CTNF 18/805,261 CTNF 87265 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION Continued Examination Under 37 CFR 1.114 07-42-04 AIA 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 05/14/2026 has been entered. Notice to Applicant In response to the communication received on 05/14/2026, the following is a Non-Final Office Action for Application No. 18805261. 12-151 AIA 26-51 12-51 Status of Claims Claims 1-7, 10-16, and 19-24 are pending. Claims 8-9 and 17-18 are cancelled. Claims 23-24 are new. Priority As required by M.P.E.P. 201.14(c) , acknowledgement is made of applicant’s claim for priority based on: 18805261 filed 08/14/2024 is a Continuation of 17898988 , filed 08/30/2022 ,now U.S. Patent # 12093863 and having 1 RCE-type filing therein; 17898988 is a Continuation of 16429960, filed 06/03/2019 ,now U.S. Patent # 11461714 and having 1 RCE-type filing therein. Response to Amendments Applicant’s amendments have been fully considered. Applicant’s amendments to the claims overcome the 35 U.S.C 101 rejection and hence the 35 U.S.C. 101 rejection has been withdrawn. Response to Arguments Applicant’s arguments with respect to the claims have been considered but are moot in light of the new grounds of rejection, as necessitated by amendment. As per the DP rejection, Applicant will hold the rejection in abeyance until claims are determined to be allowable. Double Patenting 08-33 AIA 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. 08-34 AIA Claim s 1-7, 10-16, and 19-24 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1-18 of U.S. Patent No. US 11461714 B1 . Although the claims at issue are not identical, they are not patentably distinct from each other because the claims recite substantially similar limitations as follows: receiving, by a processor and from an electronic device, image data depicting a property; executing, by the processor, a machine learning model to determine a first damage assessment of the property based on the image data, the first damage assessment being characterized by a confidence level; determining, by the processor, that the confidence level is below a threshold level; based on determining that the confidence level is below the threshold, transmitting, by the processor, to a computing device via a network, the image data, and a request executable by the computing device, the request causing the computing device to: generate a second damage assessment, and provide the second damage assessment to the processor via the network; updating, by the processor, the machine learning model using the second damage assessment . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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 of this title, 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claims 1-7, 1 0-16, and 19-24 are reje cted under 35 U.S.C. 103 as being unpatentable over Sull ivan et al. (US 20170221110 A1) hereinafter referred to as Sullivan in view of Lindeman et al. (US 20210081698 A1) hereinafter referred to as Lindeman in further view of Li et al. (US 20180260793 A1) hereinafter referred to as Li. Sull ivan teaches: Claim 1. A computer-implemented method for dynamically assessing property damage, the method comprising: receiving, by a processor and from an electronic device, image data depicting a property (¶0006 A method for improving automated damage appraisal through analysis, by an appraisal management computing apparatus, of one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output, which has stored knowledge data encoded from one or more stored property damage images, to identify which area of the property has damage. Damage data on an extent of the damage in the identified area of the property is determined, by the appraisal management computing apparatus, using the deep neural network which has stored knowledge data encoded from one or more stored property damage images. The identified area of the property with the damage is mapped, by the appraisal management computing apparatus, to one of a plurality of stored repair procedure templates to generate a list of one or more parts and one or more repair lines to make a repair.) ; executing, by the processor, a machine learning model to determine a first damage assessment of the property based on the image data, the first damage assessment being characterized by a confidence level (¶0026 FIG. 11 is a screenshot of an example of a user interface of the appraisal management computing apparatus illustrating a confidence determination with respect to a repair or replacement for one of the actionable items in FIG. 10 based on a deep neural network analysis ¶0048 Next, the deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) may determine when the images are relevant to the identified damaged property based on analyzed classifications and confidences of the one or more obtained images when compared to correlated stored images for the same type of property that are above one or more configured and stored thresholds) ; determining, by the processor, that the confidence level is below a threshold level (¶0048 Next, the deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) may determine when the images are relevant to the identified damaged property based on analyzed classifications and confidences of the one or more obtained images when compared to correlated stored images for the same type of property that are above one or more configured and stored thresholds, although other manners for qualifying the one or more images and other types of automated damage analysis may be executed at the same time or separately. Any of the one or more analyzed images that are not of the correct vehicle for damage appraisal are not qualified and may be ignored by the subsequent automated damage appraisal process executed by the appraisal management computing apparatus 12(1).