DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 2, 5, 12, and 14, is objected to because of the following informalities: the claims recite inter alia “code lookup data comprising diagnosis code meaning, estimated repair cost, estimated cost, one or more recommended items for a code lookup interface”. It is unknown what distinction is intended to be made between “estimated repair cost” and “estimated cost.” Appropriate correction is required.
Examiner Comment - 35 USC § 101
Claims 11-15 are directed to “One or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations”. This is considered a manufacture because specification ¶0096 recites, “Computer storage media excludes signals per se.”
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter) (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception (step 2B). Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 189 L. Ed. 2d 296, 2014 U.S. LEXIS 4303, 110 U.S.P.Q.2D (BNA) 1976, 82 U.S.L.W. 4508, 24 Fla. L. Weekly Fed. S 870, 2014 WL 2765283 (U.S. 2014); MPEP 2106.
Step 1:
In the instant case claims 1-10 are directed to a machine and claims 11-15 are directed to a manufacture because specification ¶0096 recites, “Computer storage media excludes signals per se” (see Examiner Comment above). All claims are therefore within statutory categories. See MPEP 2106.03, Eligibility Step 1.
Step 2A, Prong 1:
These claims also recite, inter alia,
“accessing a request associated with a diagnosis code of a vehicle associated with an item listing system; based on the request, accessing code lookup data associated with a generative AI model, the code lookup data comprising code interpretation data and recommendation data associated with the diagnosis code, the generative AI model is associated with code lookup training operations and a code lookup data structure that support providing automotive diagnosis code guidance in the item listing system; and communicating the code lookup data to cause display of the code lookup data via an item listing system client.” Claim 1.
“communicating a request associated with a diagnosis code of a vehicle associated with a user of an item listing system; based on communicating the request, accessing code lookup data associated with a generative artificial intelligence (AI) model, the code lookup data comprising code interpretation data and recommendation data associated with the diagnosis code, the generative AI model is associated with code lookup training operations and a code lookup data structure that support providing automotive diagnosis code guidance in the item listing system; and causing display of the code lookup data on an item listing system client.” Claim 11.
With recited additional elements reserved for consideration alone and all together combined with their recited role(s) in the claim under step 2A prong two, a careful analysis of the remaining limitations above results in the conclusion that each on its own recites an abstract idea and in combination they simply recite a more detailed abstract idea. The recited abstract ideas fall within the groupings of abstract ideas described as mental processes such as concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and certain methods of organizing human activity, for example commercial interactions (including marketing or sales activities or behaviors). See MPEP 2106.04(a); Eligibility Step 2A1. The claims must therefore be analyzed under the second prong of Eligibility Step 2 (Step 2A2; MPEP 2106.04(d)).
Step 2A, Prong 2:
In order to address prong 2 (MPEP 2106.04(d), Eligibility Step2A2) we must identify whether there are any additional elements beyond the abstract ideas and determine whether those additional elements (if there are any) integrate the abstract idea into a practical application. MPEP 2106.04(d), Eligibility Step 2A2. The additional elements in present claims 1-10 are one or more computer processors and computer memory storing computer-useable instructions, and the additional elements in claims 11-15 are one or more computer-storage media having computer-executable instructions embodied thereon. These additional elements have been considered individually, in combination, and altogether as a whole together with the functions they perform, e.g., the at least one processor of claims 1-10 and the executable instructions embodied within media and capable of execution by a computer with a processor, in claims 11-15, are all alone broadly and generally recited as performing all steps in terms of the intended results of functionally nonspecific activities including transmitting and looking up data. These additional elements do not integrate the judicial exception into a practical application because they amount to no more than mere instructions to apply the exception using generic computer components. The claims are otherwise entirely a recitation of abstract ideas. The substantive process is recited only by descriptions of abstract intended results of steps without indicating any particular functional acts performed by any device or structural element to perform the steps or otherwise obtain the intended results. The additional elements do not improve the functioning of any computer or other technology or technical field, they do not apply the judicial exception with or by use of a particular machine, they do not transform or reduce a particular article to a different state or thing, and they fail to apply or use the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05.
If the disclosure describes any improvements to the functioning of a computer or to any other technology or technical field this improvement would need to be identifiable as the subject matter appearing in the claims. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies technical improvements realized by the claim over the prior art. The disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. MPEP 2106.05(a).
