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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10 and 19 are rejected for failing to particularly point and distinctly claims “a process.” The claim limitation fails to point out the specifics of what the “process” is and what it is used for, in the context of the claims. For purposes of the examination, examiner interprets “a process” as applying for a loan.
The term “predefined construction quality” in claim 3, 12 and 20 is a relative term which renders the claim indefinite. The term “predefined construction quality” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For examination purposes, examiner interprets “predefined construction quality” as construction being that is designed.
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 (abstract idea) without significantly more.
Under the broadest reasonable interpretation, the following claim terms are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. MPEP § 2111.
Step 1: Does the Claim Fall within a Statutory Category? (see MPEP 2106.03) Claim 1 recites a process, which is a statutory category of invention (Step 1: YES). Claim 10 and 19 recite an apparatus, which is a statutory category of invention (Step 1: YES).
Step 2A, Prong One: Is a Judicial Exception Recited? (see MPEP 2106.04(a)). Yes.
The claims are analyzed to determine whether it is directed to a judicial exception. The following claims identify the limitations that recite additional elements in bold and the abstract idea without bold. Underlined claim limitations denote newly added claim limitations:
Claims 1, 10 and 19 recite a method for providing content-based assessments, the method being implemented by at least one processor, the method comprising: receiving, by the at least one processor, a request from a user device to initiate a process for at least one property; obtaining, by the at least one processor, a plurality of media items captured from a construction site of the at least one property at predefined time intervals, in response to the request; loading, by the at least one processor, the plurality of media items into a trained model for performing an analysis on the plurality of media items, the analysis comprises comparing the plurality of media items with a set of input data corresponding to a virtual representation of the at least one property; generating, by the at least one processor, a first report related to a construction progress and a second report related to a construction quality of the at least one property based on the analysis on the plurality of media items; generating, by the at least one processor, a third report of the at least one property based on the first report and the second report; and transmitting, by the at least one processor, at least one recommendation based on the third report, for processing the request. These limitations, as drafted, under its broadest reasonable interpretation, covers performance via certain methods of organizing human activity, but for the recitation of generic computer components. Under human activity, the limitations are fundamental economic practice. The claims are also commercial interactions, such as sales activities and business relations (See, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 123 USPQ2d 1100 (Fed. Cir. 2017)), as well as managing interactions between people, such as following instructions. The claims are also mental processes, capable of being performed in the human mind or by pen and paper. Accordingly, the claim recites an abstract idea. The mere recitation of generic computer components in the claims do not necessarily preclude that claim from reciting an abstract idea. (Step 2A-Prong 1: Yes. The claims recite an abstract idea).
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application? (see MPEP 2106.04(d)). No.
The above judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, user device, and trained model. The additional elements of a processor, user device, and trained model, are just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)). The computer components are recited at such a high-level of generality (i.e. as a generic computer components) such that it amounts to no more than mere instructions to apply the exception using generic computer components, and the claims fail to recite technological detail as to how the step of the judicial exception is accomplished. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. (Step 2A-Prong 2: NO. The judicial exception is not integrated into a practical application).
Step 2B: Does the Claim Provide an Inventive Concept? (see MPEP 2106.05). No.
The claims are next analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed with respect to Step 2A2 above, the additional elements of (a processor, user device, and trained model) in the claims amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea itself. Therefore, the claims do not amount to significantly more than the recited abstract idea (Step 2B: NO; The claims do not provide significantly more, and are not patent eligible).
Claims 2 and 11 recite wherein the plurality of media items comprises images, videos, and construction site surroundings of the at least one property. These limitations are also part of the abstract idea identified in claim 1 and 10, and is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 3, 12 and 20 recite wherein the set of input data comprises a project schedule associated with the construction site of the at least one property, a building information model, video(s) of surroundings of the construction site, a blueprint, and a predefined construction quality of the at least one property. These limitations are also part of the abstract idea identified in claim 1, 10, and 19, and is similarly rejected under the same rationale as claim 1, 10, and 19 supra.
