DETAILED ACTION
This office action is responsive to the Amendments/Request for reconsideration filed on 06/01/2026 after Non-Final filed 12/03/2025. The application contains claims 1-20, all examined and rejected.
Notice of Pre-AIA or AIA Status
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 Amendment
It is acknowledged that claims 1, 8 and 15 were amended.
Response to Arguments
Applicant's arguments filed 06/01/2026 regarding the claims being rejected under 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues that the claims are not directed to a judicial exception because the claimed operations cannot practically be performed mentally due to the volume and complexity of the production records described in the Specification. The augment is not persuasive. Although the Specification discusses processing tens of thousands of production travelers and millions of pages of manufacturing records, the claims do not cite any particular dataset size, processing throughput, computational requirements, memory architecture or performance constraints. Instead, the claims broadly recite steps including: receiving a query, accessing batch records, preprocessing data, converting data to normalized data, retrieving contextual information; generating output using a LLM, and executing compliance action. These limitations collectively recite collection information, and through mental observation and judgement with the aid of pen and paper, the data can be normalized, contextual information related to compliance with a target device can be identified, and compliance actions can be determined and implemented. The fact that computers may perform the analyses more quickly or on a larger dataset merely describes intended use and mere instructions to apply the exception using a generic computer.
Applicant further argues that the claims are directed to specialized computing architecture utilizing first and second LLM. This argument is not persuasive. The claims merely recite the use of a “first large language model” and a “second large language model” in functional terms. But the claims fail to recite any steps related to improving: to transformer architecture, neural network training, tokenization, embeddings, inference algorithms, retrieval techniques, model accuracy and efficiency. The claims merely recite generic AI models as a tool for performing conventional information extraction, normalization and reasoning.
Applicant argues that the invention employs OCR models, layout-recognition modules, RAG systems, and specialized compliance processing. Like above, the claims merely recite the use of OCR and otherwise fails to affirmatively recite the other technological features in the claims and fails to recite any steps related to improving the technological features.
Applicant argues that the claims integrate an alleged judicial exception into a practical application because they are directed to regulated medical device manufacturing and improve compliance monitoring. This argument is not persuasive. Although, the claims are limited to a particular technological environment involving manufacturing records and regulatory compliance, limiting the abstract idea to a particular field of use does not integrate the exception into a practical application. As discussed above, the claims use computer features and technology but fail to recite any steps related to improving: computer functionality, database, OCR, machine-learning, networking, storage, image-processing, or any other underlying computer technology. In other words, the claimed computer components are used as a tool to collect, normalize, analyze and evaluate information.
Applicant further argues that executing compliance actions constitutes a meaning practical application. This argument is not persuasive. The claims broadly recite: executing, based on the output results, one or more compliance actions required by the regulatory compliance rules. Under BRI, the limitation encompasses, merely: generating reports, issuing notifications, creating workflow tickets, updating tickets, requesting human review, etc. Such activities merely apply the result of the abstract analysis and constitute as insignificant extra-solution activity rather than an improvement to computer technology or another technology.
Applicant argues that the claimed combination of production traveler processing, multi-stage LLM processing, contextual retrieval, and compliance, execution is unconventional and amounts to significantly more than the alleged abstract idea. This argument is not persuasive. The additional claim elements: client devices, stored manufacturing records, preprocessing, first and second LLM, context retrieval, prompt generation, compliance action perform their expected and conventional functions. The Applicant has not identified any technological improvement related to the computer and components. As such the additional elements, considered to not amount to significantly more than implementation of the judicial exception using generic computing technology.
Applicant’s arguments with respect to amendments to claim(s) 1, 8 and 15 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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7, are method claims. Claims 8-14, non-transitory computer readable storage medium claims. Claim 15-20 are a system claims. Therefore, claims 1-20 are directed to either a process, machine, manufacture, or composition of matter.
Step 2A Prong 1:
Claim(s) 1, 8 and 15 recites the following limitation(s):
pre-processing the set of data records for extracting raw data associated with the target device from the set of data records; (nothing in the claims element precludes the “pre-processing” step from being performed in the mind with the aid of pen and paper, for example, making observations or judgment of relevant text in the data record and making record of them via pen and paper)
converting the pre-processed data to normalized data
and applying
generating a prompt to the second LLM, the prompt comprising at least the normalized data, the retrieved contextual information, and the query requesting quality information of the target device; (nothing in the claims element precludes the “generating” step from being performed in the mind with the aid of pen and paper, for example, making observations or judgment to generate an appropriate question related to the device)
Claim 2, 9 and 16 recites the following limitations:
wherein pre-processing the set of data records for extracting raw data associated with the target device from the set of data records comprises: applying an optical character recognition (OCR) to scan the set of data records; and extracting the raw data based on a result of the OCR scanning (Mental process of evaluation and judgement which can be reasonably performed in one’s mind or with the aid of pencil and paper)
Claim(s) 6, 13 and 20 recites the following limitations:
and combining the results
Accordingly, under its broadest reasonable interpretation, covers performance of the highlighted limitation(s) in the mind but for the recitation of generic computer components. That is, other than reciting “a client device,” “a target device,” “large language model,” “non-transitory computer readable storage medium,” “a processor system,” nothing in the claim element(s) precludes the step(s) from practically being performed in the human mind using observation, evaluation, judgment, and opinion. As such, the claim(s) falls within the “Mental Processes” grouping of abstract ideas. Therefore, the claim(s) recites an abstract idea.
