DETAILED ACTION
The following is a first office action upon examination of application number 19/043894. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/3/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-10 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 11-17 are directed to a system comprising one or more processors of a computing system; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claims 18-20 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to receive home rule data to generate and display a predicted home modification, which is described by claim limitations reciting:
receiving, …a query comprising home rule data from a user device;
extracting, …one or more home rules from the query comprising the home rule data;
receiving, …property data associated with a first property from one or more databases, wherein the property data includes one or more property attributes, the property data including one or more of:
location data received from one or more databases;
… historical data received from the one or more databases, wherein the historical data includes past hazard data associated with one or more properties that include at least one of the one or more property attributes of the first property;
generating, …one or more home score factors based upon the one or more home rules and the property data;
generating, …a home modification prediction for the first property based upon the one or more home score factors; and
… presenting the home modification prediction to a user …, such as a verbal or audible presentation….
The identified limitations in the claims describing receiving home rule data to generate and display a predicted home modification (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices. Dependent claims 3, 4, 5, 6, 7, 9, 10, 13, 14, 15, 16, 17, and 20 recite limitations that further narrow the abstract idea; therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the one or more processors; and user device in claim 1; the one or more processors of a computing system; at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations; and user device in claim 11; and the non-transitory computer readable medium, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations; and user device in claim 18, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer.
Additional elements such as receiving, by one or more processors…; receiving, by the one or more processors…; and outputting the home modification prediction to a display of a user device, or otherwise presenting the home modification prediction to a user via a user device… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering/display). Additional elements related to sensor data received from one or more devices located within the first property and presentation via a voice bot or chatbot do not provide an improvement to the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 8, 12, and 19, related to a natural language processing (NLP) algorithm and trained machine-learning model do not yield an improvement and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component (See Spec. [0039][0040]). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as receiving, by one or more processors…; receiving, by the one or more processors…; and outputting the home modification prediction to a display of a user device, or otherwise presenting the home modification prediction to a user via a user device… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering/display). With respect to data gathering limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). With respect to data display limitations, the courts have found the presentation of data to be a well-understood, routine, conventional activity, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93 (see MPEP 2106.05(d)). Additional elements related to sensor data received from one or more devices located within the first property and presentation via a voice bot or chatbot do not provide an improvement to the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 8, 12, and 19, related to a natural language processing (NLP) algorithm and trained machine-learning model do not yield an improvement and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s)1-3, 9, 11-13, and 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2025/0061493 (Roe).
As per claim 1, Roe teaches: a computer-implemented method for receiving home rule data to generate and display a predicted home modification, the computer-implemented method comprising: receiving, by one or more processors, a query comprising home rule data from a user device; ([0009] … acquiring a user request comprising target property data from the at least one user-entity node [0061] … acquire a user request comprising target property data from the at least one user-entity node 101 [0039] … The PES node 102 may receive user request including a list of characteristics of the property from the user-entity node 101 associated with a user 111).
extracting, by the one or more processors, one or more home rules from the query comprising the home rule data; ([0009] … acquiring a user request comprising target property data from the at least one user-entity node; parsing the user request to extract a plurality of key classifying features [0040] … The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata. In other words, the key features of the conversation data may be, advantageously, derived from the conversation data [0061] … parse the user request to extract a plurality of key classifying features).
receiving, by the one or more processors, property data associated with a first property from one or more databases, wherein the property data includes one or more property attributes, the property data including one or more of: location data received from one or more databases; sensor data received from one or more devices located within the first property; or historical data received from the one or more databases, wherein the historical data includes past hazard data associated with one or more properties that include at least one of the one or more property attributes of the first property; ([0043] The PES node 102 may query a local historical properties'-related database 103 for the historical local properties'-related data … The PES node 102 may acquire relevant remote properties'-related data from a remote database 106 residing on the cloud server 105. The properties'-related data in the database 106 may be collected from other real-estate facilities of the same type. The remote properties'-related data may be collected from the user entities of the same (or similar) type, age, gender, language, location, etc. as the local user entity 101 based in part on data extracted from the user profile data and the conversation data. [0030] … target properties' data may include data related to other properties having the same parameters such as type of location [0066] … At block 308, the processor 204 may query a local database to retrieve local historical properties'-related data based on the plurality of key classifying features and the conversation data).
