This application is a cognate of 202141002726 filed on 20-Jan-2021; 202141002727 filed on 20- Jan-2021; 202141002728 filed on 20- Jan-2021 and 202141002729 fded on 20- Jan- 2021.

FIELD

[0001] The present disclosure relates to the field of collaborative engagement between users and their heterogeneous sources of data. In particular this disclosure relates to the collaborative engagement using a human machine interface.

DEFINITIONS

[0002] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except if the context in which they are used indicates otherwise.

[0003] Feed: The term “feed” refers to content in any format created by and/or published by a user which is constantly updated with a content list.

[0004] Vault: The term “vault” refers to a store in which unstructured data can be ingested, stored, managed, controlled, and given access with the help of tokens that provide data security.

[0005] Ingestion: The term “ingestion” refers to the process of extracting or absorbing unstructured data from a wide range of data sources for immediate use or storage.

[0006] Integration: The term “integration” refers to a process of combining data including components and sub-components from different sources to provide a user a unified view of data.

[0007] Manipulation: The term “manipulation” refers to a process of organizing and controlling data for making it easier to read and more structured.

[0008] Collaborative engagement: The term “collaborative engagement” refers to the interaction of users of data integrated from various sources and enables a user to connect and share with the user themselves or other users, said data.

[0009] De-duplication - The term “de-duplication” refers to a technique for ensuring that, duplicate copies are suppressed and not provided to the user.

[0010] Normalization of data - The term “normalization of data” refers to unstructured data, which is organized according to rules designed to make a database more flexible. [0011] Heterogeneous sources: The term “heterogeneous sources” refers to sources of data which are distinct from each other include personal, private and public sources and in different formats.

[0012] Human machine interface: The term “Human Machine Interface” refers to an interface that allows a person to interact with a machine or device.

[0013] Task automation features: the term “task automation features” refers to features which reduce manual handling of simple tasks or series of complex tasks and also monitoring the implementation of the tasks automatically. Task automation involves the efficient handling of tasks defined by a user, such as defining rules for prioritization and engagement of indexed and normalized data, auto generation of feeds based on user defined rules and signals derived from observing interactions of a user with the interface, searching for data within feeds and clipping, summarizing and performing sentiment analysis on searched data, and the like.

[0014] Sentiment analysis: The term “sentiment analysis” refers to a natural language processing technique used for analysis of data to find whether the data is positive, negative or neutral in the form of sentiment.

[0015] Nested search: The term “nested search” refers to multilevel searching based on decision making logic such as Boolean logic and is typically a secondary search that is performed on selected search results.

[0016] Summarizing a nested search: The term “summarizing a nested search” refers to the use of a summarization tool to carry out a search in a search and summarize the search results.

[0017] Knowledge graph: the term “knowledge graph” refers to the representation of reasoning that uses a graph structured data model to integrate data at relevant times.

[0018] Social graphs: The term “social graph” refers to the representation of social relations of a user with other entities across the heterogeneous sources for example, whatsapp, email, telegram and the like at relevant times.

[0019] Intent graphs: The term “intent graph” refers to a dynamic representation of a user’s query and intent usderstanding and is capable of being represented in graph or other forms of data structure at relevant times.

[0020] Preference graphs: The term “preference graphs” refers to the representation of user preferences in a graph data structure, at relevant times.

[0021] Contacts - The term “contacts” includes individuals or entities, with whom a user already has relationships or entities with which a user wants to engage with. The term also includes the identity of the individuals and entities a user engages on other platform(s) irrespective of whether the individual entity is registered with the interface or not.

[0022] Slice and dice: The term “slice and dice” refers to a process which enables a user to carry out advanced and specific searches by applying specific conditional operators such as “and”, “or”, “not” and the like, to meet the requirements of a user.

[0023] UI- UX: The term “UI (User interface)” refers to the aesthetic elements by which people interact with an interface, while the term “UX (User Experience)” refers to the experience a user has with the interface.

[0024] User Ranking Parameters: The interface enables a user to set his/her priority and relevancy rules for creation of feeds at relevant times. These rules are configured by a user at relevant times for the priority and relevance of feeds. These rules typically act as ranking parameters, to enable the interface to create feeds for a user and are referred to as User Ranking Parameters.

[0025] Dynamic filters: The Interface enables a user to set his/her priority and relevancy rules to create feeds for a user, at relevant times. The Interface then enables a user to filter the feeds created by the interface by enabling the user to apply additional rules of filtering feeds created by the interface. These additional rules of filtering the feeds, for the purpose of this embodiment, typically referred to as “dynamic filters”.

BACKGROUND

[0026] The background information herein below relates to the present disclosure but is not necessarily prior art.

[0027] There are a variety of private data repository providers and application providers. These can be divided into 2 main categories:

[0028] Communication Applications: These could be chat or mail applications where all data is stored within a particular application.

[0029] Storage applications: There are a variety of storage utilities, where a user can manually upload data that the user finds important and relevant to the user.

[0030] Today there are a variety of data sources that are available to a user, these are both public and private data sources. Data is fragmented across these data sources and it is very hard for a user to find information the user needs when needed.

[0031] Knowledge workers today are getting inundated with a large volume and variety of data coming to them at a high velocity and residing in many venues or silos leading to cognitive overload in processing, searching and retrieval and sharing of data. These data sources could be public sources of data such as news, blog sites or regulatory information, or arriving from private sources. There are multiple challenges that knowledge workers face in consuming and processing this information with useful outcomes due to low signal and high noise ratio. Information from private sources is also flowing in at large volumes, velocity and variety. These could be in the form of text, documents, audio, video and the like, all of which are critical information. It is difficult to remember what information was sent and where and which application it was sent on. Further, several applications delete data after a certain duration to save on storage costs. Apart from that users browse frequently visited websites to refresh newer content of specific interests and domains.

[0032] Users collate and organize relevant information that they receive, put it into directories and folders for later retrieval. Users fire searches across multiple public platforms (public search engines, search engines of specific sites) and applications (messaging applications, email application, note taking applications, social media applications and their desktop and the like) to find information that they need. Moreover, incentives for ranking results are driven by advertising. Therefore, search results on these platforms do not yield the most relevant results for domain experts. The differing aims of user and tools result in amplification of noise and diminishing of appropriate signals for the end user.

[0033] Knowledge workers use search engines to discover new information but most of the time they are looking to refresh updated information from few sources of their relevance which are frequently visited, and searches don’t serve the results from those sources easily. For any data processing that is required, users open a separate application/tool that allows them to do the processing that requires or employs other humans to do the necessary processing. Users then share relevant, processed data with other members by first saving the processed data and then opening the relevant sharing platform and sharing it. Users face fragmented data spread across wide domains, apps and venues. The processing too is fragmented, resulting in extra effort to port data to app which can process the same or call process via API (application protocol interfaces) to process data residing in non-processing silos. Basic tasks such as View, storage, search, and retrieval and more nuanced tasks such as data manipulation happen in a fragmented manner and require manual efforts and many times there are severe data losses due to inherent limitation of processing capabilities of apps and domains in question. The non-uniformity is the Achilles heel of workflow automation and to a large extent. Information processing tools that save time and effort are not available on demand in a single location - examples include summarization of data, transcription and the like.

[0034] While processing these ever dynamic data repositories and to create a knowledge graph from the same is almost impossible. The inability to efficiently process the sheer volume, velocity and variety of high value data residing in multiple venues is a deterrent to productivity and compliance standards.

[0035] In public search engines, a search is either on public data or on a user’s private data (drives). Integrations allowed are limited for private data. There is no domain specific semantic search, no processing on demand (summarization etc.). They are good for discovery mode, but not for research mode. The messaging applications have all data within the application and that has limited storage and limited search capability within the app. No public source searches have access to this critical data residing in silos leading to data and content fragmentation. The desktop utilities/browser extensions have only Meta search possible; they have no public search possibility no domain specific search, no data processing tools on demand, and no sharing capabilities. Public search engines are not tailored for knowledge workers/domain experts who are looking for information from very specific trusted sources. As an example, if a finance domain user is researching a company, then results from regulatory sites do not rank high. Further search engines on each of these platforms are domain agnostic and do not provide for domain specific auto tagging of the data nor semantic search based on the specifics of the domain to make domain specific search relevant. Even if data is retrieved, information processing tools that save time and effort are not available on demand in a single location - examples include summarization of data, transcription and the like.

[0036] Therefore, there is a need for a human machine interface and a method of implementation thereof that alleviates the aforementioned drawbacks.

OBJECTS

[0037] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:

[0038] It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative. [0039] An object of the present disclosure is to provide a human machine interface and a method of implementation thereof.

[0040] Another object of the present disclosure is to provide an interface that does data wrangling i.e. cleaning and unifying messy and complex data sets for easy access and analysis, collation of data and processing.

