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 .
Detailed Action
In amendments dated 3/30/26, Applicant amended claims 1 and 11, canceled no claims, and added no new claims. Claims 1-20 are presented for examination.
Objections
Claims 1 and 11 are objected to because of the following informalities:
the fourth limitation recites “training the machine-learning model using training data correlating a plurality of emergent incident data to a plurality of incident remediation profile data” and the first limitation recites “emergent incident data,” so the antecedent basis of “a plurality of emergent incident data” in the fourth limitation is unclear; and
the ninth limitation recites “display, in a graphical user interface, a summary of the remediation resolve profile for a user” and the second limitation recites “… wherein the plurality of incident remediation profiles comprises a data structure representing a summary of recommended corrective actions relating to the emergent incident data …” so the antecedent basis of “a summary” in the ninth limitation is unclear.
Rejections under 35 U.S.C. 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 mental processes without significantly more. Independent claims 1 and 11 each recites classify the emergent incident data to a plurality of incident remediation profiles, , as a function of the location of the emergent incident, wherein the plurality of incident remediation profiles comprises a data structure representing a summary of recommended corrective actions relating to the emergent incident data and includes an assessment of a probability for an identified corrective action based on a predicted likelihood of that action being necessary, generating the plurality of incident remediation profiles using a machine-learning model and associated training data; training the machine-learning model using training data correlating a plurality of emergent incident data to a plurality of incident remediation profile data; classifying the plurality of emergent incident data to the plurality of incident remediation profiles using the machine-learning model; updating the training data with the emergent incident data and at least one incident remediation profile from the classifying of the emergent incident data; retraining the machine-learning model with the updated training data; and select a remediation resolve profile from the plurality of incident remediation profiles using the plurality of ability data; and the summary is generated as a function of a user strategy datum; and the at least a processor is configured to dynamically modify display fields of the summary based on the remediation resolve profile, wherein dynamically modifying display fields further comprises: identifying at least a category of emergent situation corresponding to the remediation resolve profile; associating the at least a category of emergent situation to at least a display field; and generating the at least a display field in the summary. Classifying emergent incident data is recited broadly and is a mental process accomplishable in the human mind or on paper. Generating incident remediation profiles using a machine learning model, classifying emergent incident data to incident remediation profiles using a machine learning model, and retraining the machine learning model with updated training data are each recited broadly and are mental processes accomplishable in the human mind or on paper, and using or training or retraining a machine model is merely applying it and not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Updating training data with emergent incident data is recited broadly and is a mental process accomplishable in the human mind or on paper. Selecting a remediation resolve profile, identifying a category of emergent situation, and associating the category to a display field each involve evaluating and are mental processes. Generating a summary, modifying display fields, and generating a display field are each recited broadly and are mental processes accomplishable in the human mind or on paper. Each claim recites additional elements of receiving a plurality of ability data of an entity and emergent incident data, wherein the emergent incident data comprises a location of an emergent incident, which is a data gathering step and insignificant extra-solution activity; and displaying, in a graphical user interface, a summary of the remediation resolve profile for a user, and displaying the generated at least a display field in the graphical user interface by configuring the display field, which are each output steps and insignificant extra-solution activity. Claim 1 also recites at last a processor and a memory digitally connected to the processor, which are generic components of a computer system. Examiner notes specification paragraph 0003 describes how current infrastructures for entities and equipment and their respective abilities are incongruent with execution demands in rapidly evolving situations. Paragraph 0019 describes the steps currently claimed but these steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input and output steps are recited broadly and amount to sending and receiving data across a network per paragraphs 0020 and 0026 and figure 1, which is routine and conventional per the list of such activities in MPEP 2106.05(d) part II. The at least a processor and a memory digitally connected to the processor are still generic components of a computer system. Therefore these claims do not include additional elements that are sufficient to amount to significantly more than the cited mental processes.
Claims 2 and 12 each recites wherein the emergent incident data is information aiding in an implementation of a comprehensive corrective action plan (using data is a mental process accomplishable in the human mind or on paper). Claims 3 and 13 each recites detecting a location of a remote device to determine the location of the emergent incident (detecting is evaluation and a mental process). Claims 4 and 14 each recites generating a question related to the emergent incident data (generating a question is a mental process accomplishable in the human mind or on paper); and receiving an answer related to the question from a user (receiving data across a network, routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II). Claims 5 and 15 each recites identify a governing jurisdiction of the location of the emergent incident (identification is an evaluation and a mental process); and determine the incident remediation profiles as a function of the government jurisdiction (determining is evaluating and a mental process). Claims 6 and 16 each recites identify the governing jurisdiction through a use of a machine-learning processes using prior jurisdiction identifications as training data (identification is an evaluation and a mental process, using a machine-learning algorithm is merely applying it and not significant to overcome the cited mental processes).
