DETAILED ACTION
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is a non-final, first office action in response to the application filed 13 August 2024.
Claims 1-20 are currently pending and have been examined.
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 claims recite a processor of a property evaluation server (PES) node configured to host a machine learning (ML) module coupled to a summarizer module and connected to at least one user-entity node over a network; and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire a user request comprising target property data from the at least one user-entity node; parse 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; query a local database to retrieve local historical properties’- related data based on the plurality of key classifying features and the conversation data; generate at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties’-related data; and provide 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.
The limitations of 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, acquiring 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, and providing the at least one classifier vector to a 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; as drafted, under the broadest reasonable interpretation, encompass the management of commercial activity (sales activities, pricing/valuing, business relations), and the performance of steps that can be performed in the human mind. That is, other than reciting the use of generic computer elements (processor, memory, chatbot, database, server node, machine learning), the claims recite an abstract idea. In particular, 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, acquiring conversation data from the user, and querying a local database to retrieve local historical properties’-related data based on the plurality of key classifying features and the conversation data; encompass a user requesting the valuation of a property to a service, along with the user providing information and the service retrieving information regarding the property; which encompass the management of commercial activity (sales activities, pricing/valuing, business relations). In addition, 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, and providing the at least one classifier vector to a 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; encompass generating a property evaluation report based on a user request, user provided information, historical property information, and property derived information, wherein the information is formed into a format for understanding and the output is derived from a model; which encompass the management of commercial activity (sales activities, pricing/valuing, business relations). Thus, the claims recite elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. In addition, the claims recite 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, acquiring conversation data from the user, 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, and generating a predictive model for producing a set of property evaluation parameters for the summarizer module configured to generate a property evaluation report; which encompass elements that can be performed in the human mind (observation, evaluation, judgement, and opinion). As such, the claims recite elements that fall into the “Mental Processes” grouping of abstract ideas. The claims recite an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite additional elements, when taken individually and in an ordered combination with the abstract idea, that improve the functioning of a computer, another technology, or technical field. The claims do not recite the use of, or apply the abstract idea with, a particular machine, the claims do not recite the transformation of an article from one state or thing into another. Finally, the claims do not recite additional elements, taken individually and in an ordered combination, that apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment. Instead, the claims recite the use of generic computer elements (processor, memory, chatbot, database, server node, machine learning) as tools to carry out the abstract idea. Notably, the use of “machine learning models” is at a high-level of generality, wherein no details are provided with how a solution is obtained or the model is carried out; and instead, relies on merely the recitation of its use and output, and thus renders the element merely as “apply it.” In addition, the use of a chatbot to receive user input, is deemed merely “apply it,” as this merely further recites a type of input interface used by a computer to receive data, and output interface used to output data. Therefore, the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements, when taken individually and in an ordered combination with the abstract idea, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computer elements and machines to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are directed to non-patent eligible subject matter.
The dependent claims 2-12 and 14-19, when taken individually and in an ordered combination with the abstract idea, do not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea. In particular, the claims further recite the type of format of the property data received; which merely narrows the field of use, and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 2). In addition, the claims further recite providing the evaluation report to be rendered, which merely further recites the managing commercial interactions (managing sales activities and business relations); and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (claim 3). In addition, the claims further recite extracting a language identifier of a user request and deriving features based on this language; which is deemed further reciting the managing commercial interactions (managing sales activities and business relations), and elements that can be performed in the human mind; and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes groupings of abstract ideas (claims 4, 5, 14, and 15). In addition, the claims further recite retrieving properties related data from databases based on key classifying features and conversation data; which is deemed further reciting the managing commercial interactions (managing sales activities and business relations), and elements that can be performed in the human mind; and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes groupings of abstract ideas (claims 6 and 16). In addition, the claims further recite that the database is remote, where the information in it is collected, and using the data to make the vector; which merely narrows the field of use, and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claims 6, 7, 16, and 17). In addition, the claims further recite monitoring conversation data to determine if it deviates from a previous value, and if so, then updating the classifier vector and updating the property evaluation parameters; which is deemed further reciting the managing commercial interactions (managing sales activities and business relations), and elements that can be performed in the human mind; and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” and “Mental Processes groupings of abstract ideas (claims 8, 9, 18, and 19). In addition, the claims further recite recording property evaluation parameters and the classifier vector in a ledger; which merely further recites the managing commercial interactions (managing sales activities and business relations); and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (claim 10). In addition, the claims further recite the use of a blockchain as a ledger for record and storing data; which is deemed merely a recitation of “apply it” as a blockchain ledger is a generic computer element being used as a tool to carry out the abstract idea, in addition to narrowing the field of use, by defining the type of database (a blockchain) used; and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claims 10-12). In addition, the claims further recite retrieving property parameters from the ledger responsive to nodes reaching a consensus; which is deemed extrasolution activity, which is well-understood, routine, and conventional activity (See paragraphs 37, 50, 72, and 77 which describe the processes of writing, storing, and retrieval of information from a blockchain needing a consensus at such a high level of generality, that one of ordinary skill in the art would understand it to be well-understood, routine, and conventional in order to satisfy 112a); and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 11). In addition, the claims further recite executing a smart contract to generate a token corresponding to the evaluation report, that comprises the metrics; which merely further recites the managing commercial interactions (managing sales activities and business relations); and thus, recites elements that fall into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas (claim 12). In addition, the claims recite that the token generated is an NFT token, which merely a narrowing of the field of use by defining the type of token/receipt that represents the report; and thus does not recite additional elements that integrate the abstract idea into a practical application, or add significantly more to the abstract idea (claim 12).
