Prosecution Insights
Last updated: April 19, 2026
Application No. 18/397,822

SYSTEMS AND METHODS FOR ANALYZING AND MITIGATING COMMUNITY-ASSOCIATED RISKS

Final Rejection §101§103§112
Filed
Dec 27, 2023
Examiner
O'SHEA, BRENDAN S
Art Unit
3626
Tech Center
3600 — Transportation & Electronic Commerce
Assignee
State Farm Mutual Automobile Insurance Company
OA Round
4 (Final)
30%
Grant Probability
At Risk
5-6
OA Rounds
3y 4m
To Grant
67%
With Interview

Examiner Intelligence

Grants only 30% of cases
30%
Career Allow Rate
54 granted / 178 resolved
-21.7% vs TC avg
Strong +36% interview lift
Without
With
+36.3%
Interview Lift
resolved cases with interview
Typical timeline
3y 4m
Avg Prosecution
51 currently pending
Career history
229
Total Applications
across all art units

Statute-Specific Performance

§101
28.2%
-11.8% vs TC avg
§103
40.1%
+0.1% vs TC avg
§102
11.0%
-29.0% vs TC avg
§112
19.0%
-21.0% vs TC avg
Black line = Tech Center average estimate • Based on career data from 178 resolved cases

Office Action

§101 §103 §112
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 . Status of the Claims Claims 1-17 and 21-23 are all the claims pending in the application. Claims 1, 4-6, 9, 12-14, 17, 22 and 23 are amended. Claims 1-17 and 21-23 are rejected. The following is a Final Office Action in response to amendments and remarks filed Nov. 4, 2025. Response to Arguments Regarding the 101 rejections, the rejections are maintained for the following reasons. First, Applicant asserts the rejections should be withdrawn because the claims recite activity the human mind is not equipped to perform. Examiner respectfully does not find this assertion persuasive because Applicant does not explain how or why the human mind is not equipped to analyze and mitigate risk based on weather and building materials. Second, Applicant asserts the claims reflect a technical improvement because claims solve the problems of being unable to organize data and identify risks. Examiner respectfully does not find this assertion persuasive because the present claims do not require addressing vast amounts of data and because addressing vast amounts of data is only using the improved speed or efficiency inherent with applying the abstract idea on a computer which does not provide a sufficient inventive concept. See MPEP 2106.05(f)(2) (discussing Intellectual Ventures I LLC v. Capital One Bank (USA)). Accordingly the 101 rejections are maintained, please see below for the complete rejections of the claims as amended. Regarding the 103 rejections, the rejections are maintained because Applicant does not explain how or why the present claims are not taught by the prior art. Accordingly the 103 rejections are maintained, please see below for the complete rejections of the claims as amended. In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by Applicant in regards to distinctly and specifically pointing out the supposed errors in Examiner's prior office action (37 CFR 1.111). Examiner asserts that Applicant only argues that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art. Claim Rejections - 35 USC § 112 The following is a quotation of the first paragraph of 35 U.S.C. 112(a): (a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention. The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112: The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. Claims 22 and 23 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. Regarding claim 22, claim 22 recites the newly amended limitation “…determine a location of the at least one material associated with the at least one potential risk … adjust a field of vision associated with the security camera from a first field of vision that does not include the location of the at least one material to a second field of vision that does include the location of the at least one material in order to monitor the at least one potential risk” however there is no discussion, throughout the entirety of the specification and drawings, of adjusting camera to monitor building materials. For example, the Specification discusses using cameras to monitor security risks, ¶[0087] but does not discuss using cameras to monitor building materials. Regarding claim 23, claim 23 recites the newly amended limitation of training the machine learning model in three stages however there is no discussion, throughout the entirety of the specification and drawings, of stages of training the machine learning model. For example, the Specification discusses using various training data, e.g., ¶¶[0166]-[0167] but does not discusses organizing that data into stages (e.g., curriculum learning). As such, the Examiner asserts this as evidence that the newly amended claims 22 and 23 are new matter. 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 1-17, 21 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Under Step 1 of the patent eligibility analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying Step 1 to the claims it is determined that: claims 1-16 and 21-23 are directed to a machine; and claim 17 is directed to a process as suggested. Therefore, we proceed to Step 2. Independent Claims Under Step 2A Prong 1 of the patent eligibility analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories or “buckets” of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability. The independent claims recite an abstract idea. Specifically, the independent claim recite an abstract idea in the limitations (emphasized)1: … train a machine learning model using training data comprising historical weather event data by location, building material data used to construct buildings by location, and damage data representing damage to the buildings resulting from historical weather events; receive environment data generated by the at least one sensor, the environment data associated with a plurality of historical weather events that previously occurred at a location of the building; receive building data from the at least one memory