GUIDED CONTENT CAPTURE

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 . Continued Examination Under 37 CFR 1.114 A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/20/2026 has been entered. Information Disclosure Statement The information disclosure statements filed on 08/21/2026 and 08/23/2026 fail to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Response to Amendment This is in response to applicant’s amendment/response filed on 08/20/2026, which has been entered and made of record. Claims 1, 4, 10-11, 13, 16, and 20 have been amended. Claims 1-20 are pending in the application. Response to Arguments Applicant's arguments filed on 08/20/2026 have been fully considered but they are not persuasive or are rendered moot in view of the new grounds of rejection presented below (as necessitated by the amendment to claims 1, 13, and 20). Claim Rejections - 35 USC § 103 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(s) 1-5, 7-11, 13-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPubs 2023/0419413 to Jones et al. in view of U.S. PGPubs 2025/0126353 to Ben Hariz et al., further in view of U.S. PGPubs 2021/0334767 to Utke et al., further in view of U.S. 2023/0113765 to Patt et al.. Regarding claim 1, Jones et al. teach a computing system comprising (Fig 1, par 0073, “FIG. 1 depicts an exemplary environment in which embodiments of a system 10 may be utilized for guiding and aiding a user in collecting information relevant to an insurance claim”): a network communication interface (par 0074, “The network 14 may generally allow communication between the mobile electronic device 12 and the server 16”); one or more processors; and a memory storing instructions that, when executed by the one or more processors (par 0076-0078, “The server 16 may include a communication element, a memory element, and a processing element (not shown). The communication element generally allows communication with external systems or devices (such as the mobile electronic device 12) and/or between one or more individual servers”), cause the computing system to: process incident information corresponding to an incident involving a vehicle (par 0082, “The start data collection request may be received as user input via the user interface. The start data collection request may also be received as user input via a microphone sensor of the mobile electronic device, for example where embodied by a verbal command, instruction, notice, etc. referencing the occurrence of an incident or otherwise indicating a start data collection request”, Fig 12, par 0086-0090, “following initiation of automated data collection guidance, an incident type may be received, for example via receipt of user input comprising selection of an incident type from an automatically populated set of incident types presented by instruction of the program. FIG. 4 depicts an exemplary user interface 410 displaying an automatically populated set of incident types 412. The automatically populated set of incident types 412 may be a list of incident types populated and optionally periodically updated by an insurance provider, artificial intelligence and/or the program”); initiate a guided content capture process with a user to capture images of a vehicle of the user (Fig 7, par 0094-0095, “a data collection request may comprise a request for the user to capture sensor data, for instance by taking a picture with a photographic element of the user mobile electronic device. In connection therewith, an immediate reference image may be displayed via the user interface. The immediate reference image may be a user-friendly, highly-readable graphical display of post-accident instructions. FIG. 7 depicts an exemplary user interface 710 displaying a plurality of views of a vehicle that the user is requested to emulate in collecting incident data using the photographic element of the user mobile electronic device. For instance, a data collection request 712 for a picture of a front-left portion of the user's vehicle may include an immediate reference image 714 displayed at the user interface 710 and comprising a generic drawing of a front-left portion of a vehicle. Where exemplary drawings and/or photographs more representative of the user's specific vehicle are available for use, a corresponding immediate reference image may include same. One of ordinary skill will immediately appreciate that a wide variety of requests and visual and/or audio aides may be incorporated into data collection requests within the scope of the present invention”); generate a sequential set of vehicle outlines of the vehicle on an image interface presented on the computing device of the user to implement the guided content capture process (Fig 7, par 0094, “ a data collection request 712 for a picture of a front-left portion of the user's vehicle may include an immediate reference image 714 displayed at the user interface 710 and comprising a generic drawing of a front-left portion of a vehicle. Where exemplary drawings and/or photographs more representative of the user's specific vehicle are available for use, a corresponding immediate reference image may include same”), wherein the sequential set of vehicle outlines is configured based on input received via the damage interface to capture images of the one or more portions of the vehicle (par 0094, “Exemplary data collection requests and corresponding received incident data may take any of a variety of forms. For instance, a