[0041] Yet another object of the present disclosure is to provide an interface that reduces productivity loss and prevents exhaustion in knowledge workers.

[0042] Still another object of the present disclosure is to provide an interface that has all data in one place, seamless and uniform data processing agnostic to sources, seamless single searching, and seamless sharing.

[0043] Another object of the present disclosure is to provide an interface that has a logic-based workflow automation engine which works on a variety of applications and public data sources.

[0044] Another object of the present disclosure is to provide an interface that reduces the digital footprint

[0045] Still another object of the present disclosure is to provide an interface that suppresses noise and enhances relevant signals.

[0046] Another object of the present disclosure is to provide an interface that reduces tediousness and increase productivity.

[0047] Yet another object of the present disclosure is to provide an interface that is entirely application agnostic and applies uniform data processing, and ranking results across all data sources.

[0048] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.

SUMMARY

[0049] The present disclosure envisages a human machine interface for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts of data stored in heterogeneous sources.

[0050] The interface comprises a first repository, a second repository, a vault creator module, an, ingestion module, a de-duplication module, a processing module, a customization module, searching module and a collaborative engagement module. [0051] The first repository is configured to store machine defined rules including Al and ML rules and models. The second repository is configured to store user defined rules.

[0052] The vault creator module is configured to create a unique vault, for a user. In one embodiment, the vault creator module includes:

• a permission and access granting module configured to permit the user to select sources from which the interface can be configured to retrieve data;

• a registration unit configured to facilitate the user to register within the interface by providing login credentials; and

• a sign-in unit configured to login to the vault based on the login credentials.

[0053] In another embodiment, the permission and access granting module includes a token dispensing and retrieval unit configured to grant permission and access.

[0054] Yet in another embodiment, the vault creator module is configured to provide data security to the data in the vault by allowing access via login credentials.

[0055] Also in another embodiment, the vault creator module stores retrieved data based on indexes and AI/ML rules and models stored in the first repository.

[0056] The ingestion module is configured to ingest data from selected heterogeneous sources into the vault.

[0057] In one embodiment, the ingestion module is configured to synchronize the ingested data in the vault uni-directionally.

[0058] In another embodiment, the ingested data and the customized data can be used by an individual, a group and a company.

[0059] The de-duplication module can be configured to remove duplicate items from the ingested data.

[0060] The processing module can be configured to process ingested data to obtain normalized data, the processing can module include: o an indexing unit, configured to index the ingested data based on proprietary said human machine interface process; and o a tagging unit, configured to tag the indexed data with a variety of semantic signals based on AL/ML rules and models stored in the first repository, to enable quick and efficient retrieval of indexed and tagged data based on the data format.

[0061] In one embodiment, the processing module is implemented with the help of machine learning rules. [0062] In an embodiment, the normalized data is generated based on Al and ML rules and models stored in the first repository.

[0063] In another embodiment, the normalized data is selected from the group consisting of a knowledge graph, a social graph and a preference graph.

[0064] In another embodiment, the knowledge graph at relevant times is created from the data of the User, context & information present in the data and the social graph for the User at relevant times is created based on the interaction/ relation of the User with his contacts and thereby permits the User to prioritise contacts.

[0065] The customization module can be configured to customize normalized data to generate feeds.

[0066] The customization module comprises a sorting unit configured to sort the ingested and normalized data while processing search queries and at the stage of generation of feeds based on user defined relevancy parameters and machine learning rules or models.

[0067] In one embodiment, the normalized data is obtained from by means of a processing module of the human machine interface to tag the ingested data based on AI/ML rules and models, sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface.

[0068] In one embodiment, the normalized data generates feeds based on the results derived from the knowledge graphs, social graphs, intent graphs and preference graphs.

[0069] The searching module can be configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters.

[0070] In one embodiment, the searching unit includes:

• an input unit configured to accept at least one input as a search query;

• a crawler and extractor configured to crawl through the data and extract the data from the vault based on the search query;

• a filtering unit configured to filter the data by the user based on selected selection from a set of pre-determined filters; and

• a ranking unit configured to generate prioritized search result based on relevance, user behavior and interest.

[0071] In another embodiment, the searching module further facilitates the user to perform nested searching and provide summarized search results [0072] The collaborative engagement module is configured to enable the user to engage with the feeds and includes: o a converting unit configured to convert a selected file from the feeds features to a user defined format; and o an enabling unit configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts not registered with the interface, through signed URLs with the feeds features, the vault creator module, the ingestion module, the de-duplication module, the processing module and the collaborative engagement are implemented using at least one processor and rules stored in the first repository of the human machine interface.

[0073] In one embodiment the interface can further include a recommendation module configured to provide at least one recommendation to a user based on the feeds.

[0074] In another embodiment, the interface can reduce the digital footprint by facilitating a user to generate and share a link to others associated with the interface and third party users instead of sharing media.

[0075] In another embodiment, the interface integrates together on demand secure data storage, data ingestion, search and data processing.

[0076] In an embodiment, the interface further includes an editor to enable the user to edit user defined rules.

[0077] The human machine interface for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources, the interface comprising:

• a first repository configured to store machine defined rules including Al and ML rules and models;

• a second repository configured to store user defined rules;

• a vault creator module for creating a unique vault, for a user;

• an ingestion module configured to ingest the data from the selected heterogeneous sources into the vault;

• a de-duplication module configured to process ingested data while generating feeds for a user, at relevant times;

• a processing module configured to process the ingested data to obtain normalized data, the processing module including: o an indexing unit, configured to index the ingested data based indexing rules stored in the first repository; o a tagging unit, configured a variety of semantic signals based on AL/ML rules and models stored in first vault, to enable quick and efficient retrieval of indexed and tagged data;

• a customization module configured to customize normalized data to generate feeds specific to the user at relevant times;

• a searching module configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• a collaborative engagement module configured to enable the user to engage with the feeds, includes: o a converting unit configured to convert a selected file from the feeds to a user defined format; and o an enabling unit configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts registered or not registered with the interface, through signed URL through third party application, wherein the processing module is configured to use the artificial intelligence and machine learning rules to generate a social graph for a user assigning priority values to the contacts of the user and thereby enable collaborative engagement with the help of the collaborative engagement module with contacts based on the priority value determined by the processing module.

[0078] The human machine interface for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources, the interface comprising:

• a first repository configured to store machine defined rules including Al and ML rules or models;

• a second repository configured to store user defined rules;

• a vault creator module configured to create a unique vault , for a user ;

• an ingestion module configured to ingest the data from the selected heterogeneous sources into the vault;

• a de-duplication module configured to process ingested data while generating feeds for a user, at relevant times; • a processing module configured to process the ingested data to obtain normalized data, the processing module including: o an indexing unit, configured to index the ingested data based indexing rules stored in the first repository; o a tagging unit, configured a variety of semantic signals based on AL/ML rules and models stored in first repository to enable quick and efficient retrieval of indexed and tagged data;

• a customization module configured to customize normalized data to generate feeds specific to the user at relevant times;

• a searching module configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• a collaborative engagement module configured to enable the user to engage with the feeds, which includes: o a converting unit configured to convert a selected file from the feeds to a user defined format; and o an enabling unit configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts registered or not registered with the interface, through signed URL and through third party application, wherein the customization module is configured to enable a user to generate a curated feed wherein the feed is partly curated by the processing module using the artificial intelligence and machine learning rules stored in first repository and partly curated by the user by manual intervention which may include application of Boolean logic and keywords.

[0079] The present disclosure further envisages a method of collaborative engagement of data between a user, the user itself and its contacts, of a data of the user stored in heterogeneous sources the method of collaborative engagement implemented by the user using a human machine interface , the method comprising the following steps:

• creating, a unique vault for the user within which the data associated with the user can be stored;

• ingesting, the data from selected heterogeneous sources into the vault; • removing, duplicated items from the ingested data with the help of a deduplication module of the human machine interface.

• processing, the data, stored inside the vault by means of a processing module of the human machine interface to tag the ingested data, sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface to obtain normalized data;

• customizing, the normalized data to generate feeds;

• searching, by searching module, configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters;

• converting, the feeds to a user defined format; and

• enabling, collaborative engagement of the user and selected contacts of the user with the feeds. wherein the steps of creating the vault, ingesting data, removing duplicated items, processing the data, converting the feeds and enabling collaborative engagement are implemented using rules stored in a first and second repository of the human machine interface.

[0080] In one embodiment the method includes the step of conducting sentiment analysis on at least a portion of the normalized data.

[0081] In another embodiment the method includes the steps of conducting a nested search on the normalized, and customized data and further summarizing the nested search results.

