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 .
Status of the Application
Claims 1-20 have been examined in this application.
The filling date of this application number recited above is 11-August-2023. Domestic Benefit/National Stage priority has been claimed for Provisional Application 63/405,766 in the Application Data Sheet, thus the examination will be undertaken in consideration of 12-September-2022, as the priority date, for applicable claims.
There is no information disclosure statement (IDS) filed to date.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The Claims are directed to an abstract idea, Methods of Organizing Human Activity and/or Mental Process. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
As per Claims 1, 8, and 15, the claim recites “a … method comprising:
obtaining, by [a worker] of [an entity], an image of a first damaged vehicle;
selecting, by [the worker] of [the entity], a set of images of second damaged vehicles that are similar to the first damaged vehicle;
finding, by [the worker] of [the entity], a set of one or more images of the second vehicles showing damage similar to the damage to the first vehicle;
obtaining, by [the worker] of [the entity], a set of vehicle repair claims corresponding to the set of one or more images of the second vehicles;
selecting, by [the worker] of [the entity], a subset of line items from the set of vehicle repair claims;
filtering, by [the worker] of [the entity], the subset of line items based on one or more of a frequency gate, a set of predefined rules, or an application in which a decision is made regarding the line item;
adding, by [the worker] of [the entity], the filtered subset of line items to a repair estimate data structure for the first damaged vehicle;
generating, by [the worker] of [the entity], a [poster] for presentation to a user on a [board], wherein the [poster] includes display elements that represent the filtered subset of line items in the repair estimate data structure;
receiving, by [the worker] of [the entity], first user input from the [poster], wherein the first user input represents line items that are chosen by the user;
generating, by [the worker] of [the entity], a vector that represents the line items chosen by the user, wherein a number of dimensions in the vector is based on a vehicle type for the first damaged vehicle, wherein the vector comprises a binary representation of line items in the repair estimate data structure, wherein a binary value of each bit of the binary representation indicates of non-presence or presence of each line item in the vehicle repair estimate data structure, and wherein a magnitude of the vector is based on a number of line items present in the vehicle repair estimate data structure;
applying, by [the worker] of [the entity], the vector as an inference input to a … model … with correspondences between historical examples of the vectors and corresponding line items, wherein responsive to the inference input, the … model outputs a refined subset of line items;
modifying, by [the worker] of [the entity], the repair estimate data structure to include the refined subset of line items; and
presenting, by [the worker] of [the entity], a view of the modified repair estimate data structure in the [poster].”
The limitation of the claims recited above, considering the claims without the additional elements (e.g. system, processor, device, etc.), under its broadest reasonable interpretation (BRI), recite certain methods of organizing human activities, specifically under fundamental economic principles or practices and/or commercial or legal interactions. The method recited above is a process of providing the user with repair estimates for the damaged vehicle, wherein the steps involve providing images of the damaged vehicle, comparing similar images, filering information, generating data, applying a model with an input to provide an output, and displaying information. These steps of providing a “claim refinement” for vehicle repair estimation is fundamental economic principles or practices, wherein the claims processing associated with vehicle insurance may be commercial or legal interactions, by which are all under certain methods of organizing human activities. Therefore, the claims recite an abstract idea.
Additionally, under BRI, the claims also recite Mental Process. As discussed above, the process recited by the claims is to obtain an image, compare the image to similar stored images, select an image along with the associated information, filter data, generate data, apply a model for input/output of data, and modify the information. All of these steps can be performed in the human mind, or by a human with pen and paper, as disclosed by MPEP 2106.04(a)(2)(III):
“The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011)”
“The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand.")”
“Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015)”
Although the claims may recite using a computer to receive, determine, compare, filter, generate, and display data, performing a mental process on a generic computer still recite a mental process. See MPEP 2106.04(III)(C):
“Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").”
Therefore, the claims recite an abstract idea.
This judicial exception is not integrated into practical application. In particular, the claims recite additional elements of “processor”, “server”, “system”, “hardware processor”, “non-transitory machine-readable storage medium”, “computer, “user interface”, and “user device”, to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. These general computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. These additional elements are generic, off-the-shelf components available to the public, and does not require any specialized equipment or hardware to perform the basic functionalities as claimed, such as: receive data, compare data, modify data, and provide data, as disclosed by Specification:
[0019] “Each device may be implemented as a computer, smart phone, smart glasses, electronic embedded computers and displays, and the like.”
