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 .
Status of Application
This communication is a Final Office Action in response to the Amendments and Remarks filed on the 2nd day of March, 2026. Claims 1-20 are pending. No Claims are allowed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/26/2025, 3/19/2026 and 6/29/2026 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
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Claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-20 of copending Application No. 17972261 in view of U.S. Patent Application Publication No. US 20220405856 A1 to Hedges et al. (hereinafter Hedges). Examiner notes that the instant application amounts to substantially similar scope and claim limitations as found in Claims 1-20 of the copending application ‘261.
The instant application contains additional limitations directed to the determination and use of inputted weights, similarity metrics, and severity risks, which are taught by Hedges:
determining second home score factors for the second property based at least upon the past hazard data (see at least Hedges: ¶ 33 “method can be performed by a system including a set of attribute models (e.g., configured to extract values for one or more attributes), and a set of hazard models (e.g., configured to determine a hazard score for one or more properties)”; see also Hedges: ¶ 37 “determining a single property, determining a set of properties, and/or any other suitable number of properties”; see also Hedges: ¶ 72 “The hazard score can be: a vulnerability score (e.g., an unmitigated vulnerability score and/or a mitigated vulnerability score), a regional exposure score, a risk score, a combination of scores, and/or any other metric for one or more properties”; see also Hedges: ¶ 32 “method can be performed for a single property, iteratively for a list of properties, for a group of properties as a whole (e.g., for the properties as a batch), for a property class, responsive to receipt of a request for a hazard score for a given property, responsive to receipt of a new image depicting the property, and/or at any other suitable time”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 70 “Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties.”; see at least Hedges: ¶ 120 “the outputs can be used to identify a group of properties and/or modify property groupings”; see also Hedges: ¶ 98),
determining, based upon the home characteristic data for the first property and the second home score factors for the second property, the one or more first home score factors (see at least Hedges: ¶ 72 “The hazard score can be: a vulnerability score (e.g., an unmitigated vulnerability score and/or a mitigated vulnerability score), a regional exposure score, a risk score, a combination of scores, and/or any other metric for one or more properties”; see also Hedges: ¶ 78 “the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data”; see also Hedges: ¶ 80 and 82 “the risk score can be predicted based on another hazard score (e.g., the regional exposure score)”; see also Hedges: ¶ 28 “subsets of properties can be identified using a combination of (e.g., a comparison between): unmitigated vulnerability scores, mitigated vulnerability scores, regional exposure scores, risk scores, and/or any other hazard scores”; see also Hedges: ¶ 108 “the hazard output is directly comparable to the training target for each training property. In a first example, both the hazard model output and the training target”),
determining, based upon the home characteristic data for the first property and the past hazard data, similar characteristics of the second property and the first property, the similar characteristics associated with the second home score factors and at least some of the one or more first home score factors (see at least Hedges: ¶ 70 “Determining a hazard score for the property S400 can function to determine a score for the property associated with a vulnerability and/or risk to one or more hazards, to determine the potential for mitigation of the vulnerability and/or risk, to determine a metric associated with a claim for the property (e.g., a hypothetical or real claim), and/or to determine any other metric for the property associated with a hazard. Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties”; see also Hedges: ¶ 98 “The set of training properties can be selected based on: property location (e.g., associated with a hazard exposure and/or lack of exposure), weather and/or hazard data (e.g., hazard perimeter data such as wildfire perimeter, hail-effected perimeter, flood perimeter, etc.), historical homeowners' policies, any property outcome data (e.g., described below). Examples of sets of training properties include: properties within a given region (e.g., hazard perimeter, geographic region, etc.), properties exposed to a hazard (e.g., within a given time frame), all properties regardless of hazard exposure (e.g., all properties within a set of regions, of a property type, associated with a given insurance policy, etc.), properties that have experienced damage, properties that have filed a claim, properties that have received a response from an insurance company regarding a filed claim, and/or any other property group. Preferably, the set of training data includes properties from multiple geographic regions (e.g., multiple regions across a country or multiple countries, wherein the regions can share environmental commonalities or not share environmental commonalities), but alternatively the set of training data includes properties from a single geographic region (e.g., a state, a region within a state, etc.).”; see also Hedges: ¶ 120 “In a third example, the outputs can be used to identify a group of properties and/or modify property groupings. In a first specific example, a targeted list of properties (e.g., a subset of an insurance portfolio) can be identified in a high regional exposure score region (e.g., a high likelihood of hazard exposure) that have low mitigated vulnerability scores (e.g., a desirable vulnerability rating with a lower probability of claim occurrence and/or damage). In a second specific example, properties can be grouped using one or more unmitigated hazard score(s) and then re-grouped using one or more mitigated hazard score(s), wherein the properties that switch groups (e.g., from a high underwriting risk group to a low underwriting risk group) are provided to a user. In a third specific example, a targeted list of properties can be identified that have changed their vulnerability score over time (e.g., wherein properties with a decrease in vulnerability score may be eligible for an additional credit or lower insurance premium, whereas properties with a positive change may necessitate an underwriting action; or vice versa).”),
(see at least Hedges: ¶ 82 “the risk score can be a combination of the vulnerability score, the regional exposure score, another risk score, and/or other hazard scores” and ¶ 90: discussing the regional exposure score; see also Hedges: ¶ 96-97; see also Hedges: ¶ 77 “The set of properties can be the set of training properties (S500), a set of test properties, and/or any other set of properties. In a specific example, the hazard scores for each property are binned such that each bin corresponds to approximately a predetermined proportion (e.g., 10%, 20%, 25%, 50%, etc.) of the population of properties. In a second example, the continuous hazard model output is mapped to a bin such that the bin values for a set of properties have a distribution matching that of third-party hazard scores (e.g., the distributions match for the same set of properties)”; see also Hedges: ¶ 34 “where attribute values have been previously determined for each of a set of properties”; see also Hedges: ¶ 70 “Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties.”; see at least Hedges: ¶ 120 “the outputs can be used to identify a group of properties and/or modify property groupings”; see also Hedges: ¶ 98); and
generating a weight for at least some of the one or more first home score factors in accordance with corresponding weights for the second home score factors based upon the similar characteristics, determining a severity of a risk associated with at least one of the at least some of the one or more first home score factors (see at least Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 74 “In a third specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests attribute values for the property and weather data. In a fourth specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests a determined hazard score (e.g., vulnerability score) and weather data. In a fifth specific example, the hazard model (e.g., any one of those described above or another model) ingests property measurements in addition to or instead of attribute values. Optionally, weights for one or more model inputs can be determined during model training S500, based on a decision tree, based on any neural network, based on a set of heuristics, manually, and/or otherwise determined.”; see also Hedges: ¶ 78 “Alternatively, the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data. In an illustrative example, the vulnerability score is representative of the vulnerability of a property to a hazard (e.g., probability of claim occurrence, severity of damage, etc.) assuming exposure to the hazard, wherein the vulnerability model (e.g., trained in S500) ingests