DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 have been reviewed and are under consideration by this office action.
Notice to Applicant
The following is a Final Office action. Applicant, on 06/16/2026, amended claims. Claims 1-20 are pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are received and acknowledged.
The amended claims overcome the 102/103 rejections by adding new limitations to the independent claims and are therefore withdrawn.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, but they are not persuasive.
Applicant contends that similar to Example 39, the present claims recite a closed loop training method which improves the functioning of the AI model.
Examiner respectfully disagrees. The cited example requires applying transformation to digital images to create a modified set of digital images, training a neural network on a plurality of image types including the images, modified images, and non-facial images; creating a second training set; and retraining the neural network with the specification providing further details regarding transformations and backpropagation methods that uses a gradient of a mathematical loss to adjust weights to provide a robust facial detection model that can detect faces in distorted images. The present claims require training based on spatial data and retraining using incorrect mapping. Further the courts determined that training/retraining ML/AI is incident to the nature of the technology. have Appeal 2025-003304 Application 17/304,491 contrasts DesJardins and REcentive: As the Federal Circuit explained, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement.” Recentive at 1212.
The 101 Rejection is updated and maintained below.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claim(s) is/are directed to statutory categories.
Step 2A, Prong One – The claims are found to recite limitations that set forth the abstract idea(s), namely in independent claims recite a series of steps for the abstract idea recited below.
Regarding independent claim(s) 1, 8, and 18, (additional elements bolded)
A method comprising/A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to/A non-transitory, computer-readable storage medium comprising instructions recorded there on, wherein the instructions when executed by at least one data processor of a system, cause the system to:
obtaining a spatial data structure and an updated construction data regarding a construction of a particular structure,
wherein the spatial data structure represents multiple elements of construction projects other than the particular structure,
wherein the spatial data structure is standardized;
using an artificial intelligence (AI) model, trained based on the spatial data structure excluding the particular structure, generate a mapping between the updated construction data regarding the construction of the particular structure and the spatial data structure,
wherein the updated construction data indicates whether a particular element associated with the particular spatial data is installed;
based on the mapping generated by the Al model, determining a status of the construction of the particular structure;
obtaining the mapping, that includes an incorrect mapping between the updated construction data regarding the construction of the particular structure and the spatial data structure; and
retraining the Al model (recited at a high level of generality) using the mapping, including the incorrect mapping, between the updated construction data regarding the construction of the particular structure and the spatial data structure.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea groupings of “Mental processes—concepts performed in the human mind” (observation, evaluation, judgment, opinion) as the claims are directed towards obtaining data representing multiple elements of construction projects, generate mapping between construction data, and determining a status of the structure all of which are concepts capable of being performed in the human mind (i.e. via pen and paper).
Further the claims are directed towards the abstract idea grouping of “Certain methods of organizing human activity” — commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) as the claims are directed towards collection and comparison of status data for multiple construction projects (See Specification,[04]).
Step 2A, Prong Two - This judicial exception is not integrated into a practical application. The independent claims utilize at least the additional elements bolded above. The additional elements are performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Step 2B - The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are just “apply it” on a computer. (See MPEP 2106.05(f) – Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Regarding Claim(s) 2, 3, 6, 9, 10, 14, 15, 16, 19, and 20, the claim further narrows the abstract idea or recite additional elements previously addressed in the independent claims. Examiner notes that claim 15 recites LIDAR or image data but does not explicitly recite the use of LIDAR but merely the data.
Regarding Claim(s) 4-5 and 11-12, the claim further recite the additional element(s) of training the Al model (recited at a high level of generality). This element(s) is performing the steps would be no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f) and/or amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) in Steps 2A-Prong 2 and 2B.
Accordingly, the claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or 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.
