CONFIDENTIAL INFORMATION

Applicant: MoFalP, LLC

Address: 4327 Pine Ridge Circle, Monclova, OH 43542, USA

Title: Symbol Delimited and Defined Data Blocks To Write Rich

Data Stories For Use With Artificial Intelligence.

First Named Inventor: Matthew A. Molenda

Attorney: McCarthy, Lebit, Crystal & Liftman, Co. L.P. A.

Customer No.: 113863

Attorney Docket No.: AML.P026.PCT

RELATED APPLICATIONS

[1] This application claims priority from each of U.S. Provisional Patent Application Serial No. 63/265,216 and its filing date December 10, 2021 , and U.S. Provisional Patent Application Serial No. 63/267,269 and its filing date January 28, 2022. Each of these applications is hereby incorporated by reference in their entireties for all purposes.

FIELD OF THE INVENTION

[2] This invention relates to medical systems, and more particularly, to using symbolic delimiters and symbolic definitions to automatically categorize and collate data blocks to write rich stories about a file or area of a medical record that can be analyzed and retrieved by a search engine, artificial intelligence, or machine learning.

SUMMARY OF THE INVENTION [3] The present invention includes a method and system for data collation, retrieval, organization, analysis, and summarization of health data by utilizing order-agnostic symbol delimited data linked to standardized symbols, such as emojis or unicode characters. A file name and metadata building method uses a data-blocking engine with low-character-count, symbolic delimiters automatically applied to each data field. The data blocks are orderless and structureless, with no header requirements. Similar to physical construction blocks, the digital data blocks can be constructed, built upon, deconstructed, rearranged, or modified. The data blocks build digital foundations on which artificial intelligence (Al) and/or machine learning can gather, collate, modify, and serve language agnostic, database agnostic, and platform agnostic data for individual patients or for populations, such as in a research search engine that retrieves automatically de-identified datasets of data tagged as “OK” to use in research, among other tags, solving privacy concerns at the same time.

[4] Currently, across the world different documentation systems and Electronic Health Records (EHRs) with different data and database structures are used. No uniform system is in place. While different standards like Health Level 7 (HL7) have been proposed, complex bridges among systems are required and interoperability among systems remains an elusive and unfulfilled promise, especially in the United States. In the US, different organizations, hospitals, and practices can utilize an EHR of their choosing, thus creating further fragmentation of the country’s health data. And, while EHRs may try to integrate existing standards, existing standards have challenges like legacy support of outdated database structures and fields, different naming conventions, forward- and backward- compatibility, and poor image support.

[5] Another problem with existing standards is the complexity of applying standards. Published standards are most useful when they are automatically applied, easy to use, and require medical knowledge and health literacy in only limited circumstances. Existing published standards also typically address patient demographics, facility demographics, and encounter demographics separately from diagnoses, procedures, treatments, and anatomic sites or regions; with no single method to link or unlink all of these standards together. Still further, different data headers in different countries presents meaningful challenges when performing multi-national research - where just one data point has multiple different data headers - for example "date of birth” may be abbreviated as “DOB” or "BDAY” in a US record, and as “FDN” for “fecha de nacimiento” in a Spanish system, and something else in another language, thus creating a challenge for analysis that must be manually overcome.

[6] Categorizing and linking health data into standardized and symbol-delimited data blocks presents a unique opportunity to automatically collate, display, and analyze health information with Al assistance. The symbol-delimited and symbol-defined data blocks can be stored in whole, or in part, into existing systems and become part of the file names themselves or the individual file’s metadata regardless of the existing system’s database structure or available fields. For data block storage, the two most common non-proprietary file formats in healthcare records are documents saved in Portable Document Format (.PDF) and images saved with compression in Joint Photographic Experts Group (.JPEG) format. These file formats already support additional metadata, like Exchangeable Image File Format (EXIF) in .JPEG. Since these ubiquitous file types can be stored either inside or outside of electronic medical records, they are more accessible and platform agnostic than medical images stored with other proprietary data types, and therefore easier to store metadata within regardless of the user’s operating system or record system.

