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 .
This action is in response to communications filed on 07/09/2026. Claims 1-20 are pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/09/2026 has been entered.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Response to Arguments
Applicant's arguments filed have been fully considered but they are not persuasive.
Applicant argues in substance that “all the original images that contain the part” of Li allegedly are only from the same post damage point in time and allegedly do not present the images upon selection. However, examiner respectfully disagrees. Lee specifically describes “reference images can, in some embodiments, be…from images of the same vehicle taken prior to the occurrence of damage in the current claim , e.g., at the time of purchase of the auto policy” (e.g. in paragraph 63), i.e. at a first point in time, “images (e.g., photos or videos) showing damage to the vehicle are captured soon after the damage occurs” (e.g. in paragraph 58), i.e. at a second point in time. Thus, “all the original images that contain that part on the side” (e.g. in paragraph 164) would include both “images of the same vehicle taken prior to the occurrence of damage in the current claim” and “images (e.g., photos or videos) showing damage to the vehicle are captured soon after the damage occurs” and “interface may show all” such images. Li further describes “damaged area in each input image are marked [i.e. tag; note this is an element to allow a user to recognize where damage is]… label can be put onto the damaged part [i.e. tag; note this is an element to allow a user to recognize where damage is]… project…onto the 3D model [which is shown on a UI]… model then shows the damage to the vehicle… When the adjustor clicks on a damaged part [i.e. clicks on the mark and/or label on the UI], the interface may show all the original images that contain that part on the side” (e.g. in paragraph 164), i.e. selection of the tag shows the images.
Applicant then appears to argue that a discrete “tag” corresponds to a location which is different from “a labeled damaged region”. However, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The claims do not preclude the tag corresponding to a location from being a labeled damage region. The term “location” broadly includes a region. Li describes “damaged area in each input image are marked [i.e. tag]… label can be put onto the damaged part [i.e. tag]… project…onto the 3D model… model then shows the damage to the vehicle” (e.g. in paragraph 164), which reads on the claimed “tag”.
As such, applicant’s arguments are not persuasive. However, in the interest of advancing prosecution, Vercollone is alternatively provided. See below for details.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over by Li et al. (US 20180260793 A1) in view of Musuluri (US 20130080291 A1) [or, alternatively, and Vercollone et al. (US 20150287130 A1)].
As per independent claim 1, Li teaches a method comprising:
determining first orientation information for first image data of an object via a processor (e.g. in paragraphs 57-59 and 75, “capturing images of…vehicle… mobile phone is used to collect the images, information about…the camera's orientation from the mobile phone's gyroscope and accelerometer… processor…within client device”), the first orientation information identifying a first camera location and a first camera orientation for the first image data with respect to an object model representing the object (e.g. in paragraphs 59 and 164, determines “camera's location…the camera's orientation” and figures 28 and 51 showing model), the first image data being associated with a first point in time (e.g. in paragraphs 59, 61, 63, and 124, “the time at which the images are taken… reference image is found for the same vehicle type that is taken from the same camera position and orientation… reference images can, in some embodiments, be…from images of the same vehicle taken prior to the occurrence of damage in the current claim , e.g., at the time of purchase of the auto policy”, i.e. at a first/earlier time);
determining second orientation information for second image data of the object via the processor (e.g. in paragraphs 57-59, “information about…the camera's orientation from the mobile phone's gyroscope and accelerometer” for an image taken “after the damage occurs” to the vehicle), the second orientation information identifying a second camera location and a second camera orientation for the second image data with respect to the object model (e.g. in paragraphs 59 and 164, determines “camera's location…the camera's orientation” for that image and figures 28 and 51 showing model), the second image data being associated with a second point in time occurring after the first point in time (e.g. in paragraph 58, an image taken “after the damage occurs” to the vehicle);
identifying a change to the object between the first point in time and the second point in time by identifying a difference between the first image data and the second image data (e.g. in paragraph 139, “image of the damaged vehicle and the corresponding reference image are compared for significant differences that are attributable to damage to the vehicle. The reference image can be the image of the same vehicle prior to occurrence of damage”), the difference identified at least in part by aligning the first image data with the second image data based on the first orientation information and second orientation information (e.g. in paragraphs 91 and 124, “performs image processing on the one or more images to detect external damage of the vehicle… image alignment to an undamaged version of the vehicle… overlay the two images on top of each other so that the vehicle boundaries within them more or less coincide. This is called image alignment”), wherein the first image data and the second image data are included in visual data of the object captured during at least three points in time, the first point in time associated with when the object was added to a collection, the second point in time associated with when the object was damaged (e.g. in paragraphs 59, 63, 67, 158, and 161, “reference images can, in some embodiments, be…from images of the same vehicle taken prior to the occurrence of damage in the current claim , e.g., at the time of purchase of the auto policy [i.e. when object was added to a collection]… The orientation of the camera when used to take images of the vehicle, as well as its location and time, can also assist the damage detection system in carrying out various image processing operations… the user is prompted to take photos of the damaged parts of the vehicle [i.e. when object was damaged]” including e.g. “prompted to capture photos of eight views of the vehicle”, each taken/captured photo occurs at a different point in time, i.e. at least 3 points in time); and
