DETAILED ACTION
This office action is in response to Applicant’s submission filed on 3/12/2026. Claims 1 - 6, 11-16 and 20 were amended. Claims 1-20 are pending in the application of which Claims 1, 6, and 16 are independent and have been examined.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission filed on 3/12/2026 has been entered.
Response to Arguments
Applicant’s arguments and amendments in the RCE filed on 3/12/2026 (herein “Amendment”) with respect to the 35 USC §101 rejection of claims 1-20 raised in the previous office action have been fully considered but are not persuasive. Consequently, 35 U.S.C. 101 rejection is maintained.
The examiner attempted to contact the applicant’s representative, but was unsuccessful.
The applicant draws a parallel to Ex Parte Desjardins on pages 11-13 and 18 of the Amendment. The Examiner traverses these arguments and provides the following comments:
MPEP § 2106.04(d), subsection III:
… the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).
MPEP § 2106.04(d)(1):
… the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
… method of training a machine learning model were directed to improvements in the machine learning technology itself and additionally included data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values).
The second paragraph of MPEP § 2106.05(a):
xiii. An improved way of training a machine learning model that protected the model’s knowledge about previous tasks while allowing it to effectively learn new tasks; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential); and
xiv. Improvements to computer component or system performance based upon adjustments to parameters of a machine learning model associated with tasks or workstreams; Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
The ninth paragraph of MPEP § 2106.05(f):
… the claims reflected a specific improvement that addressed the technical problem of “catastrophic forgetting” in continual learning systems, while allowing artificial intelligence systems to variously optimize system performance, use less storage capacity and reduce system complexity. Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential).
The Examiner finds the applicant’s arguments and citations unpersuasive, as the Dejardins decision addresses a specific method for training a machine learning model, rather than a generic machine learning model functioning as a general-purpose processor.
As an example, applicant on page 13 argues “AI specific claim language" alleged in Ex parte Desjardins that is not sufficiently dissimilar to Applicant's recited subject matter such that the case in inapplicable.”
The examiner contents that Desjardins is distinguishable because it focuses on a specific method for training a machine learning model. In contrast, the instant application does not disclose training, rendering Desjardins irrelevant. Furthermore, simply reciting a 'machine learning model' constitutes a generic functional limitation that fails to overcome §101 rejection. Substituting this terminology with 'processor' yields the same outcome, as it imposes no meaningful limitations on the underlying abstract idea.
On page 15, applicant continues with: But Applicant's claim language instead explicitly states one or more computing devices to: [obtain the pairs of text collections], select [...] a first automated evaluation methodology for evaluating the evidence mapping machine learning model us the pairs, using a question generation machine learning model to automatically generate a first question [...], obtain a first answer from a question answering generation machine learning model [...], obtain a second answer from the question answering machine learning model [...], determining a similarity metric between the first and second answer, determine [...] a first quality metrics of the evidence mapping machine learning model, and provide via one or more programmatic interfaces (a) the first quality metric, [...] and (b) an explanation of the first quality metric [...]. Such is clearly descriptive of a particular machine a machine that includes instructions that cause one or more computing devices to perform the particularly-recited functionality.
The Examiner has failed to identify which aspects of the claims cannot be performed by the human mind. Disregarding references to computing devices, a human agent can perform all steps described. Furthermore, reciting 'automated evaluation' or 'automatically generating a question' constitutes merely automating a manual process, which does not provide an inventive concept [MPEP 2106.05 (I) ( a ) mere automation of a manual process does not amount to an inventive concept.]
Applicant still furthers on page 15:” … Just reciting a model without sufficient specificity and arguing that what model can do, cannot be performed by human mind is not sufficient.
The Examiner contends that the claimed model is merely a functional equivalent of a generic processor, as it is described only in high-level of generality. The central issue is not whether the task is performable by a human, but rather that the model is indistinguishable from a generic processor. Because no meaningful distinction between the model and a generic processor was provided, the Examiner treats them as same.
Applicant still furthers on page 17:” … instead of considering the claim "as-a-whole," as instructed by the MPEP and by the ARP (described above) over pages 8-12 of the Office Action improperly breaks Applicant's claim down in a piece-meal fashion that intentionally ignores as-a-whole examination to make an overbroad rejection. For example, over pages 8-9, 13 and 14 the office improperly conflates the concepts of "recites" with "involves."