[0082] In another embodiment, the method includes the step of allowing the user to access the human machine interface from a user device using a web based application, the step comprising the following sub- steps:

• permitting and granting access to permit a user to select sources from which the interface can retrieve data;

• registering, by inputting credentials to create a user account or accessing an already created account; and

• storing, within a second repository, user credentials and user defined rules for customization. [0083] The step of permitting and granting access can include a step of a token dispensing and retrieval configured to granting permission and access.

[0084] The step of ingesting, by means of an ingestion module, data from the selected sources can include the sub-step of synchronizing the ingested data into the vault unidirectionally.

[0085] The step of processing, by means of a processing module the ingested data in the vault can include the sub-step of providing at least one recommendation to the user based on the processed data the recommendation based on rules stored in the first repository and second repository.

[0086] The step of customizing can customizes data to create a curated feed wherein the feed is partly curated by the processing module using the artificial intelligence and machine learning rules stored in first repository and partly curated by the user defined rules by manual intervention which may typically include application of Boolean logic and keywords by the user in a plurality of formats.

[0087] In another embodiment, the step of customizing can allow a user to search normalized data in a plurality of formats using user defined filters which may be static and/or dynamic.

[0088] In one embodiment, the step of processing normalized data can include the sub - step of, using AI/ML rules and models stored in the first repository while observing the user behaviour and interaction with the Interface to generate graphs selected from the group consisting of knowledge graphs, social graphs, intent graphs and preference graphs, at relevant times.

[0089] In one embodiment, the knowledge graph at relevant times is created from the of data of the User, context & information present in the data and the social graph of the user is generated by observing the collaborative engagement of the User with his/her contacts and by assigning priority values for the contacts of the user based on the collaborative engagement of the user with its contacts.

[0090] In one embodiment, the step of carrying out a nested search can include the sub - steps of clipping and editing the data generated from a nested search and creating a summary of search results generated from the nested search which can be used by the user during collaborative engagement.

[0091] In one embodiment, the method for collaborative engagement can further include the step of permitting a user to edit user defined rules stored in the second repository. [0092] In accordance with another aspect of disclosure, there is provided a method of collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources the method implemented by the user using a human machine interface (100), the method (200) comprising the following steps:

• creating, a unique vault for the user within which the data associated with the user can be stored;

• ingesting , the data from the selected heterogeneous sources into the vault ;

• removing duplicated items from the ingested data with help of a deduplication module, typically at the discretion of a User of the human machine interface;

• processing, the data, stored inside the vault by means of a processing module of the human machine interface to tag the ingested data of the user to obtain normalized data and generate a social graph of the user by assigning priority values for the contacts of the user.

• Customizing, the normalized data to generate feeds specific to a user at relevant times and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface to obtain normalized feeds enabling the customization of feeds through task automation features;

• searching , with the help of searching module, configured to search on the normalized and customized data in a plurality of formats using static or dynamic user defined filters;

• convert , the feeds to a user defined format; and

• enabling, collaborative engagement of the user and the contacts of the user based on the assigned priority values to the contacts and thereby enabling collaborative engagement with the help of the collaborative engagement module with contacts based on the priority value determined by the processing module.

[0093] In one embodiment, the method of collaborative engagement, wherein the user is enabled to alter the priority values assigned to at least one contact of the user, either temporarily or permanently, and thereby enable the collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources based on priority values set by the processing module and the user. [0094] In accordance with yet another aspect of disclosure, there is provided a method of collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources the method implemented by the user using a human machine interface, the method comprising the following steps:

• creating, a unique vault for the user within which the data associated with the user can be stored;

• ingesting, the data from the selected heterogeneous sources into the vault;

• removing, duplicated items from the ingested data with help of a deduplication module, of the human machine interface;

• processing, the data, stored inside the vault by means of a processing module of the human machine interface to tag the ingested data to obtain normalized data and generate a social graph of the user by assigning priority values for the contacts of the user;

• customizing, the normalized data to generate feeds specific to a user at relevant times and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface to obtain normalized feeds enabling the customization of feeds through task automation features;

• searching , with the help of searching module, configured to search on the normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• convert , the feeds to a user defined format, wherein the step of customization includes a step of enabling a user to generate a curated feed wherein the feed is partly curated by the processing module using the artificial intelligence and machine learning rules stored in the first repository and partly curated by the user defined rules stored in the second repository and partly by manual intervention which may include application of Boolean logic and keywords.

[0095] The disclosure also extends to the use of a human machine interface to enable collaborative engagement by a user with the user itself and with user contacts of data , from heterogeneous sources the collaborative engagement involving, ingestion, normalization and customization of data corresponding to the user and enabling the user to collaboratively engage with the ingested, normalized and customized data with the user itself or with contacts of the user, wherein the collaborative engagement of the user and the contacts of the user is based on assigned priority values to the contacts, thereby enabling efficient collaborative engagement with the user and its contacts based on the assigned priority values and further the user is able to receive a curated feed wherein the feed is partly curated by a processing module using artificial intelligence and machine learning rules stored in a first repository user defined rules stored in second repository and partly curated by the user by manual intervention which may typically include application of Boolean logic and keywords.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING

[0096] A human machine interface and a method thereof of the present disclosure will now be described with the help of the accompanying drawings, in which:

[0097] Figure 1 illustrates a block diagram of a human machine interface, in accordance with the present disclosure;

[0098] Figures 2a, 2b a figure 2c illustrate a flow diagram for a method for collaborative engagement for a user with its contacts using the human machine interface of Figure 1 ;

[0099] Figures 3a, Figure 3b and figure 3c illustrates a flowchart for the method illustrated in the figures 2a, 2b and 2c;

[0100] Figure 4 illustrates a data flow diagram depicting a user registration process in the method illustrated in the figures 2a, 2b and 2c;

[0101] Figure 5 illustrates a data flow diagram depicting the vault population process in the method illustrated in the figures 2a, 2b and 2c;

[0102] Figure 6 illustrates a data flow diagram for the process of processing and normalizing data in the method illustrated in the figures 2a, 2b and 2c;

[0103] Figure 7 illustrates a data flow diagram depicting the process of collaborative engagement in the method illustrated in the figures 2a, 2b and 2c;

[0104] Figure 8 illustrates a data flow diagram depicting the process of sentiment analysis in the method illustrated in the figures 2a, 2b and 2c;

[0105] Figure 9 illustrates an example of a knowledge graph generated by in the method illustrated in the figures 2a, 2b and 2c which shows the collaborative relationship between the user of the HMI of figure 1 and user’s five different contacts in respect to different areas of interest. [0106] Figure 10 illustrates an example of a social graph which shows collaborative engagement of a user of HMI of figure 1 with the user’s five different contacts

[0107] Figure 11 illustrates an example of an social graph which show the dynamic representation of social behavior of a user of HMI of figure lin relation to its contacts;

[0108] Figure 12 illustrates an example of an Intent graph which show the dynamic representation of a user of HMI of figure 1 intent in a particular time period;

[0109] Figure 13 illustrates an example of a preference graph which represents the preferences of a user of HMI of figure 1 for different areas of interest.

LIST OF REFERENCE NUMERALS:

100 - Interface, 102 - First repository, 104 - Second repository, 106 - Vault creator module, 106a - vault, 106b - permission and access grating module,

106c - registration unit, 106d - sign-in unit, 108 - Ingestion module, 110 - De-duplication module, 112 - Processing module, 112a - Indexing unit, 112- tagging unit, 114 — Customization module, 114a - sorting unit, 116 - Searching module,

116a - input unit, 116b - Crawler and extractor, 116c - filtering unit, 116d - ranking unit, 118 - Collaborative engagement module, 118a - converting unit, 118b enabling unit, 122 - Recommendation module, 124 - Editor and 10 - User device.

DETAILED DESCRIPTION

[01 10] Embodiments, of the present disclosure, will now be described with reference to the accompanying drawing.

[01 11] Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to a person skilled in the art. Numerous details, are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well- known apparatus structures, and well-known techniques are not described in detail.

[01 12] The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms "comprises," "comprising," “including,” and “having,” are open ended transitional phrases and therefore specify the presence of stated features, elements, modules, units and/or components, but do not forbid the presence or addition of one or more other features, elements, components, and/or groups thereof.

[01 13] The present disclosure envisages an interface (hereinafter indicated by the reference numeral (100) for a human machine interface and a method of using a human machine interface (hereinafter indicated by the reference numeral (200). The interface (100) and the method (200) are now described with reference to figure 1, figure 2a and figure 2b, Figure 3a, 3b, figure 4, figure 5, figure 6, figure 7, figure 8

[01 14] Referring to figure 1, the interface (100) comprises a first repository (102), a second repository (104), a vault creator module (106), and an ingestion module (108), de-duplication module (110), a processing module (112), a customization module (114), a searching module (116) and a collaborative engagement module (118).

[01 15] The present disclosure envisages a human machine interface for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts of data stored in heterogeneous sources.

