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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 4/22/2026. Claims 1-20 are pending in this office action. As of the date of this communication no Information Disclosure Statement (IDS) has been filed on behalf of this case.
Response to Amendment
Applicant’s amendments to claims 1 and 19-20 are acknowledged.
Response to Arguments
On Remarks pages 9-11, Applicant argues the 101 rejection in view of Applicant’s amendments in particular with respect to the water sensor. The Examiner understands Applicant’s arguments here however the Examiner respectfully disagrees. The 101 rejection has been updated below to reflect Applicant’s amendments.
As per Applicant’s amendments filed 4/22/2026 the claim limitations recite mental process or certain method of organizing human activity steps (as broadly recited in the claims) of receiving information from a water sensor, e.g. reading information off of a water sensor, determine based on the received information that the water sensor is operating properly (for example comparing collected and expected data), modify a score based on a determination that the water sensor is operating properly, and determine an overall home score for the subject property score based on the modified plumbing subscore. This is part of the abstract idea. The additional element of these limitations are being performed by one or more processors merely result in apply it or generally linking it to the field of computers as discussed in the 101 rejection below with respect to claim 1.
Therefore the Examiner respectfully disagrees.
On Remarks page 12, Applicant argues the prior art rejection in view of Applicant’s amendments. In view of Applicant’s amendments, the Examiner has applied a newly cited prior art of Devereaux et al. (United States Patent Number: US 10,679, 296) as detailed in the prior art rejection below rendering Applicant’s arguments moot.
Though not relied upon for prior art purposes other art considered relevant to Applicant’s amendments with respect to water sensors and home scores is Marotta et al. (United States Patent Application Publication Number: US 2022/0013222) (see paragraphs 0056, 0070, and 0102).
On Remarks page 13, Applicant argues the 112 a/first rejection with respect to claim 14. Here Applicant argues (1) one of ordinary skill in the art at the time of filing would have understood this function being implemented using known techniques and (2) the claim language does not require a particular algorithm approach. The Examiner respectfully disagrees.
With respect to (1) above, MPEP 2161 clearly states that it is not enough that one of skill in the art could write a program to achieve the claimed function because the specification must explain how the invention intends to achieve the claimed function to satisfy the written description requirement, cited herein:
“ It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made. For more information regarding the written description requirement, see MPEP § 2162- § 2163.07(b).”
Therefore the Examiner finds the argument non-persuasive.
With respect to (2) above, MPEP 2161 clearly states original claims may lack written description when the claims specify in functional language a desired result but the specification does not describe how the function is performed or the result is achieved, cited herein:
“Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV.
The level of detail required to satisfy the written description requirement varies depending on the nature and scope of the claims and on the complexity and predictability of the relevant technology. Ariad, 598 F.3d at 1351, 94 USPQ2d at 1172; Capon v. Eshhar, 418 F.3d 1349, 1357-58, 76 USPQ2d 1078, 1083-84 (Fed. Cir. 2005). Computer-implemented inventions are often disclosed and claimed in terms of their functionality. For computer-implemented inventions, the determination of the sufficiency of disclosure will require an inquiry into the sufficiency of both the disclosed hardware and the disclosed software due to the interrelationship and interdependence of computer hardware and software. The critical inquiry is whether the disclosure of the application relied upon reasonably conveys to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date. Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 682. 114 USPQ2d 1349, 1356 (citing Ariad Pharm., Inc. V. Eli Lilly & Co, 598 F.3d 1336, 1351, 94 USPQ2d 1161, 1172 (Fed. Cir. 2010) in the context of determining possession of a claimed means of accessing disparate databases).
This is the case here as the claims recite functional language of a result of verifying, via the one or processors, the at least one insight has been completed based upon the imagery data, and in response to the verification, updating, via the one or more processors, the overall home score for the subject property” but the specification does not disclose how that result in achieved.
Therefore the Examiner finds the argument not persuasive.
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 an abstract idea without significantly more.
The claim(s) recite(s) collecting data, generating scores based on collected data, comparing scores to other scores based on a parameter like location, updating and validating information that changes scores, and then updating scores.
The claims recite observations, evaluations, judgements, and opinions that can be performed by the human mind or with use of a physical aid (e.g. pen and paper) to perform the claim limitations therefore the claims recite a mental process.
Further the collecting data, generating scores based on collected data, comparing scores to other scores based on a parameter like location, updating and validating information that changes scores, and then updating scores is subject matter related to “commercial interactions” or “legal interactions” including agreements in the forms of contracts, legal obligations, advertising, marketing or sales activities or behaviors or business relations. This is certain method of organizing human activity.
Mental processes or certain methods of organizing human activities are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) And (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
Examiner notes that the Examiner has bolded the additional elements, limitations not bolded are considered part of the abstract idea.
1. A computer-implemented method for improved generation of home scores and/or improved comparison of the home scores, the method comprising:
Receiving, via one or more processors, from a water sensor of a subject matter property, water sensor data;
Determining, via the one or more processors, based upon the water sensor data, that the water sensor is operating properly;
Modifying, via the one or more processors, a plumbing subs score of the subject property based upon the determination that the water sensor is operating properly;
determining, via the one or more processors, an overall home score for the subject property based upon the modified pluming subscore of the subject property;
receiving, via the one or more processors, a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores;
receiving, via the one or more processors, a geographic filtering parameter;
filtering, via the one or more processors, the plurality of overall home scores based upon the geographic filtering parameter;
and displaying, via the one or more processors, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores.
2. The computer-implemented method of claim 1, wherein the geographic filtering parameter includes: (i) a distance filter; (ii) a zip code filter; and/or (iii) a jurisdiction filter.
3. The computer-implemented method of claim 1, further including:
displaying, via the one or more processors, on the display, a map;
receiving, via the one or more processors, user input on the map;
and setting, via the one or more processors, the geographic filtering parameter according to the user input.
The computer-implemented method of claim 1, further including:
receiving, via the one or more processors, the respective subscores;
receiving, via the one or more processors, a subscore filtering parameter;
filtering, via the one or more processors, the respective subscores based upon the subscore filtering parameter;
and displaying, via the one or more processors, on a display: (i) an average or median of the filtered respective subscores, (ii) a comparison of a subscore for the subject property to the average or median of the filtered respective subscores, and/or (iii) at least one subscore of the filtered respective subscores.
