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 § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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, 8 and 15 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of U.S. Patent Application Pub. No.; US20180260793A1 to Li and in view of U.S. Patent Application Pub. No.: US20140365029A1 to Sugimoto that was filed in 2014 (hereinafter “Sugimoto”) and in view of United States Patent Application Pub. No.: US 20210256616 A1 to Hayward that was filed in 2018 and assigned to STATE FARM™ and in view of NPL, Kwasnokis, John, Crash Reconstruction Basics for Prosecutors Targeting Hardcore Impaired Drivers, American Prosecutors Institute, https://ndaa.org/wp-content/uploads/Crash-Reconstruction-Basics.pdf (March 2003).
In regard to claim 1, and 8, and 15, Li discloses “A system, comprising: at least one processor; and memory storing instructions that, when executed by the at least one processor, cause the system to:
generate a graphical user interface comprising a graphical representation of a
particular geographic location, wherein the graphical representation comprises a
graphical representation of a vehicle that is generated based on vehicle operational
data associated with the vehicle and receive at least one indicator of damage to a vehicle; (see FIG. 4, blocks 402-408 where the camera can take photos of a damaged vehicle and then perform image processing on the photos to infer damage to the vehicle and external damage and then calculate a repair cost in blocks 402-408)
….. (see paragraph 60 where the telematics system is installed in the vehicle and then can provided before and after the accident the time, velocity, speed and acceleration of the vehicle and profile of the airbag and turn signals)
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Li is silent but Sugimoto teaches “… a particular geographic location; ”. (see paragraph 83 where the vehicle can move into a predetermined travel section within a legal speed limit; see vehicle GPS sensor 108 and the vehicle speed sensor 105 and acceleration sensor 106 which is transmitted via a signal to the vehicle data analysis server 200 and is classified as within a speed limit and provided as analysis)
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Hayward teaches “... regenerate the graphical user interface; and (see FIG. 9b where the photograph of the vehicle can be uploaded as 912 to the machine trained ai expert and then the damage of 1. Bumper, wheel well and the tire and window and a type of collision is provided by 2. The neural network ai device and see paragraph 47 where the scene of the environment can be provided including 1. Weather 2. Traffic, 3. Hair pin turn 4. No guardrails and 5. A winding road)... generate, based at least in part on the re-generated graphical representation,
(see paragraph 190-192) (see paragraph 48-51 where data for the auto insurance can be from third party data that is someone other than the owner of the system 100 that can identify a risk factor to assist with the current claim and see paragraph 181-182 where the third party system can determine 1. Fraud or inflation of a legitimate claim)
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It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li with the teachings of Hayward since Hayward teaches that an AI neural network that receive inputs that includes a first input from a third party application that can determine information to assist the neural network with assisting the resolution of the claims.
In regard to claim 18, this can include audio data as an audio recording in block 520c of FIG. 5.
It would have been obvious for one of ordinary skill in the art to combine the primary reference with the teachings of Hayward at before the time of the effective filing date since Hayward teaches on a first pass the neural network AI engine can use a photo to determine a loss and damage but then it can also provide an input as a third party data information to further refine the AI system to detect fraud or an investigation from a third party data source. For example, the notice of loss may indicate from the third party that this individual has created many of the same claims and this can flag the claim for insurance fraud. See paragraph 48-51 and 180-187.
Li discloses “…event interpretation data, wherein the event interpretation data comprises a
confidence metric., (see abstract where the device can scan a damage part and a second damaged part and then access a database and then the repair cost for the first and the second damaged part can be determined)
(see paragraph 60 where the vehicle telematics data including a speed of the vehicle is provided to the processor and the route and acceleration and see FIG. 4 where an inference is reached as to how the damage occurred in block 406; see paragraph 153-156 where a neural network of past accidents is accessed and then an indicator of a damage can be shown and then other second parts in the vehicle based on prior accidents that may have been damaged as well from a different accident can be accessed to link other parts that are likely damaged but hidden from the view of the camera)
The claim is an apparatus claim which recites an intended use. Intended use forms little patentable weight to the claims.