Claim Rejections - 35 USC § 103
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 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 6-9, 13, 16-19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Smith (US 2016/0292800 A1) (hereinafter Smith), in view of Stewart (US 2022/0084079 A1) (hereinafter Stewart), in view of Stillwell, Jr et al. (US 11595333 B1) (hereinafter Stillwell).
With respect to claims 1, 13, and 20, Smith teaches:
A processor of a property evaluation server (PES) node configured to host a module coupled to a summarizer module and connected to at least one user-entity node over a network; A memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: Acquire a user request comprising target property data from the at least one user-entity node (See at least paragraphs 72, 73, and 184 which describe a user requesting a valuation of a target property).
Parse the user request to extract a plurality of key classifying features; Acquire conversation data from the user (See at least paragraphs 25, 26, 72, 73, 184, 185, and 190 which describe evaluating the user request and identifying details regarding the request, including customer interests).
Query a local database to retrieve local historical properties’- related data based on the plurality of key classifying features and the conversation data (See at least paragraphs 25, 26, 72, 73, 184, 185, and 190 which describe accessing a database stored on the server which stores property information for the target property and local properties, wherein the information includes historic information, and wherein the additional properties are selected based on similar attributes as the user’s request).
Generate at least one classifier vector based on the plurality of the key classifying features, the conversation data and the local historical properties’-related data; Provide the at least one classifier vector to the 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 (See at least paragraphs 72, 184, 185, and 190 which describe generating input for a valuation model based on classifying features, user input, and historical properties’ data, wherein the valuation model generates an value for a target property and provides it to the user).
Smith discloses all of the limitations of claims 1, 13, and 20 as stated above. Smith does not explicitly disclose the following, however Stewart teaches:
A processor of a property evaluation server (PES) node configured to host a machine learning (ML) module coupled to a summarizer module and connected to at least one user-entity node over a network… and Provide 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 (See at least paragraphs 17, 18, 21, 46, and 52 which describe providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart. By utilizing a machine learning model to generate a valuation for a property, an appraiser would predictably be able to quickly, efficiently, and automatically generate valuation for products, while removing human error and bias.
The combination of Smith and Stewart discloses all of the limitations of claims 1, 13, and 20 as stated above. Smith and Stewart do not explicitly disclose the following, however Stillwell teaches:
Activate a chatbot running on the PES node to acquire conversation data from the user (See at least column 3 lines 26-67, column 10 line 48 through column 11 line 2, column 11 line 60 through column 12 line 38, column 14 line 12 through column 16 line 6, and column 54 line 1 through column 55 line 22 which describe utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell. By utilizing a chatbot to interact with a user, a valuation system will predictably be able to converse with the user, without requiring humans, thus reducing costs via automation.
With respect to claim 2, the combination of Smith, Stewart, and Stillwell discloses all of the limitations of claim 1 as stated above. In addition, Smith teaches:
Wherein the target property data comprising any of: audio data; video data; imaging data; and textual data (See at least paragraphs 72, 73, and 184 which describe a user requesting a valuation of a target property, wherein the information is in text format).
With respect to claim 3, Smith/Stewart/Stillwell discloses all of the limitations of claim 1 as stated above. In addition, Smith teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to provide property evaluation report to the user-entity node (See at least paragraphs 72, 184, 185, and 190 which describe generating input for a valuation model based on classifying features, user input, and historical properties’ data, wherein the valuation model generates an value for a target property and provides it to the user).
