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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/01/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US Patent Publication No. 2015/0172628 to Brown et al.
Claim 1. A system, comprising:
at least one computing device comprising a processor (Brown, fig.5 #500); and
a wireframe verification application (Brown, fig.5 #530) stored in memory, where execution of the wireframe verification application by the processor causes the at least one computing device to:
a. provide at least one 2D image or a 3D representation of a scene incorporating a structure or element of interest (Brown, para. [0026], disclosing a user interface that includes photographs of the street scene taken from different perspectives, para. [0027], disclosing each of the photographic images show a building and a tree, the photographic images may be captured from street level, such as a portion of a panoramic image, FIGs. 2A and 2B show the photographs of the street scene as 2D images of a scene including a building, indicating the photographs can correspond to at least one 2D image of a scene including at least one structure or element. In addition, para. [0045], disclosing retrieving three-dimensional shapes and two-dimensional image at a specified location), wherein a plurality of regions is present in the at least one 2D image or the 3D representation (Brown, para. [0021], disclosing LIDAR data representing the building and the tree may be sampled, the LDIAR data may include a cloud of points, each point in cloud of points may be a position in three-dimensional space detected by a LIDAR sensor, para. [0022], disclosing cloud of points may be generated using a structure-from-motion algorithm where a vehicle may take photographs of building and tree, features from the photographs may be detected and matched with one another, and points in three-dimensional space may be calculated, para. [0038], disclosing three-dimensional shapes representing a three-dimensional model can be determined, para. [0039], disclosing the LIDAR data may include a cloud of points in a three-dimensional space, indicating the cloud of points can correspond to a 3D representation of the scene including the building and/or tree as the at least one structure or element. In addition, para. [0045], disclosing retrieving three-dimensional shapes and two-dimensional image at a specified location);
b. identify the structure or element of interest in a first region in the plurality of regions, thereby providing a first identified structure or element (Brown, para. [0022], disclosing cloud of points may be generated using a structure-from-motion algorithm where a vehicle may take photographs of building and tree, features from the photographs may be detected and matched with one another, and points in three-dimensional space may be calculated, indicating the features can be identified by the computer as a structure or element present in the scene from either or both of the at least one 2D image or the 3D representation, thereby providing a first identified structure or element, para. [0037], disclosing the user may select a position on a map, para. [0038], disclosing LIDAR data in proximity to the geographic point is retrieved, and three-dimensional shapes representing a three-dimensional model are determined, para. [0039], disclosing the LDIAR data may include a cloud of points in a three-dimensional space, para. [0040], disclosing an input that constraints a shape of the three-dimensional model to a point on a two-dimensional image is received, the two-dimensional image has a field of view encompassing at least a portion of the shapes, para. [0054], disclosing user input to indicate positions on the two-dimensional photographic image corresponds to a position on the three-dimensional model, the user input may indicate a feature located at a position in the images, indicating the feature can correspond to a structure or element present in the scene from the at least one 2D image identified by the user, thereby providing a first identified structure or element);
c. process the at least one 2D image or 3D representation to generate a processed wireframe for the first identified structure or element, wherein the processing incorporates applying a machine learning system configured to simulate user action in wireframe verification processes (Brown, para. [0022], disclosing cloud of points may be generated using a structure-from-motion algorithm where a vehicle may take photographs of building and tree, features from the photographs may be detected and matched with one another, and points in three-dimensional space may be calculated, para. [0024], disclosing generating three dimensional model from the LIDAR data, para. [0025], disclosing once a three-dimensional model has been created, a user alters the model to correct for inaccuracies based on photographed images of the scene, a user may user an interface illustrated in FIGs. 2A and 2B, para. [0026], disclosing FIG. 2A illustrating a user interface that includes a photograph of the street scene and a wireframe of the three-dimensional model overlaid from a perspective of the photograph, and FIG. 2B illustrating a user interface that include a photograph of the street scene taken from a different perspective, para. [0028], disclosing the three-dimensional model may be displayed as a wireframe structure, para. [0029], disclosing the user may constrain model the model to the images by indicating a position on the model corresponds to a position on the images in the interfaces, by inputting constraints for the images in both interfaces, a user can specify where the model appears in each of the images, and the user may alter the model to model building using the images of the building, indicating the user altering the model to correct for inaccuracies based on photographed images of the scene can correspond to deriving the wireframe rendering of the first identified structure or element, wherein the altered model can be the wireframe rendering derived from both of the photographed images as the at least one 2D image and the generated 3D model derived from the cloud of points corresponding to the 3D representation); and
d. verify the processed wireframe as corresponding to the first identified structure or element, thereby providing a first verified wireframe for the first identified structure or element (Brown, para. [0029], disclosing by selecting points on the wireframe representation of the three-dimensional model, a user may constrain the three-dimensional model to the images, a user may indicate that a position on the three-dimensional model corresponds to a position on the photographic images, by inputting constraints for the images in the interfaces, a user can specify where the three-dimensional model appears in each of the images, based on the user specifications, the geometry of the three-dimensional model may be determined using a photogrammetry algorithm, so that a user may alter the three-dimensional model to model building using images of the building, indicating the constraints as the rules that define an allowed positioning for points corresponding to the one or more structural aspects can be applied to the generated wireframe to be verified as conforming to allowed geometric positionings associated with the structure or element in the scene).Claim 2. The system of claim 1, wherein identifying the structure or element of interest is based at least in part upon user interaction (Brown, para. [0024], disclosing the three-dimensional shapes determined according to the cloud of points, features from the building and the tree are missing from the generated shapes because inaccuracy of the LIDAR sensors, para. [0025], disclosing the user can alter the model to correct for inaccuracies based on the photographic images of the scene).
Claim 3. The system of claim 1, wherein verifying the processed wireframe is based at least in part upon user interaction (Brown, para. [0024], disclosing the three-dimensional shapes determined according to the cloud of points, features from the building and the tree are missing from the generated shapes because inaccuracy of the LIDAR sensors, para. [0025], disclosing the user can alter the model to correct for inaccuracies based on the photographic images of the scene).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
US 20180336722 A1 Methods and systems for roof estimation are described. Example embodiments include a roof estimation system, which generates and provides roof estimate reports annotated with indications of the size, geometry, pitch and/or orientation of the roof sections of a building. Generating a roof estimate report may be based on one or more aerial images of a building. In some embodiments, generating a roof estimate report of a specified building roof may include generating a three-dimensional model of the roof, and generating a report that includes one or more views of the three-dimensional model, the views annotated with indications of the dimensions, area, and/or slope of sections of the roof. This abstract is provided to comply with rules requiring an abstract, and it is submitted with the intention that it will not be used to interpret or limit the scope or meaning of the claims.
US 20180247416 A1 The method involves receiving an image (312) of a rooftop at a device. The image is divided into a set of subdivision images by the device based on multiple characteristics of rooftop shingles or tiles depicted in the subdivision images. A machine learning-based classifier is applied to the subdivision images by the device, where the classifier is configured to detect damage occurred to the particular shingles or tiles depicted in a particular subdivision image under analysis and assign a damage type to the detected damage. Display data is transmitted for display that is indicative of an extent of damage to the rooftop associated with the assigned damage type by the device, where the device is a computing device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARTIN MUSHAMBO whose telephone number is (571)270-3390. The examiner can normally be reached Monday-Friday (8:00AM-5:00PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571) 272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARTIN MUSHAMBO/Primary Examiner, Art Unit 2615 7/4/2026
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