generating, by the one or more processors, one or more home score factors based upon the one or more home rules and the property data; ([0009] … parsing the user request to extract a plurality of key classifying features; activate a chatbot running on the PES node to acquire conversation data from the user; querying a local database to retrieve local historical properties'-related data based on the plurality of key classifying features and the conversation data; generating at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties'-related data [0031]… a property evaluation report including various evaluation metrics. [0032] … home scores and estimates based on property condition… [0049] The PES node 102 may generate a feature vector or classifier based on the user entity-related data, a request data and the collected heuristics data (i.e., pre-stored local historical data 103 and remote historical data 106) [0082] Generation of Home Scan Condition Score for the property.
generating, by the one or more processors, a home modification prediction for the first property based upon the one or more home score factors; and ([0009] … providing the at least one classifier vector to the ML module configured to generate a predictive model for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report. [0031] … system may integrate advanced technologies discussed above, such as Artificial Intelligence (AI) and machine-learning (ML) and Blockchain. The AI may be leveraged for several key functions discussed below. In one embodiment, the AI-based property evaluation system may be used to predict the condition of a home, the costs associated with upgrading the home, and the resulting value. The system may employ a summarizer module for generation of a property evaluation report including various evaluation metrics. [0032] …The predictive report may produce accurate true cost of home ownership with preventative maintenance suggestions. [0033] … The predictive outputs may include repair plan and cost estimation. [0034] … a summarizer module may be implemented to generate estimates and key condition functionality of the home based on the predictive property evaluation parameters based on reports and estimated repair costs. In this interactive search, data from PDFs, images, and public data may be used to create an idea of the property condition. A summary may be provided with the estimated cost of repairs and grading on the severity of the problem. [0083] Repair Plan and Cost Estimator provides a detailed repair plan and cost estimation based on the predicted condition of the property and geographic labor and material costs. [0084] Search Based on Repair Cost and After Repair Value: allows users to search for properties based on repair costs and the expected value after repairs are made).
outputting the home modification prediction to a display of a user device, or otherwise presenting the home modification prediction to a user via a user device, such as a verbal or audible presentation via a voice bot or chatbot ([0033] … The predictive outputs may include repair plan and cost estimation [0044] … The AI/ML module 107 may generate a predictive model(s) 108 based on the feature vector/classifier data to predict property evaluation parameters for the summarizer module for automatically generating property evaluation report that may be provided to the broker-entity nodes 113. [0057] … the AI/ML module 107 may provide predictive outputs data in the form of property evaluation parameters for automatic generation of property evaluation report (see FIG. 1B). [0069] … provide property evaluation report for the chatbot to render to the user-entity node [0037] … the property evaluation report reviewers (e.g., broker entities) can accept or edit the machine generated property evaluation report)
As per claim 2, Roe teaches: applying, by the one or more processors, a natural language processing (NLP) algorithm to the query to extract text data; and ([0029] … models derived from pre-trained language models to extract and process the user request and conversation information, irrespective of data format, style, or data type. By leveraging the capabilities of the pre-trained language models and predictive models [0040] … The conversation data may refer to any communications via a chatbot 114 application. In one embodiment, the conversation data may be processed by the PES node 102 using the pre-trained large language models. The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata. [0041] … node 102 could engage specialized language models or apply unique natural language processing techniques optimized for that language. [0066] … parse the user request to extract a plurality of key classifying features
determining, by the one or more processors, a semantic meaning or a contextual alignment between the text data and the one or more home rules stored in one or more reference datasets ([0040] … the conversation data may be processed by the PES node 102 using the pre-trained large language models. The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata… the key features of the conversation data may be, advantageously, derived from the conversation data based on the language of the communication [0066] …processor 204 may parse the user request to extract a plurality of key classifying features)
As per claim 3, Roe teaches: analyzing, by the one or more processors, the text data to determine one or more patterns corresponding to the one or more home rules ([0066] …the processor 204 may parse the user request to extract a plurality of key classifying features [0040] …the conversation data may be processed by the PES node 102 using the pre-trained large language models. The PES node 102 may derive the language indicator and parse out the user request data and/or conversation data based on the language indicator metadata. [0070] …the processor 204 may generate the at least one classifier vector based on the plurality of the key classifying features).