Claim limitations can integrate a judicial exception into a practical application by implementing the judicial exception with or using it in conjunction with a particular machine or manufacture that is integral to the claim. A general purpose computer that applies a judicial exception by use of generic computer functions does not qualify as a particular machine. Ultramercial, Inc. v. Hulu, LLC, (Fed. Cir. 2014); MPEP 2106.05(b),(f). There are no particular machines or manufactures identified in the present claims. Claimed elements that are not abstract are identified broadly and generally as applying the method, and the method itself is described only by way of the intended functional results of unidentified activities.
The claims do not affect the transformation or reduction of a particular article to a different state or thing. Changing to a different state or thing means more than simply using an article or changing the location of an article. A new or different function or use can be evidence that an article has been transformed. Purely mental processes in which data, thoughts, impressions, or human based actions are "changed" are not considered a transformation. MPEP 2106.05(c).
The claims do not apply or use the judicial exception in any other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As a result the claim as a whole appears to be a drafting effort designed to monopolize the exception. MPEP 2106.05(e),(h).
The additional elements have not been found to integrate the abstract idea into a practical application.
Step 2B:
Although the additional elements have not been found to integrate the abstract idea into a practical application the claims could still be eligible if they recite additional elements that amount to an inventive concept (“significantly more” than the judicial exception). MPEP 2106.05, Eligibility Step 2B.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sparse additional elements of the claim are mere props supporting instructions to implement an abstract idea or other exception on a computer. MPEP 2106.05(f). The claims invoke computers or other machinery merely as tools to perform an abstract process. Simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. MPEP 2106.05(f)(2); see also OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 2015 U.S. App. LEXIS 9721, 115 U.S.P.Q.2D (BNA) 1090 (Fed. Cir. 2015) (“relying on a computer to perform routine tasks more quickly or more accurately is insufficient to render a claim patent eligible.”). The elements are recited at a high level of generality, merely implement abstract ideas using generic computers, and fail to present a technical solution to a technical problem created by the use of the surrounding technology. Limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself. See Ret. Capital Access Mgmt. Co. v. U.S. Bancorp, 611 Fed. Appx. 1007, 2015 U.S. App. LEXIS 14351 (Fed. Cir. 2015) (“It may be very clever; it may be very useful in a commercial context, but they are still abstract ideas,” said Circuit Judge Alan Lourie.). MPEP 2106.05(h).
Finally, it is reiterated that remaining dependent claims 2-10 and 12-15 do not contribute any additional elements other than those already discussed and do not add "significantly more" to establish eligibility because they merely recite additional abstract ideas that further describe the identification and manipulation of data used in implementing the abstract idea. A more detailed abstract idea is still abstract. PricePlay.com, Inc. v. AOL Adver., Inc., 627 Fed. Appx. 925, 2016 U.S. App. LEXIS 611, 2016 WL 80002 (Fed. Cir. Jan. 7, 2016) (in addressing a bundle of abstract ideas stacked together during oral argument, U.S. Circuit Judge Kimberly Moore said, "All of these ideas are abstract…. It’s like you want a patent because you combined two abstract ideas and say two is better than one.").
All of the above leads to the conclusion that additional claim elements do not provide meaningful limitations to transform the claimed subject matter into significantly more than an abstract idea. MPEP 2106.05; Eligibility Step 2B. As a result the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter because they recite an abstract idea without being directed to a practical application, and they do not amount to significantly more than the abstract idea. MPEP 2106.05, supra..
The preceding analysis applies to all statutory categories of invention. Accordingly, claims 1-15 are rejected as ineligible for patenting under 35 USC 101 based upon the same analysis.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ebrahimi et al. (Pub. No.: US 2023/0083255 A1).
