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.
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 and 11 (claim 1 being representative) is:
acquiring data associated with a target area and a task type;
structuring at least part of information included in the acquired data into a unified machine-readable 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 correlation-measurement function, the correlation-measurement function defining rules for evaluating relationships between the target area, the task type, and the structured information;
selecting portions of the structured information having correlation values exceeding a preset correlation threshold, the selecting reducing computational load for subsequent analysis by limiting processing to correlation-relevant information;
classifying the structured information into a plurality of analysis categories including a Transportation category and a Safety and Security category, each analysis category having a respective set of analysis criteria different from a set of analysis criteria of at least one other analysis category;
calculating based on the selected portions of the structured information and the respective analysis criteria for each of the plurality of analysis categories, assessment values of real estate development positioning factors for the target area and the task type within each of the plurality of multiple analysis categories, wherein the assessment values represent structured evaluation values generated per distinct analysis category;
providing, based on the assessment values and preset criteria for an objective architectural task, a final real-estate development positioning for the target area and the task type, the providing (i) comprising applying respective weight values to the assessment values according to the preset criteria and combining the weighted values into the final real estate development positioning, and (ii) representing a derivation of the final real-estate development positioning based on analysis category-specific assessment values and the preset criteria.
The abstract idea of claim 19 is:
acquiring data associated with a target area and a task type;
structuring at least part of information included in the acquired data into a unified machine-readable 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 correlation-measurement function, the correlation-measurement function defining rules for evaluating relationships between the target area, the task type, and the structured information;
selecting portions of the structured information having correlation values exceeding a preset correlation threshold, the selecting reducing computational load for subsequent analysis by limiting processing to correlation-relevant information;
classifying the structured information into a plurality of analysis categories including a Transportation category, a Safety and Security category, and a Development Pattern category, each analysis category having a respective set of analysis criteria different from a set of analysis criteria of at least one other analysis category;
based on the at least part of information included in the data and on criteria for each of the plurality of analysis categories, generating prompting information to request calculation of assessment values of real estate development positioning factors for a target area and task type within each of plurality of analysis categories;
generating a result code comprising a score assigned for each real estate development positioning factor;
checking assessment values of the real estate development positioning factors within each of the plurality of analysis categories based on the generated result code, wherein the assessment values represent structured evaluation values generated per distinct analysis category;
providing, based on the assessment values and preset criteria for an objective architectural task, a final real-estate development positioning for the target area and the task type, the providing (i) comprising applying respective weight values to the assessment values according to the preset criteria and combining the weighted values into the final real estate development positioning, and (ii) representing a derivation of the final real-estate development positioning based on analysis category-specific assessment values and the preset criteria.
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: electronic device comprising a processor and a memory; using an artificial intelligence (AI) server; the Al server comprising a first generative Al agent associated with the Transportation category and a second generative AI agent associated with the Safety and Security category; 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 an artificial intelligence (AI) server; the Al server comprising a first generative AI agent associated with the Transportation category, a second generative AI agent associated with the Safety and Security category, and a third generative Al agent associated with the Development Pattern category; machine-generated.
Claim 19 recites the following additional elements: electronic device comprising a processor and a memory; using an artificial intelligence (AI) processor of the electronic device; the Al processor comprising a first generative AI agent associated with the Transportation category, a second generative AI agent associated with the Safety and Security category, and a third generative Al agent associated with the Development Pattern category; transmitting the prompting information to the (AI) processor; 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, 7-8, 12, and 15-16, 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 4, 9, and 17 include further additional elements: processor; AI server. 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.
Claims 5 and 13 include further additional elements: the AI server further comprises a plurality of generative classification Al agents including (i) a scraping AI agent configured to support web scraping and data classification (ii) a PDF AI agent configured to extract and classify data from PDF documents: (iii) an Excel Al agent configured to extract and classify data from Excel files: (iv) an image Al agent configured to recognize and classify information from image files; and (v) a validity AI agent; using one or more of the plurality of generative classification Al agents; 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 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.
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.
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.
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.
