EARLIEST PRIORITY DATE

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202231047262, filed on August 19, 2022, and titled “SYSTEM AND METHOD FOR GENERATING AND OPTIMIZING A DECENTRALIZED AUTONOMOUS GROUP OF ASSETS FOR INVESTMENT”

FIELD OF INVENTION

Embodiments of a present disclosure relate to a field of providing assistance in investment-related decision making, and more particularly to a system and a method for generating and optimizing a decentralized autonomous group of one or more assets for investment.

BACKGROUND

A basket in finance is a weighted collection (linear, non-linear or based on machine learning) of various financial instruments. The aim could be simultaneous buying or selling, for instance during trading. Financial instruments are monetary contracts between parties and may be categorized by asset class. For example, a group of financial instruments that have similar financial characteristics and behave similarly in the marketplace is categorized under a particular asset class, and that group is generally termed as a basket or a korb. However, if a person is willing to create a basket having the group of financial instruments belonging to different asset classes, then the privacy and security of the person would be at risk, and there are fewer chances that the basket created in this would be profitable in future upon investment.

Moreover, currently, people choose a single asset, basket, or quant strategy based on the awareness they have acquired from painstakingly exploring privately or publicly available news across many channels. The process of gaining awareness takes time since it involves gathering information from various sources and making decisions. This awareness is used in the following phase to manually create each basket one at a time while maintaining bias. Creating baskets manually has drawbacks such as being time-consuming, biased, and un-compared. There are multiple approaches available in the market to overcome these drawbacks.

One such approach includes a system that uses artificial intelligence-based models for prediction and making baskets. However, such an approach has two major issues such as trust and prediction risk. Users are worried about losing sensitive data when sharing decisions or info for making the basket. The analyst has an implied concern that the original analysis will be lost, copied, and misused. Further, the investment strategies and black box of making decentralized autonomous korb is not always explained to the investors. This cultivates a lack of responsibility.

Hence, there is a need for an improved system and method for generating and optimizing a decentralized autonomous group of one or more assets for investment which addresses the aforementioned issues.

BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system for generating and optimizing a decentralized autonomous group of one or more assets for investment is provided. The system includes a processing subsystem configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an input module. The input module is configured to receive event information corresponding to one or more events relevant to pricing of the one or more assets using deep learning. The one or more assets include at least one of one or more discrete assets and one or more predetermined groups-of-assets. The processing subsystem also includes an awareness analysis module operatively coupled to the input module. The awareness analysis module is configured to generate a central-prediction model using reinforcement machine learning based on at least one of the event information received in real-time from one or more information sources, anonymized investor behavior data, and one or more strategies. The awareness analysis module is also configured to generate a central awareness score corresponding to a system-central awareness about real-time pricing of the one or more assets, using the central-prediction model. Further, the awareness analysis module is configured to generate a cumulative estimator awareness score based on the central awareness score and an estimator opinion corresponding to the pricing of the one or more assets. The cumulative estimator awareness score is tokenized on a first local distributed node of a blockchain. The awareness analysis module is further configured to generate a cumulative analyst awareness score based on the central awareness score and an analyst micro research input. The cumulative analyst awareness score is tokenized on a second local distributed node of the blockchain. Furthermore, the awareness analysis module is also configured to generate a strategy score corresponding to the one or more strategies shared by a strategist, based on the central awareness score and an explainable artificial intelligence associated with the one or more strategies. The strategy score is tokenized on a third distributed node of the blockchain. The awareness analysis module is also configured to generate a collective risk score using a central risk analysis model trained using ensemble learning, based on weights and non-sensitive data transferred from the first local distributed node, the second local distributed node, and the third local distributed node to the central node, when the central node is accessed by an investor in context of decision-making for investment on the one or more assets. Further, the processing subsystem also includes a ranking module operatively coupled to the awareness analysis module. The ranking module is configured to rank the one or more assets in a context of decision-making for investment, based on the collective risk score generated for each of the one or more assets. Furthermore, the processing subsystem also includes a grouping module operatively coupled to the ranking module. The grouping module is configured to generate the decentralized autonomous group of the one or more assets using a decentralized artificial intelligence model and privacy by design architecture, by selecting the one or more assets with a preferred rank based on at least one of the event information and one or more preferences of the investor. Subsequently, the processing subsystem includes a group optimization module operatively coupled to the grouping module. The group optimization module is configured to determine a performance of the decentralized autonomous group upon execution by investment in real-time, by analyzing a performance of each of the one or more assets in the decentralized autonomous group using a comparison mechanism. The group optimization module is also configured to generate one or more recommendations corresponding to an optimization of the generation of the decentralized autonomous group, at the central node by referring to the first local distributed node, the second local distributed node, and the third local distributed node, thereby generating and optimizing the decentralized autonomous group.

