DETAILED ACTION
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
This Office Action is in response to the Amendment filed on 03/02/2026.
Claim 6 is canceled.
Claims 1, 8, 15, and 17 are currently amended.
Claims 1-5 and 7-20 are currently pending and examined below.
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.
Claims 1-5 and 7-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-5 and 7-20 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Step 2A Prong One:
Claim 1 recites (additional elements underlined):
A tangible computer readable storage medium comprising computer executable instructions including:
instructions executable to generate a plurality of topic segments from a product feedback data with a topic segmentation model, which includes a pre-trained Large Language model, wherein the topic segments include segments of text from the product feedback data in which a plurality of topics is discussed, and wherein the topic segments correspond to a plurality of technical issues with a product;
instructions executable to generate a plurality of sentiments expressed in the topic segments about the technical issues corresponding to the topic segments, wherein the sentiments are generated by first pre-trained Machine Learning model, wherein the sentiments provide an indication of negative or positive emotion expressed in the topic segments about the technical issues;
instructions executable to generate a diagram of the topics indicating a degree of negative or positive emotion expressed about the topics;
instructions executable to identify, from the diagram and based on an image classification model, a technical issue having a high degree of negative emotion expressed about the technical issue, wherein the image classification model is a second pre-trained Machine Learning model, wherein the high degree of negative emotion is a degree of negative emotion that exceeds a threshold level of negative emotion and/or is the highest degree of negative emotion depicted in the diagram; and
instructions executable to generate a resource allocation to resolve the technical issue identified by the image classification model.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements identified below, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgement, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements identified below do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth a commercial interaction. Commercial interactions fall within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because commercial interactions are related to commerce and economy. The limitations outlined above also describe or set forth the managing of personal behavior or relationships or interactions between people. Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
Step 2A Prong Two:
In Step 2A Prong Two, the additional element(s) outlined above are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. The Examiner notes that “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent eligible subject matter" (see pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., in a computer environment). The courts have found that simply limiting the use of the abstract idea to a particular environment does not integrate the judicial exception into a practical application. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation (Step 2A Prong Two, No).
Step 2B:
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than a mere instruction to apply the abstract idea using generic computer(s) and/or generic computer component(s) (Step 2B, No).
Claims 2-5 and 7 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 2 recites the additional elements of “heat map”. The Examiner notes that a “heat map” can be considered to be part of the abstract idea (i.e., mental process). Claims 3-5 recite the additional elements of “wherein the instructions executable to”. Claim 7 recites the additional elements of “by the instructions executable”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 8 recites (additional elements underlined):
A system comprising:
a topic segmentation model hardware module including a topic segmentation model executable to generate a plurality of topic segments from a product feedback data, wherein the topic segmentation model includes a pre-trained Large Language model executable to generate the topic segments, wherein the topic segments include segments of text from the product feedback data in which a plurality of topics is discussed, and wherein the topic segments correspond to a plurality of technical issues with a product;
a sentiment analysis engine hardware module including a sentiment analysis engine configured to generate a plurality of sentiments expressed in the topic segments about the technical issues corresponding to the topic segments, wherein the sentiment analysis engine is configured to generate the sentiments by application of a first pre- trained Machine Learning model, and wherein the sentiments provide an indication of negative or positive emotion expressed in the topic segments about the technical issues;
a diagram generator hardware module including a diagram generator configured to generate a diagram of the topics indicating a degree of negative or positive emotion expressed about the topics;
an image classification model hardware module including an image classification model configured to identify, from the diagram, a technical issue having a high degree of negative emotion expressed about the technical issue, wherein the high degree of negative emotion is a degree of negative emotion that exceeds a threshold level of negative emotion and/or is the highest degree of negative emotion depicted in the diagram, wherein the image classification model is a second pre-trained Machine Learning model; and
a resource allocation recommender hardware module including a resource allocation recommender configured to generate a resource allocation to resolve the technical issue identified by the image classification model.
For the same reasons explained above with respect to claim 1, claim 8 also recites an abstract idea. For the same reasons explained above with respect to claim 1, claim 8 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 9-14 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claims 9 and 14 do not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 8, claims 9 and 14 also do not integrate the judicial exception into a practical application or amount to significantly more.
