DETAILED ACTION
1. This office action is in response to application 18/766,697 filed on 7/9/2024. Claims 1-20 are pending in this office action.
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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 .
Claim Rejections - 35 USC § 102
3. 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 US 2024/0126794 (hereinafter Cook).
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).
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.
Information
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 March 7, 2026
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