[7] Automatic Al-assisted collation and aggregation of data blocks, especially in the context of blending anatomy and non-anatomy health data through multiple interconnected neural networks, allows for innumerable opportunities in clinical practice and research. In the clinical setting, this saves countless hours of searching through different medical databases and tabs to find relevant information for a single patient. Applying data block aggregation and automatic deidentification to research creates paradigm shifts in research capabilities. This invention applies to surface and deep anatomy; and applies to aggregation of non-anatomy associated diagnoses and other data as well. Automatically linking records, images, and reports related to a basal cell carcinoma on the “left ala nasi” with a “nose emoji” for example, create a single-character symbol- defined filter point within the patient record to easily find all records dealing with the nose. Adding a laterality symbolic character for “left” plus the nose emoji creates an even more powerful filter point that only brings up records related to the left nose. Combining a large number of these filter points creates targeted data retrieval; and creating these filter points across many patients, with the nose emoji for example, allows for retrieval of records related to the nose across populations. It can be contemplated that coupling a nose emoji data block with a diagnosis code block, like 2C32 for basal cell carcinoma from the International Classification of Disease, 1 1th revision (ICD- 11 ), that the search engine included in the present invention could collate information about all patients in a record system who have basal cell carcinoma on their nose, including photos, records, and reports - which dramatically simplifies manual collation to create automatic data sets for epidemiology research.

[8] A research search engine (a “reSearch engine”) is also included in this invention, which for individual patients can retrieve specific health data from a plurality of databases, file formats, and bookmarks; and for populations can retrieve automatically de-identified datasets of data tagged as “OK” to use in research among other tags, solving privacy concerns at the same time.

[9] Automatic anatomy categorization linked to other categorizations (such as country, region, sex, age, race, diagnosis, diagnosis group, procedure, procedure group, and other health data) can happen simultaneously into various and unlimited categories with data blocks. F or example, just for anatomy, a point of interest on the mid left lower anterior thoracic region can be categorized into a symbolic emoji group of a US-based CPT coding group of “trunk arms or legs”; a descriptive group of “milk line”, and ICD-11 hierarchy including “anterior thoracic region”, “chest wall”, “thorax”, “upper trunk,” and “trunk” a cross-mapping group of NYU number 217; a SNOMED CT group of 264242009 (with its own hierarchical structure); and other categorizations that can be automatically applied to additional steps, such as assigning a country- or region-specific billing code based on the appropriate categorization or assigning a tracking ID to join the categorizations into research applications, triage applications, file naming conventions, or search engines. Photos and other attachments can automatically be added to these categorizations and defined anatomic sites, and the metadata can be combined from the encounter, the anatomic site, and the additional metadata. For example, some cameras use geographic GPS coordinates in (Exchangeable Image File Format (EXIF)) a photo’s metadata to document the location of a photo. It is contemplated that the defined anatomic sites and the encounter data blocks are automatically combined with the geographic GPS data from the photos, and the data blocks can further be used to triage wartime injuries to the most appropriate medical outpost. It is contemplated that burns, percentage body surface area, gunshot wounds, chemical injuries are examples of critical data points that need to be communicated quickly, perhaps initially even over radio communication with low data bandwidth methods. In a triage example, like in a military operation (even where operatives speak different languages), injuries are documented and communicated in any coded, linguistic, or symbolic language and automatically triaged based on anatomic sites of involvement, body surface area, injury type, injury intensity, injury count, injury distribution, and geographic GPS location. It is contemplated that the anatomic site encoding system is even customizable and encryptable, so even if intercepted, the coded communications would need a decoder to make sense of it.