Claim 10 recites the additional elements of “wherein the resource allocation recommender is configured to”. Claim 11 recites the additional elements “further comprising a simulation engine hardware module including a simulation engine, the simulation engine configured to”. Claim 12 recites the additional elements of “wherein the resource allocation recommender is configured to”. Claim 13 recites the additional elements of “wherein the sentiment analysis engine is configured to” and “audio”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 15 recites (additional elements underlined):
A computer-implemented method comprising:
generating a plurality of topic segments from a product feedback data with a topic segmentation model including a pre-trained Large Language model, wherein the topic segments include segments of text from the product feedback data in which a plurality of topics is discussed, and wherein the topic segments correspond to a plurality of technical issues with a product;
generating a plurality of sentiments expressed in the topic segments about the technical issues corresponding to the topic segments, wherein the sentiments provide an indication of negative or positive emotion expressed in the topic segments about the technical issues, and wherein the sentiments are generated by a first pre-trained Machine Learning model;
generating a diagram of the topics indicating a degree of negative or positive emotion expressed about the topics, where a point in the diagram corresponds to a respective topic segment and a color of the point represents a degree of negative or positive emotion;
identifying, from the diagram and based on an image classification model including a second pre-trained Machine Learning model, a technical issue having a high degree of negative emotion expressed about the technical issue, wherein the high degree of negative emotion is a degree of negative emotion that exceeds a threshold level of negative emotion and/or is the highest degree of negative emotion depicted in the diagram; and
generating a recommended resource allocation to resolve the technical issue identified by the image classification model by accessing historical technical issue resolution data via an application programming interface.
For the same reasons explained above with respect to claim 1, claim 15 also recites an abstract idea. For the same reasons explained above with respect to claim 1, claim 15 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 16-20 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 15 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 16 recites the additional element “heat map”. The Examiner notes that a “heat map” can also be considered a mental process. Claim 17 recites the additional element “by applying an optimization technique, which includes linear programing or genetic algorithm.” The Examiner notes that “by applying an optimization technique, which includes linear programming or genetic algorithm” can also be considered a mental process and mathematical concept. Claim 18 recites the additional element “audio”. Claim 20 recites the additional element “by a simulation engine”. However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 19 does not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 15, claim 19 also does not integrate the judicial exception into a practical application or amount to significantly more.
Prior Art
The Examiner notes that after an exhaustive search, the claims currently overcome prior art. While the prior art teach some of the elements of the claimed invention, one of ordinary skill in the art would not have arrived at Applicant’s claimed invention unless one was using Applicant’s claims and specification as a roadmap, thus using impermissible hindsight. The closest prior art found to date are the following:
Robinson et al. (US 2021/0150564 A1) discloses the concept of generating a plurality of topic segments from product feedback data with a topic segmentation model. Wherein the topic segments include segments of text from product feedback data.
Garlapati et al. (US 2020/0356633 A1) discloses a diagram that includes positive and negative sentiments.
Dal Zooto (US 2024/0020319 A1) discloses technical issues with products.
Rais (US 2024/0118687 A1) discloses the use of an image classification model.
Arumae et al. (US 2024/0403565 A1) discloses positive and negative emotions.
Ravichandran (US 2019/0244225 A1) analyzes feedback data to identify categories of sentiment (e.g., positive, neutral, or negative). The sentiment can be indicative of an issue (e.g., identifying negative sentiment from a customer support voice call can indicate a problem, issue, or other case with software product). See at least ¶¶ 47, 65, and 103.
Gangireddy et al. (US 2023/0196032 A1) discloses a heat map depicting sentiments.
Ipsen et al. (US 2025/0060997 A1) discloses the concept of resource constraints.
Mohanty et al. (US 2024/0095750 A1) discloses historical technical issue resolution data.
Cantor et al. (US 2016/0307134 A1) discloses a simulation engine configured to display a burn down chart.
Doshi et al. (US 2025/0156911 A1) discloses the concept of receiving changes to recommendation resource allocation as feedback.
Oleinik et al. (US 2023/0025698 A1) discloses the concept of generating sentiments from voice tonality and/or speech temp in audio.
Coady et al. (US 2023/0177425 A1) discloses the concept of simulating resource allocation scenarios.
Response to Arguments
Applicant's arguments filed 03/02/2026 have been fully considered but they are not persuasive. In the Remarks, Applicant argues:
Argument A: “In the instant application, the features of claim 1 of "instructions executable to generate a plurality of topic segments from a product feedback data with a topic segmentation model, which includes a pre-trained Large Language model" cannot be practically performed in the human mind. Generating a data structure with a Large Language model (LLM) cannot practically be performed in the human mind because of the large number of nodes of a neural network required for an LLM. In addition, claim 1 expressly requires generating sentiments using a Machine Learning model, creating a diagram in which each point corresponds to a topic segment and each color corresponds to a sentiment value, and analyzing that diagram using an image classification model, including a pre-trained Machine Learning model. Therefore, claim 1 is not directed to a mental process and is instead directed to patentable subject matter. Further, the features described in claim 1 cannot practically be performed in the human mind, especially when applied to large volumes of product feedback data, multiple topic segments identified by an LLM, multiple sentiment outputs computed by ML models, and diagram analysis dependent on machine-based color-gradient evaluation (as expressly described in paragraph [0022]). For such reasons, the claims do not fall into the "mental process" grouping of abstract ideas.”
In response, the Examiner respectfully disagrees. As explained above, the limitations that describe or set forth the abstract idea in Step 2A Prong One can be practically performed in the human mind. The Examiner notes that the additional elements are addressed in Step 2A Prong Two and in Step 2B of the analysis.
