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 Non-Final Office Action in response to the Amendments and Remarks filed on the 11th day of July, 2025. Claims 1-20 are pending. No Claims are allowed.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/17/2024 and 10/01/2025 were filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/11/2025 has been entered.
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).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
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.
Claims 1, 8, and 15 of instant application:
“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 and second risk scores for the second property based at least upon the past hazard data, 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, 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, determining, based upon the second risk scores and the similar characteristics, first risk scores associated with the first home score factors, and generating weights for the 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, wherein the weights for the at least some of the one or more first home score factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk scores; determining, by the one or more processors and via the trained machine learning model, one or more influential home score factors, wherein the one or more influential home score factors include a subset of the one or more first home score factors with a highest subset of weights; 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; 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”.
Claims 1, 8, and 15 of copending Application No. 17972261:
“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, 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, wherein the trained machine learning model is trained with home telematics data to determine home characteristic data, determining second home score factors for the second property based at least upon the past hazard data, 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, determining, based upon the home characteristic data for the first property, one or more similarity metrics associated with at least some of the one or more first home score factors, and generating a weight for [[each]]at least some of the one or more first home score factors by modifying corresponding weights for the second home score factors based upon the one or more similarity metrics; 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; 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; and training, by the one or more processors, the trained machine learning model using the home characteristic data.”
Claims 1-20 of the instant application contain additional limitations directed to the determination and use of inputted weights and similarity metrics which is taught by Hedges:
determining second home score factors for the second property and second risk scores 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).”),
determining, based upon the second risk scores and the similar characteristics, first risk scores associated with the first home score factors (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, wherein the weights for the at least some of the one or more first home scores factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk 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: ¶ 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)”);
determining, by the one or more processors, and via the trained machine learning model, one or more influential home score factors, wherein the one or more influential home score factors include a subset of the one or more first home score factors with a highest subset of weights (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);
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 (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 first risk scores, the weights, and the similar characteristics (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 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). One of ordinary skill in the art would have been motivated 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 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 (i.e., predictable results are obtained by adding the well-known 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 into the method and system for evaluating and generating a home score for a property). 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 and second risk scores for the second property based at least upon the past hazard data, 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, 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, determining, based upon the second risk scores and the similar characteristics, first risk scores associated with the first home score factors, and generating weights for the 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, wherein the weights for the at least some of the one or more first home score factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk scores; determining, by the one or more processors and via the trained machine learning model, one or more influential home score factors, wherein the one or more influential home score factors include a subset of the one or more first home score factors with a highest subset of weights; 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; 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, the claims are similar to the abstract idea found in 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, 11, and 16 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 § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by 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 and second risk scores 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).”),
determining, based upon the second risk scores and the similar characteristics, first risk scores associated with the first home score factors (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, wherein the weights for the at least some of the one or more first home scores factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk 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: ¶ 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)”);
determining, by the one or more processors, and via the trained machine learning model, one or more influential home score factors, wherein the one or more influential home score factors include a subset of the one or more first home score factors with a highest subset of weights (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);
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 (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 first risk scores, the weights, and the similar characteristics (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).
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 under 101 have been considered but found to be unpersuasive.
“Applicant respectfully contends that amended claim 1 is allowable under 35 U.S.C. § 101 at least because it is not directed to a judicial exception”
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 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)), mental processes (see MPEP 2106.04(a)(2)(III), and mathematical equations (see MPEP 2106.04(a)(2)(II)).
Examiner notes that the system is directed to a mental process. The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 (2012) ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
Claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); and a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011);
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 and second risk scores for the second property based at least upon the past hazard data, 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, 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, determining, based upon the second risk scores and the similar characteristics, first risk scores associated with the first home score factors, and generating weights for the 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, wherein the weights for the at least some of the one or more first home score factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk scores; determining, by the one or more processors and via the trained machine learning model, one or more influential home score factors, wherein the one or more influential home score factors include a subset of the one or more first home score factors with a highest subset of weights; 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; 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, the claims are similar to the abstract idea found in 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.
Examiner notes that the claimed invention amounts to a mental process in that the system is collecting information related to properties, comparing the properties to identify scores, and presenting the scores to the users based on the analysis, and the subsequently performing the command based on the determination which is similar to the abstract ideas identified in Electric Power Group and Classen.
