DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
1/30/2026. In Applicant’s amendment, claims 1-7, and 10 were amended. Claims 8, 9, 11, and 12 are cancelled.
Claims 1-7, and 10 are currently pending and have been rejected as follows.
Response to Amendments
Rejections under 35 USC 101 are maintained. Applicant’s amendments necessitated new grounds of rejection under 35 USC 103.
Response to Arguments
Applicant’s 35 USC 101 rebuttal arguments and amendments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 9-10 that the claims are note directed to the abstract idea groupings of mathematical concepts and mental processes, stating, “they outline a specific technical solution for efficiently identifying important causal relationships, a complex computational task that cannot realistically be performed mentally or through the application of generic computer functions alone. The claims delineate a structured methodology for processing and analyzing data in a manner that intrinsically requires dedicated computer implementation to achieve the specified, non-trivial results, thereby ensuring that the claimed subject matter, when construed as a whole, does not fall within the enumerated groupings of abstract ideas. Further, MPEP § 2106.04(a)(2), subsection III.A clearly distinguishes between mental processes that can be practically performed in the human mind with pen and paper and those that cannot. The specific steps recited in claim 1, such as the generation of causal graphs from multi-featured data, the calculation of first and second similarities for heterogeneous data types, and the subsequent composite classification into third clusters, involve complex data structures and algorithms that require extensive computational processing far beyond rudimentary human mental capabilities or simple pen-and-paper methods. For example, simultaneously assessing similarities across both causal graph structures and data group contents, and then identifying discriminative conditions through machine learning, represents a computational complexity that transcends mere "observation, evaluation, judgment, [or] opinion." Therefore, the claims do not recite any judicial exception (Step 2A - Prong 1: No), but rather employs these as tools for a concrete and specialized technical solution.”Examiner respectfully disagrees. The claims set forth comparative and logical relationships characterized by the steps based on similarity, causal relationships, common cluster association, and a first condition that partitions data. As such, those limitations are directed to the abstract idea grouping of mathematical concepts. Regarding the mental processes grouping, the claim does not require the use of any machine learning. Additionally, under the broadest reasonable interpretation, “plurality” can be two.
Applicant argues on p. 10-11 that the claims are eligible at Step 2A, Prong Two, stating, “The claimed invention provides a clear and specific technological improvement to the field of data analysis, particularly causal inference. The paragraphs [0029-30] of the specification explicitly identify the technical problem: the difficulty in identifying important causal relationships from an overwhelming number of extracted relationships using conventional methods. As elaborated in the specification in paragraphs [0029-30], the conventional methodologies for identifying causal relationships for various conditions notoriously encounter significant difficulties when confronted with a large number of such relationships, impeding the effective pinpointing of truly important causal insights. The claims offer a specific technical solution through its unique, multi-faceted process. That is, the claimed invention offers a novel and specific solution to a recognized technical problem prevalent in the field of artificial intelligence and data analysis. The claimed invention directly addresses this technical challenge by introducing a structured, multi-stage clustering mechanism designed to aggregate inherently similar data groups and their corresponding causal graphs. This includes condition-based data extraction for specific data groups as disclosed in paragraph [0044], specialized generation of causal relationships within causal graphs as disclosed in paragraphs [0045, 47], and particularly the novel classification into "third clusters" achieved by combining two fundamentally heterogeneous dimensions of similarity: similarity of causal graphs (first clusters) AND similarity of data groups (second clusters) as disclosed in paragraphs [0055, 58, 111-112]. This composite clustering structure, integrating distinct types of analytical insights (causal inference and intrinsic data content), represents a highly unconventional and technical method for organizing and analyzing data. Such a method provides a significant improvement to the functioning of a computer by enabling it to process and derive insights from complex relationships in a manner previously unavailable or inefficient. Therefore, the claimed invention, when considered as a whole, is not directed to an abstract idea, but rather integrates it into a practical application that provides a substantial technical improvement.”Examiner respectfully disagrees. The asserted improvement is an improvement to the abstract analysis, rather than an improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a). Applicant’s description of the alleged technical problem being the difficulty in identifying important causal relationships from an overwhelming number of extracted relationships using conventional methods confirms the focus of the claim improving the abstract analytical process and information presented/output. Lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. See MPEP 2106.05.I.
Further, Desjardins is not analogous to the claimed invention. Desjardins found eligibility at Step 2A, Prong 2 because the claims, considered as a whole, reflected a specific improvement to machine learning technology itself, including protecting prior task knowledge while learning new tasks. The present claims do not recite any comparable training and improvement to machine learning technology.