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of Sugimoto since LI discloses that a computer with a camera can scan a vehicle and formulate an image. The vehicle can then be determined, for example, to have damage to a panel of the vehicle. The computer also includes a neural network. The neural network has information from thousands of past accidents and also thousands of past repairs. The computer can then determine that in addition to the obvious panel that needs to be repaired there is likely other second different components behind the panel that also necessitate being repaired as well that are hidden from view. This is from the neural network and prediction from the history of observed past accidents and thousands of repairs. As described, the method shown in FIG. 27 provides a technique to identify which external parts of a vehicle are damaged, and also which portions of those parts are damaged. As described in FIG. 4 at step 406, from this information, the server may also infer internal damage to the vehicle from detected external damage. Once the externally damaged parts are identified, the server can look up in a database which internal parts are also likely to be repaired or replaced based on the set of damaged external parts. This inference can be based on historical models for which internal parts needed to be replaced given certain external damage in prior repairs. This can provide an advantageous computerized tool as a repair and repair cost can be formulated instantly without having to take apart the vehicle and merely from an exterior photo which saves time and expenses. See paragraph 60-70 and 153-170 and 354 and claims 1-12 and the abstract.
Li is silent but Kwasnoksi teaches “... and one or more movable objects;
after receiving an indication to move one or more of the one or more movable
objects from one or more respective first locations within the graphical
representation to one or more respective second locations within the graphical
representation, regenerate the graphical user interface; and (see page 19 where the user can use ED crash computer software to reconstruct the accident with different frontal impact values to crush the four different zones of the vehicle to show a reconstruction of the accident with different damage parameters from a pole impact)
(See page 20-23 where the drag of the tires can be added to the reconstruction scene and the approach angle of the vehicle to the pole and the weight and the speed of the vehicle can be added to the reconstruction).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present disclosure to combine the teachings of Kwanoski with the disclosure of Li since Kwanoski teaches that using a software ED Crash, the user can model different parameters of the accident by adding these variables including the force on the different crash zones, whether the wheels were turning or locked and whether the vehicle was rotating for a different impact point with the pole. This can provide a 1. Different speed, 2. Crush impact and 3. Crush depth and crush zones and the angle of impact and if the wheels were dragging or turning to provide an improved reconstruction of the zone. See page 19-24.
Claims 2-3 and 9-10 and 16-17 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of U.S. Patent Application Pub. No.; US20180260793A1 to Li and in view of U.S. Patent Application Pub. No.: US20140365029A1 to Sugimoto that was filed in 2014 (hereinafter “Sugimoto”) and in view of United States Patent No.: US 10497108 B1 to Knuffman et al. that was filed in 12-2016 (hereinafter ‘Knuffman”) and in view of Hayward and in view of Kwasnokis.
In regard to claim 2 and 9 and 16, LI is silent but Knuffman teaches “…2. The system of claim 1, wherein the system further includes instructions that, when
executed, cause the system to: generate notification data identifying the indicator of damage when the confidence metric is below a threshold value for an indicator of damage; receive additional data based on the the notification data; regenerate the graphical representation based on the additional data; and regenerate the event interpretation data based on the regenerated graphical representation. (see col. 27, lines 20-60 where the damage can indicate a damage to the undercarriage of the vehicle or a defective part and a replacement is ordered; see claims 2-7 where the damage part is verified using the training data and the damage is serious so as to warrant a replacement of the part and the part is ordered immediately; however if there is no damage then this is indicated as fraud ad col. 14, line 60 to col. 15, line 25) (See col. 16, lines 10 to 32 where if a damaged section is captured and based on a review of the trained images it is deemed to be damaged the processor 350 can then capture images of the surrounding portions to determine if they are consistent and that all of the other portions have been searched and corrected; see col. 16, lines 55 to 65 where the processor can indicate that other damages that are consistent with fraud).
