DETAILED ACTION
A. This action is in response to the following communications: Transmittal of New Application filed 03/15/2024.
B. Claims 1-20 remains pending.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Burton, Andrew John (US Pub. 2025/0165717 A1), priority to provisional application No. 63/499,489 filed on May 1, 2023; herein referred to as “Burton”.
As for claims 1, 12 and 19, Burton teaches. A method for generating a web form comprising:
receiving, by a provider computing system, a source file (par. 139, fig. 1 depicts a repository storing source data);
determining, by the provider computing system, case data based on the source file (par. 142 ingestor module determines new case data from source database);
generating, by the provider computing system, a case dataset including the case data (par. 146 digester module which will generate based upon extracts, classifies and sequences markup from specific sections in documents from repository, fig. 1);
selecting, by the provider computing system, a rule including a rule criteria and a destination address from a first repository of the provider computing system (par. 147 an XML parser such as lxml is used to attempt to structure the markup by producing an XML element tree that can be traversed and tags subjected to fuzzy matching which will discover location of MD&A start and end; this will save the address of the parsed content within the document in a tree, the correlation is that the XML parser follows a rule based criteria to build the XMOL element tree that is used to by traversing for structure the document for storage in linkable addresses in the XML element tree; par. 148 At the end of the digestion process, this markup can be wrapped in a newly-generated envelope, archive metadata tags for ingestion data lineage can be added, and the tags may be assigned internal digestion sequence numbers which also persist in the preserved markup);
determining, by the provider computing system, the case data of the case dataset fulfills the rule criteria (par. 147 the XML parser is following specific rule based criteria to find and structure document for traversing; For example, if a high-confidence match arises with the DISCUSSION AND ANALYSIS fragment in the region of the document typically associated with the Table of Contents of the filing, this may be hypothesized to explicitly be the beginning of the section in the ToC. The system can then attempt to look for an href attribute constituting a suspicious link directly to the potentially unlabeled element which logically commences the section content);
selecting, by the provider computing system, a web form template from a second repository of the provider computing system (par. 26 system purpose is to generate “insight-generating functions” which are dynamically generated visualizations (e.g. web form equitant) in further details this insight generated function also tend to produce metadata and vector graphics with correlating information sufficient to draw from a list of statistical anomaly or clustering functions to discover interesting data points, series, objects or other elements, and produce mechanical descriptions using templating that summarize facets related to the discovered plot points of interest. The mechanical descriptions, while not particularly discursive and analytical, may be written so as to produce a prompt that can be dispatched to an LLM to produce a discursive synthetic analysis of the anomaly, point or region of interest, or data cluster in the vicinity of the plot elements themselves.);
generating, by the provider computing system, the web form based on the web form template and the case dataset (FIG. 29 illustrates a report generator tool which allows the user to select one or more report templates, which will be filled by the system with the relevant information from the document in accordance with one embodiment.),
wherein the web form includes at least a portion of the case data of the case dataset (par. 152 the amasser module is used to build departmented textual artifact representation (rendered in template insight -generating functions on the user interface));
generating, by the provider computing system, a link associated with the web form (par. 152 the amasser module builds links to local and remote information sources from the document source; par. 201-203 discuss the user graphical interface for presenting linkage of components as nodes that the user is able to interactively digest information about a document source) ;
outputting, by the provider computing system, the link to the destination address of the rule (par. 201-203 graphical user interface for outputting tree of nodes that are interactive for user to digest information about document source that was processed through the ingestor, digester an amasser);
receiving, by the provider computing system, a request to access the web form (par. 9 as noted in summary the mention of user interface is part of a user-facing form of a SaaS-type web application and par. 205 discusses on user interface of web form pre-filled with derived information from a document source of fig. 1 as discussed above);
outputting, by the provider computing system, the web form based on the request; and receiving, by the provider computing system, follow-up case data (par. 205; fig. 21 user interface example presented to user for interaction presents information inferred by document source and wherein updates (follow-up case data) will be updated here dynamically; note par. 201-204).
Specifically for claim 12, modifying, by the provider computing system, the case dataset to include the follow-up case data; and storing, by the provider computing system, the modified case dataset in the first repository (par. 143 While the archive is being written in memory, the ingestor adds the original outer SEC data control header and form metadata (e.g. filing url, primary document filename, filing and submission date, and form type, but also EIN, tickers, exchanges, and the common entity name) to the mix of XML, document, web page, script, and image or graphics files typical to a EDGAR submission. When the ingestor is in a local caching mode, a hierarchical filesystem holds a codex file listing the CIKs held in the cache and each CIK directory holds updated metadata files listing EDGAR's information about the entity and the form metadata for each retrieved filing outside of the archives for inexpensive querying).
Specifically for claim 19, selecting, by the provider computing system, a second web form template including text data in a second language from the third repository of the provider computing system; determining, by the provider computing system, a language of the reporter based on the reporter data; selecting, by the provider computing system and in response to the language of the reporter being the first language, the first web form template (par. 402 FIGS. 64A and 64B include in common elements such as how LLMs associated with the system may translate user intentions into query language which references, e.g. user-defined notions of semantic similarity which may be expanded into semantic stored procedures run one or multiple (i.e., to get a stochastic estimate) times to produce a value usable with, e.g. a threshold test in an encapsulating evaluation cell of the query language).
As for claim 2, Burton teaches. The method of claim 1, further comprising:
outputting, by the provider computing system, the case dataset including the case data as an E2B XML file (par. 147 XML parser; par. 146 web markup includes XML).
As for claim 3, Burton teaches. The method of claim 2, wherein the rule includes a rule trigger, and wherein the method further comprising: modifying, by the provider computing system and in response to outputting the case dataset, a state of the case dataset from a first stage to a second stage; and selecting, by the provider computing system and in response to the rule trigger of the rule being met based on the state of the case dataset being the second stage, the rule from the first repository of the provider computing system (fig. 5; par. 150-152 the amasser module will modify “case” dataset through XML parser and marking tags, assign sequence numbers and inject data metadata tags this is done through rules of the amasser module).
As for claim 4, Burton teaches. The method of claim 2, further comprising:
modifying, by the provider computing system, the case dataset to include the follow-up case data; and (par. 149 while the digester can produce raw text output, its design may be intended to preserve as much of the non-visual logical HTML/XML markup as practical, since the interactive system was designed to be compatible in theory with web pages dynamically retrieved by the user via AJAX/XMLHttpRequest/fetch or other related browser APIs);
outputting, by the provider computing system and after outputting the case dataset, the modified case dataset including the follow-up case data as an E2B XML file (par. 147 XML parser; par. 146 web markup includes XML).
As for claim 5, Burton teaches. The method of claim 4, wherein the case dataset includes a version of the case dataset, and wherein modifying the case dataset includes: generating, by the provider computing system, a new version of the case dataset; and modifying, by the provider computing system, the case dataset to include the follow-up case data and the new version (fig. 5 a new version of digest markup file is created).
As for claim 6, Burton teaches. The method of claim 4, further comprising:
receiving, by the provider computing system, one or more mapping preferences; and modifying, by the provider computing system, the case dataset to include the follow-up case data based on the one or more mapping preferences (par. 152 the amasser module that builds the parsed object into a tree and par. 155-157 fig. 6a-c which goes into detail of the mapping of those nodes of the tree wherein iterations through the reenter logic of 6a for mapping (subsequent matches)).
As for claim 7, Burton teaches. The method of claim 1, wherein the case data includes reporter data, wherein the web form template includes first text data in a first language and second text data in a second language, and wherein the method further comprises: determining, by the provider computing system, a language of the reporter based on the reporter data; and generating, by the provider computing system and in response to the language of the reporter being the first language, the web form including the first text data in the first language based on the web form template (par. 402 FIGS. 64A and 64B include in common elements such as how LLMs associated with the system may translate user intentions into query language which references, e.g. user-defined notions of semantic similarity which may be expanded into semantic stored procedures run one or multiple (i.e., to get a stochastic estimate) times to produce a value usable with, e.g. a threshold test in an encapsulating evaluation cell of the query language).