Step 2A Prong 2: The judicial exception(s) are not integrated into a practical application. The claim(s) recites the following additional elements:
Claim 1, recites: a client device, a target device; Claim 8, recites: non-transitory computer readable storage medium; a processor system; a client device, a target device Claim 15 recites: computer processors; one or more computer-readable mediums; a client device, a target device; all of which are recited at high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer.
Claim(s) 1, 12 and 20 further recites:
receiving, from a client device, a query requesting quality information of a target device, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity)
accessing a set of data records associated with the target device the data records including batch records; (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity), comprising production travelers for a plurality of components of the target device (merely defining the type of data records, which is merely associating the abstract idea to a particular field of use, not integrating the exception into a practical application.)
wherein the applying the second LLM comprises: retrieving contextual information related to the target device, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Also, the LLM is used to generally apply the abstract idea without limiting how the LLM functions. The LLM is described at a high level such that it amounts to using a computer with a generic LLM to apply the abstract idea. These limitations only recite the outcomes of “retrieving contextual information” and without any details about how the outcomes are accomplished) the contextual information comprising at least regulatory compliance rules associated with a type of the target device (merely defining the type of contextual information as regulatory compliance rules, is merely associating the abstract idea to a particular field of use, not integrating the exception into a practical application.)
and providing the generated prompt to the second LLM to receive the requested quality information of the target device as the output result, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. Also, the LLM is used to generally apply the abstract idea without limiting how the LLM functions. The LLM is described at a high level such that it amounts to using a computer with a generic LLM to apply the abstract idea. These limitations only recite the outcomes of “receive the requested quality information of the target device as the output result” and without any details about how the outcomes are accomplished)
executing based on the output results one or more compliance actions required by the regulatory compliance rules (Such activities merely apply the result of the abstract analysis and constitute as insignificant extra-solution activity)
Claim 3, 10 and 17 recites the following limitations:
comprising updating the second LLM by: updating a training dataset of the second LLM with new data records comprising the quality information of the target device; and fine-tuning the second LLM with the updated training dataset, (Merely training, or updating LLM represents adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).)
Claim 4, 11 and 18 recites the following limitations:
wherein the output result identifies a misclassification of a quality issue from the set of data records associated with the target device, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.)
Claim 5, 12 and 19 recites the following limitations:
wherein the output result identifies a trend of a quality issue associated with the target device, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra/post-solution activity MPEP 2106.05(g))
Claim(s) 6, 13 and 20 recites the following limitations:
applying one or more additional LLMs to the normalized data, each of the one or more additional LLMs comprising a different function adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).)
receiving a result from each of the one or more additional LLMs, (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.)
adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f)).)
Claim(s) 7 and 14 recites the following limitations:
displaying, via a user interface displayed at the client device, a notification comprising the requested quality information of the target device (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity.)
Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claim(s) are directed to an abstract idea.
Step 2B: The claim(s) does not include additional element(s) that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) amounts to no more than mere instructions to apply the exception using a generic computer and thus are mere instructions to apply an exception using a generic computer component-see MPEP 2106.05(f). Also, the additional element(s) amounts to no more than mere data gathering and output recited at a high level of generality and thus are insignificant extra-solution activity - see MPEP 2106.05(g). (see MPEP 2106.05(g). Specifically, Parker v. Fook: adjusting a system setting after doing math (post-solution activity); Electric Power Group: selecting/analyzing information and displaying results (data gathering/output).)
Claim Rejections - 35 USC § 103
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, 3, 5, 6, 7, 8, 10, 12, 13, 14, 15, 17, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenewegen et al. (US 20250077487 A1) in view of Mably et al. (US 20240386203 A1) and Dries et al. (US 20080154749 A1).