property attribute values (e.g., intrinsic property attribute values, independent from regional location) and does not ingest weather and/or hazard data.”; see also Hedges: ¶ 27 “to provide additional information to a user (e.g., a summary of the most impactful property-specific attributes on a given hazard score)”; see also Hedges: ¶ 112 “Methods used to debias the training data and/or model can include: disparate impact testing, data pre-processing techniques (e.g., suppression, massaging the dataset, apply different weights to instances of the dataset), adversarial debiasing, Reject Option based Classification (ROC), Discrimination-Aware Ensemble (DAE), temporal modelling, continuous measurement, converging to an optimal fair allocation, feedback loops, strategic manipulation, regulating conditional probability distribution of disadvantaged sensitive attribute values, decreasing the probability of the favored sensitive attribute values, training a different model for every sensitive attribute value, and/or any other suitable method and/or approach. Additionally or alternatively, bias can be reduced using any interpretability method (e.g., an example is described in S340).”; see also Hedges: ¶ 121 “the outputs can be used to determine a set of mitigation measures for the property (e.g., high-impact mitigation measures that change the hazard score above a threshold amount). In an illustrative example, an unmitigated hazard score can be compared to each of a set of mitigated hazard scores, wherein each mitigated hazard score corresponds to a different mitigation measure, to determine one or more high-impact mitigation measures (e.g., with the largest difference between the unmitigated and mitigated hazard scores)”);
(see at least Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it);
adjusting a corresponding weight for the at least one of the at least some of the one or more first home score factors based upon the determined severity; generating, by the one or more processors and based upon at (see also Hedges: ¶ 56 “Condition-related attributes can be a rating for a single structure, a minimum rating across multiple structures, a weighted rating across multiple structures, and/or any other individual or aggregate value. Condition-related attributes can additionally or alternatively be attributes subject to weather-related conditions; for example: average annual rainfall, presence of high-speed and/or dry seasonal winds (e.g., the Santa Ana winds), vegetation dryness and/or greenness index, regional hazard risks, and/or any other variable parameter”; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 74 “In a third specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests attribute values for the property and weather data. In a fourth specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests a determined hazard score (e.g., vulnerability score) and weather data. In a fifth specific example, the hazard model (e.g., any one of those described above or another model) ingests property measurements in addition to or instead of attribute values. Optionally, weights for one or more model inputs can be determined during model training S500, based on a decision tree, based on any neural network, based on a set of heuristics, manually, and/or otherwise determined.”; see also Hedges: ¶ 78 “Alternatively, the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data. In an illustrative example, the vulnerability score is representative of the vulnerability of a property to a hazard (e.g., probability of claim occurrence, severity of damage, etc.) assuming exposure to the hazard, wherein the vulnerability model (e.g., trained in S500) ingests property attribute values (e.g., intrinsic property attribute values, independent from regional location) and does not ingest weather and/or hazard data.”); and
training, by the one or more processors, the trained machine learning model using the determined severity (see at least Hedges: ¶ 39 “Determining measurements for the property S200 can function to determine property-specific data (e.g., an image or other visual representation) for the property. The measurements can be determined after S100, iteratively for a list of properties, in response to a request, when updated or new region or property imagery is available, when one or more property components and/or attributes are added (e.g., to a database), during hazard model training S500, and/or at any other suitable time.”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 67-68 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 68 “In a first variant, the set of attributes is selected such that a hazard score determined based on the set of attributes is indicative of a key metric. The metric can be a training target (e.g., the same training target used in S500, the key metric in S400, a different training target, etc.), and/or any other metric. For example, the key metric can be: the probability of a claim being filed for the property (e.g., claim occurrence) (e.g., within a given timeframe), claim acceptance probability, claim rejection probability, an expected loss amount, a hazard exposure probability, a claim and/or damage occurrence, a combination of the above (e.g., claim occurrence and acceptance probability) and/or any other metric. The claims can be: insurance claims, aid claims (e.g., FEMA claims), and/or any other suitable claim. In an example, a statistical analysis of training data can be used to select attributes that have a nonzero statistical relationship (e.g., correlation, interaction effect, etc.) with the key metric (e.g., positive or negative correlation with claim filing occurrence). In a second variant, the set of attributes is selected using a combination of an attribute selection model and a supplemental validation method.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 75 “The hazard score can be a label, a probability, a metric, a monetary value, and/or any parameter. The score can be binary, continuous, discrete, binned, and/or otherwise configured. The hazard score can optionally include an uncertainty parameter (e.g., variance, confidence score, etc.) associated with: the hazard model, a training data set (e.g., based on recency), attribute value uncertainty parameters, and/or any other parameter. The hazard score can be—or be calculated from—the hazard model output.”; see also Hedges: ¶ 94-109 “Examples of sets of training properties include: properties within a given region (e.g., hazard perimeter, geographic region, etc.), properties exposed to a hazard (e.g., within a given time frame), all properties regardless of hazard exposure (e.g., all properties within a set of regions, of a property type, associated with a given insurance policy, etc.), properties that have experienced damage, properties that have filed a claim, properties that have received a response from an insurance company regarding a filed claim, and/or any other property group”; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the lacking features (disclosed by Hedges) into the method and system for evaluating and generating a home score for a property (as disclosed by copending Application No. 17972261). One of ordinary skill in the art would have been motivated to incorporate the features because it would determine a risk score for the property [(e.g., hazard risk score) can additionally or alternatively be determined based on the property attribute values and a regional exposure score (e.g., regional risk score), using a trained risk model (see Hedges ¶ 18)].
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the feature of determining, by the one or more processors, one or more influential home score factors, wherein the home score factors include a subset of the one or more first home score factors with a highest subset of weights and generating, by the one or more processors and based upon at least the one or more influential home score factors and a corresponding weight for each of the one or more influential home score factors, a home score for the first property (as disclosed by Hedges) into the method and system for evaluating and generating a home score for a property (as disclosed by copending Application No. 17972261), because the claimed invention is merely a simple arrangement of old elements, with each performing the same function it had been known to perform, yielding no more than one would expect from such arrangement. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention See also MPEP § 2143(I)(A).
This is a provisional nonstatutory double patenting rejection.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under MPEP 2106, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims 1-20 are directed to a method, device and a tangible, non-transitory computer-readable medium. Thus, each of the claims fall within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea. Although claims 1, 8, and 15 are directed to different categories the claim language is substantial similar and will be addressed together below.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. Examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mathematical calculations (see MPEP 2106.04(a)(2)(I), certain methods of organizing human activity specifically commercial interactions and behaviors and managing personal behavior and/or interactions between people (see MPEP 2106.04(a)(2)(II)) and mental processes (see MPEP 2106.04(a)(2)(III).
