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 .
Status of Claims
Claims 1-20 have been amended and are hereby entered.
Claims 1-20 are pending and have been examined.
This action is made FINAL.
Response to Arguments
Applicant's arguments filed July 6, 2026 have been fully considered but they are not persuasive.
Regarding the applicant's arguments for Double-Patenting Rejection in p.9: Applicant did not take any action and/or did not file an Electronic-Terminal Disclosure or e-td to obviate the Obviousness-type Double Patenting (ODP) rejection(s), Thus, the ODP rejection(s) will be maintained. Applicant’s arguments related to refraining from responding to this rejection has been considered, but are not persuasive to overcome the ODP rejection. Thus, the outstanding ODP was updated according to the Applicant amendments and will be maintained.
Regarding the applicant's arguments against the 101 rejection of pending claims on pages 9-18: Applicant’s arguments directed to 101 analysis were considered. However, these arguments are not persuasive and the examiner respectfully disagrees for the following reasons:
For Step 2A-Prong 1 starting in p. 13: The Applicant argues that the pending claims are not directed to any of the abstract ideas identified because “independent Claim 1 includes recitations that process electronic data and execute an ML tool in a manner that falls outside of any alleged certain method of organizing human activity” and “the human mind cannot perform, as a mental process, executing an ML tool and/or electronically generating documents”. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Because the Examiner concluded that the claimed invention and its pending claims were part of an abstract idea by analyzing the claim language. Firstly, the pending claims were analyzed and “evaluated after determining what [the] applicant has invented by reviewing the entire application disclosure and construing the claims in accordance with their broadest reasonable interpretation (BRI)” for Step 2A-Prong 1 and 2 (See MPEP § 2106, subsection II, for more information about the importance of understanding what the applicant has invented, and MPEP § 2111 for more information about the BRI). Further, each claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim (see MPEP 2106.04, subsection II). Specifically, for this case, the abstract idea (e.g. judicial exception) of a certain method of organizing human activity in the form of “commercial or legal interactions” is recited in claims 1, 8 and 15 and in their at least limitation steps of “access the one or more artificial intelligence (AI) models trained to analyze input data…” and “input the smart building analytics data and the claims data into the one or more AI models to generate one or more outputs including recommendations for a building plan…” related to home insurance and construction standards (i.e. providing home/construction consulting services). Thus, these steps involving accessing and analyzing “smart building analytics” and “claims data” to recommend tasks or actions related to home insurance, inspections and/or smart building plans based on construction standards encompass commercial and legal interactions related to legal obligations and the advertisement of such services as the recommendation outputs generated. As for the Applicant arguments regarding the reasoning of using Example 39 and/or hypothetical example vii from MPEP 2106.04(a)(1) for training/re-training the AI model claimed in claims 6 - 7,13 - 14 and 19 - 20, these do not apply since the Examiner did not consider the respective limitation steps to fall under mathematical concepts, rather these dependent claim limitation steps fell under certain method of organizing human activity (and under mental processes), as discussed above, due to their dependency to claims 1, 8 and 15.
For Step 2A-Prong 2 and Step 2B starting in p. 15: The Applicant alleges that the claims integrate, the judicial exception identified, into a practical application in view of the Ex Parte Desjardins case (i.e. referred to the “Precedential ARP Decision”) because “one of ordinary skill in the art would at minimum recognize improvements to computing systems for generating building plans and/or ML tools utilized in connection therewith as improvements to technology and/or a technical field, especially in view of the Precedential ARP Decision”. Thus, the claims are “clearly more than a drafting effort designed to monopolize any alleged abstract idea, and it is respectfully submitted that the recitations extend well beyond the scope of-and apply meaningful and compelling limits on-” any of the abstract ideas identified. However, the Examiner finds these arguments unpersuasive and respectfully disagrees. Firstly, the Ex Parte Desjardins case that is directed to evaluating claims related to training machine learning (ML) or Artificial Intelligence (AI) for learning new tasks while protecting knowledge about previous tasks, for this instant case, the alleged improvements are not reflected and does not cure the high level of generality that the computer components that further use the AI models, in the claims are still reciting. But mainly because, implementing an abstract idea on a generic computer that further utilizes ML tools, as asserted by the Applicant, for building planning decisions to identify/utilize location’s data that have similar characteristics to the selected location and to achieve the intended result of outputting recommendations “(e.g., home maintenance tasks, building layouts, community layouts, etc.)” (see ¶0008 from Applicant disclosure), does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer (see MPEP 2106.05 (f)). Further, the recited additional elements, individually and/or in combination that are allegedly improving “building planning systems”, allegedly, does not integrate a judicial exception into a practical application or provide an inventive concept” at Step 2B as these are invoking computers or other machinery (e.g. AI models) merely as a tool to perform an existing process (see MPEP 2106.05 (f)).