The computer-implemented method of claim 1, further including:
receiving, via the one or more processors, a home score filtering parameter including: (i) a home age parameter, (ii) a square footage parameter, (iii) a number of bathrooms parameter, (iv) a number of bedrooms parameter, (v) a school district score parameter, and/or (vi) an estimated home value parameter;
filtering, via the one or more processors, the respective home scores of the plurality of home scores based upon the home score filtering parameter;
and displaying, via the one or more processors, on a display, the filtered respective home scores of the plurality of home scores.
The computer-implemented method of claim 1, wherein the respective subscores include the respective safety subscores, and the method further includes:
determining, via the one or more processors, the respective safety subscores based upon: fire protection attributes, weather hazard attributes, and/or crime attributes.
The computer-implemented method of claim 6, wherein:
the fire protection attributes include a grade based upon a distance from a property to water and/or a distance from the property to a fire station;
the weather hazard attributes include: 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;
and the crime attributes include (i) a burglary grade based upon a burglary likelihood, and/or (ii) a motor vehicle theft grade based upon a motor vehicle theft likelihood.
8. The computer-implemented method of claim 1, wherein the respective subscores include the respective structural subscores, and the method further includes: determining, via the one or more processors, the respective structural subscores based upon: a structural grade, and/or a home age.
9. The computer-implemented method of claim 1, wherein the respective subscores include the respective plumbing subscores, and the method further includes:
determining, via the one or more processors, the respective plumbing subscores based upon: a plumbing grade, and/or a date of a most recent plumbing inspection.
10. The computer-implemented method of claim 1, wherein the respective subscores include the respective appliances subscores, and the method further includes:
determining, via the one or more processors, the respective appliances subscores based upon an energy grade, an appliances maintenance grade, and/or a heating, ventilation, and air conditioning (HVAC) attribute.
11. The computer-implemented method of claim 1, wherein the subject property corresponds to a first user, and the method further includes:
receiving, via the one or more processors, a request from a second user to view the overall home score for the subject property;
in response to receiving the request, requesting, via the one or more processors, permission from the first user to allow the second user to view the overall home score for the subject property;
receiving, via the one or more processors, from the first user, the permission;
and in response to receiving the permission, allowing, via the one or more processors, the second user to view the overall home score for the subject property.
12. The computer-implemented method of claim 1, further including: presenting, via the one or more processors, one or more insights to a user corresponding to the subject property;
receiving, via the one or more processors, from the user, an indication that at least one insight of the one or more insights has been completed;
and in response to receiving the indication, updating, via the one or more processors, the overall home score for the subject property.
13. The computer-implemented method of claim 12, wherein the one or more insights include: replacing a smoke detector battery; installing a support beam; replacing at least one pipe; replacing an air filter; and/or installing a water sensor.
14. The computer-implemented method of claim 1, further including:
presenting, via the one or more processors, one or more insights to a user corresponding to the subject property;
receiving, via the one or more processors, from the user, an indication that at least one insight of the one or more insights has been completed;
in response to receiving the indication, requesting, via the one or more processors, from the user, imagery data associated with the at least one insight;
receiving, via the one or more processors, the imagery data from the user;
verifying, via the one or more processors, that the at least one insight has been completed based upon the imagery data;
and in response to the verification, updating, via the one or more processors, the overall home score for the subject property.
15. The computer-implemented method of claim 1, wherein the respective subscores include the respective safety subscores, and the method further includes:
training a safety subscore machine learning algorithm by inputting historical information into the safety subscore machine learning algorithm, the historical information including: (i) independent variables comprising (a) historical fire protection attributes, (b) historical weather hazard attributes, and/or (c) historical crime attributes, and/or (ii) dependent variables comprising historical safety subscores;
and determining the respective safety subscores by routing information of properties into the safety subscore machine learning algorithm.
16. The computer-implemented method of claim 1, wherein the respective subscores include the respective structural subscores, and the method further includes:
training a structural subscore machine learning algorithm by inputting historical information into the structural subscore machine learning algorithm, the historical information comprising: (i) independent variables including: (a) historical structural grades, and/or (b) historical home ages, and/or (ii) dependent variables comprising historical structural subscores;
and determining the respective structural subscores by routing information of properties into the structural subscore machine learning algorithm.
17. The computer-implemented method of claim 1, wherein the respective subscores include the respective plumbing subscores, and the method further includes:
training a plumbing subscore machine learning algorithm by inputting historical information into the plumbing subscore machine learning algorithm, the historical information comprising: (i) independent variables including: (a) historical plumbing grades, and/or (b) historical dates of a most recent plumbing inspections, and/or (ii) dependent variables comprising historical plumbing subscores; and determining the respective plumbing subscores by routing information of properties into the plumbing subscore machine learning algorithm.
18. The computer-implemented method of claim 1, wherein the respective subscores include the respective appliances subscores, and the method further includes:
training an appliances subscore machine learning algorithm by inputting historical information into the appliances subscore machine learning algorithm, the historical information comprising: (i) independent variables including (a) historical energy grades, (b) historical appliances maintenance grades, and/or (c) historical heating, ventilation, and air conditioning (HVAC) attributes, and/or (ii) dependent variables comprising historical appliances subscores;
and determining the respective appliances subscores by routing information of properties into the appliances subscore machine learning algorithm.
19. A computer device for improved generation of home scores and/or improved comparison of the home scores, the computer device comprising one or more processors configured to:
Receive, from a water sensor of a subject property, water sensor data;
Determine, based upon the water sensor data, that the water sensor is operating properly;
Modify a plumbing score of the subject property based upon the determination that the water sensor is operating properly;
determine an overall home score for a subject property based upon the modified pluming subscore of the subject property;
receive a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores;
receive a geographic filtering parameter;
filter the plurality of overall home scores based upon the geographic filtering parameter;
and display, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores.