¶0050 the deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) based on assessed classifications and confidences in the identified images above one or more corresponding configured stored threshold may identify damage and determine the extent of the damage, although other manners for automated identifying damage and the extent of the damage may be used. By way of example only, a diagram illustrating the identification and determination of the extent of damage, including a repair or replace analysis by using the deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) is illustrated in FIG. 7B.) ; based on determining that the confidence level is below the threshold, transmitting, by the processor, to a computing device via a network, the image data, and a request executable by the computing device, the request causing a physical component of the computing device to (¶0052 in FIG. 10 a user interface of the appraisal management computing apparatus with actionable items of an automated damage appraisal based on a deep neural network analysis is illustrated. Even further by way of example, in FIG. 11 another user interface of the appraisal management computing apparatus with a confidence determination with respect to a repair or replacement for one of the actionable items from FIG. 10 based on a deep neural network analysis by the appraisal management computing apparatus 12(1) is illustrated ¶¶0055-0056 based on the stored customer profile setting, the estimate generation module 34 may automatically add, change, and/or remove a part or parts and/or repair lines to account for overlapping labor operations performed on nearby or adjacent parts, as well as apply specific labor rate and tax rule profiles to accommodate various geographically relevant laws and/or business rules, to produce a final estimate. By way of further example only for a damaged vehicle, based on the rules and data contained in the estimate profile corresponding to specific state or county, the appraisal management computing apparatus 12(1) may use body repair labor rates of a particular rate per hour and may add a hazardous materials disposal fee, although other types of customer profile settings may be applied. In step 414 the appraisal management computing apparatus 12(1) may disseminate the final generated estimate for the automated damage appraisal and then this example of the method may end in step 416.) : generate a second damage assessment, the second damage assessment including a cost to repair the damage, andprovide the second damage assessment to the processor via the network (¶0055 In step 412, the estimation generation module 34 in the appraisal management computing apparatus 12(1) may perform further calculations using one of a plurality of stored customer profile settings to further refine the updated list of one or more parts and/or one or more repair lines based on one or more customer requirements and/or preferences, although other types of refining calculations may be applied. By way of example, based on the stored customer profile setting, the estimate generation module 34 may automatically add, change, and/or remove a part or parts and/or repair lines to account for overlapping labor operations performed on nearby or adjacent parts, as well as apply specific labor rate and tax rule profiles to accommodate various geographically relevant laws and/or business rules, to produce a final estimate… ¶0057 This technology also may be utilized in other manners and approaches. For example, this method for improving automated damage appraisal and devices may be used to provide an automated review of completed appraisals to identify and correct errors and provide enhanced consistency of appraisal results. By way of example only, FIG. 13 is a screenshot of an example user interface depicting results from an automated review of a completed appraisal indicating errors and inaccuracies detected by the appraisal management computing apparatus.) ; updating, by the processor, the machine learning model using the cost to repair the damage (¶0047 the deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) in this example has a structure and synaptic weights trained using semi-supervised machine learning techniques in conjunction with labelled and unlabeled data to encode knowledge obtained from earlier property information data images stored and retrieved as needed from the property information storage server device 16. This deep neural network (DNN) executed by the appraisal management computing apparatus 12(1) in this example provides significantly more effective and cost efficient assessments for automated