Argument B: “The Office Action also asserts that, in its Step 2A, Prong One analysis, claim 1 "recites a certain method of organizing human activity." (Office Action, p. 4.) In particular, the limitations are alleged to "describe or set forth a fundamental economic principle or practice because commercial interactions are related to commerce and economy, a commercial interaction (e.g., advertising, marketing or sales activities or behaviors, business relations), and managing personal behavior or relationships or interactions between people." (Id.) The Applicant respectfully disagrees. Instead, claim 1 provides a technical solution for the technical problem of addressing technical issues with a product.”
In response, the Examiner respectfully disagrees. As explained above, the limitations outlined above also describe or set forth a commercial interaction. Commercial interactions fall within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because commercial interactions are related to commerce and economy. The limitations outlined above also describe or set forth the managing of personal behavior or relationships or interactions between people. Therefore, the claims describe or set forth a certain method of organizing human activity.
Argument C: “In its Step 2A, Prong One analysis, the Office Action has applied an overly broad reasoning. "Categorically excluding Al innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology." (In re Desjardins, September 26, 2025, Serial No. 16/319,040, p. 9, first full paragraph) Under the Office Action's reasoning, "many Al innovations are potentially unpatentable-even if they are adequately described and nonobvious" because the Office Action essentially equated technical solution steps with an unpatentable fundamental economic principle or practice "and the remaining additional elements as 'generic computer components,' without adequate explanation." (In re Desjardins, p. 9, first full paragraph.) No specific "fundamental economic principle or practice" was identified in the Office Action. "Examiners and panels should not evaluate claims at such a high level of generality." (Id.) As can be seen from its features, claim 1 provides a technical solution for the technical problem of addressing technical issues with a product. This improves the technical functioning of the product. For example, the claims recite an LLM-based segmentation system, an ML-based sentiment analysis engine, an ML-based image classification model that analyzes a sentiment diagram, and automated generation of a recommended resource allocation based on historical technical issue resolution data accessed via an API. The specification explains that the claims describe technical solutions to the technical problem of identifying technical issues and allocating technical resources more accurately and efficiently (paragraph [0013]). What is claimed is not a business or organizational method; rather, a machine-implemented Al pipeline for processing and classifying complex data and producing a technical output.”
In response, the Examiner respectfully disagrees. First, allocating resources more accurately and efficiently is an improvement entirely in the realm of the abstract idea. Similar to SAP America Inc. v. InvestPic LLC (Fed. Cir. 2018), the advance here lies entirely in the realm of the abstract idea, with no plausible alleged innovation in the non-abstract application realm.
Second, unlike in Ex Parte Desjardins which provided an improvement to machine learning itself, here the claims and specification are completely silent with regard to such technical improvements. The models here are recited at a high level of generality, and are merely used as tools, in their ordinary capacity, to perform the abstract idea. “[P]atents that do not more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101” (p. 18 of Recentive Analytics Inc. v. Fox Corp. (Fed. Cir. 2025)).
Argument D: “Furthermore, even if it is somehow construed that the claims do not satisfy the Step 2A, Prong One, which they do, the claims satisfy the Step 2A, Prong Two. The claims describe features that are not field-of-use limitations or generic computer implementation. Instead, as noted herein, they recite a specific, machine-implemented sequence of operations that transforms raw product feedback data into a technical diagnostic output. Thus, the claims satisfy Step 2A, Prong Two.”
In response, the Examiner respectfully disagrees. Unlike in Bascom in which the particular arrangement of known elements provided a technical improvement over prior art ways of filtering content, here looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation.
Argument E: “Additionally, the claims recite significantly more than well-understood, routine, conventional activity (Step 2B). The Office Action asserted that the limitations are generic computer functions. However, the claims describe a use of pre-trained LLMs and ML models for segmentation, sentiment generation, and diagram analysis, a gradient-based identification of issues from the diagram, and an access of historical technical issue resolution data via an API. The Office Action provides no evidence that this particular ordered combination of LLM-based topic segmentation, ML-based sentiment extraction, ML-based diagram analysis, and API-based historical-data retrieval was well-understood, routine, or conventional at the time of filing. To the contrary, the specification explains the technical advantages of this approach, including more accurate identification of technical issues and improved resource allocation (paragraph [0013]). Thus, the claims recite at least one inventive concept that is significantly more than well-understood, routine, conventional activity (Step 2B).”
In response, the Examiner respectfully disagrees. The Office Action does not take the position that any of the additional elements amount to adding insignificant extra-solution activity in Step 2A Prong Two that would warrant analysis to determine that the additional element also amounts to simply appending well-understood, routine, and conventional activity in the field. The Examiner notes that revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than a mere instruction to apply the abstract idea using generic computer(s) and/or generic computer component(s) (Step 2B, No).
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/SAM REFAI/Primary Examiner, Art Unit 3621
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