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 .
Application Status
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 06/26/2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
With regards to the IDS the NPL of the extended European search report was not considered. Applicant appears to have attached the ESR for the wrong application, and the information on it does not match what the IDS says it is.
Response to Amendment
This action is in response to amendments and remarks filed on 10/20/2025. Claim(s) 1, 3-6, 12, 15-16, 18, and 20 have been amended. Claim(s) 2, 7-10, and 17 have been cancelled. Claim(s) 1, 3-6, 11-16, and 18-20 are pending examination. Objections to the specification have been withdrawn in light of the instant amendments. Rejection to claim(s) 2, 4-5, and 9 over the 112(b) and 112(d) rejection has been withdrawn in light of the instant amendments. This action is made final.
Response to Arguments
Applicant presents the following argument(s) regarding the previous office action:
Applicant asserts that the 35 USC 101 rejection of the claims is improper. Applicant argues that the claims are integrated into a practical application and that the claim is significantly more than an abstract idea.
Applicant asserts that the 35 USC 103 rejection of the claims is improper. Applicant asserts that the claim as amended is not taught by the prior cited art.
Applicant's arguments filed 10/20/2025 have been fully considered but they are not persuasive.
Regarding applicant’s argument A, the examiner respectfully disagrees. The examiner finds the independent claims, 1, 15, and 20; as merely using generic terms to carry out an abstract idea.
Regarding applicant’s argument about step 2A prong 2, the examiner disagrees with the applicant’s assertion that the claim is a specific technical solution to a problem. Applicant alleges that their solution improves search functionality and prevents so called “irrelevant search results.” Applicant cites Ex Parte Dumais et al. and Ex Parte Johnson et al. as evidence that this specific technical improvement is not an abstract idea. However, the claimed subject matter does not provide a better search result nor does it remove irrelevant searches. Applicant claims a generic generative AI system that they allege improves searches, based on vehicle and user specific information. But a user of a vehicle that would be taking steps to perform maintenance would indeed have all of this relevant information. They would merely perform the relevant searches. A user that has a torque wrench, for example, would not be required to search for such a tool if they wanted to perform maintenance that required a torque wrench, they could simply not perform that search. Similarly the user would not be required to search for Lexus parts if their car is a Ford, the user performing a mental process with a computer would merely not search these parts. As for creating the maintenance schedule based on vehicle information, again the user could perform this in their mind. A user that lives in a snowier environment could come to the conclusion that they need to perform maintenance regarding parts that are eroded by salt from the road likewise a user that drives their vehicle more often could reasonably conclude they should perform oil changes more regularly. Additionally, in citing Ex Parte Bostick et al. the applicant is arguing that their search results are ordered based on some level of expertise on the web page, however, this type of organization is not claimed, applicant merely presents to the user “a plurality of links to listing pages for the plurality of items.” There is no ordered of the results, it is merely a page of links. In light of these arguments, and the claims as amended, the examiner does not find the claims to be a practical application. The Ai model does not “enhance the performance of the search engine,” as the AI model merely is using the search engine like an end user would.
Regarding applicant’s argument about step 2B, the examiner does not find the claim to recite “an inventive concept.” Applicant alleges that their claim is technical solution using a unique ordering of steps. The steps of “receiving information,” and “accessing user-specific contextual information,” are merely data gathering steps. These steps are carried out by a computer recited at a high level of generality. The steps are followed by the use of a generic AI model that can “generate” and “query” based on the data gathered. The generative AI is also recited at a high level of generality. The generation of the maintenance schedule and the querying of the parts catalog to find relevant parts are things a person could do in a computer environment, Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360, and/or using a computer as a toll to perform a mental process, Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699. In both Symantec Corp and Mortgage Grader, the courts have found that using computers as tools for something a human could do in their mind is merely an extension of the abstract idea. Lastly applicant alleges that their system generates a user interface, however, this is not claimed. Rather the Ai merely presents links to webpages on a user interface. These steps are not a unique technical solution to the problem. Rather these are the steps any person that would want to performance maintenance on their vehicle would take. There is no specific element used rather generic computers and generic AI models are recited. In light of this the claims would not be eligible under step 2B.
In light of applicant’s arguments the examiner is not convinced. The claims are not subject matter eligible and would remain rejected under 35 USC 101. Please see the section below titled, “Claim Rejections – 35 USC 101,” for further explanation.