property damage appraisals than is possible with a simple neural network. ¶0058 Accordingly, as illustrated and described by way of the examples herein, this technology significantly improves the efficiency and accuracy of automated damage appraisal methods. This automated technology may use images or videos of the damage (received electronically from an insured/claimant, low cost resource or other methods ex. remotely controlled drone) to automatically create an appraisal which will drastically reduce the time it takes to assess the damage and estimate the cost of the repair. In addition to increasing the efficiency of this process, the accuracy and precision achieved using this automated technology will continue to increase since the automated technology will not suffer from the bias, subjectivity and variances in skill of manual appraisal techniques, but will improve its accuracy over time by leveraging validated results as feedback to further refine subsequent iterations.) ; determining, by the processor and using the updated machine learning model, a third estimate of the damage;generating, by the processor, a work order including the third estimation of the damage (¶0055 In step 412, the estimation generation module 34 in the appraisal management computing apparatus 12(1) may perform further calculations using one of a plurality of stored customer profile settings to further refine the updated list of one or more parts and/or one or more repair lines based on one or more customer requirements and/or preferences, although other types of refining calculations may be applied. By way of example, based on the stored customer profile setting, the estimate generation module 34 may automatically add, change, and/or remove a part or parts and/or repair lines to account for overlapping labor operations performed on nearby or adjacent parts, as well as apply specific labor rate and tax rule profiles to accommodate various geographically relevant laws and/or business rules, to produce a final estimate ¶0057 This technology also may be utilized in other manners and approaches. For example, this method for improving automated damage appraisal and devices may be used to provide an automated review of completed appraisals to identify and correct errors and provide enhanced consistency of appraisal results. By way of example only, FIG. 13 is a screenshot of an example user interface depicting results from an automated review of a completed appraisal indicating errors and inaccuracies detected by the appraisal management computing apparatus. ¶0058 Accordingly, as illustrated and described by way of the examples herein, this technology significantly improves the efficiency and accuracy of automated damage appraisal methods. This automated technology may use images or videos of the damage (received electronically from an insured/claimant, low cost resource or other methods ex. remotely controlled drone) to automatically create an appraisal which will drastically reduce the time it takes to assess the damage and estimate the cost of the repair. In addition to increasing the efficiency of this process, the accuracy and precision achieved using this automated technology will continue to increase since the automated technology will not suffer from the bias, subjectivity and variances in skill of manual appraisal techniques, but will improve its accuracy over time by leveraging validated results as feedback to further refine subsequent iterations) ; and transmitting, by the processor, the work order to a service provider (¶¶0055-0056 By way of further example only for a damaged vehicle, based on the rules and data contained in the estimate profile corresponding to specific state or county, the appraisal management computing apparatus 12(1) may use body repair labor rates of a particular rate per hour and may add a hazardous materials disposal fee, although other types of customer profile settings may be applied. In step 414 the appraisal management computing apparatus 12(1) may disseminate the final generated estimate for the automated damage appraisal and then this example of the method may end in step 416.) . Although not explicitly taught by Sullivan, Lindeman teaches in the analogous art of systems for physical object analysis: determining, by the processor, that the confidence level is below a threshold level (¶0051 The results of the analysis include Subject parts detection (produced by the module 132), damage levels (produced by the modules 134 and 136 of FIG. 1A), repair costs (produced by the module 144), identification and accuracy confidence levels and visual bounding boxes for parts and damage area highlights (as performed by the module 146). In some embodiments, the engine 130 may also be configured to control the data acquisition devices. For example, if a computed confidence level for a derived output is below some reference threshold value, the engine 130 may send a request to one of the data acquisition devices (e.g., one or more of the cameras 110a-n) to obtain another data capture (another image) at a higher resolution or zoom, or from a different view or perspective. Multiple processes are thus implemented to work in concert to generate results ¶0088 Based on the output of the observability code, a “safety net” exit return takes place if sub function thresholds are exceeded, in which case a human technician may intervene to provide a visual assessment of the structural state of the physical object.