data collection request may comprise a request for the user to capture sensor data, for instance by taking a picture with a photographic element of the user mobile electronic device. In connection therewith, an immediate reference image may be displayed via the user interface. The immediate reference image may be a user-friendly, highly-readable graphical display of post-accident instructions. FIG. 7 depicts an exemplary user interface 710 displaying a plurality of views of a vehicle that the user is requested to emulate in collecting incident data using the photographic element of the user mobile electronic device”). But Jones et al. keep silent for teaching perform computer vision on image data captured by a camera of the computing device to determine when the vehicle is aligned with a particular vehicle outline of the sequential set of vehicle outlines. In related endeavor, Ben Hariz et al. teach generate a sequential set of vehicle outlines of the vehicle on an image interface presented on a computing device of the user to implement the guided content capture process(par 0087, “ a vehicle overlay may have one or more at least partially transparent (e.g., partially transparent or fully transparent) in addition with an outline of a portion of the vehicle to be imaged such that the image of the vehicle can be simultaneously seen through the at least partially transparent portions along with the outline of the portion of the vehicle to be imaged. Examples of this type of display are shown in FIG. 1C “, par 0144-0152, “These general portions may be predetermined portions of the vehicle which correspond to different views of the vehicle which are essential to analyze to determine the condition of the vehicle. These general portions may be used to generate overlays for all vehicles, and therefore sets of overlays for different vehicles may contain overlays adapted to a particular vehicle which correspond to the same general portions of the vehicle to image”); perform computer vision on image data captured by a camera of the computing device to determine when the vehicle is aligned with a particular vehicle outline of the sequential set of vehicle outlines (par 0139-0142, “Based on this positioning of the vehicle within camera view 111A with respect to the overlay 114, image capture guidance software 120 may display an alignment state 122A conveying that the vehicle is not aligned with the overlay. This may signal to the user that adjustments may be needed to capture a proper image of the vehicle. The image capture guidance software may perform processing of the camera view 111 to determine the alignment of the vehicle with the overlay 114, as discussed herein. Image capture guidance software 120 may provide instructions 121 to the user. Instructions 121 may include information on how the user should position the camera in order to capture a proper image of the vehicle. As shown, instructions 121A explain the user should move the camera backwards and to the left in order to align the vehicle with the overlay 114 …. The view of mobile device 110 shown on the right of the figure illustrates a display of the mobile device when the view of the vehicle 111B is aligned with the overlay 114. As can be seen, the exterior outline of the overlay follows that of the vehicle, the wheels of both the vehicle and overlay are aligned, the windshield is aligned, and other vehicle parts are aligned with corresponding parts of the overlay 114”, par 0156, “The overlay generating module 133 may generate a corresponding vehicle overlay for each portion of the vehicle of the portions to image 302. The overlay generating module 133 may use this information on the portion of the vehicle to image 302 to ensure that when a vehicle within a camera view is aligned with an overlay, information on the portion of the vehicle to be imaged is captured within the image. After vehicle overlays 150 are generated, they may be transferred to the image capture and guidance software 120 of the mobile device 110, where they may be used in guiding a user in capturing images of a vehicle, as discussed herein”, par 0207-0210, “The parts boundaries 601, vehicle edges 602 and vehicle outline 603 may be overlayed onto each other to generate a rough vehicle overlay 604. The rough vehicle overlay 604 may undergo smoothing to generate vehicle overlay 605. The smoothing of the rough vehicle overlay 604 may include removing small and isolated curves from the rough vehicle overlay 604. Small and isolated curves may include any curve in the rough overlay 604 below a threshold size or not connected to other curves. A smoothing algorithm may also be used to generate the vehicle overlay 605. In some examples, the Ramer-Douglas-Peucker and/or Visvalingam-Whyat algorithms may be used to smooth the curves. The algorithm may analyze the curves of the rough overlay 604 and remove and/or combine similar curves to generate the vehicle overlay 605.The process of FIG. 6 may be repeated for all received vehicle-specific camera parameters 610, in order to generate a set of overlays for a particular vehicle, which may be used in guiding the capture of images and/or videos of a vehicle, as discussed herein”). It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Jones et al. to include perform computer vision on image data captured by a camera of the computing device to determine when the vehicle is aligned with a particular vehicle outline of the sequential