The phrase "methods of organizing human activity" is used to describe concepts relating to: fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions).
The Supreme Court has identified a number of concepts falling within the "certain methods of organizing human activity" grouping as abstract ideas. In particular, in Alice, the Court concluded that the use of a third party to mediate settlement risk is a ‘‘fundamental economic practice’’ and thus an abstract idea. 573 U.S. at 219–20, 110 USPQ2d at 1982. In addition, the Court in Alice described the concept of risk hedging identified as an abstract idea in Bilski as ‘‘a method of organizing human activity’’. Id. Previously, in Bilski, the Court concluded that hedging is a ‘‘fundamental economic practice’’ and therefore an abstract idea. 561 U.S. at 611–612, 95 USPQ2d at 1010.
The courts have used the phrases "fundamental economic practices" or "fundamental economic principles" to describe concepts relating to the economy and commerce. Fundamental economic principles or practices include hedging, insurance, and mitigating risks.
The term "fundamental" is not used in the sense of necessarily being "old" or "well-known." See, e.g., OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept); In re Smith, 815 F.3d 816, 818-19, 118 USPQ2d 1245, 1247 (Fed. Cir. 2016) (describing a new set of rules for conducting a wagering game as a "fundamental economic practice"); In re Greenstein, 774 Fed. Appx. 661, 664, 2019 USPQ2d 212400 (Fed Cir. 2019) (non-precedential) (claims to a new method of allocating returns to different investors in an investment fund was a fundamental economic concept). However, being old or well-known may indicate that the practice is fundamental. See, e.g., Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 219-20, 110 USPQ2d 1981-82 (2014) (describing the concept of intermediated settlement, like the risk hedging in Bilski, to be a "‘fundamental economic practice long prevalent in our system of commerce’" and also as "a building block of the modern economy") (citation omitted); Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ2d 1001, 1010 (2010) (claims to the concept of hedging are a "fundamental economic practice long prevalent in our system of commerce and taught in any introductory finance class.") (citation omitted); Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1313, 120 USPQ2d 1353, 1356 (Fed. Cir. 2016) ("The category of abstract ideas embraces ‘fundamental economic practice[s] long prevalent in our system of commerce,’ … including ‘longstanding commercial practice[s]’").
Other examples of "fundamental economic principles or practices" include: i. mitigating settlement risk, Alice Corp. v. CLS Bank,573 U.S. 208, 218, 110 USPQ2d 1976, 1982 (2014); and iii. financial instruments that are designed to protect against the risk of investing in financial instruments, In re Chorna, 656 Fed. App'x 1016, 1021 (Fed. Cir. 2016) (non-precedential).
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, 11, and 16 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).
Examiner notes that the claimed invention is more similar to the identified abstract ideas within Alice and In re Chorna.
Furthermore, "Commercial interactions" or "legal interactions" include agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations.
An example of a claim reciting a commercial or legal interaction, where the interaction is an agreement in the form of contracts, is found in buySAFE, Inc. v. Google, Inc., 765 F.3d. 1350, 112 USPQ2d 1093 (Fed. Cir. 2014). The agreement at issue in buySAFE was a transaction performance guaranty, which is a contractual relationship. 765 F.3d at 1355, 112 USPQ2d at 1096. The patentee claimed a method in which a computer operated by the provider of a safe transaction service receives a request for a performance guarantee for an online commercial transaction, the computer processes the request by underwriting the requesting party in order to provide the transaction guarantee service, and the computer offers, via a computer network, a transaction guaranty that binds to the transaction upon the closing of the transaction. 765 F.3d at 1351-52, 112 USPQ2d at 1094. The Federal Circuit described the claims as directed to an abstract idea because they were "squarely about creating a contractual relationship--a ‘transaction performance guaranty’." 765 F.3d at 1355, 112 USPQ2d at 1096.
Examiner notes that the claimed invention is similar to the abstract idea found in buySAFE v. Google, Inc., in that the claimed invention validating consent records based on stored consent and registrant information in response to receiving a command.