20. A computer system for improved generation of home scores and/or improved comparison of the home scores, the computer system comprising:
one or more processors; and one or more non-transitory memories, the one or more non-transitory memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to:
receive, from a water sensor of a subject property, water sensor data;
determine, based upon the water sensor data, that the water sensor is operating properly;
modify a plumbing subscribe of the subject property based upon the determination that the water sensor is operating properly;
determine an overall home score for the subject property based upon he modified pluming subscore of the subject property;
receive a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores;
receive a geographic filtering parameter;
filter the plurality of overall home scores based upon the geographic filtering parameter;
and display, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores.
As per claim 1, the claims recite receiving a score, receiving a plurality of home scores for a plurality of properties that are based on subscores, receiving a geographic filter, filtering based on the geographic filter, and displaying on a display an average, a comparison, or a respective overall home score. These limitations that are mental process and or certain methods of organizing human activity steps. The additional elements that this method is “computer-implemented and being performed by “one or more processors” merely results in apply it.
Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more.
Further here these mental process and or certain method of organizing human activity steps being “computer” implemented and being performed by “one or more processors” merely results generally linking it to the field of computers.
As per Applicant’s amendments filed 4/22/2026 the claim limitations recite mental process or certain method of organizing human activity steps of receiving information from a water sensor, e.g. reading information off of a water sensor, determine based on the receive information that the water sensor is operating properly (for example comparing collected and expected data), modify a score based on a determination that the water sensor is operating properly , and determine an overall home score for the subject property score based on the modified plumbing subscore. This is part of the abstract idea. The only additional element of the limitations being performed by “one or more processors” has been addressed above, as merely apply it and or generally linking it to the field of computers.
As per claim 2, the claims merely recite different types of geographic filtering parameters. These limitations that are mental process and or certain methods of organizing human activity steps. The additional element that the method is computer implemented merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 3, the claims recite displaying on a display a map, receiving user input on the map, and setting the geographic filtering parameter according to user input. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 4, the claims recite receiving respective score, receiving a subscore filtering parameter, filtering subscores based on filtering parameter, and displaying on a display an average or median, a comparison, or a subscore of the filtered respective subscores. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 5, the claims recite receiving a home score filtering parameter, filtering the respective home scores based on the filtering parameter, and displaying the filer respective score of the plurality of home scores. These limitations are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 6, the claims recite respective subscores including respective safety subscores and determining the safety subscores based on fire protection, attributes, water hazard, or crime attributes. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 7, the claims recite fire protection includes specific attributes, weather hazard includes specific listed attributes, and crime attributes include the specific listed attributes. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional element that the method is computer implemented merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 8, the claims recite respective subscores include respective structural subscores and determining the respective structural subscores based on a structural grade and or home age. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 9, the claims recite respective subscores include respective plumbing score and determining the respective plumbing subscores based on specific plumbing attributes. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 10, the claims recite respective subscores include respective appliance subscore and determining the respective appliance subscores based on specific appliance attributes. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 11, the claims recite receiving a request from another user to view a home score, requesting permission from the user, and receiving permission from the user, and in response allowing the other user to view the home score. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 12, the claims recite presenting one or more insights to the user about the property, receiving an indication that the one or more insights have been completed, and updating the overall home score. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed via one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 13, the claims recite the specific type of insights of which indications are received from claim 12. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 14, the claims recite presenting one or more insights, receiving an indication of being completed, requesting a photo of the completion, receiving an image associated with the insight, verifying that it has been completed based on the imagery, and in response updating the home score. These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented and being performed by one or more processors merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
As per claim 15, the claims recite the respective subscores including respective safety subscores and using historical information to generate a set of rules (e.g. a model or algorithm) and determining subscores by using the set of rules (e.g. model or algorithm). These limitations that are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented merely results in apply it or generally linking it to the field of computers as addressed above in claim 1.
Further the additional elements that the set of rules (e.g. model or algorithm) is a “machine learning algorithm” and historical information being used to form the set of rules is “training” the algorithm, merely results in apply it. The claim invokes a generic machine learning model for making the recited certain method of organizing human activity or mental process step without purporting to improve the computer or technology. The claim omits any details as to how the machine learning algorithm solves a technical problem and instead recites only the idea of a solution or outcome. Here the claims invokes computers or other machinery (e.g. a trained machine learning algorithm) merely as a tool to perform an existing process. Here the additional limitations provide only a result-oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words apply it.
Further the additional elements that the set of rules (e.g. model or algorithm) is a “machine learning algorithm” and historical information being used to form the set of rules is “training” the algorithm, merely results in generally linking it to the field of computers.
As per claim 16, the claims recite the respective subscores including respective structural subscores and using historical information to generate a set of rules (e.g. a model or algorithm) and determining subscores by using the set of rules (e.g. model or algorithm). These limitations are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented, the set of rules (e.g. model or algorithm) is a “machine learning algorithm” and historical information being used to form the set of rules is “training” the algorithm, results in apply it and or generally linking it to the field of computers and is addressed above in claims 1 and 15.
As per claim 17, the claims recite the respective subscores including respective plumbing subscores and using historical information to generate a set of rules (e.g. a model or algorithm) and determining subscores by using the set of rules (e.g. model or algorithm). These limitations are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented, the set of rules (e.g. model or algorithm) is a “machine learning algorithm” and historical information being used to form the set of rules is “training” the algorithm, results in apply it and or generally linking it to the field of computers and is addressed above in claims 1 and 15.
As per claim 18, the claims recite the respective subscores including respective appliance subscores and using historical information to generate a set of rules (e.g. a model or algorithm) and determining subscores by using the set of rules (e.g. model or algorithm). These limitations are mental process and or certain methods of organizing human activity steps as broadly recited in the claims. The additional elements that this method is computer implemented, the set of rules (e.g. model or algorithm) is a “machine learning algorithm” and historical information being used to form the set of rules is “training” the algorithm, results in apply it and or generally linking it to the field of computers and is addressed above in claims 1 and 15.
