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 .
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 on January 8, 2026. 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and the fee set forth in 37 CFR § 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR § 1.114.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 December 8, 2025 has been entered.
Status of the Application
This office action is prepared in response to claim amendments and Remarks submitted by Applicant on December 8, 2026 relating to U.S. Patent Application No. 18/625,926, filed on April 3, 2024. The application claims priority to U.S. Provisional Application 63/625,398, filed on January 26, 2024, and U.S. Provisional Application 63/550,285, filed on February 6, 2024. Applicant has amended independent Claims 1 and 13. Claims 1-20 are pending and have been examined. This action is non-final.
Response to Arguments
The Remarks submitted by Applicant on December 8, 2025 have been fully considered.
With respect to the Section 112(a) rejection, Applicant has amended independent Claims 1 and 13 and removed the normalizing element which was the basis of the rejection. The Section 112(a) rejection is withdrawn.
With respect to the Section 101 rejection, Applicant asserts that amended independent Claims 1 and 13 relate closely to the principles of Desjardins. In particular, by re-training the machine learning model based on a computed deviation between predicted hazards and documented hazards, and automatically adjusting model parameters to improve future hazard prediction accuracy, the claims implement a self-optimizing hazard prediction model that adapts over time to real world feedback. Applicant asserts that this is analogous to the claims held eligible in Desjardins, where the ARP found that continual-learning, parameter-updating techniques amounted to a technological improvement in the operation of the machine learning model itself rather than a mere mathematical algorithm. Applicant asserts that the re-training limitation in the instant claims similarly yields an improvement in the accuracy of hazard predictions for vegetation near properties because the machine learning model is not static; it continuously adapts based upon real-world feedback. By continuously refining the machine learning model based on documented hazards, the system ensures that subsequent analyses are more precise and reliable and thus integrates the judicial exception into a practical application by reciting a technological process that improves the functioning of the computing system. (Remarks, pp. 13-15). Examiner respectfully disagrees. The instant claims do not provide a technical solution as was provided in Desjardins. In Desjardins the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. See MPEP 2106.04(d)(1). The instant claims do not provide improvements to the functioning of a computer or to technology or to a technical field. They do not integrate the abstract idea into a practical application or amount to significantly more. (See Section 101 rejection below). The Section 101 rejection is maintained.
With respect to the Section 103 rejection, has amended independent Claims 1 and 13 and asserts that the cited references, Modugula, Ton-That, Appel and Bryant, do not teach each and every element of the amended claims, particularly, Appel (Par. 28) discloses that the risk program 132 may modify or update "reasons" associated with tree risks using historical information and machine learning, but it does not teach or suggest a machine learning model that (i) receives vegetation features and proximity as inputs, (ii) determines an association between such inputs and hazard likelihood based on learned patterns, and (iii) outputs predicted hazards. Appel does not disclose the use of "proximity of the vegetation within a distance threshold to the property" as an input to the machine learning model. The inclusion of proximity as a quantified input feature allows the machine learning model to determine the association between vegetation characteristics, spatial relationships, and potential hazards. The machine learning model in Appel is applied only to update existing reasons, and there is no disclosure of the model generating hazard predictions based on feature-proximity relationships. Applicant further asserts that Appel simply discloses that the risk program 132 update or modify "reasons" associated with tree risk, either manually or using historical information and machine learning. Appel does not disclose computing a deviation between predicted hazards and documented hazards, nor is there any teaching of adjusting model parameters based on such deviation to refine or improve future hazard predictions. Appel is limited to updating lists of risk reasons and does not describe a feedback-driven, parameter-adjusting training loop in which model outputs are compared to ground truth hazard data to iteratively improve predictive performance. (Remarks, pp. 15-17). Applicant’s argument is persuasive. The Section 103 rejection is withdrawn.
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 pursuant to 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 - Statutory Class
Claims 1-12 are directed to a method. Claims 13-20 are directed to a system. Therefore, on its face, each of the claims is directed to a statutory class of invention.
Step 2A, Prong 1 – Abstract Idea
Claim 13 recites receiving one or more images, one or more videos, LIDAR data, or satellite imagery data associated with a property of a user from one or more data sources; identifying one or more features of vegetation located at the property and a corresponding proximity of the vegetation within a distance threshold to the property, wherein the identifying includes processing the received data using an image analysis technique; inputting the one or more identified features and the corresponding proximity into a model trained on historical data, wherein the trained model determines an association between the one or more identified features and the proximity with one or more learned patterns and outputs one or more corresponding hazards; re-training the model by computing a deviation between the one or more outputted corresponding hazards and one or more documented hazards, and adjusting one or more model parameters based upon the deviation to improve hazard predictions; generating one or more recommended actions configured to reduce at least one of the one or more hazards, determining a completion of the one or more recommended actions; and determining at least one benefit to the user based upon the completion of the one or more recommended actions. The abstract idea recited in Claim 13 recites receiving imaging data associated with a property of a user from one or more data sources and inputting the data into a model to generate a prediction of one or more hazards to the property based upon the imaging data and one or more of policy data associated with the user or historical data, generating one or more recommended actions configured to reduce at least one of the one or more hazards, determining a completion of the one or more recommended actions, and determining at least one benefit to the user based upon the completion of the one or more recommended actions which involves fundamental economic practices including mitigation of risk falling under “Certain Methods of Organizing Human Activity” according to MPEP 2106. Claim 1 recites the same abstract idea.