Further, the claimed invention, as recited in the claim limitations and their additional elements, must demonstrate and improve “upon conventional functioning of a computer, or upon conventional technology or technological processes a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art” (see MPEP 2106.05(a) and 2106.04(d)(1)). Therefore, “to show that the involvement of a computer assists in improving the technology, the claims must recite the details regarding how a computer aids the method, the extent to which the computer aids the method, or the significance of a computer to the performance of the method. Merely adding generic computer components to perform the method is not sufficient. Thus, the claim must include more than mere instructions to perform the method on a generic component or machinery to qualify as an improvement to an existing technology”. see MPEP 2106.05(a)(II) for more details. For instance, the Applicant disclosure lacks technological discussion/details of how the AI model is being used to improve building plans and merely recites the end result of “reduce an overall likelihood of loss at the enhanced building, to improve energy efficiency, to mitigate and/or prevent risk factors, and/or to decrease maintenance frequency” (see ¶0085 – 86 and also see ¶0035 and ¶0089 – 90 from Specs).
Thus, for all the reasons stated above, the Examiner respectfully disagrees, and maintains 35 USC § 101 rejection for these pending claims.
Regarding to Applicant's arguments of rejection under 35 USC §102 for the pending claims on page 19: Applicant’s arguments regarding these amended limitation steps in the pending claims are not persuasive. Applicant’s arguments with respect to claim(s) 1, 8 and 15 in view of Tehranchi, maintained herein, are unpersuasive because Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to general allegation(s) that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the Tehranchi reference. Further, claims 3, 5, 10, 12 and 17 are now taught by the combination of Tehranchi and Geylani under 35 USC § 103 rejection. Please, refer to the respective Claim Rejection - 35 USC §102 and 35 USC § 103 sections for further details. Therefore, the Examiner respectfully disagrees, and maintains 35 USC § 102 rejection for these pending claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 6, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
At least claims 1, 8 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over at least claims 1, 8 and 15 of Application No. 19/207,882 (reference application referred as ‘882 hereafter). Although the claims at issue are not identical, they are not patentably distinct from each other because the differences between the claims are considered to be anticipated as set forth below:
Instant claims
Co-pending or reference claims
(US App No. 19/207,882)
Claims 1, 8 and 15: A building planning computer system for generating a building plan using an artificial intelligence (AI) model component, the building planning computer system comprising at least one processor, an AI model component comprising one or more AT models, and at least one memory device, wherein the at least one processor is programmed to: (claim 1)
receive smart building analytics data associated with a first plurality of buildings each located at different locations, wherein the smart building analytics data includes at least electrical sensor data from each of the first plurality of buildings;
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings;
receive input data including at least a select location;
access the one or more artificial intelligence (AI) models trained to analyze input data associated with the select location;
input the smart building analytics data and the claims data into the one or more AI models to generate one or more outputs including recommendations for a building plan associated with the select location based upon the smart building analytics data from buildings at locations having similar characteristics to the select location and the claims data; and
transmit the one or more recommendations to a user computing device.
Claims 1, 8 and 15: A building planning computer system for generating a building plan using an artificial intelligence (AI) model component, the building planning computer system comprising at least one processor, an AI model component comprising one or more AI models, and at least one memory device, wherein the at least one processor is programmed to: (claim 1)
receive smart building analytics data associated with a first plurality of buildings each located at different locations;
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings;
train the one or more Al models using the smart building analytics data and the claims data, the one or more Al models trained to output a building plan for constructing an enhanced subdivision of a plurality of enhanced buildings at a select location, wherein each enhanced building includes materials and/or features customized for the select location that reduce an overall likelihood of loss at the corresponding enhanced building, and wherein the enhanced subdivision includes features and/or building positioning that improve an overall energy efficiency of the plurality of enhanced buildings;
input construction data into the one or more AI models for constructing the enhanced subdivision at the select location; and
output the building plan for the enhanced subdivision and each of the enhanced buildings including a materials list and design drawings.
Consequently, at least instant claims 1, 8 and 15 are covered by reference claims 1, 8 and 15 of app ‘882. Thus, these instant claims are anticipated by the application reference claims 1, 8 and 15 because both applicant’s pending application and the reference application cover every feature claim in which the instant claims are broadly recited and encompass the same disclosed technology. Moreover, both the instant claims and the reference application claims share identical invention titles which are directed to a method and a system for using artificial intelligence tools to combine smart building analytics data with actuarial data to generate recommendations for improved residential construction and/or maintenance, subdivision design, and community planning. Thus, this invention scope is further defined by both the instant claims as well as being covered by the independent reference claims 1, 8 and 15 from the reference application (see MPEP 804 (II)(B)(2) for more details).