Examining Claims with Respect to Prior Art
Claims 1, 8, and 18, though directed to non-statutory subject matter, are deemed to define over the currently known prior art under 35 USC 102 and 103. Examiner interprets based upon the claim limitations that there is no currently known prior art that discloses the features relating to: “A method comprising/A system comprising: at least one hardware processor; and at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to/A non-transitory, computer-readable storage medium comprising instructions recorded there on, wherein the instructions when executed by at least one data processor of a system, cause the system to: obtaining a spatial data structure and an updated construction data regarding a construction of a particular structure, wherein the spatial data structure represents multiple elements of construction projects other than the particular structure, wherein the spatial data structure is standardized; using an artificial intelligence (AI) model, trained based on the spatial data structure excluding the particular structure, generate a mapping between the updated construction data regarding the construction of the particular structure and the spatial data structure, wherein the updated construction data indicates whether a particular element associated with the particular spatial data is installed; based on the mapping generated by the Al model, determining a status of the construction of the particular structure; obtaining the mapping, that includes an incorrect mapping between the updated construction data regarding the construction of the particular structure and the spatial data structure; and retraining the Al model using the mapping, including the incorrect mapping, between the updated construction data regarding the construction of the particular structure and the spatial data structure.”
The reason to withdraw the 35 USC 103 rejection of claims 1-20 in the instant application is because the prior art of record fails to teach the overall combination as claimed. Therefore, it would not have been obvious to one of ordinary skill in the art to modify the prior art to meet the combination above without unequivocal hindsight and one of ordinary skill would have no reason to do so. Upon further searching the examiner could not identify any prior art to teach these limitations. The prior art on record, alone or in combination, neither anticipates, reasonably teaches, not renders obvious the Applicant’s claimed invention.
The closest relevant art is as follows:
Golparvar et al. (US 20190325089 A1) – The prior art teaches a computer-implemented method of evaluating progress of a structure undergoing construction in near real-time., the method comprising: receiving building information modeling (BIM) data in a non-standardized format for a set of structures undergoing construction, receiving scheduling data associated with construction of each structure in the set of structures, wherein the scheduling data for each structure includes an expected progress of construction activities at different points in time, and wherein the BIM data for each structure includes digital drawings of elements of the structure.. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Clay et al. (US 20060074608 A1) – The prior art teaches accessing a database storing construction data that associates multiple elements of construction projects and associated construction activities in a hierarchical configuration and wherein the hierarchical configuration defines branches that each have one or more types of elements and construction activities including a group and a system, a subsystem, a component, or a subcomponent at respective levels, and wherein the hierarchical configuration defines unidirectional paths between the levels of the branches. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Murphy et al. (US 20220292230 A1) – The prior art teaches wherein the ML model is trained based on a training dataset including multiple mappings of elements of structures other than the particular structure or construction activities for the structures to the multiple elements or the associated construction activities in the hierarchical configuration and retraining the ML model based on the status of the construction including a comparison of multiple elements or construction activities for the particular structure to the multiple elements or the associated construction activities in the hierarchical configuration. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Song et al. (US 20060044307 A1) – The prior art teaches predicting, based on the model, a cost to complete the construction of the particular structure relative to an expected cost to complete the construction of the particular structure and estimating, based on the model, a cost of the construction of the particular structure at a point in time relative to an expected cost of the construction of the particular structure at the point in time. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Gardner et al. (US 20230162083 A1) – The prior art teaches machine learning techniques are provided for ensuring quality and consistency of the data in a digital representation of infrastructure (e.g., a BIM or digital twin). A machine learning model learns the structure of the digital representation of infrastructure, and then detects and suggests fixes for data errors. The machine learning model may include an embedding generator, an autoencoder, and decoding logic, employing embeddings and metamorphic truth to enable the handling of heterogenous data, with missing and erroneous property values. The machine learning model may be trained in an unsupervised manner from the digital representation of infrastructure itself. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Sudry et al. (US 20220198709 A1) – The prior art teaches an expected construction state of the corresponding CAE. Optionally, BuildMonitor 130 comprises a classifier database 142 storing a plurality of classifiers (which may be referred to herein as a “classifier pool”). A classifier comprised in classifier database 142 may be designated to evaluate images comprising of views of a given object to classify a state of a given CAE associated with the given object. Optionally, the classifier is a classifier designated evaluate and classify the at least one image as indicating the CAE to be in one of a plurality of possible states, or to provide respective likelihoods of the CAE to be in two or more of a plurality of possible states. Optionally, the classifier is a binary classifier providing a yes/no output regarding one or more CAE state. The classifiers stored in classifier database 142 may be generated through a machine learning process that trains classifiers using reference images of an object designated as indicating a particular state of a CAE. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Powles et al. (US 20220391627 A1) – The prior art teaches autonomous, near real-time, and highly accurate and comprehensive building take-offs, complete construction detailing or estimates, detailed bill of materials, plan analysis (including detection of a number of non-standardized objects, such as doors or windows), as well as transforming 2D drawings into 3D and/or providing Building Information Modeling (BIM). The two dimensional real-world architectural plan can include multivariate non-standardized architectural symbols, which define numerous objects including trees, bathrooms, doors, stairs, windows, and floor finishes, lines, including solid, hollow, dashed and dotted lines. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Sariskan et al. (US 20200279364 A1) – The prior art teaches one or more inventions relating to a system or tool using machine learning in connection with assessments of structures. This new tool is mainly referred to herein as a machine learning tool for structures, although sometimes it is also referred to simply as the tool or the machine learning tool. This machine learning tool for structures is specially trained and programmed to use machine learning to assess performance of structures, identify entireties or portions of structures from images or drawings, assess damage to structures, or any combination of the foregoing. References throughout the present disclosure to machine learning encompass deep learning. It is to be understood that the present invention(s) fall within the deep learning subset of machine learning. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Ladha et al. (US 20180012125 A1) – The prior art teaches a technique for monitoring construction of a structure. In an example, a robot with a sensor, such as a LIDAR device, enters a building and obtains sensor readings of the building. The sensor data is analyzed and components related to the building are identified. The components are mapped to corresponding components of an architect's three dimensional design of the building, and the installation of the components is checked for accuracy. When a discrepancy above a certain threshold is detected, an error is flagged and project managers are notified. Construction progress updates do not give credit for completed construction that includes an error, resulting in improved accuracy progress updates and corresponding improved accuracy for project schedule and cost estimates.. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
Budlong et al. (US 20210342962 A1) – The prior art teaches a method described herein extend the technology used for standardizing zoning and/or future land use by looking for patterns in the data that can be made used—with “truth data”—to better identify optimal land use utility. The invention also includes provisions for incorporating machine learning into the data standardization module. Also, one of the invention's improvements pertains to data outputs ranging from all spatial to tabular to a hybrid which has geographical features with non-spatial attributes and the noted improvement of vector tiles which are “vessels” that contain geometries and metadata.. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
The closest relevant foreign art is as follows:
Kanner et al. (CN102884532B) – The prior art teaches methods for construction field management and operations with building information modeling. In certain embodiments, the invention provides systems for construction field management and operations, that include a central processing unit (CPU), and storage coupled to the CPU for storing instructions that when executed by the CPU cause the CPU to: encode and map data structures and data sets received from Building Information Modeling software; select particular data structures and data sets relevant to at least one person associated with a construction project; transmit the selected data structures and data sets to a user terminal operated by the person; receive inputs made by the person to the selected data structures and data sets; and synchronize and update the data structures and data sets received from Building Information Modeling software based on the inputs received from the person. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
The closest relevant non-patent literature is as follows:
Y. Srewil and R. J. Scherer, "Construction objects recognition in framework of CPS," 2017 Winter Simulation Conference (WSC), Las Vegas, NV, USA, 2017, pp. 2472-2483, doi: 10.1109/WSC.2017.8247976. – The prior art teaches a method to bridge the information gaps between the digital models and real construction site. These solutions promote the collaboration between digital, spatial and physical construction. Cyber-physical systems offer a tight integration between real physical and virtual “cyber” models. This collaborative approach supports the digital transformation in construction domain. A cyber-physical framework is proposed to provide consistent relationships and allow bidirectional data flow. In the framework the recognition of objects successes by linking physical objects to the digital product models using RFID. Next, these objects are equipped with global positions data and pinned to semantic and functional enrichment construction places. The results are objects at a level of “smartness” with enhanced digital capabilities and the ability of context-awareness. The cyber-physical objects are embedded in the process models in order to support tracking activities and facilitate process monitoring and control close to real-time. However, the cited prior art neither alone nor in combination fails to teach the limitations described above.
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 JEREMY L GUNN whose telephone number is (571)270-1728. The examiner can normally be reached Monday - Friday 6:30-4:30.
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, Jerry O'Connor can be reached on (571) 272-6787. 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.
/JEREMY L GUNN/ Primary Examiner, Art Unit 3624
Prosecution Insights