As per claim 20, the claims recite receiving a score, receiving a plurality of home scores for a plurality of properties that are based on subscores, receiving a geographic filter, filtering based on the geographic filter, and displaying on a display an average, a comparison, or a respective overall home score. These limitations are mental process and or certain methods of organizing human activity steps. The additional elements that these mental process and or certain methods of organizing human activity steps are being performed by a “computer device comprising or more processors configured to” merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
As per Applicant’s amendments filed 4/22/2026 the claim limitations recite mental process or certain method of organizing human activity steps of receiving information from a water sensor, e.g. reading information off of a water sensor, determine based on the receive information that the water sensor is operating properly, modify a score based on a determination that the water sensor is operating properly, and determine an overall home score for the subject property score based on the modified plumbing subscore. This is part of the abstract idea. There are no additional elements beyond the abstract idea discussed above in these amended limitations.
As per claim 20, the claims recite receiving a score, receiving a plurality of home scores for a plurality of properties that are based on subscores, receiving a geographic filter, filtering based on the geographic filter, and displaying on a display an average, a comparison, or a respective overall home score. These limitations are mental process and or certain methods of organizing human activity steps. The additional elements that these mental process and or certain methods of organizing human activity steps are being performed by “a computer” comprising “ one or more processors; and one or more non-transitory memories, the one or more non-transitory memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to:” merely results in apply it or generally linking it to the field of computers as discussed above in claim 1.
As per Applicant’s amendments filed 4/22/2026 the claim limitations recite mental process or certain method of organizing human activity steps of receiving information from a water sensor, e.g. reading information off of a water sensor, determine based on the receive information that the water sensor is operating properly (for example comparing collected and expected data), modify a score based on a determination that the water sensor is operating properly, and determine an overall home score for the subject property score based on the modified plumbing subscore. This is part of the abstract idea. There are no additional elements beyond the abstract idea discussed above in these amended limitations.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words “apply it” ( or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) And (2) Generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)), as detailed above under the practical application step.
Claim Objections
Claims 1, 19, and 20 are objected to because of the following informalities: Applicant recites the following limitation in the claim determine an overall home score for the subject property based upon the modified pluming score of the subject property. There appears to be a typo in the claim. For the purposes of this examination based on Applicant’s other terms in the claim the Examiner will interpret “pluming” as “plumbing”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
10. Claim 14 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 14, Applicant recites “receiving, via one or more processors, the imagery data from the user; verifying, via the one or more processors, that the at least one insight has been completed based upon the imagery data; and in response to the verification, updating, via the one or more processors, the overall home score for the subject property.”
Applicant’s specification merely discloses the same results based function claimed here. See Applicant’s specification at paragraphs 0084 and 0020. Here Applicant merely recites inputs (imagery) and outputs (verifying the insights have been completed based on the imagery), but does not recite how to the processors (e.g. computer or machine) get from the image (e.g. input) to the verification of the insight(e.g. output).
See MPEP 2161 states the following “Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed.” The term algorithm is defined as the following in MPEP 2161 as “ An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure. Finisar Corp. v. DirecTV Grp., Inc., 523 F.3d 1323, 1340, 86 USPQ2d 1609, 1623 (Fed. Cir. 2008) (internal citation omitted). It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. ”
Therefore the claim is rejected under 112 1st/a as the claim lacks the algorithm for the how the function is performed. Specifically the specification does not explain how the inventor intends to achieve the claimed function, rather the specification merely recites inputs and resulting outputs as discussed above. Accordingly claim 14 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim Rejections - 35 USC § 103
11. 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.
12. 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.
13. Claims 1-2, 4-10, 12-13, and 15-20 are rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Gross (United States Patent Application Publication Number: US 2016/0027051) further in view of Devereaux et al. (United States Patent Number: US 10,679,296).
As per claim 1, Gross teaches A computer-implemented method for improved generation of home scores and/or improved comparison of the home scores, the method comprising: (see paragraphs 0004 and 0600-0601, Examiner’s note: automated method to cluster properties based on location or common maintenance needs, has utility in insurance and home improvement (see paragraph 0004). Further teaches this is implemented by software stored on a medium running on a computer (see paragraphs 0600-0601).
receiving, via the one or more processors, a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores; receiving, via the one or more processors, a geographic filtering parameter; filtering, via the one or more processors, the plurality of overall home scores based upon the geographic filtering parameter; and displaying, via the one or more processors, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores (see paragraphs 0320, 0322, and 0324-0325, Examiner’s note: teaches generating a home score for a house, which is based on various attributes including for example roof, windows, etc., then providing a comparison to other houses in a same zip code, where this maybe an average. It is noted that many of the claim limitations are listed in the alternative only requiring one of the limitations be to used, specifically the second receiving step and the displaying step).
Gross does not expressly teach the amended providing a home score based on water sensor data or more specifically as recited in the claims receiving, via one or more processors, from a water sensor of a subject property, water sensor data; determining via the one or more processors, based upon the water sensor data, that the water sensor is operating properly; modifying, via the one or more processors, a plumbing subscore of the subject property based upon the determination that the water sensor is operating properly; determining, via the one or more processors, an overall home score for the subject property based upon the modified pluming subscore of the subject property
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches providing a home score based on water sensor data or more specifically as recited in the claims receiving, via one or more processors, from a water sensor of a subject property, water sensor data; determining via the one or more processors, based upon the water sensor data, that the water sensor is operating properly; modifying, via the one or more processors, a plumbing subscore of the subject property based upon the determination that the water sensor is operating properly; determining, via the one or more processors, an overall home score for the subject property based upon the modified pluming subscore of the subject property(see column 10 lines 13- column 11 lines 25, column 21 lines 25-40, column 24 lines 35-50, column 24 lines 35-50, column 27 lines 64-column 28 lines 7, and Column 27 lines 10-20, Examiner’s note: teaches various water sensors that track plumbing stress, water leaks, and water flow and providing home insurance and guidance based on such detections (see column 10 lines 13- column 11 lines 25). If a user agrees to upgrade or install a sensor reducing an insurance policy payment (see column 21 lines 25-40). Score related to sensors of objects to make a determination of providing recommendations (see column 24 lines 35-50 and column 27 lines 64-column 28 lines 7). Column 27 lines 10-20 teaches generating multiple scores were the scores may be averaged together to generate an overall score).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross with the aforementioned teachings from Deveraux et al. with the motivation of to have included other known risk aspects of a house like plumbing in generating a home insurance score (see Deveraux et al. column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance risk is known (see Gross paragraphs 0591 and 0004) is known.