[01 16] The vault creator module (106), the ingestion module (108), the de-duplication module (110), the processing module (112) and the collaborative engagement module (114) are implemented using one or more processor(s) not shown in the drawing.

[01 17] The vault creator module (106) can be configured to create a vault (106a). The module (106) can include a permission and access granting module (106b) configured to permit a user to select sources from which the interface (100) can be configured to retrieve data, a registration unit (106c) configured to facilitate a user to register within the interface by providing login credentials and a sign-in unit (106d) configured to enable the user to login to the vault (106a) based on the login credentials. The permission and access granting module (106b) further includes a token dispensing and retrieval unit (not particularly shown) configured to permit and give access configured to permit a user to select sources from which the interface (100) can be configured to retrieve data.

[01 18] An exemplified pseudo-code for the application of the permission and access granting module (106) is as follows:

Class Permission

{ Access ()

{ Read

{ user to select sources }

Write

{ permit the user to select sources from which said interface can be configured to retrieve data

}

}

} Permission exe=new PermissionQ;

Exe.access();

[01 19] The vault creator module (106) can be further configured to create data stores (not shown), for a user in the vault (106a). The vault creator module (106) provides data security in the vault (106a) by allowing access via login credentials. Further, the vault creator module (106) can store the retrieved data based on indexes and AI/ML rules and models.

[0120] The ingestion module (108) can be configured to ingest data from selected heterogeneous sources into the vault (106a). The ingestion module (108) can further synchronize the ingested data into the vault (106a) in a unidirectional manner.

[0121 ] The de-duplication module configured to process ingested data while generating feeds for a user, at relevant times.

An exemplified pseudo-code for the application for the de duplication module (110) is shown as follows: de-duplication()

{ scan said retrieved data and de-duplicate items of duplicate data typically at the discretion of the user;

} Run.de-duplicationQ;

[0122] The processing module (112) can process the ingested data to obtain normalized data. The processing module can further include an indexing unit (112a) which can be configured to index the ingested data based on indexing rules stored in the first repository (102) and a tagging unit (112b) which can be configured to tag the indexed data, to enable quick and efficient retrieval of indexed and tagged data. The processing module (112) can be further implemented on the basis of machine learning rules.

[0123] An exemplified pseudo-code for the processing module (112) is as follows: Class processing { indexing() {

Ingested data is indexed on proprietary human machine intercede (100) process; } Sort ()

{ sort said retrieved data based on user-defined relevancy parameters;

}

Tagging ()

{ Tag the indexed data;

}

CustomizationQ

{ enable the user and said interface to customize said retrieved data to generate feeds;

}

} processing Run=new processingQ;

Run. indexing();

Run. sort();

Run. tagging();

Run. CustomizationQ;

[0124] The indexing unit (112a) of the Interface(lOO) can be configured (a) to define the ingested data and organize it by its relationship to each other and to understand the meaning of the ingested data, (b) to create common understanding of the meaning of the ingested data, (c) to understand common meaning in semantic language to help the Interface (100) more accurately interpret the ingested data, (d) to transform the ingested data into vector representations to enable application of machine learning on the indexed data, (e) to create labelled graphs in which the labels have well-defined meanings (for example, a knowledge graph) for the ingested data to enable efficient use of the Ingested data for further processing and analysis.

[0125] Further, the normalized data can be selected from the group consisting of signals from a knowledge graph, a social graph, an intent graph and a preference graph wherein the knowledge graph is generated based on data of the User, context & information present in the data and the social graph is generated observing the collaborative engagement of the User with his/her contacts and by assigning priority values for the contacts of the user based on the collaborative engagement of the user with its contacts. The normalized data is generated based on Al and ML rules and models stored in the first repository (102). [0126] The customization module (114) can be configured to customize normalized data to generate feeds.

[0127] The searching module (116) can be configured to search on the normalized and customized data in plurality of formats using static or dynamic user defined filters. The searching module (116) can further include an input unit (116a) configured to accept at least one input as a search query, a crawler and extractor pair (116b) configured to crawl through the data and extract the data from said vault (106a) based on the search query, a filtering unit (116c) configured to filter the data by the user based on selection from a set of pre-determined filters and a ranking unit (116d) configured to generate prioritized search results based on relevance, user behavior and interest. The searching module (116) can further facilitate the user to perform nested searching and provide summarized search results.

[0128] The collaborative engagement module (118) can be configured to enable the user to engage with the feeds. The collaborative engagement module (118) can further include a converting unit (118a) configured to convert a selected file from the feeds to a user defined format and an enabling unit (118b) which can be configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts not registered with the interface, through signed URLs with the feeds and third party aplications.

[0129] An exemplified pseudo-code for the collaborative engagement module (118) is as follows:

Class engagement()

{ ConvertQ

{ convert the selected file from feeds from one format to a user-defined format;

}

Collaboration()

{ enable the user to collaborate and engage the user and selected contacts of the user as well as user selected contacts not registered with the interface, through signed URL and third party applications;

}

} engagement Runl=new engagement);

Runl. Collaboration(); [0130] In one embodiment, the interface (100) can include the searching module (118) comprising the input unit (118a), the crawler and extractor pair (118b), the filtering unit (118c) and the ranking unit (118d).

[0131] The input unit (118a) can be configured to accept at least one input as a search query. The crawler and extractor pair (118b) can be configured to crawl through the data and extract data from the vault (106a) based on the search query. The filtering unit (118c) can be configured to filter the data by a user based on a selection from the set of pre-determined filters and the ranking unit (118d) can be configured to generate a prioritized search result based on relevance, user behavior and interest.

[0132] The searching module (116) can further facilitate the user to perform nested searching.

[0133] In another embodiment, the analysis unit (not shown) can further include a knowledge extractor (not shown) configured to achieve a plurality of signals and implement a knowledge extraction method to:

• Allow for data storage in a variety of ways based on user preferences.

• Allow for auto organization of the data based on user specified rules and machine learning rules.

• Allow for specific knowledge extraction methods that get triggered based on user preferences and user ontologies.

• Allow for the creation of private and public semantic signals (tagging) repositories - based on user provided input and Human Machine Interface input.

• Ability to index either only semantic signals or the entire data based on user preferences.

• Ability to store the entire data in a compressed vectorized form for later retrieval and to enable deep search in a cost-effective manner.

• Ability to create a private knowledge graph for the user that is constructed only from the private sources of data that the user specifies.

• Ability to create a knowledge graph across all the sources of data that is accessible across users.

[0134] In an embodiment, the knowledge extractor can optionally include a classifier unit, a tagging unit (112b), a reading unit and a graph unit. [0135] The classifier unit can be configured to classify the document into a particular domain based on the signals.

[0136] The tagging unit (112b)can be configured to auto tag/classify a data based on a domain specific ontology and a user input. The tags may be stored in the semantic signal store.

[0137] In one embodiment, the reading unit can be configured to read through all the text in the document, and further can be configured to extract vectorized representations of sentences/paragraphs/pages. These vectorized representations may be beneficial to:

• capture the meaning encapsulated in the document in different parts and

• store the information in a compressed format as opposed to a standard reverse index used in information retrieval applications stored in a vector store.

[0138] In one embodiment, the reading unit (not shown) can be configured to read data in a format selected from the group consisting of, but not limited to, text, multimedia, audio and video.

[0139] In one embodiment the graph unit can be configured to read through each sentence and extract entities and relationships based on vectorized representations. Depending on whether the data source is private or public, the entities and relationships may be added to either a private knowledge graph or a public knowledge graph.

[0140] In another embodiment, the graph unit can be configured to read data in a format selected from the group consisting of, but not limited to, text, multimedia, audio and video.

[0141] The Interface (100) can be configured to take auto back up with user intervention from at least one data source.

[0142] In an embodiment, the vectorized data and the semantic signals can be stored in the vault (106a).

[0143] In one embodiment, the interface (100) can optionally further comprise a recommendation module (122) configured to provide at least one recommendation to the user based on the analyzed data.

[0144] In another embodiment, the interface (100) can reduce the digital footprint by facilitating a user to generate and share a link to contacts associated with the interface (100) and third party users instead of sharing media.

[0145] The processor may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any device that manipulates signals based on operational instructions. Among other capabilities, the processor may be configured to fetch and execute the set of predetermined rules stored in the repositories to control the operation of different modules/units of the system.

[0146] In another embodiment, the interface (100) can be configured to facilitate a user to create a new feed, delete feeds, edit feed and provide feed preferences. The preferences for the feed may be assigned a plurality of attributes relevant to the feed. These attributes can be:

• trust levels of the source;

• filter criteria specific to the source;

• originating entities - e.g. people, organizations;

• topics, keywords of interest;

• other users who are invited to the feed; and

• fine grained access control criteria for each of these users.