Claims 1, 8, and 15 recite a computer-implemented method for evaluating and generating a home score for a property, the computer-implemented method comprising: retrieving, by one or more processors, home data for a first property; retrieving, by the one or more processors, past hazard data associated with a second property; determining, by the one or more processors and based upon at least the home data for the first property and the past hazard data, one or more first home score factors using a trained machine learning model, wherein the determining includes: analyzing, using the trained machine learning model, the home data for the first property to determine home characteristic data for the first property, determining second home score factors for the second property determining a severity of a risk associated with at least one of the at least some of the one or more first home score factors, and adjusting a corresponding weight for the at least one of the at least some of the one or more first home score factors based upon the determined severity; the weights for the at least some of the one or more first home score factors determined severity, Electric Power Group.
Examiner notes that claim 1-20 recite a system for receiving a plurality of attributes related to a property, and calculating an overall rating and score related to the property which is directed to concepts that are performed mentally and a product of human mental work. The limitations suggest a process similar to standard practice risk management when buying or insuring a property where historical data and historical attributes related to the house are considered prior to purchase. Because the limitations above closely follow the steps of receiving information, processing the information, and displaying the results of the processing, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the see MPEP 2106.04(a)(2)(III). If a claim, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor executing computer code stored on a computer medium, then it falls within the “Mental Processes” grouping of abstract idea. Accordingly, the claims recite an abstract idea.
Furthermore, the claims recite the familiar concept of property valuation. As the Supreme Court explained in Alice, claims involving “a fundamental economic practice long prevalent in our system of commerce,” such as the concepts of hedging and inter-mediated settlement, are patent-ineligible abstract ideas. Alice, 134 S. Ct. at 2356 (quoting Bilski v. Kappos, 561 U.S. 593, 611 (2010)). It follows that the claims at issue here are directed to an abstract idea. Applicants’ claims recite one or more computers configured to receive a user’s property valuations, and display that information. Like the risk hedging in Bilski and the concept of intermediated settlement in Alice, the concept of property valuation, that is, determining a property’s market value, is “a fundamental economic practice long prevalent in our system of commerce.” Id. (quoting Bilski, 561 U.S. at 611). Prospective sellers and buyers have long valued property and doing so is necessary to the functioning of the residential real estate market. As such, claims 1, 8, and 15 are directed to the abstract idea of property valuation. and is similar to the abstract idea identified in MPEP 2106.04(a)(2)(II) in grouping “II” in that the claims recite certain methods of organizing human activity such as fundamental economic practices. This is merely further embellishments of the abstract idea and does not further limit the claimed invention to render the claims patentable subject matter. The limitations, substantially comprising the body of the claim, recite standard processes found in standard practice in property valuations. This is common practice when purchasing or insuring a piece of property. Because the limitations above closely follow the steps standard in fundamental economic practices such as process valuation, and the steps of the claims involve organizing human activity, the claim recites an abstract idea consistent with the “organizing human activity” grouping set forth in the see MPEP 2106.04(a)(2)(II).
Additionally, Examiner notes that the claims contain language directed to “analyzing, using a trained machine learning model”, “determining, by the one or more processors and via the trained machine learning model” and “training, by the one or more processors, the trained machine learning model using the determined first risk scores, the weights, and the similar characteristics”, which amounts to, under the broadest reasonable interpretation, the system requires specific mathematical calculations (training the algorithm using stored hazard information related to properties). “Although the methods described elsewhere herein may not directly mention machine learning techniques, such methods may be read to include such machine learning for any determination or processing of data that may be accomplished using such techniques. In some embodiments, such machine-learning techniques may be implemented automatically upon occurrence of certain events or upon certain conditions being met. Use of machine learning techniques, as described herein, may begin with training a machine learning program, or such techniques may begin with a previously trained machine learning program. A processor or a processing element may be trained using supervised or unsupervised machine learning, and the machine learning program may employ a neural network, which may be a convolutional neural network, a deep learning neural network, or a combined learning module or program that learns in two or more fields or areas of interest. Machine learning may involve identifying and recognizing patterns in existing data (such as customer financial transaction, location, browsing or online activity, mobile device, vehicle, and/or home sensor data) in order to facilitate making predictions for subsequent customer data. Models may be created based upon example inputs of data in order to make valid and reliable predictions for novel inputs.” (See at least Specification ¶ 76-77) and therefore encompasses mathematical concepts. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the claimed invention falls within the mental process/certain method of organizing human activity grouping of abstract ideas, and steps fall within the mathematical concepts grouping of abstract ideas. The limitations are considered together as a single abstract idea for further analysis. (Step 2A, Prong One: YES).
For the above reasons the examiner concludes that the claimed invention has a concept similar to those that the courts have found to be abstract and that the claims are directed to a judicial exception fin the form of an abstract idea.
The conclusion that the claim recites an abstract idea within the groupings of the MPEP 2106.04(a)(2) remains grounded in the broadest reasonable interpretation consistent with the description of the invention in the specification. For example, (App. Spec. ¶ 2), the system amounts to a “method and system for evaluating and generating a home score for a property”. Accordingly, the Examiner submits claims 1, 8, and 15, recite an abstract idea based on the language identified in claims 1, 8, and 15, and the abstract ideas previously identified based on that language that remains consistent with the groupings of Step 2A Prong 1 of the MPEP 2106.04(a)(1).
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to a method instructing the reader to implement the abstract idea identified method of organizing human activity of fundamental business and economic practices such as property valuation and risk mitigation, the mathematical calculations, and the mental processes. For instance, the additional elements or combination of elements other than the abstract idea itself include the elements such as a “processor”, “memory”, and “analyzing, using a trained machine learning model” and “train the trained machine learning model using the home characteristic data” recited at a high level of generality. The claimed computer structure read in light of the specification can be “processor”, “memory”, and “analyzing, using a trained machine learning model” and “train the trained machine learning model using the home characteristic data” and includes any wide range of possible devises comprising a number of components that are “well-known” and include an indiscriminate “computer” (e.g., processor, memory). Thus, the claimed structure amounts to appending generic computer elements to abstract idea comprising the body of the claim. Examiner notes that the use and overall description of the machine learning techniques are broadly addressed and claimed. Nothing amounts to improvement to the machine learning techniques or processes. The system is merely appending the computer processes to perform thein intended purposes to the abstract idea. The computing elements are only involved at a general, high level, and do not have the particular role within any of the functions but to be a generically claimed “device” and “trained machine learning”.
Similarly, reciting the abstract idea as software functions used to program a generic computer is not significant or meaningful: generic computers are programmed with software to perform various functions every day. A programmed generic computer is not a particular machine and by itself does not amount to an inventive concept because, as discussed in MPEP 2106.05(a), adding the words “apply it” (or an equivalent) with the judicial exception, or more instructions to implement an abstract idea on a computer, as discussed in Alice, 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)), is not enough to integrate the exception into a practical application. Further, it is not relevant that a human may perform a task differently from a computer. It is necessarily true that a human might apply an abstract idea in a different manner from a computer. What matters is the application, “stating an abstract idea while adding the words ‘apply it with a computer’” will not render an abstract idea non-abstract. Tranxition v. Lenovo, Nos. 2015-1907, -1941, -1958 (Fed. Cir. Nov. 16, 2016), slip op. at 7-8.