[10] The present invention applies a vocabulary builder and a site naming sequence configuration through artificial neural networks to break down anatomic site descriptions into data blocks including site name, laterality, prefixes, suffixes, enhanced modifiers to describe direction, custom descriptions and triangulations, automatic relationship descriptions with magnitude modifiers, code sequences, translations, synonyms, groupings, symbolic references, crossmappings, and other metadata. In other words, these are some of the “anatomic components” or data blocks of the anatomic site name, and the present invention can arrange these in customized ways based on user preference or language. An example of this rearrangement is with natural linguistic sequencing applied through natural language processing to show the anatomic site name first in Spanish; followed by laterality (“left hand” in English is most naturally “mano izquierda” in Spanish (which sequentially translates to “hand left”). The invention also detects, translates, and visualizes combinations of coded, linguistic, and symbolic inputs like “Spenglish” “left mano” for left hand could automatically be translated to the correct linguistic and coded language for mano izquierda, and thus the correct symbolic language outputs into delimiters or standalone symbols, aka “symbol-defined data blocks.” For example, if someone has limited anatomy knowledge of one linguistic spoken language, they could mix languages to achieve desired translations. Language inputs can also be verbal or spoken and translated into a standardized anatomic site name and visualization. Automatic language modification also applies language specific considerations, such as changing laterality endings for masculine vs. feminine terms (izquierdo vs izquierda); or to display considerations for languages that read from right-to- left, such as Hebrew and Arabic. This, combined with applying neural networks to data block translations of the user interface, symptoms, morphologies, durations, numbers/alphabets, descriptions, diagnoses, diagnoses extensions, tags, visualizations, legends, and all other components of the software engine allow of the present invention for automatic, enhanced translation of the entire medical encounter, with or without anatomy visualizations and images.

[11] The anatomic site, standardized anatomy codes, names, and symbols, patient data, diagnosis data, encounter data, tags, and other data can be used to generate language-agnostic file naming, grouping, and exporting function with optional universal symbolic low-character-count delimiters to automatically write a language agnostic story about files, documentation blocks, bookmarks within patient charts, labeled specimens, photos, attachments, links, and other metadata. This symbolic delimited and defined data (termed as “data blocks” in the present invention) can be truncated in a file name, encrypted into static or evolving QR codes (or other codes with or without encryption), stored in exported file metadata, exported to a database or file wrapper (such as a Digital Imaging and Communications in Medicine (DICOM) wrapper), filtered, searched, de-identified, encoded, and tagged. In other words, the data blocks are combined and separated with meaningful delimiters, such as symbolic delimiters like universally translatable emojis, into an order-independent, structureless, meaningful story. Reiterating this, the invention writes a “novel” about the file using data blocks that does not have to fit into an EHR or other defined data structure (since the invention is database independent and agnostic, and data structure independent), and include that “novel” in the filenames, file metadata, or both, or include that novel as bookmarks within other records, thus automatically creating a filter and data target point. A reSearch engine, used for research on individual patients or populations, can retrieve identifiable or scrubbed (de-identified) health data based on the data blocks. In another application Al collates these data blocks to put together a history or timeline for an individual patient related to a specific anatomic region of interest, by using a single anatomic site or category, or a group of anatomic sites or categories, or other non-anatomy data blocks. Still other benefits and advantages of the invention will become apparent to those skilled in the art to which it pertains upon a reading and understanding of the following detailed specification.

BRIEF DESCRIPTION OF DRAWINGS

[12] FIG. 1 is an omnidirectional data model that illustrates the capabilities of the data block engine.

[13] FIG. 2 is a screenshot showing map synchronization and pin-level data containing symbolic tags and categorizations.

[14] FIG. 3 is the screenshot of FIG. 2 with portions translated to Chinese and added NovelFile™.

[15] FIG. 4 is a screenshot showing options to customize the anatomic site name sequence configuration and the data blocks within a file name builder. [16] FIG. 5 is a screenshot of a modal view of a thumbnail image and its accompanying symbol delimited and symbol defined file name.

