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 Arguments
Applicant's arguments filed on 09/13/2025 have been fully considered but they are not persuasive.
Regarding arguments based on 101 abstract idea rejection:
Applicant argues: “The Examiner asserted that the claims are directed to an abstract idea of "organizing human activity.” Applicant respectfully disagrees. The claims are not directed to an abstract idea but rather to a specific technical solution addressing computational challenges in distributed processing systems. The claims solve a concrete technical problem rooted in computer science: the computational complexity challenges that arise when attempting to meaningfully process pairwise comparisons across large datasets in distributed systems. Traditional approaches become statistically intractable due to the sparse comparison problem, where the number of required pairwise comparisons grows exponentially with dataset size (e.g., n(n-1)/2 comparisons for n items), making comprehensive evaluation infeasible. The claims recite a technical solution to enabling a distributed evaluation of inputs from multiple devices…The claims address the scalability challenge through technological means. Unlike abstract concepts that could theoretically be performed manually, the claimed invention addresses problems that are inherently technological. The specification describes how the system manages “hundreds or thousands of different client devices interacting simultaneously" while maintaining statistical validity across distributed processing nodes. This represents a technical challenge that exists only in computer network architectures and cannot be performed through human mental processes alone…The claims recite specific technological implementations rather than generic computer components. Similar to Example 39 of the USPTO's Subject Matter Eligibility Examples, which found neural network optimization claims patent- eligible, the present claims recite specific machine learning implementations that improve computer functionality. The claimed "machine learning model" that receives "attack scoring that updates the machine learning model to reduce similarity modeling between the two base responses" and "support scoring that updates the machine learning model to reinforce similarity modeling between the base responses" represents a concrete feedback mechanism that dynamically modifies the underlying computational model in real-time. The claimed system implements real-time distributed processing coordination across multiple client devices through the attack and support scoring mechanism”.
Examiner disagrees. The claim as a whole recite collecting responses, generating similarity models, grouping data, determining base response and computing rankings, which are all information processes and do not improve specific technology, but for the recitation of generic computer components. For example, but for the generic computer components language, the above limitations in the context of this claim encompasses observing a problem request and accepting set of responses, judging a set of responses to filter redundant responses by comparing them with set of similar responses, evaluating the responses and distributing them to the participants and evaluating a report for the problem request. Accepting a question and distributing the question to group of people and ranking or classifying responses can be done by one ordinary skilled in the art such as a professor in a classroom. Accordingly, the claim recites an abstract idea. Please see rejection below for each limitation of how each limitation is interpreted as a mathematical concept.
Applicant argues: “Step 2A Prong Two - Integrated Into a Practical Application Even if the claims were considered to involve abstract concepts, they are integrated into a practical application that provides concrete technological improvements to computer functionality and distributed computing systems. MPEP § 2106.05(a) states that "improvements to the functioning of a computer or to any other technology or technical field" demonstrate integration into a practical application. The claims recite specific improvements to distributed computing systems through real-time optimization mechanisms. The claimed system implements "automatically re-consolidating by updating processing of the response inputs by the machine learning model" based on distributed feedback, which represents a concrete technical improvement to machine learning system performance. The claimed invention addresses specific technical challenges in distributed computing that go beyond mere automation of manual processes. Managing computational complexity that scales with input size while maintaining statistical validity, maintaining data integrity across distributed processing nodes, and implementing real-time processing in multi-device architectures represent genuine technological problems. The Applicant therefore submits that the claims of the present application does integrate any perceived abstract concept into a practical application and complies with section 101 under Step 2A prong 2.”
Examiner disagrees. The claim as a whole recite mental and multiple mathematical concepts and calculations as explained in detail limitation by limitation below in the updated rejection. The steps of updating and processing are stated at high level of generality and merely describes a desired result. The claim only recites additional elements that are 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). The additional element of the “similarity engine”, “response judgment interface”, “client device”, “machine learning model” and “at a computing platform comprising one or more processors configured to communicate with multiple client devices” are recited at a high level of generality, and comprises only a processor to simply perform the generic computer functions Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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. The claim is directed to an abstract idea. Please see rejection below for each limitation of how each limitation is interpreted as a mathematical concept.