An example of a claim reciting advertising is found in Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 714-15, 112 USPQ2d 1750, 1753-54 (Fed. Cir. 2014). The patentee in Ultramercial claimed an eleven-step method for displaying an advertisement (ad) in exchange for access to copyrighted media, comprising steps of receiving copyrighted media, selecting an ad, offering the media in exchange for watching the selected ad, displaying the ad, allowing the consumer access to the media, and receiving payment from the sponsor of the ad. 772 F.3d. at 715, 112 USPQ2d at 1754. The Federal Circuit determined that the "combination of steps recites an abstraction—an idea, having no particular concrete or tangible form" and thus was directed to an abstract idea, which the court described as "using advertising as an exchange or currency."
An example of a claim reciting a commercial or legal interaction in the form of a legal obligation is found in Fort Properties, Inc. v. American Master Lease, LLC, 671 F.3d 1317, 101 USPQ2d 1785 (Fed Cir. 2012). The patentee claimed a method of "aggregating real property into a real estate portfolio, dividing the interests in the portfolio into a number of deedshares, and subjecting those shares to a master agreement." 671 F.3d at 1322, 101 USPQ2d at 1788. The legal obligation at issue was the tax-free exchanges of real estate. The Federal Circuit concluded that the real estate investment tool designed to enable tax-free exchanges was an abstract concept. 671 F.3d at 1323, 101 USPQ2d at 1789. Examiner notes that the claimed invention is similar to the abstract idea found within Fort Properties in that the system is processing information in the form of registry commands based on the “master agreement” in the form of the consent record stored within the registry.
An example of a claim reciting business relations is found in Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017). The business relation at issue in Credit Acceptance is the relationship between a customer and dealer when processing a credit application to purchase a vehicle. The patentee claimed a "system for maintaining a database of information about the items in a dealer’s inventory, obtaining financial information about a customer from a user, combining these two sources of information to create a financing package for each of the inventoried items, and presenting the financing packages to the user." 859 F.3d at 1054, 123 USPQ2d at 1108. The Federal Circuit described the claims as directed to the abstract idea of "processing an application for financing a loan" and found "no meaningful distinction between this type of financial industry practice" and the concept of intermediated settlement in Alice or the hedging concept in Bilski. 859 F.3d at 1054, 123 USPQ2d at 1108.
Examiner notes that the claimed invention is similar to the abstract idea in Credit Acceptance Corp., in that the system is processing information related to property hazards to determine risk associated to the plurality of properties.
Examiner notes that the claimed invention is more like buySAFE, Accenture, and Ultramercial, in that the invention revolves around analyzing real properties characteristics. Examiner respectfully submits that the claimed invention falls squarely in the grouping “II” and is directed to an abstract idea.
Furthermore, the mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court has identified a number of concepts falling within this grouping as abstract ideas including: a procedure for converting binary-coded decimal numerals into pure binary form, Gottschalk v. Benson, 409 U.S. 63, 65, 175 USPQ2d 673, 674 (1972); a mathematical formula for calculating an alarm limit, Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ2d 193, 195 (1978); the Arrhenius equation, Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981); and a mathematical formula for hedging, Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ 2d 1001, 1004 (2010).
When determining whether a claim recites a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations), examiners should consider whether the claim recites a mathematical concept or merely limitations that are based on or involve a mathematical concept. A claim does not recite a mathematical concept (i.e., the claim limitations do not fall within the mathematical concept grouping), if it is only based on or involves a mathematical concept. See, e.g., Thales Visionix, Inc. v. United States, 850 F.3d 1343, 1348-49, 121 USPQ2d 1898, 1902-03 (Fed. Cir. 2017) (determining that the claims to a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform did not merely recite "the abstract idea of using ‘mathematical equations for determining the relative position of a moving object to a moving reference frame’."). For example, a limitation that is merely based on or involves a mathematical concept described in the specification may not be sufficient to fall into this grouping, provided the mathematical concept itself is not recited in the claim.
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).
Examples of mathematical calculations recited in a claim include: performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018); using an algorithm for determining the optimal number of visits by a business representative to a client, In re Maucorps, 609 F.2d 481, 482, 203 USPQ 812, 813 (CCPA 1979); and calculating the difference between local and average data values, In re Abele, 684 F.2d 902, 903, 214 USPQ 682, 683-84 (CCPA 1982).