Smith discloses all of the limitations of claim 3 as stated above. Smith does not explicitly disclose the following, however Stillwell teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to provide results for the chatbot to render to the user-entity node (See at least column 3 lines 26-67, column 10 line 48 through column 11 line 2, column 11 line 60 through column 12 line 38, column 14 line 12 through column 16 line 6, and column 54 line 1 through column 55 line 22 which describe utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell. By utilizing a chatbot to interact with a user, a valuation system will predictably be able to converse with the user, without requiring humans, thus reducing costs via automation.
With respect to claims 6 and 16, Smith/Stewart/Stillwell discloses all of the limitations of claims 1 and 13 as stated above. In addition, Smith teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to retrieve properties’-related data from at least one remote database based on the plurality of the key classifying features and the conversation data, wherein the remote properties’-related data is collected at locations associated with remote real-estate outfits of the same type (See at least paragraphs 24-28 which describe the valuation system retrieving comparable property information from MLS databases (remote databases), wherein data is retrieved using classifying information and user input).
With respect to claims 7 and 17, Smith/Stewart/Stillwell discloses all of the limitations of claims 1, 6, 13, and 16 as stated above. In addition, Smith teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to generate the at least one classifier vector based on the plurality of the key classifying features and the local historical properties’-related data combined with the remote properties’-related data (See at least paragraphs 24-28, 72, 184, 185, and 190 which describe generating input for a valuation model based on classifying features, user input, and historical properties’ data, wherein information is generated from local and remote databases).
With respect to claims 8 and 18, Smith/Stewart/Stillwell discloses all of the limitations of claims 1 and 13 as stated above. In addition, Smith teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to continuously monitor the conversation data to determine if at least one value of property-related parameters contained in the conversation data deviates from a previous value of a pre-set corresponding property-related parameter value by a margin exceeding a pre-set threshold value (See at least paragraphs 29-31, 43, 184, 185, and 185 which describe monitoring user input and determined information to determine if any parameters change more than a preset amount).
Smith discloses all of the limitations of claims 8 and 18 as stated above. Smith does not explicitly disclose the following, however Stillwell teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to continuously monitor the conversation data from the chatbot (See at least column 3 lines 26-67, column 10 line 48 through column 11 line 2, column 11 line 60 through column 12 line 38, column 14 line 12 through column 16 line 6, and column 54 line 1 through column 55 line 22 which describe utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell. By utilizing a chatbot to interact with a user, a valuation system will predictably be able to converse with the user, without requiring humans, thus reducing costs via automation.
With respect to claims 9 and 19, Smith/Stewart/Stillwell discloses all of the limitations of claims 1, 8, 13, and 18 as stated above. In addition, Smith teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to, responsive to the at least one value of the property-related parameters deviating from the pre-set corresponding property -related parameter value by the margin exceeding the pre-set threshold value, generate an updated classifier vector based on the conversation data coming from the chatbot and generate updated property evaluation parameters produced in real-time by the predictive model in response to the updated classifier vector (See at least paragraphs 29-31, 43, 184, 185, and 185 which describe monitoring user input and determined information to determine if any parameters change more than a preset amount, wherein if it does change, then updating the valuation).
Claims 4, 5, 14, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Smith, Stewart, and Stillwell as applied to claims 1 and 13 as stated above, and further in view of Willis (US 2023/0188482 A1) (hereinafter Willis)
With respect to claims 4 and 14, Smith/Stewart/Stillwell discloses all of the limitations of claims 1 and 13 as stated above. Smith, Stewart, and Stillwell do not explicitly disclose the following, however Willis teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to extract a language identifier from the user request (See at least paragraphs 39 and 40 which describe extracting a language identifier from a user’s conversation with a chatbot, wherein analysis is conducted and responses are provided based on the language identifier).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell, with the system, and method of extracting a language identifier from a user’s conversation with a chatbot, wherein analysis is conducted and responses are provided based on the language identifier of Willis. By extracting a language identifier in a conversation with a chatbot and basing results on the identifier, a valuation system that runs a chatbot would predictably be able to communicate with a user in a manner that they would understand, thus leading to a productive service offering.
With respect to claims 5 and 15, Smith/Stewart/Stillwell discloses all of the limitations of claims 1, 4, 13, and 14 as stated above. In addition, Willis teaches:
Wherein the machine-readable instructions that when executed by the processor, cause the processor to derive the plurality of the key classifying features based on the language identifier (See at least paragraphs 39 and 40 which describe extracting a language identifier from a user’s conversation with a chatbot, wherein analysis is conducted and responses are provided based on the language identifier).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell, with the system and method of extracting a language identifier from a user’s conversation with a chatbot, wherein analysis is conducted and responses are provided based on the language identifier of Willis. By extracting a language identifier in a conversation with a chatbot and basing results on the identifier, a valuation system that runs a chatbot would predictably be able to communicate with a user in a manner that they would understand, thus leading to a productive service offering.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Smith, Stewart, and Stillwell as applied to claims 1 and 13 as stated above, and further in view of Okamota et al. (US 2024/0303697 A1) (hereinafter Okamota).