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the systems for physical object analysis of Lindeman with the system for improving automated damage appraisal of Sullivan for the following reasons: (1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sullivan ¶0003 teaches that it is desirable to improve automating property damage appraisals which rely on user input and predictive analysis to represent damage severity; (2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sullivan Abstract teaches improving automated damage appraisal which includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage, and Lindeman Abstract teaches obtaining physical object data for a physical object, determining a physical object type based on the obtained physical object data, and determining based on the obtained physical object data, using at least one processor-implemented learning engine, findings data comprising structural deviation data representative of deviation between the obtained physical object data and normal physical object data representative of normal structural conditions for the determined physical object type; and (3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sullivan at least the above cited paragraphs, and Lindeman at least the inclusively cited paragraphs. Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the systems for physical object analysis of Lindeman with the system for improving automated damage appraisal of Sullivan. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G). Although not explicitly taught by Sullivan in view of Lindeman, Li teaches in the analogous art of automatic assessment of damage and repair costs in vehicles: determining, by the processor and using the updated machine learning model, a third estimate of the damage;generating, by the processor, a work order including the third estimation of the damage (¶0073 As described in greater detail herein, client devices 104 are used to capture one or more images of a damaged vehicle. The images are transmitted over a network connection 108 to a server 102. The server 102 processes the images to estimate damage and repair costs. The estimates are transmitted over network connection 112 to the adjust computer device 106 for approval or adjustment. ¶0083 Server(s) 102 is at least one computing machine that can automatically calculate an estimate for vehicle repair costs based on images provided from a client device 104. The server 102 has access to one or more databases 110 and other facilities that enable the features described herein. According to certain embodiments, similar elements shown in FIG. 2 to be included in the client device 104 can also be included in the adjuster computing device 106. The adjuster computing device 106 may further include software stored in a memory and executed by a processor to review and adjust repair cost estimates generated by the server 102. ¶0112 Iterative Minimisation Assign GMM components to pixels: kn:=arg.Math..Math.minkn.Math.Dn(αn,kn,θ,zn). 2 GMM parameters from data z: θ_:=arg.Math..Math.minθ_.Math.U(α_,k,θ_,z) [0114] 3, Estimate segmentation: use min cut to min{αn:.Math.n∈TU}.Math.mink.Math.E(α_,k,θ_,z). 4 Repeat from step 1, until convergence.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman for the following reasons: (1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sullivan ¶0003 teaches that it is desirable to improve automating property damage appraisals which rely on user input and predictive analysis to represent damage severity; (2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sullivan Abstract teaches improving automated damage appraisal which includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage, and Lindeman Abstract teaches obtaining physical object data for a physical object, determining a physical object type based on the obtained physical object data, and determining based on the obtained physical object data, using at least one processor-implemented learning engine, findings data comprising structural deviation data representative of deviation between the obtained physical object data and normal physical object data representative of normal structural conditions for the determined physical object type, and Li Abstract teaches a system is provided for automatically estimating a repair cost for a vehicle; and (3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sullivan in view of Lindeman at least the above cited paragraphs, and Li at least the inclusively cited paragraphs. Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G). Sullivan teaches: Claim 2. The computer-implemented method of claim 1, further comprising: determining, by the processor and from the image data, a characteristic associated with the property; and selecting, by the processor, the computing device based on the characteristic (¶0030 Repair procedure templates with respect to automated damage appraisals are meticulously curated vehicle year/make/model-specific collections of data about replacement parts and/or repair operations determined by certified vehicle repair experts and/or from manufacturer data, following vehicle manufacturer recommended repair procedures and guidelines, to be the necessary and optimal parts and operations required to repair a specified section of a damaged vehicle and restore it to manufacturer approved specifications and safety tolerances. Rules of adjacency with respect to automated damage appraisal comprise an expertly authored hierarchal rule structure that defines the relationships between collision repair operation types by identifying how a given specific collision repair operation necessarily requires, likely requires or relates to one or more additional specific collision repair operations.) . Sullivan teaches: Claim 3. The computer-implemented method of claim 2, wherein the characteristic indicates a state in which the property is registered, and the method further comprises: determining, by the processor, a person licensed to perform the second damage assessment in the state, wherein the selected computing device is associated with the person (¶0055 In step 412, the estimation generation module 34 in the appraisal management computing apparatus 12(1) may perform further calculations using one of a plurality of stored customer profile settings to further refine the updated list of one or more parts and/or one or more repair lines based on one or more customer requirements and/or preferences, although other types of refining calculations may be applied. By way of example, based on the stored customer profile setting, the estimate generation module 34 may automatically add, change, and/or remove a part or parts and/or repair lines to account for overlapping labor operations performed on nearby or adjacent parts, as well as apply specific labor rate and tax rule profiles to accommodate various geographically relevant laws and/or business rules, to produce a final estimate. By way of further example only for a damaged vehicle, based on the rules and data contained in the estimate profile corresponding to specific state or county, the appraisal management computing apparatus 12(1) may use body repair labor rates of a particular rate per hour and may add a hazardous materials disposal fee, although other types of customer profile settings may be applied.) . Sullivan teaches: Claim 4. The computer-implemented method of claim 2, wherein the characteristic indicates a manufacturer’s suggested retail price (MSRP) value associated with the property that exceeds a threshold, and the method further comprises: determining, by the processor, a person having experience assessing other properties characterized by comparable MSRP values, wherein the selected computing device is associated with the person (¶0030 Repair procedure templates with respect to automated damage appraisals are meticulously curated vehicle year/make/model-specific collections of data about replacement parts and/or repair operations determined by certified vehicle repair experts and/or from manufacturer data, following vehicle manufacturer recommended repair procedures and guidelines, to be the necessary and optimal parts and operations required to repair a specified section of a damaged vehicle and restore it to manufacturer approved specifications and safety tolerances. Rules of adjacency with respect to automated damage appraisal comprise an expertly authored hierarchal rule structure that defines the relationships between collision repair operation types by identifying how a given specific collision repair operation necessarily requires, likely requires or relates to one or more additional specific collision repair operations. ¶0044 Referring more specifically to FIG. 4, in step 400 in this example of the method may start and then one or more of the imaging devices 14(1)-14(n) may be used to capture a number of images, such as pictures and/or videos, of property, such as a vehicle by way of example only as illustrated by way of example in FIG. 5, requested by the appraisal management computing apparatus 12(1), although the images can be obtained in other manners and the specified types and/or numbers of images can be set in other manners. By way of example only, the images may be captured by an insured/claimant or a low cost resource, such as a drone, using one of the imaging devices 14(1)-14(n). Additionally and by way of example only, the captured images, such as pictures and/or videos, may be captured using a camera or equipped mobile device as one of the imaging devices 14(1)-14(n) or a three dimensional (3D) scan generated using a scanner or other equipped mobile device as one of the imaging devices 14(1)-14(n), although other types of imaging of the property can be used. ¶0046 Next in step 404, the appraisal management computing apparatus 12(1) may perform one or more assessments on the one or more obtained images, such as pictures by way of example only, and/or one or more dynamic images, such as video by way of example only, for determining an automated property damage appraisal using a deep neural network (DNN) based on stored programmed instructions, such as in the image damage assessment module 32 by way of example only. A functional diagram of the operation