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
Status of Claims
This action is in reply to the response filed on 11th of August 2026.
Claims were not amended.
Claims 1-2 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed on 11th of August 2026 have been fully considered but they are not persuasive.
With regard to the limitations of claims 1 and 2, Applicant argues “…that independent claim 1 is akin to the identified eligibility components of Claim 3 of Example 47 of the USPTO Subject Matter Eligibility Examples.” The Examiner respectfully disagrees. Although claim 1 recites a machine-learning model and real-time retraining, these limitations are used in furtherance of the abstract idea identified in the rejection, namely, certain methods of organizing human activity, including commercial or legal interactions, specifically performing steps to provide home repair services for appliances or equipment of a subscriber. The machine learning model is used to predict whether a repair is required, determine whether a repair should be scheduled, and facilitate the provision of the home repair service. Thus, the claimed machine-learning functionality does not improve the functioning of the computer or machine learning technology itself, but instead improves the accuracy of the underlying home repair service determination.
With regard to the limitations of claims 1 and 2, Applicant argues “…the system retrains in real-time, via an algorithm, the machine learning model to reflect an accuracy or inaccuracy of the prediction value (and adjusts the prediction values, accordingly) determined for prior service requests to have an optimized model of when a service provider is legitimately required to be dispatched.” The Examiner respectfully disagrees. The claimed retraining does not provide a technological improvement to the machine-learning model itself. Rather, claim 1 expressly states that the retraining reflects the “accuracy or inaccuracy of the prediction value determined for the service request.” The resulting improvement is therefore directed to more accurately determining whether an appliance requires servicing and whether a service provider should be dispatched. Accordingly, the alleged improvement concerns the accuracy of the commercial home-repair service determination, rather than an improvement to computer functionality or machine-learning technology.
With regard to the limitations of claims 1 and 2, Applicant argues “…This cannot be practically performed in the human mind.” The Examiner respectfully disagrees. The rejection did not identify the judicial exception as a mental process. Rather, the rejection identified the abstract idea as certain methods of organizing human activity, including commercial or legal interactions, namely, performing steps to provide home repair services for appliances or equipment of a subscriber. Therefore, whether the claimed machine-learning operations can practically be performed in the human mind does not address the judicial exception identified in the rejection.
With regard to the limitations of claims 1 and 2, Applicant argues “…This contrasts with Claim 2 of Example 47 (found to be ineligible) where there is just simply analysis and output, unlike the real-time retraining of the model based on real-time analysis in claim 1 of the application.” The Examiner respectfully disagrees. Applicant’s reliance on the presence of real-time retraining does not establish that claim 1 is analogous to the eligible claim of Example 47. The claimed retraining must be considered in the context of what it accomplishes. Here, the retraining improves prediction values used to determine whether a repair service is required. Claim 1 further uses the machine learning model within the home repair service process to schedule repair jobs and determine whether a proposed repair cost is indicative of a fraudulent claim. Therefore, the recited real-time retraining is used to facilitate the identified commercial interaction and does not, merely by being performed in real time, demonstrate an improvement to computer or machine-learning technology.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claims recite abstract idea of organizing human activities. This judicial exception is not integrated into a practical application and the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Analysis
First of all, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. For claim 1, the claim recites an abstract idea of
“…receiving a service request for a repair job to be performed on appliances or equipment of a subscriber of the home services platform; (b) receiving data inputs relating to: (1) a make and model of respective appliances or equipment from respective service providers bound to prior repair jobs associated with the respective appliances or equipment; (2) a symptom associated with the respective appliances or equipment as identified by the respective service providers during the prior repair job; (3) a course of action for repairing the respective appliances or equipment as performed by the respective service providers during the prior repair job; (4) a historical cost associated with the course of action for repairing each of the respective appliances or equipment including an out-of-pocket expense amount charged by the respective service providers to the subscriber; and (5) a corresponding outcome of each of the prior repair jobs;
(c) operating on a machine learning model of the received data inputs to predict whether each of the different types of repair jobs for which a respective prior service request was initially requested does require any of the service providers to be bound to that type of repair job in the future because the respective appliance or equipment was required to be repaired, and over time improving an accuracy of the predicting whether a future repair job is required to be bound to any other service providers by: (1) continually receiving updated sets of data inputs for each type of service request performed by each of the respective service providers; (2) determining a prediction value for each of the updated sets of data inputs, wherein higher prediction values are representative of an appliance or equipment needing to be serviced by any of the other service providers and lower prediction values are representative of an appliance or equipment not needing to be serviced by any of the other service providers; (3) determining a prediction