Applicant argues: “Step 2B - Contains an Inventive Concept The claims include additional elements that provide an inventive concept sufficient to transform any abstract idea into patent-eligible subject matter. The Examiner noted that no prior art references disclose the claimed technical features, indicating the non-conventional nature of the claimed approach. The claims recite unconventional machine learning optimization through attack and support scoring mechanisms that differ from well-understood computational methods. Furthermore, the claims recite specific technical limitation that taken as a whole go well beyond conventional, well understood concepts. The specific combination of real-time distributed feedback with dynamic machine learning model updates through attack and support scoring represents more than routine computer implementation. The Applicant therefore submits that the claims of the present application does not recite an abstract idea and therefor complies with section 101 under Step 2B. For at least the reasons set forth above, the Applicant respectfully requests the rejections under 35 U.S.C. §101 be removed from the application.”
Examiner disagrees. The claim as a whole recite multiple mathematical concepts and calculations as explained in detail limitation by limitation below in the updated rejection. The “attack score” and “support scoring” for updating and real time distributing as claimed do not provide inventive concept, but merely describe mathematical adjustment based on user judgment, and also the distributed feedback is well known in computer networking. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, (see MPEP § 2106.05(d)). It neither improves the functioning of a computer, transforms an article into another article, nor is applied by a particular machine. The claim is not patent eligible. Please see rejection below for each limitation of how each limitation is interpreted as a mathematical concept.
Examiner suggest to further amend a specific distributed processing protocol and how it would reflect a practical application.
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 1-2, 4-6, 8-9, 11-14, and 16-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims’ subject matter eligibility will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
With respect to claim 1.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a method, which is directed to a process, one of the statutory categories.
Step 2A Prong One Analysis: the claim is directed to receiving a problem request, consolidating the set of responses through a classification of natural language process of similar response type, allocating the responses and distributing them based on comparison of base responses and generate a report for the problem request. Each of the following limitations:
at a similarity engine of the computing platform, consolidating, through processing of a machine learning model, the set of response inputs into a set of base responses, wherein consolidating the set of response inputs comprises:
generating, using the machine learning model, similarity modeling across the set of response inputs,
segmenting, based on the similarity modeling, the set of response inputs into responses groups,
computationally determining a representative base response for each response group by processing response inputs of each response group with a predictive language model and outputting a generated base response; (generating, segmenting and determining a representative base response are mathematical algorithms).
at the computing platform, dynamically distributing pair-wise comparisons of base responses across the multiple client devices…: (pairwise comparison are judgment activities which is a mental process)
wherein during the judgment stage, dynamically updating, based on the judgement input, the set of base responses for pair-wise comparison which includes automatically re-consolidating by updating processing of the response inputs by the machine learning model, wherein judgment input indicating preference between two base responses receives attack scoring that updates the machine learning model to reduce similarity modeling between the two base responses and judgment input indicating similarity receives support scoring that updates the machine learning model to reinforce similarity modeling between the base responses; (updating involves weighted mathematical optimization).
as drafted, is a process that, under its broadest reasonable interpretation, covers mental processes (concepts performed in the human mind (including an observation, evaluation, judgment, opinion)) but for the recitation of generic computer components. For example, but for the generic computer components language, the above limitations in the context of this claim encompasses observing a problem request and accepting set of responses, judging a set of responses to filter redundant responses by comparing them with set of similar responses, evaluating the responses and distributing them to the participants and evaluating a report for the problem request. Accepting a question and distributing the question to group of people and ranking or classifying responses can be done by one ordinary skilled in the art such as a professor in a classroom. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two Analysis: This judicial exception is not integrated into a practical application. The additional elements of:
“at a computing platform comprising one or more processors configured to communicate with multiple client devices, collecting a set of natural language response inputs to a problem prompt from the multiple client devices”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“at a similarity engine of the computing platform, consolidating, through processing of a machine learning model, the set of response inputs into a set of base responses, wherein consolidating the set of response inputs”: Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f).