As per claim 2, Gross teaches
wherein the geographic filtering parameter includes: (i) a distance filter; (ii) a zip code filter; and/or (iii) a jurisdiction filter (see paragraphs 0322, Examiner’s note: comparison based on zip code or city which reads on at least a zip code filter or jurisdiction filter, though only one of the above alternatives is required in the claims).
As per claim 4, Gross teaches
further including: receiving, via the one or more processors, the respective subscores; receiving, via the one or more processors, a subscore filtering parameter; filtering, via the one or more processors, the respective subscores based upon the subscore filtering parameter; and displaying, via the one or more processors, on a display: (i) an average or median of the filtered respective subscores, (ii) a comparison of a subscore for the subject property to the average or median of the filtered respective subscores, and/or (iii) at least one subscore of the filtered respective subscores. (see paragraphs 0322, 0463, Figure 30D, Examiner’s note: teaches presenting the subscores to a user as well not just the overall score, for but example other neighbors who have similar gutter cleaning needs (See Figure 0322 and Figure 30D). Further teaches queries like searching for poor or below average paint but above average landscaping (see paragraphs 0463)).
As per claim 5, Gross teaches
further including: receiving, via the one or more processors, a home score filtering parameter including: (i) a home age parameter, (ii) a square footage parameter, (iii) a number of bathrooms parameter, (iv) a number of bedrooms parameter, (v) a school district score parameter, and/or (vi) an estimated home value parameter; filtering, via the one or more processors, the respective home scores of the plurality of home scores based upon the home score filtering parameter; and displaying, via the one or more processors, on a display, the filtered respective home scores of the plurality of home scores (see paragraph 0143, Examiner’s note: teaches interactive interface to filter based on things like new, extreme fixer upper, etc. (which would read on the broad definition of estimated home value parameter (see paragraph 0143). Further teaches additional filter like good lawn or two stores which additionally read on estimated home value parameter (see paragraph 0143)).
As per claim 6, Gross teaches
wherein the respective subscores include the respective safety subscores, and the method further includes: determining, via the one or more processors, the respective safety subscores based upon: fire protection attributes, weather hazard attributes, and/or crime attributes (see paragraph 0085, 0293, 0307, and 0325, Examiner’s note: home score is computed off of different variables related to property (see paragraph 0325), variables related to property include crime rates (see paragraph 0085). Further paragraph 0293 teaches crime score is additional property scoring criteria. Further teaches collecting natural phenomena like weather (wing, rain, hail, lighting)(see paragraph 0307, as additional property scoring criteria). It is noted only one is required by the claims).
As per claim 7, Gross teaches
wherein: the fire protection attributes include a grade based upon a distance from a property to water and/or a distance from the property to a fire station; the weather hazard attributes include: 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; and the crime attributes include (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 paragraphs 0307, 0315, Examiner’s note: teaches wind, rain, hail, lighting, thunder etc. are used to additional property scoring criteria. Examiner further notes only one of the alternatives is required by the claims, based on claim 6 from which claim 7. In the efforts of compact prosecution, the Examiner has cited additional references (under the conclusion section in the office action below) that teach fire protection and crime attributes, though they are not required here as listed in the alternative and therefore not relied upon for this rejection).
As per claim 8, Gross teaches
wherein the respective subscores include the respective structural subscores, and the method further includes: determining, via the one or more processors, the respective structural subscores based upon: a structural grade, and/or a home age (see paragraph 0322-0323, Examiner’s note: teaches individual scores related to things like roof, windows, landscaping).
As per claim 9, Gross teaches
wherein the respective subscores include the respective roofing subscores, and the method further includes: determining, via the one or more processors, the respective roofing subscores based upon: a plumbing grade, and/or a date of a most recent roofing inspection. (see paragraph 0322-0323, Examiner’s note: teaches individual scores related to things like roof, windows, landscaping).
Gross does not expressly teach a score related to plumbing.
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches a score related to plumbing (see column 10 lines 13- column 11 lines 25, column 21 lines 25-40, column 24 lines 35-50, column 24 lines 35-50, column 27 lines 64-column 28 lines 7, and Column 27 lines 10-20, Examiner’s note: teaches various water sensors that track plumbing stress, water leaks, and water flow and providing rating home insurance and guidance based on such detections (see column 10 lines 13- column 11 lines 25). If a user agrees to upgrade or install a sensor reducing an insurance policy payment (see column 21 lines 25-40). Score related to sensors of objects to make a determination of providing recommendations (see column 24 lines 35-50 and column 27 lines 64-column 28 lines 7). Column 27 lines 10-20 teaches generating multiple scores were the scores may be averaged together to generate an overall score).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Deveraux with the aforementioned teachings from Deveraux et al with the motivation of to include other known risk aspects of a house like plumbing in generating a home insurance score (see column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance risk is known (see Gross paragraph 0591 and 0004) is known.
As per claim 10, Gross teaches
wherein the respective subscores include the respective appliances subscores, and the method further includes: determining, via the one or more processors, the respective roofing subscores based upon an roofing grade, an roofing maintenance grade, and/or a heating, ventilation, and air conditioning (HVAC) attribute. (see paragraph 0322-0323, Examiner’s note: teaches individual scores related to things like roof, windows, landscaping).