Claims 4 and 13 recite wherein the predefined time intervals comprise a first interval and subsequent intervals. These limitations are also part of the abstract idea identified in claim 1 and 10, and is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 5 and 14 recite wherein the analysis on the plurality of media items at the subsequent intervals further comprises: comparing, by the at least one processor, the plurality of media items captured at the subsequent intervals with the set of input data to determine a delay in the construction progress of the at least one property; and updating, by the at least one processor, the delay in the construction progress of the at least one property in the first report. These limitations are also part of the abstract idea identified in claim 1 and 10, and the additional elements of the processor are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 1 and 10 analysis above. Therefore, this claim is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 6 and 15 recite wherein the trained model is developed using machine learning (ML). These limitations are also part of the abstract idea identified in claim 1 and 10, and the additional elements of the trained model and machine learning are addressed in the Steps 2A2 and B as just applying generic computer components to the recited abstract limitations (MPEP 2106.05(f)) as in the claim 1 and 10 analysis above. Therefore, this claim is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 7 and 16 recite wherein the third report comprises a ratio, quality deviations within allowed thresholds as per design, a progress as per deadlines, and a location of the at least one property. These limitations are also part of the abstract idea identified in claim 1 and 10, and is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 8 and 17 recite wherein the first report comprises new changes identified compared to a previous report, deviations identified compared to a project plan, a projected completion date, and remaining construction work of the at least one property. These limitations are also part of the abstract idea identified in claim 1 and 10, and is similarly rejected under the same rationale as claim 1 and 10, supra.
Claims 9 and 18 recite wherein the second report comprises information related to construction materials, dimensions of an interior of the at least one property, shapes of structures, and types of equipment used for construction. These limitations are also part of the abstract idea identified in claim 1 and 10, and is similarly rejected under the same rationale as claim 1 and 10, supra.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1 -4, 6-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Golparvar-Fard US 20130155058, in view of Black Knight, Inc., “Black Knights New Validate Mode App for Valuations Uses AI to Speed and Simplify Home Equity Lending, Shorten Time to Close by Shaving Days, Dollars in the Process,” 2023.
Regarding Claims 1, 10 and 19, Golparvar-Ford discloses a method for providing content-based assessments (Abstract, used for construction site assessments), the method being implemented by at least one processor, the method comprising:
Golparvar-Fard can initiate a process by uploading daily photographs taken as a construction site (Fig. 2), but fails to disclose receiving, by the at least one processor, a request from a user device to initiate a process for at least one property. However, Blck Knight discloses a user being able to request a home loan from their user device application by submitting photograph’s of the condition of the property and validation using artificial intelligence to calculate the home condition.
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Golparvar-Fard with the loan request for a property from Black-Knight. Doing so expedites the process of calculating a home loan and shortens the time to close on a loan application.
Modified Golparvar-Fard also discloses obtaining, by the at least one processor, a plurality of media items captured from a construction site of the at least one property at predefined time intervals, in response to the request (Golparvar-Fard Fig. 2, 212 is a construction schedule, which is a request being made for an analysis model; Para. 64, “To present how these steps are formed, two sets of 112 and 160 images were chosen that were taken on Aug. 20 and Aug. 27 of 2008 during construction of the Ikenberry Residence Hall (RH) in Champaign, Ill. In both cases, a field engineer causally walked along the sidewalk of the project and took images within a few minutes. FIGS. 3( a) and 3(b) represent the sparsely reconstructed scene from the same image subset and illustrate five registered cameras in the D4AR environment”; Para. 152, “For example, in these projects, field engineers take about 250 photographs per day. In addition, these two projects include 18 different work packages, and for each work package, contractors take about 20 to 25 photos per day”);
loading, by the at least one processor, the plurality of media items into a trained model for performing an analysis on the plurality of media items, the analysis comprises comparing the plurality of media items with a set of input data corresponding to a virtual representation of the at least one property (Golparvar-Fard, Para. 56, “The 3D and schedule integrator 230 may generate a 4D as-planned model of the construction project, data for which may be stored in a 4D as-planned model database 230. The schedule may be fused into the IFC-based BIM by manually linking image elements to activities and creating a 4D baseline model for progress monitoring”; Para. 57-58; “Data from the sparse 3D model and camera parameters may be supplied to a Euclidean registrar 246 for registering the sparse 3D model within the system 200. Using SfM techniques, the SfM processor 214 may generate an underlying 3D geometry for the as-built scene that sets a baseline for visual navigation through registered imagery in the scene. Generation of the 3D geometry and creation of the as-built scene may be completed by the SfM processor 214 calculating camera pose (e.g., location, orientation, and field of view) and the Euclidean registrar 246 calculating sparse 3D Cartesian coordinate information of the as-built model. The Euclidean registrar 246 may then superimpose the 3D IFC-based BIM with the integrated as-built point cloud and camera model (camera 3D positions and viewing directions). For example, the Euclidean registrar 246 may create a Euclidean sparse 3D model that may be stored in a Euclidean sparse 3D model database 264 and Euclidean camera parameters that may be stored in a Euclidean camera parameters database 268.; Para. 47, “a Bayesian probabilistic model may be introduced to automatically recognize progress deviations by comparing measurements of progress with dynamic thresholds learned through a Support Vector Machine (SVM) classifier.; Para. 61, “These two labeled, as-built and as-planned spaces may be fed into a Bayesian model and used to assess progress through a Support Vector Machine (SVM) classifier 290; Para. 121, “Although currently the operational progress details may not be automatically identified, e.g., differentiation of concrete from formwork, the proposed Bayesian model accounts for that, and this in turn facilitates the extension of the proposed algorithms.”);
generating, by the at least one processor, a first report related to a construction progress (Golparvar-Fard, D4AR viewer generates visualization/report of progress – “visualize progress deviations in an integrated fashion”; Fig. 16, color-codes elements as behind, or on-schedule; progress is formulated as a binary per element and aggregated per schedule activity; Fig. 2, showing various reports being processed; Examiner notes, that based on broadest reasonable interpretation of the word “report,” the word itself can be oral, written, one page, multiple pages, or even as simple as a chart of data that is a “report”; First model 234) and a second report related to a construction quality of the at least one property based on the analysis on the plurality of media items (“Quality assessment/quality control… quality of the finished surfaces can be remotely tracked, analyzed and controlled”; Fig. 30, interactive zooming for surface quality; Fig. 2, showing various reports being processed; Examiner notes, that based on broadest reasonable interpretation of the word “report,” the word itself can be oral, written, one page, multiple pages, or even as simple as a chart of data that is a “report”; Second model 242)
generating, by the at least one processor, a third report of the at least one property based on the first report and the second report (Golparvar-Fard, Para. 61, “These two labeled, as-built and as-planned spaces may be fed into a Bayesian model and used to assess progress through a Support Vector Machine (SVM) classifier 290. Finally, the detected as-built elements, camera parameters plus 4D BIM may be stored in a detected dense 3D as-built model database 295 and be fed into a four-dimensional augmented reality (D4AR) viewer 270 to visualize the as-built and as-planned models, and to visualize progress deviations in an integrated fashion”; Fig. 2, showing various reports being processed; Examiner notes, that based on broadest reasonable interpretation of the word “report,” the word itself can be oral, written, one page, multiple pages, or even as simple as a chart of data that is a “report”); Third model 264, based on the combination of 234 and 242 which feeds into 246)
and transmitting, by the at least one processor, at least one recommendation based on the third report, for processing the request (Golparvar-Fard, Fig. 2, D2AR viewer 270 is a function of 264; for output).