As per claim 1, Groenewegen et al. (US 20250077487 A1) describes:
a method, comprising: receiving, from a client device, a query requesting quality information of a target device, at least by (paragraph [0020] searching for quality ticket data of a product that relates to incoming quality ticket, as such the quality ticket data is the quality information of a product (e.g. target device) and the incoming quality ticket is the query requesting quality information of a target device, is the query that the search is based on.)
accessing a set of data records associated with the target deviceat least by (paragraph [0020] describes assessing a database of tickets (e.g. set of data records) related to the product)
converting the pre-processed data to normalized data using a first large language model (LLM), at least by (paragraph [0047,0085] describes using a language model to calculating (e.g converting) vector embeddings from the words (e.g. pre-processed data) within the ticket, where the vector embeddings (e.g. normalized data)
and applying a second LLM to the normalized data for generating an output result comprising the requested quality information of the target device, at least by (paragraph [0079,0081-0082] describes using an AI language model to search and generate quality ticket data including data enrichment (e.g. generating an output result comprising the requested quality information of the target device) using the incoming quality ticket and prompt)
wherein the applying the second LLM comprises: retrieving contextual information related to the target device, , at least by (paragraph [0079,0081-0082] describes using an AI language model to search and generate quality ticket data (e.g. contextual information related to the target device) including data enrichment using the incoming quality ticket and prompt)
generating a prompt to the second LLM, the prompt comprising at least the normalized data, the retrieved contextual information, and the query requesting quality information of the target device, at least by (paragraph [0021] “prompt that targets data enrichment, and in response receive at least a portion of a data enrichment from the language model, which is then utilized to enrich a quality ticket. The enrichment enriches the incoming quality ticket” where the enriched incoming quality ticket is the generated prompt with the query requesting quality information of the target device and normalized data (as described above) + enriched data (e.g. the retrieved contextual information)
and providing the generated prompt to the second LLM to receive the requested quality information of the target device as the output result, at least by (paragraph [0091] “displaying 706 at least a portion of the search result ticket in the user interface.” Paragraph [0136] provides an example output related to the relevance and impact of the product (e.g. output result/quality information)
But Groenewegen fails to specifically recite:
(a) the data records including batch records comprising production travelers for a plurality of components of the target device,
(b) pre-processing the set of data records for extracting raw data associated with the target device from the set of data records
(c) the contextual information comprising at least regulatory compliance rules associated with a type of the target device
(d) and executing, based on the output result, one or more compliance actions required by the regulatory compliance rules.
However, Mably teaches the above limitation (b) at least by (paragraph [0047] “Pre-processing algorithms can detect and remove header and footer content of a comment, remove comments that are considered too short to provide useful information, and remove comments that are too long” and paragraph [0049] “machine-learning pipeline may include a TextRank model, a BERT… model and a GPT-3.5-turbo model, which are Large Language Models, and a Flair … model, which is a Natural Language Processor Named Entity Recognition Machine-Learning Model.”
Furthermore, Dries teaches the above limitation (a) at least by (paragraph [0119-0120] “parts and materials are going into a given product, and open up a material declaration file… through inspection of literature, email with the vendor” para. 0126 “provide the material data for these parts. The supplier then adds the materials (or substances) to parts to the MDO”, where the MDO has batch records comprising production travelers (literature, material data) for a plurality of components of the target device)
Dries teaches the above limitation (c) at least by (paragraph [0053] “PG&C module helps cross-correlate product material data with standards and government regulations,” paragraph [0149] “the bill of substances of each part of the declaration, and calculates parts per million (PPM) of the various substances, and matches this with the allowed regulatory specification parts per million,”, paragraph [0150] “the bill of materials data has been extended to additionally contain the environmental regulatory compliance data collected”)
Dries teaches the above limitation (d) at least by (paragraph [0014] “a compliance corrective action engine configured to generate corrective action requests when a component is determined to not be compliant… calculate a best case amount of a substance and a worst case amount of a substance contained in a product for comparison with the compliance threshold, and is further configured to cause the compliance corrective action engine to generate a request for a compliance corrective action if the amount of the substance contained in the product is above a predetermined threshold.”)
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Groenewegen with the comment pre-processing and keyword extraction provided by Mably to efficiently extract the most relevant keywords by reducing the number comments and text available for extraction, (Mably, 0050). And further combined with Dries’ product lifecycle manager to improve systems and methods for managing, tracking, validating and otherwise dealing with material or substance compliance in products, including in component parts, packaging, enclosures, and other aspects of a product where evaluation of such materials and substances is desired or even required by government regulation (Dries, para. 0204).