[0065] “FIG. 6 depicts a block diagram of an example computer system 600 in which embodiments described herein may be implemented. The computer system 600 includes a bus 602 or other communication mechanism for communicating information, one or more hardware processors 604 coupled with bus 602 for processing information. Hardware processor(s) 604 may be, for example, one or more general purpose microprocessors”.
For example, mere instructions to display information on a GUI does not improve computer functionality, see MPEP 2106.05(a)(I) example that the courts have indicated may not be sufficient to show an improvement in computer-functionality: “Arranging transactional information on a graphical user interface in a manner that assists traders in processing information more quickly, Trading Technologies v. IBG LLC, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019)”. Mere instructions to implement the abstract idea on the generic computer system, or merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of integration into a practical application; see MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, determine, or display data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activities) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims also recite “trained machine learning model”. However, this additional element is merely applied as a black-box application, wherein an input is given to the model to provide an output, without providing any technical details or additional steps on how the model is trained, modified, or improved, or how the model improves, changes, modifies, or impacts any of the underlying technology. As similarly discussed above, mere “apply it” is not indicative of integration into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, the additional element of using a computer based system is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. The claims lack sufficient technical details to provide how these limitations may provide technological steps or technical details on how it is particularly implemented on a computer to improve its system or any of its underlying hardware or components (e.g. how it is performed on the computer, how it could improve the computer itself, how it could manipulate the computer to function in a specific way other than its generic functionality, and/or how it could improve any of the underlying technology), but merely applies the generic computer system to perform its generic functionalities. Mere instructions to implement the abstract idea on the generic computer system, or merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of an inventive concept (aka “significantly more”). In view of the Specification cited above, the judicial exception is not applied with or used by a particular machine. As held in Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) and Bancorp Services v. Sun Life, 687 F.3d 1266, 1276, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), “the routine use of a computer to perform calculations cannot turn an otherwise ineligible mathematical formula or law of nature into patentable subject matter.” The claims are not patent eligible.
Regarding dependent claims, they are still directed to an abstract idea without significantly more.
Claims 2, 9, and 16 recite “wherein the instruction further cause the processor to: receive second user input from the user interface, wherein the second user input represents a decision by the user to commit the estimate; and responsive to the second user input, provide the modified repair estimate data structure to a claims adjuster.” The claims provide further steps of receiving user inputs and providing modified information, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 3, 10, and 17 recite “wherein finding one or more images of the second vehicles showing damage similar to the first vehicle comprises reverse searching the selected set of images of the second damaged vehicles using the image of the first damaged vehicle.” The claims provide further steps of finding similar images and reverse searching the images, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 4, 11, and 18 recite “wherein selecting a subset of line items from the obtained vehicle repair claims comprises selecting line items based on a frequency of occurrence of the line items.” The claims provide further steps of selecting information, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 5, 12, and 19 recite “wherein the instruction further cause the processor to: obtain one or more training data sets comprising the historical examples of the vectors and corresponding line items; and train the one or more trained machine learning models using the training data set.” The claims provide further steps of obtaining historical data to train the machine learning model, which is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 6, 13, and 20 recite “wherein the instruction further cause the processor to: generate the one or more training data sets.” The claims provide further steps of generating data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 7 and 14 recite “wherein the instruction further cause the processor to: obtain one or more further training data sets comprising further historical examples of the vectors and corresponding line items; and retrain the one or more trained machine learning models using the further training data set.” The claims provide further steps of obtaining training data to retrain the machine learning model, which is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
These additional steps of each claims fail to remedy the deficiencies of their parent claim above because they are merely further limiting the rules used to conduct the previously recited abstract idea, and are therefore rejected for at least the same rationale as applied to their parent claim above.