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). In this case, the Federal Circuit relied upon the specification in explaining that the claimed steps of voting, verifying the vote, and submitting the vote for tabulation are "human cognitive actions" that humans have performed for hundreds of years. The claims therefore recited an abstract idea, despite the fact that the claimed voting steps were performed on a computer. 887 F.3d at 1385, 126 USPQ2d at 1504. Another example is FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 USPQ2d 1293 (Fed. Cir. 2016). The patentee in FairWarning claimed a system and method of detecting fraud and/or misuse in a computer environment, in which information regarding accesses of a patient’s personal health information was analyzed according to one of several rules (i.e., related to accesses in excess of a specific volume, accesses during a pre-determined time interval, or accesses by a specific user) to determine if the activity indicates improper access. 839 F.3d. at 1092, 120 USPQ2d at 1294. The court determined that these claims were directed to a mental process of detecting misuse, and that the claimed rules here were "the same questions (though perhaps phrased with different words) that humans in analogous situations detecting fraud have asked for decades, if not centuries." 839 F.3d. at 1094-95, 120 USPQ2d at 1296.
An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. The patentee in Mortgage Grader claimed a computer-implemented system for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The interface prompts a borrower to enter personal information, which the grading module uses to calculate the borrower’s credit grading, and allows the borrower to identify and compare loan packages in the database using the credit grading. 811 F.3d. at 1318, 117 USPQ2d at 1695. The Federal Circuit determined that these claims were directed to the concept of "anonymous loan shopping", which was a concept that could be "performed by humans without a computer." 811 F.3d. at 1324, 117 USPQ2d at 1699. Another example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes. 881 F.3d at 1366, 125 USPQ2d at 1652-53.
Both product claims (e.g., computer system, computer-readable medium, etc.) and process claims may recite mental processes. For example, in Mortgage Grader, the patentee claimed a computer-implemented system and a method for enabling borrowers to anonymously shop for loan packages offered by a plurality of lenders, comprising a database that stores loan package data from the lenders, and a computer system providing an interface and a grading module. The Federal Circuit determined that both the computer-implemented system and method claims were directed to "anonymous loan shopping", which was an abstract idea because it could be "performed by humans without a computer." 811 F.3d. at 1318, 1324-25, 117 USPQ2d at 1695, 1699-1700. See also FairWarning IP, 839 F.3d at 1092, 120 USPQ2d at 1294 (identifying both system and process claims for detecting improper access of a patient's protected health information in a health-care system computer environment as directed to abstract idea of detecting fraud); Content Extraction & Transmission LLC v. Wells Fargo Bank, N.A., 776 F.3d 1343, 1345, 113 USPQ2d 1354, 1356 (Fed. Cir. 2014) (system and method claims of inputting information from a hard copy document into a computer program). Accordingly, the phrase "mental processes" should be understood as referring to the type of abstract idea, and not to the statutory category of the claim.
Examples of product claims reciting mental processes include: An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356; and A computer readable medium containing program instructions for detecting fraud – CyberSource, 654 F.3d at 1368 n. 1, 99 USPQ2d at 1692 n.1.
Examiner notes that the claimed in invention is similar to the Voter Verified, Inc., FairWarning, Mortgage Grader, Berkheimer, Content Extraction and CyberSource applications wherein the court identified computer system and “machine learning” is merely serving as the generic computer, computing environment, or tool to perform the mental process.
Here, the instructions entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the role of the generic computing elements recited in claims 1, 8, and 15, is the same as the role of the computer in the claims considered by the Supreme Court in Alice, and the claim as whole amounts merely to an instruction to apply the abstract idea on the generic computing system. Therefore, the claims have failed to integrate a practical application (2106.04(d)). Under the MPEP 2106.05, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claims 1, 8, and 15, do not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea.
Claims 2-7, 9-14, and 16-20 are directed to further embellishments of the central theme of the abstract idea which is processing information in order to the valuations provided on the property information. This is not enough, as addressed above, to provide significantly more to the claims.
Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. See MPEP 2106.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20220405856 to Hedges et al. (hereinafter Hedges).
Referring to Claim 1, 8, and 15 (substantially similar in scope and language), Hedges discloses a computer-implemented method for evaluating and generating a home score for a property, the computer-implemented method comprising (see at least Hedges: Abstract and ¶ 123):
retrieving, by one or more processors, home data for a first property (see at least Hedges: ¶ 16-17 “extracting attribute values for each of a set of property attributes from the images. The property attributes are preferably structural attributes, such as the presence or absence of a property component (e.g., roof, vegetation, etc.), property component geometric descriptions (e.g., roof shape, slope, complexity, building height, living area, structure footprint, etc.), property component appearance descriptions (e.g., condition, roof covering material, etc.), and/or neighboring property components or geometric descriptions (e.g., presence of neighboring structures within a predetermined distance, etc.), but can additionally or alternatively include other attributes, such as built year, number of beds and baths, or other descriptors. One or more hazard scores (e.g., vulnerability score, risk score, regional exposure score, etc.) can then be calculated for the property.”; see also Hedges: ¶ 33 “configured to extract values for one or more attributes”; see also Hedges: ¶ 52-62: discussing attributes);
retrieving, by the one or more processors, past hazard data associated with a second property (see at least Hedges: ¶ 19 “risk model and/or vulnerability model can be trained on historical insurance claim data, such that the respective scores are associated with a probability of or expected: claim occurrence, claim loss, damage, claim rejection, and/or any other metric”; see also Hedges: ¶ 25 “evaluating hazard exposure risk based on property location (e.g., based on historical weather data), the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property (e.g., insurance claim, aid claim, etc.) will be submitted and accepted and/or estimate other claim parameters (e.g., loss amount, etc.)”; see also Hedges: ¶ 62 “attribute values can be determined by: extracting features from property measurements (e.g., wherein the attribute values are determined based on the extracted feature values), extracting attribute values directly from property measurements, retrieving values from a database or a third party source (e.g., third-party database, MLS database, city permitting database, historical weather and/or hazard database, tax assessor database, etc.), using a predetermined value (e.g., assuming a given mitigation action has been performed as described in S400), calculating and/or adjusting a value (e.g., from an extracted value and a scaling factor; adjusting a previously determined attribute value as described in S400; etc.), and/or otherwise determined”; see also Hedges: ¶ 80-82, 84, and 90-91);
determining, by the one or more processors and based upon at least the home data for the first property and the past hazard data, one or more first home score factors using a trained machine learning model (see at least Hedges: ¶ 52-62: discussing the system determining, extracting, analyzing, and using hazard and property attributes to determine an overall hazard score; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it),
wherein the determining includes: analyzing, using a trained machine learning model, the home data for the first property to determine home characteristic data for the first property, (see at least Hedges: ¶ 81 “the hazard score is a risk score (e.g., an overall risk score). The risk score can be associated with or represent the overall likelihood of a claim loss being filed, predicted claim loss frequency, expected loss severity, and/or any other key metric. This risk score is preferably dependent on the likelihood of hazard exposure (e.g., in contrast to the vulnerability score), but can alternatively be independent of and/or conditional on the hazard exposure. The risk score can be predicted based on: property measurements (e.g., directly), property attribute values extracted from property measurements, historical weather and/or hazard data, another hazard score (e.g., regional exposure score), and/or any other suitable information.”; see also Hedges: ¶ 114 “the risk score can represent an overall risk of a claim filing, incorporating both regional risk and vulnerability”; see also Hedges: ¶ 62 “Attribute values can be determined using an attribute value model that can include: CV/ML attribute extraction, any neural network and/or cascade of neural networks, one or more neural networks per attribute, key point extraction, SIFT, calculation, heuristics (e.g., inferring the number of stories of a property based on the height of a property), classification models (e.g., binary classifiers, multiclass classifiers, semantic segmentation models, instance-based segmentation models, etc.), regression models, object detectors, any computer vision and/or machine learning method, and/or any other technique. Different attribute values can be determined using different methods, but can alternatively be determined in the same manner.”; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it).
