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 the Claims
The amendment received on 08 April 2026 has been acknowledged and entered.
Claims 1-3 and 5-20 have been amended.
No new claims have been added.
Claims 1-20 are currently pending.
Response to Amendments and Arguments
Applicant's arguments filed 08 April 2026 with respect to the rejection of claims 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant argues (in REMARKS, pages 10-11 of 14) that claim 1 has been amended to recite the additional features of "implementing, by the computing device, one or more second machine-learned models to: process the one or more codes and one or more images of the structure captured after completion of at least one of the one or more project performance operations, and generate one or more prediction scores indicating a likelihood of passing an inspection based on the at least one of the one or more project performance operations being completed." Moreover, independent claim 1 is directed to an improvement in machine learning
technologies for performing project operations.
For example, as described at paragraphs [0055]- [0057], it is submitted that technical benefits are achieved by the claimed subject matter via the recited machine-learned models which ensure that codes applicable to the project are satisfied and that project performance operations are followed such that an inspection outcome can be predicted based on a prediction score. Accordingly, the claimed subject matter can avoid a failed inspection by an actual inspector and thus can avoid the need for repeating work, prevent possible injuries or unsafe conditions associated with a project and/or structure, and reduce the consumption of resources by ensuring proper materials are utilized, not wasted, etc …Here, there is an improvement to a technological process which is recited with particular features. Therefore, the claimed subject matter - when considered as a whole - is clearly directed to a technical improvement and is a practical application of the alleged abstract idea. Accordingly, for at least the above-discussed reasons Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. §101 of independent claim 1.
In response to Applicant’s argument, the Examiner respectfully disagrees and notes that first, the claims appear to be referencing a business solution to a business problem by use of generic computer components (e.g. computing device, machine-learned models) as tools to implement the abstract idea. Secondly, the overall performance of the system, computing device, or machine-learned models are not improved. Lastly, Applicant has not shown a teaching in the specification on how the invention improves a technology or establishes a clear nexus between the claim language and the improvement to technology where both the claims and the specification support the asserted technical improvement. Therefore, the Examiner maintains the claims are patent ineligible.
Applicant argues (in REMARKS, page 11 of 14) that regarding Independent Claims 15 and 20 and Dependent Claims, for at least similar reasons as set forth above, Applicant submits that independent claims 15 and 20 and the dependent claims herein recite patent eligible subject matter under 35 U.S.C. § 101 and requests withdrawal of the rejection to these claims and allowance in due course. Accordingly, withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101 is respectfully requested and it is submitted that the application is in condition for allowance.
In response to Applicant’s argument, the Examiner respectfully disagrees for reasons stated above regarding the rejection of claim 1.
Applicant’s arguments, see REMARKS, pages 12-14 of 14, filed 08 April 2026, with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered and are persuasive. The rejection of claims 1-20 under 35 U.S.C. 103 has been withdrawn.
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-14 are directed to a method (i.e., a process); Claims 15-19 are directed to computing device (i.e., a machine); and claim 20 is directed to a non-transitory computer readable medium (i.e., a machine). Therefore, claims 1-20 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claims 1, 15 and 20, substantially recites
obtaining context data descriptive of a project associated with a structure, the context data including textual data including one or more codes associated with the structure;
generate one or more project performance operations for the project based on the context data, wherein the one or more project performance operations include operations for completing the project in compliance with the one or more codes; and
providing, the one or more project performance operations.
Implementing one or more second machine-learned models to:
process the one or more codes and one or more images of the structure captured after completion of at least one of the one or more project performance operations, and
generate one or more prediction scores indicating a likelihood of passing an inspection based on the at least one of the one or more project performance operations being completed; and
providing the one or more prediction scores as a second output.
The limitations stated above are processes/ functions that under broadest reasonable interpretation (i.e., providing project performance operation) covers “certain methods of organizing human activity” (managing personal behavior or relationships or interactions between people and commercial or legal interactions and following rules or instructions). Therefore, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claims 1, 15 and 20 as a whole, amounts to: (i) merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent).