In accordance with another embodiment, a method for generating and optimizing a decentralized autonomous group of one or more assets for investment is provided. The method includes receiving event information corresponding to one or more events relevant to pricing of the one or more assets using deep learning, wherein the one or more assets includes at least one of one or more discrete assets and one or more predetermined groups-of-assets. The method also includes generating a centralprediction model using reinforcement machine learning based on at least one of the event information received in real-time from one or more information sources, anonymized investor behavior data, and one or more strategies. Further, the method also includes generating a central awareness score corresponding to a system-central awareness about real-time pricing of the one or more assets, using the centralprediction model. The method further includes generating a cumulative estimator awareness score based on the central awareness score and an estimator opinion corresponding to the pricing of the one or more assets, wherein the cumulative estimator awareness score is tokenized on a first local distributed node of a blockchain. Furthermore, the method also includes generating a cumulative analyst awareness score based on the central awareness score and an analyst micro research input, wherein the cumulative analyst awareness score is tokenized on a second local distributed node of the blockchain. Subsequently, the method also includes generating a strategy score corresponding to the one or more strategies shared by a strategist, based on the central awareness score and an explainable artificial intelligence associated with the one or more strategies, wherein the strategy score is tokenized on a third distributed node of the blockchain. The method also includes generating a collective risk score using a central risk analysis model trained using ensemble learning, based on weights and non-sensitive data transferred from the first local distributed node, the second local distributed node, and the third local distributed node to the central node, when the central node is accessed by an investor in context of decision-making for investment on the one or more assets. In addition, the method also includes ranking the one or more assets in a context of decision-making for investment, based on the collective risk score generated for each of the one or more assets. Further, the method also includes generating the decentralized autonomous group of the one or more assets using a decentralized artificial intelligence model and a privacy by design architecture, by selecting the one or more assets with a preferred rank based on at least one of the event information and one or more preferences of the investor. Furthermore, the method also includes determining a performance of the decentralized autonomous group upon execution by investment in real-time, by analyzing a performance of each of the one or more assets in the decentralized autonomous group using a comparison mechanism. The method also includes generating one or more recommendations corresponding to an optimization of the generation of the decentralized autonomous group, at the central node by referring to the first local distributed node, the second local distributed node, and the third local distributed node, thereby generating and optimizing the decentralized autonomous group.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system for generating and optimizing a decentralized autonomous group of one or more assets for investment in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of an exemplary embodiment of a system for generating and optimizing a decentralized autonomous group of one or more assets for investment of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram of an asset grouping assistance computer or an asset grouping assistance server in accordance with an embodiment of the present disclosure; FIG. 4 (a) is a flow chart representing steps involved in a method for generating and optimizing a decentralized autonomous group of one or more assets for investment in accordance with an embodiment of the present disclosure; and

FIG. 4 (b) is a flow chart representing continued steps involved in a method of FIG. 4 (a) in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or subsystems elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system for generating and optimizing a decentralized autonomous group of one or more assets for investment. As used herein, the term “asset” is defined as an economic resource or a financial instrument that can be owned or controlled to return a profit or a future benefit. In financial trading, the term asset relates to what is being exchanged on markets. In one embodiment, the one or more assets may include stocks, bonds, currencies, commodities, equities, mutual funds, crypto, homes, commercial estates, and the like. Further, as used herein, the term “decentralized autonomous group” is defined as a group of assets that belong to either a single asset class or multiple asset classes. Furthermore, the system described hereafter in FIG. 1 is the system for generating and optimizing the decentralized autonomous group of the one or more assets for investment.

FIG. 1 is a block diagram representation of a system (10) for generating and optimizing a decentralized autonomous group of one or more assets for investment in accordance with an embodiment of the present disclosure. The system (10) includes a processing subsystem (20). The processing subsystem (20) may be hosted on a server. In one embodiment, the server may include a cloud server. In another embodiment, the server may include a local server. In yet another embodiment, the processing subsystem (20) may be configured in integrated circuits such as smart card, microchip and the like. The processing subsystem (20) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as a local area network (FAN). In another embodiment, the network may include a wireless network such as wireless fidelity (Wi-Fi), Bluetooth, Zigbee, near field communication (NFC), infrared communication, or the like. Generally, a basket or a Korb of assets, in financial trading, is made by grouping the assets belonging to a single asset class. Further, in order to make a basket or a Korb of the one or more assets belonging either to a single asset class or multiple asset classes, the system (10) proposed in the present disclosure may be used. Furthermore, one or more users willing to use the system (10) may register with the system (10). Thus, in an embodiment, the system (10) may include a registration module (as shown in FIG. 2). The registration module may register the one or more users with the system (10) upon receiving a plurality of user details via a user device. In one embodiment, the plurality of user details may be stored in a database of the system (10). In one embodiment, the database may include a local database or a cloud database. Moreover, in an embodiment, the database may be hybrid, wherein the database may include a decentralized database and a centralized database. The plurality of user details may include a name, contact details, a unique identity proof, and the like. Further, in an embodiment, the user device may include a mobile phone, a tablet, a laptop, or the like.

