DETAILED ACTION
Status of Claims
This is a first office action on the merits in response to the application filed on5/29/2025.
Claims 1-8 are currently pending and have been examined.
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 .
Priority
This application claims priority of Provisional Application 63/664218 filed on 6/26/2024 and Provisional Application 63/652944 filed on 5/29/2024. Applicant's claim for the benefit of this prior-filed application is acknowledged.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 5-8 are directed toward a process and claims 1-4 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system for generating proposals for infrastructure modalities, comprising: an input interface configured to receive and preprocess data including geographical location, site dimensions, environmental constraints, and specific electrical standards; a lightweight generative or rendering pipeline configured to generate preliminary 2D plan view designs based on the preprocessed data; a generative model selected from the group consisting of diffusion, transformer- based, GAN, or other deep generative architectures comprising a generator and a discriminator, wherein: the generator is configured to create 3D models and 2D drawing sets from selected designs, and the discriminator is configured to evaluate the generated designs against predefined criteria; a cloud-based backend infrastructure provided by one or more cloud infrastructure providers configured to support data processing and integration with third-party services; wherein the system is configured to generate optimized modality design proposals in response to user inputs (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing input data including geographical location, site dimensions, environmental constraints, and specific electrical standards to generate 2D designs and using generic generative AI to evaluate the designs and optimize the proposals based on the users inputs, which is managing how humans interact for commercial purposes.
Dependent claims 2-4 and 6-8 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “a system for: an input interface configured to; a lightweight generative or rendering pipeline configured to; comprising a generator and a discriminator, wherein: the generator is configured to; and the discriminator is configured to; a cloud-based backend infrastructure provided by one or more cloud infrastructure providers configured to; wherein the system is configured to (claim 1)”; “a generative model selected from the group consisting of diffusion, transformer- based, GAN, or other deep generative architectures (claims 1 and 5)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-4 and 6-8 further narrow the abstract idea and dependent claims 2, 4, and 6 additionally recite “a user interface (claim 2); a geographic information systems (claim 4); applying deep learning algorithms (claim 6)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05(f)).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, Method; System Independent claims 1 and 5 recite “a system for: an input interface configured to; a lightweight generative or rendering pipeline configured to; comprising a generator and a discriminator, wherein: the generator is configured to; and the discriminator is configured to; a cloud-based backend infrastructure provided by one or more cloud infrastructure providers configured to; wherein the system is configured to (claim 1)”; “a generative model selected from the group consisting of diffusion, transformer- based, GAN, or other deep generative architectures (claims 1 and 5)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification where the interface disclosed is a generic user interface running on a general purpose computer. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-4 and 6-8 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 2, 4, and 6 additionally recite “a user interface (claim 2); a geographic information systems (claim 4); applying deep learning algorithms (claim 6)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-8 is/are rejected under 35 U.S.C. 102(a)(2) as being taught by Tehranchi et al. (US 2025/0238564 A1).
Regarding Claims 1 and 5: Tehranchi et al. teach a system for generating proposals for infrastructure modalities, comprising (See Figure 1, Abstract, Paragraph 0025, and claim 1):
an input interface configured to receive and preprocess data including geographical location, site dimensions, environmental constraints, and specific electrical standards (See Figure 3A, Figure 3B, Figure 3C, Paragraph 0025 – “a user input unit 106, a data capture layer 108, a design processing layer 112, and a design routing layer 116”, Paragraph 0038 – “the data sources 110 include high-resolution elevation data 302, public property data 304, private property data 306, point cloud data 308, satellite and aerial imagery 310, codes and regulations data 312, and official property survey data 314 … the property information 336 includes county records data 316, topographic data 318, parcel boundary data 320, building footprint data 322, roof lines data 324, vegetation top view data 326, driveway location data 328, irrigation equipment locations 330, pool location data 332, and codes and setbacks data 334”, and Paragraph 0056 – “input by user in the user interface”);
a lightweight generative or rendering pipeline configured to generate preliminary 2D plan view designs based on the preprocessed data (See Paragraph 0023 – “two-dimensional (2D) and/or three-dimensional (3D) models of the design project, point cloud representations of the design, detailed blueprints, and a detailed cost estimate for the construction project”, Paragraph 0028, Paragraph 0030, and Paragraph 0037);
a generative model selected from the group consisting of diffusion, transformer- based, GAN, or other deep generative architectures comprising a generator and a discriminator, wherein: the generator is configured to create 3D models and 2D drawing sets from selected designs, and the discriminator is configured to evaluate the generated designs against predefined criteria (See Paragraph 0030 – “The artificial intelligence services platform 114 implements one or more generative models that are configured to receive textual natural language prompt as an input and to generate textual content, image content, 2D or 3D plans or models of a design, and/or other content associated with the design project”, Paragraph 0037 – “The image generative models are used by the property design pipeline 104 to generate preliminary site plans, plans for proposed designs, blueprints, and 2D and/or 3D renderings of the structures and/or landscape elements included in a design. GPT-4V models are used in some implementations to analyze sample images of structural and/or landscape elements provided by the user to extract information from the sample images that can be used to generate a design for a construction project”, Paragraph 0055, Paragraph 0063, and claim 1);
a cloud-based backend infrastructure provided by one or more cloud infrastructure providers configured to support data processing and integration with third-party services (See Paragraphs 0025-0026, Paragraph 0116, Paragraph 0122, and Paragraph 0127);
wherein the system is configured to generate optimized modality design proposals in response to user inputs (See Paragraph 0022 – “The property design pipeline implements a parallel processing design in which many of the tasks performed by the various components of the pipeline are performed by AI driven modules that collect and analyze data and generate content in parallel with other processed to substantially reduce the amount of time that the system takes to generate the proposed design. The proposed design is then presented to the user on the user interface of the design application”, Paragraph 0028, Paragraph 0037 – “one or more generative models that can generate various types of content for the property design pipeline 104 for a proposed design or finalized design”, Paragraph 0057, and claim 1).