The additional elements of (i) a computing device comprising one or more processors, one or more first machine-learned models, and one or more second machine-learned models in claim 1;
a computing device, an output, one or more memories configured to store instructions; one or more processors configured to execute the instructions to perform operations, one or more first machine-learned models, and one or more second machine-learned models in claim 15; and
non-transitory computer readable medium storing instructions, executed by a processor, one or more first machine-learned models, and one or more second machine-learned models in claim 20 are recited at a high-level of generality (See specification [0091] The user computing system 100 can include any type of computing device, such as, for example, a personal computing device (e.g., laptop or desktop), a mobile computing device (e.g., smartphone or tablet), a gaming console or controller, a wearable computing device (e.g., augmented-reality goggles), an embedded computing device, or any other type of computing device. [0094] user computing system 100 and/or server computing system 300 may form part of an application system which can provide a tool via one or more machine-learned models [0100-101] , the one or more machine-learned models 132 can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other types of machine-learned models, including non-linear models and/or linear models. [0112-116] Example machine-learned models include neural networks or other multi-layer non-linear models. Example neural networks include feed forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks. [0229]), such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
Accordingly, these additional elements, when viewed as a whole/ordered combination (as shown in Fig.2A-B& Fig 12), do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than: (i) “apply it” (or an equivalent), and are not a practical application of the abstract idea. The same analysis applies here in Step 2B, i.e., (i) merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)) does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional elements of: (i) a computing device comprising one or more processors, one or more first machine-learned models, the computing device, an output, one or more memories configured to store instructions; one or more processors configured to execute the instructions to perform operations, non-transitory computer readable medium storing instructions, executed by a processor, do not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination ( as shown in Fig.2A-B& Fig 12), nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. Thus, the claims are ineligible.
Dependent Claims Step 2A:
The limitations of the dependent claims but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Additionally, for the same reasons as above, the limitations fail to integrate the abstract idea into a practical application because they use the same general technological environment and instructions to implement the abstract idea (e.g., using computers to communicate data). Claims 2, 10, 18 and 19 add the element “images captured by a user”, this fails to integrate the abstract idea into a practical application because adds insignificant extra-solution activity to the judicial exception.
The dependent claims add the elements “ database” , “ second machine-learned model“ third machine-learned models”, “ fourth machine-learned models”, “fifth machine-learned models”, which fail to integrate the abstract idea into a practical application because merely invoking the generic components as a tool to perform the abstract idea or “apply it”. Therefore, the claims recite an abstract idea.
Dependent Claims Step 2B:
The dependent claims merely use the same general technological environment and instructions to implement the abstract idea. Accordingly, the claims are not directed to significantly more than the exception itself. ). Claims 2, 10, 18 and 19 add the element “images captured by a user”, recited at a high-level of generality ( See [0109] The user computing system 100 may include a capture device 180 that is capable of capturing media content (e.g., photos, videos, etc.), according to various examples of the disclosure. For example, the capture device 180 can include an image capturer 182 (e.g., a camera) which is configured to capture images (e.g., photos, videos, etc.).), which does not amount to significantly more for the same reasons it fails to integrate the abstract idea into a practical application
The dependent claims add the elements “captured by a user” “ database” “ second machine-learned models”, “ third machine-learned models”, “ fourth machine-learned models”, “fifth machine-learned models”, are recited at a high-level of generality ( see specification [0114] the server computing system 300 can include and/or be communicatively connected with a search engine 340 that may be utilized to crawl one or more databases (and/or resources). [0141] pre-inspection operation 3400 can implement the one or more machine-learned models 3820 (e.g., the one or more code identification models 1020) from the application system 3800 to identify codes which are applicable to the project and/or the structure associated with the project [0100-102]), therefore, they do not amount to significantly more for the same reasons they fail to integrate the abstract idea into a practical application. Therefore, the dependent claims are not eligible subject matter under § 101.
Prior Art Discussion
As per independent claims 1, 15, and 20, the best prior art:
1) Murphy et al. (US PG Pub. 20220292240 A1) discloses artificial intelligence determination of building metrics for code compliance, wherein the AI assesses whether a building described in design plans complies with a relevant code set forth by an authority having jurisdiction. Codes may include, for example, codes enforcing fire safety and the Americans with Disabilities Act.
2) Delplace et al. (US PG Pub. 20140365259 A1) discloses in-field installation record of a project where upon completion of a task, task data is sent from handheld tool to a reporting source which then generates an asset report to information management system which facilitates tracking the progress of work at the construction site and automatically updating records such as blueprints in real-time using record updater so that they reflect the as-built configuration of the building.
