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 .
Response to Arguments
Applicant’s arguments, see page 7, filed 6/23/2026 with respect to 35 USC §112(f) invocation of claims 1,20 have been fully considered and are persuasive. Applicant has amended claims to direct claim limitations to graphics processing unit. The 35 USC §112(f) invocation of claims 1,20 has been withdrawn.
Applicant’s arguments, see page 7, filed 6/23/2026 with respect to 35 USC §103 rejection of claims 1-20 have been fully considered but were not persuasive. Applicant has amended claims and argues previously cited references do not disclose a small form factor image conversion device (SFFICD) includes a graphics processing unit to generate an image and to perform a real- time neural rendering of a three-dimensional model. Applicant has provided no further arguments. Based on specifications, previously cited Benesh’s automated digital twin model of truth (DTMT) system and Golparvar-Fard’s tracking, analysis and visualization system teaches argued claim limitations. The 35 USC §103 rejection of claims 1-20 has been affirmed.
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 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.
Claim 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Benesh et al (US 20190138667 A1) in view of Golparvar-Fard et al (US 20130155058 A1)
Regarding claim 1, Benesh discloses a construction compliance system ([0036] automated digital twin model of truth (DTMT) system, also known as a digital execution verification system), comprising:
A small form factor image conversion device (SFFICD) having a graphics processing unit (GPU) configured to receive an image of a construction status of a construction project ([0041] DTMT system is operable to ingest (either periodically or continuously) reality data captured from the construction site, fabrication yard, manufacturing facility or any other location in the construction project supply chain.), and
to perform real-time neural rendering of a three-dimensional model of the construction status based on the image ([0038] Visualization of the digital twin overlaid on top of its corresponding CAD model can also be provided, as described in greater detail subsequently.),
a near edge processor configured to receive a three-dimensional model of a construction plan of the construction project ([0055] data points received (e.g., corresponding to plan data and/or reality data) can be tagged with additional metadata information in a metadata field such as the time of acquisition, geo localization data (i.e. longitude, latitude, and elevation), data accuracy, and audit data including information indicating the device that acquired the data, its accuracy, and the individual or data capture execution plan that requested the acquisition),
to compare the three-dimensional model of the construction status with the three-dimensional model of the construction plan to determine whether the construction status deviates from the construction plan ([0056] dense data capture can also facilitate the reporting of true construction progress and verify that the quality of execution is within tolerance), and
to provide an indication when the construction status deviates from the construction plan, wherein the near edge processor is located in a near edge of the construction compliance system that is remote from the construction project ([0067] This model would then be overlaid onto the Computer Aided Design (CAD)/Building Information Modelling (BIM) to identify any deviances and measure construction progress. This way, issues such as mismatches or construction inaccuracies may be addressed as they occur).
Golparvar-Fard discloses wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project ([0066] , the users may be allowed to select a set of corresponding control points from the as-built point cloud or the registered imagery and have those associated with the as-planned model, these points could be surveying control points or a set of points that represent the geospatial location of the site. In the case studies presented herein, these points are mostly chosen from corners of the foundation walls and columns as their detection and correspondence may be visually easier.)
Benesh and Golparvar-Fard are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify automated digital twin model of truth (DTMT) system of Benesh to include wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project as described by Golparvar-Fard.
The motivation for doing so would have been to generating as-built three-dimensional (3D) and four-dimensional (4D) (3D+time) point cloud models using low resolution images; generating an augmented reality environment by superimposing a building information model with the as-built point cloud model; and automated progress monitoring using the building information model integrated with a 3D or 4D point cloud model. (Golparvar-Fard, [0040]).
Therefore, it would have been obvious to combine Benesh and Golparvar-Fard to obtain the invention as specified in claim 1.
Regarding claim 2, Benesh discloses wherein the SFFICD includes a repository to store the three-dimensional model of the construction plan ([0060] , the interface may allow the user to identify objects within an image corresponding to components of the project (e.g. pipes, walls, etc.) and to map those objects to a corresponding counterpart within a CAD file).