[0147] In one embodiment, the interface (100) can ingest data from heterogeneous sources that are private, paid and public.

[0148] In other embodiment the interface (100) can further integrate together on demand secure data storage, the data ingestion, the search and the data processing.

[0149] Referring to Figures 2a and 2b, the method (200) of collaborative engagement of data between a user, the user itself and its contacts, of the data of the user stored in heterogeneous sources, can be implemented by the user using a human machine interface (100), the method (200) comprising the following steps:

• creating (202), a unique vault (106a) for the user within which the data associated with the user can be stored;

• ingesting (204), the data from the selected heterogeneous sources in to the vault (106a);

• removing (206) duplicated items from the ingested data with help of a deduplication module (110) typically at the discretion of the user of the human machine interface (100);

• processing (208), the data, stored inside the vault (106a) by means of a processing module (112) of the human machine interface (100) to tag the ingested data; • customizing (210), the normalized data to generate feeds feeds specific to a user at relevant times and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface (100) to obtain feeds enabling the customisation of feeds through task automation features;

• searching (212), by searching module (116), configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters;

• convert (214), the feeds to a user defined format; and

• enabling (216), collaborative engagement of the user and selected contacts of the user with the feeds.

[0150] An exemplified pseudo code for a method of collaborative engagement of data between a user, the user itself and its contacts, of the data of the user stored in heterogeneous sources the method implemented by the user using a human machine interface (100) is as follows: sPackage com.humanmachineinterface ;

Class humanmachineinterface

{ Perform()

{ Creating a unique vault for the user within which the data associated with the user can be stored; ingesting, the data from the selected heterogeneous sources in to the vault; removing duplicated items from the ingested data with help of a de-duplication module of the human machine interface; processing the data, stored inside the vault to tag the ingested data and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface (100) to obtain normalized data; customizing the normalized data to generate feeds; searching by searching module, configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters; convert the feeds to a user defined format; and enabling collaborative engagement of the user and selected contacts of the user with the feeds

} humanmachine obj=new humanmachine(); obj. Perform();

[0151] The step of permitting and granting access (202a) can include a step of token dispensing and retrieval and can be configured to grant permission and access.

[0152] The step of ingesting (204), by means of an ingestion module (108), data from selected sources can include the sub-step of synchronizing (204a) the ingested data into the vault (106a) uni-directionally.

[0153] The step of processing (208), by means of an processing module (112), the ingested data in the vault (106a) can include the sub-step of providing (208a) at least one recommendation to the user based on the processed data and the first repository (102) includes rules for recommendation.

[0154] The step of customizing (210) can customize data to create feeds by the user in a plurality of formats.

[0155] The step of customizing (210) can further allow user to search normalized data in a plurality of formats using user defined filters which may be static or dynamic or both.

[0156] Further, the step of processing (208) normalized data can include the step of using AI/ML rules and models stored in the firast repository (102) to generate graphs selected from the group consisting of knowledge graphs, social graphs, intent graphs and preference graphs. The knowledge graph can be generated based on data of the User, context & information present in the data and the social graph can be generated observing the collaborative engagement of the User with his/her contacts and by assigning priority values for the contacts of the user based on the collaborative engagement of the user with its contacts.

[0157] The step of carrying out the nested search can include the steps of clipping and editing the data generated from the nested search, creating summary of search results generated from the nested search which can be used by the user during collaborative engagement.

[0158] In the step of de-duplication, a de-duplication module is configured to process ingested data while generating feeds for a user, at relevant times; [0159] duplicated items from the ingested data are removed during presentation of results for a user initiated search. In one embodiment the user can have the option to directly remove duplicated data from the ingested data.

[0160] The disclosure also envisages the use of a human machine interface (100) to enable collaborative engagement of data from heterogeneous sources, selected by a user with the user itself and with user contacts of the user, the collaborative engagement involving, ingestion, normalization and customization of data corresponding to the user and enabling the user to collaboratively engage with the ingested, normalized and customized data with the user itself or with contacts of the user.

[0161] The purpose of the interface (100) is to provide the user with an interface that collects, records and process the user data from different sources defined by user. Also the purpose of the interface (100) is to provide a logic based workflow automation engine and a search engine that provides a unified view of search results across multitude of applications, allows for domain specific search ranking and allows for search across a multitude of data formats. Also, as the systems use a traditional reverse index which is expensive, the size of the index is typically 10-20% of the volume of data. Indexes have to be maintained in repository for efficient retrieval.

[0162] The flowcharts represented in figures 3a and 3b in relation to the interface (100) and the data flow diagrams represented in figures 4, figure 5, figure 6, figure 7, figure 8 for human machine interface (100) can be represented by the below use cases which have been included in the disclosure merely for the purpose of illustration and do not in any way to limit the scope of the disclosure.

[0163] The present disclosure can be configured to: allow users to create a single feed across heterogeneous sources of data across a plethora of criteria, along with trust ranking criteria specified by users and assigned by the AI/ML rues; share these dynamic data feeds with other users with the ability of other users to also contribute to these data feeds; and have fine grained access control on these dynamic hybrid data feeds.

[0164] The present disclosure can be configured to create a custom feed across a variety of data stores, each holding data from heterogeneous data sources, in a multitude of formats based on keywords, filters from the sources, topics of interest, and entities of interest and events of interest to create a single unified feed view across multiple hybrid sources. Furthermore, these customized feeds can be shared with other users with different types of access rights, i.e., read only, contribute data sources and delete data/data sources.

[0165] The present disclosure can comprise several aspects detailed below:

• ability to clip and extract from the document across the format;

• ability for the user to create a feed from hybrid data sources, with hybrid ranking criteria specified by human and combined with machine learnt ranking criteria;

• ability for the user to specify for each application source to be added to the feed, a set of filters to be applied to each application source to help narrow down the feed;

• a user can specify a set of entities who are originators of information - e.g., people or organizations who transmit information via a variety of applications;

• a user can specify certain search keywords/criteria across multiple sources to obtain information and the user can also specify complex Boolean logic on keywords to narrow down the scope of the feed;

• ability to have logic-based prioritization of data;

• ability to receive alerts and notifications on feed;

• Once a feed is created, a user can share the entire feed or partial feed with other users and give other users granular access control rights. All users subscribed to a feed receive updates automatically with no human intervention;

• ability to summarize and share across the other users; and

• these access control rights, apart from providing admin rights, read rights and delete rights, could also allow other users to contribute to the feed from their own data sources. These access control rights could also limit number of times data can be downloaded, time validity of access to data, domain validity etc.

[0166] The present disclosure envisages an interface that allows users to keep up to date on information about different areas of interest with data coming from heterogeneous multiplicity of sources, public, paid and private. The interface allows the user to specify trust levels and priority levels of these different sources of information, so the information in the feeds can also be displayed based on the levels of trust in the source. Furthermore, the interface allows a user to share the feed with other users with different granularities of access control and even the ability to contribute their own data sources to the feed. To the best of the knowledge there is no interface, method or application that allows users to do this. Currently none of the applications that exist work across heterogeneous data sources which can be private, public and paid.

[0167] The interface (100) allow an aggregation of data across heterogeneous sources and a single query across multiple platforms. The interface (100) is a live auto refreshed repository of frequently visited websites and user specified applications which are in private domain (both free and paid), with an application of standard OCR, uniform tagging, and processing across all data sources and self-operating tools for crawling and tagging. The auto tagging is a of human and machine defined tagging. Users have a unified view of data across multiple sources with a single search. The system uniformly processes data within the platform, gives a unified view to user based on prioritization and has an automation workflow on top. The interface (100) also allows users to share content based on manual sharing logic as well and rule-based workflows.

[0168] The interface may be configured to search across a variety of data sources and knowledge stores with multiple representation formats and stitching them together to provide a single unified view to the end user. The system may be configured to search across a variety of data stores, each holding data from heterogeneous data sources, in a multitude of formats and rank the results across these data sources and stitching them together to provide a single unified view of relevant results to the end user.

[0169] The proposed interface provides an efficient way to set up search across a large volume of data by using a compressed vectorized search which may imply either large machines or the index distributed over a large number of machines which becomes expensive.

[0170] In an embodiment, the interface further includes an editor to enable the user to edit user defined rules.