Applicant argues on p. 11-13 that the claims are eligible at Step 2B stating, “Independent claims 1, 7, and 10 explicitly recite a novel and intricate combination of steps that collectively provide a robust inventive concept. These steps include: referring to a memory to extract specific data groups based on a plurality of predefined conditions; obtaining a plurality of causal graphs by generating one for each extracted data group, where these graphs indicate causal relationships between features; classifying these pluralities of causal graphs into a plurality of first clusters based on their first similarity; classifying the data groups themselves, separate from their causal graphs, into a plurality of second clusters based on their second similarity; and crucially, classifying the data groups into a plurality of third clusters, where a data group is assigned to a specific third cluster only if it resides within a corresponding first cluster (of causal graphs) and a corresponding second cluster (of data groups). Subsequent steps involve identifying a first condition for each third cluster, capable of distinguishing its constituent data groups from others, and outputting this identified condition in association with its respective third cluster. This meticulously defined sequence of operations, when considered in its entirety, represents a concrete, technical solution that substantially improves the existing processes for identifying causal relationships. The constituent steps are not merely generic computer activities. The distinct, multi-layered clustering process delineated in claims 1, 7, and 10, particularly the generation of "third clusters" through the innovative integration of results from "first clusters" (based on causal graph similarity) and "second clusters" (based on data group similarity), imposes "meaningful limits" on any abstract idea that might arguably be present (MPEP § 2106.04(d)). This unique combination and sequential arrangement of elements ensure that the claimed invention is far "more than a drafting effort designed to monopolize the [judicial] exception" (MPEP § 2106.05(e)).”Examiner respectfully disagrees. Under Step 2B, examiners should evaluate whether the claim recites additional elements that amount to significantly more than the judicial exception. Although the conclusion of whether a claim is eligible at Step 2B requires that all relevant considerations be evaluated, most of these considerations were already evaluated in Step 2A Prong Two. Thus, in Step 2B, examiners should:
• Carry over their identification of the additional element(s) in the claim from Step 2A Prong Two;
• Carry over their conclusions from Step 2A Prong Two on the considerations discussed in MPEP §§ 2106.05(a) - (c), (e) (f) and (h):
• Re-evaluate any additional element or combination of elements that was considered to be insignificant extra-solution activity per MPEP § 2106.05(g), because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and
• Evaluate whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Here, the additional elements perform their ordinary functions: non-transitory computer-readable recording medium stores a program, computer executed the recited analysis, memory stores feature data, referring to and extracting from a memory is ordinary memory access and data retrieval, outputting is ordinary presentation or transmission of information. As such, the claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations that mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional referenced in 2106.05(d)(II). Further, the alleged inventive combination merely points to the limitations that constitute the abstract idea. Lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. See MPEP 2106.05.I. An improvement to an abstract idea is not amount to an inventive concept under Step 2B
Applicant's prior art arguments have been fully considered and they are persuasive to overcome the rejection. Specifically, see Applicant’s Remarks on p. 14-19.
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 1-7, and 10 are clearly drawn to at least one of the four categories of patent eligible subject matter recited in 35 U.S.C. 101 (method, system, and non-transitory computer readable recording medium). Claims 1-7, and 10 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 integrating the abstract idea into a practical application or amounting to significantly more than the abstract idea.
Regarding Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (‘2019 PEG”), Claims 1-6 are directed toward the statutory category of an article of manufacturer (reciting a “non-transitory computer readable recording medium”). Claim 7 is directed toward the statutory category of a machine (reciting an “apparatus”). Claim 10 is directed toward the statutory category of a process (reciting a “method”).
Regarding Step 2A, prong 1 of the 2019 PEG, Claims 1, 7 and 10 are directed to an abstract idea by reciting referring to … each of which consists of a plurality of features, …, data groups for a plurality of conditions by extracting, for each condition of the plurality of conditions, from among the plurality of pieces of data stored in the …, a data group consisting of one or more pieces of data that satisfy the each condition; obtaining a plurality of causal graphs by generating, for each condition of the plurality of conditions, a causal graph indicating causal relationships between the plurality of features included in the data group extracted for the each condition; classifying the plurality of causal graphs into a plurality of first clusters, based on first similarity between the plurality of causal graphs; classifying the data groups into a plurality of second clusters, based on second similarity between the data groups; classifying the data groups into a plurality of third clusters so as to classify, into a same cluster, a plurality of the data groups that are in a same one of the first clusters and are in a same one of the second clusters; identifying, for each third cluster of the plurality of third clusters, a first condition capable of classifying the plurality of data groups classified into the each third cluster and other data groups classified into other third clusters; and outputting, for each third cluster of the plurality of third clusters, the identified first condition in association with the each third cluster (Example Claim 1).