In one exemplary embodiment, the one or more users may include at least one of an investor, an estimator, an analyst, a strategist, and the like. As used herein, the term “investor” is defined as any person or other entity (such as a firm or mutual fund) who commits capital with the expectation of receiving financial returns. Similarly, as used herein, the term “estimator” is defined as an active investor who does homework or analysis before investing. Further, as used herein, the term “analyst” is defined as a person who is not actively involved in investing, but rather in quality analysis and research to share with others in return for benefits. Furthermore, as used herein, the term “strategist” is defined as a person who provides strategies or fixed plans to achieve a profitable return by going long or short in markets.

Further, for the system (10) to provide desired output, the system (10) may have to be provided with certain inputs. Therefore, the processing subsystem (20) includes an input module (30). The input module (30) is configured to receive event information corresponding to one or more events relevant to pricing of the one or more assets using deep learning. In one embodiment, the event information may correspond to at least one of a current market state, current pricing of the one or more assets in a trading market, a current market activity, and the like. Further, the one or more events may include micro news, macro news, and the like about the trading market. As used herein, the term “deep learning” is defined as a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data.

In one embodiment, the event information may be received from one or more information sources. In one exemplary embodiment, the one or more information sources may include at least one of one or more external databases, one or more internal databases, one or more cross platforms, and the like. For example, the one or more information sources may be Reuters, news channels, trading platforms, or the like. In such embodiment, the event information may be extracted from the one or more information sources by using a web-crawling mechanism. In another exemplary embodiment, the event information may be received from the one or more users via a user interface associated with the user device. In one embodiment, the user interface may include a chatbot, a dashboard, a virtual world, or the like integrated with blockchain. The event information may be received in one or more forms such as, but not limited to, text, voice, image, video, and the like. As used herein, the term “blockchain” is defined as a shared, distributed, and immutable ledger that facilitates the process of recording transactions and tracking assets in a business network.

The one or more assets include at least one of one or more discrete assets and one or more predetermined groups-of-assets. In one embodiment, the one or more predetermined groups-of-assets may be a group of the one or more assets already created by an individual and made available in the trading market. Thus, the one or more users who are actively looking for investing in the trading market, such as the investor may choose a group from the one or more predetermined groups-of-assets and invest in the same. Alternatively, if the investor is willing to create a personalized group of the one or more assets for investment, then the investor may use the system (10) proposed in the present disclosure.

Further, to enable the investor to create the personalized group of the one or more assets, an awareness of the investor may have to be analyzed. Thus, the processing subsystem (20) also includes an awareness analysis module (40) operatively coupled to the input module (30). The awareness analysis module (40) is configured to generate a central-prediction model using reinforcement machine learning based on at least one of the event information received in real-time from the one or more information sources, anonymized investor behavior data, and one or more strategies. In one embodiment, the central-prediction model may be adapted to predict an event impact and a future price of the one or more assets. As used herein the term “reinforcement machine learning” is defined as a machine learning training method based on rewarding desired behaviors and/or punishing undesired ones. In general, a reinforcement learning agent is able to perceive and interpret its environment, take actions and learn through trial and error. The awareness analysis module (40) is further configured to generate a central awareness score corresponding to a system-central awareness about real-time pricing of the one or more assets, using the centralprediction model.

Suppose the investor is an estimator. Then, the estimator may have certain opinions about the pricing of the one or more assets based on research and analysis that the estimator might have carried out. The analysis varies depending on an individual's preference for manually collecting and processing information. Estimators that use public media for their "search intention" can access information through media. Each estimator makes a different conclusion after analyzing the information. Although an estimator’s decisions are rarely consistent, a cumulative probability that exceeds a certain threshold increases a likelihood of active investment success. This is what is referred to as an estimator awareness. This understanding can be measured using artificial intelligence (Al). Numerous studies demonstrate that this consensus/collective/plurality result is superior to an expert. Further, the Estimator’s awareness value (EAV) is a score unique to each estimator and is stored with a token to maintain privacy of the estimator. The Estimator Awareness Value (EAV) may be derived from the following equation: dEAV=EAV~AI-AV wherein, dEAV is the Estimator Awareness Score,

EAV is the Estimator Awareness Value, and

AI-AV is the Central Awareness Value. Moreover, in an embodiment, the awareness analysis module (40) may also be configured to generate an estimator awareness score using an estimator awareness- related model, based on the estimator awareness. In one embodiment, the estimator awareness-related model may be generated using Al. As used herein, the term “artificial intelligence” is defined as the simulation of human intelligence processes by machines, especially computer systems. Further, the estimator opinion may be determined based on the estimator awareness score. The awareness analysis module (40) is then configured to generate a cumulative estimator awareness score based on the central awareness score and the estimator opinion corresponding to the pricing of the one or more assets. In one exemplary embodiment, the cumulative estimator awareness score may be a difference between the central awareness score and the estimator awareness score. The cumulative estimator awareness score may be a delta value. In one embodiment, the delta value may be zero, a positive value, or a negative value. The cumulative estimator awareness score is tokenized on a first local distributed node of the blockchain. Thus, in an embodiment, the estimator awareness- related model may be a distributed training model generated at the first local distributed node, and is provided with privacy by design architecture, thereby taking care of the privacy of the estimator.