Claims 1-7, 9-14, and 16-20, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are sufficient to integrate into a practical application and do not amount to significantly more than the judicial exception. Similarly to the independent claims, each claim is merely applying the generic computer system to perform the abstract idea as mentioned above. Therefore, prong 2 and step 2B analysis are similar to above and these claims are not eligible.
Therefore, Claims 1-20 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (US 10762385 B1), in view of Nelson et al. (US 10922726 B1), and in view of Valenti et al. (US 20190102752 A1) in further view of SHAH et al. (US 20200410163 A1).
As per Claims 1, 8, and 15, Yang discloses a computer-implemented method (See Figure 1 teaching the system, as disclosed [Col 4 Lines 34-45] “The server 106 may include one or more processors (not shown in FIG. 1) and a memory (also not shown in FIG. 1). The memory may be a computer-readable, non-transitory storage unit or device, or collection of units/devices, and may include persistent (e.g., hard disk) and/or non-persistent memory components. The memory may store instructions that are executable on the one or more processors to perform various operations, including the instructions of various software applications and the data that is generated and/or used by such applications. The stored instructions may include instructions for executing a predictive similarity model generator 116 and an image search engine 118”) comprising:
obtaining, by a processor of a server, an image of a first damaged vehicle (See Figure 4 – 402, as disclosed [Col 11 Lines 38-41] “The user interface 402 may enable a user to upload an image of a damaged vehicle, such as image 114 of FIG. 1 (e.g., taken using camera 112). In some embodiments, a user may upload multiple images of the damaged vehicle”);
selecting, by the processor of the server, a set of images of second damaged vehicles that are similar to the first damaged vehicle ([Col 6 Lines 6-9] “Using the predictive similarity model, the image search engine 118 may locate one or more images with a high level of predictive similarity to the damaged vehicle image 114, to be used for a repair estimate or other damage level indicator”);
finding, by the processor of the server, a set of one or more images of the second vehicles showing damage similar to the damage to the first vehicle ([Col 6 Lines 22-25] “Using the similarity scores, the comparison image selector 136 may select images from the dataset 138 to be compared with the damaged vehicle image 114 for the repair estimate or other damage level indicator”);
obtaining, by the processor of the server, a set of vehicle repair claims corresponding to the set of one or more images of the second vehicles ([Col 5 Lines 5-12] “Each of historical claim data 128, 130, 132 may include data (e.g., text, field values or codes, etc.) indicating information such as how a vehicle was damaged, where a vehicle was damaged, the qualitative severity of damage to a vehicle (e.g., “heavy,” “moderate,” etc.), the repair time for the damage to a vehicle, the repair cost for the damage to a vehicle, and/or any other information relevant to an insurance claim”);
selecting, by the processor of the server, a subset of line items from the set of vehicle repair claims ([Col 5 Lines 30-43] “By processing the historical image 122 associated with historical claim data 128, the historical image 124 associated with claim data 130, the historical image 126 associated with historical claim data 132, and so forth, the predictive similarity model generator 116 may learn which image characteristics are indicative or predictive of which claim data characteristics (e.g., indicative or predictive of damage level, repair time, repair cost, and/or any other claim data characteristics relating to damage and/or repairs). For images that show a bent bumper portion of a vehicle, for example, the predictive similarity model generator 116 may determine that the depicted degree of bending within a particular area of the bumper is predictive of damage levels, repair times, and/or repair costs.”);
filtering, by the processor of the server, the subset of line items based on one or more of a frequency gate, a set of predefined rules, or an application in which a decision is made regarding the line item ([Col 4 Lines 55-60] “Based on the learned image characteristics, the predictive similarity model generator 116 may generate a predictive similarity model, which may include data and/or rules indicating which image characteristics are relevant for predicting damage and/or repair of a vehicle depicted in the image” or see also [Col 6 Lines 6-25] “Using the predictive similarity model, the image search engine 118 may locate one or more images with a high level of predictive similarity to the damaged vehicle image 114, to be used for a repair estimate or other damage level indicator. The image search engine 118 may include a similarity score generator 134 and a comparison image selector 136 … The similarity score generator 134 may then use the rules generated by the predictive similarity model generator 116 to generate a similarity score for each image in the damaged vehicle digital image dataset 138, with the score indicating the similarity between the image and the damaged vehicle image 114. Using the similarity scores, the comparison image selector 136 may select images from the dataset 138 to be compared with the damaged vehicle image 114 for the repair estimate or other damage level indicator”);