determining second home score factors for the second property (see at least Hedges: ¶ 33 “method can be performed by a system including a set of attribute models (e.g., configured to extract values for one or more attributes), and a set of hazard models (e.g., configured to determine a hazard score for one or more properties)”; see also Hedges: ¶ 37 “determining a single property, determining a set of properties, and/or any other suitable number of properties”; see also Hedges: ¶ 72 “The hazard score can be: a vulnerability score (e.g., an unmitigated vulnerability score and/or a mitigated vulnerability score), a regional exposure score, a risk score, a combination of scores, and/or any other metric for one or more properties”; see also Hedges: ¶ 32 “method can be performed for a single property, iteratively for a list of properties, for a group of properties as a whole (e.g., for the properties as a batch), for a property class, responsive to receipt of a request for a hazard score for a given property, responsive to receipt of a new image depicting the property, and/or at any other suitable time”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 70 “Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties.”; see at least Hedges: ¶ 120 “the outputs can be used to identify a group of properties and/or modify property groupings”; see also Hedges: ¶ 98),
determining, based upon the home characteristic data for the first property and the second home score factors for the second property, the one or more first home score factors (see at least Hedges: ¶ 72 “The hazard score can be: a vulnerability score (e.g., an unmitigated vulnerability score and/or a mitigated vulnerability score), a regional exposure score, a risk score, a combination of scores, and/or any other metric for one or more properties”; see also Hedges: ¶ 78 “the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data”; see also Hedges: ¶ 80 and 82 “the risk score can be predicted based on another hazard score (e.g., the regional exposure score)”; see also Hedges: ¶ 28 “subsets of properties can be identified using a combination of (e.g., a comparison between): unmitigated vulnerability scores, mitigated vulnerability scores, regional exposure scores, risk scores, and/or any other hazard scores”; see also Hedges: ¶ 108 “the hazard output is directly comparable to the training target for each training property. In a first example, both the hazard model output and the training target”),
determining, based upon the home characteristic data for the first property and the past hazard data, similar characteristics of the second property and the first property, the similar characteristics associated with the second home score factors and at least some of the one or more first home score factors (see at least Hedges: ¶ 70 “Determining a hazard score for the property S400 can function to determine a score for the property associated with a vulnerability and/or risk to one or more hazards, to determine the potential for mitigation of the vulnerability and/or risk, to determine a metric associated with a claim for the property (e.g., a hypothetical or real claim), and/or to determine any other metric for the property associated with a hazard. Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties”; see also Hedges: ¶ 98 “The set of training properties can be selected based on: property location (e.g., associated with a hazard exposure and/or lack of exposure), weather and/or hazard data (e.g., hazard perimeter data such as wildfire perimeter, hail-effected perimeter, flood perimeter, etc.), historical homeowners' policies, any property outcome data (e.g., described below). Examples of sets of training properties include: properties within a given region (e.g., hazard perimeter, geographic region, etc.), properties exposed to a hazard (e.g., within a given time frame), all properties regardless of hazard exposure (e.g., all properties within a set of regions, of a property type, associated with a given insurance policy, etc.), properties that have experienced damage, properties that have filed a claim, properties that have received a response from an insurance company regarding a filed claim, and/or any other property group. Preferably, the set of training data includes properties from multiple geographic regions (e.g., multiple regions across a country or multiple countries, wherein the regions can share environmental commonalities or not share environmental commonalities), but alternatively the set of training data includes properties from a single geographic region (e.g., a state, a region within a state, etc.).”; see also Hedges: ¶ 120 “In a third example, the outputs can be used to identify a group of properties and/or modify property groupings. In a first specific example, a targeted list of properties (e.g., a subset of an insurance portfolio) can be identified in a high regional exposure score region (e.g., a high likelihood of hazard exposure) that have low mitigated vulnerability scores (e.g., a desirable vulnerability rating with a lower probability of claim occurrence and/or damage). In a second specific example, properties can be grouped using one or more unmitigated hazard score(s) and then re-grouped using one or more mitigated hazard score(s), wherein the properties that switch groups (e.g., from a high underwriting risk group to a low underwriting risk group) are provided to a user. In a third specific example, a targeted list of properties can be identified that have changed their vulnerability score over time (e.g., wherein properties with a decrease in vulnerability score may be eligible for an additional credit or lower insurance premium, whereas properties with a positive change may necessitate an underwriting action; or vice versa).”),
(see at least Hedges: ¶ 82 “the risk score can be a combination of the vulnerability score, the regional exposure score, another risk score, and/or other hazard scores” and ¶ 90: discussing the regional exposure score; see also Hedges: ¶ 96-97; see also Hedges: ¶ 77 “The set of properties can be the set of training properties (S500), a set of test properties, and/or any other set of properties. In a specific example, the hazard scores for each property are binned such that each bin corresponds to approximately a predetermined proportion (e.g., 10%, 20%, 25%, 50%, etc.) of the population of properties. In a second example, the continuous hazard model output is mapped to a bin such that the bin values for a set of properties have a distribution matching that of third-party hazard scores (e.g., the distributions match for the same set of properties)”; see also Hedges: ¶ 34 “where attribute values have been previously determined for each of a set of properties”; see also Hedges: ¶ 70 “Determining a hazard score can be performed once for the determined property, multiple times (e.g., for multiple hazards, for multiple score types of a given hazard, the same hazard score using different attribute sets, etc.), iteratively for each property in a group (e.g., within a predetermined region), after S300, during S500, and/or at any other suitable time. Each hazard score is preferably specific to a given property, but can alternatively be shared across multiple properties.”; see at least Hedges: ¶ 120 “the outputs can be used to identify a group of properties and/or modify property groupings”; see also Hedges: ¶ 98); and