Step 2A, Prong 2 – Practical Application
Claim 13 recites one or more processors in communication with one or more data sources, at least one non-transitory computer readable medium storing instructions and a machine learning model. Claim 1 recites one or more processors of a computing system in communication with one or more data sources and a machine learning model. The additional elements of the claims are recited at a high level of generality and are used as tools to implement the abstract idea. They do not integrate the abstract idea into a practical application. They do not provide improvements to the functioning of a computer or to technology or to a technical field because they only manipulate data. The claims do not invoke a particular machine as our guidance is clear that a generic computer is not the particular machine envisioned, they do not transform matter as they only manipulate data which is not matter.
Step 2B – Significantly more
As set forth in the discussion in Step 2A, Prong 2, above, the additional elements of Claims 13 and 1 are recited at a high level of generality and are used as tools and instructions to implement the abstract idea. They do not integrate the abstract idea into a practical application or add significantly more to the abstract idea.
Dependent claims
Claims 2 and 14 (determining, by the one or more processors, one or more trees are within the distance threshold of the property; and generating, by the one or more processors, a first hazard score indicating a probability of the one or more trees damaging the property based upon at least one of attributes of the one or more trees), Claims 3 and 15 (the attributes of the one or more trees include one or more of tree varieties at a higher hazard of falling during extreme weather conditions relative to other tree varieties, tree varieties at a higher hazard of being uprooted during the extreme weather conditions relative to other tree varieties, tree varieties with shallow or damaged roots relative to other tree varieties, tree varieties with uneven canopies relative to other tree varieties, tree varieties with multiple trunks, or tree varieties that increase a hazard of termite damage to the property relative to other tree varieties), Claims 4 and 16 (generating the one or more recommended actions comprises: causing, by the one or more processors, a presentation of the one or more recommended actions in a user interface of a device associated with the user, the one or more recommended actions arranged based upon the first hazard score indicating a probability of the one or more trees damaging the property above a first predetermined threshold, wherein the one or more recommended actions include at least one of trimming or removal of the one or more trees), Claims 5 and 17 (determining, by the one or more processors, that the property is located in a geographical area with a hazard of at least one of erosion or flooding above a second predetermined threshold; and generating, by the one or more processors, a second hazard score indicating a probability of the at least one of erosion or the flooding damaging the property based upon one or more of vegetation around the property, the historical data, or weather data), Claims 6 and 18 (generating the one or more recommended actions comprises: causing, by the one or more processors, a presentation of the one or more recommended actions in a user interface of a device associated with the user, the one or more recommended actions arranged based upon the second hazard score indicating a probability of the erosion and/or the flooding damaging the property above the second predetermined threshold, wherein the one or more recommended actions include one or more of sowing plant varieties with strong roots, landscape designs that prevent the at least one of erosion or the flooding, or identification of a service provider for implementing the landscape designs), Claims 7 and 19 (determining, by the one or more processors, that the property is located in a geographical area with a hazard of wildfire above a third predetermined threshold; and generating, by the one or more processors, a third hazard score indicating a probability of the wildfire damaging the property based upon one or more of type of vegetation within immediate surroundings of the property, the historical data, or weather data), Claims 8 and 20 (generating the one or more recommended actions comprises: causing, by the one or more processors, a presentation of the one or more recommended actions in a user interface of a device associated with the user, the one or more recommended actions arranged based upon the third hazard score indicating a probability of the wildfire damaging the property above the third predetermined threshold, wherein the one or more recommended actions include at least one of removal of flammable vegetation around the property, sowing lower-maintenance plant varieties, landscape designs that protect the property from the wildfire, or identification of a service provider for implementing the landscape designs), Claim 9 (determining, by the one or more processors, overgrown vegetation around the property having a predetermined association with attraction of pests capable of property damage; and generating, by the one or more processors, a fourth hazard score indicating a probability of the pests damaging the property based upon the determined overgrown vegetation and the historical data), Claim 10 (generating the one or more recommended actions comprises: causing, by the one or more processors, a presentation of the one or more recommended actions in a user interface of a device associated with the user, the one or more recommended actions arranged based upon the fourth hazard score indicating a probability of the pests damaging the property above a fourth predetermined threshold, wherein the one or more recommended actions include one or more of removal of the overgrown vegetation that attracts pests or identification of a service provider for pest control), Claim 11 (determining the completion of the one or more recommended actions comprises: receiving, by the one or more processors, response data to the one or more recommended actions from the one or more data sources, wherein the response data includes image data; and analyzing, by the one or more processors, the response data to determine the completion of the one or more recommended actions) and Claim 12 (the at least one benefit upon completion of the one or more recommended actions includes a policy premium reduction) include additional elements (underlined above) that are recited at a high level of generality and used as tools to implement the abstract idea and/or further define and merely add specificity to the abstract idea. The dependent claims fail to add significantly more to the abstract idea.
As such, Claims 1-20 are not patent eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE PROIOS whose telephone number is (571)272-4573. The examiner can normally be reached M-F 8-5.
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/GEORGE N. PROIOS/Examiner, Art Unit 3694
/BENNETT M SIGMOND/Supervisory Patent Examiner, Art Unit 3694