adding, by the processor of the server, the filtered subset of line items to a repair estimate data structure for the first damaged vehicle ([Col 11 Lines 59-67] “The user interface 406 may display a statistical analysis of the claim data associated with the selected and/or displayed images, such as a histogram and/or density plot. Additionally, the interface 406 may be configured to receive user selections of whether to view total repair hours and/or total repair cost, as shown in FIG. 4. In other embodiments, the user interface 406 may display more, fewer, and/or different types of information, such as a qualitative estimate of damage level (e.g., “heavy” or “totaled”), for example”);
generating, by the processor of the server, a user interface for presentation to a user on a user device, wherein the user interface includes display elements that represent the filtered subset of line items in the repair estimate data structure (See Figure 4 - 406, as disclosed [Col 7 Lines 53-58] “Based on the selection of the user, the client computing device 104 may display claim data corresponding to the selected image(s) (or other information derived therefrom, such as an average repair cost/time, etc.) via a user interface such as user interface 406 discussed below with respect to FIG. 4”);
Although Yang teaches of utilizing a machine learning model for image comparison to provide information, such as repair estimates associated with the claims, that are similar to the provided image of the damaged vehicle, the prior art does not seem to explicitly disclose of using the machine learning model to provide modified repair estimates based on the user selection. However, Nelson discloses:
generating, by the processor of the server, a user interface for presentation to a user on a user device, wherein the user interface includes display elements that represent the filtered subset of line items in the repair estimate data structure ([Col 11 Lines 48-56] “In an embodiment, the jurisdictionally-based set of repairs 38 predicted to be needed to repair the damaged vehicle may be included in a draft estimate for the repair of the damaged vehicle, where the draft estimate includes, for example, a set of line entries, each of which corresponds to a respective part that is identified by a jurisdiction-specific identifier, and which indicates whether or not that part is to be repaired or replaced” or see also Figure 5 – step 502);
receiving, by the processor of the server, first user input from the user interface, wherein the first user input represents line items that are chosen by the user (See Figure 5 – step 520, as disclosed [Col 34 Lines 7-15] “On the other hand, at the block 520, the user may indicate a modification to the displayed, current draft estimate via the user interface. The user-generated modification may be a user-generated contextual modification to the current draft estimate such as at least one of: an addition of another field, a removal of an existing field, and population of a value into an empty field, a removal of a value from a populated field, a change to an existing value of a populated field, etc.”);
generating, by the processor of the server, a vector that represents the line items chosen by the user … ([Col 34 Lines 24-44] “Additionally, at the block 522, the method 500 includes re-estimating or re-calculating the current draft estimate to which the user-generated modification has been applied to thereby generate a user-modified draft estimate, where the user-modified draft estimate includes a contextual modification and/or a calculation modification. The block 522 may performed, for example, by the user interface 405 of the system 400, by the intelligent interceptor engine 410 of the system 400, or by another engine, module, or application of the system 400 or of another suitable system … The method 500 then may operate on the user-modified draft estimate by iteratively executing the loop 510 of the blocks 505-508, in a manner similar to that discussed above for the system-modified draft estimate”);
applying, by the processor of the server, the vector as an inference input to a trained machine learning model that has been trained with correspondences between historical examples of the vectors and corresponding line items, wherein responsive to the inference input, the trained machine learning model outputs a refined subset of line items (See Figure 5 – step 508 which uses trained machine learning model to provide an output, as disclosed [Col 3 Lines 9-18] “The determination may be based on a machine learning model generated based on historical data associated with repairing vehicles and/or one or more statistical analyses of the historical data, where the historical data includes image attribute data of a plurality of historical images of damaged vehicles, respective indications of parts associated with repairing the damaged vehicles depicted in the historical images, and a plurality of other attributes corresponding to the damaged vehicles depicted in the historical images”);