[0171] In accordance with one aspect of disclosure, there is provided the human machine interface (100) for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources, comprises:

• a first repository (102) configured to store machine defined rules including Al and ML rules or models;

• a second repository (104) configured to store user defined rules;

• a vault creator module (106) configured to create a unique vault (106a), for a user ; • an ingestion module (108) configured to ingest said data from said selected heterogeneous sources into said vault (106a);

• a de-duplication module (110) configured to process ingested data while generating feeds for a user, at relevant times;

• a processing module (112) configured to process said ingested data to obtain normalized data, said processing module (112) including: o an indexing unit (112a), configured to index said ingested data based indexing rules stored in said first repository (102); o a tagging unit (112b), configured to tag said indexed data with a variety of semantic signals based on AL/ML rules and models stored in first vault, to enable quick and efficient retrieval of indexed and tagged data based on data format;

• a customization module (114) configured to customize normalized data to generate feeds specific to the user at relevant times;

• a searching module (116) configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• a collaborative engagement module (118) configured to enable the user to engage with said feeds, includes: o a converting unit (118a) configured to convert a selected file from the feeds to a user defined format; and o an enabling unit (118b) configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts registered or not registered with said interface, through signed URL with said feeds and through third party application, wherein said processing module (112) is configured to use the artificial intelligence and machine learning rules to generate among other graphs a social graph for a user assigning priority values to the contacts of the user and thereby enable collaborative engagement with the help of the collaborative engagement module (118) with contacts based on the priority value determined by the processing module.

[0172] In one embodiment the human machine interface (100) includes an editor (124) which is configured enable a user to alter the priority values assigned to at least one contact of the user, either temporarily or permanently, and thereby enable the collaborative engagement of the user with its contacts based on priority values set by the processing module (112) and the user.

[0173] In accordance with another aspect of disclosure, there is provided the human machine interface (100) for ingestion, normalization, customization and collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources, comprises:

• a first repository (102) configured to store machine defined rules including Al and ML rules or models;

• a second repository (104) configured to store user defined rules;

• a vault creator module (106) configured to create a unique vault (106a), for a user ;

• an ingestion module (108) configured to ingest said data from said selected heterogeneous sources into said vault (106a);

• a de-duplication module (110) configured to to process ingested data while generating feeds for a user, at relevant times;

• a processing module (112) configured to process said ingested data to obtain normalized data, said processing module (112) including: o an indexing unit (112a), configured to index said ingested data based indexing rules stored in said first repository (102); o a tagging unit (112b), configured to tag said indexed data with a variety of semantic signals based on AL/ML rules and models stored in first vault, to enable quick and efficient retrieval of indexed and tagged data;

• a customization module (114) configured to customize normalized data to generate feeds specific to the user at relevant times;

• a searching module (116) configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• a collaborative engagement module (118) configured to enable the user to engage with said feeds, which includes: o a converting unit (118a) configured to convert a selected file from the feeds to a user defined format; and o an enabling unit (118b) configured to enable collaborative engagement of the user and selected contacts of the user as well as user selected contacts registered or not registered with said interface, through signed

URL with said feeds and through third party application, wherein said customisation module (114) is configured to enable a user to generate a curated feed wherein the feed is partly curated by the processing module (112) using the artificial intelligence and machine learning rules stored in first repository (102), user defined rules stored in second repository (104) and partly curated by the user by manual means which may typically include application of Boolean logic and keywords.

[0174] The present disclosure further envisages a method (200) of collaborative engagement of data between a user, the user itself and its contacts, of a data of the user stored in heterogeneous sources the method of collaborative engagement implemented by the user using a human machine interface , the method comprising the following steps:

• Creating (202), a unique vault for the user within which the data associated with the user can be stored;

• Ingesting (204), the data from selected or interface recommended heterogeneous sources into the vault;;

• Removing (206), duplicated items from the ingested data with the help of a deduplication module of the human machine interface typically at the discretion of a user.

• Processing (208), the data, stored inside the vault by means of a processing module of the human machine interface to tag the ingested data, sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface to obtain normalized data;

• Customizing (210), the normalized data to generate feeds;

• Searching (212), by searching module, configured to search on normalized and customized data in a plurality of formats using static or dynamic user defined filters;

• Converting (214), the feeds to a user defined format; and

• Enabling (216), collaborative engagement of the user and selected contacts of the user with the feeds. wherein the steps of creating the vault, ingesting data, removing duplicated items, processing the data, converting the feeds and enabling collaborative engagement are implemented using rules stored in a first and second repository of the human machine interface.

[0175] In one embodiment the method (200) includes the step of conducting sentiment analysis on at least a portion of the normalized data.

[0176] In another embodiment the method (200) includes the steps of conducting a nested search on the normalized, and customized data and further summarizing the nested search results.

[0177] In another embodiment, the method (200) includes the step of allowing the user to access the human machine interface from a user device using a web based application, said step comprising the following sub- steps:

• permitting and granting access to permit a user to select sources from which the interface can retrieve data;

• registering, by inputting credentials to create a user account or accessing an already created account; and

• storing, within a second repository, user credentials and user defined rules for customization.

[0178] The step of permitting and granting access can include a step of a token dispensing and retrieval configured to granting permission and access.

[0179] The step of ingesting, by means of an ingestion module, data from the selected sources can include the sub-step of synchronizing the ingested data into the vault unidirectionally.

[0180] The step of processing, by means of a processing module the ingested data in the vault can include the sub-step of providing at least one recommendation to the user based on the processed data said recommendation based on rules stored in the first repository and second repository.

[0181] The step of customizing can customizes data to create a curated feed wherein the feed is partly curated by the processing module (112) using the artificial intelligence and machine learning rules stored in first repository (102) user defined rules stored in second repository (104) and partly curated by the user by manual means which may typically include application of Boolean logic and keywords by said user in a plurality of formats.

[0182] In another embodiment, the step of customizing can allow a user to search normalized data in a plurality of formats using user defined filters which may be static and/or dynamic.

[0183] In one embodiment , the step of processing normalized data can include the sub - step of, using AI/ML rules and models stored in the first repository to generate graphs selected from the group consisting of knowledge graphs, social graphs, intent graphs and preference graphs.

[0184] In one embodiment , knowledge graph is generated based on the collaborative engagement of said user and said social graph of the user is generated by assigning priority values for the contacts of the user and based on said collaborative engagement of said user with its contacts.

[0185] In one embodiment, the step of carrying out a nested search can include the sub - steps of clipping and editing the data generated from a nested search and creating a summary of search results generated from the nested search which can be used by the user during collaborative engagement.

[0186] In one embodiment, the method for collaborative engagement can further include the step of permitting a user to edit user defined rules stored in the second repository.

[0187] In accordance with another aspect of disclosure, the method of collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources said method implemented by the user using a human machine interface (100), comprises following steps:

• creating (202), a unique vault (106a) for said user within which the data associated with said user can be stored;

• ingesting (204), said data from said selected heterogeneous sources into said vault (106a);

• removing (206), duplicated items from the ingested data and processing ingested data while generating feeds for a user, at relevant times with help of a de-duplication module, typically at the discretion of a user (110) of the human machine interface (100);

• processing (208), the data, stored inside said vault (106a) by means of a processing module (112) of the human machine interface (100) to tag the ingested data to obtain normalized data and generate a social graph of the user by assigning priority values for the contacts of the user.

• customizing (210), the normalized data to generate feeds specific to a user at relevant times and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface (100) to obtain normalized feeds enabling the customisation of feeds through task automation features;

• searching , with the help of searching module (116), configured to search on the normalized and customized data in a plurality of formats using static or dynamic user defined filters;

• convert , the files/ document provided in the feeds to a user defined format; and

• enabling , collaborative engagement of the user and the contacts of the user based on the assigned priority values to the contacts and thereby enabling collaborative engagement with the help of the collaborative engagement module with contacts based on the priority value determined by the processing module .

[0188] In one embedment, the method of collaborative engagement includes a step, wherein the user is enabled to alter the priority values assigned to at least one contact of the user, either temporarily or permanently, and thereby enabling the collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources based on priority values set by the processing module and the user.

[0189] In accordance with yet another aspect of disclosure, there is provided a method of collaborative engagement between a user, the user itself and its contacts, of data stored in heterogeneous sources said method implemented by the user using a human machine interface, comprises the following steps:

• creating, a unique vault for said user within which the data associated with said user can be stored;

• ingesting, said data from said selected heterogeneous sources into said vault;

• removing, duplicated items and processing ingested data while generating feeds for a user, at relevant times; • from the ingested data with help of a de-duplication module, typically at the discretion of a user of the human machine interface;

• processing, the data, stored inside said vault by means of a processing module of the human machine interface to tag the ingested data to obtain normalized data and generate a social graph of the user by assigning priority values for the contacts of the user;

• customizing, the normalized data to generate feeds specific to a user at relevant times and sort the data based on user defined relevancy parameters and machine learning signals derived from the human machine interface to obtain normalized feeds enabling the customisation of feeds through task automation features;

• searching , with the help of searching module, configured to search on the normalized and customized data in a plurality of formats using static or dynamic user defined filters; and

• convert , the files/ document provided in the feeds to a user defined format, wherein said step of customisation includes a step of enabling a user to generate a curated feed wherein the feed is partly curated by the processing module using the artificial intelligence and machine learning rules stored in first repository (102), user defined rules stored in second repository (104) and partly curated by the user by manual means which may typically include application of Boolean logic and keywords.

