CTNF 18/735,558 CTNF 85541 DETAILED ACTION Status of Application This communication is a Non-Final Office Action in response to the Amendments, Arguments, and Remarks filed on 2/5/26. Claims 1-10 and 12-20 are pending. Claim 11 has been cancelled. No Claims are allowed. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-10 and 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: Claim 1 recites a method and therefore, falls into a statutory category. Similar independent claims 12 and 17 recite a system and a computer readable medium, and therefore, also fall into a statutory category. Step 2A – Prong 1 (Is a Judicial Exception Recited?): The following underlined limitations identify the abstract limitations which are considered mental processes gathering , with a smart product on the property, at a plurality of time intervals during a time period, sensor data from a plurality of sensors on the property; aggregating the sensor data gathered during the time period; sending , from the smart product on the property, the aggregated sensor data along with a hash of the aggregated sensor data to one or more processors; determining , by the one or more processors, from the sensor data, one or more attributes of the property, the one or more attributes comprising: (i) one or more safety attributes, and/or (ii) one or more property feature hazard attributes ; determining , by the one or more processors, one or more home score factors, wherein the one or more home score factors include (i) a safety score, and/or (ii) a property feature hazard score, and wherein the determining the one or more home score factors comprises determining: the safety score based upon the one or more safety attributes; and/or the property feature hazard score based upon the one or more property feature hazard attributes; generating , by the one or more processors, the overall home score of the property based upon the one or more home score factors; and displaying , via the one or more processors: (i) the overall home score, and (ii) the safety score and/or the property feature hazard score . These limitations constitute evaluating and generating a home score for a property (Specification ¶2), which are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting that the claims include a smart product, sensors, one or more processors (claim 1); a smart product, sensors, one or more processors (claim 12); and a tangible, non-transitory computer-readable medium, a smart product, sensors, one or more processors (claim 17), nothing in the claim elements precludes the step from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Alternatively, the following underlined limitations identify the abstract limitations which are considered certain methods of organizing human activity gathering , with a smart product on the property, at a plurality of time intervals during a time period, sensor data from a plurality of sensors on the property; aggregating the sensor data gathered during the time period; sending , from the smart product on the property, the aggregated sensor data along with a hash of the aggregated sensor data to one or more processors; determining , by the one or more processors, from the sensor data, one or more attributes of the property, the one or more attributes comprising: (i) one or more safety attributes, and/or (ii) one or more property feature hazard attributes ; determining , by the one or more processors, one or more home score factors, wherein the one or more home score factors include (i) a safety score, and/or (ii) a property feature hazard score, and wherein the determining the one or more home score factors comprises determining: the safety score based upon the one or more safety attributes; and/or the property feature hazard score based upon the one or more property feature hazard attributes; generating , by the one or more processors, the overall home score of the property based upon the one or more home score factors; and displaying , via the one or more processors: (i) the overall home score, and (ii) the safety score and/or the property feature hazard score . These limitations constitute generating and displaying an overall home score, thereby valuing a property, and said score may be displayed to a user searching for a home to purchase or rent (Specification ¶29), or used by an insurer (Specification ¶36), which are processes that, under their broadest reasonable interpretation, are considered certain methods of organizing human activity – commercial or legal interactions (including agreements in the form of contracts and marketing or sales activities or behaviors) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea. Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a smart product, sensors, one or more processors (claim 1); a smart product, sensors, one or more processors (claim 12); and a tangible, non-transitory computer-readable medium, a smart product, sensors, one or more processors (claim 17). Each of these additional elements are considered computer components. The computer components are recited at a high-level of generality (i.e., as a generic processing device performing generic computer functions), such that they amount to no more than mere instructions to apply the exception using a generic computer component. Additionally, the sending and displaying limitations may be considered insignificant extra-solution activity (see MPEP 2106.05(g)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claim is directed to an abstract idea. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception. Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the steps of the abstract idea amount to no more than mere instructions to apply the exception using a generic computer component. Further, the claims simply append well-understood, routine, and conventional (WURC) activities previously known to the industry, specified at a high level of generality, to the judicial exception, in the form of the extra-solution activity. The courts have recognized that the computer functions claimed (the sending and displaying limitations) as WURC (see 2106.05(d), identifying receiving or transmitting data over a network as WURC, as recognized by Symantec and identifying presenting offers as WURC, as recognized by OIP Techs ). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea. Dependent claims 2-10, 13-16, and 18-20 merely recite further embellishments of the abstract idea of independent claims 1, 12, and 17 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claims 1, 12, and 17; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits. In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea. Notice 07-06 AIA 15-10-15 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 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. Claim Rejections - 35 USC § 103 07-20-aia AIA 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 of this title, 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. 07-23-aia AIA The factual inquiries set forth in Graham v. John Deere Co. , 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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. 07-21-aia AIA Claim s 1-4, 6, 12-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Hedges et al. (US 20220405856), in view of Sanchez (US 20220335366), in view of Luiciani (US 20220245290), and further in view of Ichihashi et al. (US 20220253818) . Referring to claims 1, 12, and 17 (substantially similar in scope and language): Hedges discloses a computer-implemented method for generating and/or displaying an overall home score for a property, the computer-implemented method comprising: determining, by one of the sensors, by one or more processors, one or more attributes of the property, the one or more attributes comprising: (i) one or more safety attributes, and/or (ii) one or more property feature hazard attributes (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, one or more home score factors, wherein the one or more home score factors include (i) a safety score, and/or (ii) a property feature hazard score (see at least Hedges: ¶ 66-84 “the risk score can be determined using a risk model that ingests: property attribute values and historical weather and/or hazard data for the property location”; 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) , and wherein the determining the one or more home score factors comprises determining: the safety score based upon the one or more safety attributes; and/or the property feature hazard score based upon the one or more property feature attributes (see at least Hedges: ¶ 66-84 “the risk score can be determined using a risk model that ingests: property attribute values and historical weather and/or hazard data for the property location”; 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); generating, by the one or more processors, the overall home score of the property based upon the one or more home score factors (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.”; and displaying, via the one or more processors: (i) the overall home score, and (ii) the safety score and/or the property features hazard score (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.”). Hedges fails to disclose: gathering, with a smart product on the property, at a plurality of time intervals during a time period, sensor data from a plurality of sensors on the property; sending, from the smart product on the property, batches of the gathered sensor data to one or more processors 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 information to train the model to determine asset/property characteristics (see at least Sanchez: ¶ 81-86 “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.”). Sanchez further teaches wherein the trained machine model is trained with home telematics data to determine home scores, wherein the telematics data was gathered by: at a plurality of time intervals over a time period, collecting sensor data from one or more sensors, and transmitting (the information) the sensor data collected over the time period to the one or more processors (see at least Sanchez: ¶ 49 “The smart home computing device may automatically acquire image data using sensors on the one or more additional devices and transmit the acquired image data to PM computing device in image data message 126.”; see also Sanchez: ¶ 81 and 85-86 “the processing element may learn, with the user's permission or affirmative consent, to identify new possessions in updated image data” where updating the images indicates that the sensor data (the images) are gathered at a plurality of time intervals during a time period). Examiner notes that Hedges further discloses the hazard score and “Any score can be associated with a timeframe (e.g., the probability of hazard exposure within the timeframe, the probability of damage occurring within the timeframe, the probability of filing a claim within the timeframe, etc.) and/or unassociated with a timeframe.” (see at least Hedges ¶ 72; see also Hedges: ¶ 74 “dates (e.g., a timeframe under consideration, dates of a hypothetical or real claim filing, dates of previous hazard events, etc.)”; see also Hedges: ¶ 91; see also Hedges: ¶ 102: “the training data is segmented into positive and negative sets, wherein the positive or negative classification for each property is the binary training target. In a first example of the first embodiment, for a hazard model (e.g., vulnerability model, risk model, etc.) with binary claim occurrence as the training target, properties in the set of training properties with claims submitted for fire damage (e.g., within the historical timeframe) are in the positive dataset”). 