set of vehicle outlines as taught by Ben Hariz et al. to generate instructions for guiding the user to video the identified portions of the vehicle with one or more image quality criteria to properly captured in videos or images. Jones et al. as modified by Ben Hariz et al. keep silent for teach capture images of the one or more damaged portions of the vehicle. In related endeavor, Utke et al. teach wherein the sequential set of vehicle outlines is configured based on the input received via the damage interface to capture images of the one or more damaged portions of the vehicle (Fig 4, par 0038-0039, “the mobile estimate application 153 may instruct the user to capture a walk-around video focusing on the impact point(s). For example, if a likely impact point is on the right side of the vehicle, the mobile estimate application 153 may instruct the user to capture a walk-around video of the right side of the vehicle. Additionally or alternatively, the mobile estimate application 153 may instruct the user to record a walk-around video of whatever portion visually appears damaged to the user …. as illustrated at FIG. 4, if the detected impact point is the right front corner of the vehicle, the mobile estimate application 153 may instruct the user to capture both the right front corner and the left front corner in the walk-around video. The user may thus follow a path 401 while recording the walk-around video”). It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Jones et al. as modified by Ben Hariz et al. to include capture images of one or more portions of the vehicle identified as damaged in the incident information as taught by Utke et al. to capture a small number of images of a damaged vehicle after a damage incident to accurately represent the damaged portions of the vehicle to provide an improved ability to detect the extent of damage to the vehicle. But Jones et al. as modified by Ben Hariz et al. and Utke et al. keep silent for teaching wherein processing the incident information includes (i) generating a damage interface on a computing device of the user, the damage interface including a three-dimensional representation of the vehicle that is rotatable about a set of axes, and (ii) receiving, via the damage interface, input indicating one or more damaged portions of the vehicle. In related endeavor, Patt et al. teach process incident information corresponding to an incident involving a vehicle, wherein processing the incident information includes (i) generating a damage interface on a computing device of the user, the damage interface including a three-dimensional representation of the vehicle that is rotatable about a set of axes (par 0029, “For vehicle incidences, the system can include a three-dimensional vehicle damage assessment interface that enables the claimant to indicate vehicle damage. In one example, the system can perform a lookup or otherwise determine the claimant's vehicle, and the damage assessment interface can present a virtual representation of the claimant's vehicle, which can be rotated about multiple axes to allow the claimant to indicate all claimed damage”, Figs 9M and 9N, par 0125-0127, “Referring to FIG. 9M, a vehicle damage assessment interface 930 is generated (e.g., as a subpage of the FNOL interface) when a claimant user 197 initiates an insurance claim for vehicle damage. The vehicle damage assessment interface 930 can include a number of selectable features 934 that enable the claimant user 197 to provide a text and/or voice description of the damage, as well as record images and/or video of the damage, provide receipts for repairs, etc. The selectable features 934 can further include one or more queries that prompts the user 197 to provide additional contextual information for the subsequent claim filing …. the virtual representation 936 of the claimant user's vehicle may be three-dimensional and rotatable on multiple axes to enable the user 197 to indicate all damage to the vehicle. For example, the vehicle damage assessment interface 930 can include controls 937 that enable the user to rotate and/or move the vehicle representation 936 about multiple axes”, par 0141, “The computing system 100 may then cause the computing device 190 of the claimant user 197 to generate a three-dimensional, vehicle damage assessment interface that enables the claimant user 197 to input damage information (1010). As described above with respect to FIG. 9M and FIG. 9N, the vehicle damage assessment interface can present an interactive, virtual representation of the vehicle of the claimant user 197 (1012). As further describe above, the virtual representation of the vehicle can be rotatable on multiple axes to enable the claimant user 197 to accurately mark the location and severity of the damage (1013)” ), and (ii) receiving, via the damage interface, input indicating one or more damaged portions of the vehicle (Fig 9N, par 0125-0127, “In one example such as shown by FIG. 9N, the user 197 may provide input (e.g., through interaction with a touchscreen of a mobile device) to indicate or otherwise color in the location(s) of the damage. The input provided by the user can be rendered as damage markings 935 on the vehicle representation 936. The damage markings 935 can provide a course estimate as to the actual damage to the vehicle (based on the input of the claimant user 197). In some examples, the computing system 100 can include a process that maps the areas