DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/24/2025 has been entered.
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the RCE filed on 10/24/2025.
Claims 1, 3, 6, 8, 10, 13, 15, and 17 have been amended and hereby entered.
Claims 1-20 are currently pending and have been examined.
Response to Arguments
Applicant's arguments filed 10/24/2025 with respect to the 101 rejection have been fully considered but they are not persuasive. With respect to applicant’s arguments pertaining to Step 2A Prong 2 and Step 2B, the Examiner respectfully disagrees. With respect to using and training the machine learning model for pattern and/or feature identification in the images to identify features associated with the real properties located within the sub-region such as a terrain, a type of housing, and a type of neighborhood from the images, the Examiner fails to see how this amounts to a practical application or significantly more, as this is similar to Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025), where the Courts found that instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, rather as was in Recentive, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment and akin to Recentive, that the requirements that the machine learning model be generically trained or updated is akin to Recentive Analytics, Inc. v. Fox Corp. and not a technological improvement in that training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning (Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Fed. Cir. Apr. 18, 2025)).
For the reasons above, applicant’s arguments are not persuasive.
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 an abstract idea without significantly more, and fails step 2 of the analysis because the focus of the claims is not on the devices themselves or a practical application but rather directed towards an abstract idea, the analysis is provided below.
Step 1 (Statutory Categories) - The claims pass step 1 of the subject matter eligibility test (see MPEP 2106(III)) as the claims are directed towards a system, method and non-transitory computer-readable media.
Step 2A – Prong One (Do the claims recite an abstract idea?) - The idea is recited in the claims, in part, by:
training a machine learning model to determine a plurality of sub-regions for a geographic region using historical rating factors obtained for training and to detect patterns associated with real properties located within the geographic region having different risk profiles using historical images;
analyzing, using the trained machine learning model, one or more images corresponding to a particular geographic region to determine a plurality of sub-regions for the particular geographic region, wherein each sub-region of the plurality of sub-regions comprises one or more real properties with a shared risk profile;
determining, using the machine learning model and for one or more sub-regions of the plurality of sub- regions, a collection of coordinate pairs, wherein each coordinate pair comprises a latitude and a longitude, wherein the collection describes a boundary of a customized shape corresponding to the sub-region, wherein determining the customized shape includes identifying patterns associated with one or more real properties having a shared risk profile and wherein identifying the patterns includes identifying one or more features comprising at least one of a terrain, a type of housing, and a type of neighborhood from the one or more images;
updating the machine learning model in response to the analysis of the one or more images and the determination of the customized shape;
associating, using the machine learning model and with the customized shape. a rating factor for the one or more real properties located within the customized shape;
receiving a request for a recommendation associated with a property, the request including an address associated with the property;
converting the address to an address coordinate, the address coordinate corresponding to a geolocation of the property;
matching the address coordinate with the customized shape;
providing, based on the rating factor, associated with the customized shape and the shared risk profile for the one or more real properties located within the customized shape, an output.
The steps recited under Step 2A Prong One under the broadest reasonable interpretation covers commercial or legal interactions (including sales activities or behaviors) but for the recitation of generic computer components to analyze and organize data related to real properties into different custom generated geographic regions, receiving a request for a recommendation for a property and generating an output based on a rating factor and geolocation and region of a property base, as discussed in the specification and claimed in the dependent claims, these request are for recommending a product or service based on the ratings, such as an insurance product as described in [0058], and therefore describes sales activities and behaviors (recommending a product). That is other than reciting a computing device, a processor, memory unit, one or more non-transitory computer-readable media, and a geo-coded territory rating system nothing in the claim elements are directed towards anything other than commercial or legal interactions, then it falls within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A – Prong Two (Does the claim recite additional elements that integrate the judicial exception into a practical application?) - This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a computing device, a processor, memory unit, one or more non-transitory computer-readable media, and a geo-coded territory rating system. The computing device, processor, memory unit, one or more non-transitory computer-readable media, and geo-coded territory rating system are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components and limits the judicial exception to the particular environment of computers. Mere instructions to apply the judicial exception using generic computer components and limiting the judicial exception to a particular environment are not indicative of a practical application (see MPEP 20106.05(f) and MPEP 20106.05(h)). 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. The claims are directed towards an abstract idea.
Step 2B (Does the claim recite additional elements that amount to significantly more than the judicial exception?) - The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, with respect to integration of the abstract idea into a practical application, the additional elements of using a computing device, a graphical user interface, processor, memory unit, one or more non-transitory computer-readable media, and a geo-coded territory rating system to perform the steps recites in Step 2A Prong One amounts to no more than mere instructions to apply the exception using generic computer components. With respect to the training a machine learning model limitation, [0048] of the specification describes the algorithms which the model may utilize, such as, a linear regression, a decision tree, a support vector machine, a random forest, a k-means algorithm, gradient boosting algorithms, dimensionality reduction algorithms, and therefore under broadest reasonable interpretation, the training the machine learning model is akin to performing a series of mathematical calculations, similar to ineligible claim 2 of Example 47 in the July 2024 Subject Matter Eligibility Examples, and therefore does not amount to significantly more than the abstract idea (See also Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values) (See MPEP 2106.05(d)). Additionally, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements have been considered separately, and as an ordered combination, and do not add significantly more (also known as an “inventive concept”) to the judicial exception. Further, MPEP 2106.05(d)(ii) provides that receiving and transmitting data over a network (see buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Gathering and analyzing information using conventional techniques and displaying the result (TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);, and Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values) are well-understood routine and conventional and insufficient to amount to significantly more than the abstract idea, similar to the instant application is directed towards analyzing and organizing data related to real properties into different custom generated geographic regions, receiving a request for a recommendation, analyzing the address to match it to a geometric profile, and outputting results of the analysis. The claims are not patent eligible.
The dependent claims have been given the full analysis including analyzing the additional limitations both individually and in combination as a whole. For instance, claims 2-7 are all steps that fall within the “Certain Methods of Organizing Human Activities” groupings of abstract ideas further defining the abstract idea. The Dependent claims when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 for the same reasoning as above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY S CUNNINGHAM II whose telephone number is (313)446-6564. The examiner can normally be reached Mon-Fri 8:30am-4pm.
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GREGORY S. CUNNINGHAM II
Primary Examiner
Art Unit 3694
/GREGORY S CUNNINGHAM II/Primary Examiner, Art Unit 3694