3) Zass et al. (US PG Pub. 20200413011 A1) discloses controlling image acquisition robots in construction sites where a first image corresponding to a first point in time and a second image corresponding to a second point in time may be obtained and analyzed to determine whether a change occurred in a particular area of a construction site between the first point in time and the second point in time; and if is that a higher quality image of the particular area of the construction site is needed, an image acquisition robot may be controlled to capture images.
4) Reznek et al. (US PG Pub. 20220318980 A1) discloses computer vision methods for loss prediction and asset evaluation based on aerial images by using a computing platform that receives historical images and historical inspection results to train a roof waiver model, then the computing platform may receive a new image corresponding to a particular residential property, and using a roof waiver model, the computing platform analyzes the new image to output of a likelihood of passing inspection, and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.
However, neither Murphy et al., Delplace et al., Zass et al., and Reznek et al., alone or in combination, anticipates, reasonably teaches, nor renders obvious the below noted features of applicant's invention as the noted features amount to more than a predictable use of elements in the prior art.
The features are:
implementing, by the computing device, one or more second machine-learned models to: process the one or more codes and one or more images of the structure captured after completion of at least one of the one or more project performance operations, and
generate one or more prediction scores indicating a likelihood of passing an inspection based on the at least one of the one or more project performance operations being completed; and
providing, by the computing device, the one or more prediction scores as a second output.
As per independent claims 1, 15, and 20, the best Foreign prior art:
4) Satomi (WO 2021256141 A1) discloses a prediction score calculation device, prediction score calculation method, prediction score calculation program, and learning device used to output a prediction score of a quality determination of a final inspection when inspection data of a process inspection of a production line is input to a machine learning unit.
However, Satomi does not, alone or in combination, anticipate, reasonably teach, nor render obvious the below noted features of applicant's invention as the noted features amount to more than a predictable use of elements in the prior art.
The features are:
implementing, by the computing device, one or more second machine-learned models to: process the one or more codes and one or more images of the structure captured after completion of at least one of the one or more project performance operations, and
generate one or more prediction scores indicating a likelihood of passing an inspection based on the at least one of the one or more project performance operations being completed; and
providing, by the computing device, the one or more prediction scores as a second output.
As per independent claims 1, 15, and 20, the best NPL prior art:
1) Peter Hevesi et al. “Towards Construction Progress Estimation Based on Images Captured on Site”, 11 March 2021, Industrial IoT Technologies and Applications, pp 141-161, discloses that as a consequence of the instrumentation difficulties the use of image processing techniques, in particular in conjunction with aerial, drone-based surveillance of construction sites is a growing field. A two-stage model leveraging state-of-the-art components representing human experts knowledge about project states in different types of construction processes Isis proposed. The first stage involves image processing to extract information about the visual cues related to the targeted process; and the second stage focuses on predicting the current project state based on the detected object and resources on the images.
2) “The world’s first 3D AI recognition system for large concrete structures”, 28 September 2022, mynewsdesk.com, 8 pages discloses Kiwa and Spotscale have entered into a partnership agreement for the development of the inspection technology of the future, one that uses AI to predict the lifetime of concrete structures, and AI (artificial intelligence) provides the opportunity to use high-resolution images taken by drones to map not only very minor but also major damage to inspected constructions (such as bridges, dams, power stations and water towers, for example). This technology can be used to monitor millions of individual flaws and defects as well as to categorise them in a way that is impossible to achieve with conventional inspection methods.
However, neither Peter Hevesi et al. or “The world’s first 3D AI recognition system for large concrete structures”, alone or in combination, anticipates, reasonably teaches, nor renders obvious the below noted features of applicant's invention as the noted features amount to more than a predictable use of elements in the prior art.
The features are:
implementing, by the computing device, one or more second machine-learned models to: process the one or more codes and one or more images of the structure captured after completion of at least one of the one or more project performance operations, and
generate one or more prediction scores indicating a likelihood of passing an inspection based on the at least one of the one or more project performance operations being completed; and
providing, by the computing device, the one or more prediction scores as a second output.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDA A. NELSON whose telephone number is (571)272-7076. The examiner can normally be reached Monday-Friday, 10:00am - 6:30pm.
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/F.A.N/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628