device, the building data including materials data associated with a plurality of materials used to construct the building; utilize the trained machine learning model to determine at least one potential risk associated with at least one material of the plurality of materials based upon the used to construct the building based upon the received environment data and the received building data for the building; generate a building risk profile for the building using output from the trained machine learning model that includes the at least one potential risk associated with the at least one material; and generate a risk mitigation output based upon the building risk profile for the building, wherein the risk mitigation output includes causing a model to be displayed of the building that includes a visual indicator of the at least one potential risk of damage to the at least one material used to construct the building, and a recommendation on how to address the at least one potential risk of damage. These limitations recite an abstract idea because these limitations encompass a mental process. That is, these limitations encompass evaluating risks associated with a building and its materials, judging the risk for the building, and opining on how to mitigate the risk. Claims that encompass evaluation, judgment, and opinion fall within the mental process grouping of abstract ideas. Claims 1, 9, and 17 recite an abstract idea. Under Step 2A Prong 2 of the patent eligibility analysis, it must be determined whether the identified, recited abstract idea includes additional limitations that integrate the abstract idea into a practical application. The additional elements of independent claims do not integrate the abstract idea into a practical application. The independent claim recite additional elements in the limitations (emphasized)2: … train a machine learning model using training data comprising historical weather event data by location, building material data used to construct buildings by location, and damage data representing damage to the buildings resulting from historical weather events; receive environment data generated by the at least one sensor, the environment data associated with a plurality of historical weather events that previously occurred at a location of the building; receive building data from the at least one memory device, the building data including materials data associated with a plurality of materials used to construct the building; utilize the trained machine learning model to determine at least one potential risk associated with at least one material of the plurality of materials based upon the used to construct the building based upon the received environment data and the received building data for the building; generate a building risk profile for the building using output from the trained machine learning model that includes the at least one potential risk associated with the at least one material; and generate a risk mitigation output based upon the building risk profile for the building, wherein the risk mitigation output includes causing a model to be displayed of the building that includes a visual indicator of the at least one potential risk of damage to the at least one material used to construct the building, and a recommendation on how to address the at least one potential risk of damage. These additional elements do not integrate the abstract idea into a practical application for the following reasons. First, the additional elements of training and utilizing the machine learning model to determine the potential risk, when considered individually or in combination, does not integrate the abstract idea because the use of machine learning is recited sufficiently broadly such that it is not more than mere instructions to apply the exception, see MPEP 2106.05(f). Second, the additional elements of receiving environment data and receiving building data, as claimed, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only mere data gathering, see MPEP 2106.05(g). That is, these additional elements do not reflect significantly more than the abstract idea because the additional elements encompass obtaining information relevant to risk assessments. Claims 1, 9, and 17 further recite the additional elements: “at least one memory device; and at least one processor in communication with the at least one memory device and at least one sensor located proximate to a building”; “one non-transitory computer-readable storage medium with instructions stored thereon”; “at least one processor in communication with at least one memory” and “at least one sensor in communication with the at least one processor”, respectively. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions, see MPEP 2106.05(f). Accordingly, claims 1, 9, and 17 are directed to an abstract idea. Under Step 2B of the patent eligibility analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea (i.e., an innovative concept). The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of receiving environment data and receiving building data are only insignificant extra-solution activity because gathering data is a necessary for assessing risks. Further, as discussed above with respect to integration of the abstract idea into a practical application, the rest of the additional elements amount to no more than mere instructions to apply the exception. Insignificant extra-solution activity and mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Claims 1, 9, and 17 are not patent eligible. Dependent Claims All the dependent claims except for claim 22 are rejected under 35 USC 101 for the following reasons. Claims 2-5 and 10-13 recite the additional elements of transmitting the risk alert and precautionary measures including recommended actions and displaying a notification. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer functions of sending and displaying data (i.e. sending text), see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claims 6 and 14 recite the additional elements of transmitting instructions to alter operations of a system associated with the building. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer functions of sending data, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claims 7, 8, 15, and 16, recite the additional elements of a three-dimensional model with a visualization indicating a location of the potential risk. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements encompass a generic computer functions of storing and displaying, see MPEP 2106.05(f)(2) (noting the use of computers in their ordinary capacity to receive, store, or transmit data does not integrate a judicial exception into a practical application). Claim 21 recites the additional elements of 3D printing a model of the building. These additional elements, when considered individually or in combination, do not integrate the abstract idea into a practical application because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (3D printing) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims. Claim 23 recites the additional elements of training the machine learning model in stages. These additional elements, when considered individually or in combination, does not integrate the abstract idea because the additional elements are only a general link to a field of use or technological environment, see MPEP 2106.05(h) (discussing Affinity Labs). That is, although these additional elements do limit the use of the abstract idea, this type of limitation merely confines the use of the abstract idea to a particular technological environment (i.e., machine learning training techniques like curriculum learning) and does not integrate the abstract idea into a practical application or add an inventive concept to the claims. Please note, claim 22 is not rejected under 35 USC 101. Claim Rejections - 35 USC § 103 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim(s) 1-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pourmohammad et al, US Pub. No. 2019/0138512, herein referred to as “Pourmohammad” in view of Tohidi et al, US Pub. No. 2020/0155882, herein referred to as “Tohidi”. Regarding claim 1, Pourmohammad teaches: at least one memory device; and at least one processor in communication with the at least one memory device and at least one sensor located proximate to a building, wherein the at least one processor is configured to (processor and memory, ¶¶[0227]-[0229], and building sensors, ¶¶[0003], [0216], [0225], [0396]): train a machine learning model using training data (trains machine learning model, ¶¶[0274]-[0276]) comprising historical weather event data by location (analyzes weather patterns in the area, ¶¶[0220]-[0221] and weather data, e.g., ¶¶[0331]-[0333]), and damage data representing damage to the buildings resulting from historical weather events (uses historical threat data, e.g., ¶¶[0007], [0230] and threats include damage, ¶[0212]); receive environment data generated by the at least one sensor comprising internal data relative to the building (data sources include building sensors and building systems include fire and security systems, ¶[0396]) external environment data relative to the building (building sensors generate data on threats, ¶[0003], [0216], [0396]; see also e.g. ¶¶[0221]-[0222] discussing types of threats; and e.g. ¶¶[0331]-[0332] discussing assessing weather risks), the environment data associated with a plurality of historical weather events that previously occurred at a location of the building (records historical weather threat events and how it effects the building, e.g., ¶¶[0347]-[0348], [0360]); receive building data from the at least one memory device (obtains asset (i.e., building) information, ¶¶0211], [0248]-[0249]), utilize the trained machine learning model to determine at least one potential risk associated with at least one material of the plurality of materials used to construct the building based upon the received environment data and the received building data for the building (uses geofences to determine if potential threats are close enough to assets, ¶¶[0305], [0310]); generate a building risk profile for the building using output from the trained machine learning model that includes the at least one potential risk (provides a summary of threat an distance away from asset, ¶[0397], see also e.g., ¶[0305] discussing stores threats as historical threats in the threat database); and generate a risk mitigation output based upon the building risk profile for the building (controls building systems in response to threats and issues warning or alerts, ¶[0359]; see also ¶[0211] summarizing process), wherein the risk mitigation output includes causing a model to be displayed of the building that includes a visual indicator of the at least one potential risk of damage to the at least one material used to construct the building (displays information and location of threats, ¶¶[0398]-[0399] and Figs. 25 and 26) and a recommendation on how to address the at least one potential risk of damage (controls building systems in response to threats and issues warning or alerts, ¶[0359]; see also ¶[0211] summarizing process). However Pourmohammad does not teach but Tohidi does teach: building material data used to construct buildings by location (assessments of risk are based on buildings in the area and material used in the buildings, ¶¶[0220]-[0221]), the building data including materials data associated with a plurality of materials used to construct the building (assessments of risk are based on buildings in the area and material used in the buildings, ¶¶[0220]-[0221]); determine at least one potential risk associated with at least one material of the plurality of materials used to construct the building (assessments of risk are based on buildings in the area and material used in the buildings and weather patterns in the area, ¶¶[0220]-[0221]; see also ¶[0120] and Fig. 16 discussing lighting