At least claims 1, 8 and 15 are rejected on the ground of nonstatutory double patenting as being unpatentable over at least claims 1, 8 and 15 of Application No. 19/207,759 (reference application referred as ‘759 hereafter). Although the claims at issue are not identical, they are not patentably distinct from each other because the differences between the claims are considered to be anticipated as set forth below:
Instant claims
Co-pending or reference claims
(US App No. 19/207,759)
Claims 1, 8 and 15: A building planning computer system for generating a building plan using an artificial intelligence (AI) model component, the building planning computer system comprising at least one processor, an AI model component comprising one or more AT models, and at least one memory device, wherein the at least one processor is programmed to: (claim 1)
receive smart building analytics data associated with a first plurality of buildings each located at different locations, wherein the smart building analytics data includes at least electrical sensor data from each of the first plurality of buildings;
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings;
receive input data including at least a select location;
access the one or more artificial intelligence (AI) models trained to analyze input data associated with the select location;
input the smart building analytics data and the claims data into the one or more AI models to generate one or more outputs including recommendations for a building plan associated with the select location based upon the smart building analytics data from buildings at locations having similar characteristics to the select location and the claims data; and
transmit the one or more recommendations to a user computing device.
Claims 1, 8 and 15: A building planning computer system for generating a building plan using an artificial intelligence (AI) model component, the building planning computer system comprising at least one processor, an AI model component comprising one or more AI models, and at least one memory device, wherein the at least one processor is programmed to: (claim 1)
receive smart building analytics data associated with a first plurality of buildings each located at different locations, wherein the smart building analytics data includes at least data describing: (i) materials used to build each respective building, (ii) building features included within each respective building, and (iii) features surrounding each respective building of the first plurality of buildings;
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings, and wherein the claims data includes loss data associated with each respective building and an event associated with each loss;
train the one or more Al models using the smart building analytics data and the claims data, the one or more Al models trained to output a building plan for constructing an enhanced building at a select location, wherein the enhanced building includes recommended materials, recommended building features and/or surrounding features customized for the select location that reduce an overall likelihood of loss at the enhanced building;
input into the one or more AI models construction data for constructing the enhanced building at the select location; and
output the building plan for the enhanced building including a recommended materials list and design drawings including areas surrounding the enhanced building for constructing the enhanced building based upon the construction data.
Consequently, at least instant claims 1, 8 and 15 are covered by reference claims 1, 8 and 15 of app ‘759. Thus, these instant claims are anticipated by the application reference claims 1, 8 and 15 because both applicant’s pending application and the reference application cover every feature claim in which the instant claims are broadly recited and encompass the same disclosed technology. Moreover, both the instant claims and the reference application claims share identical invention titles which are directed to a method and a system for using artificial intelligence tools to combine smart building analytics data with actuarial data to generate recommendations for improved residential construction and/or maintenance, subdivision design, and community planning. Thus, this invention scope is further defined by both the instant claims as well as being covered by the independent reference claims 1, 8 and 15 from the reference application (see MPEP 804 (II)(B)(2) for more details).
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 analysis of this claimed invention recited in the claims begins in view of independent claim 1, the most representative claim of the independent claims set 1, 8 and 15, as follows:
At Step 1: Claims 1 – 7 falls under statutory category of a machine, while claims 15 – 20 are directed to an article of manufacture, and claims 8 - 14 are directed to a process.
At Step 2A Prong 1: Claim 1 (representative of claims 8 and 15) recites an abstract idea in the following limitations:
…receive smart building analytics data associated with a first plurality of buildings each located at different locations, wherein the smart building analytics data includes at least electrical sensor data from each of the first plurality of buildings;
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings;
receive input data including at least a select location;
access…to analyze input data associated with the select location;
input the smart building analytics data and the claims data…to generate one or more outputs including recommendations for a building plan associated with the select location based upon the smart building analytics data from buildings at locations having similar characteristics to the select location and the claims data; and
transmit the recommendations...