Suppose the investor may be an analyst. Then, the analyst may also have certain opinions about the pricing of the one or more assets based on research and analysis that the analyst might have carried out. Analysts are different from the estimators because of the availability of an information source, intention, and biasness. The purpose of the analysts is significantly different from that of estimators in that it is to share high-quality analysis and research with others in exchange for benefits rather than making active investments.

Moreover, in an embodiment, the awareness analysis module (40) may then transform the research and the analysis carried out by the analyst into an analyst micro research input using a micro-research model. In one embodiment, the analyst micro research input may include drivers, a fundamental score, and the like. Further, in an embodiment, the anonymized investor behavior data, when the investor may be an analyst, may include key drivers, fundamental score, forecasted value, and the like. This is what can be referred to as analyst awareness. This understanding can be measured using Al.

Later, in an embodiment, the awareness analysis module (40) may further generate an analyst awareness score using an analyst awareness-related model, based on the analyst awareness. In one embodiment, the analyst awareness-related model may be generated using Al, wherein the analyst awareness-related model is adapted to extract the anonymized investor behavior data and then assist in generating the analyst awareness score based on the analyst micro research input. The awareness analysis module (40) is further configured to generate a cumulative analyst awareness score based on the central awareness score and the analyst micro research input. In one embodiment, the analyst awareness score (PAAV) is obtained by the cumulative of PAAV and ALEV, as follows:

PAAV=PAAV~AI-EV

In one exemplary embodiment, the analyst awareness-related model may be referred to as a scoring model, wherein the scoring model may be performing an operation of multiplying a weight parameter with an awareness score of the micro-research. In such an embodiment, the weight parameter may be a function of market state and consensus. Further, the market state may be a function of bid, ask, and total volume.

The score obtained from the scoring model is the input to a gamification interface. Further, the gamification layer is configured to extract information subsequent to the secured gamified Al. The real market value based on each asset’s market value is displayed through the gamification interface. The future value of the basket is also displayed based on the simulated price of assets which may be challenged or accepted as a game.

In one exemplary embodiment, the cumulative analyst awareness score may be a difference between the central awareness score and the analyst awareness score. The cumulative analyst awareness score is tokenized on a second local distributed node of the blockchain. Thus, in an embodiment, the analyst awareness-related model may be a distributed training model generated at the second local distributed node, and is provided with the privacy by design architecture, thereby taking care of the privacy of the analyst.

Suppose the investor may be a strategist. A motive of the strategist may be mainly to provide one or more strategies that can be used by an active investor for making an investment. Therefore, it is understood that one or more strategies are the most important entity in active investment. Quality of the one or more strategies is most difficult to reveal. Also sharing the one or more strategies reduces the value of a reward to the strategist. However, the privacy and security of the corresponding one or more strategies can be achieved by generating a strategy score. Moreover, in an embodiment, the event information may be stored in a central storage associated with the central node.

Further, in an embodiment, the awareness analysis module (40) may perform an event simulation using a strategist awareness-related model, based on the event information extracted from the central storage. Upon performing the event simulation, an explainable Al may be needed to provide a proper explanation about each of the one or more strategies, and hence may be input to the generation of the strategy score. As used herein, the term “explainable Al” is defined as a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning techniques. The secured “explainable Al” and the secured gamified Al brings in the confidence of sharing and also eases the understanding by the one or more users.

Furthermore, the awareness analysis module (40) is configured to generate the strategy score corresponding to the one or more strategies shared by the strategist, based on the central awareness score and the explainable Al associated with the one or more strategies. The strategy score is tokenized on a third distributed node of the blockchain. Thus, in an embodiment, the strategist awareness-related model may be a distributed training model generated at the third local distributed node, and is provided with privacy by design architecture, thereby taking care of the privacy of the strategist. This may be referred as “responsible Al” along with the “explainable Al”.

In addition, in an embodiment, the awareness analysis module (40) may also be configured to perform reinforcement training and ensemble learning of the centralized-prediction model in real-time based on at least one of the cumulative estimator awareness score, the cumulative analyst awareness score, and a variation in a trend of the strategy score. As used herein, the term “ensemble learning” is defined as a process by which multiple models are strategically generated and combined to solve a particular computational intelligence problem.