Claims 1-2, 7-8, 11-12, and 15-16 are rejected under 35 U.S.C. § 103 as being unpatentable over Budlong (US 20150058233) (Budlong ‘233) in view of Budlong (US 20210342962) (Budlong ‘962) in further view of Zhang (US 20250190449).
Claims 1 and 11
Budlong ‘233 discloses (claim 1 being representative):
(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.”}
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 ‘233 does not disclose, however, Budlong ‘962, in a similar field of endeavor directed to real property development and utility, teaches:
structuring, by the processor, at least part of information included in the acquired data into a unified machine-readable 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.}
assigning, by the processor, correlation values to portions of the structured information based on a correlation-measurement function stored in the memory, the correlation-measurement function defining rules for evaluating relationships between the target area, the task type, and the structured information; {[0349], [0354], [0358] – [0360], [0393] The system applies rules, logic, and compatibility determinations to evaluate relationships between zoning attributes, parcel characteristics, and user queries.}
selecting, by the processor, portions of the structured information having correlation values exceeding a preset correlation threshold the selecting reducing computational load for subsequent analysis by limiting processing to correlation-relevant information; {[0353], 0402], [0409] The system supports filtering and retrieving only data meeting criteria selected by the user.}
classifying, by the processor and using an artificial intelligence (AI) server, the structured information into a plurality of analysis categories including a Transportation category and a Safety and Security category, {[0383], [0385], [0390] – [0392], [0397] – [0410], [0451] The system organizes and structures data into multiple categories and subcategories, including transportation and crime (i.e., safety and security).}
each analysis category having a respective set of analysis criteria different from a set of analysis criteria of at least one other analysis category, and {[0354], [0358] – [0361], [0390] – [0397], [0402] – [0409] The system applies different logic, compatibility rules, and evaluation criteria depending on the data type, category, or inquiry.}
calculating by the processor and using the first generative Al agent associated with the Transportation category and the second generative Al agent associated with the Safety and Security category, and based on the selected portions of the structured information and the respective analysis criteria for each of the plurality of analysis categories, assessment values of real estate development positioning factors for the target area and the task type within each of the plurality of multiple analysis categories {[0354], [0375, [0393] – [0397], [0413] – [0418] The system supports generating outputs such as development potential determinations, comparisons, and opportunity findings based on structured data and applied logic.}
wherein the assessment values represent structured evaluation values generated per distinct analysis category; and {[0395] – [0409] The system produces outputs based on categories, including standardized category and subcategory results, detailed breakouts, and visualizations.}
providing, by the processor and using the Al server, and based on the assessment values and preset criteria for an objective architectural task, a machine-generated final real-estate development positioning for the target area and the task type, the providing (i) comprising applying respective weight values to the assessment values according to the preset criteria and combining the weighted values into the final real estate development positioning, and (ii) representing a derivation of the final real-estate development positioning based on analysis category-specific assessment values and the preset criteria. {[0349], [0375] – [0381], [0396] The system provides automated outputs such as development suitability, hypothetical scenario results, and optimal use determinations based on structured data and applied criteria.}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the real estate information organization features of Budlong ‘233 to include the zoning and land-use development controls features of Budlong ‘962, to enhance search capabilities by land use utility and existing property use utility. (See para. [0014] of Budlong ‘962).
The combination of Budlong ‘233 and Budlong ‘962 does not teach, however, Zhang, in a similar field of endeavor directed to generative artificial intelligence-assisted analytics of structured data sets, teaches:
the Al server comprising a first generative Al agent associated with the Transportation category and a second generative AI agent associated with the Safety and Security category {An AI system includes a plurality of generative AI agents that are selected and assigned to perform analytics tasks based on the prompt and data characteristics, where different agents are associated with different analytical functions. [0027], [0080] – [0082]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233 and Budlong ‘962 to include the AI analytics features of Zhang, to improve usability of analytical applications and structured data set context by means of AI. (See para. [0003] of Zhang).