generating a weight for at least some of the one or more first home score factors in accordance with corresponding weights for the second home score factors based upon the similar characteristics, determining a severity of a risk associated with at least one of the at least some of the one or more first home score factors, and adjusting a corresponding weight for the at least one of the at least some of the one or more first home score factors based upon the determined severity; the weights for the at least some of the one or more first home score factors (see at least Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 74 “In a third specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests attribute values for the property and weather data. In a fourth specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests a determined hazard score (e.g., vulnerability score) and weather data. In a fifth specific example, the hazard model (e.g., any one of those described above or another model) ingests property measurements in addition to or instead of attribute values. Optionally, weights for one or more model inputs can be determined during model training S500, based on a decision tree, based on any neural network, based on a set of heuristics, manually, and/or otherwise determined.”; see also Hedges: ¶ 78 “Alternatively, the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data. In an illustrative example, the vulnerability score is representative of the vulnerability of a property to a hazard (e.g., probability of claim occurrence, severity of damage, etc.) assuming exposure to the hazard, wherein the vulnerability model (e.g., trained in S500) ingests property attribute values (e.g., intrinsic property attribute values, independent from regional location) and does not ingest weather and/or hazard data.”; see also Hedges: ¶ 27 “to provide additional information to a user (e.g., a summary of the most impactful property-specific attributes on a given hazard score)”; see also Hedges: ¶ 112 “Methods used to debias the training data and/or model can include: disparate impact testing, data pre-processing techniques (e.g., suppression, massaging the dataset, apply different weights to instances of the dataset), adversarial debiasing, Reject Option based Classification (ROC), Discrimination-Aware Ensemble (DAE), temporal modelling, continuous measurement, converging to an optimal fair allocation, feedback loops, strategic manipulation, regulating conditional probability distribution of disadvantaged sensitive attribute values, decreasing the probability of the favored sensitive attribute values, training a different model for every sensitive attribute value, and/or any other suitable method and/or approach. Additionally or alternatively, bias can be reduced using any interpretability method (e.g., an example is described in S340).”; see also Hedges: ¶ 121 “the outputs can be used to determine a set of mitigation measures for the property (e.g., high-impact mitigation measures that change the hazard score above a threshold amount). In an illustrative example, an unmitigated hazard score can be compared to each of a set of mitigated hazard scores, wherein each mitigated hazard score corresponds to a different mitigation measure, to determine one or more high-impact mitigation measures (e.g., with the largest difference between the unmitigated and mitigated hazard scores)”);
(see at least Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it);
(see also Hedges: ¶ 56 “Condition-related attributes can be a rating for a single structure, a minimum rating across multiple structures, a weighted rating across multiple structures, and/or any other individual or aggregate value. Condition-related attributes can additionally or alternatively be attributes subject to weather-related conditions; for example: average annual rainfall, presence of high-speed and/or dry seasonal winds (e.g., the Santa Ana winds), vegetation dryness and/or greenness index, regional hazard risks, and/or any other variable parameter”; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 74 “In a third specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests attribute values for the property and weather data. In a fourth specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests a determined hazard score (e.g., vulnerability score) and weather data. In a fifth specific example, the hazard model (e.g., any one of those described above or another model) ingests property measurements in addition to or instead of attribute values. Optionally, weights for one or more model inputs can be determined during model training S500, based on a decision tree, based on any neural network, based on a set of heuristics, manually, and/or otherwise determined.”; see also Hedges: ¶ 78 “Alternatively, the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data. In an illustrative example, the vulnerability score is representative of the vulnerability of a property to a hazard (e.g., probability of claim occurrence, severity of damage, etc.) assuming exposure to the hazard, wherein the vulnerability model (e.g., trained in S500) ingests property attribute values (e.g., intrinsic property attribute values, independent from regional location) and does not ingest weather and/or hazard data.”); and
training, by the one or more processors, the trained machine learning model using the determined severity (see at least Hedges: ¶ 39 “Determining measurements for the property S200 can function to determine property-specific data (e.g., an image or other visual representation) for the property. The measurements can be determined after S100, iteratively for a list of properties, in response to a request, when updated or new region or property imagery is available, when one or more property components and/or attributes are added (e.g., to a database), during hazard model training S500, and/or at any other suitable time.”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 67-68 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 68 “In a first variant, the set of attributes is selected such that a hazard score determined based on the set of attributes is indicative of a key metric. The metric can be a training target (e.g., the same training target used in S500, the key metric in S400, a different training target, etc.), and/or any other metric. For example, the key metric can be: the probability of a claim being filed for the property (e.g., claim occurrence) (e.g., within a given timeframe), claim acceptance probability, claim rejection probability, an expected loss amount, a hazard exposure probability, a claim and/or damage occurrence, a combination of the above (e.g., claim occurrence and acceptance probability) and/or any other metric. The claims can be: insurance claims, aid claims (e.g., FEMA claims), and/or any other suitable claim. In an example, a statistical analysis of training data can be used to select attributes that have a nonzero statistical relationship (e.g., correlation, interaction effect, etc.) with the key metric (e.g., positive or negative correlation with claim filing occurrence). In a second variant, the set of attributes is selected using a combination of an attribute selection model and a supplemental validation method.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 75 “The hazard score can be a label, a probability, a metric, a monetary value, and/or any parameter. The score can be binary, continuous, discrete, binned, and/or otherwise configured. The hazard score can optionally include an uncertainty parameter (e.g., variance, confidence score, etc.) associated with: the hazard model, a training data set (e.g., based on recency), attribute value uncertainty parameters, and/or any other parameter. The hazard score can be—or be calculated from—the hazard model output.”; see also Hedges: ¶ 94-109 “Examples of sets of training properties include: properties within a given region (e.g., hazard perimeter, geographic region, etc.), properties exposed to a hazard (e.g., within a given time frame), all properties regardless of hazard exposure (e.g., all properties within a set of regions, of a property type, associated with a given insurance policy, etc.), properties that have experienced damage, properties that have filed a claim, properties that have received a response from an insurance company regarding a filed claim, and/or any other property group”; see also Hedges: ¶ 25 “the method can include training a model to ingest property-specific attribute values to estimate the probability that a claim associated with the property”; see also Hedges: ¶ 29, 31, 34, 51 and 67-68: discussing training the hazard model to determine hazard scores; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 72-77: discussed training the model; see also Hedges: ¶ 89 and 95-114: extensive discussion on training the model and using it).
***Claims 8 and 15 contains additional language directed to computing device for evaluating and generating a home score for a property, the computing device comprising: one or more processors; a communication unit; and a non-transitory computer-readable medium coupled to the one or more processors and the communication unit and storing instructions thereon that, when executed by the one or more processors, cause the computing device (see at least Hedges: ¶ 123).