transmitting an instruction to present a visual object history for the object on a display screen having a user interface including the first image data and second image data (e.g. in paragraphs 128 and 163, runs “interface screen” with “reference image” and/or “two-dimensional images of the vehicle taken prior to occurrence of damage” and “image currently being processed is displayed”, i.e. visual object history), the first image data and second image data being aligned with a visual representation of the object model (e.g. in paragraphs 92, 128, and 163-164, “reference image is shown aligned with the image shown in portion 2204” and “project the images onto the 3D model of the vehicle using the camera angles determined during the alignment process… adjustor can rotate and [zoom] in on the 3D model”), wherein the user interface includes the object model with a first tag, the first tag corresponding to a location on the object model, and wherein selection of the first tag via the user interface causes the user interface to present visual data from the first image data and visual data from the second image data each corresponding to the location of the first tag (e.g. in paragraphs 58, 63, 128, 158, and 164, “reference images can, in some embodiments, be…from images of the same vehicle taken prior to the occurrence of damage in the current claim , e.g., at the time of purchase of the auto policy… take photos of the damaged parts of the vehicle… damaged area in each input image are marked [i.e. tag]… label can be put onto the damaged part [i.e. tag]… project…onto the 3D model… model then shows the damage to the vehicle… When the adjustor clicks on a damaged part, the interface may show all the original images that contain that part on the side”), thereby presenting the identified change at the location between the first point in time and the second point in time, wherein the first tag is generated in response to identifying the change, the first tag identifying the location on the object model at which the change has occurred between the first point in time and the second point in time (e.g. in paragraphs 58, 63, 128, 158, and 164, “images of the same vehicle taken prior to the occurrence of damage in the current claim , e.g., at the time of purchase of the auto policy… take photos of the damaged parts of the vehicle… damaged area in each input image are marked [i.e. tag]… label can be put onto the damaged part [i.e. tag]… project…onto the 3D model… model then shows the damage to the vehicle”),
but the combination does not specifically teach a third point in time after the object was repaired.
However, Musuluri teaches a third point in time after an object was repaired (e.g. in paragraphs 35, 45, and 47, “Examples of visual data for a vehicle may include, but not limited to, one or more photographs of the vehicle showing damages sustained by the vehicle in an accident [when damaged],… photographs of the vehicle at a dealer [when added to a collection], one or more photographs of the vehicle before, during and/or after a repair [when repaired]… returns the report to the client logic 141, which displays the report to the user 142 in a window… the vehicle history information transmitted to the client logic 141… the client logic 141 may retrieve and show the corresponding resource with visual data to the user 142”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Li to include the teachings of Musuluri because one of ordinary skill in the art would have recognized the benefit of providing relevant information associated with a vehicle/object.
If, alternatively, the combination is not interpreted to teach “present visual data from the first image data and visual data from the second image data each corresponding to the location” as applied above, then the teachings of Vercollone is relied upon. Vercollone teaches present visual data from the first image data and visual data from the second image data each corresponding to a location of damage (e.g. in paragraph 59, “the application will present the before and after photographs to the user of the electronic device 110 (or another electronic device) in a side-by-side manner to allow for an easy comparison between the condition of the vehicle at the various time points… the reviewer may be presented with…before and after photographs of a particular view in which the agent identified potential damage”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination (e.g. clicking on an identified damage in Lee) to include the teachings (e.g. identified damage showing before and after photographs) of Vercollone because one of ordinary skill in the art would have recognized the benefit of allowing easy comparison to be performed at various points in time.
As per claim 2, the rejection of claim 1 is incorporated and the combination further teaches wherein identifying the change to the object involves identifying a location on the object model corresponding with the identified difference (e.g. Li, in paragraphs 139 and 164, “compared for significant differences that are attributable to damage… damaged area in each input image are marked”).
As per claim 3, the rejection of claim 2 is incorporated and the combination further teaches wherein the identified change is indicated in the user interface by a tag located on the object model at the identified location (e.g. Li, in paragraph 164, “damaged area in each input image are marked”).
As per claim 4, the rejection of claim 3 is incorporated and the combination further teaches wherein selecting the tag via the user interface causes the user interface to display a first portion of the first image data corresponding to the identified location (e.g. Li, in paragraph 164, “When the adjustor clicks on a damaged part, the interface may show all the original images that contain that part on the side”).
As per claim 5, the rejection of claim 1 is incorporated and the combination further teaches wherein the change represents damage to the object (e.g. Li, in paragraph 164, “damaged area in each input image are marked”), and wherein the method further comprises: estimating a characteristic is selected from the group consisting of: an estimated probability of damage to the object, an estimated severity of damage to the object, and an estimated type of damage to the object (e.g. Li, in paragraphs 151 and 163, “whether the fraction of "damaged" windows to the total number of windows covering the part exceeds a threshold… indicator of the severity of damage to the part… identify the external parts that are damaged… total repair costs are estimated”).