Claims 2 and 12
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
wherein the data associated with the target area and the task type comprises: quantitative data comprising at least one of population density data, economic indicators data, traffic statistics data, architectural infrastructure data, or environmental data regarding the target area and the task type; and {[0245] “The structured database, 4, combines spatial data, zoning controls in the form of structured data, environmental data, transportation, neighborhood, property data, historic and economic.”}
qualitative data comprising at least one of SNS data, local community feedback data, cultural value data, or quality-of-life data associated with the target area and the task type. {[0518] “Data retrieval includes the development potential for the identified properties and identifies future land use with the option to map outputs color coded by future land use.” [0519] “unexpected results include:” [0544] “Monitoring community sentiment about specific properties in their community that could be candidates for redevelopment or development, building types they support or not, businesses they support or not, zoning policy such as preservation, design etc. they support or not.”}
Claims 7 and 15
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
wherein the real estate development positioning factors comprise at least one of the following: Service Level and Quality, Themed Experience, Recreation and Leisure, History & Culture, Community and Social Engagement, Eco-Friendliness, Location and Accessibility, or Functional Amenities. {[0554] “Monitoring community sentiment about specific properties in their community that could be candidates for redevelopment or development, building types they support or not, businesses they support or not, zoning policy such as preservation, design etc. they support or not.”}
Claims 8 and 16
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
calculating, by the processor, assessment values indicating a current status of the target area; {The system calculates development potential and compatibility metrics for properties within a target area using current zoning, environmental, and spatial data. [0245], [0250], [0254]}
determining, by the processor, a numerical difference between the assessment values for the current status and the assessment values for the final real estate development positioning; and {The system compares current development conditions of a target area with proposed development scenarios to obtain quantifiable results. [0011], [0250], [0263]}
generating, by the processor, at least one of text data or image data representing the numerical difference. {[0254] “Spatial data,40, forms the association of a property's location with land-use controls which are often set up geographically. The non-spatial data, 41, provides contextual data regarding the land-use controls and the property.”}
Claims 4, 6, 9-10, 14, and 17-20 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Budlong ‘233, Budlong ‘962, and Zhang, in further view of Kimchi (US 20060241860).
Claim 4
While the combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above, it does not explicitly teach, however, Kimchi, in a similar field of endeavor directed to capturing, connecting, sharing, and visualizing information based on a geographic location, teaches:
generating by the processor, prompting information requesting evaluation of correlation between the structured information and at least one of the target area and the task type; {The system [0113] “can analyze the current route for traffic and notify the user that the system determined that an alternate route might be quicker, and [0129] “can create the zone by monitoring messages between the contacts and certain trigger words or phrase (e.g., "Lunch", "Chinese", "can't leave before 11:45", "be back by 1", "within a 15 minutes drive") can be utilized to auto-create the search query and results while the users are conversing.” (i.e., the system generates prompting information regarding a specific context and request assessment or confirmation of correlation between data).}
transmitting, by the processor, the prompting information to the Al server configured to evaluate correlation based on learned models; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing ([0049], [0082-0084]).}
receiving, by the processor, a result code from the Al server, the result code indicating a correlation score assigned to each portion of structured information; and {After receiving data (e.g., prompting information) to an AI or inference component, the system receives an output generated by AI (i.e., result code) [0082-0084]. The inference results represent probabilistic determinations (i.e., scores) associated with analyzed data. [0045] }
assigning by the processor, a correlation value to each portion of the structured information by mapping a correlation score indicated in the result code to a numeric representation according to the correlation-measurement function stored in the memory. {The machine learning component analyzes user and data inputs to generate probabilistic or weighted associations among data items (i.e., assigning correlation scores that reflect similarity) ([0045], [0082-0084].}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, and Zhang to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
Claims 6 and 14
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
identifying, by the processor, topics corresponding to analysis criteria within each of the plurality of analysis categories; {The system identifies topics within each analysis category to calculate development potential and compatibility for a target area. ([0098], [0250], [0263], [0554]).}