of the deep neural network (DNN) during automated damage appraisal is illustrated by way of example only in FIG. 6.) . Sullivan teaches: Claim 5. The computer-implemented method of claim 4, wherein the characteristic indicates an age of the property, and the method further comprises: determining, by the processor, and based on at least one of the age, the MSRP, or the first damage assessment, an additional computing device needed to perform the second damage assessment (¶0046 Next in step 404, the appraisal management computing apparatus 12(1) may perform one or more assessments on the one or more obtained images, such as pictures by way of example only, and/or one or more dynamic images, such as video by way of example only, for determining an automated property damage appraisal using a deep neural network (DNN) based on stored programmed instructions, such as in the image damage assessment module 32 by way of example only. A functional diagram of the operation of the deep neural network (DNN) during automated damage appraisal is illustrated by way of example only in FIG. 6.) . Sullivan teaches: Claim 6. The computer-implemented method of claim 1, wherein the first damage assessment indicates at least one of a type of a damage, an estimated amount of the damage, or an estimated repair cost (¶0043 An example of a method for improving automated damage appraisal and devices thereof will now be described with reference to FIGS. 1-13, although this technology can be used in the same manner for other types of applications, such as using the same process that is illustrated and described by way of the examples herein to review existing property damage estimate repair operation data lists by analyzing the corresponding photos to determine if the repair procedures in the estimate data are optimal and accurately reflect the damage in the photos, thus automating the estimate review process.) . Sullivan teaches: Claim 7. The computer-implemented method of claim 1, further comprising: receiving, by the processor and from the computing device, an annotation associated with the image data; and updating, by the processor, the machine learning model using the annotation (¶0055 In step 412, the estimation generation module 34 in the appraisal management computing apparatus 12(1) may perform further calculations using one of a plurality of stored customer profile settings to further refine the updated list of one or more parts and/or one or more repair lines based on one or more customer requirements and/or preferences, although other types of refining calculations may be applied. By way of example, based on the stored customer profile setting, the estimate generation module 34 may automatically add, change, and/or remove a part or parts and/or repair lines to account for overlapping labor operations performed on nearby or adjacent parts, as well as apply specific labor rate and tax rule profiles to accommodate various geographically relevant laws and/or business rules, to produce a final estimate…) . As per claims 10-16,and 19-20, the system and computer-readable storage medium tracks the method of claims 1-7, and 1,7, respectively, resulting in substantially similar limitations. The same cited prior art and rationale of claims 1-7, and 1,7 are applied to claims 10-16, and 19-20, respectively. Sullivan discloses that the embodiment may be found as a system and non-transitory computer-readable storage medium (Fig. 1 and ¶0007). Sullivan teaches: Claim 21. The computer-implemented method of claim 1, wherein the first damage assessment of the property includes a second confidence level determined based on a parameter associated with the property, and the computer-implemented method further comprises:determining, by the processor, that the second confidence level is greater than or equal to an audit threshold level;based on determining that the second confidence level is greater than or equal to the audit threshold level, determining, by the processor, a portion of the image data to be used for the damage assessment; and transmitting, by the processor, the portion of the image data to the computing device, wherein receipt of the portion of the image data by the computing device causes the computing device to:generate an annotation corresponding to the portion of the image data;associate the annotation with the portion of the image data, andgenerate the second damage assessment based on the portion of the image data and the annotation (¶0053 FIG. 9B depicts a user interface showing an example application of rules of adjacency in which a L Fender determined to require a repair operation of Replace, shown by the checked box, will necessitate the L Front Door Shell to require a repair operation of type Blend, shown by the checked box, based on rules of adjacency. Further by way of example, in FIG. 9C, a Hood determined to require a repair operation of type Repair, shown by the checked box, may or may not necessitate the L Fender and/or the R Fender to require a repair operation of type Blend, shown by the yellow highlighted unchecked box, depending on the location and extent of the damage to the hood, based on rules of adjacency.) . Sullivan teaches: Claim 22. The computer-implemented method of