Generally, and as disclosed in the specification in ¶0032, this claimed invention “generate predictions and recommendations for a homes, communities, or other structures based upon documents and/or data including information about the homes or communities, such as home inspection reports, data containing information about the location and past insurance incidents, for example, (i) smart building analytics data relating to individual homes and structures, (ii) actuarial data such as past claims, insurance policies, and risk models, claims, (iii) construction standards data such as standards, codes, and plans, and/or (iv) location-based data, including topography information, vegetation information, and climate and weather information.” Further such recommendations can be “home maintenance tasks, building plans, community layouts, etc.” and “home maintenance tasks, scheduling further inspections, community layout recommendations, home construction recommendations, etc.” (see ¶0038 and ¶0053 from Applicant disclosure). However, the abstract idea(s) of a certain method of organizing human activity (See MPEP 2106.04(a)(2), subsection II) is recited in claim 1 in the form of “commercial or legal interactions”. Specifically, the abstract idea is recited in the step of “access the one or more artificial intelligence (AI) models trained to analyze input data…” and “input the smart building analytics data and the claims data into the one or more AI models to generate one or more outputs including recommendations for a building plan…” related to home insurance and construction standards (i.e. providing home/construction consulting services). Because accessing and analyzing “smart building analytics” and “claims data” to recommend tasks or actions related to home insurance, inspections and/or smart building plans based on construction standards at least encompasses commercial and legal interactions related to legal obligations and the advertisement of such services as the recommendations generated.
Similarly, these specific steps previously mentioned are also falling under the abstract idea of mental processes that can be practically be performed in the human mind or in pen and paper (See MPEP 2106.04(a)(2), subsection III). Because analyzing the input data of “smart building analytics” and “claims data” to recommend tasks or actions related to home insurance, inspections and/or smart building plans encompass evaluation, judgement and opinion. Also, these steps can either be done with the help of physical aid such as pen and paper or can be performed by humans without or with the assistance (e.g. tool) a computer. Thus, the steps do not negate and further still reads in the mental nature of the limitation(s) even when accessing trained AI models, when analyzing such information, as well as the concept is merely claimed to be performed on a generic computer that further access AI models trained and is merely using a computer as a tool to perform the concept of generating such recommendations for the select location based upon the smart building analytics data and the claims data (see MPEP 2106.04(a)(2)(III)(B & C)).
At Step 2A Prong 2: For independent claims 1, 8 and 15, The judicial exception(s) or abstract idea previously identified is not integrated into a practical application (see MPEP 2106.04 (d)). The claims recite the additional element(s) of a building planning computer system, (from claim 1); at least one non-transitory computer-readable storage media (from claim 15); at least one processor, an AI model component comprising one or more AI models, at least one memory device and a user computing device (from claims 1, 8 and 15). These additional elements, individually and in combination, and while considering the claims as a whole, are merely used as a tool to perform the abstract idea (See MPEP 2106.05(f)). Specifically, the steps are recited as being performed by the computer. The computer and the one or more artificial intelligence (AI) models trained accessed and used for data analysis are recited at a high level of generality that is being used as a tool to perform the generic computer functions for analyzing input data and to generate recommendation outputs related to home insurance, inspections and/or smart building plans. Thus, these steps mentioned above are further describing and applying the abstract idea without placing any limits on how the technological components are being improved, while distinguishing in the claim language, the performing limitations from functions that generic computer components can perform.
Finally, the steps directed to “receive” different types of data and “transmit” the recommendations in the representative claim are really nothing more than links to computer for implementing the use of ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components (refer to MPEP 2106.05 f (2)). Thus, in these limitation steps, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer.
Step 2B: For independent claims 1, 8 and 15, these claims do not provide an inventive concept. The recited additional elements of the claim(s) are the following: a building planning computer system, (from claim 1); at least one non-transitory computer-readable storage media (from claim 15); at least one processor, an AI model component comprising one or more AI models, at least one memory device and a user computing device (from claims 1, 8 and 15)., including the step of “accessing” the AI models trained to analyze input data. These additional elements are not sufficient to amount significantly more than the judicial exception or abstract idea (see MPEP 2106.05). Because, as indicated in Step 2A Prong 2, these additional element(s) claimed are merely, instructions to “apply” the abstract ideas, which cannot provide an inventive concept. Thus, even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer, which do not provide an inventive concept at Step 2B.
For dependent claims 2-7, 9-14 and 16 - 20, the same analysis is incorporated. Due to their dependency to the independent claims analyzed, these claims cover or fall under the same abstract idea(s) of a method of organizing human activity and mental processes. They describe additional limitations steps of:
Claims 2-7, 9-14 and 16 - 20: further describes the abstract idea of the method for generating a building which further discloses the generation and output of a building plan for AR/VR display and different simulations to determine the select location state and predicted state as well as a loss likelihood based on their comparison, the association of existing structure in the smart building analytics data as well as the type of recommendations and training data that is used to train and re-train the AI models based on location selection changes. Thus, being directed to the abstract idea group of “managing personal behavior or relationships or interactions between people” as it is related to commercial and legal interactions, specifically directed to legal obligations and the advertisement of such services as the recommendations generated that further requires evaluation, judgement and opinion.