Examiner draws attention to Recentive Analytics vs. Fox Corp., 692 F.Supp.3d 438 (D. Del. 2023), in which the Court decided in a case with a more specific use of machine learning than instant application that the claims involving a trained model updating upon new information amounts to merely applying the known computer elements as a tool to implement the method. The claimed invention is similar to the claims found within Recentive in that the model is being trained to output information and the instant application is similar to the identified use of “training” a model for the output of information. No improvement to the overall method of machine learning or algorithm is presented. The mere use of machine learning as a tool to update a displayed information regarding properties does not render the claims patentable subject matter. “Considering the focus of the disputed claims, Alice, 573 U.S. at 217, it is clear that they are directed to ineligible, abstract subject matter. Recentive has repeatedly conceded that it is not claiming machine learning itself. See Appellant’s Br. 45; Transcript at 26:14–15. Both sets of patents rely on the use of generic machine learning technology in carrying out the claimed methods for generating event schedules and network maps. See, e.g., ’367 patent, col. 6 ll. 1–5, col. 11–12; ’811 patent, col. 3, l. 23, col. 5 l. 4. The machine learning technology described in the patents is conventional, as the patents’ specifications demonstrate. See, e.g., ’367 patent, col. 6 ll. 1–5 (requiring “any suitable machine learning technology . . . such as, for example: a gradient boosted random forest, a regression, a neural network, a decision tree, a support vector machine, a Bayesian network, [or] other type of technique”); ’811 patent, col. 3 l. 23 (requiring the application of “any suitable machine learning technique.”).” (Recentive Analytics vs. Fox Corp., 692 F.Supp.3d 438 (D. Del. 2023)).
“Instead of disclosing “a specific implementation of a solution to a problem in the software arts,” Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016), or “a specific means or method that solves a problem in an existing technological process,” Koninklijke, 942 F.3d at 1150, the only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment.” (Recentive Analytics vs. Fox Corp., 692 F.Supp.3d 438 (D. Del. 2023)).
“the claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity. See, e.g., Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333. Whether the issue is raised at step one or step two, the increased speed and efficiency resulting from use of computers (with no improved computer techniques) do not themselves create eligibility. See, e.g., Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) (rejecting argument that “humans could not mentally engage in the ‘same claimed process’ because they could not perform ‘nanosecond comparisons’ and aggregate ‘result values with huge numbers of polls and members’”) (internal citation omitted); Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1365 (Fed. Cir. 2020) (holding claims abstract where “[t]he only improvements identified in the specification are generic speed and efficiency improvements inherent in applying the use of a computer to any task”); compare McRo, 837 F.3d at 1314-16 (finding eligibility of claims to use specific computer techniques different from those humans use on their own to produce natural-seeming lip motion for speech)” (Recentive Analytics vs. Fox Corp., 692 F.Supp.3d 438 (D. Del. 2023)).
Similar to Recentive “nothing in the claims, whether considered individually or in their ordered combination, that would transform the Machine Learning Training and Network Map patents into something “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning. See SAP Am., 898 F.3d at 1169–70; Broadband iTV, 113 F.4th at 1372. Recentive has also failed to identify any allegation in its complaint that would suffice to plausibly allege an inventive concept to defeat Fox’s motion to dismiss. Trinity, 72 F.4th at 1365” (Recentive Analytics vs. Fox Corp., 692 F.Supp.3d 438 (D. Del. 2023)). Examiner notes that the claims are similar to the identified unpatentable subject matter in that the system is merely applying standard machine learning elements in the abstract idea of processing hazard information related to properties.
Examiner notes that the claimed invention is similar to the identified use of mathematical calculations discussed in Bilski, SAP America, Inc., In re Maucorps. and In re Abele.
Examiner notes that the claimed invention is additionally similar to the decision found in Recentive
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.
Applicant argues “claim 1 shows that the claim as a whole successfully integrates the judicial exception”, “claim 1 further provides improvements to the machine learning model by training the model using the determined first risk scores, weights, and similar characteristics” and “Applicant respectfully contends that the instant claims provide an improvement in the functioning of a computer or another technology or technical field”. Examiner respectfully disagrees.