claim 21, wherein the property is a vehicle, and the computer-implemented method further comprises: receiving, by the processor, additional information associated with the vehicle; andinputting, by the processor, the image data and the additional information as the input to the machine learning model,wherein the additional information includes at least one of a make, a model, a year,or an odometer reading of the vehicle (¶0045 Next, in step 402 the appraisal management computing apparatus 12(1) may obtain one or more the captured images from one of the imaging devices 14(1)-14(n) via an internet connection or other communication network 20, although the appraisal management computing apparatus 12(1) can obtain the necessary images in other manners. Additionally, the appraisal management computing apparatus 12(1) may receive or otherwise obtain other data relating to the property to be appraised for damage, such as a vehicle identification number (VIN) of the vehicle to use as an identifier (ID) of the property, a year and a make, and/or model of the vehicle by way of example only from a received data input from a user computing device or other device used by the insured/claimant coupled to the appraisal management computing apparatus 12(1)) . Although not explicitly taught by Sullivan in view of Lindeman, Li teaches in the analogous art of automatic assessment of damage and repair costs in vehicles: Claim 23. The computer-implemented method of claim 1, further comprising: transmitting, to an electronic device associated with the service provider, a notification indicating the work order, the notification causing a physical component of the electronic device to display the work order together with a prompt, the prompt requesting an input indicating acceptance or rejection of the work order (¶0073 As described in greater detail herein, client devices 104 are used to capture one or more images of a damaged vehicle. The images are transmitted over a network connection 108 to a server 102. The server 102 processes the images to estimate damage and repair costs. The estimates are transmitted over network connection 112 to the adjust computer device 106 for approval or adjustment. FIG. 2 is a block diagram of basic functional components for a client device 104 according to some aspects of the disclosure. In the illustrated embodiment of FIG. 2, the client device 104 includes one or more processors 202, memory 204, network interfaces 206, storage devices 208, power source 210, one or more output devices 212, one or more input devices 214, and software modules—operating system 216 and a vehicle claims application 218—stored in memory 204.) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman for the following reasons: (1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sullivan ¶0003 teaches that it is desirable to improve automating property damage appraisals which rely on user input and predictive analysis to represent damage severity; (2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sullivan Abstract teaches improving automated damage appraisal which includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage, and Lindeman Abstract teaches obtaining physical object data for a physical object, determining a physical object type based on the obtained physical object data, and determining based on the obtained physical object data, using at least one processor-implemented learning engine, findings data comprising structural deviation data representative of deviation between the obtained physical object data and normal physical object data representative of normal structural conditions for the determined physical object type, and Li Abstract teaches a system is provided for automatically estimating a repair cost for a vehicle; and (3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sullivan in view of Lindeman at least the above cited paragraphs, and Li at least the inclusively cited paragraphs. Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G). Although not explicitly taught by Sullivan in view of Lindeman, Li teaches in the analogous art of automatic assessment of damage and repair costs in vehicles: Claim 24. The computer-implemented method of claim 23, wherein based on receiving the input indicating rejection of the work order, the notification causes the physical component of the electronic device to: determine an alternative service provider, and transmit the work order to the alternative service provider (¶0058 In one example implementation, images (e.g., photos or videos) showing damage to the vehicle are captured soon after the damage occurs. The images can be taken with a mobile phone and sent to a server by the vehicle owner or driver over a cellular or wireless network connection, either through a proprietary platform such a mobile application or through a web-based service. In some embodiments, an insurance company field inspector or adjustor visits the vehicle site, captures the requisite images and uploads them to the server, as is currently done in some jurisdictions or countries. In further embodiments, the images can be captured by an auto repair shop to which the vehicle is taken after an accident ¶0073 As described in greater detail herein, client