Ebrahimi teaches all of the limitations of claims 1-15. For example, Ebrahimi discloses automotive diagnosis code lookup data associated with a generative AI model wherein the generative AI model is trained to provide automotive diagnosis code guidance including interpretation data and recommendation data associated with the diagnosis code. Ebrahimi further discloses pertaining to
Claim 1. A computerized system comprising: ● one or more computer processors (see at least Ebrahimi fig. 1, ¶0062 “computing system 100 can contain at least one processor”); and ● computer memory storing computer-useable instructions that, when used by the one or more computer processors, cause the one or more computer processors to perform operations (Ebrahimi fig. 1, ¶0010 “present disclosures provide a technology, including any combination of methods, systems, and computer readable medium,” ¶0062 “Processor 110 can perform actions responsive to instructions and in conjunction with one or more memory devices,”), the operations comprising: ● accessing a request associated with a diagnosis code of a vehicle associated with an item listing system (see at least Ebrahimi abstract “a repair order to generate a CEICA or line code. … a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair,” figs. 6-8, 10B, ¶0004 “vehicle identification number (VIN) or other identifier can be obtained to determine the make, model, and from that information, the specific parts used to build a particular vehicle. This in turn can allow for error codes or other data from that vehicle to be used,” ¶0012 “receiving, from a scan tool a first diagnostic code,” ¶0051 “CIECA Codes (parts code table): Codes that were created from CEICA standardization repair standards the CEICA codes. CEICA codes can be back-end codes used in collision estimate software to define parts or components on a vehicle”); ● based on the request, accessing code lookup data associated with a generative AI model, the code lookup data comprising code interpretation data and recommendation data associated with the diagnosis code, the generative AI model is associated with code lookup training operations and a code lookup data structure that support providing automotive diagnosis code guidance in the item listing system (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair,” figs. 9, 10B-10F, ¶¶0012-0013 “receiving, from a scan tool a first diagnostic code; analyzing, using a first class of rules, the first diagnostic code; analyzing, when a first condition based on the first class of rules is not met, a repair order … repair data may be analyzed using a machine learning model to obtain a first line code. … The first diagnostic code is analyzed against a list of Golden DTCs. … The diagnostic trouble codes can be classified as Golden DTCs using a machine learning model. … [0013] … generating, using a second trained machine learning model, a set of vehicle components for calibration,” ¶0034 “analysis using a trained machine learning model, such as for example, a specific DTC … a parameter combining vehicle data and CEICA related codes, and outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0039 “a CEICA code corresponding to repair information can be obtained using a … a machine-based learning categorization method,” ¶0072 “specific parts can be identified, and listed by their CIECE code and description based on a correlation to an ADAS related part of that particular vehicle. … Column 272 can describe a code, such as a CEICA code. Column 273 can include a description of the CEICA code. In some examples, column 273 and 272 can be correlated based on a machine learning or other model”); and ● communicating the code lookup data to cause display of the code lookup data via an item listing system client (see at least Ebrahimi fig.9 (identifies steps involved in user interaction, e.g., output to user (i.e., display) and responsive input from user), ¶0034 “outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0036 “output of the model can be translated to a "universal" ADAS system and provided to a user through a console,” ¶0051 “CEICA codes can be back-end codes used in collision estimate software to define parts or components on a vehicle. They may be organized and displayed on software,” ¶0083 “Operations Console can refer to a user interface or console, which can allow for reporting of diagnostics from scans (and ADAS identification),” ¶0143 “model can provide outcomes or outputs, which can be used, along with selected golden-DTCs and correlations of ADAS to line codes …. These outputs can be used and provided on a console for end-users”).Claim 2. The system of claim 1, further comprising a generative AI code lookup engine that uses the generative AI model to generate the code lookup data comprising diagnosis code meaning, estimated repair cost, estimated cost, one or more recommended items for a code lookup interface for presenting instances of code lookup data for requests processed using the generative AI presentation engine (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair…. Machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate,” figs.8-9).Claim 3. The system of claim 1, further comprising a generative AI code lookup engine that is associated with a machine learning engine, the machine learning engine is associated with training the generative AI model that supports image generation and text generation for instances of code lookup data (see at least Ebrahimi ¶0034 “outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0039 “repair information can be obtained using … a machine-based learning categorization method. Repair information related to a vehicle, whether textual, visual … can be used to categorize a particular repair and identify CEICA codes or ADAS modules which may be associated with that repair,” ¶0093 “Looker visualization module 326 can allow for visualization of data or outputs from a trained machine learning model”).Claim 4. The system of claim 1, wherein the code lookup training operations support training generative AI models based on training data comprising user data, automotive data, image data, text data and item listing interfaces data, wherein the code lookup training operations support generating instances of code lookup data for a plurality of item listing interfaces of the item listing system (see at least Ebrahimi figs.4, 9, ¶0013 “outputting, using a trained machine learning model, a second set of vehicle areas, … a set of vehicle components … providing, to an end-user in a human readable format …. trained machine learning model may be retrained or reconfigured upon receiving a threshold level of false negatives,” ¶0034 “inputs can be used … in connection with training of a machine learning model … a specific DTC … a parameter combining vehicle data and CEICA related codes, and outputs … interpreting repair information, accident information, or other textual information related to a vehicle,” ¶0087 “feedback report can … in turn be used in training … machine learning