The claims are considered abstract because these steps recite mathematical concepts including mathematical relationships and mental processes including an observation, evaluation, judgment, opinion. The claims recite steps relationships between features, classifying the relationships into clusters based on a first similarity, classifying the data groups into second clusters based on a second similarity, classifying the data groups into third clusters, identifying first conditions capable of classifying the data groups, and outputting the first conditions with a classification result. It is understood that the claimed steps aim to identify an important causal relationship leading to a solution to a problem in cases where a large number of causal relationships corresponding to each condition are identified (Applicant’s Specification, [0029]-[0030]). By this evidence, the claims recite a type of mathematical concepts including mathematical relationships and mental processes including an observation, evaluation, judgment, opinion common to judicial exception to patent-eligibility. By preponderance, the claims recite an abstract idea (e.g., apparatus, machine, and process for identifying an important causal relationship leading to a solution to a problem).
Regarding Step 2A, prong 2 of the 2019 PEG, the judicial exception is not integrated into a practical application because the claims (the judicial exception and the additional elements such as non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute a process; an information processing apparatus comprising: a memory; and a processor coupled to the memory; referring to a memory that stores a plurality of pieces of data; to extract, from the memory) are not an improvement to a computer or a technology, the claims do not apply the judicial exception with a particular machine, the claims do not effect a transformation or reduction of a particular article to a different state or thing nor do the claims apply 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 (see MPEP §§ 2106.05(a-c, e)).
Claim 4 recites training neural network model. However, the claims merely recite machine learning at a high level, amounting to mere instructions to apply the abstract idea to a computing environment.
Dependent claims 2-6 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations recite 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).
Regarding Step 2B of the 2019 PEG, the additional elements have been considered above in Step 2A Prong 2. The claim limitations do not amount to significantly more than the judicial exception because they are directed to limitations referenced in MPEP 2106.05I.A. that are not enough to qualify as significantly more when recited in a claim with an abstract idea because the limitations recite 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).
Applicant's claims mimic conventional, routine, and generic computing by their similarity to other concepts already deemed routine, generic, and conventional [Berkheimer Memorandum, Page 4, item 2] by the following [MPEP § 2106.05(d) Part (II)]. The claims recite steps like: “Receiving or transmitting data over a network, e.g., using the Internet to gather data,” Symantec, “Performing repetitive calculations,” Flook, and “storing and retrieving information in memory,” Versata Dev. Group, Inc. v. SAP Am., Inc. (citations omitted), by performing steps to “referring” to a memory, “extract” data groups, “identifying” relationships, “classifying” the relationships, “classifying” the data groups, “obtaining” a first condition capable of classifying the plurality of data groups, and “outputting” the identified first condition (Example Claim 1).
By the above, the claimed computing “call[s] for performance of the claimed information collection, analysis, and display functions ‘on a set of generic computer components' and display devices” [Elec. Power Group, 830 F.3d at 1355] operating in a “normal, expected manner” [DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d at 1245, 1258 (Fed. Cir. 2014)].
Conclusively, Applicant's invention is patent-ineligible. When viewed both individually and as a whole, Claims 1-7, and 10 are directed toward an abstract idea without integration into a practical application and lacking an inventive concept.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2023/0060252 A1: A system and associated methods for organizing, representing, finding, discovering, and using data. Embodiments represent information and data in the form of a data structure termed a Feature Graph, that includes nodes and edges, where the edges serve to connect a node to one or more other nodes. A node in a Feature Graph may represent a variable, such as a measurable object, characteristic, or factor of a study. An edge in a Feature Graph may represent a measure of a statistical association between a node and one or more other nodes. Datasets that demonstrate or support the statistical association or measure the associated variable may be accessed through an identifier in a Feature Graph. An application may traverse a Feature Graph and aggregate and process data associated with a set of nodes or edges.
WO 2014/168981 A1: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems. Classification can involve the automatic generation of classes, states, observations, predictions, norms, objectives, and the processing of sample intervals having irregular durations.
Chen et al, Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers, 2021: The volume of scientific publications in organizational research becomes exceedingly overwhelming for human researchers who seek to timely extract and review knowledge. This paper introduces natural language processing (NLP) models to accelerate the discovery, extraction, and organization of theoretical developments (i.e., hypotheses) from social science publications. We illustrate and evaluate NLP models in the context of a systematic review of stakeholder value constructs and hypotheses. Specifically, we develop NLP models to automatically 1) detect sentences in scholarly documents as hypotheses or not (Hypothesis Detection), 2) deconstruct the hypotheses into nodes (constructs) and links (causal/associative relationships) (Relationship Deconstruction ), and 3) classify the features of links in terms causality (versus association) and direction (positive, negative, versus nonlinear) (Feature Classification). Our models have reported high performance metrics for all three tasks. While our models are built in Python, we have made the pre-trained models fully accessible for non-programmers. We have provided instructions on installing and using our pre-trained models via an R Shiny app graphic user interface (GUI). Finally, we suggest the next paths to extend our methodology for computer-assisted knowledge synthesis.
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED EL-BATHY whose telephone number is (571)270-5847. The examiner can normally be reached on M-F 8AM-4:30PM.
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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
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