DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-2, and 4-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/30/2026 has been entered.
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 1-2, and 4-20 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claims 1 and 19 are directed to a method (i.e., a process), and claim 11 is directed to a device (i.e., machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. § 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1, 11, and 19 (claim 1 being representative) is:
acquiring data associated with a target area and a task type, the data comprising heterogeneous data obtained from a plurality of different data source types, wherein the plurality of different data source types includes quantitative data and qualitative data;
extracting, classifying, recognizing, or evaluating information from the heterogeneous data;
structuring at least part of the extracted, classified, recognized, or evaluated information into an analyzable format by performing at least one of data refinement, data transformation, and data integration, the structuring comprising harmonizing heterogeneous data sources to improve machine-based analysis accuracy;
assigning correlation values to portions of the structured information based on a preset correlation-measurement function, each correlation value representing a correlation among a corresponding portion of the target area, the task type, and the structured information;
identifying selected information having correlation values exceeding a preset correlation threshold, wherein information not exceeding the preset correlation threshold is excluded from subsequent category-based assessment processing;
determining a plurality of analysis categories based on at least one of the target area and the task type, the determining comprising excluding at least one analysis category based on the at least one of the target area and the task type;
classifying the selected information into the plurality of analysis categories having different analysis criteria;
providing the selected information classified into respective analysis categories to a plurality of category-specific generative Als corresponding to the respective analysis categories;
processing the selected information classified into the respective analysis categories according to analysis criteria corresponding to the respective analysis categories;
calculating assessment values of real estate development positioning factors for the target area and the task type within each of the plurality of analysis categories based on outputs;
processing the assessment values according to preset criteria for an objective architectural task and generating a final real-estate development positioning for the target area and the task type based on the assessment values.
The abstract idea steps italicized above recite real-estate development positioning (i.e., where to develop or renovate property), which constitutes a process that, under its broadest reasonable interpretation (BRI), covers commercial activity. This is further supported by [0055] – [0056] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim recites urban planning evaluative reasoning, which could be performed mentally, including with pen and paper. This is further supported by [0065] – [0067] of applicant’s specification as filed. If a claim limitation, under its BRI, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: computer-implemented; electronic device comprising a processor and a memory; using a plurality of data-type-specific artificial intelligence (Al) agents included in or associated with an Al server; wherein the plurality of data-type-specific Al agents comprise at least two of: (i) a classification scraping agent configured to support web scraping and data classification, (ii) a PDF agent configured to extract or classify text or other data from a PDF document, (iii) an Excel agent configured to extract or classify data from an Excel file, (iv) an image agent configured to recognize or classify information from image data, and (v) a validity agent configured to evaluate validity or reliability of data; category-specific generative Als; using a data- classification generative Al; using a final-assessment generative Al; machine-generated.
Claim 11 recites the following additional elements: An electronic device comprising: a memory storing instructions, and a processor configured to execute the instructions to; using a plurality of data-type-specific artificial intelligence (Al) agents included in or associated with an Al server; wherein the plurality of data-type-specific Al agents comprise at least two of: (i) a classification scraping agent configured to support web scraping and data classification, (ii) a PDF agent configured to extract or classify text or other data from a PDF document, (iii) an Excel agent configured to extract or classify data from an Excel file, (iv) an image agent configured to recognize or classify information from image data, and (v) a validity agent configured to evaluate validity or reliability of data; category-specific generative Als; using a data- classification generative Al; using a final-assessment generative Al; machine-generated.
Claim 19 recites the following additional elements: computer-implemented; electronic device comprising a processor and a memory; using a plurality of data-type-specific artificial intelligence (Al) agents included in or associated with an Al server; wherein the plurality of data-type-specific Al agents comprise at least two of: (i) a classification scraping agent configured to support web scraping and data classification, (ii) a PDF agent configured to extract or classify text or other data from a PDF document, (iii) an Excel agent configured to extract or classify data from an Excel file, (iv) an image agent configured to recognize or classify information from image data, and (v) a validity agent configured to evaluate validity or reliability of data; category-specific generative Als; using a data- classification generative Al; using a final-assessment generative Al; machine-generated.
These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0087] – [0088], and [0235] – [0237] of applicant’s specification as filed, for example. Further, the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Regarding claims 2, 4, 7-9, 12, and 15-17, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible.