Step 2A Prong 2 and Step 2B: For dependent claims 2, 6 – 7, 9, 13 – 14, 16 and 19 – 20, these claims recite the additional elements of: a user interface (UI) (from claim 2, 9 and 16); train the one or more models using training data (from claim 6, 13 and 19); and re-train the one or more AI models based upon one or more changes to the select location (from claim 7, 14 and 20). These additional elements recited are invoking computers merely used as a tool to perform or “apply” the abstract idea(s) to the existing process of outputting building plans for display and use AI models to feed training data to further adjust and optimize the models based on location selection changes. Thus, amounting to no more than mere instructions to “apply” the exception using a generic computer component (MPEP 2106.05(f) and (f)(2)). Accordingly, for the same reasons stated above, these additional element(s) claimed cannot provide an inventive concept at Step 2B.
Finally, the additional elements previously mentioned above, are nothing more than descriptive language about the elements that define the abstract idea, and these claims remain rejected under 101 as well.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1- 2, 4, 6 - 8 - 9, 11, 13 - 16 and 18 - 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tehranchi (U.S. Pub No. 20250238564 A1).
Regarding claims 1, 8 and 15:
Tehranchi teaches:
the building planning computer system comprising at least one processor, an AI model component comprising one or more AI models, and at least one memory device, wherein the at least one processor is programmed to: (See Fig. 1 (106, 108, 112 and 114); Fig. 2 (204 and 114); Fig. 3C (112, 370 and 376); Fig. 7 (700 and 710): Refer to ¶0124 – 126 for more information about the computer system and its processor(s) and memory and refer to ¶0025 for AI models access through “a cloud-based design services platform” and “a design application on a client device”.)
receive smart building analytics data associated with a first plurality of buildings each located at different locations, wherein the smart building analytics data includes at least electrical sensor data from each of the first plurality of buildings; (In ¶0027 – 28; Figs. 1 – 2 (110); Fig. 3A (304, 306 and 310); Fig. 3B (336 and 342, 348 and 350); Fig. 4B (412); Fig. 5A (502 – 504): teaches that the system’s “data capture layer 108 receives the property location information input by the user of the client application 102” and “utilizes multiple AI driven module that operate in parallel to rapidly obtain and analyze data from numerous data sources 110” wherein the data sources can “provide information about the property and/or local building regulations” such as “access information, privacy information, regulatory information, utility information, and/or natural elements of the site of the property” (i.e. “mandatory information”) and “utility locations”, in accordance to smart building analytics data examples given in ¶0073 from Applicant’s disclosure. See ¶0038 – 40 also. As for receiving smart building analytics data including electrical sensor data per building, this is already taught by this prior art since “the property design pipeline automatically obtains information from various data sources based on property address information provided by the user to access numerous sources of information” (see ¶0022) wherein such information can include “utility information” (see ¶0028) which is further directed to electrical sensor data claimed.)
receive claims data associated with a second plurality of buildings each located at different locations, wherein the first plurality of buildings includes at least some of the second plurality of buildings; (In ¶0027 - 28; Figs. 1 – 2 (110); Fig. 3A (306, 312 and 314); Fig. 3B (344 and 334); Fig. 4B (412); Fig. 5A (502 – 504); Fig. 5B (544): teaches that the system’s “data capture layer 108 receives the property location information input by the user of the client application 102” and “utilizes multiple AI driven module that operate in parallel to rapidly obtain and analyze data from numerous data sources 110” wherein the data sources can “provide information about the property and/or local building regulations” such as “access information, privacy information, regulatory information” (i.e. “mandatory information”), in accordance to claims data examples given in ¶0073 from Applicant’s disclosure. See ¶0038 – 40 also. Further, in ¶0049, “data retrieval and analysis modules 352 extract mandatory information module 340 from the property information 336” that further includes “information related to access, structural, and landscaping information 342. The access information identifies any roads, driveways, easements, or other means of accessing the property. The structural information identifies and buildings which currently exist on the property” which is directed to the second plurality of buildings each located at different locations.)
receive input data including at least a select location; (In ¶0027; Figs. 4A – 4B (402 and 404); Fig. 5A (502 – 504); Fig. 5B (542): teaches that the “data capture layer 108 receives the property location information input by the user of the client application 102” wherein “property location information can include, but is not limited to a street address, an APN, geographical coordinates, or other information identifying the location of the property”. See ¶0067 also.)