indicated by the damage markings 935 to vehicle parts which will likely need replacement or repair. The mapping of damage markings 935 to vehicular parts can be based on user-specific information, including the make, model, year of the user's vehicle, as well as the geographic location of the user”). It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Jones et al. as modified by Ben Hariz et al. and Utke et al. to include wherein processing the incident information includes (i) generating a damage interface on a computing device of the user, the damage interface including a three-dimensional representation of the vehicle that is rotatable about a set of axes, and (ii) receiving, via the damage interface, input indicating one or more damaged portions of the vehicle as taught by Patt et al. to provide a three-dimensional damage assessment interface for display on the computing device of the claimant, the damage assessment interface enabling the claimant to provide damage information for a vehicle based on the vehicular incident to calculate and refine reserve estimates, adjusted loss predictions, and/or predicted exposure risks for any given area and for any given claim event based on accurately received information. Regarding claim 2, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 1, and Ben Hariz et al. further teach wherein the executed instructions further cause the computing system to: in response to detecting the vehicle being aligned with the particular vehicle outline, trigger a manual capture indicator on the computing device of the user to enable the user to manually capture an image of the vehicle (par 0127, “ the mobile device may automatically capture the image of the vehicle when it is determined the view of the vehicle is aligned with the displayed vehicle overlay. In some embodiments, the mobile device may provide instructions to the user to capture the image, after which the user may provide an input to the mobile device to capture the image”, par 0141, “Image capture guidance software 120 may provide instructions 121 to the user. Instructions 121 may include information on how the user should position the camera in order to capture a proper image of the vehicle. As shown, instructions 121A explain the user should move the camera backwards and to the left in order to align the vehicle with the overlay 114.”, par 0143, “The image capture guidance software 120 may display an alignment state 122B indicating the vehicle is properly aligned with the overlay. In addition, the image capture guidance software 120 may display instructions 121B indicating an image of the vehicle has been captured and to move to the next position. As discussed herein, the images of the vehicle may be captured automatically or via a user input”). Regarding claim 3, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 1, and Ben Hariz et al. further teach wherein the executed instructions further cause the computing system to: in response to detecting the vehicle being aligned with the particular vehicle outline, trigger an automated image capture on the computing device of the user to automatically capture an image of the vehicle (par 0112, “the image capture guidance software 120 may analyze the camera display 111 and automatically capture an image when the current image on the camera display is of a sufficient quality.”, par 0127, “ the mobile device may automatically capture the image of the vehicle when it is determined the view of the vehicle is aligned with the displayed vehicle overlay. In some embodiments, the mobile device may provide instructions to the user to capture the image, after which the user may provide an input to the mobile device to capture the image”, par 0143, “The image capture guidance software 120 may display an alignment state 122B indicating the vehicle is properly aligned with the overlay. In addition, the image capture guidance software 120 may display instructions 121B indicating an image of the vehicle has been captured and to move to the next position. As discussed herein, the images of the vehicle may be captured automatically or via a user input”). Regarding claim 4, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 1, and further teach wherein the sequential set of vehicle outlines corresponds to predetermined angles to capture images of an exterior of the vehicle (Jones et al.: Fig 7, par 0094, “a data collection request 712 for a picture of a front-left portion of the user's vehicle may include an immediate reference image 714 displayed at the user interface 710 and comprising a generic drawing of a front-left portion of a vehicle. Where exemplary drawings and/or photographs more representative of the user's specific vehicle are available for use, a corresponding immediate reference image may include same “…take picture at 4 position around vehicle, Ben Hariz et al.: Fig 2, par 0144-0152, “These general portions may be predetermined portions of the vehicle which correspond to different views of the vehicle which are essential to analyze to determine the condition of the vehicle. These general portions may be used to generate overlays for all vehicles, and therefore sets of overlays for different vehicles may contain overlays adapted to a particular vehicle which correspond to the same general portions of the vehicle to image”). Regarding claim 5, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 4, and further teach wherein the guided content capture