While Hedges does not utilize the terminology, “severity of risk”, the terminology “severity of risk” is broad terminology and Hedges teaches various risk factors and weighting considerations, as described above. But, if it were argued that Hedges does not teach “severity risk” per se, it is old and well known, and specifically regarding the region exposure score disclosed in Hedges, that the insurance industry considers coastal communities, for example, at a higher risk for hurricane issues than communities inland and therefore the coastal communities are weighted more heavily for that type of risk than a community in say the Rocky Mountains. Therefore, it would have been obvious to one of ordinary skill in the art to modify Hedges to adjust weights depending on known hazards (such as hurricanes), as is known in the art and as described in the rejection above.
Referring to Claim 2, 9, and 16 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 1, computing device of 8, and non-transitory computer-readable medium of claim 15, including further comprising: receiving, from a user, a request for the home score; and displaying, responsive to the request, the home score for the first property (see at least Hedges: ¶ 32 “The method can be performed for a single property, iteratively for a list of properties, for a group of properties as a whole (e.g., for the properties as a batch), for a property class, responsive to receipt of a request for a hazard score for a given property, responsive to receipt of a new image depicting the property, and/or at any other suitable time. The hazard information (e.g., attribute values, hazard score, etc.) can be stored in association with the property identifier for the respective property. All or parts of the hazard information can be determined: in real or near-real time; responsive to a request; pre-calculated; asynchronously; and/or at any other time. The hazard score can be calculated in response to a request, be pre-calculated, and/or calculated at any other suitable time. The hazard score(s) can be returned (e.g., sent to a user) in response to the request, published, and/or otherwise presented. An example is shown in FIG. 2 .”; see also Hedges: ¶ 37 and 39 “S100 can include determining a single property, determining a set of properties, and/or any other suitable number of properties. In a first variant, the property can be determined via an input request including a property identifier. The received input can be communicated via a user device (e.g., smartphone, tablet, computer, etc.), an API, GUI, third-party system, and/or any suitable system (e.g., from a requestor, a user, etc.). In a second variant, the property can be extracted from a map, image, geofence, and/or any other representation of a geographic region. In this variant, each property within the geographic region can be identified (e.g., corresponding to a predetermined region exposed to a given hazard, based on an address registry, database, image segmentation, based on claim data, etc.), wherein all or parts of the method is executed for each identified property.”; see also Hedges: ¶ 48 “The measurements can be received as part of a user request, retrieved from a database, determined using other data (e.g., segmented from an image, generated from a set of images, etc.), synthetically determined, and/or otherwise determined.”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 110 “The method can optionally include determining a key attribute Shoo. Shoo can function to explain a hazard score (e.g., what attribute(s) are causing the hazard model to output a hazard score indicating a high or low probability of filing a claim). Shoo can occur automatically (e.g., for each property), in response to a request, when a hazard score falls below or rises above a threshold, and/or at any other time.”).
Referring to Claim 3, 10, and 17 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 2, computing device of 9, and non-transitory computer-readable medium of claim 16, including further comprising: displaying, responsive to the request, the home characteristic data for the first property (see at least Hedges: ¶ 32 “The method can be performed for a single property, iteratively for a list of properties, for a group of properties as a whole (e.g., for the properties as a batch), for a property class, responsive to receipt of a request for a hazard score for a given property, responsive to receipt of a new image depicting the property, and/or at any other suitable time. The hazard information (e.g., attribute values, hazard score, etc.) can be stored in association with the property identifier for the respective property. All or parts of the hazard information can be determined: in real or near-real time; responsive to a request; pre-calculated; asynchronously; and/or at any other time. The hazard score can be calculated in response to a request, be pre-calculated, and/or calculated at any other suitable time. The hazard score(s) can be returned (e.g., sent to a user) in response to the request, published, and/or otherwise presented. An example is shown in FIG. 2 .”; see also Hedges: ¶ 37 and 39 “S100 can include determining a single property, determining a set of properties, and/or any other suitable number of properties. In a first variant, the property can be determined via an input request including a property identifier. The received input can be communicated via a user device (e.g., smartphone, tablet, computer, etc.), an API, GUI, third-party system, and/or any suitable system (e.g., from a requestor, a user, etc.). In a second variant, the property can be extracted from a map, image, geofence, and/or any other representation of a geographic region. In this variant, each property within the geographic region can be identified (e.g., corresponding to a predetermined region exposed to a given hazard, based on an address registry, database, image segmentation, based on claim data, etc.), wherein all or parts of the method is executed for each identified property.”; see also Hedges: ¶ 48 “The measurements can be received as part of a user request, retrieved from a database, determined using other data (e.g., segmented from an image, generated from a set of images, etc.), synthetically determined, and/or otherwise determined.”; see also Hedges: ¶ 51 “Determining attribute values for the property S300 can function to determine property-specific values of one or more components of the property of interest. S300 can be performed after S200, in response to a request (e.g., for a property), in batches for groups of properties, iteratively for each of a set of properties, at regular time intervals, when new data (e.g., measurements) for the property is received, during and/or after model training S500, during S400, and/or at any other suitable time.”; see also Hedges: ¶ 110 “The method can optionally include determining a key attribute Shoo. Shoo can function to explain a hazard score (e.g., what attribute(s) are causing the hazard model to output a hazard score indicating a high or low probability of filing a claim). Shoo can occur automatically (e.g., for each property), in response to a request, when a hazard score falls below or rises above a threshold, and/or at any other time.”).
Referring to Claim 4, 11, and 17 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 1, computing device of 8, and non-transitory computer-readable medium of claim 15, including computing device of 9, and non-transitory computer-readable medium of claim 16, including wherein the home characteristic data includes at least one of: location data, environment data, first responder data, home structure data, adherence to local construction codes, average power consumption, average water consumption, average security score, and average occupancy score (see at least Hedges: ¶ 25, 56 “subject to weather-related conditions; for example: average annual rainfall, presence of high-speed and/or dry seasonal winds (e.g., the Santa Ana winds), vegetation dryness and/or greenness index, regional hazard risks, and/or any other variable parameter.”; see also Hedges: ¶ 62 “extracting attribute values directly from property measurements, retrieving values from a database or a third party source (e.g., third-party database, MLS database, city permitting database, historical weather and/or hazard database”; see also Hedges: ¶ 74, 78, 80-82, and 90-91).