[17] FIG. 6 is a screenshot of a coordinated anatomy data in correspondence with a color- coded legend; and symbolic definitions for the anatomic site group.

[18] FIG. 7 is a legend of representative symbolic searches within a reSearch engine.

[19] FIG. 8 is a legend of representative application examples to re-create pins on regions of interest

[20] FIG. 9 is a screenshot of an artificial intelligence collated patient history in an anatomic region of interest made possible by data blocks.

DETAILED DESCRIPTION OF DRAWINGS

[21] FIG. 1 depicts an omnidirectional data model 330 where uncoordinated 332 and coordinated 334 anatomy data 336, non-anatomy data 338, geographic data 342, tags 344, and data buckets 340 containing photos, attachments, and links enable the capabilities 350 of the data block engine 352. For anatomy 336, an uncoordinated data 332 example would be a linguistic description like “right ear.” Coordinated data 334 examples would be the position of a pin on a visual anatomic map, or selection of an anatomic region of interest on an image that has an anatomic map. A membership and category 346 example would be the “right ear” belonging to the “head and neck” in a hierarchical relationship, and the “auditory system” in a functional system. Data buckets 340 overlap with anatomy data 336, tags 344, non-anatomy data 338, and communicate with geographic 342 and linguistic language 348, and contain data such as photographs, attachments such as pdf reports, and links such as hyperlinks to a specific bookmark in a medical record. A geographic coordinated data 342 example includes the geographic GPS coordinates of a photograph captured by a GPS enabled phone with a camera that stores the GPS data in the photograph. In this embodiment, tags 344 are used to supply data block tags to the data block engine 352, with an example being “OK” tag symbolized by the “OK emoji” signifying that the patient has approved to allow their data to be used in research, it is contemplated that the data block engine 352 has capabilities 350 in record generation, retrieval, deidentification, translation to any coded, linguistic or symbolic language, sequencing timed data into timelines, form generation, visualization of healthcare data (such as re-creating a point on an anatomic map to show the location of a disease, or selecting a region of interest on an anatomic map to search the research engine), tracking of records and health data including monitoring of populations, and filtering and scrubbing structureless, orderless health data to deliver the required results. The data blocks flow in all directions, making it omnidirectional. It is contemplated that all of the data are connected to a neural network that can be used by Al and/or machine learning.

[22] FIG. 2: depicts a screen shot with dynamic patient and encounter synchronization of data to map. Real-time synchronized map data 60 includes text like patient name, symbolic translations like for male sex as a symbolic definition, and symbolic delimiters like the birthday cake emoji to represent date of birth and the calendar emoji to represent encounter date. It is contemplated that some symbols such as those for patient sex stand on their own as symbolic definitions, and do not need additional data, while others like the birthday cake emoji would typically be tied to other data like a date, and thus the date of birth would be delimited by the birthday cake emoji in an order independent and structureless string of data blocks. The cursor 14 is shown over the right (superior) paramedian forehead with a corresponding color-coded legend 12 to the bottom left of the figure. On that same diagram, two pins are dropped, with a brown representing cryosurgery procedure to a diagnosis of an inflamed seborrheic keratosis, and a red A” representing a shave biopsy procedure on the diagnosis of a neoplasm of uncertain behavior of skin (2F72.Y), with the representing the precise point of the biopsy procedure. The OR code 62 that contains dynamic anatomic address (a multidimensional and reproducible anatomic site that is trackable at different time points) information is partially redacted, but it contains patient data, re-creation data, anatomic site data, and other data blocks. This embodiment also depicts the icons for changing procedures, diagnoses, list memberships, and pins to distribution segments, along with a dropdown that shows the pin preview and description of the pin should the user wish to automatically change multiple components of the dynamic anatomic address but keep the selected anatomic site and site descriptions constant. In this embodiment, the user could easily change from “. A” representing a shave biopsy procedure on the diagnosis of a neoplasm of uncertain behavior of skin (2f72.y) to a diagnosis of melanoma, thus dynamically changing some of the data blocks related to this pin and the other data attached to the pin, such as the file name diagnosis. In other words, the diagnosis component and other components like the pin description change dynamically in the dynamic anatomic address, but the anatomic site information and visualization of the location remains static.