Regarding claim 3, Benesh discloses wherein the GPU utilizes the three-dimensional model of the construction plan stored in the repository in rendering the three-dimensional model of the construction status ([0061] computer vision techniques can be applied to segment and classify objects within a point cloud gathered and link these objects to a corresponding CAD object in a CAD file., [0065] If the digital twin and the plan data do not match each other in the virtual world, then mismatches are automatically identified,).
Regarding claim 4, Benesh discloses a back end processor configured to receive the construction plan and to render the three-dimensional model of the construction plan based on the construction plan, wherein the back end processor is located in a datacenter of the construction compliance system ([0039] The DTMT system may alternately be hosted on a distributed or cloud-based environment located remotely at a data center (e.g. Google™ Cloud Services or Amazon™ Web Service))
Regarding claim 5, Benesh discloses wherein the back end processor further renders the three-dimensional model of the construction plan based on a regulation governing the construction plan ([0060] the interface may allow the user to identify objects within an image corresponding to components of the project (e.g. pipes, walls, etc.) and to map those objects to a corresponding counterpart within a CAD file.).
Regarding claim 6, Benesh discloses wherein the three-dimensional model of the construction plan includes a tolerance based on the regulation ([0060] Once this association is created, a tolerance threshold can be applied by the project manager or project controls team to identify all components which have been installed within tolerance, installed out of tolerance, or not installed).
Regarding claim 7, Benesh discloses wherein in the determination of whether the construction status deviates from the construction plan, the near edge processor is further configured to determine that the three-dimensional model of the construction status is outside of the tolerance ([0060] Once this association is created, a tolerance threshold can be applied by the project manager or project controls team to identify all components which have been installed within tolerance, installed out of tolerance, or not installed).
Regarding claim 8, Benesh discloses wherein in the determination of whether the construction status deviates from the construction plan, the near edge processor is further configured to determine that the three-dimensional model of the construction status violates a regulation governing the construction plan ([0060] if a re-installation is required to address the mismatch, the time required to conduct this re-installation should be considered and may affect the progress by causing a decrease in the percentage progress.).
Regarding claim 9, Benesh discloses wherein the indication identifies the regulation ([0065] digital twin can then be overlaid with the construction design as represented by the plan data (i.e., the plan model) and to digitally verify whether the real world construction matches the design)
Regarding claim 10, Benesh discloses wherein the SFFICD includes an image capture device configured to generate the image ([0046] 3D point cloud data acquired from a laser scanner [0047] camera photos acquired from a person, tripod, unmanned aerial vehicle (ie. drone) or unmanned ground vehicle. [0048] satellite imagery [0049] 360° images acquired from 360° cameras [0050] RFID tag data read by RFID readers).
Regarding claim 11, Benesh discloses a method ([0036] automated digital twin model of truth (DTMT) system, also known as a digital execution verification system), comprising:
providing, in a construction compliance system, a small form factor image conversion device (SFFICD) having a graphics processing unit (GPU) ([0041] DTMT system is operable to ingest (either periodically or continuously) reality data captured from the construction site, fabrication yard, manufacturing facility or any other location in the construction project supply chain.),,
receiving, by the SFFICD, an image of a construction status of the construction project ([0056] a sparse data capture is carried out for the full facility (e.g. the entire construction site) to facilitate early stage planning and opportunity identification);
rendering in real-time, by the GPU, a three-dimensional model of the construction status based on the image utilizing neural rendering ([0038] Visualization of the digital twin overlaid on top of its corresponding CAD model can also be provided, as described in greater detail subsequently.),;
providing, in the construction compliance system, a near edge processor, wherein the near edge processor is located in a near edge of the construction compliance system that is remote from the construction project ([0055] data points received (e.g., corresponding to plan data and/or reality data) can be tagged with additional metadata information in a metadata field such as the time of acquisition, geo localization data (i.e. longitude, latitude, and elevation), data accuracy, and audit data including information indicating the device that acquired the data, its accuracy, and the individual or data capture execution plan that requested the acquisition),;
receiving, by the near edge processor, a three-dimensional model of a construction plan of the construction project ([0059] point cloud data generated by a 3D scanner can be classified on the basis of matching the data point to an object in a design saved within a CAD file.);
comparing, by the near edge processor, the three-dimensional model of the construction status with the three-dimensional model of the construction plan to determine whether the construction status deviates from the construction plan ([0056] dense data capture can also facilitate the reporting of true construction progress and verify that the quality of execution is within tolerance), and; and
providing, by the near edge processor, an indication when the construction status deviates from the construction plan ([0067] This model would then be overlaid onto the Computer Aided Design (CAD)/Building Information Modelling (BIM) to identify any deviances and measure construction progress. This way, issues such as mismatches or construction inaccuracies may be addressed as they occur).