generating, by the processor, prompting information requesting the Al server to assign a score for each real estate development positioning factor based on the structured information corresponding to the topics; {The system uses logic based user interface prompts (e.g., visual guideposts) to guide the user toward generating assessment values on specific identified topics within multiple data categories ([0013,], [0245], [0250], [0254]).}
While the combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above, it does not explicitly teach, however, Kimchi, in a similar field of endeavor directed to capturing, connecting, sharing, and visualizing information based on a geographic location, teaches:
transmitting, by the processor, the generated prompting information to the AI server; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing ([0049], [0082-0084]).}
receiving, by the processor, a result code comprising the score assigned for each real estate development positioning factor; and {After receiving data (e.g., prompting information) to an AI or inference component, the system receives an output generated by AI (i.e., result code) [0082-0084].}
calculating, by the processor, the assessment values by converting the score indicated in the result code into a numerical assessment value according to scoring rules stored in the memory. {The system applies ML and utility based analytical techniques to process inference outputs (e.g., probability values) using defined evaluation criteria that weigh benefits and costs. [0045], [0047]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, and Zhang to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
Claims 9 and 17
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
identifying by the processor, comparative real estate development positioning relating to a different area regarding an architectural status of a different area; {[0250] “The logic, 5, allows for the application to automatically calculate development potential for a property or properties and provide compatibility analysis to identify situations that don't meet requirements for distance-to for protected uses such as churches and schools, adjacency zoning issues such as a commercial district adjacent to residential and/or street type controlling uses to protect or promote identifiable developments and/or uses.” [0254] “The logic, 5, handles hierarchy and compatibility type of logic using rules associated with property characteristics, 121, and GIS spatial data structuring, 122.”}
based on the comparative real estate development positioning, generating, by the processor, prompting information requesting adjustment of at least a portion of the assessment values for the target area; {When comparative analyses or community evaluations reveal differences between proposed and existing developments, the system generates notifications or feedback prompts that request updates to the proposed development positioning. [0329-0330], [0554]}
While the combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above, it does not explicitly teach, however, Kimchi, in a similar field of endeavor directed to capturing, connecting, sharing, and visualizing information based on a geographic location, teaches:
transmitting, by the processor the generated prompting information to the AI server; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing. [0049], [0082-0084]}
receiving, by the processor, a result code comprising updated values; and {After receiving data (e.g., prompting information) to an AI or inference component, the system receives an output generated by AI (i.e., result code). [0082-0084]}
updating, by the processor, the final real estate development positioning by applying the updated values indicated in the result code. {Based on output results from the AI component, the system recalculates routes, zones, or map views (i.e., positioning). [0045], [0113], [0129-0130]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, and Zhang to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
Claims 10 and 18
The combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above. Budlong ‘233 further discloses:
generating by the processor, prompting information requesting projection data regarding financial or operational characteristics of the target area based on the final real-estate development positioning; {Once the system establishes a final development positioning (e.g., development scenario), it can prompt further analysis related to the target area (e.g., policy effect or future development outcomes). The system includes logic and user interface features that encourages users to generate projection data (e.g., transportation). [0011], [0013], [0250]}
While the combination of Budlong ‘233, Budlong ‘962, and Zhang teaches the limitations set forth above, it does not explicitly teach, however, Kimchi, in a similar field of endeavor directed to capturing, connecting, sharing, and visualizing information based on a geographic location, teaches:
transmitting, by the processor the prompting information to the Al server; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing. [0049], [0082-0084]}
receiving, by the processor, a result code from the Al server indicating projection data; and {After receiving data (e.g., prompting information) to an AI or inference component, the system receives an output generated by AI (i.e., result code) ([0082-0084]).}
verifying by the processor, the projection data based on projection rules stored in the memory. {[0113] “the system can analyze the current route for traffic and notify the user that the system determined that an alternate route might be quicker.” [0129] “the system can determine and create a drive-time zone based on the location of the contacts. This zone can be displayed to the users showing only the targeted search results within that zone.”}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, and Zhang to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