[0190] The disclosure also extends to the use of a human machine interface to enable collaborative engagement by a user with the user itself and with user contacts of data , from heterogeneous sources said collaborative engagement involving, ingestion, normalization and customization of data corresponding to the user and enabling the user to collaboratively engage with said ingested, normalized and customized data with the user itself or with contacts of the user, wherein the collaborative engagement of the user and the contacts of the user is based on assigned priority values to the contacts, thereby enabling collaborative engagement with the user and its contacts based on the assigned priority values and further the user is able to receive a curated feed wherein the feed is partly curated by a processing module using alrtificial intelligence and machine learning rules stored in a first repository (102) ), user defined rules stored in second repository (104) and partly curated by the user by manual means which may typically include application of Boolean logic and keywords by said user in a plurality of formats partly curated by the user defined rules stored in second repository (104) and partly by manual intervention with the help of Boolean logic and keywords

[0191] Referring to figure 9, a knowledge graph is prepared depicting relationship of a human machine interface (HMI) (100) user with his/her contacts, referred by way of example to C1-C5 along with the frequency of sharing/collaboration in respect to each area of interest like finance, business, education, health care, legal and social. The knowledge graph of HMI (100) establishes the underlying dynamic context and the semantics for the ingested and normalized data of the user and enables user to:

(i) trust the provenance of each of data point and use the data,

(ii) personalize the feeds based on the underlying dynamic context and the semantics for the ingested and normalized data,

(iii) collaborate the feeds that are generated by the interface based on the underlying dynamic context and the semantics for the ingested and normalized data with the contacts, team members, and groups of the user selected by the user based on the common interest, purpose of the user and his contact, team members, groups of the user.

(iv) contact team members, groups of the user based on common interest, purpose of the user and his/her contact(s), wherein the interface analyses and establishes the relationship present for each contact and creates graphs with underlying dynamic context for the ingested and normalized data of the User.

[0192] Referring to Figure 10 and Figure 11, the HMI (100) integrates large number of private apps of users which provides a rich data about a user’s interaction with his/her other contacts through multiple apps such as whatsapp, email, telegram, social media - twitter and the like. Therefore, what the HMI is able to accomplish is build a unique identity using a social graph of a user cutting across multiple apps, multiple mobile numbers, multiple interactions and based on the user’s usage pattern, the frequency, the reciprocity, the freshness of the conversation and based on all these parameters assign a value to the user for his/her current contacts. These values indicate to the HMI how valuable that contact is in terms of priority: high, medium or low. Therefore, the social graph cuts across multiple diverse applications and allows the HMI to create a unique identity of the user. This may be referred to as a cross app identity and a value is assigned based on interactions observed over multiple apps.

[0193] By way of example suppose a user who can be called user@needl.ai is interacting with other users namely, user2 and user3. The users are interacting on a regular basis, daily there are emails, zoom calls, chats, social media messages and the like. Many user’s interactions are happening on social media networks. Therefore, what the HMI is able to assess is (a) there is a rich to and fro communication between user@needl.ai and user2 by way of example. This interaction and communication is happening on email, it is happening on whatsapp, it is happening on zoom, it is happening on slack and other multiple apps. Therefore, user2 and the user@needl.ai have very high touch point contacts and every email written by user2 to user@needl.ai is answered back, the frequency is high, reciprocity is high, and the two users are forwarding messages of others to each other so based on this usage pattern the machine learning rules embedded in the HMI are able to assign a high score for user2 in relation to user@needl.ai amongst the rest of the users/peers and contacts of user@needl.ai. Suppose user@needl.ai has 500 hot contacts with whom user@needl.ai is interacting daily, the machine learning rule can put user2 at number 8 or 9 position in the list of contacts very accurately and when user@needl.ai searches his/her data the priority results will get ranked accordingly. Therefore, user@needl.ai is able to get more information from his/her closer contacts and the information from a contact not close enough gets buried which is many a times noise. A message containing the word “patent” might be sent by user3 to user@needl.ai on an email, but user3 and user@needl.ai are not communicating regularly. But user2 and user@needl.ai are communicating regularly on “patents”. So even though the search word is “patent” and user3 has sent user@needl.ai an email recently but yet when user@needl.ai does a feed or a search, user2 will be bumped up though user2 has sent user@needl.ai a message/email much earlier. In relevance because user2 and user@needl.ai are closer in high priority contacts than user3 and user@needl.ai, because user3 is just forwarding user@needl.ai articles on emails, therefore the HMI will recognize that user@needl.ai has not reciprocated any of user3’s emails/messages but on patent related matters user@needl.ai has reciprocated to user2’s emails/messages. This is how user2 will get prioritized in user@needl.ai contacts social graph and this has an implication on user@needl.ai’s search results and feed results. A unique graph is created which has been referred to as the social graph in this disclosure. [0194] Social graphs therefore cut across multiple apps, observes the users behaviour patterns, frequency, reciprocity, relevancy and all other parameters to bring about clustering and closeness of the contact with the user or his relevance/importance in the life of a user. Therefore, all things being equal the person with whom a user is in regular touch is far more important than the person with whom a user is in touch only occasionally. In accordance with the disclosure the HMI is able to reduce noise and amplify a relevant signal. The user is not aware of this and this happens in the background using the Al and machine learning rules of the HMI. The user doesn’t explicitly see this happening but he/she feels it when the user searches it and when the results are arranged according to a priority based on the social graph created for the user.

[0195] In one embodiment of the disclosure, a user can edit the priority value assigned to a contact either temporarily or permanently. For instance, if a contact is assigned as a medium value contact but in reality this contact is of high value, then a user will be able to make changes explicitly and correct this. This will require manually intervention on the part of the user. What the HMI is doing is giving the user its own suggestion but the user can control the HMI on this parameter.

[0196] Therefore, in another embodiment of the disclosure the social graph is created on a combination of machine indicated and user manipulated priority parameters which result in hybrid relevancy signals given by both - the user and the machine. Therefore, in this embodiment, the hybrid relevancy signals are included in the social graph.

[0197] In yet another embodiment, the user intervention can be applied on a time duration for example 15 days. During this time period, a contact of the user can be given a higher priority and either change the priority value after 15 days manually or ask the machine to do this by default.

[0198] Another feature of the HMI is that change in the contact details can be updated automatically and merged or over written on the current contact details.

[0199] Figure 11 shows a representative diagram of the contacts of a user designated as user@needl.ai. The figure shows a schematic diagram for sets of message exchanged across email, whatsapp and telegram platforms. The HMI builds a cross identity graph for user@needl.ai and each contact of user@needl.ai is assigned a numerical value. For instance, the contact user2 is assigned the value 0.67. This is a numerical value assigned to the contact user2 for user@needl.ai. Similarly, the contact user3 is assigned the value 0.12. This means that user3 is at a lower priority and user2 who has been assigned the value 0.67 is at a higher priority. This is how the HMI across different platforms puts down a numerical value for each contact of the user and this value is based on the frequency of communication and reciprocity. The length of time that a user takes to respond to a message/email/chat in an example of a parameter that is taken in assigning the score. Also, if the user’s email id with the email id of the contact that may show that the user and the contact belong to the same organization, but at the same time the contact may have a private email id. This is mixed and matched in one single social graph. Therefore, the value will be the same for the contact in any platform such as telegram, whatsapp and in emails. The contact interacts with many other contacts with whom the user also interacts, if the contact is interacting, for instance, with 50 people with whom the user also interacts, the HMI will recognize that the user and the contact live in the same world.

[0200] Therefore, the more the common contacts the user and a contact will have the more proximately they will be placed with each other. The lesser the contacts between them, that means the two are just working on an assignment and have come together for a limited time and then they will move away.

[0201 ] This is what the social graph does and thereby reduces noise and amplifies the relevant signal.

[0202] Another embodiment of the disclosure is the ability to generate a hybrid feed wherein the feed is partly curated by the HMI and partly curated by the user.

[0203] The current feeds for example a facebook feed or a twitter feed, are essentially completely based Al driven algorithms based on what people click, what other people like the user click. It does not give the user control over what the user sees, what the user wants to prioritize. The HMI of this disclosure is essentially a hybrid mechanism, again to enable eliminating noise and amplifying desired signals. Therefore, the HMI will allow the user to get a variety of signals on what is important for the user. For instance, the user does not want to see anything with particular keywords but other keywords are important to the user. The HMI takes inputs from the user and combines it with the Al and machine learning rules stored in the HMI.