Gross does not expressly teach a score related to appliances
However, Devereaux et al. et al. which is in the art of determining score related to aspects of a home (see Figure 7) teaches appliances (see column 11 lines 10-15, column 17 lines 5-30, column 24 lines 55- column 25 lines 3, and column 28 lines 50-62, Examiner’s note: teaches determining information like condition related to appliances (see column 11 lines 10-15, column 17 lines 5-30), then teaches calculating a score related to a condition of the appliance to make a recommendation (see column 24 lines 55- column 25 lines 3 and column 28 lines 50-62)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Devereaux et al. et al. with the aforementioned teachings from Devereaux et al. et al. with the motivation of providing a way to take into account another aspect of the house when generating information relating to a house and home insurance (see Devereaux et al. et al column 11 lines 10-15, column 17 lines 5-30, column 24 lines 55- column 25 lines 3, and column 28 lines 50-62), when taking inconsideration numerous aspects of a house when generating a home insurance score is known (see Gross paragraphs 0332-0325)
As per claim 12, Gross teaches
further including: presenting, via the one or more processors, one or more insights to a user corresponding to the subject property; receiving, via the one or more processors, from the user, an indication that at least one insight of the one or more insights has been completed; and in response to receiving the indication, updating, via the one or more processors, the overall home score for the subject property (see paragraphs 0178 and 0249, Examiner’s note: teaches updating information over time as it is completed, and identifying changes in property condition over time).
As per claim 13, Gross does not expressly teach wherein the one or more insights include: replacing a smoke detector battery; installing a support beam; replacing at least one pipe; replacing an air filter; and/or installing a water sensor.
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches wherein the one or more insights include: replacing a smoke detector battery; installing a support beam; replacing at least one pipe; replacing an air filter; and/or installing a water sensor (see column 21 lines 25-40, Examiner’s note: If a user agrees to upgrade or install a sensor reducing an insurance policy payment).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Deveraux with the aforementioned teachings from Deveraux et al with the motivation of including other known risk aspects of a house like plumbing when generating a score related to insurance (see Deveraux et al column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance is risk is known (see Gross paragraph 0591 and 0004).
As per claim 15, Gross teaches
wherein the respective subscores include the respective safety subscores, and the method further includes: training a safety subscore machine learning algorithm by inputting historical information into the safety subscore machine learning algorithm, the historical information including: (i) independent variables comprising (a) historical fire protection attributes, (b) historical weather hazard attributes, and/or (c) historical crime attributes, and/or (ii) dependent variables comprising historical safety subscores; and determining the respective safety subscores by routing information of properties into the safety subscore machine learning algorithm(see paragraphs 0084, 0101, 0128, 0293, 0377, and 0322-0323, Examiner’s teaches algorithms for performing the functions (See paragraph 0084) to determine property scores based on multiple features (see paragraphs 0322-0323). Further teaches training a classifier to assess and rate different properties (see paragraphs 0101 and 0128). Further teaches additional property scoring criteria include elements of weather or crime (see paragraph 0293 and 0307). It is noted that many of the limitations are referenced in the alternative therefore only requiring one of the list).
As per claim 16, Gross teaches
wherein the respective subscores include the respective structural subscores, and the method further includes: a training structural subscore machine learning algorithm by inputting historical information into the structural subscore machine learning algorithm, the historical information comprising: (i) independent variables including: (a) historical structural grades, and/or (b) historical home ages, and/or (ii) dependent variables comprising historical structural subscores; and determining the respective structural subscores by routing information of properties into the structural subscore machine learning algorithm (see paragraphs 0084, 0101, 0128, 0322-0323, Examiner’s teaches algorithms for performing the functions (See paragraph 0084) to determine property scores based on things like roof and window conditions which can be things like historical structural grades (see paragraphs 0322-0323). Further teaches a training a classifier to assess and rate different properties (see paragraphs 0101 and 0128)).
As per claim 17, Gross teaches
wherein the respective subscores include the respective home subscores, and the method further includes: training a home subscore machine learning algorithm by inputting historical information into the home subscore machine learning algorithm, the historical information comprising: (i) independent variables including: (a) historical home grades, and/or (b) historical dates of a most recent home inspections, and/or (ii) dependent variables comprising historical home subscores; and determining the respective home subscores by routing information of properties into the home machine learning algorithm. (see paragraphs 0084, 0101, 0128, 0322-0323, Examiner’s teaches algorithms for performing the functions (See paragraph 0084) to determine property scores based on things like roof and window conditions which can be things like historical subscores or grades (see paragraphs 0322-0323). Further teaches a training a classifier to assess and rate different properties (see paragraphs 0101 and 0128). It is further noted that these are listed in the alternative therefore only requiring one. ).
Gross does not expressly teach that plumbing is used to determine a score.
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches that plumbing is used to determine a score. (see column 10 lines 13- column 11 lines 25, column 21 lines 25-40, column 24 lines 35-50, column 24 lines 35-50, column 27 lines 64-column 28 lines 7, and Column 27 lines 10-20 Examiner’s note: teaches various water sensors that track plumbing stress, water leaks, and water flow and providing rating home insurance and providing guidance based on such detections (see column 10 lines 13- column 11 lines 25). If a user agrees to upgrade or install a sensor reducing an insurance policy payment (see column 21 lines 25-40). Score related to sensors of objects to make a determination of providing recommendations (see column 24 lines 35-50 and column 27 lines 64-column 28 lines 7). Column 27 lines 10-20 teaches generating multiple scores were the scores may be averaged together to generate an overall score).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Deveraux with the aforementioned teachings from Deveraux et al with the motivation of to include other known risk aspects of a house like plumbing (see Deveraux et al column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance is risk is known (see Gross paragraph 0591 and 0004).
As per claim 18, Gross teaches
wherein the respective subscores include the respective home subscores, and the method further includes: training an home subscore machine learning algorithm by inputting historical information into the home subscore machine learning algorithm, the historical information comprising: (i) independent variables including (a) historical energy grades, (b) historical home maintenance grades, and/or (c) historical heating, ventilation, and air conditioning (HVAC) attributes, and/or (ii) dependent variables comprising historical home subscores; and determining the respective home subscores by routing information of properties into the home subscore machine learning algorithm. (see paragraphs 0084, 0101, 0128, 0322-0323, Examiner’s note: teaches algorithms for performing the functions (See paragraph 0084) to determine property scores based on things like roof and window conditions which can be things like home subscroes or grades (see paragraphs 0322-0323). Further teaches a training a classifier to assess and rate different properties (see paragraphs 0101 and 0128). It is further noted that these are listed in the alternative therefore only requiring one.).