Claims 7 and 17 each recites determine the remediation resolve profile as a function of the governing jurisdiction (determining is evaluation and a mental process). Claims 8 and 18 each recites receive a feedback from a user (receiving feedback is receiving data across a network, routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II); and refine the machine-learning model as a function of the feedback, wherein the feedback is incorporated as the training data (refining a machine-learning model with the feedback is reusing the model and not significant to overcome the cited mental processes). Claims 9 and 19 each recites wherein receiving the plurality of ability data and the emergent incident data comprises using a machine-learning model to populate any incomplete data based on heuristic schemes (using a machine-learning model with the feedback is merely applying the model and not significant to overcome the cited mental processes). Claims 10 and 20 each recites determining a lack of the training data of the machine-learning model (determining is evaluation and a mental process); and receiving additional training data for the machine-learning model, as a function of the lack of the training data, from a user (receiving additional data is receiving data across a network, routine and conventional per the list of routine and conventional activities in MPEP 2106.05(d) part II).
Relevant Prior Art
During his search for prior art, Examiner found the following references to be relevant to Applicant's claimed invention. Each reference is listed on the Notice of References form included in this office action:
Bopardikar et al (US 11,887,003) teaches techniques for training and retraining a machine learning model with inputs and outputs, does not teach ability data of an entity and emergent incident data, wherein the emergent incident data comprises a location of an emergent incident , classifying the emergent incident data to emergent incident profiles, selecting a remediation resolve profile, displaying a summary of a remediation resolve profile, and modifying and displaying a display field of a remediation resolve profile (column 3 lines 13-56, columns 6-8 lines 13-20 figure 2).
Responses to Applicant’s Remarks
Regarding rejections of claims 1-20 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered and are not persuasive. On pages 3-4 of Applicant’s Remarks Applicant discusses Step 2A Prong One and asserts claim 1 recites limitations “which are not directed to an abstract idea and provides an inventive concept amounting to significantly more than any alleged abstract idea.” Examiner identified some of the limitations as additional elements above but disagrees that the limitations provide an inventive concept amounting to significantly more than the recited mental processes. On pages 4-7 Applicant discusses Step 2A Prong Two and asserts “unlike the examples cited, by the MPEP guidance, the amended claim 1 in the instant application contain limitations that amount to more than mere instructions to apply an exception at least because the limitations include steps such as the training of a machine learning model.” Examiner disagrees as the Courts have found that training a machine learning model is not significantly more than an abstract idea per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Applicant excerpts from MPEP 2106.04(d) and quotes from the Ex Parte Donovan and McRo decisions (“Similar to the claims in Donovan, amended claim 1 recites a specifically trained classifier to display a summary of remediation resolve profiles for a user, with the use of emergent incident data and incident remediation profiles as inputs. Similarly to McRo, Applicant asserts that these specific means are not merely directed to a result or effect that itself is the abstract idea, because claim 1 as a whole is limited to rules, such as training the classifier using training data, where the classifier is retrained with updated training data.”) Examiner notes the claims in Ex Parte Donovan recited a machine learning model plus specific “statistical functionality (linear discriminant analysis)” while the instant claims lack such specific techniques and only recite “a data structure,” “an assessment f a probability,” “based on a predicted likelihood” with no details on how cush data structures or quantities are calculated or used by the invention. Furthermore, while the claims in McRo recited specific rules, the instant claims recite training a machine learning model using training data and retraining the machine learning model using updated training data, which do not recite any specific rules. Applicant quotes from the opinion in Ex Parte Baughman, “claim 1 recites a set of steps for a particular query-and hypothesis-based processing sequence and set of rules, executed by a QA system, that amounts to ‘us[ing] the limited rules in a process specifically designed to achieve an improved technological result in conventional industry practice,’ i.e., to improve the technology of QA systems,” and Examiner notes in the instant claims the classifier is not trained with specific rules but uses the machine learning model which is trained with training data that correlates emergent incident data to incident remediation profiles with no details on how the invention does the correlating. Thus Examiner disagrees that the claims limitations that are more than instructions to apply the mental processes of classifying data and training a machine learning model.
On page 6 Applicant assets “claim 1 as amended is not directed to an abstract idea, because claim 1 as amended is directed to an improvement to a computer process.” Examiner disagrees and notes the mental processes identified in the rejections above. These mental process steps (classifying emergent incident data, generating incident remediation profiles, training a machine learning model, updating training data, retraining a machine learning model, selecting a remediation resolve profile, modifying display fields, identifying a category of emergent situation, associating the category to a display field, and generating the display field), are each recited broadly and without details shown how the invention performs the steps and thus might show an improvement to a computer process or to a technology. On page 7 of her Remarks Applicant discusses Step 2B of the Eligibility Analysis and asserts claim 1 recites an inventive concept. In evaluating the additional elements (receiving ability data and emergent incident data, displaying a summary of a remediation resolve profile, displaying a display field from said summary of a remediation reserve profile), Examiner believes each of them does not affect the mental process steps recited. For example, receiving data does not affect classifying it or training a machine learning model with it and displaying a summary of or a display field from the summary of a remediation resolve profile does not affect how the remediation resolve profile is selected or how the display field is modified. Examiner believes each of these additional elements are routine and conventional activities per MPEP 2106.05(d) part II as shown in the rejection above and does not believe the claims amount to an inventive concept.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 5/22/26
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