With respect to claim 10, Smith/Stewart/Stillwell discloses all of the limitations of claim 1 as stated above. Smith, Stewart, and Stillwell do not explicitly disclose the following, however Okamoto teaches:
Wherein the machine-readable instructions that when executed by the processor, further cause the processor to record the set of set of property evaluation parameters on a permissioned blockchain ledger along with the at least one classifier vector (See at least paragraphs 10, 30, 41-43, 48, 49, 51, 52, 60, and 61 of requesting an appraisal of property, such as real estate, wherein the appraisal system records the appraisal and feature information on a permissioned blockchain ledger)
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell, with the system and method of requesting an appraisal of property, such as real estate, wherein the appraisal system records the appraisal and feature information on a permissioned blockchain ledger of Okamoto. By recording appraisals and properties of the appraised property on a blockchain ledger, a valuation system will predictably add security to the valuation process, by utilizing the known techniques of a blockchain, thus reducing the likelihood of fraud occurring between parties.
With respect to claim 11, Smith/Stewart/Stillwell/Okamoto discloses all of the limitations of claims 1 and 10 as stated above. In addition, Okamoto teaches:
Wherein the machine-readable instructions that when executed by the processor, further cause the processor to retrieve at least one of set of property evaluation parameters for the chatbot from the blockchain responsive to a consensus among user-entity nodes onboarded onto the permissioned blockchain (See at least paragraphs 10, 30, 41-43, 48, 49, 51, 52, 60-62, and 70 of requesting an appraisal of property, such as real estate, wherein the appraisal system records the appraisal and feature information on a permissioned blockchain ledger, and wherein the system can write and retrieve information responsive to a consensus among the blockchain nodes)
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell, with the system and method of requesting an appraisal of property, such as real estate, wherein the appraisal system records the appraisal and feature information on a permissioned blockchain ledger, and wherein the system can write and retrieve information responsive to a consensus among the blockchain nodes of Okamoto. By recording appraisals and properties of the appraised property on a blockchain ledger, a valuation system will predictably add security to the valuation process, by utilizing the known techniques of a blockchain, thus reducing the likelihood of fraud occurring between parties.
With respect to claim 12, Smith/Stewart/Stillwell/Okamoto discloses all of the limitations of claims 1 and 10 as stated above. In addition, Okamoto teaches:
Wherein the machine-readable instructions that when executed by the processor, further cause the processor to execute a smart contract to generate at least one NFT corresponding to the property evaluation report comprising a plurality of property evaluation metrics on the permissioned blockchain (See at least paragraphs 10, 30, 44, 62, and 68-70 which describe executing a smart contract regarding the appraisal, wherein the contract generates an NFT corresponding the property valuation, and contains the valuation and parameters used to make the valuation).
It would have been obvious to one of ordinary skill in the art at the time of filing the claimed invention to combine the system and method of generating an valuation report for a property targeted by a user, wherein the user requests the valuation and provides information to the system, and wherein the system uses the user information and stored information regarding comparable properties to generate the valuation of Smith, with the system and method of providing a machine learning model with property class information, including request parameters, property characteristics, and comparable properties’ information, wherein the machine learning model generates an valuation report for the user of Stewart, with the system and method of utilizing a chatbot to respond to real estate queries, wherein users input their query and parameters into the chatbot, the system analyzes the conversation in order to extract information, and then the system uses the information to formulate results, such as pricing for the real estate, which are provided to the user in the chatbot of Stillwell, with the system and method of requesting an appraisal of property, such as real estate, wherein the appraisal system records the appraisal and feature information on a permissioned blockchain ledger, and wherein the system executes a smart contract regarding the appraisal, wherein the contract generates an NFT corresponding the property valuation, and contains the valuation and parameters used to make the valuation of Okamoto. By generating an NFT that represents the appraisal of property, that also contains the parameters used for the valuation, a valuation system will predictably be able to generate a token that can be distributed to parties that provides evidence of the valuation and the blockchain location, thus, reducing fraudulent activities and providing assurance regarding valuations.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL P HARRINGTON whose telephone number is (571)270-1365. The examiner can normally be reached Monday-Friday 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Zimmerman can be reached on (571) 272-4602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Michael Harrington
Primary Patent Examiner
11 December 2025
Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628