models,” ¶¶0097-0098 “the set of training data being received and being trained for …. The set of training data may be a range of ADAS modules, vehicle data, such as year, make, model, or sub-model …. [0098] In yet other examples, the set of training data can include…” (et seq.)).Claim 5. The system of claim 1, wherein the code lookup data structure is associated with code lookup logic that includes instructions for providing the code lookup data including diagnosis code meaning, estimated repair cost, estimated cost, one or more recommended items to a code lookup interface (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair…. Machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate,” figs.8-9).Claim 6. The system of claim 1, wherein code lookup training operations are based on one or more prompt templates comprising structured input queries or instructions designed to train or instruct the generative AI model to generate instances of code lookup data (see at least Ebrahimi fig.4, ¶0034 “parameters or inputs can be used as rules, that can be used in connection with training of a machine learning model,” ¶0087 “feedback report can include information related to a prediction, such as a false positive, which can in turn be used in training,” ¶0096 “training data can be input as a string, such as an input and desired outcome,” ¶¶0097-0098 “the set of training data being received and being trained for …. The set of training data may be a range of ADAS modules, vehicle data, such as year, make, model, or sub-model …. [0098] In yet other examples, the set of training data can include…” (et seq.))).Claim 7. The system of claim 6, wherein a prompt template comprises a plurality of code lookup data categories including two or more of the following Diagnosis Trouble Code (DTC), additional vehicle information, and historical automotive context (see at least Ebrahimi ¶0075 “additional modules or information can be included or used in conjunction with system 200, such as parts build data, emissions data, or live or historically captured sensor data,” ¶0097 “training data may be a range of ADAS modules, vehicle data, such as year, make, model, or sub-model,” ¶0109 “model can be trained on all the historical data where line category codes and line descriptions are present,” ¶0118-0129 “a person of skill in the art will appreciate that another set of features, including those containing hidden variables or hidden features, or an equivalent formulation of features can be discovered. The following are a non-limited example set of features or parameters which can be trained on: [0119] … [0129]”).Claim 8. The system of claim 1, wherein the code lookup data comprises two or more of the following: ● a non-generative AI data element, a generative AI data element, and a generative AI item listing interface element (see at least ¶0012 “outputted from a trained machine learning model. … generated based on supervised machine learning or manually set by a human operator. … The first diagnostic code is analyzed against a list of Golden DTCs. The Golden DTCs can be manually tagged or created by a human operator,” ¶0034 “outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0035 “information generated or analyzed using artificial intelligence”).Claim 9. The system of claim 1, the operations further comprising: ● communicating a request instance associated with a diagnosis code of a vehicle associated with a user (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair…. Machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate,” figs.8-9, ¶0012 “receiving, from a scan tool a first diagnostic code”); ● based on communicating the request associated with the user, accessing an instance of code lookup data associated with user data of the user, the instance of code lookup data is associated with a code lookup interface (see at least Ebrahimi figs.4, 9, ¶0013 “outputting, using a trained machine learning model, a second set of vehicle areas, … a set of vehicle components … providing, to an end-user in a human readable format …. trained machine learning model may be retrained or reconfigured upon receiving a threshold level of false negatives,” ¶0073 “DTCs codes may be provided …. provided information can be mined or processed to provide specific recommendations to an end-user without the end-user having to manually look up a specific error code,” ¶¶0097-0098 “the set of training data being received and being trained for …. The set of training data may be a range of ADAS modules, vehicle data, such as year, make, model, or sub-model …. [0098] In yet other examples, the set of training data can include…. dataset on which the machine learning model is trained can be updated based on feedback from an end-user…” (et seq.)); and ● causing display of the instance of code lookup data on the code lookup interface (see at least Ebrahimi fig.9 (identifies steps involved in user interaction, e.g., output to user (i.e., display) and responsive input from user), ¶0034 “outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0036 “output of the model can be translated to a "universal" ADAS system and provided to a user through a console,” ¶0051 “CEICA codes can be back-end codes used in collision estimate software to define parts or components on a vehicle. They may be organized and displayed on software,” ¶0083 “Operations Console can refer to a user interface or console, which can allow for reporting of diagnostics from scans (and ADAS identification),” ¶0143 “model can provide outcomes or outputs, which can be used, along with selected golden-DTCs and correlations of ADAS to line codes …. These outputs can be used and provided on a console for end-users”).Claim 10. The system of claim 9, the operations further comprising: ● accessing a training dataset associated with training an instance of a generative AI model for an item listing system (see at least Ebrahimi figs. 5A, 10B-10F, ¶0099 “datasets illustrated with respect to FIGS. 10A to 10F and FIGS. 13A and 13B can be used for training of a machine learning model”); ● executing code lookup training operations on the training dataset to generate the instance of the generative AI model (see at least fig.5A, ¶0110 “data can be aggregated to create a more robust data set for model training,” ¶¶0118-0129 (listing datasets used in executing code lookup training operations), ¶0141 “a DTC to ADAS mapping generation module can be included. Golden-DTCs can be provided to the outcome generation notebook as part of use in a machine learning training of, for example, the XGBoost model. Similarly, ADAS to line code mappings can be stored in the DTC-sys to ADAS mapping generation notebook, and provided through the outcome generation notebook to the XGBoost model module for training”); and ● deploying the instance of the generative AI model to support generating instances of code lookup data in the item listing system (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair…. Machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate,” figs.8-9).
