DETAILED ACTION
This office action is responsive to the above identified application filed 03/17/2025. 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 .
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; (receiving” and “outputting” are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity)
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.)
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)
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-solution activity.)
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).
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 device, at 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: pre-processing the set of data records for extracting raw data associated with the target device from the set of data records,
However, Mably teaches the above limitations 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.”
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).
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 describe:
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 describe:
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 describe:
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 and Mably 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 and Mably 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].
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/DENNIS TRUONG/Primary Examiner, Art Unit 2152 11/26/2025