[23] It is contemplated that files named with symbolic delimiters and symbolic definitions could easily name photographs and attachments with a meaningful written story or novel about that photo or attachment, automatically. Dynamic changes in portions of the data blocks are especially helpful at different time points, since diagnoses can change with additional information, like a pathology report from the biopsy, but anatomic site data blocks will remain the same. The notes section was typed in with free text, and it is not currently tagged for translation. There are “chips” of data blocks relevant to the encounter that can be used to insert automatic, automatically translatable text blocks into the notes box, such as the procedure name (“shave biopsy” in this example). There is also a morphology selector and a symptom selector (not shown) in which the user can enter these automatically translatable features. It is contemplated that the user is a computer, and for example, the morphology data blocks can be detected from the images and added automatically. It is contemplated that other features could be added into different parts of the application from automatic image detection, such as skin type and skin tone (not shown here). It is contemplated that skin subtyping and sub-toning can also be done (not shown here), such as a customized skin type and tone that has a much more granular scale and based on dynamic anatomic address average calculations and confidence intervals, and other features such as age. For example, sun exposed skin on the face may be more darkly pigmented in older patients who spent a lot of time in the sun, and the skin on their inner arm or buttock where there is typically less sun exposure would provide a more accurate skin type and tone measurement for the overall patient, and is therefore weighted more heavily in the skin subtype and subtone calculations from the different skin tone and skin type data blocks based on anatomic locations.

[24] It is contemplated that lighting features captured by geographic GPS coordinates, weather, indoor/outdoor settings, lighting, and camera settings, and other EXIF metadata can also be data blocked and analyzed for more accurate readings. It is further contemplated that standardized color wheels can also be used along with camera capture. In the depicted embodiment, each photo and attachment are joined into a collated bucket for this pin and dynamic anatomic address which has a symbolic definition, and are tagged and notated with other symbolic tags, definitions, and delimiters. It is contemplated that a symbolic definition can also serve as a symbolic delimiter, with the “tag” emoji being an example of this. The “tag” emoji symbolically delimits other symbolic defined tags, such as a magnifying glass emoji to tag a photo as a closeup, or an “OK” emoji tagging the image as approved for use in research. Thumbnails are shown for each photo and attachment in the currently joined bucket and clicking on it will bring up a modal to view the thumbnails as navigable and editable images in larger size (shown in FIG. 3 ). Symbolic emoji tags are shown for brevity, but these tags are also translated with linguistic parallels in any coded or linguistic language using a neural network. Photo notes and link descriptions are only translated based on user preference. Links are also categorized with symbolic delimiters and tagged with language agnostic symbolic and translatable text tags.