Regarding Claims 2 and 11, modified Golparvar-Fard discloses wherein the plurality of media items comprises images, videos, and construction site surroundings of the at least one property (Para. 64, To present how these steps are formed, two sets of 112 and 160 images were chosen that were taken on Aug. 20 and Aug. 27 of 2008 during construction of the Ikenberry Residence Hall (RH) in Champaign, Ill. In both cases, a field engineer causally walked along the sidewalk of the project and took images within a few minutes. FIGS. 3( a) and 3(b) represent the sparsely reconstructed scene from the same image subset and illustrate five registered cameras in the D4AR environment. Once a camera is visited, the camera frustum may be texture-mapped with a full resolution of the image so users (i.e., owner, project executive, or the architect) can interactively zoom in and visually acquire information on progress, quality, safety and productivity as well as workspace logistics of a construction site. FIGS. 3( a) and 3(b) visualize the as-built point cloud model from synthetic views. FIG. 3( c) shows location of a camera frustum. FIG. 3( d) shows the site through the same camera viewpoint. FIG. 3( e) demonstrates the image textured on a viewing plane of the camera; Para. 138/142, video streams; Images noted through; See figures 3, 10, 11, and 13)
Regarding Claims 3, 12 and 20, modified Golparvar-Fard discloses wherein the set of input data comprises a project schedule associated with the construction site of the at least one property (Dozens of property photos and videos described throughout), a building information model (BIM described throughout as intelligent frame correction, Para. 52, “In regards to the as-planned model, it may be assumed that (1) an intelligent frame correction (IFC)-based BIM is generated based on the most updated construction drawings. ASIs (Architect's Supplemental Instructions), RFIs (Requests for Information), RFPs (Requests for Proposal) or change orders are reflected in the revised plan model; (2) the most updated project schedule may be used to generate the underlying 4D model. For the as-built model, it may further be assumed that the photographs are all collected on one particular day or in a short period of time (e.g., a couple of days) where no significant progress is made in construction. In the proposed approach there is no need to infer temporal order from images. Rather, such information may be automatically extracted from exchange image file format (EXIF) tag of JPEG images (available in all cameras). Finally, for registration of as-planned and as-built models, it may be assumed that at least three distinct control points are available so that the as-planned model may be superimposed with the as-built sparse point cloud model,” video(s) of surroundings of the construction site (Images can be both time lapses or video, Para. 138, “Video streams and time-lapsed images allow contractors to manually measure and analyze performance of their work force and machinery away from the jobsites and revise work processes or sequence of activities to improve productivity”; Para. 66, “In addition, the point cloud model produces a significantly large number of points that do not belong to the building model itself (e.g., generated from the façade of surrounding buildings, machinery, or even people and plants on or around the site)”; Para. 177, “ The SIFT feature detection technique does not limit the detection to corners of various objects on the construction site. On the contrary, the SIFT feature detection technique allows distinct feature points to be detected from surrounding environment (e.g., trees, machinery, or periphery of the construction site) as well”; Para. 198, “he SfM procedure may estimate relative camera locations. In addition, the point cloud model may result in a significantly large number of points that do not belong to the building model itself, e.g., may belong to the façade of surrounding buildings, machinery, or even people and plants on or around the site”), a blueprint (Para. 20, blueprint is implicit in IFC-based BIM, “FIG. 13 includes (a) an image taken on the RH project dated Aug. 27, 2008; (b) range image generated for the expected intelligent frame correction (IFC) elements in which color-coding shows the ratio of depth along the camera line-of-sight compared to the rear foundation wall; and (c) the expected as-built progress voxels detected and projected back on the image plane”, Note, IFC-based BIM described throughout), and a predefined construction quality of the at least one property (Claim 4, “ schedule quality control during building”; Para. 37, “FIG. 30 is a series of images illustrating interactive zooming, which captures high-resolution images along with the implemented, interactive zooming that allows the quality of the finished surface to be studied remotely”; Para. 64, “Once a camera is visited, the camera frustum may be texture-mapped with a full resolution of the image so users (i.e., owner, project executive, or the architect) can interactively zoom in and visually acquire information on progress, quality, safety and productivity as well as workspace logistics of a construction site”; Para. 131, “it may also be used as a tool for automated and remote monitoring of progress and safety, quality control and site layout management, enabling enhanced coordination and communication”).