Also, in an embodiment, the awareness analysis module (40) may be configured to generate the cumulative estimator awareness score, the cumulative analyst awareness score, and the strategy score using one or more federated distributed models. In one embodiment, the one or more federated distributed models may be generated using federated learning. As used herein, the term “federated learning” is defined as a machine learning technique that trains a model across multiple decentralized edge devices or servers holding local data samples, without exchanging them. Moreover, in an embodiment, the one or more federated distributed models may include the estimator awareness-related model, the analyst awareness-related model, and the strategist awareness-related model.

Subsequently, the awareness analysis module (40) is also configured to generate a collective risk score using a central risk analysis model trained using ensemble learning, based on weights and non-sensitive data transferred from the first local distributed node, the second local distributed node, and the third local distributed node to the central node, when the central node is accessed by the investor in context of decision-making for investment on the one or more assets. In one embodiment, the non-sensitive data correspond to data that may not reveal identity-related information about the one or more users sharing respective awareness with the central node. Therefore, in an embodiment, the non-sensitive data may include the estimator awareness, the analyst awareness, the strategist awareness, and the like corresponding to the pricing of the one or more assets. Basically, based on the collective risk score, risk associated with investing in the one or more assets chosen based on the estimator awareness, the analyst awareness, and the strategist awareness may be identified. In one embodiment, the collective risk score may be set as a central benchmark for the risk and stored in the central storage associated with the central node.

Additionally, in an embodiment, the processing subsystem (20) may also include an incentivizing management module (as shown in FIG. 2) operatively coupled to the awareness analysis module (40). The incentivizing management module may be configured to generate and allocate a predetermined incentive value to each of the one or more users involved in sharing an awareness corresponding to the pricing of the one or more assets to the central node, based on a stake of the corresponding awareness using crypto-economics. As used herein, the term “stake” is defined as partial ownership or a position in which you stand to gain when a company performs well in a business. Further, as used herein, the term “crypto-economics” refers to the combinations of cryptography, computer networks, and game theory that provide secured and decentralized systems which use some set of economic incentives to provide for their maintenance.

In one exemplary embodiment, the processing subsystem (20) may also include an interface customizing module (as shown in FIG. 2) operatively coupled to the awareness analysis module (40). The interface customization module may be configured to customize data appearing on one or more successive interfaces, based on the event information, the estimator opinion, the analyst micro research input, and the one or more strategies received on one or more preceding interfaces. The one or more preceding interfaces and the one or more successive interfaces may be operatively coupled to the processing subsystem (20). Moreover, in an embodiment, the one or more successive interfaces and the one or more preceding interfaces may correspond to one or more user interfaces via which the one or more users may be accessing the system (10) from the user device.

The processing subsystem (20) also includes a ranking module (50) operatively coupled to the awareness analysis module (40). The ranking module (50) is configured to rank the one or more assets in a context of decision-making for investment, based on the collective risk score generated for each of the one or more assets. Further, the processing subsystem (20) also includes a grouping module (60) operatively coupled to the ranking module (50). The grouping module (60) is configured to generate the decentralized autonomous group of the one or more assets using a decentralized Al model and the privacy by design architecture, by selecting the one or more assets with a preferred rank based on at least one of the event information and one or more preferences of the investor. The preferences may be mutual funds and specific sectors such as technology sector. Subsequently, the processing subsystem (20) includes a group optimization module (70) operatively coupled to the grouping module (60). The group optimization module (70) is configured to determine a performance of the decentralized autonomous group upon execution by investment in real-time, by analyzing a performance of each of the one or more assets in the decentralized autonomous group using a comparison mechanism. Basically, in an embodiment, a performance of each of the one or more assets in a first group may be compared with a performance of each of the one or more assets in one or more second groups, for identifying a performance of the first group.

The group optimization module (70) is also configured to generate one or more recommendations corresponding to an optimization of the generation of the decentralized autonomous group, at the central node by referring to the first local distributed node, the second local distributed node, and the third local distributed node, thereby generating and optimizing the decentralized autonomous group.

In one exemplary embodiment, a stake of one or more tokens may be a dynamic function of the central benchmark. The dynamic function may include multiplying a weight parameter with a market state and with the collective risk score. In one embodiment, the weight parameter may be a function of the impact of an event. Similarly, in an embodiment, the market state may be a function of a difference in a central prediction and a real price corresponding to the one or more assets. Further, in an embodiment, a stake for a preferred currency may be dependent on investment by the creator of the strategy, the decentralized autonomous group, and advice. The stake is a dynamic function of a weight, market and state score, weight (impact of the event) and market state (difference in central prediction and real price). Further, in an embodiment, an investment amount may be a demand where qualified inputs such as the analysis, the one or more strategies, and the estimation are supplied. Furthermore, in an embodiment, an exchange rate of the decentralized autonomous group may be dependent on the investment amount, the qualified inputs, and the market state.