Applicant’s arguments with respect to claim(s) 1, 3-6, 11-16, and 18-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding applicant’s argument B, the examiner finds it moot. In light of the amendments and further search and consideration, the examiner would reject at least the independent claims as obvious in light of previously cited Kuznetsova in view of Johnson (US PG Pub 2023/0289744). Broadly, Johnson teaches a system and process of repairing a vehicle that has sustained structural or cosmetic damage. The system reviews reported repaired orders and compares those using artificial intelligence to an analysis of the vehicle. Repair procedures for the specific vehicle are then relayed to a user. The user is informed of any specialty equipment needed to perform a portion of that repair. The specialty equipment may then receive a repair program from the system to enable the repair. The use of the Generative AI enables the system to operate quicker and more efficiently than before. In light of the cited portions of Johnson the examiner would find at least the independent claims 1, 15, and 20 as obvious and would be rejected under 35 USC 103. The dependent claims would be rejected at least due to their dependence on rejected subject matter. Further detailed explanation and mapping can be found below in the section titled, “Claim Rejections – 35 USC 103.”
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-6, 11-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Analysis of the claim(s) regarding subject matter eligibility utilizing the 2019 Revised Patent Subject Matter Eligibility Guidance is described below.
STEP 1: STATUTORY CATEGORIES
Claim(s) 1, 3-6, 11-16, and 18-20 do fall into at least one of the four statutory subject matter categories. Claim 1 and its dependents are directed to a method which is the statutory category of a process. Claim 15 and its dependents are directed to a non-transitory computer readable medium which is the statutory category of a manufacture. Claim 20 is directed to a system which is the statutory category of a machine.
STEP 2A: JUDICIAL EXCEPTIONS
PRONG 1: RECITATION OF A JUDICIAL EXCEPTION
The claim(s) recite(s):
- Claims 1, 15, and 20 recite(s) an abstract idea belonging to the grouping of mental processes. As the claims are substantially similar only one explanation will be given. Claim 1 recites, “receiving information corresponding to a vehicle of a user;” “accessing user-specific contextual information;” “based on the information corresponding to the vehicle and the user-specific contextual information, generating, by a generative AI model a maintenance schedule corresponding to the vehicle;” “generating, by the generative AI model, an indication of a plurality of items for tasks included in the maintenance schedule;” “generating, by the generative AI model, a plurality of queries for the plurality of items;” “querying a keyword index using the plurality of queries to identify a plurality of item listings from an item database;” and “providing, for presentation on a user device of the user, a user interface presenting the maintenance schedule and a plurality of links to listing pages for the plurality of item listings.” A person using a generic computing device could reasonably accomplish this task using the mind. A person could reasonably receive vehicle information, assess their own skill level and/or vehicle environment, determine a maintenance schedule for the vehicle, and then recommend products/parts to perform this task and search results for the parts. The use of a generative AI when recited at such a high level is not enough to remove this task from a mental process or provide an improvement to the existing art. The list based on the maintenance schedule would be understood in the elements of claim 1, and using generative AI to recommend the purchase of “oil” for an “oil change” is nothing more than a mental process. Presentation of links to a purchase page is merely a generic computer environment to enable a purchase.
- Claim 3 recites, “wherein the information corresponding to the vehicle is input by the user via a search engine and communicated to the generative AI model.” This would be considered mere data gathering.
- Claim 4 recites, “wherein the information corresponding to the vehicle is received at the search engine from sensors of the vehicle via an application programming interface and communicated to the generative AI model.” This would be considered mere data gathering.
- Claim 5 recites, “wherein the information corresponding to the vehicle is received at the search engine from an On-Board Diagnostic II scanner via an application programming interface and communicated to the generative AI model.” This would be considered mere data gathering.
- Claim 6 recites, “wherein the user interface also presents a description of the tasks described in the maintenance schedule, a level of difficulty to perform the tasks, or instructions to perform the tasks.” A person with a generic computing device could accomplish this task with a generic computer tool.
- Claim 11 recites, “receiving event information corresponding to an upcoming event.” This would be considered mere data gathering.
- Claim 12 recites, “based on the upcoming event, modifying the maintenance schedule.” A person with a generic computing device could accomplish this task in their mind.
- Claim 13 recites, “receiving part order information corresponding to non-maintenance parts that have been ordered for other vehicles.” This would be considered mere data gathering.