[0204] For example, a user wants to create a feed of news items on Tesla but where the word “Elon Musk” is excluded. The user types in and selects all sources but provides a logic of “Tesla” not “Elon Musk”, “Tesla” not “Elon”, “Tesla” not “Musk”. The user as created this to view all results relating to Tesla where Elon Musk is not mentioned. The HMI will then supress all news items in all sources which include Tesla but do not include “Elon Musk”, “Elon” or “Musk” and create a hybrid feed.

[0205] Boolean logic is one method by which such a hybrid feed can be created using the HMI. There are variety of other ways that include stating what is relevant for the user in terms of priority, it could be in terms of Boolean logic or keywords, it could be in terms of likes, dislikes. There are a variety of input signals that can be given to the HMI and the input signals are combined with the relevancy and ranking factors that the machine determines and a hybrid result is provided to the user.

[0206] Referring to figure 12, an intent graph is prepared for depicting percentage HMI user intent with respect a particular time period. The Y- axis denotes percentage of search intentions of user for different areas of interest, and X- axis denotes number of time in a month user visited to relevant area of interest. In figure the user intent for business related search is high in second week of the month whereas user intent for finance is low in third week of the month. As the intent of the user changes, the graph changes dynamically.

[0207] The Intent graph of the HMI (100) establishes the ranking for each HMI (100) user in reference to his/her intent in different areas, for a particular time period. This intent graph provides the user:

(i) with each ingested data referred as intent,

(ii) with populated list of frequently searched areas,

(iii) with customization and collaborative engagement of user defined rules to define trusted sources of data and ranking for each source

[0208] The intent graphs are created within the HMI (100) at relevant times to understand user’s intent. The HMI ( !00) enables the user to connect with heterogeneous data sources to ingest data, for customization and collaborative engagement by user defined rules and the intent behind each action of user at relevant times with HMI (100) and the other sources. This results in creation of a graph underlying relationship of user intent and collaborative engagement of the intent for a particular time period.

[0209] Referring to figure 13, a preference graph is prepared for depicting relationship of human machine interface (100) user’s interest with user’s area of interest. The X - axis denotes search preferences of the user for different areas of interest and Y- axis denotes the percentage of search preferences for respective user’s area of interest. The preference graph of HMI (100) establishes the ranking for each HMI (100) user in reference to his/her collaborative engagement with his/her search preferences based on number of times an engagement occurs for different areas of interest. This preference graph provides user:

(i) with populated list of, collaborative engagement of frequent preferences of user and customization rules defined by user,

(ii) to define trusted sources of data and ranking for each source and sender of data,

(iii) for collaboration and data sharing with HMI user contacts.

[0210] The preference graphs are created within the interface based on user preferences, social graph and user defined customization rules. The HMI (100) enables user to connect to heterogeneous sources and user’s contacts to define customization and collaborative engagement rules by user. Each customization and collaborative engagement rule is referred as a user’s preference. This results in creation of a graph underlying relationship of user preferences and collaborative engagement of each preference with each contact of user.

[0211] The use cases provided below are merely examples where the interface (100) can be use and do not in any way limit the disclosure.

[0212] Use case : This use case illustrates the use of the system and method of this invention of this disclosure in a legal services environment, compliance team environment or a finance team environment wherein teams of individual users are required to (i) keep a track of the legislative changes, ongoing cases, hearing dates and calls with clients, managing drafts, client lists, and perform conflict checks or (ii) to keep track of compliance with amendments to rules, circulars/ guidelines issued by a Regulator, interaction with auditors and managing audit schedules; or (iii) are required to handle huge volumes of financial data and research reports coming into user devices via various platforms, such as email alerts, broker updates, group messages and paid subscriptions, respectively.

[0213] Team members who will be the users referred to in this disclosure can access the interface (100) through (a) Google/FB login (single sign on) or (b) a dedicated human machine interface (100) user id and login.

[0214] Successful login credentials provides the user team member individual access to the team member’s secured vault (106a) created by the interface for the user.

[0215] The logged in user is now permitted to use the functions of the Interface (referred to as HMI) (100) as described in this disclosure.

[0216] The user will now give permission to the HMI (100) to ingest the data from the user devices (10) such as data integrated from user’s hard disk, integrate third party multiple apps, and connect paid data sources, free public data sources, connecting RSS feeds, data related to third party website crawling. This permits the HMI (100) to create a comprehensive data repository for the team user to keep track of regulatory changes, updates, client lists, hearing dates and the fora in which cases are being conducted, case precedents, plaints and other pleadings from multiple drafts. All this data is stored in the vault (106a) of the user.

[0217] All team members of the legal service firm are mandated to use the interface (100) and connect all the sources and communication channels to the Interface (100). This enables the compliance department of the firm to monitor compliance with the data security and privacy rules of the Firm.

[0218] Each team member of the firm has the option to select inbuilt pre-defined public data sources based on the expertise of the team member or the general legal services provides by the firm for example Securities Law or Tax laws, area of practice and domain interests. The user has the option to connect these pre-defined data resources and connect them to the user data repository in the user’s vault (106a). This enables each team member to create an updated data repository as per area of expertise, area of practice and area of interest and when put together for all team members, will result in a comprehensive data repository for the compliance department of the Firm to oversee.

[0219] Now the data of each of the Team members and that of the Firm is ingested into vaults (106a) created by the HMI (100)

[0220] After ingestion of the user’s data, the data is indexed based on the features of an Search on search or a nested search as directed by the user to the interface (100). The interface (100) also does tagging of the data of the User. The processes carried out by the interface (100), enables the user to detect, a case, name of the case, author/ entity name. Tagging is an important step in indexing, making the search process easy, quicker, efficient and eventually enables the consumption of less processing power and reduce the processing cost for the user and the interface (100) and for the firm in which the interface is installed.

[0221] From the ingested and indexed data, the interface (100) permits each user to process the ingested data to create customized user defined Feed(s) for the user and for other members of the firm.

[0222] The feed creation is an independent process inside the interface (100). To create/ define a feed, the interface (100) permits each user to define (depending on interest area, area of expertise, and area of practice) the sources, keywords, data range and contacts, thereby enabling customizing of data in the vault (106a) to create user defined Feed(s). Feeds are dynamic as the rules defining the feeds can be altered or edited by the Team member. The user can edit or clip the feed depending upon the interest of the user.

[0223] The interface (100) has a nested search facility which permits any of the users of the interface, to filter the data of the primary search results generated through feeds, which is typically of the UI-UX type. The changes based on results shown are contextual to the search term and there are options to slice and dice differently. The nested search facility provides further processing ability to the user. The user is able to process and extract relevant data through a query/ feature that searches not only the documents and chats but also scanned contents, images, audio and video.

[0224] The processing Module (112) of the HMI (100) permits the user to search relevant data from all user defined integrated sources, public data of interest, paid data, private data, personal notes, data from third party apps, data from RSS feeds, data from crawled/ curated websites, email, files, video, images, based on the ML based search rules built in the interface, thus enabling collaborative engagement between the user and user’s data. The processing module (112) permits the user to apply Boolean logic to further refine the product.

[0225] The interface (100) also assists the user with certain task automation features like but not limited to searching within a document for example the Income Tax Act, clipping of relevant data from the document for example a pdf copy of the Income Tax Act, changing a data or a part of it in one format to another and converting it to a user defined format for example converting selected portion of a source pdf document to a word file format, summarization feature, which permits the user to carry out a nested search on the underlying document/ data and create summary reports/ notes based on the requirements of the user for example a user can select parts of the Income Tax Act based on an audit query and create summary notes, and based on key word search / nested search performed by the user. The resulting summary notes are editable by the user and the interface has the ability to enable the user to collaboratively engage with third parties by sharing of the created data with third parties for an example auditor and with the other Team Members for review and affecting edits.

[0226] The interface also allows the user to collaboratively engage to share data from multiple data sources of other users of the Firm and with the client and user itself. Data sharing can be done directly or through signed URL (date and time for granting temporary access.

TECHNICAL ADVANCEMENTS

[0227] The present disclosure described herein above has several technical advantages including, but not limited to, the realization of a human machine interface, that:

• does data wrangling, collation of data and processing;

• reduces productivity loss and prevents exhaustion in knowledge workers;

• reduces the digital footprint by facilitating the user to generate and share a link to others associated with the interface and third party users instead of sharing media;

• has all data in one place, seamless and uniform data processing agnostic to source, seamless single searching and seamless sharing;

• has a logic-based workflow automation engine which works in a variety of applications and on public data sources;

• provides an interface that suppresses noise and enhances desired signals;

• reduces tediousness and increase productivity; and

• Is entirely application agnostic and applies uniform data processing and ranking results across all data sources.

[0228] The embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0229] The foregoing description of the specific embodiments so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.

[0230] The use of the expression “at least” or “at least one” suggests the use of one or more elements, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.

[0231] Any discussion of documents, acts, materials, devices, articles or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.

[0232] While considerable emphasis has been placed herein on the modules and module units of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation.