DETAILED ACTION
Status of Claims
The following is a FINAL OFFICE ACTION in response to applicant’s amendments to and response for Application #18/327,270, filed on 03/16/2026.
Claims 9-14 are now pending and have been examined.
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 “criteria information…for processing repair orders according to rules and policies…, receiving input repair order data for a service event performed on a vehicle, the input repair order data comprising a plurality of data entry lines, each data entry line comprising at least one data entry field and a corresponding identifier descriptive of information to be entered into the at least one data entry field, analyzing repair order data on a field-by-field basis to determine, from the corresponding identifiers and he criteria information, whether a target data entry field lacks an allowable value, generating output repair order data by modifying the input repair order data to generate and insert a compliant data entry value into the target data entry field, thereby transforming the input repair order data into an acceptable repair order, and using 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 using criteria information according to rules and policies, identify on a field by field basis using the identifiers and criteria any entries that lack allowable values, modify the repair order data by inserting a compliant value into the field, and then allow the modified order record to be used to provide determinations on a service event. There is no part of these steps that could not be done mentally by a human operator with access to the data, albeit potentially at a slower rate. The computer and components only automate this process with the aid of a machine learning model. Therefore, the claims are determined to recite and abstract idea, namely a mental process.
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 by constraining a machine learning algorithm using training parameters…,” “applying the training data comprising a plurality of repair orders to the constrained machine learning algorithm,” “applying the trained order creation model to the input repair order data…,” 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, in the applying and generating steps, 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 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 by constraining a machine learning algorithm using training parameters…,” “applying the training data comprising a plurality of repair orders to the constrained machine learning algorithm,” “applying the trained order creation model to the input repair order data…,” 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, in the applying and generating steps, 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 modified repair order data based on the analysis, and using 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 algorithm is constrained by applying the training parameters to the MLM to define acceptable and unacceptable repair orders simply describes at a high level of generality what all training of a model is, inputting training data so the model “learns.” There are no detailed technical steps as to how the model is trained. 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 13-14 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 and in further view of Witter, et al., Pre-Grant Publication No. 2015/0220692 A1.
Regarding claim 9, Ranca teaches:
A method for generating repair orders, the method comprising:
training a repair order creation model by constraining a machine learning algorithm using training parameters comprising criteria information that are derived from one or more processing routines for processing repair order according to rules and policies of one or more external systems, and applying the training data comprising a plurality of repair orders to the constrained machine learning algorithm (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)
receiving input repair order data for a service event performed on a vehicle (see [0226]-[0230])
applying the trained repair order creation model to the input repair order data (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 to the output repair order data 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.
Ranca and Junik, however, does not appear to specify:
the input repair order data comprising a plurality of data entry lines, each data entry line comprising at least one data entry field and a corresponding identifier descriptive of information to be entered into the at least one data entry field
applying…to the input repair order data on a field-by-filed basis to determine, from the corresponding identifiers and the criteria information, whether a target data entry field lacks an allowable value according to the one or more processing routines
generating…output repair order data by automatically modifying the input repair order data to generate and insert a compliant data entry value into the target data entry field, thereby transforming the input repair order data into an acceptable repair order for the one or more external systems
Witter teaches:
the input repair order data comprising a plurality of data entry lines, each data entry line comprising at least one data entry field and a corresponding identifier descriptive of information to be entered into the at least one data entry field, applying…to the input repair order data on a field-by-filed basis to determine, from the corresponding identifiers and the criteria information, whether a target data entry field lacks an allowable value according to the one or more processing routines, and generating…output repair order data by automatically modifying the input repair order data to generate and insert a compliant data entry value into the target data entry field, thereby transforming the input repair order data into an acceptable repair order for the one or more external systems (see Figure 1 and [0040] in which each data segment in the claim, which are the equivalent of “data entry lines,” have corresponding predefined criteria, which are considered the equivalent of “identifiers,” as the applicant’s published specification describes the identifiers in paragraph [0084] as “descriptive of the information to be entered into the field,” and as these data segment fields are analyzed, it is determined whether the field lacks an allowable value, such as ranges of data, acceptable length, predefined code being correct, formatting, or other such values, and if the value is incorrect the field is modified as a processing routine is initiated to auto-correct the data segment by modifying it with a correct value)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Witter with Ranca and Junik because Ranca already teaches pre-processing and processing of repair orders using machine learning including identifying errors and omissions, and correcting any lack of allowable values with modifying of the field would allow for an automated process for correcting RO’s, making the process better for service providers while divulging them of the need to know the requirements of every benefits provider and negating the need for the system to communicate back with the service provider to correct errors.