As per claim 9, Roe teaches: wherein the one or more property attributes include one or more of: a location attribute, a climate attribute, a power consumption attribute, or a water consumption attribute ([0048] …The remote properties'-related data may be collected from the user entities of the same (or similar) type, age, gender, language, location, etc. as the local user entity 101 [0030] …properties having the same parameters such as type of location, size, physical conditions, appraisals and sales data, language of the jurisdiction, nationality of the owners or locations, etc.).
As per claims 11 and 18, these claims recite limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claims 12 and 19, these claims recite limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
As per claims 13 and 20, these claims recite limitations substantially similar to those addressed by the rejection of claim 3, above; therefore, the same rejection applies.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-6 and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0061493 (Roe); in view of US 2011/0029401 (Granger).
As per claim 4, although not explicitly taught by Roe, Granger teaches: retrieving, by the one or more processors, a plurality of predetermined home modification plans from a data store. ([0026] … designs from a selection of different pre-packaged or pre-rendered designs… [0075] … database with room designs which can include renderings, paint selections, furnishing selections, finishes, descriptive text, and other design characteristics. [0077] …database disposed to allow entry and storage of a plurality of characteristics of a design; uploading pictures, texts, renderings, or graphics to a pre-packaged room designs database; displaying the prepackaged room designs to a customer, wherein the customer can view or sort the display by any of the characteristics of the design; sorting, viewing, and selecting the pre-packaged room designs by the customer; and receiving detailed information of the full pre-packaged room design via a mobile device or the internet. [0082] Once the design is in the database, the application can use design characteristics such as room function, style, size or any other relevant design characteristic to allow the client to inquire the database for design packages. The customer may view, sort, or otherwise manipulate the design packages).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Roe with the aforementioned teachings of Granger with the motivation of selecting prepackaged designs/plans (Granger [0026]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Granger to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for use of stored designs/plans.
As per claim 5, Roe teaches: determining, by the one or more processors, a first … home modification plan …. that best corresponds to the one or more home score factors, wherein the home modification prediction includes the first predetermined home modification plan ([0032] … home scores and estimates based on property condition [0083] … provides a detailed repair plan and cost estimation based on the predicted condition of the property and geographic labor and material costs).
Although not explicitly taught by Roe, Granger teaches: determining, by the one or more processors, a first predetermined home modification plan of the plurality of predetermined home modification plans that best corresponds to the one or more home score factors, wherein the home modification prediction includes the first predetermined home modification plan ([0077] …database disposed to allow entry and storage of a plurality of characteristics of a design; uploading pictures, texts, renderings, or graphics to a pre-packaged room designs database; displaying the prepackaged room designs to a customer, wherein the customer can view or sort the display by any of the characteristics of the design; sorting, viewing, and selecting the pre-packaged room designs by the customer; and receiving detailed information of the full pre-packaged room design via a mobile device or the internet. [0082] Once the design is in the database, the application can use design characteristics such as room function, style, size or any other relevant design characteristic to allow the client to inquire the database for design packages. The customer may view, sort, or otherwise manipulate the design packages).
One of ordinary skill in the art would have recognized that applying the teachings of Granger to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of designs/plans that match a home.
As per claim 6, although not explicitly taught by Roe, Granger teaches: wherein each of the plurality of predetermined home modification plans comprises one or more predetermined home score factors. ([0077] …database disposed to allow entry and storage of a plurality of characteristics of a design; uploading pictures, texts, renderings, or graphics to a pre-packaged room designs database; displaying the prepackaged room designs to a customer, wherein the customer can view or sort the display by any of the characteristics of the design; sorting, viewing, and selecting the pre-packaged room designs by the customer; and receiving detailed information of the full pre-packaged room design via a mobile device or the internet. [0082] Once the design is in the database, the application can use design characteristics such as room function, style, size or any other relevant design characteristic to allow the client to inquire the database for design packages. The customer may view, sort, or otherwise manipulate the design packages).
One of ordinary skill in the art would have recognized that applying the teachings of Granger to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for identification of designs/plans that match a home.
As per claims 14, 15, and 16, these claims recite limitations substantially similar to those addressed by the rejection of claims 4, 5, and 6, respectively; therefore, the same rejection applies.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0061493 (Roe); in view of US 2011/0029401 (Granger); in view of Case-based reasoning approach for decision-making in building retrofit: A review (Li).
As per claim 7, although not explicitly taught by Roe, Li teaches: calculating, by the one or more processors, a similarity score for each of one or more predetermined home score factors and each of the plurality of predetermined home modification plans; and selecting, by the one or more processors, the predetermined home modification plan with the highest similarity score ([Page 4] … effective to draw on previous experiential cases, especially those similar to successful cases [Page 5] … provide solutions by analogy or referring to previous similar cases … [Page 6] … measure how similar a case is to the decision maker’s demands. The best cases for the customer can then be identified and matched. To this end, Case-based reasoning (CBR) could attain this goal [88]. In this method, similar cases are searched from the corresponding database to match potential project solutions. [Page 7] … select the most suitable case for their needs in terms of candidate building information. The core of the CBR method is to extract successful previous cases or solutions from the datasets by measuring the similarity level. [Page 8] … similarity calculation, which computes the weight coefficients for diverse cases to find the most similar case.)
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Roe with the aforementioned teachings of Li with the motivation of providing solutions by referring to previous similar cases (Li [Page 5]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Li to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for use of similarity to identify a case/plan.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 7, above; therefore, the same rejection applies.
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2025/0061493 (Roe); in view of US 2019/0304026 (Lyman).
As per claim 8, although not explicitly taught by Roe, Lyman teaches: receiving, by the one or more processors, user data from a user device; generating, by the one or more processors, one or more user-adjusted home score factors based upon the user data; and modifying, by the one or more processors, via a trained machine-learning model, one or more of the one or more home score factors based upon the one or more user-adjusted home score factors ([0134] … a user interface is provided for a user to customize features. Examples of customization include setting a risk tolerance for a portfolio, such as customization of detection and their priority [0042] … ongoing data may be used to improve the results of the machine learning model. That is, the historical database is collected over time and used as training data to train an AI engine to perform image analysis, such as damage assessment, risk assessment, and scoring. It will be understood throughout the following discussion that a variety of machine learning techniques may be used to implement the AI engine, including, for example, neural networks, supervised learning, unsupervised learning, and semi-supervised learning. [0049] … The AI engine is trained in block 410 to analyze and score selected property attributes [0138] FIG. 17 illustrates a user interface in which a user can select a detection type, such as boundary drawings, roof material, swimming pool, damage, wear & tear, patching, roof shape, tree overhang, ponding, staining, debris, and rust. [0139] FIG. 18 illustrates an example of a user interface to label attributes of an image. In this example, the user interface permits a selection of the type of roof material. The labelling of attributes may occur, for example, to generate training data during a quality assurance step. While roof material is one example, more generally the user interface may provide options for labelling attributes of other risk factors. [0297] … utilizing the selections and customizations as training data).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Roe with the aforementioned teachings of Lyman with the motivation of providing an interface for users to customize features (Lynman [0134]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Lyman to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow users to adjust scoring factors.
As per claim 10, although not explicitly taught by Roe, Lyman teaches: wherein the one or more home score factors include one or more of: a fire hazard score, a safety score, a weather hazard score, a property feature hazard score, or a resiliency score ([0038] … Data 112 on historical weather data, natural disaster data, or historical insurance data may be accessed to provide data regarding general information of the region a property is located in. For example, historical weather data may include data on storms that are likely to cause property damage to roofs or other portions of a building. Natural disaster data may, for example, include historical data on wildfires or flooding. [0039] … an analysis for particular types of hazards and risks. [0048] … assess fire hazards historical data on wildfires in a geographic region [0052] … analysis also analyzes tree overhang of the roof and generates a score. In one embodiment, the AI analysis determines the presence and severity of water ponding and gives that a score. This can be continued for multiple factors, including AI analysis of rust, hail damage, storm damage, etc. [0073] … accessing weather databases to identify weather related risks, such as data on windstorm risks, hail risks, etc. This may include, for example, a frequency and severity of extreme weather events. Optionally, other natural disaster risks may be obtained for the property based on the geographical coordinates, such as historical earthquake, wildfire, flood, or other natural disaster risks.).
One of ordinary skill in the art would have recognized that applying the teachings of Lyman to the system of Roe would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the scoring of different types of hazards.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2024/0420214 (Wilson) – discloses a large language trained generative AI model to create recommendations of additional home improvement products to be installed in a home.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625
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