FIG. 2 is a block diagram representation of an exemplary embodiment of the system (10) for generating and optimizing the decentralized autonomous group of the one or more assets for the investment of FIG. 1 in accordance with an embodiment of the present disclosure. Suppose a user ‘A’ (80) is willing to be a part of a trading market or investment and hence registers with the system (10). The system (10) includes the processing subsystem (20) hosted on a cloud server (90). The processing subsystem (20) includes the input module (30), the awareness analysis module (40), the ranking module (50), the grouping module (60), and the group optimization module (70). The processing subsystem (20) also includes the registration module (100), via which the user ‘A’ (80) is registered with the system (10) upon providing a plurality of user details via a personal mobile phone (110). The system (10) includes the decentralized database (120) adapted to store the plurality of user details, input from the user ‘A’ (80), and the one or more information sources as a user’s asset. The system (10) also includes the centralized database (130) that holds the weights and the non-sensitive data transferred from one or more decentralized nodes (140) of the blockchain (150) to a centralized node (160).

Basically, the system (10) receives the event information corresponding to the one or more events pertaining to the pricing of the one or more assets available in the trading market for investment, via the input module (30). The event information is generally received from the one or more information sources such as one or more news platforms (170), one or more estimators (180), one or more analysts (190), and one or more strategists (200). For, the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) to be able to share the event information and respective awareness corresponding to the pricing of the one or more assets, the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) are also registered with the system (10) via the registration module (100) by providing a corresponding plurality of personal details via corresponding one or more personal mobile phones (210).

The centralized node (160) is provided with the central-prediction model trained for predicting real-time future pricing for the one or more assets. Thus, a central awareness score corresponding to a system-central awareness about the real-time pricing of the one or more assets is generated via the awareness analysis module (40). Then, as the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) also have a respective awareness about trading in the trading market, the cumulative estimator awareness score, the cumulative analyst awareness score, and the strategy score is generated for each of the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) via the awareness analysis module (40). Based on these scores, the collective risk score is generated via the awareness analysis module (40), which gives an insight into an amount of risk associated with the investment in the one or more assets that may be chosen by the user ‘A’ (80) based on these scores.

Further, as the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) provide the respective awareness about the trading market, the corresponding one or more estimators (180), the corresponding one or more analysts (190), and the corresponding one or more strategists (200) may be rewarded with a predetermined incentive value, based on the stake of the awareness shared via the incentivizing management module (220). Furthermore, the user ‘A’ (80) is interacting with the system (10) via a dashboard appearing as an interface for the system (10) on the personal mobile phone (110). Suppose the user ‘A’ (80), based on the awareness of the system (10), is interacting with the one or more estimators (180), the one or more analysts (190), and the one or more strategists (200) about crypto. Later, suppose the user ‘A’ (80) switched to a browser platform. Then, since on a dashboard, a topic of discussion was crypto, data appearing on an interface of the browser platform may also be about crypto only. This customization of the interface of the browser platform is done via the interface customization module (230).

Moreover, based on the collective risk score, the one or more assets are ranked via the ranking module (50). Suppose the one or more assets available in the trading market are stocks including stockl and stock2, bonds including bondl, bond2, and bond3, and cryptos including crypto 1 and crypto2. Consider an example in which, in the trading market, currently, stock2, bondl, bond3, and crypto 1 were predicted to be at the highest risk of facing loss upon investment. Further, stockl is predicted to be the safest one for investment, next safe is bond2, and then is crypto2. Therefore, stockl is ranked first, bond2 is ranked second and crypto2 is ranked third, and the rest of the one or more assets are ranked after this via the ranking module (50). Based on this type of ranking, the decentralized autonomous group is generated by grouping the stockl, bond2, and crpto2 in it via the grouping module (60).

Further, a performance of the decentralized autonomous group is determined and the one or more recommendations for optimizing the decentralized autonomous group are provided via the group optimization module (70). Upon receiving the one or more recommendations, the user ‘A’ (80) can then create a new decentralized autonomous group. This is how the system (10) is used by the user ‘A’ (80) for generating and optimizing the decentralized autonomous group for investment.

FIG. 3 is a block diagram of an asset grouping assistance computer or an asset grouping assistance server (240) in accordance with an embodiment of the present disclosure. The asset grouping assistance server (240) includes processor(s) (250), and memory (260) operatively coupled to a bus (270). The processor(s) (250), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (250).

The memory (260) includes a plurality of subsystems stored in the form of executable program which instructs the processor(s) (250) to perform method steps illustrated in FIG. 4. The memory (260) includes a processing subsystem (20) of FIG 1. The processing subsystem (20) further has following modules: an input module (30), an awareness analysis module (40), a ranking module (50), a grouping module (60), a group optimization module (70), an incentivizing management module (220), and an interface customizing module (230).

The input module (30) is configured to receive event information corresponding to one or more events relevant to pricing of the one or more assets using deep learning, wherein the one or more assets include at least one of one or more discrete assets and one or more predetermined groups-of-assets.