Claim 19
Budlong ‘233 discloses:
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.”}
based on the at least part of information included in the data and on criteria for each of the plurality of analysis categories, generating prompting information to request calculation of assessment values of real estate development positioning factors for a target area and task type within each of the plurality of analysis categories; {The system integrates data and analysis criteria across multiple categories and uses logic and user interface prompts to initiate the calculation of development assessment values. The prompting information is implemented through visual guideposts and lookup functions ([0013], [0245], [0250], [0254]).}
Budlong ‘233 does not disclose, however, Budlong ‘962, in a similar field of endeavor directed to real property development and utility, teaches:
structuring, by the processor, at least part of information included in the acquired data into a unified machine-readable 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.}
assigning, by the processor, correlation values to portions of the structured information based on a correlation-measurement function stored in the memory, the correlation-measurement function defining rules for evaluating relationships between the target area, the task type, and the structured information: {[0349], [0354], [0358] – [0360], [0393] The system applies rules, logic, and compatibility determinations to evaluate relationships between zoning attributes, parcel characteristics, and user queries.}
selecting by the processor portions of the structured information having correlation values exceeding a preset correlation threshold, the selecting reducing computational load for subsequent analysis by limiting processing to correlation-relevant information; {[0353], 0402], [0409] The system supports filtering and retrieving only data meeting criteria selected by the user.}
classifying, by the processor and using an artificial intelligence (AI) processor of the electronic device, the structured information into a plurality of analysis categories including a Transportation category, a Safety and Security category, and a Development Pattern category, {[0383], [0385], [0390] – [0392], [0397] – [0410], [0451] The system organizes and structures data into multiple categories and subcategories, including transportation, crime, and development. }
each analysis category having a respective set of analysis criteria different from a set of analysis criteria of at least one other analysis category, and {[0354], [0358] – [0361], [0390] – [0397], [0402] – [0409] The system applies different logic, compatibility rules, and evaluation criteria depending on the data type, category, or inquiry.}
providing, by the processor and using the Al processor, and based on the assessment values and preset criteria for an objective architectural task, a machine-generated final real estate development positioning for the target area and the task type the providing (i) comprising applying respective weight values to the assessment values according to the preset criteria and combining the weighted values into the final real-estate development positioning, and (i) representing a derivation of the final real-estate development positioning based on analysis category-specific assessment values and the preset criteria. {[0349], [0375] – [0381], [0396] The system provides automated outputs such as development suitability, hypothetical scenario results, and optimal use determinations based on structured data and applied criteria.}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the real estate information organization features of Budlong ‘233 to include the zoning and land-use development controls features of Budlong ‘962, to enhance search capabilities by land use utility and existing property use utility. (See para. [0014] of Budlong ‘962).
The combination of Budlong ‘233 and Budlong ‘962 does not teach, however, Zhang, in a similar field of endeavor directed to generative artificial intelligence-assisted analytics of structured data sets, teaches:
the Al processor including a first generative AI agent associated with the Transportation category, a second generative Al agent associated with the Safety and Security category and a third generative Al agent associated with the Development Pattern category {An AI system includes a plurality of generative AI agents that are selected and assigned to perform analytics tasks based on the prompt and data characteristics, where different agents are associated with different analytical functions. [0027], [0080] – [0082]}
wherein the assessment values represent structured evaluation values generated per distinct analysis category, that are output by the first generative AI agent associated with the Transportation category, the second generative AI agent associated with the Safety and Security category, and the third generative Al agent associated with the Development Pattern category {An agent orchestrator selects one or more generative AI agents based on the prompt and data characteristics, and an ensemble model executes these agents to generate outputs that may be combined or individually selected as the final response. [0040], [0042]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233 and Budlong ‘962 to include the AI analytics features of Zhang, to improve usability of analytical applications and structured data set context by means of AI. (See para. [0003] of Zhang).
