DETAILED ACTION
Status of Claims
The following is a NON-FINAL OFFICE ACTION for Application #18/327,270 in response to applicant’s response to Restriction Requirement sent on 10/03/2025. The response was filed on 11/26/2025. This application was originally filed on 06/01/2023.
Claims 9-14 are now pending and have been examined.
Claims 1-8 and 15-20 are withdrawn as the non-elected claims.
Restriction/Election
A Restriction Requirement was sent on 10/03/2025. In response to the Restriction Requirement, the applicant elected Group II, claims 9-14. The election was made without traverse. Therefore, claims 1-8 and 15-20 have been withdrawn as the non-elected claims.
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 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The rationale for this finding is explained below.
Per Step 1 of the analysis, the claims are analyzed to determine if they are directed to statutory subject matter. Claim 9 claims a method, or process. A process is a statutory category for patentability.
Per Step 2A, Prong 1 of the analysis, the examiner must now determine if the claims recite an abstract idea or eligible subject matter. In the instant case, the independent claims recite an abstract idea. Specifically, independent claim 9 recites “a plurality of repair orders and information derived from repair orders, analyzing repair order data for a service event performed on a vehicle, generating output repair order data that is predictive of information, and storing the output repair order data for providing determinations on the service event.” Therefore, the claims recite an abstract idea, namely a mental process. A human operator with access to the plurality of repair orders from service events and other information could analyze the data, generate further repair order data based on the analysis, and initiate storage of the data. The computer and components only automate this process with the aid of some kind of model or machine learning. Therefore, the claims are determined to recite and abstract idea, namely a mental process. The claims are secondarily directed to an abstract idea, namely a “certain method of organizing human activity.” Specifically, the claims are directed to “business activities.” A repair business such as a vehicle repair shop would generally store and analyze service event data and analyze repair information in order to project everything from efficiency and time management to projections for reminder to customers of repair needs and projecting revenue. The computer and components only automate this process. Therefore, the claims are secondarily determined to recite and abstract idea, namely “business activities.”
Per Step 2A, Prong 2 of the analysis, the examiner must now determine if the claims integrate the abstract idea into a practical application. The additional elements of the claims include “one or more external systems,” “one or more processing routines for processing repair order,” and a “database.” However, these recited elements are considered generic recitations of technical elements as they are recited at a high level of generality. These elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and do not integrate the abstract idea into a practical application. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The claims also include the actual storage of data in the database. Absent further detail, this additional element is listed in the MPEP 2106.05 (d) (II) (iii-iv) as an example of conventional computer functioning- see “electronic recordkeeping,” citing Alice Corp., and “storing and retrieving information in a memory,” citing Versata Dev Grp v SAP. Therefore, this additional element does not integrate the abstract idea into a practical application. The additional elements also include “training a repair order creation model based on training data…and training parameters…,” “applying the trained repair order creation model,” and “generating, by the repair order creation model….” However, the training, applying, and generating steps being done with and by the model are recited at a high level of generality and with very little detail of how the model is trained, how the model is applied, or how the model specifically generates an output. In fact, the applying and generating step, if you removed the “by the model” limitation, would read just as a mental process or a step that could be done without the model mentally or with aid of a generic computer. These additional elements are considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05 (f)). Therefore, these additional elements do not integrate the abstract idea into a practical application.
Per Step 2B of the analysis, the examiner must now determine if the claims include limitations that are “significantly more” than the abstract idea by demonstrating an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements of the claims include “one or more external systems,” “one or more processing routines for processing repair order,” and a “database.” However, these recited elements are considered generic recitations of technical elements as they are recited at a high level of generality. These elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and are not considered significantly more than the abstract idea itself. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The claims also include the actual storage of data in the database. Absent further detail, this additional element is listed in the MPEP 2106.05 (d) (II) (iii-iv) as an example of conventional computer functioning- see “electronic recordkeeping,” citing Alice Corp., and “storing and retrieving information in a memory,” citing Versata Dev Grp v SAP. Therefore, this additional element is not considered significantly more than the abstract idea itself. The additional elements also include “training a repair order creation model based on training data…and training parameters…,” “applying the trained repair order creation model,” and “generating, by the repair order creation model….” However, the training, applying, and generating steps being done with and by the model are recited at a high level of generality and with very little detail of how the model is trained, how the model is applied, or how the model specifically generates an output. In fact, the applying and generating step, if you removed the “by the model” limitation, would read just as a mental process or a step that could be done without the model mentally or with aid of a generic computer. These additional elements are considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05 (f)). Therefore, these additional elements are not considered significantly more than the abstract idea itself.
When considered as an ordered combination, the claim is still considered to be directed to an abstract idea as the claims in the ordered combination simply recite the logical steps for accessing a plurality of repair orders from service events and other information, analyzing the data, generate further repair order data based on the analysis, and initiating storage of the data. The computer and components only automate this process with the aid of some kind of model or machine learning. Therefore, the ordered combination does not lead to a determination of significantly more.