Gross does not expressly teach that appliances is used to determine a score
However, Devereaux et al. et al. which is in the art of determining score related to aspects of a home (see Figure 7) teaches that appliances is used to determine a score (see column 11 lines 10-15, column 17 lines 5-30, column 24 lines 55- column 25 lines 3, and column 28 line 50-62, Examiner’s note: teaches determining information like condition related to appliances (see column 11 lines 10-15, column 17 lines 5-30), then teaches calculating a score related to a condition of the appliance to make a recommendation (see column 24 lines 55- column 25 lines 3 and column 28 lines 50-62).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Devereaux et al. et al. with the aforementioned teachings from Devereaux et al. et al. with the motivation of providing a way to take into account another aspect of the house when generating information relating to a house (see Devereaux et al. et al column 11 lines 10-15, column 17 lines 5-30, column 24 lines 55- column 25 lines 3, and column 28 liens 50-62), when taking inconsideration numerous aspects of a house when generating an insurance score is known (see Gross paragraphs 0332-0325) .
As per claim 19, Gross teaches A computer device for improved generation of home scores and/or improved comparison of the home scores, the computer device comprising one or more processors configured to: (see paragraphs 0004 and 0600-0601, Examiner’s note: automated system to cluster properties based on location or common maintenance needs, has utility in insurance and home improvement (see paragraph 0004). Further teaches this is implemented by software stored on a medium running on a computer (see paragraphs 0600-0601).
receive a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores; receive a geographic filtering parameter; filter the plurality of overall home scores based upon the geographic filtering parameter; and display, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores. (see paragraphs 0320, 0322, and 0324-0325, Examiner’s note: teaches generating a home score for a house, which is based on various attributes including for example roof and windwos, then providing a comparison to other houses in a same zip code, where this maybe an average. It is noted that many of the claim limitations are listed in the alternative only requiring one of the limitations be to used, specifically the second receive step and the display step).
Gross does not expressly teach the amended generating a score from water sensor information or more specifically as recited in the claims of receive, from a water sensor of a subject property, water sensor data; determine, based upon the water sensor data, that the water sensor is operating properly; modify a plumbing score of the subject property based upon the determination that the water sensor is operating properly; determine an overall home for the subject property based upon the modified pluming subscore of the subject property.
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches the amended generating a score from water sensor information or more specifically as recited in the claims of receive, from a water sensor of a subject property, water sensor data; determine, based upon the water sensor data, that the water sensor is operating properly; modify a plumbing score of the subject property based upon the determination that the water sensor is operating properly; determine an overall home for the subject property based upon the modified pluming subscore of the subject property. (see column 10 lines 13- column 11 lines 25, column 21 lines 25-40, column 24 lines 35-50, column 24 lines 35-50, column 27 lines 64-column 28 lines 7, and Column 27 lines 10-20, Examiner’s note: teaches various water sensors that track plumbing stress, water leaks, and water flow and providing rating home insurance and providing guidance based on such detections (see column 10 lines 13- column 11 lines 25). If a user agrees to upgrade or install a sensor reducing an insurance policy payment (see column 21 lines 25-40). Score related to sensors of objects to make a determination of providing recommendations (see column 24 lines 35-50 and column 27 lines 64-column 28 lines 7). Column 27 lines 10-20 teaches generating multiple scores were the scores may be averaged together to generate an overall score).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross with the aforementioned teachings from Deveraux et al of the motivation of to include other known risk aspects of a house like plumbing in generating an insurance score (see Deveraux et al column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance is risk is known (see Gross paragraph 0591 and 0004).
As per claim 20, Gross teaches A computer system for improved generation of home scores and/or improved comparison of the home scores, the computer system comprising: (see paragraphs 0004 and 0600-0601, Examiner’s note: automated system to cluster properties based on location or common maintenance needs, has utility in insurance and home improvement (see paragraph 0004). Further teaches this is implemented by software stored on a medium running on a computer (see paragraphs 0600-0601).
one or more processors; and one or more non-transitory memories, the one or more non-transitory memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to: (see paragraphs 0100 and 0600-0601, Examiner’s note: teaches a tangible machine readable form for causing a computer system to execute instructions).
receive a plurality of overall home scores for respective properties of a plurality of properties, wherein the plurality of properties does not include the subject property, and wherein respective overall home scores of the plurality of overall home scores are determined based upon respective subscores including: (i) respective safety subscores, (ii) respective structural subscores, (iii) respective plumbing subscores, and/or (iv) respective appliances subscores; receive a geographic filtering parameter; filter the plurality of overall home scores based upon the geographic filtering parameter; and display, on a display, (i) an average or median of the filtered plurality of overall home scores, (ii) a comparison of the overall home score for the subject property to the average or median of the filtered plurality of overall home scores, and/or (iii) respective overall home scores of the plurality of overall home scores. (see paragraphs 0320, 0322, and 0324-0325, Examiner’s note: teaches generating a home score for a house, which is based on various attributes including for example roof and windows, then providing a comparison to other houses in a same zip code, where this maybe an average. It is noted that many of the claim limitations are listed in the alternative only requiring one of the limitations be to used, specifically the second receive step and the display step).
Gross does not expressly teach the amended generating a score from water sensor information or more specifically as recited in the claims of receive, from a water sensor of a subject property, water sensor data; determine, based upon the water sensor data, that the water sensor is operating properly; modify a plumbing score of the subject property based upon the determination that the water sensor is operating properly; determine an overall home for the subject property based upon the modified pluming subscore of the subject property.