Golparvar-Fard discloses wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project ([0066] , the users may be allowed to select a set of corresponding control points from the as-built point cloud or the registered imagery and have those associated with the as-planned model, these points could be surveying control points or a set of points that represent the geospatial location of the site. In the case studies presented herein, these points are mostly chosen from corners of the foundation walls and columns as their detection and correspondence may be visually easier.)
Benesh and Golparvar-Fard are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify automated digital twin model of truth (DTMT) system of Benesh to include wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project as described by Golparvar-Fard.
The motivation for doing so would have been to generating as-built three-dimensional (3D) and four-dimensional (4D) (3D+time) point cloud models using low resolution images; generating an augmented reality environment by superimposing a building information model with the as-built point cloud model; and automated progress monitoring using the building information model integrated with a 3D or 4D point cloud model. (Golparvar-Fard, [0040]).
Therefore, it would have been obvious to combine Benesh and Golparvar-Fard to obtain the invention as specified in claim 11.
Regarding claim 12, Benesh discloses wherein the SFFICD includes a repository to store the three-dimensional model of the construction plan ([0060] , the interface may allow the user to identify objects within an image corresponding to components of the project (e.g. pipes, walls, etc.) and to map those objects to a corresponding counterpart within a CAD file).
Regarding claim 13, Benesh discloses wherein the GPU utilizes the three-dimensional model of the construction plan stored in the repository in rendering the three-dimensional model of the construction status ([0061] computer vision techniques can be applied to segment and classify objects within a point cloud gathered and link these objects to a corresponding CAD object in a CAD file., [0065] If the digital twin and the plan data do not match each other in the virtual world, then mismatches are automatically identified,).
Regarding claim 14, Benesh discloses providing, in the construction compliance system, a back end processor, wherein the back end processor is located in a datacenter of the construction compliance system; receiving, by the back end processor, the construction plan; and rendering, by the back end processor, the three-dimensional model of the construction plan based on the construction plan ([0039] The DTMT system may alternately be hosted on a distributed or cloud-based environment located remotely at a data center (e.g. Google™ Cloud Services or Amazon™ Web Service))
Regarding claim 15, Benesh discloses wherein the back end processor further renders the three-dimensional model of the construction plan based on a regulation governing the construction plan ([0060] the interface may allow the user to identify objects within an image corresponding to components of the project (e.g. pipes, walls, etc.) and to map those objects to a corresponding counterpart within a CAD file.).
Regarding claim 16, Benesh discloses wherein the three-dimensional model of the construction plan includes a tolerance based on the regulation ([0060] Once this association is created, a tolerance threshold can be applied by the project manager or project controls team to identify all components which have been installed within tolerance, installed out of tolerance, or not installed).
Regarding claim 17, Benesh discloses wherein in determining whether the construction status deviates from the construction plan, the method further comprises: determining, by the near edge processor, that the three-dimensional model of the construction status is outside of the tolerance ([0060] Once this association is created, a tolerance threshold can be applied by the project manager or project controls team to identify all components which have been installed within tolerance, installed out of tolerance, or not installed).