Claims 5 and 13 include further additional elements: a third generative Al agent associated with the Macro Industry Trend category; a fourth generative Al agent associated with the Micro Industry Trend category; a fifth generative Al agent associated with Demographic Trend category; a sixth generative AI agent associated with the Development Pattern category; a seventh generative Al agent associated with the Local Business Ecosystem category; an eighth generative Al agent associated with the History & Culture category; and a ninth generative Al agent associated with the Urbanistic Quality category; and wherein the assessment values of the real estate development positioning factors are calculated using each of the third, the fourth, the fifth, the sixth, the seventh, the eighth, and the ninth generative Al agents. The additional elements are directed toward AI interaction such as receiving a result code, and calculating or updating positioning values based on the received result. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f) (see paragraphs [0087] – [0088] of applicant’s specification as filed, for example). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claims 6, 10, 14, 18 include further additional elements: processor; AI server; memory. The additional elements are directed toward AI interaction such as transmitting prompting information, receiving a result code, and calculating or updating positioning values based on the received result. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f) (see paragraphs [0087] – [0088] of applicant’s specification as filed, for example). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Claim 20 includes further additional elements: AI processor. Similar to above, these additional elements do no more than apply the abstract idea to a computer, per MPEP 2106.05(f). When viewed alone or in combination, this does not integrate the abstract idea into practical application and is not significantly more.
Accordingly, claims 1-2, and 4-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
No Prior Art Applied to Claims 1-2, and 4-20
Claims 1, 11, and 19
There is no prior art applied to claims 1, 11, and 19 because the cited prior art fails to disclose or suggest the complete feature set recited in the claims. Budlong (US 20150058233) (Budlong ‘233), considered the closest prior art, discloses:
(claim 1) A computer-implemented method executed by an electronic device comprising a processor and a memory, the method comprising: {[0003] “[T]he embodiments relate to computer enabled search of structured data with business logic and business methods specific to the complex subject of zoning and land-use development controls.”}
(claim 11) An electronic device comprising: {[Abstract] “Computer implemented application to provide automated answers to zoning and real estate development questions by approaching the complex subject through the creation of modules representing the rules, property and process and accounting for user perspective.”}
(claim 11) a memory storing instructions, and {[0248] “The structured database, 4, and logic, 5, store data and support the execution and retrieval of queries.”}
(claim 11) a processor configured to execute the instructions to: [0003] “the embodiments relate to computer enabled search of structured data with business logic and business methods specific to the complex subject of zoning and land-use development controls.”}
(claim 19) A computer-implemented performed by an electronic device comprising a processor and a memory, the method comprising {[0003] “[T]he embodiments relate to computer enabled search of structured data with business logic and business methods specific to the complex subject of zoning and land-use development controls.”}
acquiring, by the processor, data associated with a target area and a task type; {[0629] “Data associated with changes for a location comprising demographics, neighborhood boundaries, zoning permits, zoning cases, building permits, retail sales, real estate sales is imported into a database.”}
Budlong (US 20210342962) (Budlong ‘962) teaches:
the data comprising heterogeneous data obtained from a plurality of different data source types, wherein the plurality of different data source types includes quantitative data and qualitative data; {[0248] – [0252], [0410] – [0416] The system supports importing heterogeneous datasets (e.g., GIS, zoning parcel, and external data), parsing and intersecting such data, and standardizing them into a common classifier format.}
structuring, by the processor, at least part of the extracted, classified, recognized, or evaluated information into an analyzable format by performing at least one of data refinement, data transformation, and data integration, the structuring comprising harmonizing heterogeneous data sources to improve machine-based analysis accuracy; {[0248] – [0252], [0410] – [0416] The system supports importing heterogeneous datasets (e.g., GIS, zoning parcel, and external data), parsing and intersecting such data, and standardizing them into a common classifier format.}
However, nor (Budlong ‘233) or (Budlong ‘962) disclose or suggest “extracting, classifying, recognizing, or evaluating, by the processor using a plurality of data-type-specific artificial intelligence (Al) agents included in or associated with an Al server, information from the heterogeneous data, wherein the plurality of data-type-specific Al agents comprise at least two of: (i) a classification scraping agent configured to support web scraping and data classification, (ii) a PDF agent configured to extract or classify text or other data from a PDF document, (iii) an Excel agent configured to extract or classify data from an Excel file, (iv) an image agent configured to recognize or classify information from image data, and (v) a validity agent configured to evaluate validity or reliability of data; assigning, by the processor, correlation values to portions of the structured information based on a preset correlation-measurement function, each correlation value representing a correlation among a corresponding portion of the target area, the task type, and the structured information; identifying, by the processor, selected information having correlation values exceeding a preset correlation threshold, wherein information not exceeding the preset correlation threshold is excluded from subsequent category-based assessment processing by category-specific generative Als; determining, by the processor, a plurality of analysis categories based on at least one of the target area and the task type, the determining comprising excluding at least one analysis category based on the at least one of the target area and the task type; classifying, by the processor using a data- classification generative Al, the selected information into the plurality of analysis categories having different analysis criteria; providing, by the processor, the selected information classified into respective analysis categories to a plurality of category-specific generative Als corresponding to the respective analysis categories; processing, by the processor using the plurality of category-specific generative Als, the selected information classified into the respective analysis categories according to analysis criteria corresponding to the respective analysis categories; calculating, by the processor, assessment values of real estate development positioning factors for the target area and the task type within each of the plurality of analysis categories based on outputs from the plurality of category-specific generative Als; processing, by the processor using a final-assessment generative Al, the assessment values according to preset criteria for an objective architectural task and generating, by the processor, a machine- generated final real-estate development positioning for the target area and the task type based on the assessment values as processed by the final-assessment generative Al.