As for claim 8, Burton teaches. The method of claim 1, wherein the case data includes reporter data, wherein the web form template includes text data in a first language, and wherein the method further comprises: determining, by the provider computing system, a language of the reporter based on the reporter data; translating, by the provider computing system and in response to the language of the reporter being a second language, the text data of the web form template from the first language to the second language; and generating, by the provider computing system, the web form including the translated text data (par. 402 FIGS. 64A and 64B include in common elements such as how LLMs associated with the system may translate user intentions into query language which references, e.g. user-defined notions of semantic similarity which may be expanded into semantic stored procedures run one or multiple (i.e., to get a stochastic estimate) times to produce a value usable with, e.g. a threshold test in an encapsulating evaluation cell of the query language).
As for claims 9, 16 and 20, Burton teaches. The method of claim 1, wherein the link is a public access link, and wherein the method further comprises: generating, by the provider computing system, a token; generating, by the provider computing system, the public access link including a uniform resource locator (URL), wherein the URL includes the token; generating, by the provider computing system, a correspondence including the public access link; outputting, by the provider computing system, the correspondence including the public access link to the destination address; receiving, by the provider computing system, a request to access the web form including the token from a client computing device; validating, by the provider computing system, the client computing device based on the token; and outputting, by the provider computing system and in response to validating the client computing device, the web form to the validated client computing device (par. 232, fig. 26a and par. 286 “token trend” visualization of similar character to that displayed in the annotator. The user may, as with the instrumentality of a single-selection radio button UI element, designate named entities, channels, or tags to view the cumulative incidence lines thereof).
As for claim 10, Burton teaches. The method of claim 1, wherein the rule is a first rule, wherein the rule criteria is a first rule criteria, and wherein the method further comprises: selecting, by the provider computing system, a second rule including a second rule criteria from the first repository of the provider computing system; determining, by the provider computing system, the case data of the case dataset does not fulfill the second rule criteria; and selecting, by the provider computing system and in response to determining the case data of the case dataset does not fulfill the second rule criteria, the first rule including the first rule criteria and the destination address from the first repository of the provider computing system (fig. 5; par. 150-152 the amasser module will modify “case” dataset through XML parser and marking tags, assign sequence numbers and inject data metadata tags this is done through rules of the amasser module).
As for claim 11, Burton teaches. The method of claim 1, wherein the web form includes one or more text sections, wherein the web form is output to a client computing device for display on a user interface including the one or more text sections, and wherein the follow-up case data is received from the client computing device via the one or more text sections of the user interface (par. 170-171 one example as shown in the user interface are text sections from document source fig. 1 processing to be shown to the user via GUI wherein follow up case data is derived through user iteration and reiteration of processing dynamically called upon the system as noted above).
As for claim 13, Burton teaches. The method of claim 12, wherein the case data includes reporter data, wherein the first section of the web form template and the second section of the request each include text data in a first language, and wherein the method further comprises: determining, by the provider computing system, a language of the reporter based on the reporter data; translating, by the provider computing system and in response to the language of the reporter being a second language, the text data of the first section and the second section from the first language to the second language; and generating, by the provider computing system, the web form including the first section including the text data of the first section in the second language and the second section including the text data of the second section in the second language (par. 402 FIGS. 64A and 64B include in common elements such as how LLMs associated with the system may translate user intentions into query language which references, e.g. user-defined notions of semantic similarity which may be expanded into semantic stored procedures run one or multiple (i.e., to get a stochastic estimate) times to produce a value usable with, e.g. a threshold test in an encapsulating evaluation cell of the query language).