The awareness analysis module (40) is configured to generate a central-prediction model using reinforcement machine learning based on at least one of the event information received in real-time from one or more information sources, anonymized investor behavior data, and one or more strategies. The awareness analysis module (40) is also configured to generate a central awareness score corresponding to a system-central awareness about real-time pricing of the one or more assets, using the central-prediction model. The awareness analysis module (40) is also configured to generate a cumulative estimator awareness score based on the central awareness score and an estimator opinion corresponding to the pricing of the one or more assets, wherein the cumulative estimator awareness score is tokenized on a first local distributed node of a blockchain. The awareness analysis module (40) is also configured to generate a cumulative analyst awareness score based on the central awareness score and an analyst micro research input, wherein the cumulative analyst awareness score is tokenized on a second local distributed node of the blockchain. The awareness analysis module (40) is also configured to generate a strategy score corresponding to the one or more strategies shared by a strategist, based on the central awareness score and an explainable artificial intelligence associated with the one or more strategies, wherein the strategy score is tokenized on a third distributed node of the blockchain. The awareness analysis module (40) is also configured to generate a collective risk score using a central risk analysis model trained using ensemble learning, based on weights and non-sensitive data transferred from the first local distributed node, the second local distributed node, and the third local distributed node to the central node, when the central node is accessed by an investor in context of decision-making for investment on the one or more assets

The ranking module (50) is configured to rank the one or more assets in a context of decision-making for investment, based on the collective risk score generated for each of the one or more assets.

The grouping module (60) is configured to generate the decentralized autonomous group of the one or more assets using a decentralized artificial intelligence model and privacy by design architecture, by selecting the one or more assets with a preferred rank based on at least one of the event information and one or more preferences of the investor.

The group optimization module (70) is configured to determine a performance of the decentralized autonomous group upon execution by investment in real-time, by analyzing a performance of each of the one or more assets in the decentralized autonomous group using a comparison mechanism. The group optimization module (70) is also configured to generate one or more recommendations corresponding to an optimization of the generation of the decentralized autonomous group, at the central node by referring to the first local distributed node, the second local distributed node, and the third local distributed node, thereby generating and optimizing the decentralized autonomous group.

The incentivizing management module (220) is configured to generate and allocate a predetermined incentive value to each of one or more users involved in sharing an awareness corresponding to the pricing of the one or more assets to the central node, based on a stake of the corresponding awareness using crypto-economics.

The interface customizing module (230) is configured to customize data appearing on one or more successive interfaces, based on the event information, the estimator opinion, the analyst micro research input, and the one or more strategies received on one or more preceding interfaces, wherein the one or more preceding interfaces and the one or more successive interfaces are operatively coupled to the processing subsystem (20).

The bus (270) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (270) includes a serial bus or a parallel bus, wherein the serial bus transmits data in a bit-serial format and the parallel bus transmits data across multiple wires. The bus (270) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.

FIG. 4 (a) is a flow chart representing steps involved in a method (280) for generating and optimizing a decentralized autonomous group of one or more assets for investment in accordance with an embodiment of the present disclosure. FIG. 4 (b) is a flow chart representing continued steps involved in the method (280) of FIG. 4 (a) in accordance with an embodiment of the present disclosure. The method (280) includes receiving event information corresponding to one or more events relevant to pricing of the one or more assets using deep learning, wherein the one or more assets include at least one of one or more discrete assets and one or more predetermined groups-of-assets in step 290. In one embodiment, receiving the event information may include receiving the event information via an input module (30).

The method (280) also includes generating a central-prediction model using reinforcement machine learning based on at least one of the event information received in real-time from one or more information sources, anonymized investor behavior data, and one or more strategies in step 300. In one embodiment, generating the central-prediction model may include generating the central-prediction model via an awareness analysis module (40).

Further, the method (280) includes generating a central awareness score corresponding to a system-central awareness about real-time pricing of the one or more assets, using the central-prediction model in step 310. In one embodiment, generating the central awareness score may include generating the central awareness score via the awareness analysis module (40).

Furthermore, the method (280) also includes generating a cumulative estimator awareness score based on the central awareness score and an estimator opinion corresponding to the pricing of the one or more assets, wherein the cumulative estimator awareness score is tokenized on a first local distributed node of a blockchain in step 320. In one embodiment, generating the cumulative estimator awareness score may include generating the cumulative estimator awareness score via the awareness analysis module (40).

Furthermore, the method (280) also includes generating a cumulative analyst awareness score based on the central awareness score and an analyst micro research input, wherein the cumulative analyst awareness score is tokenized on a second local distributed node of the blockchain in step 330. In one embodiment, generating the cumulative analyst awareness score may include generating the cumulative analyst awareness score via the awareness analysis module (40). Subsequently, the method (280) also includes generating a strategy score corresponding to the one or more strategies shared by a strategist, based on the central awareness score and an explainable artificial intelligence associated with the one or more strategies, wherein the strategy score is tokenized on a third distributed node of the blockchain in step 340. In one embodiment, generating the strategy score may include generating the strategy score via the awareness analysis module (40).