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of KNUFFMAN since KNUFFMAN teaches that a drone can be dispatched to a vehicle upon a trigger that an insurance claim is made. The drone can be an unmanned aerial vehicle or a ground based drone that can look under the car. The drone can be trained to detect damage using images that are correctly showing an indication of damage. This provides a training. Using this image capture and comparing these new images to the trained images the UAV can determine the amount of damage. If there is damage the UAV can also immediately order a repair of the damage to put the vehicle back on the road fast. However, if there is no damage, then the insurance carrier can then alternatively adjust the risk as the driver is reporting a claim that has no damage or is presenting a false claim that has inconsistent damage with the claim. For example, the drone can look and image the bumper and other areas to see if it is consistent with the type of accident. See col. 2, lines 65 to col. 3, line 35 and col. 9 lines1-61 and col. 15. 1-20 and claims 1-10 and the abstract.
In regard to claim 3 and 10 and 17, Li is silent but Knuffman teaches “…3. The system of claim 2, wherein the event interpretation data is regenerated for each of the
indicators of damage. (See col. 16, lines 10 to 32 where if a damaged section is captured and based on a review of the trained images it is deemed to be damaged the processor 350 can then capture images of the surrounding portions to determine if they are consistent and that all of the other portions have been searched and corrected; see col. 16, lines 55 to 65 where the processor can indicate that other damages that are consistent with fraud)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of KNUFFMAN since KNUFFMAN teaches that a drone can be dispatched to a vehicle upon a trigger that an insurance claim is made. The drone can be an unmanned aerial vehicle or a ground based drone that can look under the car. The drone can be trained to detect damage using images that are correctly showing an indication of damage. This provides a training. Using this image capture and comparing these new images to the trained images the UAV can determine the amount of damage. If there is damage the UAV can also immediately order a repair of the damage to put the vehicle back on the road fast. However, if there is no damage, then the insurance carrier can then alternatively adjust the risk as the driver is reporting a claim that has no damage or is presenting a false claim that has inconsistent damage with the claim. For example, the drone can look and image the bumper and other areas to see if it is consistent with the type of accident. See col. 2, lines 65 to col. 3, line 35 and col. 9 lines1-61 and col. 15. 1-20 and claims 1-10 and the abstract.
Claims 4 and 11 and 18 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of Li and Sugimoto and in view of United States Patent Application Pub. No.: US20130317864A1 to Toffe et al. that was filed in 2012 and in view of Hayward and Kwasnokis.
In regard to claim 4 and 11 and 18, Li is silent but Toffe et al teaches “4. The system of claim 1, wherein: the at least one indicator of damage to the vehicle comprises audio data; and further
including instructions that, when executed, cause the system to:
generate text data based on the audio data; and
generate the graphical representation based on the text data. (See claim 15; paragraph 21-32).
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It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of LI and the teachings of TOFFE since TOFFE teaches that a user using a smartphone can report a claim. See block 510. A model of an accident scene can be determined and the user can provide audio and text data and indicating the details of the accident. Then using the user interface the claim can be reported. If additional information is required then the GUI can ask for additional data. This can provide increased automation in claim reporting without using a costly human to aggregate the data and instead using an application on a smartphone to collect the data. See Toffe at FIG. 1-5 and paragraph 21-32 and the abstract.
Claim 5 and 12 and 19 are rejected under 35 U.S.C. sec. 103(a) as being unpatentable as obvious in view of Li and Sugimoto and in view of United States Patent No.: US 10497108 B1 to Knuffman et al. that was filed in 12-2016 (hereinafter ‘Knuffman”) and in view of Hayward and in view of Kwasnokis
In regard to claim 5 and 12 and 19, Li is silent but Knuffman teaches “…5. The system of claim 1, wherein:
the at least one indicator of damage to the vehicle comprises image data of the at
least one indicator of damage; and
the event interpretation data comprises a damage model generated by the at least one
machine classifier based on the image data and the graphical representation. (see col. 14, line 61 to col. 16, line 45)”.