As per claim 3, claim 1 is incorporated and Groenewegen further describes:
further comprising updating the second LLM by: updating a training dataset of the second LLM with new data records comprising the quality information of the target device; and fine-tuning the second LLM with the updated training dataset, at least by (paragraph [0059-0066] which describes training the large language model via supervised learning such supervised learning describes fine-tuning the second LLM with the updated training dataset, where the training dataset are the describes example tickets (e.g. new data records comprising the quality information of the target device)
As per claim 5, claim 1 is incorporated and Groenewegen further describes:
wherein the output result identifies a trend of a quality issue associated with the target device, at least by (paragraph [0087] describes collective negative user sentiment related to a product, paragraph [0089] describes identifying previous ticket intents that reports the same bug of a particular product, both of which provides a trend of the quality issue associated with the product)
As per claim 6, claim 1 is incorporated and Groenewegen further describes:
further comprising: applying one or more additional LLMs to the normalized data, each of the one or more additional LLMs comprising a different function that identifies quality information of the target device; receiving a result from each of the one or more additional LLMs; and combining the results from the one or more additional LLMs to generate at least a portion of the requested quality information of the target device, at least by (paragraph [0085] which describes different language models being used “ (BERT) language model 212, or another language model 212” and further paragraph [0059-0066] describes how the language model 212 (which includes different language models) are trained to provide different outputs: target audience description, tag suggestion, impact description, relevance description, workaround suggestion, resolution description; from incoming tickets, as such a different language model can be used for each output)
As per claim 7, claim 1 is incorporated and Groenewegen further describes:
further comprising: displaying, via a user interface displayed at the client device, a notification comprising the requested quality information of the target device, at least by (paragraph [0091] “displaying 706 at least a portion of the search result ticket in the user interface.” Paragraph [0136] provides an example output related to the relevance and impact of the product (e.g. output result/quality information)
Claims 8, 10, 12, 13 and 14 recite equivalent claim limitations as claims 1, 3, 5, 6, 7 above, except that they set forth the claimed invention as a non-transitory computer readable storage medium; Claims 15, 17, 19 and 20 recite equivalent claim limitations as claims 1, 3, 5, 6 above, except that they set forth the claimed invention as a system, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 2, 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenewegen, Mably and Dries in view of Edward et al. (US 20240135017 A1).
As per claim 2, claim 1 is incorporated but Groenewegen and Mably fails to describe:
wherein pre-processing the set of data records for extracting raw data associated with the target device from the set of data records comprises: applying an optical character recognition (OCR) to scan the set of data records; and extracting the raw data based on a result of the OCR scanning.
However, Edward et al. (US 20240135017 A1) teaches the above limitations at least by (paragraph [0013] “data analysis system may perform optical character recognition (OCR) on the review (e.g., using a screenshot of the review from a website) to detect and extract the text of the review from the screenshot. The data analysis system may use machine learning text-analysis techniques to analyze the text of the review to determine whether the review is relevant and/or authentic.”
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Groenewegen and Mably which is designed to “allow any keyword extraction model to be substituted in and out” (Mably, 0049), with Edward’s optical character recognition (OCR) to extract text from formats including images improving text extraction from different formats (Edward, 0025).
Claim(s) 9 recite equivalent claim limitations as claim(s) 2 above, except that they set forth the claimed invention as a non-transitory computer readable storage medium; Claim(s) 16 recite equivalent claim limitations as claim(s) 2 above, except that they set forth the claimed invention as a system, as such they are rejected for the same reasons as applied hereinabove.
Claim(s) 4, 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Groenewegen, Mably and Dries in view of Ravichandran et al. (US 20190244225 A1).
As per claim 4, claim 1 is incorporated and Groenewegen fails to describe:
wherein the output result identifies a misclassification of a quality issue from the set of data records associated with the target device.
However, Ravichandran teaches the above limitations at least by (paragraph [0077] “errors from the initial classification of the first record can be fed back into the network, and used to modify the algorithm of the network for further iterations”
Therefore, before the effective filing date of the invention it would have been obvious to one of ordinary skill in the art to combine the system of Groenewegen and Mably with Ravichandran ability to identify errors retrain the algorithm of the network to improve the accuracy of classification algorithm (Ravichandran, 0077)
Claim(s) 11 recite equivalent claim limitations as claim(s) 2 above, except that they set forth the claimed invention as a non-transitory computer readable storage medium; Claim(s) 18 recite equivalent claim limitations as claim(s) 4 above, except that they set forth the claimed invention as a system, as such they are rejected for the same reasons as applied hereinabove.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mali et al. “Safety Concerns in Mobility-Assistive Products for Older Adults: Content Analysis of Online Reviews.” (see Method section).
LeewayHertz “AI in customer complaint management: The way to faster and more efficient complaint handling” (see sec: “Identifying patterns and trends”; “Real-time Montoring”; “Sentiment Analysis”; “Automated ticket classification”)
Rivichandran (US 20190244225 A1), see paragraph [0044, 0047-0049, 0065, 0079].
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 DENNIS TRUONG whose telephone number is (571)270-3157. The examiner can normally be reached Monday - Friday 8:30 am - 5:30 pm PT.
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 07/29/2026
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