devices 104 are used to capture one or more images of a damaged vehicle. The images are transmitted over a network connection 108 to a server 102. The server 102 processes the images to estimate damage and repair costs. The estimates are transmitted over network connection 112 to the adjust computer device 106 for approval or adjustment. FIG. 2 is a block diagram of basic functional components for a client device 104 according to some aspects of the disclosure. In the illustrated embodiment of FIG. 2, the client device 104 includes one or more processors 202, memory 204, network interfaces 206, storage devices 208, power source 210, one or more output devices 212, one or more input devices 214, and software modules—operating system 216 and a vehicle claims application 218—stored in memory 204..) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman for the following reasons: (1) a finding that there was some teaching, suggestion, or motivation, either in the references themselves or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings, e.g. Sullivan ¶0003 teaches that it is desirable to improve automating property damage appraisals which rely on user input and predictive analysis to represent damage severity; (2) a finding that there was reasonable expectation of success since the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference, e.g. Sullivan Abstract teaches improving automated damage appraisal which includes analyzing one or more obtained images of property using a deep neural network with multiple hidden layers of units between an input and output and which has stored knowledge data encoded from one or more stored property damage images to identify which area of the property has damage, and Lindeman Abstract teaches obtaining physical object data for a physical object, determining a physical object type based on the obtained physical object data, and determining based on the obtained physical object data, using at least one processor-implemented learning engine, findings data comprising structural deviation data representative of deviation between the obtained physical object data and normal physical object data representative of normal structural conditions for the determined physical object type, and Li Abstract teaches a system is provided for automatically estimating a repair cost for a vehicle; and (3) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness, e.g. Sullivan in view of Lindeman at least the above cited paragraphs, and Li at least the inclusively cited paragraphs. Therefore, it would be obvious to one skilled in the art at the time of the invention to combine the automatic assessment of damage and repair costs in vehicles of Li with the system for improving automated damage appraisal of Sullivan in view of Lindeman. The rationale to support a conclusion that the claim would have been obvious is that "a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention and whether there would have been a reasonable expectation of success in doing so." DyStar Textilfarben GmbH & Co. Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006). See MPEP 2143(G). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to KURTIS GILLS whose telephone number is (571)270-3315. The examiner can normally be reached on M-F 8-5 PM. 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, Jerry O’Connor can be reached on 571-272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. 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If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KURTIS GILLS/Primary Examiner, Art Unit 3624 Application/Control Number: 18/805,261 Page 2 Art Unit: 3624 Application/Control Number: 18/805,261 Page 3 Art Unit: 3624 Application/Control Number: 18/805,261 Page 4 Art Unit: 3624 Application/Control Number: 18/805,261 Page 5 Art Unit: 3624 Application/Control Number: 18/805,261 Page 6 Art Unit: 3624 Application/Control Number: 18/805,261 Page 7 Art Unit: 3624 Application/Control Number: 18/805,261 Page 8 Art Unit: 3624 Application/Control Number: 18/805,261 Page 9 Art Unit: 3624 Application/Control Number: 18/805,261 Page 10 Art Unit: 3624 Application/Control Number: 18/805,261 Page 11 Art Unit: 3624 Application/Control Number: 18/805,261 Page 12 Art Unit: 3624 Application/Control Number: 18/805,261 Page 13 Art Unit: 3624 Application/Control Number: 18/805,261 Page 14 Art Unit: 3624 Application/Control Number: 18/805,261 Page 15 Art Unit: 3624 Application/Control Number: 18/805,261 Page 16 Art Unit: 3624 Application/Control Number: 18/805,261 Page 17 Art Unit: 3624 Application/Control Number: 18/805,261 Page 18 Art Unit: 3624 Application/Control Number: 18/805,261 Page 19 Art Unit: 3624 Application/Control Number: 18/805,261 Page 20 Art Unit: 3624 Application/Control Number: 18/805,261 Page 21 Art Unit: 3624 Application/Control Number: 18/805,261 Page 22 Art Unit: 3624 Application/Control Number: 18/805,261 Page 23 Art Unit: 3624 Application/Control Number: 18/805,261 Page 24 Art Unit: 3624 Application/Control Number: 18/805,261 Page 25 Art Unit: 3624 Application/Control Number: 18/805,261 Page 26 Art Unit: 3624 Application/Control Number: 18/805,261 Page 27 Art Unit: 3624
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