The combination of Budlong ‘233, Budlong ‘962, and Zhang does not explicitly teach, however, Kimchi, in a similar field of endeavor directed to capturing, connecting, sharing, and visualizing information based on a geographic location, teaches:
transmitting the prompting information to the Al processor; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing ([0049], [0082-0084]).}
generating, by the Al processor, a result code comprising a score assigned for each real estate development positioning factor; {The AI component processes the prompting information and outputs an inference (i.e., result code) that includes the AI’s computed determination. [0045], [0082-0084]}
checking by the processor assessment values of the real estate development positioning factors within each of the plurality of analysis categories based on the generated result code {The AI component evaluates data across different categories to generate probabilistic results. The AI combines multiple factors to produce learned recommendations. [0045], [0083-0084]}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, and Zhang to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
Claim 20
The combination of Budlong ‘233, Budlong ‘962, Zhang, and Kimchi teaches the limitations set forth above. Budlong ‘233 further discloses:
identifying comparative real estate development positioning relating to a different area regarding an architectural status of a different area; {[0250] “The logic, 5, allows for the application to automatically calculate development potential for a property or properties and provide compatibility analysis to identify situations that don't meet requirements for distance-to for protected uses such as churches and schools, adjacency zoning issues such as a commercial district adjacent to residential and/or street type controlling uses to protect or promote identifiable developments and/or uses.” [0254] “The logic, 5, handles hierarchy and compatibility type of logic using rules associated with property characteristics, 121, and GIS spatial data structuring, 122.”}
based on the comparative real estate development positioning, generating prompting information requesting adjustment of at least a portion of the assessment values for the target area; {When comparative analyses or community evaluations reveal differences between proposed and existing developments, the system generates notifications or feedback prompts that request updates to the proposed development positioning. [0329-0330], [0554]}
Kimchi further teaches:
transmitting the generated prompting information to the Al processor; {[0045] “Artificial intelligence based systems (e.g., explicitly and/or implicitly trained classifiers) can be employed in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations”. Further, the receiver component and inference/machine learning component facilitate transmitting collected or generated information to these AI components for processing ([0049], [0082-0084]).}
generating, by the Al processor, a result code comprising updated values in response to the prompting information; and {The AI component processes the prompting information and outputs an inference (i.e., result code) that includes the AI’s computed determination ([0045], [0082-0084]).}
updating by the processor the final real estate development positioning by applying the updated values indicated in the result code. {Based on output results from the AI component, the system recalculates routes, zones, or map views (i.e., positioning) ([0045], [0113], [0129-0130]).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Budlong ‘233, Budlong ‘962, Zhang, and Kimchi to include the AI data processing features of Kimchi, to consolidate geographic information for ease of use and time efficiency for consumers. (See paragraphs [0005] – [0007] of Kimchi).
No Prior Art Applied to Claims 5 and 13
Claims 5 and 13
There is no prior art applied to claims 5 and 13 (claim 5 being representative)because the cited prior art fails to disclose or suggest the complete feature set recited in the claims. Budlong ‘233, considered the closest prior art, discloses:
wherein the AI server further comprises a plurality of generative classification Al agents including (i) a scraping AI agent configured to support web scraping and data classification (ii) a PDF AI agent configured to extract and classify data from PDF documents: (iii) an Excel Al agent configured to extract and classify data from Excel files: (iv) an image Al agent configured to recognize and classify information from image files; and (v) a validity AI agent configured to evaluate validity and reliability of data, wherein the structured information is classified into the plurality of analysis categories using one or more of the plurality of generative classification Al agents;
wherein the plurality of analysis categories further includes each of the following: Macro Industry Trend, Micro Industry Trend, Demographic Trend, Development Pattern, Local Business Ecosystem, History & Culture, and Urbanistic Quality, wherein the AI server further includes:
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. {A database processes and organize different data types based on rules [0245], [0251]. The system supports scoring and evaluation outputs [0665], [0680]. Overall, the system is directed to rule based data structuring, querying, and scoring of zoning information.
However, there is no disclosure of an AI driven classification architecture with multiple specialized generative agents assigned to specific analysis categories.