access the one or more artificial intelligence (AI) models trained to analyze input data associated with the select location; (In ¶0028; Fig. 5A (506 – 508); Fig. 5B (546 – 548): teaches that the “data capture layer 108 generates a preliminary site plan that includes various elements of the property based on the mandatory information” and “provides this preliminary site plan to the design processing layer 112”. Further “design processing layer 112 analyzes the preliminary site plan customizes the preliminary site plan according to the natural language prompts entered by the user that describe the features of the design project” which include “structural and/or landscape design features of the construction project”. Finally, the “design processing layer 112” utilizes “one or more machine learning models provided by the artificial intelligence services platform 114 to generate a proposed design for the construction project based on the preliminary site plan and the natural language prompts provided by the user.” See ¶0085 – 86 and ¶0094 – 95 also.)
input the smart building analytics data and the claims data into the one or more AI models to generate one or more outputs including recommendations for a building plan associated with the select location based upon the smart building analytics data from buildings at locations having similar characteristics to the select location and the claims data; and transmit the recommendations to a user computing device. (In ¶0028; Figs. 4C – 4I; Fig. 5A (508 – 514); Fig. 5B (544 – 556): teaches that the “design processing layer 112 provides the content associated with the preliminary site plan to the client application 102 and causes the client application 102 to present these on a user interface of the client application 102.” Further, the generated “design content 118 can include content such as but not limited to 2D and/or 3D models of the design project, point cloud representations of the design, detailed blueprints, and a detailed cost estimate for the construction project.” Further, the “design processing layer enables the user to provide feedback on the proposed design via the chat user interface shown in the examples which follow” wherein “the user is able to describe in natural language how the user would like to modify a proposed design without the user having an understanding of how these changes would be impacted by local results and regulations, setbacks, utility locations and accessibility, and other factors that constrain the proposed design” which is directed to considering similar characteristics to the selected location and the claims data. Finally, “the design processing layer 112 analyzes the natural language prompt to understand how the user would like to modify the proposed design, determines the various factors that constrain the proposed design, and modifies the design to satisfy the intent of the user as well as these factors” and “if an aspect of the modifications proposed by the user cannot be satisfied for various reasons, such as but not limited to code or regulations, setback requirements, or cost, the design processing layer 112 notifies the user of the aspects that could not be satisfied and why these aspects could not be satisfied.” See ¶0087 – 90 and ¶0093 – 99 also.)
Regarding claims 2, 9 and 16:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Tehranchi further teaches:
wherein the at least one processor is further programmed to: output the building plan on a user interface (UI) showing the select location by overlaying the building plan on at least one of a map, image, or video feed associated with the select location. (In ¶0070 – 71; Figs 4A – 4I; Fig. 5A (514); Fig. 5B (556): teaches in Fig. 4D the “user interface 400 presenting a preliminary site plan of the property” that “provides a representation of the current state of the property and may include shading, colorization, and/or other indicators identify various elements of the property, such as property boundaries, structures, pools, landscaping features, driveways, roads, and/or other means of accessing the property” and “the property design pipeline 104 proceeds to generate and present the proposed design as shown in FIGS. 4E-4I”. Further, in “FIG. 4E shows an example of the user interface 400 in which the property design pipeline 104 has generated the proposed design and is presenting a proposed site plan 420 for the project” wherein all these figures are showing the selected location being overlayed with the proposed design in the form of an image and/or map. See ¶0037 wherein “other generative models 220” can generate a proposed/finalized design by using images and output respective “2D and/or 3D renderings of the structures and/or landscape elements included in a design”.)
Regarding claims 4, 11 and 18:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Tehranchi further teaches:
wherein the smart building analytics data is associated with an existing structure, and the recommendations include at least one of a maintenance task recommendation or a recommendation to alter the existing structure. (In ¶0049; Fig. 4B; Figs. 4G – 4I; Fig. 5B (554 – 556); Fig. 5B (578 and 584): teaches that “preliminary site plan will identify” features of the property, and “the design processing layer 112 will attempt to incorporate these features into the proposed design or suggest that such features be modified or removed if necessary” wherein such features can include property structure (i.e. “structural information”) and “vegetation, decks or patios, pergolas, pools, gazebos, greenhouses, and/or other structures” (i.e. “landscaping information”). See ¶0075 also.)
Regarding claims 6, 13 and 19:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Tehranchi further teaches:
wherein the at least one processor is further programmed to: receive first training data including at least one of a plurality of historical location data, actuarial data, home data, or construction data; and train the one or more models using first training data. (In ¶0054; Fig. 5C (576, 580 and 592): teaches “data capture model training unit 357 of the data capture layer 108 trains the design capture black box AI model 214 using the property information 336, the mandatory information 340, and/or other information obtained or generated by the data capture layer 108” and “the data capture model training unit 357 trains the design capture black box AI model 214 which is used to generate at least a portion of the site plan data”. Refer to ¶0058, ¶0065 and ¶0103 – 105 also.)