[25] FIG. 3 depicts the screenshot of FIG. 2 with portions translated to Chinese. This embodiment has been simultaneously and real-time translated to Chinese 244 in all areas except select manual inputs still in English 240. This embodiment also shows a symbolically delimited and symbolically defined NovelFile™ filename 249 for the image 247 in bucket belonging to this pin at this dynamic anatomic address. The NovelFile™ filename 249 tells a language agnostic story about the pin by using symbols, and order does not matter because of the symbolic delimitation. The symbolic story telling file name can also be included with the metadata of the exported or saved file, based on user preference. The exemplar photo 247 in this embodiment belongs to the dynamic anatomic address and pin and is editable and able to be marked up. Additionally, the same photos or attachments or links or forms can simultaneously belong to other dynamic anatomic addresses, such as the other * pin (obscured in this screenshot by the translated photo modal 248) and exist in multiple buckets and dynamic anatomic addresses simultaneously. Some useful examples of multimedia have multiple dynamic anatomic addresses are illustrated by the photo in this figure. On the map, there is a shave biopsy to rule out melanoma (. A, red); and an inflamed seborrheic keratosis (*, brown) treated with cryosurgery right above it. the recent surgical scar above that can also have details automatically pulled about that surgery based on its data blocks derived from its dynamic anatomic address (not shown) and other data blocks, into a shadow chart or timeline view, for example. This photo 247 can belong to all three dynamic anatomic addresses and have differentiated, and the same data blocks related to the anatomic locations, in the ongoing example simultaneously, because it is relevant to each. It is contemplated that timeline views, data collation, anatomic region filtering, communication, translation, and future documentation into the correct buckets are all achieved through the dynamic anatomic addressing and data blocking models applied by the described software engines and artificial neural networks working together. Also shown in this embodiment is a translated dropdown that allows for conversion of dynamic anatomic address component of a pin to be converted to a translated distribution segment that is visually represented on the map (not shown), and has other differences such as diagnosis, but maintain its pin position through an invisible anchor pin, and bucket contents. It is contemplated that some of the data blocks remain the same (like anatomy data blocks), while other data blocks (like non-anatomy data blocks such as diagnosis), would change or evolve under such circumstances, and as one example. [26] FIG. 4 is a screenshot showing options to customize the shown data with options to automatically and dynamically order, categorize, and show data 270 like coded translations, symbolic categorizations of anatomy and health data, and symbolic delimiters and symbolic definitions related to data blocks 274. The naming sequence can individually toggle data blocks 272 related to anatomic site name components, code translations and options, optional separators, and symbolic categorizations and symbolic definitions such as emoji groups in this exemplar. As shown in FIG. 4 the images and attachments can be customized 274 to a desired configuration as well. The images and attachment of present invention uses symbolic delimiters (such as a birthday cake emoji for date of birth) and definitions or categorizations (such as patient sex) automatically, allowing for automatic storytelling, data aggregation, and data filtering through filenames, file metadata, bookmarks, links, file wrappers, and other digital repositories for data and metadata. Importantly, it is contemplated that anatomic site and dynamic anatomic address component data blocks can be represented simultaneously in multiple ways in the story, including but not limited to emoji groups as a symbolic category or definition, code strings, linguistic description and categories, separated laterality and site name components, test ID, pin ID, pin coordinates, pin angles and deviations, pin relationships, pin level of hierarchy, pin organ system, pin anatomy system, site relationships, site segmentations, site level of hierarchy, site organ system, site anatomy system, and other representations.