While a piecemeal analysis was not intended, it is noted that pages 10-13, which pertain to Step 2A, prong 1, were previously misplaced. This error is corrected herein.
The Applicant referencing on Enfish (pages 12, 15, 18); that case is distinguishable, as it focuses on computer database improvements, not the specific evidence mapping models in the instant application.
Applicant contends on pages 19 and 22 that improvements are achieved via LLM fine-tuning, citing paragraphs 19 and 20 of the specification. As this feature is absent from the current claims, the argument is not persuasive.
In summary, the Examiner has considered the Applicant’s arguments but maintains that all claimed steps are performable by a human. Specifically, the task of generating a question based on a particular sentence within an annotated summary is a conventional cognitive activity. A human can readily review summary notes and related annotations to pose follow-up questions, a routine behavior that does not inherently require a Question Answering (QA) model.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The flowchart in MPEP 2106, subsection III, is used to determine whether a claim satisfies the criteria for subject matter eligibility. For analysis purposes, one can follow the flowchart for subject matter eligibility.
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Step 1: The independent Claims is directed to statutory categories:
Step 1: Abstract Idea Groupings – MPEP 2106.04(a)(2)
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
Claim 1 is a system claim and directed to the machine or manufacture category of patentable subject matter.
Claim 6 is a method claim and directed to the process category of patentable subject matter.
Claim 16 is a machine-readable medium claim and is directed to the machine or manufacture category of patentable subject matter.
Step 2A is a two-prong test.
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Step 2A, Prong One: Does the Claim recite a Judicially Recognized Exception? Abstract Idea? Are these Claims nevertheless considered Abstract as a Mathematical Concept (mathematical relationships, mathematical formulas or equations, mathematical calculations), Mental Process (concepts performed in the human mind (including an observation, evaluation, judgment, opinion), or Certain Methods of Organizing Human Activity (1-fundamental economic principles or practices (including hedging, insurance, mitigating risk), 2-commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations), 3- managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and fall under the judicial exception to patentable subject matter?)
The broadest reasonable interpretation of steps in the claim limitations is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
A system, comprising: one or more computing devices; wherein the one or more computing devices include instructions that upon execution on or across the one or more computing devices cause the one or more computing devices to:
obtain, at a network-accessible service of a cloud provider network, [This is merely amount to gaining access to a server. Transmission to and from a server can be a data gathering operation. See MPEP 2106.05(d)(II) which also provides case law establishing such operations as being well-known, routine, and conventional.]
a plurality of pairs of text collections, wherein an individual pair of text collections comprises (a) a source text collection which includes a first plurality of sentences and (b) an annotated summary of the source text collection, comprising a second plurality of sentences and [This is merely a data gathering activities where as an example a body of a text such as a transcript of a conversation along with it summary is obtained, where the summary is annotated where it required per desire.]
a set of evidence mappings, wherein corresponding to a first sentence of the second plurality of sentences, the set of evidence mappings includes a first evidence mapping, wherein the first evidence mapping indicates that a second sentence within the source text collection provides evidence for the first sentence, and wherein the set of evidence mappings is generated by an evidence mapping machine learning model trained to generate the annotated summaries comprising the sets of evidence mappings; [This involves annotating the summary report to substantiate where that specific portion of the summary is obtained from the original text/transcript. Human can mark the end of each sentence in the summary with a number or otherwise, which also point to the specific portion of the original document. The reference from the summary report to the original document is named as evidence mapping which is basically pointing to evidence. The additional element of a trained machine learning model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea.]
select, at the network-accessible service, from a plurality of automated evaluation methodologies, at least a first automated evaluation methodology for evaluating the evidence mapping machine learning model using the plurality of pairs of text collections; [This is amount to choosing a method of evaluation of the appropriateness/correctness of the evidence pointing from the summary report to the original text. The additional element of evidence mapping machine learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea.]
in accordance with the first automated evaluation methodology, using a question generation machine learning model trained to generate questions corresponding to a particular sentence of a particular annotated summary, automatically generate a first question corresponding to a particular sentence of a particular annotated summary included in a particular pair of text collections, wherein the particular pair of text collections comprises a particular source text collection; [This is amount to using a process by which the correctness is ascertained by asking a question regarding the annotated portion in the summary and its validity of the corresponding portion in the original text. Such as: are you sure the given sentence in the summary is correctly reflecting the sentence on page x of the original text, is that appropriately reflected in the summary report? The additional element of question generation machine learning model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea.]