Regarding claims 7, 14 and 20:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Tehranchi further teaches:
wherein the at least one processor is further programmed to generate second training data based on the input data and one or more changes to the select location; and re-train the one or more AI models based upon the second training data (In ¶0071; Fig. 4E (424 and 426) and Figs. 4F – 4H: teaches that “the design processing layer 112 constructs prompts to one or more models of the artificial intelligence services platform 114 to cause the models to generate a revised version of the proposed design that reflects the changes requested by the user”. Thus, “the revised version of the proposed design is presented to the user on the user interface 400 in response to each prompt” and “the revision process may be an iterative process in which the user provides prompts to further revise the proposed design until the user is satisfied with the proposed design” which is directed to re-training the AI models responsible of outputting the revised “proposed design” version and the prompt and revision data of the iterative revision process for the proposed design as the second training data being generated and used for the re-training of the AI model. Further, in ¶0054 the “data capture model training unit 357 of the data capture layer 108 trains the design capture black box AI model 214” also by using “other information obtained or generated by the data capture layer 108” which can be directed to second training data. Refer to ¶0109 – 111 for more details.)
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.
Claims 3, 5, 10, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tehranchi (U.S. Pub No. 20250238564 A1) in view of Geylani (U.S. Pub No. 20200234379 A1).
Regarding claims 3 and 10:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1 and 8, respectively.
Tehranchi teaches that “other generative models 220” can generate a proposed/finalized design by using images and output respective “2D and/or 3D renderings of the structures and/or landscape elements included in a design” (see ¶0037 and Figs 4C – 4I). However, Tehranchi does not explicitly teach the ability of outputting the building plan by specifically displaying an AR or VR overlay at the selected location. Thus, Geylani teaches:
wherein the at least one processor is further programmed to: output the building plan by displaying the building plan as an AR or VR overlay at the select location. (In ¶0024: teaches that the “graphical representation can include augmented and virtual reality” and the “calculation unit can be further configured to compute a simulation to determine a probable maximum loss of insured interest, property damage, or business interruption”. Refer to ¶0060 wherein the “End users will be able to see the augmented reality of the process diagram along with the structural 3D model of the insured asset. Structural damage simulations can project process disruption effects on the operation of the insured asset. SANCAR algorithms can then generate probable maximum loss of business interruption loss simulation” and see ¶0076 wherein “SANCAR then provides data using augmented reality to the GDMCU and UAV/Drone to compare the initial condition and the after loss condition of the insured location”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Tehranchi to provide the ability of outputting the building plan by specifically displaying an AR or VR overlay at the selected location, as taught by Geylani in order to allow “detailed automated data analytics to more accurately estimate probable maximum property and business interruption loss scenarios” and “minimize asymmetric information and moral hazard issues during the risk acceptance process of the insurance policies by using newly available technologies such as smart devices, sensors, virtual reality, augmented reality and 360 degree cameras, etc.” (¶0011 – 12; Geylani), see also MPEP 2143.I.G.
Regarding claims 5, 12 and 17:
Tehranchi, as shown in the rejection above, discloses the limitations of claims 1, 8 and 15, respectively.
Tehranchi further teaches:
further comprising: generate a first simulation for the select location to determine a present state of the select location; (In ¶0070; Fig. 8 (804); Fig. 4D: teaches that in ¶0076 “FIG. 4D shows an example of the user interface 400 presenting a preliminary site plan of the property” providing “a representation of the current state of the property and may include shading, colorization, and/or other indicators identify various elements of the property, such as property boundaries, structures, pools, landscaping features, driveways, roads, and/or other means of accessing the property”. Further, in ¶0076 the “property design pipeline 104 automatically obtains data from various data sources 110 that is provided to one or more generative models to create a preliminary site plan for the property”.)
generate a second simulation for the select location based on the recommendations to determine a predicted state of the select location; and (In ¶0071; Fig. 8 (806); Fig. 4E: teaches that in “FIG. 4E shows an example of the user interface 400 in which the property design pipeline 104 has generated the proposed design and is presenting a proposed site plan 420 for the project”. Then “user interface 400 includes prompts that request the user to input any changes that they would like to the structures in the proposed design in the prompt field 424 and to input any changes that they would like to make to the landscaping in the prompt field 426” and in response, “the design processing layer 112 constructs prompts to one or more models of the artificial intelligence services platform 114 to cause the models to generate a revised version of the proposed design that reflects the changes requested by the user. The revised version of the proposed design is presented to the user on the user interface 400 in response to each prompt.” Further in ¶0077, the “design processing layer 112 of the property design pipeline 104 generates the proposed designs for the project based on the natural language prompts input by the user and the preliminary site plan”.)