However, Devereaux et al. which is in the art of determining risks related to properties (see abstract) teaches generating a score from water sensor information or more specifically as recited in the claims of receive, from a water sensor of a subject property, water sensor data; determine, based upon the water sensor data, that the water sensor is operating properly; modify a plumbing score of the subject property based upon the determination that the water sensor is operating properly; determine an overall home for the subject property based upon the modified pluming subscore of the subject property. (see column 10 lines 13- column 11 lines 25, column 21 lines 25-40, column 24 lines 35-50, column 24 lines 35-50, column 27 lines 64-column 28 lines 7, and Column 27 lines 10-20 Examiner’s note: teaches various water sensors that track plumbing stress, water leaks, and water flow and providing rating home insurance and guidance based on such detections (see column 10 lines 13- column 11 lines 25). If a user agrees to upgrade or install a sensor reducing an insurance policy payment (see column 21 lines 25-40). Score related to sensors of objects to make a determination of providing recommendations (see column 24 lines 35-50 and column 27 lines 64-column 28 lines 7). Column 27 lines 10-20 teaches generating multiple scores were the scores may be averaged together to generate an overall score).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross with the aforementioned teachings from Deveraux et al with the motivation of including other known risk aspects of a house like plumbing in generating an insurance score (see Deveraux et al column 27 lines 64- column 28 lines 7), when calculating risk related to various elements of a house to generate an insurance is risk is known (see Gross paragraph 0591 and 0004).
14. Claims 3 is rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Gross (United States Patent Application Publication Number: US 2016/0027051) further in view of Devereaux et al. (United States Patent Number: US 10, 679, 296) further in view of Tadman et al. (United States Patent Application Publication Number: US 2010/0094548).
As per claim 3, Gross teaches
receiving, via the one or more processors, user input on the map; and setting, via the one or more processors, the geographic filtering parameter according to the user input (see paragraphs 0155, 0322, 0376, Examiner’s note: teaches filtering based on distance and presenting information in response to an interactive interface including cursor location).
Gross in view of Devereaux et al. does not expressly teach selecting the distance or range for the search from a map or more specifically as recited in the claims further including: displaying, via the one or more processors, on the display, a map.
However, Tadman et al. which is in the art of real estate (see abstract) teaches selecting the distance or range for the search from a map or more specifically as recited in the claims further including: displaying, via the one or more processors, on the display, a map (see paragraphs 0076-0078, Figure 6, Examiner’s note: teaches selecting a radius on a map to be displayed real estate results).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Devereaux et al.with the aforementioned teachings from Tadman et al. with the motivation of providing a known way for a user to select an input to be provided corresponding results (see Tadman et al. paragraphs 0076-0078 and Figure 6) when users filtering based on distance is known (see Gross paragraphs 0155, 0322, and 0376).
15. Claim 11 is rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Gross (United States Patent Application Publication Number: US 2016/0027051) further in view of Devereaux et al. (United States Patent Number: US 10, 679, 296) further in view of King et al. (United States Patent Application Publication Number: US 2007/0162347).
As per claim 11, Gross teaches
wherein the subject property corresponds to a first user, and the method further includes: receiving, via the one or more processors, a request from a second user to view the overall home score for the subject property; and in response to receiving the permission, allowing, via the one or more processors, the second user to view the overall home score for the subject property. (see paragraphs 0244 and 0325, Examiner’s note: targeting users for services based on home scores and permissions allowing users to view information ).
While Gross clearly teaches permissions for sharing data (see paragraph 0244) Gross in view of Devereaux et al. does not expressly teach requesting permissions after receiving a request or more specifically as recited in the claim of in response to receiving the request, requesting, via the one or more processors, permission from the first user to allow the second user to view the requested information; receiving, via the one or more processors, from the first user, the permission;
However, King et al. which is in the art of acting as an intermediary for the buying and selling of projects (see abstract) teaches requesting permissions after receiving a request or more specifically as recited in the claim of in response to receiving the request, requesting, via the one or more processors, permission from the first user to allow the second user to view the requested information; receiving, via the one or more processors, from the first user, the permission (see paragraph 0041, Examiner’s note: teaches permissions for contacting other users and requesting permissions after a request).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Devereaux et al. with the aforementioned teachings from King et al. with the motivation of providing a common way of requesting permissions when someone else asks for information (see King et al. paragraph 0041), when using permissions to provide information other entities with (see Gross paragraphs 0244 and 0325) is known.
16. Claim 14 is rejected under pre-AIA 35 U.S.C. 103 as being unpatentable over Gross (United States Patent Application Publication Number: US 2016/0027051) further in view of Devereaux et al. (United States Patent Number: US 10, 679, 296) further in view of Faulkner (United States Patent Application Publication Number: US 2018/0174250).
As per claim 14, Gross teaches
further including: presenting, via the one or more processors, one or more insights to a user corresponding to the subject property; receiving, via the one or more processors, from the user, an indication that at least one insight of the one or more insights has been completed; and in response to the verification, updating, via the one or more processors, the overall home score for the subject property. (see paragraphs 0178 and 0249, Examiner’s note: teaches updating information over time as it is completed, and identifying changes in property condition over time).
Gross in view of Devereaux et al. does not expressly teach verifying changes through images or more specifically as recited in the claims of in response to receiving the indication, requesting, via the one or more processors, from the user, imagery data associated with the at least one insight; receiving, via the one or more processors, the imagery data from the user; verifying, via the one or more processors, that the at least one insight has been completed based upon the imagery data.
However, Faulkner which is in the art of project completion (see abstract) teaches verifying changes through images or more specifically as recited in the claims of in response to receiving the indication, requesting, via the one or more processors, from the user, imagery data associated with the at least one insight; receiving, via the one or more processors, the imagery data from the user; verifying, via the one or more processors, that the at least one insight has been completed based upon the imagery data (see paragraph 0107, Examiner’s note: verifying images to determine project completion).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified Gross in view of Devereaux et al. with the aforementioned teachings from Faulkner with the motivation of providing a way to validate completion of a project (see Faulkner paragraph 0107), when receiving completion of projects and updating scores based on completion is known (see Gross paragraphs 0178 and 0249).
Conclusion
17. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
18. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Marotta et al. (United States Patent Application Publication Number: US 2022/0013222) teaches water sensors and generating score based on water sensor information (see paragraphs 0056, 0070, and 0102).
Harvey et al. (United States Patent Number: US 9,875,509) teaches a system and method to determine condition of insured properties in a neighborhood (see abstract)
Williams et al. (United States Patent Application Publication Number: US 2021/0133886) teaches insurance and a high level of recent burglaries in an area (see paragraph 0078)
Chapin et al. (United States Patent Application Publication Number: US 2011/0295624) teaches risks based on distance to a fire station (see abstract)
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626
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