modifying, by the processor of the server, the repair estimate data structure to include the refined subset of line items ([Col 32 Lines 37-50] “As such, the system-modified draft estimate includes both the system-generated contextual modifications (e.g., the updates to entries, fields, and/or populated values of the draft estimate), and also includes system-generated computed or calculated modifications (e.g., line item costs, overall estimated costs, other costs, time intervals, etc. of the draft estimate) that reflect the application of the system-generated contextual modifications to the received draft estimate. In an embodiment, the estimate calculation engine 412 may operate on the contextually-modified draft estimate (which was generated by the intelligent interceptor engine 410) to determine the corresponding calculation modifications and thereby generate the system-modified draft estimate”); and
presenting, by the processor of the server, a view of the modified repair estimate data structure in the user interface (See Figure 5 – step 518, as disclosed [Col 33 Lines 52-57] “As such, in view of the above discussion, generally the block 518 may include presenting, on the user interface, the contents of a current draft estimate (which may be the initially received draft estimate associated with the block 502, or a system-modified draft estimate generated at the block 508)”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize modification to the repair estimate of the given damaged vehicle image by using trained machine learning model as in Nelson in the system executing the method of Yang, wherein Yang already teaches of using trained machine learning model to provide information such as repair estimates with images of similar damaged vehicles, with the motivation of offering to [Col 1 Lines 61-67 to Col 2 Lines 1-9] “greatly improve the speed for generating such estimates over the currently known electronic templates or forms” and provide “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” as taught by Nelson over that of Yang.
Although Yang teaches of generating a user interface displaying the uploaded image of the damaged vehicle to provide repair estimates, the prior art does not seem to explicitly disclose the full claim limitation associated with generating a vector that represents the line item chosen by the user. However, Valenti discloses:
generating, by the processor of the server, a vector that represents the line items chosen by the user ([0010] “A condition report and its damage line item(s), or damage line item list and corresponding work order and other workflow processes, can be initiated and/or created by an inspector at one or more locations and received by a main server system”), wherein a number of dimensions in the vector is based on a vehicle type for the first damaged vehicle, wherein the vector comprises a binary representation of line items in their repair estimate data structure, wherein a binary value of each bit of the binary representation indicates of non-presence or presence of each line item in the vehicle repair estimate data structure ([0069] “The “Damages” Tab 46 allows the inspector to report on and document specific parts, by area of the vehicle, and generate damage line items to the options, equipment, accessories, interior, exterior, body, glass, trim, mechanical, wheels, tires, frame, unibody or structural components of the vehicle 16, such as a dent, scratch, rust, or any other damage to the vehicle 16, as well as the corresponding severity, remedy, notes, damage line item photos and repair estimates as displayed in FIGS. 11A-11G) … Each of these tabs, the groupings, the fields, and the corresponding quick add damage line items can be dynamically configured uniquely or determined by editing rows in a table for the following, including, but not limited to, … by any vehicle type, … any vehicle attribute or vehicle specific data, or combinations thereof”. See also Figure 11A-11B for non-presence and presence of selected line item (i.e. binary representation used to display data)), and wherein a magnitude of the vector is based on a number of line items present in the vehicle repair estimate data structure ([0072] “Each collection can be configured to have unique requirements and configurations for each Part, Damage, Severity, Note, Remedy, or specific combinations of each damage line item including, but not limited to, requiring a photo or video 99 for a specified damage line item combination, updating a vehicle heat map image for a specified damage line item combination, allowing specified parts and/or damage line item combinations for specific vehicle type”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the condition report comprising damage line items specific to the vehicle type as in Valenti in the system executing the method of Yang, wherein Yang already teaches of user interface displaying vehicle damage with repair estimates, with the motivation of offering to provide [0003-0008] “a condition report application that is accurate, user friendly, and is generated quickly and efficiently” as taught by Valenti over that of Yang.