process includes instructions to the user to move around the vehicle to align the vehicle with a respective vehicle outline to capture an image of each predetermined angle of the vehicle (Jones et al.: par 0146, “the program may instruct the processor to direct the user to capture images with the camera from various angles, such as views of the vehicles from each corner of the vehicles”, Fig 7, par 0094, “a data collection request 712 for a picture of a front-left portion of the user's vehicle may include an immediate reference image 714 displayed at the user interface 710 and comprising a generic drawing of a front-left portion of a vehicle. Where exemplary drawings and/or photographs more representative of the user's specific vehicle are available for use, a corresponding immediate reference image may include same“ …take picture at 4 position around vehicle, Ben Hariz et al.: par 0110, ” Instructions 121 may additionally include how a user is to move the mobile device 110, for example a speed at which to move the device at. Instructions 121 may also indicate how a user should align the vehicle in camera display 111 with a vehicle overlay”, par 0223, “he instructions may include information on how to orient the camera of the mobile device relative to the vehicle in order to capture video of the vehicle. The instructions may include a direction for the user to move in, an angle for the user to position the camera at, and information on how to improve camera movement for capturing video, among other instructions”). Regarding claim 7, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 1, and Jones et al. further teach wherein the executed instructions cause the computing system to initiate the guided content capture process by initially requesting that the user capture an image of at least one of a vehicle identification number or a license plate of the vehicle (par 0027, “An exemplary data capture process may generate important incident data such as information about one or more other drivers—e.g., if a third party was involved—such as name, phone number, insurance company, policy number, license plate, vehicle identification number (VIN), driver's license number and/or issuing state, photographs of indicia embodying any of the foregoing information (e.g., of driver's license, license plate, etc.), witness names, witness phone numbers, witness statements and/or audio files and/or text files thereof (e.g., generated using talk-to-text software), images of the user's car and/or of any other car involved, makes and/or models of involved cars, year of manufacture of involved cars, images of the incident scene, images of collateral damage and/or personal injuries, metadata generated from any of the aforementioned or other relevant images (such as location and time of the incident and/or of image capture), data related to weather conditions, the location, date, and time of the incident, whether the incident occurred on a one-way or two-way street, etc. “, also par 0050 and par 0126). Regarding claim 8, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 7, and Jones et al. further teach wherein the executed instructions further cause the computing system to perform optical character recognition on the image to (i) determine the at least one of the vehicle identification number or the license plate of the vehicle, and (ii) perform a lookup in a vehicle database to determine a vehicle type for the vehicle (par 0052, “The program may also be configured to determine—for example, using object recognition software—and/or receive information related to the make, model, and/or year of vehicles involved in and/or proximate the accident “, par 0051, “ the program may be configured to utilize optical character recognition (OCR) and/or object recognition software to analyze images for additional incident data. In an embodiment, the program may be configured to direct the user to capture images of one or more third-party driver documents via the camera of the user's mobile electronic device and may execute OCR software to recognize text on the images for storage and/or inclusion in a structured incident data report or the like. More particularly, the program may be configured to request and receive images of an insurance card, driver's license, VIN, and/or license plate, and utilize OCR to determine phone number, legal name, driver's license number, state of licensure, license plate number, insurance company, policy number and/or other information from the image(s)”, par 0099, “the user may be directed to capture images of one or more third-party driver documents via the camera of the user's mobile electronic device and may execute OCR software to recognize text on the images for storage and/or inclusion in a structured incident data report or the like. More particularly, images of an insurance card, driver's license, and/or license plate may be requested, received, and utilized via OCR to determine phone number, legal name, driver's license number, state of licensure, license plate number, insurance company, policy number and/or other information from the image(s).”, also par 0126). Regarding claim 9, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 8, and Ben Hariz et al. further teach wherein the executed instructions cause the computing system to generate the sequential set of vehicle outlines of the vehicle based on determining the vehicle type for the vehicle (par 0086-0088, “Examples of vehicle