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 data evaluation model is trained with home telematics data to determine home characteristic data and transmit the information to the central server periodically (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 data evaluation model is trained with home telematics data to determine home characteristic data and transmit the information to the central server periodically 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 data evaluation model is trained with home telematics data to determine home characteristic data and transmit the information to the central server periodically (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 data evaluation model is trained with home telematics data to determine home characteristic data and transmit the information to the central server periodically 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). Hedges, as modified by Sanchez, discloses a system for determining a score for a property (Hedges abstract). Hedges, as modified by Sanchez, does not disclose aggregating the sensor data gathered during the time period. However, Luciana discloses a similar system for aggregating the sensor data gathered during the time period {Luciana [0029]; the sensor data may be periodically sampled, aggregated over time, and transmitted to smart home modeling system 120 [0029] } . It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Hedges and Sanchez to incorporate aggregating the sensor data as taught by Luciana because this would provide a manner for allowing a smart home modeling system to periodically receive sensor data during a time interval (Luciana [0029]), thus aiding the system in analyzing the sensor data. Hedges, as modified by Sanchez and Luciana, discloses a system for determining a score for a property (Hedges abstract). Hedges, as modified by Sanchez and Luciana, does not disclose sending a hash of the aggregated sensor data to one or more processors. However, Ichihashi discloses a similar system for a sensor unit (abstract). Ichihashi discloses sending a hash of the aggregated sensor data to one or more processors {Ichihashi [0006]; generate a hash value obtained by hashing sensor information through a hash function, and transmit the hash value to the terminal apparatus [0006] } . It would have been obvious for a person of ordinary skill in the art (PHOSITA) before the effective filing date of the claimed invention to modify the system disclosed in Hedges, Sanchez, and Luciana to incorporate sending a hash of the sensor data as taught by Ichihashi because this would provide a manner for communication (Ichihashi [0006]), thus aiding the system in analyzing the sensor data. Referring to claims 2, 13, and 18 (substantially similar in scope and language): Hedges, a modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1, the computer system of claim 12, and the non-transitory computer readable medium of claim 17, wherein: the retrieved one or more attributes further comprise (iii) one or more fire hazard attributes, and/or (iv) one or more weather hazard attributes; and the determining the one or more home score factors comprises determining the property feature hazard score based upon the one or more fire hazard attributes, and/or the one or more weather hazard attributes (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 at least Hedges: ¶ 66-84 “the risk score can be determined using a risk model that ingests: property attribute values and historical weather and/or hazard data for the property location”; 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). Referring to claims 3, 14, and 19 (substantially similar in scope and language): Hedges, a modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 2, the computer system of claim 13, and the non-transitory computer readable medium of claim 18, wherein the determining the one or more home score factors comprises determining the property feature hazard score based upon the one or more fire hazard attributes, and wherein the one or more fire hazard attributes comprise a grade based upon a distance from the property to water and/or a distance from the property to a fire station (see at least Hedges: ¶ 74 and 100 “property measurements, other hazard scores (e.g., calculated using a hazard model, retrieved from a third-party hazard database, etc.), property location, data from a third-party database (e.g., property data, hazard exposure risk data, claim/loss data, policy data, weather and/or hazard data, fire station locations, insurer database, etc.” and “training inputs for each training property can include and/or be based on: property measurements (e.g., acquired before a hazard event, after a hazard event, and/or unrelated to a hazard event), property attribute values, a property location, a hazard score, data from a third-party database (e.g., property data, hazard risk data, claim/loss data, policy data, weather data, hazard data, fire station locations, tax assessor database, insurer database, etc.), dates, and/or any other input (e.g., as described in S400)”). Referring to claims 4, 15, and 20 (substantially similar in scope and language): Hedges, a modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 2, the computer system of claim 13, and the non-transitory computer readable medium of claim 18, wherein the determining the one or more home score factors comprises determining the property feature hazard score based upon the one or more weather hazard attributes, and wherein the one or more weather hazard attributes comprise: an earthquake grade, a wind grade, a hail grade, a tornado grade, a lightning grade, a flood grade, a wildfire grade, a drought grade, a tsunami grade, a hurricane grade, a volcano grade, a wind born debris grade, a costal storm surge grade, and/or a convection storm grade (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: Hedges, a modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1, wherein the determining the one or more home score factors comprises determining the property feature hazard score based upon the one or more property feature hazard attributes, and wherein the one or more property feature hazard attributes comprise: a roof condition rating, a tree overhang rating, a radon grade, a mold index grade, a slope risk grade, an aspect risk grade, an ice damage grade, and/or a frozen pipe grade (see at least Hedges: ¶ 56 “roof condition (e.g., tarp presence, material degradation, rust, missing or peeling material, sealing, natural and/or unnatural discoloration, defects, loose organic matter, ponding, patching, streaking, etc.)… accessory structure condition, yard debris and/or lot debris (e.g., presence, coverage, ratio of coverage, etc.), lawn condition, pool condition, driveway condition, tree parameters (e.g., overhang information, height, etc.), vegetation parameters (e.g., coverage, density, setback, location within one or more zones relative to the property),”) . 