Examiner notes that the instant application is merely using computing elements as a tool and not providing a technological improvement to machine learning or to the functioning of a computer or another technology.
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 or using “machine learning” as merely serving as a the generic computer, computing environment, or tool to perform the mental process or abstract idea.
The second part of the Alice/Mayo test is often referred to as a search for an inventive concept. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014) (citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 71-72, 101 USPQ2d 1961, 1966 (2012)).
Evaluating additional elements to determine whether they amount to an inventive concept requires considering them both individually and in combination to ensure that they amount to significantly more than the judicial exception itself. Because this approach considers all claim elements, the Supreme Court has noted that "it is consistent with the general rule that patent claims ‘must be considered as a whole.’" Alice Corp., 573 U.S. at 218 n.3, 110 USPQ2d at 1981 (quoting Diamond v. Diehr, 450 U.S. 175, 188, 209 USPQ 1, 8-9 (1981)). Consideration of the elements in combination is particularly important, because even if an additional element does not amount to significantly more on its own, it can still amount to significantly more when considered in combination with the other elements of the claim. See, e.g., Rapid Litig. Mgmt. v. CellzDirect, 827 F.3d 1042, 1051, 119 USPQ2d 1370, 1375 (Fed. Cir. 2016) (process reciting combination of individually well-known freezing and thawing steps was "far from routine and conventional" and thus eligible); BASCOM Global Internet Servs. v. AT&T Mobility LLC, 827 F.3d 1341, 1350, 119 USPQ2d 1236, 1242 (Fed. Cir. 2016) (inventive concept may be found in the non-conventional and non-generic arrangement of components that are individually well-known and conventional).
Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality: ii. Accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016), iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential); vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018).
To show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology. See MPEP § 2106.05(f) for more information about mere instructions to apply an exception.
Examples that the courts have indicated may not be sufficient to show an improvement to technology include: i. A commonplace business method being applied on a general purpose computer, Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
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”.
Examiner notes that the claimed invention is more like the implementations of computer elements found in FairWarning IP, LLC, Credit Acceptance Corp. v. Westlake Services, LendingTree, LLC v. Zillow, Inc., BSG Tech LLC v. Buyseasons, Inc., Alice Corp. and Versata Dev. Group, Inc. and fail to implement a technical improvement, practical application, or significantly more than the abstract idea.
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 a the generic computer, computing environment, or tool to perform the mental process.
Therefore, the claims stand rejected.
103 Rejections
Applicant’s arguments with respect to claim(s) 1-20 under 103 have been considered but found to be unpersuasive.
Applicant argues that neither Hedges nor Sanchez, individually or in combination, teaches the elements of amended claim 1 and “Applicant respectfully contends that such a disclosure of a weighted rating across multiple structures differs from generating weights in accordance with corresponding weights for second home score factors based upon determined similar characteristics between the properties”. Examiner respectfully disagrees and submits that applicant has limited the comparison to one citation and not the full analysis.
Specifically Hedges discloses 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, wherein the weights for the at least some of the one or more first home scores factors include an overall impact percentage based upon the corresponding weights for the second home score factors in accordance with the determined first risk scores and the second risk 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: ¶ 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.”).
Examiner notes that the Hedges reference discloses determining... one or more similarity metrics (regional exposure risk for a set of properties) ... and generating a weight for at least some of the one or more first home score factors by modifying corresponding weights for the second home score factors based upon the one or more similarity metrics (regional exposure risk). Applicant ignores the remaining citation applied in the rejection directed to the application of weights being applied to every attribute which amounts to using existing weights to determine weights for a property based upon similarities and/or differences.
Applicant further alleges that Hedges does not seem to disclose such details regarding the weights for the home score factors, nor does Hedges seem to indicate that the weights are determined in accordance with the risk scores. Examiner respectfully disagrees and submits that Hedges discloses presenting overall impact measures to the users (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)”).
Additionally, Hedges discloses weights are determined in accordance with the risk scores (see at least 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.).
Therefore, the claims stand rejected for the reasons and rationales presented above.
Conclusion
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/Michael Young/Examiner, Art Unit 3626
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