overlays are provided herein including in FIG. 2. Vehicle overlays may be specific to a particular vehicle make and model. In some examples, vehicle overlays may be general overlays to a particular type of vehicle, such as a general sedan overlay or a general truck overlay …. since multiple images of multiple portions of any one vehicle are of interest (e.g., for identifying vehicle defects, for inclusion in a vehicle condition report, etc.), multiple vehicle overlays need to be generated for each type of vehicle and there is a multitude of vehicle makes and models for which vehicle overlays are to be generated. The conventional manual techniques are therefore tedious, time consuming, and impractical”, par 0123, “the server may maintain a set of general vehicle overlays for multiple types of vehicles including, but not limited to a sedan, a coupe, a sports car, a crossover, a full-size car, a sports utility vehicle, a pickup truck, and a compact car. The server 130 may provide the general vehicle overlays corresponding to a vehicle type determined from the vehicle information 112 to the mobile device 110”). Regarding claim 10, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 8, and further teach wherein determining the vehicle type comprises determining a make, a model, and a model year of the vehicle (Jones et al.: par 0027, “images of the user's car and/or of any other car involved, makes and/or models of involved cars, year of manufacture of involved cars, images of the incident scene”, par 0033, “images of the user's car and images of any other car involved, makes of the relevant cars, models of the relevant cars, year of manufacture of the relevant cars, images of the incident scene, metadata generated from the images (such as location and time of the incident)”, par 0050, “images of the user's car and images of any other car involved, makes of the relevant cars, models of the relevant cars, year of manufacture of the relevant cars, and/or any other information regarding any of the parties and/or properties involved in the incident that is pre-existing (stored prior to the accident in question) and stored on an insurance provider and/or third-party database”, par 0127, “ The program may also be configured to determine—for example, using object recognition software—and/or receive information related to the make, model, and/or year of vehicles involved in and/or proximate the accident”, Ben Hariz et al.: par 0120, par 0221, “The vehicle information may include any suitable information, including, but not limited to a make of the vehicle, a model of the vehicle, a model year of the vehicle, a trim level of the vehicle, and a VIN of the vehicle. The vehicle information 112 is then transmitted to the server(s) 130”). Regarding claim 11, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 8, and further teach wherein determining the vehicle type comprises determining whether the vehicle is a van, a semi-truck, a cargo truck, a pickup truck, a full size sedan, a station wagon, a mid-size sedan, compact sedan, a hatchback, or a motorcycle (Ben Hariz et al.: par 0123, par 0222, “the server 811 may obtain a generic 3D model corresponding to a vehicle type associated with the received vehicle information 820. Examples of vehicle types for generic 3D models may include a sedan, a coupe, a sports car, a crossover, a full-size car, a sports utility vehicle, a pickup truck, and a compact car”). Regarding claim 13, Jones et al. teach a non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to (par 0007). The remaining limitations of the claim are similar in scope to claim 1 and rejected under the same rationale. Regarding claim 14-17 and 19, Jones et al. as modified by Ben Hariz et al. and Utke et al. teach all the limitation of claim 13, the claims 14-17 and 19 are similar in scope to claim 2-5 and 7 and are rejected under the same rational. Regarding claim 20, Jones et al. teach a non-transitory computer readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to (par 0006). The remaining limitations of the claim are similar in scope to claim 1 and rejected under the same rationale. Claim(s) 6, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pubs 2023/0419413 to Jones et al. in view of U.S. PGPubs 2025/0126353 to Ben Hariz et al., further in view of U.S. PGPubs 2021/0334767 to Utke et al., further in view of U.S. Patent 10573012 to Collins et al. Regarding claim 6, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 4, but keep silent for teaching wherein the executed instructions cause the computing system to determine the predetermined angles based on damage to the vehicle as indicated in the incident information. In related endeavor, Collins et al. teach wherein the executed instructions cause the computing system to determine the predetermined angles based on damage to the vehicle as indicated in the incident information (col 8:33-53, “The enhanced claims processing server 101 may be configured to receive and analyze the photos to determine if they meet a predefined set of criteria (e.g., not too blurry, correct angles, etc.) for completeness, accuracy, etc. … This process of assuring that the photos are compliant for further analysis may be repeated until the user of device 201 has complied with all of the rules set forth by enhanced claims processing server 101. Server 101 may then analyze the photos to generate an output, including a cost estimate to repair the damage associated