Regarding claim 18, Benesh discloses wherein in determining whether the construction status deviates from the construction plan, the method further comprises: determining, by the near edge processor, that the three-dimensional model of the construction status violates a regulation governing the construction plan ([0060] if a re-installation is required to address the mismatch, the time required to conduct this re-installation should be considered and may affect the progress by causing a decrease in the percentage progress.).
Regarding claim 19, Benesh discloses wherein the indication identifies the regulation ([0065] digital twin can then be overlaid with the construction design as represented by the plan data (i.e., the plan model) and to digitally verify whether the real world construction matches the design)
Regarding claim 20, Benesh discloses A construction compliance system ([0036] automated digital twin model of truth (DTMT) system, also known as a digital execution verification system), comprising:
A small form factor image conversion device (SFFICD) including an image capture device configured having graphics processing unit (GPU) to generate an image of a construction status of a construction project ([0041] DTMT system is operable to ingest (either periodically or continuously) reality data captured from the construction site, fabrication yard, manufacturing facility or any other location in the construction project supply chain.),
the GPU being configured to perform a real-time neural rending of a three-dimensional model of the construction status based on the image ([0038] Visualization of the digital twin overlaid on top of its corresponding CAD model can also be provided, as described in greater detail subsequently.),
a near edge processor configured to receive a three-dimensional model of a construction plan of the construction project ([0055] data points received (e.g., corresponding to plan data and/or reality data) can be tagged with additional metadata information in a metadata field such as the time of acquisition, geo localization data (i.e. longitude, latitude, and elevation), data accuracy, and audit data including information indicating the device that acquired the data, its accuracy, and the individual or data capture execution plan that requested the acquisition), and
to compare the three-dimensional model of the construction status with the three-dimensional model of the construction plan to determine whether the construction status deviates from the construction plan ([0056] dense data capture can also facilitate the reporting of true construction progress and verify that the quality of execution is within tolerance), and
to provide an indication when the construction status deviates from the construction plan, wherein the near edge processor is located in a near edge of the construction compliance system that is remote from the construction project ([0067] This model would then be overlaid onto the Computer Aided Design (CAD)/Building Information Modelling (BIM) to identify any deviances and measure construction progress. This way, issues such as mismatches or construction inaccuracies may be addressed as they occur).
a back end processor configured to receive the construction plan and to render the three-dimensional model of the construction plan based on the construction plan, wherein the back end processor is located in a datacenter of the construction compliance system ([0039] The DTMT system may alternately be hosted on a distributed or cloud-based environment located remotely at a data center (e.g. Google™ Cloud Services or Amazon™ Web Service)).
Golparvar-Fard discloses wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project ([0066] , the users may be allowed to select a set of corresponding control points from the as-built point cloud or the registered imagery and have those associated with the as-planned model, these points could be surveying control points or a set of points that represent the geospatial location of the site. In the case studies presented herein, these points are mostly chosen from corners of the foundation walls and columns as their detection and correspondence may be visually easier.)
Benesh and Golparvar-Fard are combinable because they are from the same field of invention.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify automated digital twin model of truth (DTMT) system of Benesh to include wherein the SFFICD is located in a far edge of the construction compliance system that is proximate to the construction project as described by Golparvar-Fard.
The motivation for doing so would have been to generating as-built three-dimensional (3D) and four-dimensional (4D) (3D+time) point cloud models using low resolution images; generating an augmented reality environment by superimposing a building information model with the as-built point cloud model; and automated progress monitoring using the building information model integrated with a 3D or 4D point cloud model. (Golparvar-Fard, [0040]).
Therefore, it would have been obvious to combine Benesh and Golparvar-Fard to obtain the invention as specified in claim 20.
Conclusion
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/SHIVANG I PATEL/Primary Examiner, Art Unit 2615
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