07-21-aia AIA Claim s 5, 7-10, & 16 are rejected under 35 U.S.C. 103 as being unpatentable over Hedges et al. (US 20220405856), in view of Sanchez (US 20220335366), in view of Luiciani (US 20220245290), and further in view of Ichihashi et al. (US 20220253818), and further in view of Shoup (US 20210150651) . Referring to claim 5 & 16: Hedges, as modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1; Hedges, as modified by Sanchez, Luciana, and Ichihashi, does not explicitly state wherein the determining the one or more home score factors comprises determining the safety score based upon the one or more safety attributes, and wherein the one or more safety attributes comprise: (i) a burglary grade based upon a burglary likelihood, and/or (ii) a motor vehicle theft grade based upon a motor vehicle theft likelihood. However, Shoup, which discloses a method and system for determining property and neighborhood metrics, teaches determining the one or more home score factors comprises determining the safety score based upon the one or more safety attributes, and wherein the one or more safety attributes comprise: (i) a burglary grade based upon a burglary likelihood, and/or (ii) a motor vehicle theft grade based upon a motor vehicle theft likelihood (see at least Stoup: ¶ 78 “In some embodiments, the system 10 may use the data rating application 118 to rate the categorized safety data using a variety of criteria. For example, the categorized data may be rated by the type and severity of the crime committed, with violent crimes such as murder, assault and battery, armed robbery, etc. rated higher than non-violent crimes such as shop lifting, theft and drug related offenses. In this way, the severity of the crimes may be rated and classified as a crime severity rating. In another example, the data may be rated according to the perpetrators, with repeat offenders being rated higher than first-time offenders. In another example, the data may be rated by amount of time that has passed since the crime(s) were committed, the amount of time between each crime committed, and/or the frequency or patterns of the crimes committed. Other types of rating criteria also may be used. In this way, each crime may be given one or more scores that may represent the severity of the crime committed. Once the scores have been calculated, the scores and the rated data may be stored in the rated data database 128”; see also Stoup: ¶ 44-45 “As used herein, the term “safety information” may include information related to (without limitation): violent crimes committed, non-violent crimes committed, registered sex offenders, and other types of safety information. The safety information may include the type and/or category of the offense(s) committed, the location of the offense (exact and/or approximate), the distance of the offense from the location of the property (exact and/or approximate), the date and time of the offense(s), whether or not the perpetrators were apprehended (if available), whether or not the perpetrators are still in custody (if available), other information regarding the perpetrators (such as his/her prior record, release date if previously incarcerated and/or institutionalized, terms served, all if available, etc.), and other types of information. The safety information may be aggregated over specified time periods (e.g., over the past week, month, year, etc.).”; see also Shoup: ¶ 71-75, 77-80, and 88). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood safety score and accounting for crimes (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources. One of ordinary skill in the art would have been motivated to apply the known technique of generating a property and neighborhood safety score and accounting for crimes because it would assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources and to quantify the overall safety of the property and of the surrounding neighborhood (see Shoup: ¶ 2 and 46). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood safety score and accounting for crimes (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. 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 applying the known technique of generating a property and neighborhood safety score and accounting for crimes to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources). See also MPEP § 2143(I)(D). Referring to claim 7: Hedges, as modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1 and applying weights to selected attributes (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.”), including wherein the generating the overall home score comprises: applying, via the one or more processors, a safety score weight to the safety score; applying, via the one or more processors, a property feature hazard score weight to the property feature hazard score, wherein the safety score weight is larger than the property feature hazard score; and generating, via the one or more processors, the overall home score based upon the weighted safety score and the weighted property feature hazard score. Shoup, which talks about a method and system for determining a property and neighborhood assessment system and method, teaches it is known to provide a map of properties to a user wherein the properties are color coded based on the assessed data which teaches wherein the determining the two or more home score factors comprises determining, by the one or more processors: the fire hazard score by applying a fire hazard score weight to the one or more fire hazard attributes; the safety score by applying a safety score weight to the one or more safety attributes; the weather hazard score by applying a weather hazard score weight to the one or more weather hazard attributes; and/or the property feature hazard score by applying a property feature hazard score weight to the one or more property feature hazard attributes (see at least Shoup: ¶ 79 “the data rating application 118 may calculate one or more overall safety ratings for each property of interest by applying weight factors and/or algorithms to each type of safety data associated with the property. For example, a property located near the locations of identified violent crimes may receive a lower overall safety score compared to a property associated with a lesser number or less severe crimes within the same or similar geographic radius and/or time frame. In this way, the property's overall safety rating may generally represent a quality-of-life aspect associated with the property”). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood score and applying various weights (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources. One of ordinary skill in the art would have been motivated to apply the known technique of generating a property and neighborhood score and applying various weights because it would assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources and to quantify the overall safety of the property and of the surrounding neighborhood (see Shoup: ¶ 2 and 46). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood score and applying various weights (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. 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 applying the known technique of generating a property and neighborhood score and applying various weights to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources). See also MPEP § 2143(I)(D). Referring to claim 8: Hedges, as modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1; Hedges, as modified by Sanchez, Luciana, and Ichihashi, does not explicitly state wherein the displaying further comprises displaying a map showing indications of home scores of other properties, and wherein the indications of home scores of other properties comprise color coded indications of overall home scores of other properties. Hedges disclose displaying, via the one or more processors, the overall home score (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.”). However, Shoup, which discloses a method and system for determining a property and neighborhood assessment system and method, teaches it is known to provide a map of properties to a user wherein the properties are color coded based on the assessed data which teaches displaying, via the one or more processors, a map including indications of other properties (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”); in response to receiving the selection, displaying, via the one or more processors, information of the second property including: (i) a fire hazard score of the second property, (ii) a weather hazard score of the second property, and/or (iii) a property feature hazard score of the second property (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”; see also Shoup: ¶ 85 “the system 10 may create and provide various reports to the user(s) Un via the data reporting application 121. The reports may include the time period over which the safety information has been aggregated, and textual data describing the safety data for each applicable offense On, the safety ratings(s) (scores) of the property and its surrounding neighborhood, charts that graphically display the safety data and the ratings (e.g., bar charts, pie charts, etc.), maps, any other types of data representations and any combinations thereof. In some embodiments, safety data and/or ratings contained in the report(s) may be color-coded as described above (with respect to FIG. 3) to provide a visual representation of the data”; see also Shoup: ¶ 86: “he system 10 may intake background information relating to the residents of the neighboring properties adjacent to or in close proximity to the property of interest. For example, the system 10 may intake information such as criminal records, sex offences and other types of information. This information may then be provided to the user Un via the reports and/or maps as described in other sections”; see also Shoup: ¶ 94-96: “Once the safety data has been processed, the system 10 (via the data reporting application 121) may provide safety and rating report(s), interactive map(s) (FIG. 3) and/or other formats of safety data as described herein.”). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources. One of ordinary skill in the art would have been motivated to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map because it would assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources and to quantify the overall safety of the property and of the surrounding neighborhood (see Shoup: ¶ 2 and 46). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. 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 applying the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources). See also MPEP § 2143(I)(D). Referring to claim 9: Hedges, as modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1; Hedges, as modified by Sanchez, Luciana, and Ichihashi, fails to disclose wherein the displaying further comprises displaying a map showing indications of home scores of other properties, wherein the property is a first property, and wherein the method further comprises: receiving, via the one or more processors, a user selection of a second property on the map; and in response to receiving the selection, displaying, via the one or more processors, information of the second property including: (i) an overall home score of the second property, (ii) a safety score of the second property, and/or (iii) a property feature hazard score of the second property. Hedges disclose displaying, via the one or more processors, the overall home score (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.”). However, Shoup, which discloses a method and system for determining a property and neighborhood assessment system and method, teaches it is known to provide a map of properties to a user wherein the properties are color coded based on the assessed data which teaches displaying, via the one or more processors, a map including indications of other properties (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”); in response to receiving the selection, displaying, via the one or more processors, information of the second property including: (i) a fire hazard score of the second property, (ii) a weather hazard score of the second property, and/or (iii) a property feature hazard score of the second property (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”; see also Shoup: ¶ 85 “the system 10 may create and provide various reports to the user(s) Un via the data reporting application 121. The reports may include the time period over which the safety information has been aggregated, and textual data describing the safety data for each applicable offense On, the safety ratings(s) (scores) of the property and its surrounding neighborhood, charts that graphically display the safety data and the ratings (e.g., bar charts, pie charts, etc.), maps, any other types of data representations and any combinations thereof. In some embodiments, safety data and/or ratings contained in the report(s) may be color-coded as described above (with respect to FIG. 3) to provide a visual representation of the data”; see also Shoup: ¶ 86: “he system 10 may intake background information relating to the residents of the neighboring properties adjacent to or in close proximity to the property of interest. For example, the system 10 may intake information such as criminal records, sex offences and other types of information. This information may then be provided to the user Un via the reports and/or maps as described in other sections”; see also Shoup: ¶ 94-96: “Once the safety data has been processed, the system 10 (via the data reporting application 121) may provide safety and rating report(s), interactive map(s) (FIG. 3) and/or other formats of safety data as described herein.”). Shoup teaches wherein the property is a first property, and wherein the method further comprises: displaying, via the one or more processors, a map showing indications of home scores of other properties (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”; see also 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.”); receiving, via the one or more processors, a user selection of a second property on the map (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”; see also Shoup: ¶ 85 “the system 10 may create and provide various reports to the user(s) Un via the data reporting application 121. The reports may include the time period over which the safety information has been aggregated, and textual data describing the safety data for each applicable offense On, the safety ratings(s) (scores) of the property and its surrounding neighborhood, charts that graphically display the safety data and the ratings (e.g., bar charts, pie charts, etc.), maps, any other types of data representations and any combinations thereof. In some embodiments, safety data and/or ratings contained in the report(s) may be color-coded as described above (with respect to FIG. 3) to provide a visual representation of the data”; see also Shoup: ¶ 86: “he system 10 may intake background information relating to the residents of the neighboring properties adjacent to or in close proximity to the property of interest. For example, the system 10 may intake information such as criminal records, sex offences and other types of information. This information may then be provided to the user Un via the reports and/or maps as described in other sections”; see also Shoup: ¶ 94-96: “Once the safety data has been processed, the system 10 (via the data reporting application 121) may provide safety and rating report(s), interactive map(s) (FIG. 3) and/or other formats of safety data as described herein.”); and in response to receiving the selection, displaying, via the one or more processors, information of the second property including: (i) an overall home score of the second property, (ii) a fire hazard score of the second property, (ii) a safety score of the second property, (iii) a weather hazard score of the second property, and/or (iv) a property feature hazard score of the second property (see at least Shoup: ¶ 81-83 “the geographic map database 132 may include maps of each geographical area that the system 10 may support, with the maps including street address and other types of identifying information for each property included within the geographic regions”; see also Shoup: ¶ 84-85 “the system 10 may create and provide various reports to the user(s) Un via the data reporting application 121. The reports may include the time period over which the safety information has been aggregated, and textual data describing the safety data for each applicable offense On, the safety ratings(s) (scores) of the property and its surrounding neighborhood, charts that graphically display the safety data and the ratings (e.g., bar charts, pie charts, etc.), maps, any other types of data representations and any combinations thereof. In some embodiments, safety data and/or ratings contained in the report(s) may be color-coded as described above (with respect to FIG. 3) to provide a visual representation of the data”; see also Shoup: ¶ 86: “he system 10 may intake background information relating to the residents of the neighboring properties adjacent to or in close proximity to the property of interest. For example, the system 10 may intake information such as criminal records, sex offences and other types of information. This information may then be provided to the user Un via the reports and/or maps as described in other sections”; see also Shoup: ¶ 94-96: “Once the safety data has been processed, the system 10 (via the data reporting application 121) may provide safety and rating report(s), interactive map(s) (FIG. 3) and/or other formats of safety data as described herein.”). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi,) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources. One of ordinary skill in the art would have been motivated to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map because it would assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources and to quantify the overall safety of the property and of the surrounding neighborhood (see Shoup: ¶ 2 and 46). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by Hedges, as modified by Sanchez, Luciana, and Ichihashi) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. 