[27] FIG. 5: This embodiment shows an example file name 246 for a photo 247 that is named with symbolic delimiters, health data including anatomic site data, and symbolic definitions. Within the filename, unique symbolic characters (emojis and unicode characters) construct a rich story about the photo and automatically link different data concepts together into data blocks such as encounter demographics, patient demographics, anatomic sites, diagnoses, and even free text symptoms. Even more data blocks can be saved into the actual file metadata or into different sections of a progress note or report, for example. It is contemplated that file names may have size limitations (such as 256 characters), the data blocks may be truncated in the filename and simultaneously placed into metadata fields for the file which have larger data storage capabilities. It is also contemplated that data blocks can document changes and histories over time, such as when some data blocks change at different time points. Just from the exemplar filename 246, which has written a novel or rich story about the file, including details about its dynamic anatomic address, it is contemplated that even a human reader could ascertain a lot of information about the file from the file name. Each symbolic delimited and symbolic defined component of the exemplar file name 246 is broken down in a table 245. It is contemplated that even more data blocks can be stored within the file metadata or within file wrappers, such as DICOM wrappers. It is contemplated that symbolic delimiters and symbolic definitions can optionally use standard character-based delimiters and text abbreviations or descriptions of the symbols for legacy systems that do not support all the unicode and emoji symbols (not shown), so they may be used in parallel with legacy datasets. There are a multitude of benefits in storing data blocks as described, including but not limited to: (1) platform agnosticism: single- or low-character count categorizations that are stored in the filename and/or file metadata, or within sections or progress notes or reports, allowing this concept to work regardless of the electronic health record system or database in use; (2) order agnosticism: the order of the data blocks does not matter; (3) allowing for structureless and orderless data; (3) no database is required; (4) language agnosticism: standardized symbolic delimiters and symbolic definitions confer meaning regardless of language, and construct a human readable story just with the data blocks; (5) modifiable: as additional information becomes available, additional data blocks can be added to existing records without actually altering the integrity of the record. For example, in the exemplar, the diagnosis might have been neoplasm of uncertain behavior (2F72.Y) at the time of the photo; but after biopsy it was determined to be basal cell carcinoma, nodular type (2C32-XH2CR0); (6) data collation, aggregation and de-identification: select data blocks can be searched for, collated and/or aggregated, and automatically de-identified on an as-needed basis simply by removing the health data blocks that contain Protected Health Information (PHI). For example, for compliance with PHI and Health Insurance Portability and Accountability Act (HIPAA); (7) uniquely targetable: currently, it is exceedingly rare to see emojis and unicode symbols in medical records or their metadata, so their use in the method set forth by this invention would mitigate any legacy issues while simultaneously unlocking new data frontiers. I f found visually distracting, the data blocks and symbols can also be targeted to hide from the user’s view, serving as invisible data blocks or “bookmarks” within a progress note, for example. Unique targeting also enables future search, collation, and aggregation capabilities.

[28] It is contemplated that from a research perspective, formulaic searches through health data blocks can enable filtered, de-idenlified, aggregated research data that has been preapproved for use in research (i.e., tagged as “OK” to indicate patient approval;. Granular tagging and data block application of dynamic anatomic addresses, or individual photos or tiles, can address patient privacy concerns automatically by only allowing appropriately tagged content into research search results. Standardized and symbol-delimited health data blocks create a foundation for a research search engine (a “reSearch engine”) for healthcare research data. The included formulaic search example searches, aggregates, and delivers de-identified photos symbol for "camera”) for all male patients with basal cell carcinoma (2C32) on the nose between ages 30-40 (calculated as age at time of encounter, by encounter date minus birthdate) by searching and modifying the health metadata blocks that have been tagged as “OK to use in resear It is contemplated that the order of the formulaic query does not matter. It is further contemplated that the files are automatically scrubbed of identifiable patient information and delivered to the researcher with relabeled data blocks file naming protocols. Standardized and symbol-delimited and symbol defined data blocks represent a new frontier in medicine and research and are the key to innumerable new clinical and research capabilities. Applying health data block labeling and tagging to health information creates foundations for Al-assisted collation, retrieval, organization, and summarization of health data and records. For a single patient, multiple diagnoses and treatments can be linked simultaneously to a dynamic anatomic address and different anatomic regions and dynamic anatomic addresses through their data block components. It can be contemplated that machine learning and Al generate a history about a region of interest by using health data blocks including blocks from dynamic anatomic addresses, diagnoses, treatments, and dates in an area of interest. To restate, it can be contemplated that just like physical blocks, the health data blocks can be combined, deconstructed, and built upon, and exist simultaneously in unlimited dynamic anatomic addresses and in unlimited block data structures, including a block chain. It can further be contemplated that a context aware, automatically generated history includes direct links to relevant photos or other imaging (like X-rays, ultrasounds, etc.), documents, forms, reports, and data collated from and made possible by the data blocks.

[29] FIG 6. depicts a screenshot illustrating a selected anatomic site 18 at the left dorsal proximal interphalangeal joint of little finger, coordinated anatomy data 16 is shown in correspondence with a color-coded legend 12; and symbolic definitions 19 for the anatomic site group 20 are shown for each dimension, level and layer in an English embodiment at the end of each anatomic site description. It is contemplated that the symbolic delimited and symbolic defined emoji categorization captures all of anatomic site data in a granular way simultaneously regardless of language or code set through a cross-mapping data set, neural networks, and data block engine.