Tehranchi teaches that “the revision process may be an iterative process in which the user provides prompts to further revise the proposed design until the user is satisfied with the proposed design” (see ¶0071) wherein such revision includes “design proposal information 358” such as “constraints placed on the design by codes and setbacks 334, the property characteristics” (see ¶0056 – 57), “natural factors information 364, and other proposed design data 366” (see ¶0059 and ¶0062), as well as “user requirements” (see ¶0065 and see ¶0028 for general details). However, Tehranchi does not explicitly teach the ability of determining a change in a specific likelihood of loss at the selected location by comparing the location’s current and future states. Thus, Geylani further teaches:
determine a change in a likelihood of loss at the select location by comparing the present state of the location to the predicted state of the select location. (In ¶0076 – 77: teaches wherein the “SANCAR then provides data using augmented reality to the GDMCU and UAV/Drone to compare the initial condition and the after loss condition of the insured location” and “GDMCU and UAV/Drone also capture the damaged product line/processing unit 520 and compare their after loss condition with the initial condition. Additionally, the functional part of PNSN can also provide data about the conditions of the process units. Along with this data, the SANCAR can provide predictive analytics for the potential loss of business interruption at the insured location”, in accordance to the example of the determination of likelihood of loss changes given in ¶0080 - 83.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify Tehranchi to provide the ability of determining a change in a specific likelihood of loss at the selected location by comparing the location’s current and future states, as taught by Geylani in order to allow “detailed automated data analytics to more accurately estimate probable maximum property and business interruption loss scenarios” and “minimize asymmetric information and moral hazard issues during the risk acceptance process of the insurance policies by using newly available technologies such as smart devices, sensors, virtual reality, augmented reality and 360 degree cameras, etc.” (¶0011 – 12; Geylani), see also MPEP 2143.I.G.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Messervy (U.S. Patent No. 12518067 B2) is pertinent because it “relates to determining building damage and, more particularly, to systems and methods for determining roof damage of a building based upon a historical damage data.”
Li (U.S. Pub No. 20260043658 A1) is pertinent because it “describes techniques for using computing devices to perform automated operations related to analyzing visual data from images acquired in multiple rooms of a building to generate multiple types of building information including a building floor plan with interconnected polygonal room shapes, and for subsequently using the generated building information (referred to herein at times as “mapping information”) in one or more further automated manners.”
Splittstoesser (U.S. Pub No. 20220292822 A1) is pertinent because it “relates to determining building damage and, more particularly, to systems and methods for determining roof damage of a building based upon a historical damage data.”
Connaughton (U.S. Pub No. 20250005531 A1) is pertinent because it “generally relates to artificial intelligence in building construction.”
Kikuchi (U.S. Pub No. 20260044636 A1) is pertinent because it “relates to a construction process support system, method, and program that improve construction processes by enabling the use of natural language messages.”
Jha (U.S. Pub No. 20220327254 A1) is pertinent because it is about “systems and/or techniques for generating floor layouts associated with buildings and/or determining locations of units.”
Brown (U.S. Pub No. 20240346036 A1) is pertinent because it “relates more particularly to systems for managing and processing data of the building system.”
Graham (U.S. Pub No. 20220407598 A1) is pertinent because it “relates to a system providing an intuitive and semi-automated means of collecting and managing crowdsourced data via a crowdsourcing platform, and further leveraging the crowdsourced data to provide various features and services in the field of workplace and building automation and management.”
Little (U.S. Patent No. 11436828 B1) is pertinent because it “provide systems and methods to assist insurance policy holders to create and maintain an inventory of property. It is further desirable to provide systems and methods to assist policyholders in preparing and/or documenting a claim after an incident.”
Maestas (U.S. Patent No. 11055531 B1) is pertinent because it “generally relates to an augmented reality method of determining damage to physical objects and repairing or replacing those physical objects.”
Reddy (U.S. Pub No. 20160110722 A1) is pertinent because it is about “relates to utilizing digital objects associated with products that provide a variety of functions, including enabling certain particular services, such as warranty services and insurance services, related to the products.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ivonnemary Rivera Gonzalez whose telephone number is (571)272-6158. The examiner can normally be reached Mon - Fri 9:00AM - 5:30PM.
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/IVONNEMARY RIVERA GONZALEZ/Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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