As per claim 6, the rejection of claim 1 is incorporated and the combination further teaches wherein the user interface allows for navigation of the first image data and second image data based on user input applied to the object model (e.g. Li, in paragraph 164, “rotate and [zoom] in on the 3D model”)
As per claim 7, the rejection of claim 1 is incorporated and the combination further teaches wherein identifying the change to the object comprises applying a neural network to the first image data and second image data (e.g. Li, in paragraph 174, “uses a machine learning method called Convolutional Neural Network (CNN)”)
As per claim 8, the rejection of claim 1 is incorporated and the combination further teaches determining the object model by applying a neural network to estimate one or more skeleton joints for a respective one of a plurality of images included in the first image data (e.g. Li, in paragraphs 174, 235, 239, 251, 253, and 334, “uses a machine learning method called Convolutional Neural Network (CNN)… segmentation includes identifying the outline of the object of interest (e.g., the car) and removing all other parts of the image (e.g., the background and other cars that are not of interest)” to identify “external body parts”, i.e. skeleton joints, and figures 28 and 51).
As per claim 9, the rejection of claim 1 is incorporated and the combination further teaches wherein the object model is selected from the group consisting of: a top-down view of the object, a three-dimensional skeleton of the object, and a two-dimensional skeleton of the object (e.g. Li, in paragraphs 249 and 344, “identifying the outline of the object of interest… segmented 3D model”, i.e. skeleton, and figures 28 and 51)
As per claim 10, the rejection of claim 1 is incorporated and the combination further teaches wherein the first image data includes a multi-view representation of the object that includes a plurality of perspective view images of the object, the multi-view representation being navigable in one or more directions (e.g. Li, in paragraph 164, “camera angles… can rotate and [zoom] in on the 3D model”, i.e. showing multiple views in one or more directions).
As per claim 11, the rejection of claim 1 is incorporated and the combination further teaches wherein the object is a vehicle (e.g. Li, in paragraph 55, “vehicle”), and wherein the object model includes a three-dimensional skeleton of the vehicle (e.g. Li, in paragraphs 249 and 344, “identifying the outline of the object of interest… segmented 3D model”, i.e. skeleton, and figures 28 and 51), and wherein the object model components include each of a left vehicle door, a right vehicle door, and a windshield (e.g. Li, in paragraph 235, “external body parts” which includes both doors and windshields as shown in figures 28 and 51).
As per claim 12, the rejection of claim 1 is incorporated and the combination further teaches wherein the first image data includes a video of the object captured by a camera as the camera moves around the object (e.g. Li, in paragraphs 56, 59, and 161, “In other embodiments, the captured images include not only still images, but also video… capture…eight views of the vehicle” using “camera”).
As per claim 13, the rejection of claim 1 is incorporated and the combination further teaches wherein the first image data includes one or more images of the object captured by a camera as the camera moves around the object (e.g. Li, in paragraphs 59 and 161, “capture photos of eight views of the vehicle” using “camera”).
Claims 14-19 are the device claims corresponding to method claims 1-8 and are rejected under the same reasons set forth and the combination further teaches a processor and a display screen (e.g. Li, in paragraphs 75 and 79, “processor… display screen”).
Claim 20 is the media claim corresponding to method claim 1 and is rejected under the same reasons set forth and the combination further teaches one or more non-transitory computer readable media (e.g. Li, in paragraphs 75-76, “memory”, etc.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Nelson et al. (US 20200410278 A1) teaches “receiving, by the processor, a second image of the object, the second image being captured at a second time different from the first time. The method further includes determining, by the processor, that the object in the first image is a same object in the second image. The method further includes aligning, by the processor, features of the object in the first image to the features of the object in second image. The method further includes determining, by the processor, at least one difference associated with the object based on a comparison between the first image and the second image. The method further includes determining, by the processor, that the at least one difference indicates damage to the object that exceeds wear and tear depreciation damage based at least in part on an amount of elapsed time between the first time and the second time… image may also include metadata related to…information input by the user as described herein, such as a condition of the object, damage identified in the image” (e.g. in paragraphs 3 and 85).
Altermatt et al. (US 20150032580 A1) teaches “operated by different drivers at different times and may become damaged during such use… accidents, collisions, and the like… tracking damage to rental vehicles over time and tracing such damage to particular rental transactions and/or points in time… receiving rental vehicle images and associated metadata from a number of different types of image acquisition systems (e.g., gate camera systems, mobile cameras, etc.) at a number of different rental sites where the images will document the body damage condition of the rental vehicles at the start and conclusion of each rental transaction with respect to such rental vehicles… the vehicle images in database 210 associated with the condition reports for that vehicle to generate a consolidated vehicle history that visually documents the damage history for the vehicle during its time as a rental vehicle” (e.g. in paragraphs 2, 9, 27-28, 30, and 64).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM WONG whose telephone number is (571)270-1399. The examiner can normally be reached Monday-Friday 9am-5pm.
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, TAMARA KYLE can be reached at (571)272-4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/W.W/Examiner, Art Unit 2144 07/25/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144
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