It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of KNUFFMAN since KNUFFMAN teaches that a drone can be dispatched to a vehicle upon a trigger that an insurance claim is made. The drone can be an unmanned aerial vehicle or a ground based drone that can look under the car. The drone can be trained to detect damage using images that are correctly showing an indication of damage. This provides a training. Using this image capture and comparing these new images to the trained images the UAV can determine the amount of damage. If there is damage the UAV can also immediately order a repair of the damage to put the vehicle back on the road fast. However, if there is no damage, then the insurance carrier can then alternatively adjust the risk as the driver is reporting a claim that has no damage or is presenting a false claim that has inconsistent damage with the claim. For example, the drone can look and image the bumper and other areas to see if it is consistent with the type of accident. See col. 2, lines 65 to col. 3, line 35 and col. 9 lines1-61 and col. 15. 1-20 and claims 1-10 and the abstract.
Claims 6-7 and 13-14 and 20 are rejected under 35 U.S.C. sec. 103 as being unpatentable as obvious in view of Li and in view of Sugimoto and in view of U.S. Patent No.: US10373387B1 to Fields et al. and in view of Hayward and Kwasnokis.
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In regard to claim 6 and 14, Li is silent but Fields teaches “…6. The system of claim 1, wherein the instructions further cause the system to:
obtain satellite image data for the particular geographic location; and
generate the graphical representation further based on the satellite image data. (See col. 14, lines 40 to 67 and col. 15, lines 1-60)
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It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of FIELDS since FIELDS teaches that a user using a smartphone can report a claim. See FIG. 5a. A model of an accident scene can be determined via 360 degree photos, sat. images, GoogleMaps™ streetview™ and text. The text can include the claim information, and details about the loss and a description of the facts. Using a smartphone then the user can provide audio and text data and indicating the details of the accident. Then using the user interface the claim can be reported using immersive multimedia images that can be annotated. This allows the claims handler to view the details to verify the veracity of the claim using the images as evidence. This can provide increased automation of the claim to determine a damage and assess the fault without having to go to the scene of the accident. For example a rear end collision can be claimed but the images can reveal a side collision which can apportion the claim fault differently. See Fields at col. 14 line 40 to col. 15, line 60 and the abstract.
In regard to claim 7 and 14, Li is silent but Fields teaches “…7. The system of claim 6, further including instructions that, when executed, further cause the system to:
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generate a user interface comprising the event interpretation data and the graphical representation; and(See col. 14, lines 40 to 67 and col. 15, lines 1-60);
provide the user interface. (see FIG. 5a and 5b where the user interface comprises an image of the accident of the scene 518 and a description of the loss 516 and that includes a touch screen GUI) ;
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It would have been obvious for one of ordinary skill in the art before the effective filing date to combine the disclosure of Li and the teachings of FIELDS since FIELDS teaches that a user using a smartphone can report a claim. See FIG. 5a. A model of an accident scene can be determined via 360 degree photos, sat. images, GoogleMaps™ streetview™ and text. The text can include the claim information, and details about the loss and a description of the facts. Using a smartphone then the user can provide audio and text data and indicating the details of the accident. Then using the user interface the claim can be reported using immersive multimedia images that can be annotated. This allows the claims handler to view the details to verify the veracity of the claim using the images as evidence. This can provide increased automation of the claim to determine a damage and assess the fault without having to go to the scene of the accident. For example a rear end collision can be claimed but the images can reveal a side collision which can apportion the claim fault differently. See Fields at col. 14 line 40 to col. 15, line 60 and the abstract.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected under obviousness double patenting in view of claim 1 of U.S. Patent No.: 12 322 219 that recites generating event interpretation data.
The only difference is in claim of the present claims it recites an event interpretation data which is obvious in a user interface to deny the claim due to fraud when no damage is seen.
The claims are otherwise identical.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEAN PAUL CASS whose telephone number is (571)270-1934. The examiner can normally be reached Monday to Friday 7 am to 7 pm; Saturday 10 am to 12 noon.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott A. Browne can be reached on 571-270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEAN PAUL CASS/Primary Examiner, Art Unit 3668
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