with the insured vehicle and/or to replace a damaged part of the insured vehicle”, col 9:63-67 and col 10:1-11, “The user may receive instructions on various types of photos/video, including photos/video of the entire vehicle, VIN door tag, current odometer reading, and/or the damaged areas. In some aspects, the user may capture image data related to at least two different angles of the damage for each panel (e.g., hood, fender, door, bumper, etc.) based on an initial claim description.”, col 10:50-67 and col 11:1-12, “server 101 may determine that the photos are too blurry and/or that the photos do not capture the correct angles to clearly show damage associated with the insured vehicle.”, Fig 15, col 29:53-67 and col 30:1-4, “ window 1503a and instruction 1503b relate to capturing damage to an insured vehicle from different angles”, col 36:3-34, “ the user may be instructed to take at least four photos of the vehicle, each from a different angle …. Screen 2701a may include on or more guides 2701b. For example, screen 2701a displays guides for orienting a user that is taking a picture from an angle that captures the front and driver-side portions of a vehicle. In aspects of the disclosure, screen 2701a may include guides for taking pictures of a vehicle from various angles, for taking pictures of a certain portions of a vehicle, for instance pictures of damaged portions or pictures of a VIN number, or for any other suitable purpose”). It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. to include wherein the executed instructions cause the computing system to determine the predetermined angles based on damage to the vehicle as indicated in the incident information as taught by Collins et al. to provide corrected image data based on a predetermined set of criteria through obtaining advice associated with loss prior to submission of an insurance claim with efficient claims processing to better serve customers. Regarding claim 12, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 1, but keep silent for teaching wherein the executed instructions further cause the computing system to: perform automated image analysis on the image data captured by the camera of the computing device to determine a predicted estimate of damage to the vehicle of the user. In related endeavor, Collins et al. teach wherein the executed instructions further cause the computing system to: perform automated image analysis on the image data captured by the camera of the computing device to determine a predicted estimate of damage to the vehicle of the user (col 6:25-33, “The automated process may utilize various hardware components (e.g., processors, communication servers, memory devices, sensors, etc.) and related computer algorithms to generate image data related to damage associated with an insured item, determine if the image data conforms to a predetermined set of criteria, analyze the image data to assess loss associated with the insured item, and determine if a payment is appropriate to the claimant as compensation for assessed loss”, col 6:42-63, “ Processor 103 and its associated components may allow the device 101 to run a series of computer-readable instructions to analyze image data depicting damage to an insured item (e.g., vehicle, etc.). Processor 103 may determine the general location of damage associated with the vehicle by analyzing images of the vehicle and comparing these images with reference images of a similar vehicle with no damage or with similar damage”, col 47:62-67 and col 48:1-67, “ the estimate data may be received based on one or more photos being displayed to the user. The received estimate data may comprise data related to a cost for repairing damage to the insured item and/or replacing portions of the insured item ….. a device (e.g., server 101, terminal 141 and/or terminal 151) may receive estimate data about an insured item and calculate an estimate based on the received estimate data. In this example, the received estimate data may comprise data related to a cost for repairing damage to the insured item and/or replacing portions of the insured item, and the estimate may be calculated based on the received data”). It would have been obvious to a person of ordinary skill in the art at the time before the effective filing data of the claimed invention to modified Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. to include wherein the executed instructions further cause the computing system to: perform automated image analysis on the image data captured by the camera of the computing device to determine a predicted estimate of damage to the vehicle of the user as taught by Collins et al. to provide corrected image data based on a predetermined set of criteria through obtaining advice associated with loss prior to submission of an insurance claim with efficient claims processing to better serve customers. Regarding claim 18, Jones et al. as modified by Ben Hariz et al., Utke et al., and Patt et al. teach all the limitation of claim 16, the claim 18 is similar in scope to claim 6 and is rejected under the same rational. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jin Ge whose telephone number is (571)272-5556. The examiner can normally be reached 8:00 to 5:00. 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, Jason Chan can be reached at (571)272-3022. 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. 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