“communicating the pair-wise comparisons of base responses to a judgment interfaces of multiple client device and collecting judgement input during a judgement stage, which comprises: through a response judgment interface at a client device, retrieving judgment input that includes a preference selection of one of the two base responses or a similarity selection of the two base responses”: Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g).
“and generating a response report on preference ranking of a resulting set of base responses based on collected judgement input”. Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g).
In particular, the claim only recites additional elements that are 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). The additional element of the “similarity engine”, “response judgment interface”, “client device”, “machine learning model” and “at a computing platform comprising one or more processors configured to communicate with multiple client devices” are recited at a high level of generality, and comprises only a processor to simply perform the generic computer functions Generic computers performing generic computer functions, alone, do not amount to significantly more than the abstract idea. The generic computer components in these steps are recited at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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. The claim is directed to an abstract idea.
Step 2B Analysis: The claim does not include additional elements 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 of using generic computer components to perform the abstract idea amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, (see MPEP § 2106.05(d)). It neither improves the functioning of a computer, transforms an article into another article, nor is applied by a particular machine. The claim is not patent eligible.
Regarding Claim 2,
The claim recites “wherein, based on the judgment input, reinforcing the similarity modeling output of the machine learning model.”: This limitation merely recites generic training, Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Regarding Claim 4,
The claim recites “wherein a response group of a base response is a group of responses segmented according to set consolidation threshold configuration within the computing platform”: This limitation mere gathering of data, which is insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g) under step 2A prong 2 and its well-understood, routine, conventional activity in the field under step 2B.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Regarding Claim 5,
Step 2A Prong 1: The claim recites “wherein the number of base responses in the set of base responses is altered when automatically re-consolidating”: This limitation merely recites a mental process of updating the grouping of responses.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Regarding Claim 6,
Step 2A Prong 1: The claim recites “wherein automatically re-consolidating comprises re-segmenting response inputs of a response group into two or more distinct base responses.”: This limitation merely recites a mental process of updating the grouping of responses.
Step 2A Prong 2, Step 2B: This judicial exception is not integrated into a practical application. Mere recitation of generic computer components neither integrates the judicial exception into a practical application nor provides an inventive concept.
Regarding Claim 8,
Claim 8 is directed to a non-transitory computer-readable medium, one of the statutory categories. Claim 8 recites: non-transitory computer-readable medium storing instructions that, when executed by the one or more computer processors to perform a process that has limitations similar to the limitations of claim 1. Thus, claim 8 is rejected with the same rationale applied against claim 1. As performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 8 remains subject matter ineligible.
Regarding claims 9 and 11-12,
claims 9 and 11-12 are dependent to claim 8 and recites limitations that are similar to the limitations recited in claims 2 and 4-5. Therefore, claims 9 and 11-12 are rejected with the same rationale applied against claims 2 and 4-5 above.
Regarding Claim 13,
Claim 13 is directed to a system, which is directed to a machine, one of the statutory categories. Claim 13 recites: system, comprising of one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors to perform a process that has limitations similar to the limitations of claim 1. Thus, claim 13 is rejected with the same rationale applied against claim 1. As performing a mental process or abstract idea on a generic computer component cannot integrate the abstract idea into a practical application and cannot provide an inventive concept, claim 13 remains subject matter ineligible.
Regarding claims 14 and 16-18,
claims 14 and 16-18 are dependent to claim 13 and recites limitations that are similar to the limitations recited in claims 2 and 4-6. Therefore, claims 14 and 16-18 are rejected with the same rationale applied against claims 2 and 4-6 above.