Referring to Claim 5, 12, and 19 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 1, computing device of 8, and non-transitory computer-readable medium of claim 15, including wherein the one or more first home score factors include: (i) a fire hazard score, (ii) a safety score, (iii) a weather hazard score, (iv) a property feature hazard score, and (v) a potential hazards score (see at least Hedges: ¶ 16-17 “extracting attribute values for each of a set of property attributes from the images. The property attributes are preferably structural attributes, such as the presence or absence of a property component (e.g., roof, vegetation, etc.), property component geometric descriptions (e.g., roof shape, slope, complexity, building height, living area, structure footprint, etc.), property component appearance descriptions (e.g., condition, roof covering material, etc.), and/or neighboring property components or geometric descriptions (e.g., presence of neighboring structures within a predetermined distance, etc.), but can additionally or alternatively include other attributes, such as built year, number of beds and baths, or other descriptors. One or more hazard scores (e.g., vulnerability score, risk score, regional exposure score, etc.) can then be calculated for the property.”; see also Hedges: ¶ 33 “configured to extract values for one or more attributes”; see also Hedges: ¶ 52-62: discussing attributes; see at least Hedges: ¶ 25, 56 “subject to weather-related conditions; for example: average annual rainfall, presence of high-speed and/or dry seasonal winds (e.g., the Santa Ana winds), vegetation dryness and/or greenness index, regional hazard risks, and/or any other variable parameter.”; see also Hedges: ¶ 62 “extracting attribute values directly from property measurements, retrieving values from a database or a third party source (e.g., third-party database, MLS database, city permitting database, historical weather and/or hazard database”; see also Hedges: ¶ 74, 78, 80-82, and 90-91).
Referring to Claim 6, 13, and 20 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 1, computing device of 8, and non-transitory computer-readable medium of claim 15, including wherein each of the one or more first home score factors has an equal weight (see also Hedges: ¶ 56 “Condition-related attributes can be a rating for a single structure, a minimum rating across multiple structures, a weighted rating across multiple structures, and/or any other individual or aggregate value. Condition-related attributes can additionally or alternatively be attributes subject to weather-related conditions; for example: average annual rainfall, presence of high-speed and/or dry seasonal winds (e.g., the Santa Ana winds), vegetation dryness and/or greenness index, regional hazard risks, and/or any other variable parameter”; see also Hedges: ¶ 67 “The set of attributes (e.g., for a given hazard model) can be selected: manually, automatically, randomly, recursively, using an attribute selection model, using lift analysis (e.g., based on an attribute's lift), using any explainability and/or interpretability method (e.g., as described in S600), based on an attribute's correlation with a given metric (e.g., claim frequency, loss severity, etc.), using predictor variable analysis, through hazard score validation, during model training (e.g., attributes with weights above a threshold value are selected), using a deep learning model, based on the mitigation and/or zone classification, and/or via any other selection method or combination of methods.”; see also Hedges: ¶ 73 “Each hazard score is preferably determined using a hazard model (e.g., a model trained in S500), but can alternatively be retrieved (e.g., from a third-party hazard risk database) and/or otherwise determined. The hazard model can be or use: regression, classification, neural networks (e.g., CNNs, DNNs, etc.), rules, heuristics, equations (e.g., weighted equations with a predetermined weight for each input attribute, etc.), selection (e.g., from a library), instance-based methods (e.g., nearest neighbor), regularization methods (e.g., ridge regression), decision trees (e.g., random forest, gradient boosted, etc.), Bayesian methods (e.g., Naïve Bayes, Markov), kernel methods, probability, deterministics, genetic programs, support vectors, or any other suitable method. The hazard model can be the same or different for each hazard score, hazard, region, property type, time period, and/or any other parameter.”; see also Hedges: ¶ 74 “In a third specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests attribute values for the property and weather data. In a fourth specific example, the hazard model (e.g., a damage model, a claim rejection model, etc.) ingests a determined hazard score (e.g., vulnerability score) and weather data. In a fifth specific example, the hazard model (e.g., any one of those described above or another model) ingests property measurements in addition to or instead of attribute values. Optionally, weights for one or more model inputs can be determined during model training S500, based on a decision tree, based on any neural network, based on a set of heuristics, manually, and/or otherwise determined.”; see also Hedges: ¶ 78 “Alternatively, the vulnerability can be dependent on the exposure risk (e.g., weighted and/or otherwise adjusted based on the regional exposure score) and/or any regional data. In an illustrative example, the vulnerability score is representative of the vulnerability of a property to a hazard (e.g., probability of claim occurrence, severity of damage, etc.) assuming exposure to the hazard, wherein the vulnerability model (e.g., trained in S500) ingests property attribute values (e.g., intrinsic property attribute values, independent from regional location) and does not ingest weather and/or hazard data.”).
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20220405856 to Hedges et al. (hereinafter Hedges) in view of U.S. Patent Application Publication No. 20220335366 to Sanchez.
Referring to Claim 7 and 14 (substantially similar in scope and language), Hedges discloses the computer-implemented method of claim 1, and computing device of 8; Hedges fails to state that the collection units include wherein the home data includes at least one of smart device-mounted sensor data, home- mounted sensor data, or mobile device-mounted sensor data
However, Sanchez, which talks about a method and system for processing information for insurance purposes, teaches it is known to incorporate machine learning techniques when processing asset information such as home properties using intelligent home telematics (home mounted) information to train the model to determine asset/property characteristics (see at least Sanchez: ¶ 81-85 “the machine learning programs may be trained by inputting sample data sets or certain data into the programs, such as image, mobile device, vehicle telematics, autonomous vehicle, and/or intelligent home telematics data.”).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the feature of wherein the trained machine learning model is trained with home telematics data to determine home characteristic data (as disclosed by Sanchez) into the method and system for home scoring based on property characteristics determining and applying a weighting factor when scoring a home based on property characteristics using trained machine learning algorithms (as disclosed by Hedges). One of ordinary skill in the art would have been motivated to incorporate the feature of wherein the trained machine learning model is trained with home telematics data to determine home characteristic data because it would aid the insurance provider in determining policy rates and additionally aid the policyholder in determining the amount of coverage they will need (see Sanchez ¶ 6).
Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the feature of wherein the trained machine learning model is trained with home telematics data to determine home characteristic data (as disclosed by Sanchez) into the method and system for home scoring based on property characteristics determining and applying a weighting factor when scoring a home based on property characteristics using trained machine learning algorithms (as disclosed by Hedges), because the claimed invention is merely a simple arrangement of old elements, with each performing the same function it had been known to perform, yielding no more than one would expect from such arrangement. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 406 (2007). In other words, all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results to one of ordinary skill in the art at the time of the invention (i.e., predictable results are obtained by adding the well-known feature of wherein the trained machine learning model is trained with home telematics data to determine home characteristic data into the method and system for home scoring based on property characteristics determining and applying a weighting factor when scoring a home based on property characteristics using trained machine learning algorithms). See also MPEP § 2143(I)(A).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but found to be unpersuasive for the reasons/rationale provided in the rejection above.
The claims stand rejected.
With respect to Hedges, applicant argues the following:
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However, the term “severity of risk” is broad terminology and Hedges teaches various risk factors and weights. For example, regarding the region exposure score disclosed in Hedges, it is old and well known in for example the insurance industry that coastal communities are at higher risk for hurricane issues and therefore are weighted more heavily for that type of risk than a community in the Rocky Mountains. Therefore, it would have been obvious to one of ordinary skill in the art to modify Hedges to adjust weights depending on known hazards, as described in the rejection above.
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.
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/FLORIAN M ZEENDER/Supervisory Patent Examiner, Art Unit 3627
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