Pertaining to computer-storage media claims 11-15
Rejection of claims 11-15 is based on the same rationale explained above with regard to claims 1-2, 4-5, and 8. In addition Ebrahimi discloses regarding
Claim 11. One or more computer-storage media having computer-executable instructions embodied thereon that, when executed by a computing system having a processor and memory, cause the processor to perform operations (see at least Ebrahimi fig. 1, ¶0010 “present disclosures provide a technology, including any combination of methods, systems, and computer readable medium,” ¶0062 “computing system 100 can contain at least one processor”), the operations comprising: ● communicating a request associated with a diagnosis code of a vehicle associated with a user of an item listing system (see at least Ebrahimi abstract “a repair order to generate a CEICA or line code. … a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair,” figs. 6-8, 10B, ¶0004 “vehicle identification number (VIN) or other identifier can be obtained to determine the make, model, and from that information, the specific parts used to build a particular vehicle. This in turn can allow for error codes or other data from that vehicle to be used,” ¶0012 “receiving, from a scan tool a first diagnostic code,” ¶0051 “CIECA Codes (parts code table): Codes that were created from CEICA standardization repair standards the CEICA codes. CEICA codes can be back-end codes used in collision estimate software to define parts or components on a vehicle”); ● based on communicating the request, accessing code lookup data associated with a generative artificial intelligence (AI) model, the code lookup data comprising code interpretation data and recommendation data associated with the diagnosis code, the generative AI model is associated with code lookup training operations and a code lookup data structure that support providing automotive diagnosis code guidance in the item listing system (see at least Ebrahimi abstract “a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair,” figs. 9, 10B-10F, ¶¶0012-0013 “receiving, from a scan tool a first diagnostic code; analyzing, using a first class of rules, the first diagnostic code; analyzing, when a first condition based on the first class of rules is not met, a repair order … repair data may be analyzed using a machine learning model to obtain a first line code. … The first diagnostic code is analyzed against a list of Golden DTCs. … The diagnostic trouble codes can be classified as Golden DTCs using a machine learning model. … [0013] … generating, using a second trained machine learning model, a set of vehicle components for calibration,” ¶0034 “analysis using a trained machine learning model, such as for example, a specific DTC … a parameter combining vehicle data and CEICA related codes, and outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0039 “a CEICA code corresponding to repair information can be obtained using a … a machine-based learning categorization method,” ¶0072 “specific parts can be identified, and listed by their CIECE code and description based on a correlation to an ADAS related part of that particular vehicle. … Column 272 can describe a code, such as a CEICA code. Column 273 can include a description of the CEICA code. In some examples, column 273 and 272 can be correlated based on a machine learning or other model”); and ● causing display of the code lookup data on an item listing system client (see at least Ebrahimi fig.9 (identifies steps involved in user interaction, e.g., output to user (i.e., display) and responsive input from user), ¶0034 “outputs from a Bidirectional Encoder Representations from Transformers (BERT model) interpreting repair information, accident information, or other textual information related to a vehicle or vehicle repair,” ¶0036 “output of the model can be translated to a "universal" ADAS system and provided to a user through a console,” ¶0051 “CEICA codes can be back-end codes used in collision estimate software to define parts or components on a vehicle. They may be organized and displayed on software,” ¶0083 “Operations Console can refer to a user interface or console, which can allow for reporting of diagnostics from scans (and ADAS identification),” ¶0143 “model can provide outcomes or outputs, which can be used, along with selected golden-DTCs and correlations of ADAS to line codes …. These outputs can be used and provided on a console for end-users”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
● CHIN, KR 20230046557 A: teaches fault diagnosis code analyzed by neural net and connected with inventory of parts available at various repair shops.
● ディクシット、スニール, JP 2023536677 A: teaches various codes for identifying issues with a vehicle and includes identifying maintenance and parts/components in an inventory.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM LEVINE whose telephone number is (571)272-8122. The examiner can normally be reached Monday - Thursday 9am-7:30pm.
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/ADAM L LEVINE/Primary Examiner, Art Unit 3689 May 2, 2026
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