Status of Prior Art
Claims 1-2, 4-6, 8-9, 11-14, and 16-18 would be allowable if rewritten or amended to overcome the rejection(s) under 101 abstract idea, set forth in this Office action.
claims 1-2, 4-6, 8-9, 11-14, and 16-18 are allowable over prior art since the prior art taken individually or in combination fails to particularly disclose, fairly suggest, or render obvious the independent claim as a whole.
In addition, examiner notes, the claims should also be amended to overcome the claim rejections indicated in this Office action; and the claim amendments do not raise new issues that would require an updated rejection of claims.
Closest prior art of record:
Li et al. (US 20080215541 A1) teaches the search system may identify alternate queries for an initial query submitted by a user to a search system. Upon receiving the initial query, the search system identifies questions that are related to the initial query and presents to the user the related questions as alternate queries. The search system may also identify messages within a discussion thread that include answers. The search system may also identify an expert relating to the subject of a query by searching through expert profiles containing keywords of discussion threads in which the expert participated.
Gruber et. al. (US 20120016678 A1) teaches automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.
Tran et al. (US 20170140474 A1) teaches crowd-sourced expert system marketplace in which answers from human experts are provided within a guaranteed period of time to electronically submitted questions by human users. The invention is a computer-implemented system and method that provides a micro-transaction based marketplace in which electronically submitted questions in text or picture form from human users are parsed, categorized and routed in real-time and then transmitted electronically to the best available human experts with a response guaranteed within a specified time.
Ho et al. (US 20170154281 A1) teaches information handling system identifies an endorser classifier from the multiple classifiers that generates the highest proportion of correct decisions among the endorser classifier's decisions matching the leader classifier's decisions, and combines the leader classifier and the endorser classifier into a combined classifier stage. In turn, the information handling system utilizes the combined classifier stage to process inquiries and generate results.
Luo et al. (US 20150120718 A1) teaches first similarity calculation and selects an at least one model answerer based on the similarity between elements of a question on the QA forum, and the elements of the QA forum activity associated and corresponding to each of the users of the QA forum. Based on a second similarity calculation, the processor determines a similarity between the QA forum user elements and the at least one model answerer elements, and identifies at least one prospect answerer from the QA forum users.
Song et al. (US 20130097178 A1) teaches In a QA thread, a ranking of answers may include an initial labeling of the longest answer in each thread as the best answer. Such a labeling provides an initial point of reference. Then, in an iterative manner answerers are ranked using the labeling. The ranking of answerers allows selection of experts and poor or inexpert answerers. A label update is performed using the experts (and perhaps inexpert answerers) as input. The label update may be used to train a model, which may describe quality of answers in one or more QA threads and an indication of expert and inexpert answerers.
Bechtel et al. (US 20100185498 A1) teaches For each pair of responses, the processor may receive, from the devices of the users, a selection of a response. The processor may calculate scores for each response based on the number of times each response was presented to the users for selection, the number of times each response was selected by the users, and an indication of the other responses of the plurality of responses each response was presented with.
Micro et al. (“Using Randomization to Attack Similarity Digests”. ATIS 2014, CCIS 490, pp. 199–210, 2014) teaches three similarity digest schemes (Ssdeep, Sdhash and TLSH) work when exposed to random change. Various file types are tested by randomly manipulating source code, Html, text and executable files. In addition, we test for similarities in modified image files that were generated by cybercriminals to defeat fuzzy hashing schemes (spam images). The experiments expose shortcomings in the Sdhash and Ssdeep schemes that can be exploited in straight forward ways. The results suggest that the TLSH scheme is more robust to the attacks and random changes considered.
In summary, the references made of record, fail to disclose the required claimed technical features recited by the independent claim limitations as a whole.
The dependent claims, being further limiting to the independent claims, definite, and enable by the Specification would also be considered allowable if the noted rejections were overcome.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/IMAD KASSIM/Examiner, Art Unit 2129