Regarding Claims 4 and 13, modified Golparvar-Fard discloses wherein the predefined time intervals comprise a first interval (Para. 151, “In these projects, field engineers take about 250 photographs per day” and subsequent intervals (Para. 5, “Quality of the daily progress reports also highly depends on the data collected by field personnel which tends to be based on their interpretation of what needs to be measured, the way it needs to be measured and the way it needs to be presented, and therefore, it may not reveal the actual impact of site circumstances on the construction project”; Para. 231, “o that end, D4AR (4D Augmented Reality) models may be developed, and explored in detail herein includes the application of unsorted daily progress photograph collections available on any construction site as an easy and ready-to-use data collection technique”; Claim 6, “where the processor is further configured to track building progress based on an application of the unordered group of images to which is iteratively added on a daily basis, to update the 3D as-built model as compared to the 4D as-planned model”; Para. 64, “To present how these steps are formed, two sets of 112 and 160 images were chosen that were taken on Aug. 20 and Aug. 27 of 2008 during construction of the Ikenberry Residence Hall (RH) in Champaign, Ill. In both cases, a field engineer causally walked along the sidewalk of the project and took images within a few minutes”, One photo taken on one day, with subsequent photo on Aug. 27th).
Regarding Claims 6 and 15, modified Golparvar-Fard discloses wherein the trained model is developed using machine learning (ML) (Para. 92, “Thus a machine learning methodology may be used to estimate such dynamic thresholds in a principled way”; Para. 47, “Finally, a Bayesian probabilistic model may be introduced to automatically recognize progress deviations by comparing measurements of progress with dynamic thresholds learned through a Support Vector Machine (SVM) classifier”; Para. 102, “In experiments, the SVM model was trained over the RH 112 image dataset”; Para. 94, “The optimal hyper-plane that separates the two classes may be learned in a supervised fashion using a linear support vector machine (SVM)”).
Regarding Claims 7 and 16, modified Golparvar-Fard discloses wherein the third report comprises a ratio (Para. 21, “FIG. 14 are diagrams of: (a) the ratio of expected progress P(θT i|ηi) to the expected observable regions, P(θp i) for progress detection results from RH # 1 experiment; and (b) the ratio of accuracy of detection to the percentage of visibility (1-occlusion)”; aggregated progress ratios per activity), quality deviations within allowed thresholds as per design (Para. 47, “Finally, a Bayesian probabilistic model may be introduced to automatically recognize progress deviations by comparing measurements of progress with dynamic thresholds learned through a Support Vector Machine (SVM) classifier. Not only does this model quantify progress automatically, but it also accounts for occlusions and recognizes if reconstructed building elements are missing because of occlusions or because of changes. This makes the presented model the first probabilistic model for automated progress tracking and visualization of deviations that melds both as-planned models and unordered daily photographs in a principled way”; Para. 49, “The detection of progress deviations may be based on a priori information such as available in a 4D Building Information Model (BIM), as well as daily construction photographs”), a progress as per deadline (“linking image elements to activities” in 4D schedule; Para. 165, “The construction schedule database 2012 may store milestones with reference to the as-planned project in relation to dates by which the milestones are to be met”; as well as Fig. 18), and a location of the at least one property (Para. 192, “The resulting 4D as-built model allows project participants to select a specific location of a project and study that location within a specific day using all images that have captured ongoing work in that area”).
Regarding Claims 8 and 17, modified Golparvar-Fard discloses wherein the first report comprises new changes identified compared to a previous report (Para. 68, “From then after, any new point cloud model may only need to be registered to the underlying point cloud models”; Fig. 13b-13c), deviations identified compared to a project plan (Para. 61, “Finally, the detected as-built elements, camera parameters plus 4D BIM may be stored in a detected dense 3D as-built model database 295 and be fed into a four-dimensional augmented reality (D4AR) viewer 270 to visualize the as-built and as-planned models, and to visualize progress deviations in an integrated fashion” versus 4D as-planned BIM; See also Fig. 16 and 29), a projected completion date and remaining construction work of the at least one property (“Remaining work…visibility/per-activity metrics”; facilitates “schedule revisions”; Para. 195, “linking the schedule to the as-planned model; and (3) updating the model based on schedule revisions, approved RFIs, RPFs and change orders to continuously revise the as-planned model based on scheduled changes”; Para. 217, “The underlying basis of the system that visualizes the 4D, as-planned model allows prompt look-ahead schedule updating. Based on observations of as-built progress, completed construction process and the conditions under which they were completed, as well as the way resources were allocated can be understood. Comparing the as-built observations with the 3D planned model, allows different alternatives to be studied over the 4D model. It further allows constructability analysis to be performed in presence of the as-built imagery and this may enable better decision-making during scheduled revisions by extending application of the 4D model.”).