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 applying the known technique of displaying properties in adjacent to other properties that have similar information, hazard features, and safety ratings within a displayed map to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources). See also MPEP § 2143(I)(D). Referring to claim 10: Hedges, as modified by Sanchez, Luciana, and Ichihashi, discloses the computer-implemented method of claim 1; Hedges, as modified by Sanchez, Luciana, and Ichihashi, fails to state further comprising generating, via the one or more processors, a neighborhood score by determining an average of overall home scores of nearby properties. Hedges discloses determining overall home scores of nearby properties (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.”). Shoup, which discloses a method and system for determining a property and neighborhood assessment system and method, teaches it is known to provide a map of properties to a user wherein the properties are color coded based on the assessed data which teaches generating, via the one or more processors, a neighborhood score by determining an average of overall home scores of nearby properties (see at least Shoup: ¶ 81-86: discussing mapping properties and neighborhoods; see also Shoup: ¶ 41, 44-46, 85, and 88-89: also discussing scoring properties and neighborhoods). Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood score (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by the combination of Hedges and Sanchez) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources. One of ordinary skill in the art would have been motivated to apply the known technique of generating a property and neighborhood score because it would assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources and to quantify the overall safety of the property and of the surrounding neighborhood (see Shoup: ¶ 2 and 46). Furthermore, it would have been obvious to one of ordinary skill in the art at the time of filing to apply the known technique of generating a property and neighborhood score (as disclosed by Shoup) to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes (as disclosed by the combination of Hedges, Sanchez, Luciana, and Ichihashi,) to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources, because the claimed invention is merely applying a known technique to a known method ready for improvement to yield predictable results. 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 applying the known technique of generating a property and neighborhood score to the known determining, based on first sensor readings for at least one sensor associated with the first one of the plurality of homes and second sensor readings from corresponding sensors associated with at least a second one of the plurality of homes, a live home score for the first one of the plurality of homes; and sending the live home score to the computing device associated with the first one of the plurality of homes to assess the safety of a property and its associated neighborhood, including a system that aggregates, standardizes, and transforms safety information from third party sources). See also MPEP § 2143(I)(D). Response to Arguments 35 USC 101 Rejections Applicant argues that the use of a transmitting a cryptographic hash alongside the aggregated sensor data enables the processors to verify that the sensor data has not been corrupted or tampered with during transmission and as such, provides a technical solution to a technical problem, which “demonstrates subject matter eligibility.” Remarks 9. Examiner respectfully disagrees. The consideration referenced by Applicant, while often referred to as the search for a technological solution to a technological problem, requires Examiners to consider whether the claim "purport(s) to improve the functioning of the computer itself" or "any other technology or technical field." Alice Corp. Pty. Ltd. v. CLS Bank Int’l , 573 U.S. 208, 225, 110 USPQ2d 1976, 1984 (2014). If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. MPEP 2106.05(a). In this case, there is no technical explanation as to how to implement the invention in the specification; in other words, the disclosure does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Rather, the specification sets forth the alleged improvement in a conclusory manner, not providing any technical details regarding transmitting a hash value. As such, the claim does not improve technology. Applicant then argues that the claims provide a technical improvement to (1) computer network efficiency; by aggregating the data and transmitting it together rather than continuously sending data, the system reduces network congestion and communication overhead, and to (2) scalability of the system. Remarks 10. Examiner respectfully disagrees. Applicant has not provided any evidence that the claim limitations provide technical improvements. Aggregating data and transmitting it together is well-known in the art and does not provide a technical improvement. Neither does the Specification identify computer network efficiency or scalability as technical problems which are solved by the limitations. Applicant further argues that the combination of these “three advantages” demonstrate subject matter eligibility. Remarks 10. Examiner respectfully disagrees. Applicant does identify what is added by the computer components that is not already present when the steps are considered separately. Further, the additional elements have all been considered individually and do no more than require a generic computer to perform generic computer functions. As such, this is not a persuasive argument. 35 USC 103 Applicant argues that the prior art does not disclose the sending of hash data. Examiner has provided new art which discloses this feature (see above). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /CARRIE S GILKEY/Primary Examiner, Art Unit 3626 Application/Control Number: 18/735,558 Page 2 Art Unit: 3626 Application/Control Number: 18/735,558 Page 4 Art Unit: 3626 Application/Control Number: 18/735,558 Page 5 Art Unit: 3626 Application/Control Number: 18/735,558 Page 6 Art Unit: 3626 Application/Control Number: 18/735,558 Page 7 Art Unit: 3626 Application/Control Number: 18/735,558 Page 8 Art Unit: 3626
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