Examiner also considered the following additional references:
Tippens (US 20220172282), which teaches: The present system and methods allow for receiving first customer data, storing the first customer data in one or more databases, automatically determining whether the customer data satisfies a predetermined condition, in response to the customer data satisfying the predetermined condition, converting the customer data to a standardized format based on one or more conversion rates, automatically determining whether the converted customer data in the standardized format satisfies a predetermined criteria, and in response to the converted customer data in the standardized format satisfying the predetermined criteria, sending a notification to a real estate management platform, wherein the notification includes content based on the converted customer data in the standardized format. Via the system and methods, an employee or a commercial property tenant may receive a rent reduction or eligibility for affordable real estate.
Bomze (US 20230089025), which teaches: Disclosed embodiments provide a real estate property analysis system and method. A user provides search criteria, lifestyle options, and/or style preferences. This data is provided to a machine learning system to select real estate properties based on the provided information. The selected real estate property information is provided in a list to a user. The user interface enables positive or negative feedback of each of the items in the list. The machine learning system periodically adjusts the list based on user feedback. Thus, disclosed embodiments streamline and sort listings based on user preferences and lifestyle priorities, thereby introducing them to refined real estate property choices. Taking into account a user's unique style and lifestyle priorities, disclosed embodiments can provide a personalized feed of real estate property listings ranked such that the closest matches appear first in the list, thereby simplifying the complex task of searching for a home.
However, neither reference disclose or suggest “the AI server further comprises a plurality of generative classification Al agents including (i) a scraping AI agent configured to support web scraping and data classification (ii) a PDF AI agent configured to extract and classify data from PDF documents: (iii) an Excel Al agent configured to extract and classify data from Excel files: (iv) an image Al agent configured to recognize and classify information from image files; and (v) a validity AI agent configured to evaluate validity and reliability of data, wherein the structured information is classified into the plurality of analysis categories using one or more of the plurality of generative classification Al agents” nor “a third … a ninth generative Al agent, 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.”
Accordingly, there is no prior art applied to claims 5 and 13.
Response to Arguments
Applicant’s arguments filed on 01/09/2026 have been carefully considered but they are not persuasive.
Rejections under 35 U.S.C. §101
Applicant has conflated the abstract idea, considered at Step 2A Prong One, with the additional elements, considered at Step 2A Prong Two and Step 2B. Here, Examiner identified the following steps as part of the abstract idea (claim 1): acquiring data associated with a target area and a task type;
structuring at least part of information included in the acquired data into a unified machine-readable 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 correlation-measurement function, the correlation-measurement function defining rules for evaluating relationships between the target area, the task type, and the structured information;
selecting portions of the structured information having correlation values exceeding a preset correlation threshold, the selecting reducing computational load for subsequent analysis by limiting processing to correlation-relevant information;
classifying the structured information into a plurality of analysis categories including a Transportation category and a Safety and Security category, each analysis category having a respective set of analysis criteria different from a set of analysis criteria of at least one other analysis category;
calculating based on the selected portions of the structured information and the respective analysis criteria for each of the plurality of analysis categories, assessment values of real estate development positioning factors for the target area and the task type within each of the plurality of multiple analysis categories, wherein the assessment values represent structured evaluation values generated per distinct analysis category;
providing, based on the assessment values and preset criteria for an objective architectural task, a final real-estate development positioning for the target area and the task type, the providing (i) comprising applying respective weight values to the assessment values according to the preset criteria and combining the weighted values into the final real estate development positioning, and (ii) representing a derivation of the final real-estate development positioning based on analysis category-specific assessment values and the preset criteria.
The electronic device comprising a processor and a memory; using an artificial intelligence (AI) server, etc. are considered additional elements, which are merely facilitating the tasks of said abstract idea. MPEP 2106.05(f) is clear that this generic recitation does not integrate the abstract idea into practical application and/or add significantly more. This interpretation holds whether the additional elements are viewed alone or in combination, where the combination of elements is nothing more than a network-enabled computing system.
Rejections under 35 U.S.C. §§ 102/103
Applicant’s arguments with respect to patentability under 35 U.S.C. §§ 102/103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding any arguments concerning the dependent claims, examiner notes that they are predicated on the independent claims, which have been amended. For the same reason as above, these arguments are moot. Examiner directs applicant’s attention to the claim analysis above.
In summary, examiner has responded to all arguments and found them unpersuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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