When considering the dependent claims, claim 10 is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05 (f)). The training step is recited at a high level of generality with very little detail of how the model is trained. The addition of some repair orders being unacceptable for training does not change the analysis as the limitation simply is describing what data was NOT used for training. Therefore, this limitation is considered insignificant extra-solution activity. Therefore, these additional elements are not considered significantly more. Claim 11 is considered conventional computer functioning. The MPEP 2106.05 (d) (II) (i) lists examples of conventional computer functioning to include “receiving or transmitting data over a network,” citing Symantec, and “sending messages over a network,” citing buySAFE v Google. Therefore, this additional element is not considered significantly more than the abstract idea itself. The real-time aspect of the data being received at the end-user system is at the time of filing of the application also considered conventional computer functioning that is old and well known. For Claim 12, the applying of the repair order data to the trained model is not considered significantly more for reasons already covered in the analysis of claim 10 above, and the receiving from an end-user system of the input repair order data is not considered significantly more because of the rationale given in the analysis of claim 11 above. Claim 13 is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05 (f)). The training step is recited at a high level of generality with little detail of how the model is trained. Saying the model is constrained using training parameters to teach the model is simply describing all training of a model at a high level of generality with no specificity. Applying the data to the constrained model likewise simply describes inputting data to train the model with no detail. Therefore, these additional elements are not considered significantly more. Claim 14 is considered “receiving and/or transmitting of data over a network,” listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmitting data over a network,” citing Symantec, and “sending messages over a network,” citing buySAFE v Google. Therefore, this additional element is not considered significantly more than the abstract idea itself. The establishing of communicative connections to a plurality of end-user systems is considered a generic recitation of a technical element as they are recited at a high level of generality. This element is considered the equivalent of “apply it,” or using the components as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and is not considered significantly more than the abstract idea. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)).
Therefore, claims 9-14 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. Vs. CLS Bank International et al., 2014 (please reference link to updated publicly available Alice memo at http://www.uspto.gov/patents/announce/alice_pec_25jun2014.pdf as well as the USPTO January 2019 Updated Patent Eligibility Guidance.)
Claim Rejections - 35 USC § 103
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 9, 11, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ranca, et al., Pre-Grant Publication No. 2021/0272271 A1 in view of Junik, et al., Pre-Grant Publication No. 2023/0334603 A1.
Regarding claim 9, Ranca teaches:
A method for generating repair orders, the method comprising:
training a repair order creation model based on training data comprising a plurality of repair orders and training parameters comprising criteria information that are derived from one or more processing routines for processing repair order according to rules and polices of one or more external systems (see [0226]-[0230] in which the repair order model is trained based on a plurality of historical repair orders including what is required for processing routines for external systems such as insurance companies to which the claims will be submitted)
applying the trained repair order creation model on input repair order data for a service event performed on a vehicle (see [0226]-[0230] in which the trained model analyzes input current repair orders for a service event on a vehicle for such as an insurance claim and identifies any errors or omissions that might need to be included for submission)
generating, by the repair order creation model, output repair order data that is predictive of information required by the one or more processing routines (see [0226]-[0230] in which the trained model analyzes input current repair orders for a service event on a vehicle for such as an insurance claim and identifies any errors or omissions that might need to be included for submission; see also [0186]-[0189] , [0194]-[0197], and [0226]-[0228] in which the system outputs the results of the analysis including areas that need to be added or improved for submission of the repair order)
wherein the one or more external systems apply the one or more processing routines on the output repair order for providing determinations on the service event (see [0180]-[0181], [0196], [0232], in which the insurance company or other third party external system to which the output repair order is submitted applies their processing routines to the repair order)
Ranca, however, does not appear to specify:
storing the output repair order data in a database
Junik teaches:
storing the output repair order data in a database (see [0051]-[0055] in which repair orders that have been analyzed and need additional information prior to submission to the third party external systems are held in storage queues during the processing stages)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Junik with Ranca because Ranca already teaches pre-processing and processing of repair orders involving multiple parties and steps, and storing the output repair order in a database prior to being finalized allows for secure keeping during the process and for easy access for all parties as needed.
Regarding claim 11, the combination of Ranca and Junik teaches:
the method of claim 9
Ranca further teaches:
receiving the input repair order data in real-time as part of preparing a repair order for the service event at an end-user system (see [0186]-[0189], [0194]-[0197], and [0226]-[0230] in which input repair order data is submitted as part of a repair order for the service event through an API/computer at an end-user system; the examiner notes that while the reference does not use the words “real-time,” this teaching is considered inherent because the end-user is submitting the data through a connected API in the system electronically versus through the mail or other process, and the data would be transmitted practically instantaneously as is understood at the time of filing of an application when submitting data through such as an API into a system)
Regarding claim 14, the combination of Ranca and Junik teaches:
the method of claim 9
Ranca further teaches:
establishing communicative connections to a plurality of end-user systems (see [0186], [0194]-[0196], and [0231]-[0232])
responsive to establishing communicative connects, obtaining repair order data from each of the plurality of end-user systems, wherein the repair order data is applied to the trained repair order creation model as the input repair order data (see [0226]-[0232])
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ranca, et al., Pre-Grant Publication No. 2021/0272271 A1 in view of Junik, et al., Pre-Grant Publication No. 2023/0334603 A1 and in further view of Bagherinia, et al., Pre-Grant Publication No. 2024/0127446 A1.