value for the service request based on information associated with the service request provided by at least the subscriber regarding the appliance or equipment of the subscriber; (4) designating a threshold prediction value representative of when an appliance or equipment of the subscriber is required to be serviced; (5) comparing the prediction value determined for the service request to the threshold prediction value and if the prediction value is equal to or greater than the threshold prediction value, allowing for scheduling of the repair job for that appliance or equipment; and (6) receiving data after completion of the repair job corresponding to whether the appliance or equipment was repaired and retraining in real-time, via an algorithm, the machine learning model to reflect an accuracy or inaccuracy of the prediction value determined for the service request; and (d) designating a specific service provider from the any of the other service providers for the repair job of the appliance or equipment by: (1) transmitting a repair job request corresponding to the repair job via a user device of the specific service provider; (2) receiving confirmation via the user device that the specific service provider accepted the repair job request; (3) receiving data via the user device corresponding to a cost associated with the repair job; (4) determining in real-time based on the machine learning model of the received data inputs including the historical cost associated with the course of action for repairing each of the respective appliances or equipment that an intended amount to be charged by the specific service provider to the subscriber for a repair of the appliance or equipment is indicative of a fraudulent claim; (5) based on the fraudulent claim, indicating in real-time via the user device to the specific service provider that the repair should not be made; and (6) transmitting in real-time a second repair job request corresponding to the repair job via another user device to another service provider.”
This is an abstract idea of a certain method of organizing human activity, since it recites a commercial or legal interactions, namely perform steps to provide home repair services for the appliance or equipment of the subscriber. Besides reciting the abstract idea, the remaining claim limitations recite generic computer components/processes (e.g., computers/processors, computer readable mediums, machine learning model). “We conclude that claim 1 is “directed to a result or effect that itself is the abstract idea and merely invoke[s] generic processes and machinery” rather than “a specific means or method that improves the relevant technology.” Smart Sys. Innovations, LLC v. Chi. Transit Authority, 873 F.3d 1364, 1371
This recited abstract idea is not integrated into a practical application. In particular, the claim only recites generic computer components/processes (e.g., computers/processors, computer readable mediums, machine learning model) to receive/transmit data (extra-solution activities) and perform the abstract idea mentioned above. (See at least MPEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); buySafe, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Ultramercial, Inc. v. Hulu, LLC, 772 F.3D 709, 715 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1328-29 (Fed. Cir. 2017); Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857; Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)). The additional elements (e.g., computers/processors, computer readable mediums, machine learning model) are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components or merely uses a computer as a tool to perform an abstract idea. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
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 elements - (e.g., computers/processors, computer readable mediums, machine learning model) amount to no more than mere instructions to apply the abstract idea using generic computer components or merely uses a computer as a tool to perform an abstract idea. In conclusion, merely “applying” the exception using generic computer components cannot provide an inventive concept. Therefore, the claim is not patent eligible under 35 USC 101.
Again, the insignificant extra-solution activities mentioned above were re-evaluated in step 2B. The limitations do not amount to significantly more than the abstract idea because the courts found sending/receiving of data to be well understood, routine, and conventional activities. (See at least MPEP 2016.05(g): CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375 (Fed. Cir. 2011); buySafe, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); Ultramercial, Inc. v. Hulu, LLC, 772 F.3D 709, 715 (Fed. Cir. 2014); Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55 (Fed. Cir. 2016); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1328-29 (Fed. Cir. 2017); Ameranth, 842 F.3d at 1245, 120 USPQ2d at 1857; Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)). Thus again, claims were not patent eligible under 35 USC 101.
Dependent claim 2 has been given the full two-part analysis, analyzing the additional limitations both individually and in combination. The dependent claim when analyzed individually and in combination, are also held be patent ineligible under 35 U.S.C. 101.
For claim 2, the recited limitations of this claim merely further narrow the abstract idea discussed above. This claim further adds, “…wherein the algorithm is a backpropagation algorithm and the retraining is performed using the one or more computers.” The limitations of this claim fail to integrate the abstract idea into a practical application because this claim does not introduce additional elements other than the generic components discussed above. This dependent claim, therefore, also amounts to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Finally, the additional recited limitation of this dependent claim fails to establish that the claim provides an inventive concept because claim that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept.
Conclusion
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 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD CHANG whose telephone number is (571)270-3092. The examiner can normally be reached M - F, 9-5.
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/EDWARD CHANG/Primary Examiner, Art Unit 3696 09/01/2026
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