Examiner also considered the following additional references:
US 20140122299 A1, which teaches facilitating the selection of a real estate agent online, which includes storing agent location data relating to a plurality of real estate agents, comparing property location data with the agent location data to identify the one or more real estate agents from the agent location data who are the geographically closest agents to the property listed for sale, and displaying at least a portion of the identified real estate agents and information regarding the property through a user interface.
US 20210125294 A1, which teaches identifying a potential buyer of real estate from real estate transaction public records, comprising identifying two or more real estate transaction public records that include matching buyers and different property addresses; identify real estate listings having at least some transaction attributes matching those of the two or more real estate transaction public records; and identifying the buyer included in the two or more real estate transaction public records as a potential buyer of property associated with the real estate listing(s). Systems and methods for identifying a potential buyer of real estate from real estate transaction public records and matching the potential buyer with an available real estate listing, comprising identifying contact information for a potential buyer selected from a plurality of real estate transaction public records; and notifying the potential buyer of available real estate listing(s) having matching transaction attributes.
US 20220172309 A1, which teaches solutions for matching real-property listings with users (consumers) based on user demographic and preference data. In some aspects, a matching system of the disclosed technology utilizes a machine-learning model for identifying and performing relevance ranking of real-property listings based on the demographic and preference information. In some aspects, a process of the technology includes steps for receiving demographic data associated with a user, receiving preference data associated with the user, receiving listing data comprising property listings associated with one or more parcels of real property, and determining a matching relevance for one or more of the property listings based on the demographic data.
US 20180096362 A1, which teaches an e-commerce real estate marketplace and platform includes one or more systems and methods for facilitating all aspects of cross-border real-estate transactions. Included are one or more unique machine learning techniques that together with multiple data processing steps perform mathematical calculations and/or automated reasoning tasks that allow buyers, sellers, and third parties to execute satisfactory transactions in a secure and confidential environment with complete certainty.
However, neither reference disclose or suggest the aforementioned claim limitations.
Accordingly, there is no prior art applied to claims 1, 11, and 19. The rest of the claims, by virtue of their dependency, also have no prior art applied.
Response to Arguments
Applicant’s arguments filed on 06/30/2026 have been carefully considered but they are not persuasive.
Rejections under 35 U.S.C. §101
Applicant argues that the amended claims are not directed to mental processes or methods of organizing human activity because they recite heterogeneous data sources, data type specific AI agents, data harmonization, threshold filtering, AI models specific to categories, and a final assessment AI that allegedly cannot practically be performed in the human mind. However, the amendments do not change the focus of the claims. The claims reman directed to collecting, analyzing, classifying, filtering, and evaluating information to generate a real estate development assessment, which recites mental processes and certain methods of organizing human activity. The recited AI components merely automate these abstract analytical steps using generic computing technology.
Applicant’s reliance on Enfish and Ex-parte Desjardin is also unpersuasive. Unlike those decisions, the present claims do not recite a specific improvement to computer functionality or AI technology itself. The claimed AI agents, data harmonization, threshold filtering, category selection, and orchestration of multiple AI models may improve the analysis performed on the data and the resulting recommendation, rather than the operation of the computer or AI system.
Applicant further contends that the claimed architecture addresses technical problems such as heterogenous data processing, processing noise, domain mismatch, distributed processing, and AI orchestration. However, these alleged improvements relate to improving the quality or accuracy of the information analysis, not to improving the functioning of the computer or another technology. The claims recite functional results without specifying a technological improvement to the computing system.
Accordingly, the rejections to the claims under 35 USC §101 are maintained.
Rejections under 35 U.S.C. §103
Applicant’s arguments with respect to patentability under 35 U.S.C. §103 have been considered but are moot because no art has been applied to the claims. Examiner directs applicant’s attention to the claim analysis above.
In summary, examiner has responded to all arguments and found them unpersuasive.
Conclusion
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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