Regarding claim 10, the combination of Ranca and Junik teaches:
the method of claim 9
Ranca further teaches:
wherein the training data comprises a first plurality of repair orders that are acceptable according to the rules and policies of the one or more external systems and a second plurality of repair orders that are unacceptable according to the rules and policies of the one or more external systems (see [0226]-[0230] in which the repair order model is trained based on a plurality of historical repair orders including what is required for processing routines for external systems such as insurance companies to which the claims will be submitted)
Ranca and Junik, however, does not appear to specify:
wherein the training data comprises a first plurality of repair orders that are acceptable according to the rules and policies of the one or more external systems and a second plurality of repair orders that are unacceptable according to the rules and policies of the one or more external systems
Bagherinia teaches:
wherein the training data comprises a first plurality of repair orders that are acceptable according to the rules and policies of the one or more external systems and a second plurality of repair orders that are unacceptable according to the rules and policies of the one or more external systems (see [0011], [0061], and [0074] in which the model is trained with images that are of acceptable and unacceptable quality so that the model can make a determination submitted images for such as radiological analysis)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Bagherinia with Ranca and Junik because Ranca and Junik already teach submission of repair orders that optionally include images and a trained model that determines if the orders are submitted correctly or need to be changed due to deficient information prior to submitting to a third party external system, and training the model on both acceptable and unacceptable data such as images would ensure proper submission and timely processing of the repair orders, especially when information such as images are crucial in making repair determinations for such as vehicles.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ranca, et al., Pre-Grant Publication No. 2021/0272271 A1 in view of Junik, et al., Pre-Grant Publication No. 2023/0334603 A1 and in further view of Official Notice.
Regarding claim 12, the combination of Ranca and Junik teaches:
the method of claim 9
Ranca further teaches:
receiving, from an end-user system, the input repair order data comprising a repair order for the service event (see [0186]-[0189], [0194]-[0197], and [0226]-[0230] in which input repair order data is submitted as part of a repair order for the service event through an API/computer at an end-user system)
Ranca and Junik, however, does not appear to specify:
wherein the input repair order data is applied to the trained repair order creation model responsive to receiving the input repair order data
The examiner, however, takes Official Notice that it is old and well known in the computer arts to use a feedback loop or update training of a model with new relevant data as it comes available, thus keeping the model updated and current. Companies such as Microsoft, IBM, and Google have done this for many years prior to the effective filing date of the application.
Therefore, it would be obvious to one of ordinary skill in the art at the time of filing of the application to combine wherein the input repair order data is applied to the trained repair order creation model responsive to receiving the input repair order data with Ranca and Junik because Ranca and Junik already teach input repair order data and training a model on historical data, and continuing to train the model on incoming current data would keep the model relevant and updated based on the most current available data.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ranca, et al., Pre-Grant Publication No. 2021/0272271 A1 in view of Junik, et al., Pre-Grant Publication No. 2023/0334603 A1 and in further view of Zhang, et al., Pre-Grant Publication No. 2024/0188895 A1.
Regarding claim 13, the combination of Ranca and Junik teaches:
the method of claim 9
Ranca and Junik, however, does not appear to specify:
constraining a machine learning model using the training parameters to teach the machine learning model
applying the training data to the constrained machine learning model to generate the trained repair order creation model
Zhang teaches:
constraining a machine learning model using the training parameters to teach the machine learning model and applying the training data to the constrained machine learning model to generate the trained repair order creation model (see [0086]; the examiner notes that the other references already teach the repair order creation aspect of the claims, so the cited reference is being used to teach the other aspects of the claim)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Zhang with Ranca and Junik because Ranca and Junik already teach training of a model to analyze repair orders, and constraining a mode with various parameters would allow the model to better analyze specific aspects of the repair orders.
Conclusion
The following prior art references was not relied upon in this office action but is considered pertinent to the claimed invention:
Paukkeri, et al., Patent No. 11,531,834 B2- teaches training a model on unacceptable variables in data analysis.
Bukhamsin, et al., Pre-Grant Publication No. 2023/0385324 A1- teaches the use of machine learning models to analyze maintenance orders and identify missing parts of the orders.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Luis A. Brown whose telephone number is 571.270.1394. The Examiner can normally be reached on M-F 8:30am-4:30pm EST. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, JESSICA LEMIEUX can be reached at 571.270.3445.
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/LUIS A BROWN/Primary Examiner, Art Unit 3626
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