As for claim 14, Burton teaches. The method of claim 12, further comprising:
receiving, by a provider computing system, a source file; determining, by the provider computing system, the case data based on the source file; generating, by the provider computing system, the case dataset including the case data; and storing, by the provider computing system, the case dataset in the first repository (fig. 1 overview of the system functionality for receiving a document source and processing it and storing it for user digestion through interaction with the user interface; note analysis of claim 1 above).
As for claim 15, Burton teaches. The method of claim 12, wherein the case data includes reporter data including a reporter address, and wherein the destination address is determined based on the reporter address (par. 203 example of linked information).
As for claim 17, Burton teaches. The method of claim 12, wherein the case data includes a patient's initials, and wherein the first section of the web form includes the patient's initials of the case data (par. 174 example of classified object found by fig. 1 as mentioned in claim 1 analysis above).
As for claim 18, Burton teaches. The method of claim 12, wherein the rule is a first rule, wherein the rule criteria is a first rule criteria, and wherein the method further comprises: selecting, by the provider computing system, a second rule including a second rule criteria from the second repository of the provider computing system; determining, by the provider computing system, the case data of the case dataset does not fulfill the second rule criteria; and selecting, by the provider computing system and in response to determining the case data of the case dataset does not fulfill the second rule criteria, the first rule including the first rule criteria and the destination address from the second repository of the provider computing system (par. 170-174 user interaction with a web form application for depicting derived information in view of “annotations” wherein user is able to set rules that will alter the presentation of the information presented on the user interface that includes removal of information through dynamic information calls through recursive tree processing and/or information sources updating).
(Note: ) It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275, 277 (CCPA 1968)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Telehealth Solutions For Early Personalization Of Healthcare Data Support Via Methods, Communications, Data Networks, Biometrics At Point Of Care, Services And Apparatuses Therefor.
Document ID
US 20210209249 A1
Date Published
2021-07-08
Abstract
These scalable solutions concern transforming each communication network into a network to also automate personalized rapid healthcare support. They integrate biometric identification capabilities into a network entity of, or a resource communicably connectible with, a serving network by using computers to mediate biometric identification and location data. Network operators will provide always on enhanced emergency connectivity for mobility and roaming for user equipment to enable leveraging biometric identification for rapid healthcare support and to produce a unified result set, without risk of undue disclosure of raw biometric data or of selected portions of health profile information. A specially adapted serving network that manages or mediates rapid health care support is supported by a computer system having access to databases with biometric identity or health profile information to be shared as needed with authorized requesters, under confidentiality rules, privacy rules, gating policies, or other pre-defined constraints.
SYSTEMS, METHODS, AND APPARATUSES FOR DYNAMICALLY ASSIGNING NODES TO A GROUP WITHIN BLOCKCHAINS BASED ON TRANSACTION TYPE AND NODE INTELLIGENCE USING DISTRIBUTED LEDGER TECHNOLOGY (DLT)
Document ID
US 20200250747 A1
Date Published
2020-08-06
Abstract
Systems, methods, and apparatuses for dynamically assigning nodes to a group within blockchains based on transaction type and node intelligence using Distributed Ledger Technology (DLT) in conjunction with a cloud based computing environment. For example, according to one embodiment there is a system having at least a processor and a memory therein executing within a host organization, in which such a system includes means for operating a blockchain interface to the blockchain on behalf of a plurality of tenants of the host organization, in which each one of the plurality of tenants operate as a participating node with access to the blockchain; creating a consensus group on the blockchain and associating the consensus group with a specific transaction type for transactions to be processed via the blockchain; assigning a subset of the participating nodes to the consensus group; granting increased weight consensus voting rights to any participating nodes assigned to the consensus group; receiving a transaction at the blockchain having a transaction type matching the specific transaction type associated with the consensus group; and determining consensus for the transaction based on the consensus votes of the participating nodes assigned to the consensus group. Other related embodiments are disclosed.
Inquires
Any inquiry concerning this communication should be directed to NICHOLAS AUGUSTINE at telephone number (571)270-1056.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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/NICHOLAS AUGUSTINE/Primary Examiner, Art Unit 2178 March 3, 2026