Although Valenti teaches of generating a condition report and its damage line items, wherein a selection of the line item or the part of the vehicle to indicate damage as shown in Figure 11A – 172 would highlight the area as shown in Figure 11B – 891, which would be obvious to one of ordinary skilled in the art to interpret that the data being selected would already comprise binary representation of the item with a binary value to be displayed as non-presence in Figure 11A and the part being selected or being greyed out represents as presence in Figure 11B. However, for the purposes of compact prosecution, SHAH also teaches:
generating, by the processor of the server, a vector that represents the line items chosen by the user, wherein a number of dimensions in the vector is based on a vehicle type for the first damaged vehicle, wherein the vector comprises a binary representation of line items in their repair estimate data structure, wherein a binary value of each bit of the binary representation indicates of non-presence or presence of each line item in the vehicle repair estimate data structure … ([0020] “The text processing unit 106 is configured to receive a service request as logged by a user … Further, text processing unit 106 is configured to filter out descriptive words from the service request. Further, the text processing unit 106 is configured to create a first binary vector based on the list of descriptive words of the service dictionary. For each service request, a first binary vector is determined based on the service dictionary. The first binary vector is representative of presence of descriptive words in the service dictionary.” or see also [0027] “The method 150 further includes processing the service request based on the service dictionary and a text parsing technique to generate a list of descriptive words as illustrated in step 156. The text processing includes removing special characters, proper nouns and dates from the service request to generate the list of descriptive words. Further, the text processing technique includes creating a first binary vector based on the service dictionary and list of descriptive words corresponding to service request. The first binary vector is representative of presence of descriptive words in the service dictionary”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize binary vector representative of presence of data as in SHAH in the system executing the method of Valenti, wherein Valenti already teaches of updating the display to show presence or non-presence of selected line item, with the motivation of offering to [0031] provide automatic processing to reduce processing overhead with minimized improved response times to enhance customer satisfaction at optimized service contract cost as taught by SHAH over that of Valenti.
As per claims 2, 9, and 16, Yang may not explicitly disclose, but Nelson teaches the system of claim 1, the one or more non-transitory machine-readable storage media of claim 8, and the computer-implemented method of claim 15, wherein the instruction further cause the processor to:
receive second user input from the user interface, wherein the second user input represents a decision by the user to commit the estimate (See Figure 5 – step 520, as disclosed [Col 33 Lines 66-67 to Col 34 Line 1] “At block 520, the method 500 includes receiving either a user approval of the presented draft estimate, or a user modification to the presented draft estimate.”); and
responsive to the second user input, provide the modified repair estimate data structure to a claims adjuster ([Col 34 Lines 1-6] “When, at the block 520, the user indicates approval of the displayed draft estimate, e.g., upon activation of the approval user control via the user interface, the approved vehicle repair estimate may be stored, printed, and/or transmitted for ingestion by another application or system.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize transmitting the approved estimate to another application or system (e.g. claims adjuster) as in Nelson in the system executing the method of Yang, with the motivation of offering to [Col 1 Lines 61-67 to Col 2 Lines 1-9] “greatly improve the speed for generating such estimates over the currently known electronic templates or forms” and provide “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” as taught by Nelson over that of Yang.
As per claims 3, 10, and 17, Yang teaches the system of claim 1, the one or more non-transitory machine-readable storage media of claim 8, and the computer-implemented method of claim 15, wherein finding one or more images of the second vehicles showing damage similar to the first damaged vehicle comprises reverse searching the selected set of images of the second damaged vehicles using the image of the first damaged vehicle ([Col 6 Lines 6-9] “Using the predictive similarity model, the image search engine 118 may locate one or more images with a high level of predictive similarity to the damaged vehicle image 114, to be used for a repair estimate or other damage level indicator” and see also Figure 2 teaching the similarity comparison of the set of damaged vehicle images to the received damaged vehicle image).
As per claims 4, 11, and 18, Yang may not explicitly disclose, but Nelson teaches the system of claim 1, the one or more non-transitory machine-readable storage media of claim 8, and the computer-implemented method of claim 15, wherein selecting a subset of line items from the obtained vehicle repair claims comprises selecting line items based on a frequency of occurrence of the line items ([Col 14 Lines 30-43] “When multiple possible parts remain on the list of possible parts 37 corresponding to the particular Global ID, the context mapping module 35 may narrow the list based on which of the multiple possible parts have been most commonly utilized in historical vehicle repairs. For example, the jurisdictional requirements database 40 may contain historical data corresponding to a frequency of parts being ordered and/or utilized in repairs based on jurisdiction, vehicle make/model/year, insurance claims submitted, etc. over a particular, most recently occurring length of time (e.g., the last year). The context mapping module 35 may automatically identify/select the particular one of the multiple possible parts remaining on the list that has been most frequently utilized in the most recently-occurring repairs”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize selecting line items based on the frequency from historical data as in Nelson in the system executing the method of Yang, with the motivation of offering to [Col 1 Lines 61-67 to Col 2 Lines 1-9] “greatly improve the speed for generating such estimates over the currently known electronic templates or forms” and provide “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” as taught by Nelson over that of Yang.
