DETAILED ACTION
Response to Amendment
1. The amendment filed on 6/22/2026 has been entered. Claims 1, 11 and 20 have been amended. No claims have been added or cancelled. Accordingly, claims 1-20 are pending in this office action.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
3. Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
4. 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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0126794 (hereinafter Cook) in view of 2025/0307640 (hereinafter Belgi).
As for claim 1 Cook discloses: A system for handling of JSON objects by a large language model, comprising: one or more processors (See paragraphs 0018 and 0019); a memory (See paragraph 0018); and one or more programs stored in the memory (See paragraphs 0022 and 0102), the one or more programs comprising instructions configured to: receive a user input requesting an analysis of one or more quality records (See paragraphs 0039-0041 note the LLM and data are analyzed using analytics using one or more quality metric records including inventory, financial, human resources, sales etc.); extract the one or more of quality records, wherein each quality record comprises one or more JSON objects (See paragraphs 0032, 0039-0041 and 0103 note the system is designed to work on JSON objects to extract datasets to be used within the system); identify an applicable rule for parsing each of the one or more JSON objects, wherein the applicable rule is based on one or more of the user input, the one or more quality records, and the JSON objects (See paragraphs 0053-0055 and 0087 note multiple sets of rules are used to process the JSON objects including stored rules, fuzzy rules, predefined algorithms and the like); parse each of the one or more JSON objects based on the identified applicable rule (See paragraph 0040 note the JSON objects are parsed according to the rule set), wherein the parsing is performed by requesting an API corresponding to the identified applicable rule (See paragraphs 0139- 0144 note the rules govern output and they can be replaced with functions based on the input from the API); feed the parsed JSON objects to the large language model to obtain the analysis of the one or more quality records (See paragraphs 0055-0062 note the large language mode is trained on public or private datasets, inputs; and other information)
display the obtained analysis of the one or more quality records to the user (See paragraph 0018 note the system will display the results of the analysis to the user).
Cook does not explicitly disclose: wherein the instructions to parse each of the one or more JSON objects are further configured to convert a JSON string into a structured language-based data, wherein interpretation of data stored in each of the one or more JSON objects is converted into contextual language- based textual data, and wherein an output of the parsing is machine understandable semantically correct English text that provides context regarding the data stored in the one or more JSON objects, wherein the contextual language-based textual data is processed to obtain vectors nor , wherein the instructions to feed the parsed JSON objects to the large language model are further configured to provide the vectors generated from the contextual language-based textual data as input to the large language model.
Belgi however renders obvious: wherein the instructions to parse each of the one or more JSON objects are further configured to convert a JSON string into a structured language-based data, wherein interpretation of data stored in each of the one or more JSON objects is converted into contextual language- based textual data (See paragraphs 0041-0044 and 0067 note the JSON object is converted based on context and determines permissions), and wherein an output of the parsing is machine understandable semantically correct English text that provides context regarding the data stored in the one or more JSON objects (See paragraphs 0029 and 0062 note the system will take structured or non-English and provide English to the user), wherein the contextual language-based textual data is processed to obtain vectors (See paragraph 0062 note comprehensive vectors are created and used to provide the results) and , wherein the instructions to feed the parsed JSON objects to the large language model are further configured to provide the vectors generated from the contextual language-based textual data as input to the large language model (See paragraph note the vectors are updated and used as input to the permissions learning system). It would have been obvious to an artisan of ordinary skill in the pertinent at the time the instantly claimed invention was filed to have incorporated the teaching of Belgi into the system of Cook. The modification would have been obvious because the two references are concerned with the solution to problem of data extraction (See Cook and Belgi abstract), therefore there is an implicit motivation to combine these references (i.e. motivation from the references themselves). In other words, the ordinary skilled artisan, during his/her quest for a solution to the cited problem, would look to the cited references at the time the invention was made. Consequently, the ordinary skilled artisan would have been motivated to combine the cited references since Belgi teaching would enable users of the Cook system to have more efficient processing.
As for claim 2 the rejection of claim 1 is incorporated and further Cook discloses: wherein the instructions are further configured to: extract text of one or more attachments of the one or more JSON objects by one or more pre-defined templates (See paragraphs 0032 and 0041 note background data and the metadata are attachments).
As for claim 3 the rejection of claim 2 is incorporated and further Cook discloses: wherein the text extracted from the one or more attachments of the one or more JSON objects is combined with the parsed JSON objects to obtain a vector text of the JSON objects (See paragraphs 0060-0063).
As for claim 4 the rejection of claim 1 is incorporated and further Cook discloses: wherein the one or more quality records comprise complaints, deviations, risks, and change controls (See paragraph 0114 note the quality records can contain risk/loss calculations).
As for claim 5 the rejection of claim 1 is incorporated and further Cook discloses: wherein the obtained analysis of the quality records comprises summarization of the quality records, questions and answers of the quality records using a virtual assistant (See paragraphs 0043-0049 and 0055 note the system will use a digital assistant or chatbot the summarize information to the user and interact with the user).
As for claim 6 the rejection of claim 1 is incorporated and further Cook discloses: wherein the API corresponding to the identified applicable rule is based on metadata of the one or more JSON objects (See paragraph 0041 note the metadata can determine the content, context and structure of the data which determines the rules).
As for claim 7 the rejection of claim 2 is incorporated and further Cook discloses: wherein the one or more attachments of the JSON objects comprises pdf, word, and txt file types (See paragraph 0041 note textual files are used within the system).
As for claim 8 the rejection of claim 1 is incorporated and further Cook discloses: wherein the parsing of the one or more JSON objects provides a meaningful contextual text (See paragraph 0042 note contextual data is extracted from the dataset).
As for claim 9 the rejection of claim 1 is incorporated and further Cook discloses: wherein the large language model is trained offline, and the parsed JSON objects are fed to the large language model in runtime (See paragraph 0033 note processes such as the OCR can be performed offline for collection and training).
As for claim 10 the rejection of claim 9 is incorporated and further Cook discloses: wherein the large language model is further trained by the user input and the obtained analysis of the one or more quality records (See paragraphs 0040-0043 note the system analyzes the quality records and further trains the system).
Claims 11-19 are method claims substantially corresponding to the system of claims 1-6 and 8-10 and are thus rejected for the same reasons as set forth in the rejection of claims 1-6 and 8-10.
Claim 20 is a non-transitory computer readable medium claim substantially corresponding to the method of claim 1 and is thus rejected for the same reasons as set forth in the rejection of claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIYAH STONE HARPER whose telephone number is (571)272-0759. The examiner can normally be reached on Monday-Friday 10:00 am - 6:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached on (571) 272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Eliyah S. Harper/Primary Examiner, Art Unit 2166 August 8, 2026
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