[30] FIGs. 7 and 8 depicts exemplar emoji searches 65 and application examples 66 with their unicode backup. While emoji characters may render differently on different systems, they are backed up by unicode and confer the same meaning nearly universally. Some emojis may even display differently in different countries based on inherent emoji localization features. It is contemplated that the birthday cake emoji may display differently in Japan than in the US; and the birthday cake emoji is closer to a global universal symbol for “date of birth” than “DOB.” Furthering this example, in Spanish, “fecha de nacimiento” means “date of birth” or “birthday” and may be abbreviated as FDN in a record system, thus it is contemplated that these different data headers in different languages create the need for manual data cross-mapping in multi-national research an health data.

[31] FIG. 9 depicts an exemplar single Al, context aware, automatically generated history includes direct links to relevant photos or other imaging (like X-rays, ultrasounds, etc.), documents, forms, reports, and health data. The underlined sections 64 shown in the present embodiment represent hyperlinks directly to the relevant notes, reports, photos, and other health record information. Al and machine learning generated single patient history in English collated, organized, and presented from data blocks in the selected anatomic site. It is contemplated that data are organized in a timeline based on the selected anatomic site 52, and account for regional anatomic sites as well (with information delivered for the left cheek, which is automatically included in the specialty context and the anatomy context). It is contemplated that automatic cross-links are generated to procedure summaries, photos, results, prescriptions, and other health data associated with the dynamic anatomic addresses and data blocks, represented as blue hyperlinks in the figure. It is further contemplated that progression, transformation, recurrence, growth, resolution, and other changes can be tracked, documented, and analyzed automatically because of the platform that includes the dynamic anatomic addressing method and the data blocking engine. It is further contemplated that symbolic delimitations and symbolic definitions and symbolic categorization in all systems form artificial neural networks that enable language agnostic, order agnostic, platform agnostic, modifiable, and targetable results, collation, and aggregation.

[32]

[33] It is contemplated that an example of a more global result, with deep anatomic sites (not shown), includes applying the dynamic anatomic address method and data blocks, and the data block engine of this invention, to answer global questions that affect superficial, deep, or systems- based anatomy. For example, a standardized distribution tracking output that automatically segments the lungs in layered or three-dimensional space tracks how a respiratory virus affects different areas of the lungs with fibrosis, inflammation, hemorrhage, and other morphology features identified on medical imaging or biopsies. Taking this example further, the inflammatory profile is linked to different dynamic anatomic address areas of the lung where fluid samples were taken, providing dynamic collated answers to questions like: Is the inflammatory response different in the lower lung versus the upper lung? Does the inflammatory profile of the different lung areas change over time during a disease course? How does drug X affect the inflammatory profiles in different areas of the lung? Does drug X alter progression to pulmonary fibrosis? Is the left or right lung more likely to progress to fibrosis?

[34] The foregoing descriptions of specific embodiments of the present invention are presented for purposes of illustration and description. They are not intended to be exhaustive nor are they intended to limit the invention to precise forms disclosed and, obviously, many modifications and variations are possible in light of the above teaching. The embodiments are chosen and described in order to best explain principles of the invention and its practical application, to thereby enable others skilled in the art to best utilize the invention and its various embodiments with various modifications as are suited to the particular use contemplated. It is intended that a scope of the invention be defined broadly by the drawings and specification appended hereto and to their equivalents. Therefore, the scope of the invention is in no way to be limited only by any adverse inference under the rulings of Warner-Jenkinson Company, v. Hilton Davis Chemical, 520 US 17 (1997) or Festo Corp. v. Shoketsu Kinzoku Kogyo Kabushiki Co., 535 U.S. 722 (2002), or other similar caselaw or subsequent precedent should not be made if any future claims are added or amended subsequent to this patent application.