The method (280) also includes generating a collective risk score using a central risk analysis model trained using ensemble learning, based on weights and non-sensitive data transferred from the first local distributed node, the second local distributed node, and the third local distributed node to the central node, when the central node is accessed by an investor in context of decision-making for investment on the one or more assets in step 350. In one embodiment, generating the collective risk score may include generating the collective risk score via the awareness analysis module (40).

In one exemplary embodiment, the method (280) may further include performing reinforcement training and ensemble learning of the centralized-prediction model in real-time based on at least one of the cumulative estimator awareness score, the cumulative analyst awareness score, and a variation in a trend of the strategy score. In such embodiment, performing the reinforcement training and the ensemble learning may include performing the reinforcement training and the ensemble learning via the awareness analysis module (40).

In one embodiment, the method (280) may also include generating the cumulative estimator awareness score, the cumulative analyst awareness score, and the strategy score using one or more federated distributed models. In such embodiment, generating the cumulative estimator awareness score, the cumulative analyst awareness score, and the strategy score using the one or more federated distributed models may include generating the cumulative estimator awareness score, the cumulative analyst awareness score, and the strategy score using the one or more federated distributed models via the awareness analysis module (40).

In a further embodiment, the method (280) may include customizing data appearing on one or more successive interfaces, based on the event information, the estimator opinion, the analyst micro research input, and the one or more strategies received on one or more preceding interfaces, wherein the one or more preceding interfaces and the one or more successive interfaces are operatively coupled to the processing subsystem (20). In such an embodiment, customizing the data appearing on the one or more successive interfaces may include customizing the data appearing on the one or more successive interfaces via an interface customizing module (230).

Subsequently, in a specific embodiment, the method (280) may also include generating and allocating a predetermined incentive value to each of one or more users involved in sharing an awareness corresponding to the pricing of the one or more assets to the central node, based on a stake of the corresponding awareness using crypto-economics. In such embodiment, generating and allocating the predetermined incentive value to each of the one or more users may include generating and allocating the predetermined incentive value to each of the one or more users via an incentivizing management module (220).

In addition, the method (280) also includes ranking the one or more assets in a context of decision-making for investment, based on the collective risk score generated for each of the one or more assets in step 360. In one embodiment, ranking the one or more assets may include ranking the one or more assets via a ranking module (50).

Further, the method (280) also includes generating the decentralized autonomous group of the one or more assets using a decentralized artificial intelligence model and a privacy by design architecture, by selecting the one or more assets with a preferred rank based on at least one of the event information and one or more preferences of the investor in step 370. In one embodiment, generating the decentralized autonomous group may include generating the decentralized autonomous group via a grouping module (60). Furthermore, the method (280) also includes determining a performance of the decentralized autonomous group upon execution by investment in real-time, by analyzing a performance of each of the one or more assets in the decentralized autonomous group using a comparison mechanism in step 380. In one embodiment, determining the performance of the decentralized autonomous group may include determining the performance of the decentralized autonomous group via a group optimization module (70). The method (280) also includes generating one or more recommendations corresponding to an optimization of the generation of the decentralized autonomous group, at the central node by referring to the first local distributed node, the second local distributed node, and the third local distributed node, thereby generating and optimizing the decentralized autonomous group in step 390. In one embodiment, generating the one or more recommendations may include generating the one or more recommendations via the group optimization module (70).

Further, from a technical effect point of view, the implementation time required to perform the method steps included in the present disclosure by the one or more processors of the system is very minimal, thereby the system maintains very minimal operational latency and requires very minimal processing requirements.

Various embodiments of the present disclosure provide a system with a trustable usercentric interface, for generating and optimizing the decentralized autonomous group of the one or more assets for investment. Thus, the decentralized autonomous group can be a group of the one or more assets belonging either to a single asset class or multiple asset classes. The system proposed in the present disclosure solves problems such as privacy, processing power, and biasness. Further, the user-centric interface accelerates the overall improvement of the system.

Further, since the system is utilizing an explainable Al method, the system is better able to grasp Al tactics outside of back testing and identify potential areas of failure. Investors and strategists benefit from greater transparency without sacrificing privacy in the method. Active investment requires making decisions, and an Al model can increase the accuracy and caliber of those decisions. In this system, continuous human intelligence input helps the Al model to be developed. The model can be improved more quickly by facilitating input. Additionally, the method is enhanced by increasing stakeholder dependability via a continuous channel of analyst and strategist awareness input.

Further, in an environment where user experience is prioritized and "awareness value" is kept as an asset in the cryptocurrency economy, blockchain and decentralization deliver robustness and security through greater ownership control. Deterministic and non-deterministic jobs might be divided differently to increase decentralization while conserving computing and hardware resources.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.