As per claims 5, 12, and 19, Yang teaches the system of claim 1, the one or more non-transitory machine-readable storage media of claim 8, and the computer-implemented method of claim 15, wherein the instruction further cause the processor to:
obtain one or more training data sets comprising the historical examples of the vectors and corresponding line items ([Col 1 Lines 60-65] “Training the deep learning neural network may include learning the damaged vehicle image characteristics that are predictive of one or more of damage level, repair time, or repair cost, as well as the degree to which each of the damaged vehicle image characteristics is predictive of one or more of damage level, repair time, or repair cost.”); and
train the one or more trained machine learning models using the training data set ([Col 1 Lines 54-59] “In one aspect, a computer-implemented method is provided that may include accessing a historical training dataset populated with digital images of damaged vehicles and historical claim data related to damaged vehicles and generating a predictive similarity model by using the historical training dataset to train a deep learning neural network”).
As per claims 6, 13, and 20, Yang teaches the system of claim 5, the one or more non-transitory machine-readable storage media of claim 12, and the computer-implemented method of claim 19, wherein the instruction further cause the processor to:
generate the one or more training data sets ([Col 9 Lines 53-58] “In the method 300, a historical training dataset populated with digital images of damaged vehicles and historical claim data related to the damaged vehicles may be accessed (block 302). The historical training dataset (e.g., historical training dataset 120 of FIG. 1) may include one or more images associated with each vehicle and/or claim data”).
As per claims 7 and 14, Yang may not explicitly disclose, but Nelson teaches the system of claim 5, and the one or more non-transitory machine-readable storage media of claim 12, wherein the instruction further cause the processor to:
obtain one or more further training data sets comprising further historical examples of the vectors and corresponding line items ([Col 11 Lines 11-13] “Training or updating may be triggered when a particular quantity of additional data points have been added to the historical data store 32.”); and
retrain the one or more trained machine learning models using the further training data set ([Col 11 Lines 5-9] “In some embodiments, the parts prediction model 30 is re-trained or updated to account for additional data (e.g., additional image attribute data and other data related to repairing damaged vehicles) that has been added to the historical data store 32”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize re-training or updating the machine learning model with additional data as in Nelson in the system executing the method of Yang, with the motivation of offering to [Col 1 Lines 61-67 to Col 2 Lines 1-9] “greatly improve the speed for generating such estimates over the currently known electronic templates or forms” and provide “maximum improvements in accuracy, completeness, and processing speed in electronic vehicle repair estimating” as taught by Nelson over that of Yang.
Response to Arguments
Applicant's arguments, see page 9, filed 05-August-2026, with respect to 35 U.S.C. 101 rejection have been fully considered but they are not persuasive. The amended claims still recite an abstract idea, and the additional elements are merely applied to implement the abstract idea, wherein mere “apply it” is not “significantly more”. Therefore, the 35 U.S.C. 101 rejection is maintained for at least the reasons as discussed above under the 35 U.S.C. 101 rejection.
Applicant’s arguments, see pages 9 to 12, with respect to 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Adegan (US 20130041786 A1) discloses [0036] “FIG. 4 shows an identification request 420, according to an example embodiment of the present subject disclosure. Identification request 420 includes vehicle identifiers such as a make/model/year field 422 and a VIN field 423. A point-of-impact field 424, and a parts field 425 including line item 426 may be among the additional information in identification request 420”;
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H JUNG whose telephone number is (571)270-5018. The examiner can normally be reached Mon - Fri 9:30 - 5:30.
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/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