and wind modeling for fire forecasting; and ¶[0212]-[0213] discussing analyzing combustible materials in the region including material in the home such as roof tiles) generate a building risk profile for the building using output from the trained machine learning model that includes the at least one potential risk associated with the at least one material (prediction of fire risks based on weather and building structures, e.g., ¶¶[0063], [0207] and assessments of risk are based on buildings in the area and material used in the buildings and weather patterns in the area, ¶¶[0220]-[0221]) Further, it would have been obvious before the effective filing date of the claimed invention, to combine the building risk analysis of Pourmohammad with the assessment of building materials of Tohidi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the risks assessment of Pourmohammad would likely be improved by considering additional factor such as the weather patterns and buildings materials, i.e., as taught by Tohidi. Regarding claim 2, the combination Pourmohammad and Tohidi teaches all the limitations of claim 1 and Pourmohammad further teaches: wherein the risk mitigation output is a risk alert and the at least one processor is further configured to transmit the risk alert to at least one of an external computer device or a building management computer system in communication with the at least one processor (provides risk scores and contextual information to a user for monitoring and/or responding to threats of a building or campus, ¶[0234], and issues warning or alerts, ¶[0359]; see also ¶¶[0214], [0235] and Fig. 1 discussing user devices). Regarding claim 3, the combination Pourmohammad and Tohidi teaches all the limitations of claim 2 and Pourmohammad further teaches: wherein the external computer device is a user computer device and wherein the risk alert causes the user computer device to display a notification (provides risk scores and contextual information to a user for monitoring and/or responding to threats of a building or campus, ¶[0234], and issues warning or alerts, ¶[0359]; see also ¶[0235] discussing user devices). Regarding claim 4, the combination Pourmohammad and Tohidi teaches all the limitations of claim 1 and Pourmohammad further teaches: wherein the risk mitigation output includes precautionary measures for individuals associated with the building (warnings include, evacuate a building, take cover, move to a basement area, etc., ¶[0359]), and wherein the at least one processor is further configured to transmit the risk mitigation output to an external computer device (provides risk scores and contextual information to a user for monitoring and/or responding to threats of a building or campus, ¶[0234], and issues warning or alerts, ¶[0359]; see also ¶¶[0214], [0235] and Fig. 1 discussing user devices). Regarding claim 5, the combination Pourmohammad and Tohidi teaches all the limitations of claim 4 and Pourmohammad further teaches: wherein the external computer device is associated with a building management computer system in communication with the at least one processor (building control equipment, ¶[0359]) and wherein the recommendation contains recommended actions for mitigating the at least one potential risk associated with the building (warnings include, evacuate a building, take cover, move to a basement area, etc., ¶[0359]). Regarding claim 6, the combination Pourmohammad and Tohidi teaches all the limitations of claim 5 and Pourmohammad further teaches: wherein the risk mitigation output is configured to cause the building management computer system to alter operations of a system associated with the building comprising at least one of halting operation of the system, rebooting the system, or activating or deactivating the system (building control equipment issues warning or alerts, [0359]). Regarding claim 7, the combination Pourmohammad and Tohidi teaches all the limitations of claim 1 and Pourmohammad further teaches: wherein the building risk profile further includes a three-dimensional model of the building including risk information associated with the at least one potential risk overlaid thereon (schematic drawing of buildings, Fig. 16; see also ¶[0313] discussing Fig. 16). Regarding claim 8, the combination Pourmohammad and Tohidi teaches all the limitations of claim 7 and Pourmohammad further teaches: wherein the risk information overlaid upon the three-dimensional model of the building includes a visualization indicating a location of the at least one potential risk (schematic drawing of buildings includes threat locations, Fig. 16; see also ¶[0313] discussing Fig. 16). Regarding claims 9-17, claims 9-17 recite similar limitations as claims 1-8 and accordingly are rejected for similar reasons. Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pourmohammad and Tohidi further in view of Rietman, US Pub. No. 2013/0095927, herein referred to as “Rietman”. Regarding claim 21, the combination Pourmohammad and Tohidi teaches all the limitations of claim 7 and does not teach but Rietman does teach: wherein the at least one processor is further configured to cause a 3D printing system to print the three-dimensional model of the building (three dimensional printer produces physical mock ups of building designs, ¶[0037]; see also ¶[0022] discussing assessing safety). Further, it would have been obvious before the effective filing date of the claimed invention, to combine the building risk analysis of Pourmohammad and Tohidi with the 3D printing of Rietman because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the users of Pourmohammad and Tohidi might appreciate being able to view physical models of the threat assessment and accordingly would have modified Pourmohammad and Rietman to produce models, e.g., using 3D printing as taught by Rietman. Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pourmohammad and Tohidi further in view of Myslinski, US Pub. No. 2018/0305017, herein referred to as “Myslinski”. Regarding claim 22, the combination Pourmohammad and Tohidi teaches all the limitations of claim 1 and Tohidi further teaches: determine a location of the at least one material associated with the at least one potential risk (assessments of risk are based on buildings in the area and material used in the buildings, ¶¶[0220]-[0221]). Further, it would have been obvious before the effective filing date of the claimed invention, to combine the building risk analysis of Pourmohammad with the assessment of building materials of Tohidi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the risks assessment of Pourmohammad would likely be improved by considering additional factor such as the weather patterns and buildings materials, i.e., as taught by Tohidi. However the combination of Pourmohammad and Tohidi does not teach but Myslinski doe teach: and transmit the risk mitigation instructions to a computing system associated with the building, wherein the risk mitigation instructions cause the computing system to adjust at least one security camera associated with the building to adjust a field of vision associated with the security camera from a first field of vision that does not include the location of the at least one material to a second field of vision that does include the location of the at least one material in order to monitor the at least one potential risk (moves to location to view fire, ¶[0164], to acquire imagery, ¶¶[0172]-[0173])). Further, it would have been obvious before the effective filing date of the claimed invention, to combine the building risk analysis with the assessment of building materials of Pourmohammad and Tohidi with drone monitoring of Myslinski because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the building monitoring of Pourmohammad would likely be improved by the inclusion of drones and accordingly would have modified Pourmohammad and Tohidi to use the drone monitoring of Myslinski. Claim(s) 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pourmohammad and Tohidi further in view of Mitchell, Tom, et al. "Never-ending learning." Communications of the ACM 61.5 (2018): 103-11, herein referred to as “Mitchell”. Regarding claim 23, the combination Pourmohammad and Tohidi teaches all the limitations of claim 1 and Pourmohammad further teaches: historical weather event data (analyzes weather patterns in the area, ¶¶[0220]-[0221] and weather data, e.g., ¶¶[0331]-[0333]), and damage data (uses historical threat data, e.g., ¶¶[0007], [0230] and threats include damage, ¶[0212]). However Pourmohammad does not teach but Tohidi does teach: train the machine learning model: in a first stage (trains machine learning model based on actual fires, e.g., ¶¶[0113], [0144]) building material data (assessments of risk are based on buildings in the area and material used in the buildings, ¶¶[0220]-[0221]). Further, it would have been obvious before the effective filing date of the claimed invention, to combine the building risk analysis of Pourmohammad with the assessment of building materials of Tohidi because known work in one field of endeavor may prompt variations of it for use in the same field based on design incentives, see MPEP 2143.I.F. That is, one of ordinary skill would have recognized the risks assessment of Pourmohammad would likely be improved by considering additional factor such as the weather patterns and buildings materials, i.e., as taught by Tohidi. However the combination of Pourmohammad and Tohidi does not teach training the machine learning in the first, second, and third stages as claimed but Mitchell does teach curriculum learning (staged learning, pg. 2304). Further, it would have been obvious before the effective filing date of the claimed invention, to combine with the building risk analysis with the assessment of building materials of Pourmohammad and Tohidi with curriculum learning because Mitchell explicitly suggests doing so minimize the amount of supervision need, pg. 2308; see also MPEP 2143.I.G. Conclusion Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 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. Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRENDAN S O'SHEA whose telephone number is (571)270-1064. The examiner can normally be reached Monday to Friday 10-6. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRENDAN S O'SHEA/Examiner, Art Unit 3626 1 Examiner notes the claim language of the independent claims varies but does not find the differences in claim language significantly alters the eligibility analysis and analyses the independent claims concurrently here for the sake of brevity. 2 Examiner notes the claim language of the independent claims varies but does not find the differences in claim language significantly alters the eligibility analysis and analyses the independent claims concurrently here for the sake of brevity.
Read full office action

Prosecution Timeline

Dec 27, 2023
Application Filed
Sep 21, 2024
Non-Final Rejection — §101, §103, §112
Jan 10, 2025
Applicant Interview (Telephonic)
Jan 11, 2025
Examiner Interview Summary
Jan 23, 2025
Response Filed
Apr 05, 2025
Final Rejection — §101, §103, §112
Jul 08, 2025
Request for Continued Examination
Jul 11, 2025
Response after Non-Final Action
Jul 26, 2025
Non-Final Rejection — §101, §103, §112
Oct 28, 2025
Examiner Interview Summary
Oct 28, 2025
Applicant Interview (Telephonic)
Nov 04, 2025
Response Filed
Feb 15, 2026
Final Rejection — §101, §103, §112 (current)

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Patent 12373795
SYSTEM AND METHOD OF DYNAMICALLY RECOMMENDING ONLINE ACTIONS
2y 5m to grant Granted Jul 29, 2025
Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

5-6
Expected OA Rounds
30%
Grant Probability
67%
With Interview (+36.3%)
3y 4m
Median Time to Grant
High
PTA Risk
Based on 178 resolved cases by this examiner. Grant probability derived from career allow rate.

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