Regarding Claim 2: Tehranchi et al. teach the limitations of claim 1. Tehranchi et al. further teach wherein the input interface includes a user interface configured to display the preliminary 2D plan view designs and receive user selections of the designs for further processing by the generative model (See Paragraph 0030 – “The artificial intelligence services platform 114 implements one or more generative models that are configured to receive textual natural language prompt as an input and to generate textual content, image content, 2D or 3D plans or models of a design, and/or other content associated with the design project”, Paragraph 0037 – “The image generative models are used by the property design pipeline 104 to generate preliminary site plans, plans for proposed designs, blueprints, and 2D and/or 3D renderings of the structures and/or landscape elements included in a design. GPT-4V models are used in some implementations to analyze sample images of structural and/or landscape elements provided by the user to extract information from the sample images that can be used to generate a design for a construction project”, Paragraph 0055, Paragraph 0057, Paragraph 0063, and claim 1).
Regarding Claim 3: Tehranchi et al. teach the limitations of claim 1. Tehranchi et al. further teach wherein the generator utilizes input parameters comprising the user inputs, geographical constraints, construction standards and electrical requirements to simulate realistic infrastructure layouts (See Figure 3A, Figure 3B, Figure 3C, Paragraph 0025 – “a user input unit 106, a data capture layer 108, a design processing layer 112, and a design routing layer 116”, Paragraph 0030 – “The artificial intelligence services platform 114 implements one or more generative models that are configured to receive textual natural language prompt as an input and to generate textual content, image content, 2D or 3D plans or models of a design, and/or other content associated with the design project”, Paragraph 0038 – “the data sources 110 include high-resolution elevation data 302, public property data 304, private property data 306, point cloud data 308, satellite and aerial imagery 310, codes and regulations data 312, and official property survey data 314 … the property information 336 includes county records data 316, topographic data 318, parcel boundary data 320, building footprint data 322, roof lines data 324, vegetation top view data 326, driveway location data 328, irrigation equipment locations 330, pool location data 332, and codes and setbacks data 334”, and Paragraph 0056 – “input by user in the user interface”).
Regarding Claim 4: Tehranchi et al. teach the limitations of claim 1. Tehranchi et al. further teach further comprising a hybrid retrieval pipeline using dense, sparse, or graph embeddings and post-retrieval re-ranking or answer verification for integration with geographic information systems (GIS), weather and environmental services and design tools for enhancing data accuracy and design detail (See Figure 3A, Figure 3B, Figure 3C, Paragraph 0079, Paragraph 0081, Paragraph 0129).
Regarding Claim 6: Tehranchi et al. teach the limitations of claim 5. Tehranchi et al. further teach wherein generating preliminary 2D plan view designs includes applying deep learning algorithms implemented in one or more ML frameworks selected from PyTorch 2.x, TensorFlow, JAX, or their successors to explore a range of design possibilities based on the input data (See Paragraph 0030 – “The artificial intelligence services platform 114 implements one or more generative models that are configured to receive textual natural language prompt as an input and to generate textual content, image content, 2D or 3D plans or models of a design, and/or other content associated with the design project”, Paragraph 0036 – “implemented using a GPT model, such as but not limited to a GPT-3 model, a GPT-4 model, and GPT-4 with Vision (GPT-4V) models. Other generative models, such as a Bidirectional Encoder Representations from Transformers (BERT) model, a Pathways Language Model (PaLM), a Unified Pre-Trained Language Model (UniLM), XLNet, or other such models may be utilized in other implementations”, Paragraph 0037 – “The image generative models are used by the property design pipeline 104 to generate preliminary site plans, plans for proposed designs, blueprints, and 2D and/or 3D renderings of the structures and/or landscape elements included in a design. GPT-4V models are used in some implementations to analyze sample images of structural and/or landscape elements provided by the user to extract information from the sample images that can be used to generate a design for a construction project”, Paragraph 0055, Paragraph 0057, Paragraph 0063, and claim 1).
Regarding Claim 7: Tehranchi et al. teach the limitations of claim 5. Tehranchi et al. further teach wherein refining the selected designs includes an iterative process between the generator and discriminator components of the generative model to enhance design accuracy and compliance with regulatory standards (See Figure 4E and Paragraph 0071 – “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”).
Regarding Claim 8: Tehranchi et al. teach the limitations of claim 5. Tehranchi et al. further teach further comprising integrating feedback from engineers and stakeholders during the refining step to incorporate practical insights and requirements into the modality design (See Paragraph 0023, Paragraph 0028, Paragraph 0030, and Paragraph 0077).
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached 9-5 Monday-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
Prosecution Insights