obtain, from a question answering machine learning model trained to answer questions generated by the question generation machine learning mode, in response to a first input which comprises the particular sentence and the first question, a first answer to the first question; [This is amount to providing a response (answer #1) to the question. Such as yes, it is correctly reflecting the substance of the original text. The additional element of question answering machine learning model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea.]
obtain, from the question answering machine learning model, in response to a second input, a second answer to the first question, wherein the second input comprises another sentence and the first question, wherein the other sentence is in the particular source text collection, and wherein a particular evidence mapping included in the particular annotated summary indicates that the other sentence is evidence for the particular sentence; [This is amount to repeat the previous process by way of using the original question by pointing at different portion of the original text and gather the response (answer #2) to the second attempt. The additional element of question answering machine learning model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea.]
determine a similarity metric indicating similarity between the first and the second answer; and [This is amount to comparing answer #1 with answer #2 and figure out as how similar the answers are since the question for both scenarios were the same, which is a mental process activity.]
determine, based at least in part on the similarity metric, a first quality metric of the evidence mapping machine learning model, wherein the quality metric indicates a likelihood the evidence mapping machine learning model generates incorrect or nonsensical statements; and [Based on the similarity between the two answers, one can define a quality measure as how effective the refernce pointing were performed. As an example, if the similarity is high, one can conclude the evidence pointing were having high quality, and if the similarity metric is low, conclude the answer could be faulty etc. The additional element of evidence mapping machine learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea].
provide, via one or more programmatic interfaces, (a) the first quality metric of the evidence mapping machine learning model and (b) an explanation of the first quality metric, wherein the explanation is based at least in part on the similarity metric. [This is amount to have a post solution activity (interface) of having a display to present to the user the quality measure of the evidence mapping along with the justification/explanation of the pointing/mapping which is based on the similarity score, such as since the similarity score is as such then it justifies the high/low quality of mapping, etc. The additional element of evidence mapping machine learning Model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea]
The rejected Claims recite Mental Processes.
Step 2A, Prong Two: Additional Elements that Integrate the Judicial Exception into a Practical Application? Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. “Integration into a practical application” requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Uses the considerations laid out by the Supreme Court and the Federal Circuit to evaluate whether the judicial exception is integrated into a practical application.
The rejected Claims do not include additional limitations that point to integration of the abstract idea into a practical application. Accordingly, the rejected Claims are directed to the abstract idea that they recite.
Claim 1 is a generic automation of a mental process since a human agent can obtaining, providing, generating, selecting, and determining, etc. Other than the mental process under the BRI, there is only the mention of a network-accessible service, evidence mapping machine learning model, question generation model, programmatic interface, which is considered to be generic processor. None of these extra elements are invented nor improved by the applicant, and as such they are considered a generic processor/computer due to lack of specificity. With such a generic extra element, one cannot identify anything that can be relied upon as an improvement. Prong 2 of step 2A, in the 101 analysis, asks whether the abstract idea is integrated into a practical application. The answer is no in this instance because there is no technological solution in the Claim that “integrates” the abstract idea. The Claim only suggests that the abstract idea be applied. It does not describe an application.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “evidence mapping machine learning Model”, “question generation machine learning model”, “question answering machine learning model” and “computing device/processors” nothing in the claim element precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of using a “evidence mapping machine learning Model”, “question generation machine learning model”, “question answering machine learning model” and “computing device/processors” to perform all of the above-mentioned steps. The use of a “evidence mapping machine learning Model”, “question generation machine learning model”, “question answering machine learning model” and “computing device/processors” is recited at a high-level of generality (i.e., as a generic computer/processor device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component See MPEP2106.05(f) Mere Instructions to Apply an Exception [R-10.2019].
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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Step 2B: Search for Inventive Concept: Additional Element Do not amount to Significantly More: The limitations of "evaluation based on evidence mapping model”, “generating question based on question generation model”, and “obtaining answer from question answering model” are a well-understood, routine, and conventional machine components that and are being used for their well-understood, routine, and conventional and rather generic functions. Additionally, these limitations are expressed parenthetically and lack nexus to the claim language and as such are a separable and divisible mention to a machine. Merely reciting “evidence mapping machine learning Model”, “question generation machine learning model”, and “question answering machine learning model” without significantly more appears to be equivalent to a generic computer/processor to process a task that a human can process in their mind or with the aid of a paper/pen.