Regarding Claims 9 and 18, modified Golparvar-Fard discloses wherein the second report comprises information related to construction materials (“concrete, formwork, steel”; “visual appearance of the element (i.e. concrete, formwork, steel)”), dimensions of an interior of the at least one property, shapes of structures (“3D as-built point cloud,” “dimensions of…structures” via reconstruction and zooming),, and types of equipment used for construction (“FIG. 1 highlights the technical challenges of a vision-based progress monitoring system, showing different areas of a construction site with various real-world issues due to passage of time, including areas categorized as: visible unchanged, occluded changed, occluded unchanged, visible changed, which may be observed under static occlusion (progress on the project itself), dynamic occlusion (movement of equipment and workers) and shadows.”; Equipment also shown throughout various photos present in disclosure).
Claim(s) 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Golparvar-Fard US 20130155058, in view of Black Knight, Inc., “Black Knights New Validate Mode App for Valuations Uses AI to Speed and Simplify Home Equity Lending, Shorten Time to Close by Shaving Days, Dollars in the Process,” 2023, as applied to claims 4 and 13 above, further in view of Board of Trustees, University of Illinois (BOT-UI) US 11288412.
Regarding Claims 5 and 14, modified Golparvar-Fard fails to discloses wherein the analysis on the plurality of media items at the subsequent intervals further comprises: comparing, by the at least one processor, the plurality of media items captured at the subsequent intervals with the set of input data to determine a delay in the construction progress of the at least one property; and updating, by the at least one processor, the delay in the construction progress of the at least one property in the first report. However, BOT-UI discloses a second 3D point cloud model (“The processing device may retrieve a second 3D point cloud model generated at a later time than the first 3D point cloud model with known 3D pose relative to the BIM. The processing device may further execute an alignment tool to display, in the GUI, a visual instantiation of the first 3D point cloud model and a visual instantiation of the second 3D point cloud model”) with root causes for a six-week delay (Fig. 16A) and the ability to measure for a risk of delay (“By integrating these point cloud models with 3D BIMs and a schedule, the system may measure progress and analyze risk for delay at each location”) with a comparison to a 4D BIM/schedule to show as-built construction progress compared to the construction schedule (“The processing device may also display, in the GUI, at least one of the first 3D point cloud model or the second 3D point cloud model superimposed on the 4D BIM to illustrate as-built construction progress over time with reference to the construction schedule”) with the ability to update as-built construction progress over time (Fig. 26; “3D reconstruction to generate an updated 3D point cloud model that is displayable in a graphical user interface”; “The 4D BIM 233 may be the original construction site plan (building information model) with integrated scheduling information, or an updated plan created after construction began”; “Using the system 100 during weekly work plan meetings, project teams can compare 4D as-built and plan models, tracking work crew assignments via a color-coded model, update the 4D model in real time, document manpower per task per location, and enable documentation of root-causes for delays and task constraints. Note the metrics Readiness Index (RI) and Readiness Reliability (RR), which are described in more detail below, may be employed for measuring reliability of schedule tasks in the current weekly work plans and look-ahead schedules based on the status of predecessor tasks (tasks that must be finished before the next task can start) and task constraints”).
It would have been obvious to one of ordinary skill in the art, before the effective date of filing, to have modified Golparvar-Fard with the comparison and updating of BOT-UI. Doing so makes the model more accurate, saving time and money for the overall construction project.
Conclusion
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/BRANDON M DUCK/Examiner, Art Unit 3693
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