**The examiner notes that while Witter’s claims are directed to a claim for a payout of medical procedures that have been provided by a medical provider rather than a repair order claim for repairs that have been provided by an automobile service provider, the process is still the same when it comes to the claim and identifying and correcting a lack of allowable values in a data field and more importantly Ranca already teaches the other aspects of the claim as applied to vehicle repair and repair orders.**
Regarding claim 11, the combination of Ranca, Junik, and Witter 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 13, the combination of Ranca, Junik, and Witter teaches:
the method of claim 9
Ranca further teaches:
wherein constraining the machine learning algorithm comprises applying the training parameters to the machine learning algorithm to define acceptable and unacceptable repair orders according to the one or more processing routines (see [0229]-[0232] in which the processing routines use the trained model to pre-check the repair estimate prior to providing to insurer to see if there are any errors that would make the order unacceptable or to verify it is acceptable)
Regarding claim 14, the combination of Ranca, Junik, and Witter 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 Witter, et al., Pre-Grant Publication No. 2015/0220692 A1 and in further view of Bagherinia, et al., Pre-Grant Publication No. 2024/0127446 A1.
Regarding claim 10, the combination of Ranca, Junik, and Witter 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, Junik, and Witter, 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, Junik, and Witter 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 Witter, et al., Pre-Grant Publication No. 2015/0220692 A1 and in further view of Official Notice.
Regarding claim 12, the combination of Ranca, Junik, and Witter 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, Junik, and Witter, 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, Junik, and Witter 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.
Response to Arguments
Regarding the rejections based on 35 USC 101
Regarding the applicant’s argument on page 8 of the response that the claims do not recite an subtract idea and “the claim recites a particular computerized operation performed on a structured repair-order record having data-entry lines, data-entry fields, and corresponding identifiers, in which a trained repair-order creation model is applied on a field-by-field basis to detect a target field lacking an allowable value and automatically insert a compliant value into that field”:
Claims being computerized, having “electronic records,” applying of a model, and other such aspects does not make the claims eligible. Under the Step 2A Prong 1 analysis the examiner determines if the claims RECITE an abstract, not if each and every limitation of the claims is encompassed by the abstract idea. There are many recent Court decisions such as buySAFE v Google and OPI Techs v Amazon.com which recite e-commerce systems with multiple interacting components including processors, servers, databases, interfaces, etc performing multiple steps and yet the Court still found the claims to be ineligible and directed to an abstract idea.
Regarding the applicant’s argument on page 9 of the response that the claims do not recite an abstract idea and “the amended claim 9 now recites a specific field-level record-processing workflow…. There is a specific computer-implemented sequence for modifying a structured electronic repair-order record…”:
The claims including a workflow that processes “electronic” records, uses an MLM, or is computer-implemented again only automates the mental process. The claims do not cease to recite an abstract idea because the mental process is implemented using a computer or involves electronic records rather than manual. The abstract idea can still be performed manually and using mental steps. The recognition of a field having a lack of an allowable value and then modifying the specific field of the record can be done mentally and the claims do not present some kind of technical improvement to address the problem.
Regarding the applicant’s argument on page 9 of the response that “the specification identifies a technical problem arising in computerized repair-order environments…”:
Electronic records being incomplete, inconsistent, or improperly structured is not a TECHNICAL problem. Electronic records are simply automated manual records. Manual records can have the same issues. The fact something is computerized does not take it out of the realm of the abstract. A human operator fixing electronic records would use the same mental process as fixing manual records. Again, the workflow for addressing this problem being “computerized” or “using a MLM” does not take the solution out of the realm of the abstract and make it a technical solution.
Regarding the applicant’s argument on page 10 of the response that “the claim recites that the machine-learning algorithm is constrained using training parameters comprising criteria information derived from one or more processing routines” and that the amendment now specifies how the model is used:
The amendment merely describes how any training process for an MLM is conducted. Specific data is inputted so that the model is trained to identify data a certain way, such as what is allowable and what is not. The “use” is not any kind of technical improvement and there are no specifics as to how the MLM specifically is used. The MLM is simply “used” to perform what could be done as mental steps, such as analyzing the repair orders on a field-by-field basis and modifying values that lack an allowable value.
Therefore, the applicant’s arguments in light of the amendments to the claims are not persuasive and the rejection is sustained.
Regarding the rejections based on 35 USC 103
The applicant’s arguments in light of the amendments to the claims have been considered but are moot in light of the new grounds of rejection necessitated by the applicant’s amendments.
Conclusion
Applicant amendment(s) necessitated any new grounds of rejection set forth in this Office Action. Therefore, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
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.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://portal.uspto.gov/external/portal/pair . Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866.217.9197 (toll-free).
Any response to this action should be mailed to:
Commissioner of Patents and Trademarks
Washington, D.C. 20231
or faxed to 571-273-8300.
Hand delivered responses should be brought to the United States Patent and Trademark Office Customer Service Window:
Randolph Building
401 Dulany Street
Alexandria, VA 22314.
/LUIS A BROWN/Primary Examiner, Art Unit 3626
Prosecution Insights