As mentioned, the only additional element to be considered, is the recitation of “evidence mapping machine learning Model”, “question generation machine learning model”, and “question answering machine learning model”. However, according to the as-filed specification (Par. 0021, 0038, 0068 and 0074 as example) it disavows specificity of the “evidence mapping Model”, “question answering model”, and “question answering model” used and referenced to “comprise LLMs”, or “evidence mapping LLM”, to be used, or when it quoted “LLM, now capable of generating annotated summaries of conversations” which is attestation for model used to be a generic model. Therefore, the cited additional element of “evidence mapping Model”, “question answering model”, and “question answering model” do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, it is not sufficient to cause the Claim, as a whole, to amount to significantly/substantially more than the underlying abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements of using a “evidence mapping machine learning Model”, “question generation machine learning model”, “question answering machine learning model” and “computing device” to perform all of the above-mentioned steps. The use of a “evidence mapping machine learning Model”, “question generation machine learning model”, “question answering machine learning model” and “computing device” is recited at a high-level of generality (i.e., as a generic computer/processor device performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component See MPEP2106.05(f) Mere Instructions to Apply an Exception [R-10.2019]. Also, in case of “evidence mapping Model”, “question answering model”, and “question answering model”, it is described in a broad manner such that it could include techniques that may be performed by a human, like a rule-based learning for example. However, “evidence mapping Model”, “question generation model”, and “question answering model” are all LLM based and are a “well-understood, routine, conventional elements, for example, US20240430216A1- discloses LLMs that links identifying a source of the relevant. US20240403112A1- discloses LLM identifying the specified data source. US20240370709A1- disclose large language models for identifying source material from an enterprise data system that corroborate the response. US12093299B1- discloses large language model to generate a block summary. US20240427994A1- discloses LLM models to answer questions. US20240403453A1- discloses LLM to answer questions. US20240402705A1- discloses large language model to answer the plurality of questions in a specified order to obtain a plurality of answers; US20240386214A1- discloses a large language model to generate answers for customer questions. US20240370769A1- discloses LLM to answer questions/queries based on a predefined ground truth defined by a corpus of information. US20240152767A1- discloses LLM for generating questions. US20240119257A1- discloses LLM with a question generation model that is used to generate questions. The additional element of a “computing device”, as cited the as-filed specification of the instant application appears to disclose a general-purpose computer component which are well-understood, routine and conventional elements. The use of an “computer and/or components of a computer” is recited at a high-level of generality (i.e., as a generic computer device performing a generic computer function of capturing input data, storing data and retrieval data) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
With respect to independent Claim 6, there is no additional component which is sufficient to make the claim as a whole to amount to substantially more than the underlying abstract idea.
With respect to independent Claim 16, the additional component is a non-transitory computer-accessible storage media which is not sufficient to make the claim as a whole to amount to substantially more than the underlying abstract idea.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim:
Claims 2 recites: “select, at the network-accessible service, from the plurality of automated evaluation methodologies, a second automated evaluation methodology for obtaining a second quality metric of the evidence mapping machine learning model; and in accordance with the second automated evaluation methodology, provide, as input to a textual entailment model, (a) the other sentence and (b) the particular sentence; and obtain, from the textual entailment model, a first score indicative of an extent of an entailment relationship between the other sentence and the particular sentence, wherein the second quality metric is based at least in part on the first score.” Steps recited are similar to the discussion above with the exception of textual entailment model. The additional element of textual entailment model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 3 recites: “compute an aggregated quality metric of the evidence mapping machine learning model from the first quality metric and a second quality metric of the evidence mapping machine learning model, wherein the second quality metric is determined using a textual entailment model; and provide, via the one or more programmatic interfaces, the aggregated quality metric.” Obtaining different quality metric based on a different methodology is a process that human can undertake. Once the two are identified/obtained they can be written on a piece of paper for sharing purposes. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 4 recites: “obtain, via the one or more programmatic interfaces, a request to evaluate at least the evidence mapping machine learning model, wherein the first automated evaluation methodology is implemented in accordance with the request.” The selection of evaluation methodology is not an inventive concept and human can look at the command and perform accordingly. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 5 recites: “obtain, via the one or more programmatic interfaces, an indication of a problem domain for which the evidence mapping machine learning model is to be utilized, wherein the first automated evaluation methodology is selected based at least in part on the problem domain.” Conduction evidence pointing for a domain specific content can be carried out by a human and essentially domain specific or otherwise does not impact the step to be taken as discussed in claim 1. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 7 recites: “receiving, via the one or more programmatic interfaces, a request for an explanation of the quality metric; and providing, via the one or more programmatic interfaces, an explanation of the quality metric, wherein the explanation includes a result of the analysis of the first answer and the second answer.” Human agent can explain the reason for a given outcome. Looking at the quality score and justify it by explanation is a mental process. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 8 recites: “wherein the analysis of the first answer and the second answer comprises generating a score of a similarity between the first answer and the second answer.” Human can generate a similarity score of a pair of text which is a mental process. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 9, recites: “providing, to a textual entailment model, a third input which includes (a) the other sentence and (b) the particular sentence; and obtaining, from the textual entailment model, a first score indicative of an extent of an entailment relationship between the other sentence and the particular sentence, and wherein the quality metric is based at least in part on the first score.” This is simply a duplication effort of previously discussed situation where a textual entailment model is used to score the pointing evidence for the purpose of scoring the relatedness of the two text and as such arrive at a quality score. All such steps are mental process. The additional element of textual entailment model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 10 recites: “wherein the textual entailment model comprises a large language model (LLM).” The additional element of large language model does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claims 11 and 20 recites: “wherein the first evidence mapping machine learning model comprises an LLM.” The additional element of first evidence mapping model comprises an LLM does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 12 recites: “wherein the question generation machine learning model comprises an LLM.” The additional element of question generation model comprises an LLM does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 13 recites: “wherein the question answering machine learning model comprises an LLM.” The additional element of question answering model comprises an LLM does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing of the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 14 recites: “obtaining, via the one or more programmatic interfaces, a request to evaluate at least the first evidence mapping machine learning model, wherein the request indicates the plurality of pairs of text collections, and wherein the quality metric is provided in response to the request.” Human agent can receive a request from another agent to evaluate evidence pointing of one document to another, and once the process/evaluation is completed provide a score as how the task had been done. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 15 recites: “ obtaining, via the one or more programmatic interfaces, an indication of a problem domain for which the first evidence machine learning mapping model is to be employed; and selecting, from a plurality of automated evaluation methodologies for evidence mapping machine learning models, based at least in part on the problem domain, one or more automated evaluation methodologies for the first evidence mapping machine learning model, including a first automated evaluation methodology, wherein the question generation machine learning model and the question answering machine learning model are utilized in the first automated evaluation methodology.” The entire process is a mental process by which a human agent can identify a potential problem domain for usage in evidence pointing activity. Based on such assessment, then he chooses an appropriate method (domain based) to evaluate the evidence pointing. One such method is the usage of question/answer technique in gauging the problem issue at hand. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claims 17 recites: “wherein the annotated analysis result comprises a summary of the source text collection.” Human can arrive at an annotated summary of the source/original text and uses that as a basis for his analysis. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 18 recites: “wherein the source text collection comprises a transcript of a conversation between two or more entities.” Human can choose as a source document o deal with a transcript of a given conversation. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Claim 19 recites: “wherein the quality metric is based at least in part on an entailment score generated for a portion of the annotated analysis result and a portion of the source text collection.” This is a matter design choice where quality metric is based on an entailment score as opposed to other choices. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim directed toward abstract idea. The claim is not patent eligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Aggarwal et al. (US 20240119220 A1) teaches in Par. 0026:” … identify a simplified text that includes original information from a complex text as well as additional information that is not in the complex text. A neural network of the text simplification apparatus computes an entailment score for each sentence of the complex text, where the entailment score indicates whether the sentence of the simplified text includes information from a sentence of the complex text corresponding to the sentence of the simplified text. Then the text simplification apparatus generates a modified text based on the entailment score, the simplified text, and the complex text, where the modified text includes the original information and excludes the additional information.”
Examiner's Note: Examiner has cited particular columns and line numbers and/or paragraph numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
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DARIOUSH AGAHI, P.E.
Primary Examiner
/DARIOUSH AGAHI/Primary Examiner, Art Unit 2656
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