- Claim 14 recites, “utilizing the part order information to predict non-maintenance parts of the vehicle that are likely to fail.” A person with a generic computing device could accomplish this task in their mind.
- Claims 16, and 18-19 are substantially similar to assorted claims 2-14. They would be rejected for the reasons recited above.
PRONG 2: INTEGRATION INTO A PRACTICAL APPLICATION
The additional element(s) recited in the claim(s) beyond the judicial exception are the use of generic search engines, generic computing devices, and generic AI models. The additional element(s) do not integrate the judicial exception into a practical application because the additional element(s) do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception and add insignificant extra-solution activity to the judicial exception. The computer elements are merely used as a tool to perform the abstract idea, and the use of the judicial exception is generally linked to the particular technological environment of vehicle maintenance without using the judicial exception in some other meaningful way (MPEP 2106.04(d)).
STEP 2B: INVENTIVE CONCEPT/SIGNIFICANTLY MORE
The additional elements recited in the claim(s) are not sufficient to amount to significantly more than the judicial exception because they do not add more than insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), and the computer functions of receiving and transmitting data have been recognized by the courts as well-understood, routine, and conventional functions when they are claimed in a merely generic manner or as insignificant extra-solution activity (MPEP 2106.05(d)). Further, the additional elements of a “memory” and a “processor” recited in the claim(s) are well-understood, routine, and conventional activities previously known to the industry, specified at a high level of generality (MPEP2106.05 (d)).
Based on the above analysis, claim(S) 1, 3-6, 11-16, and 18-20 is/are not eligible subject matter and is/are rejected -under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 3, 6, 11-13, 15-16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsova (US Pat 8, 185, 446) in view of Johnson (US PG Pub 2023/0289744).
Regarding claim 1, Kuznetsova teaches a method comprising:
receiving information corresponding to a vehicle of a user; (Figs. 2-7 item 201 and Col. 6, lines 38-42; Col. 7, lines 39-45; teach a user inputting information into a website)
accessing user-specific contextual information comprising at least one of: a skill set of the user, a purchase history of the user, a driving pattern associated with the user or the vehicle, a climate or geographical location associated with the vehicle, or event information associated with the user or the vehicle; (Col. 3, lines 17-26; teach the system storing and using the order history of a user. Fig. 5 and Col. 7, lines 39-47; teach the vehicle information being the mileage of the vehicle in question. Col. 7, lines 48-61; teach receiving information about an upcoming maintenance event of a vehicle based on historical vehicle usage data)
based on the information corresponding to the vehicle and the user-specific contextual information, generating (Col. 4, lines 19-21; and Col. 5, lines 23-49; teach the system pulling maintenance data on a vehicle to determine the next maintenance task for a user to complete, this can include regular and otherwise necessary maintenance)
generating(Col. 5, lines 23-49; teach providing the user with the maintenance task next on the schedule as well as the parts necessary to complete the task, known as a parts bundle.)
;
querying a keyword index using the plurality of queries to identify a plurality of item listings from an item database; (Col. 5, lines 4-23; teach the system as able to query various databases, including parts databases, in order to identify a series of parts/tools needed for the maintenance task) and
providing, for presentation on a user device of the user, a user interface (Col. 6, lines 35-47; teach the system having a user interface for presenting data to the user) presenting the maintenance schedule and a plurality of links to listing pages for the plurality of item listings. (Col. 7, lines 20-47; teach displaying the maintenance schedule as well as the parts bundles with links to allow the user to purchase the parts. Col. 6, lines 48-56; further teaches the system presenting the hyperlinks to the user.)
Kuznetsova does not teach by a generative AI model, and generating, by the generative AI model, a plurality of queries for the plurality of items.
However, Johnson teaches “a generative AI model,” ([0030], [0033], and [0057] teach an AI module that can generate content specific to the situation, in this case, vehicle repair) and “generating, by the generative AI model, a plurality of queries for the plurality of items.” ([0035]-[0036] and [0038] teach the system as whole with the AI as able to query various databases in order to determine the parts/tools available for the repair. As the system can perform the queries it is implicitly disclosed that the system can generate the query.)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kuznetsova and Johnson; and have a reasonable expectation of success. Both relate to systems that present vehicle repair schedules, parts, and other general maintenance systems. As Johnson teaches in [0030] the use of the repair AI model allows the system to evaluate all potential maintenance pathways and further determine what the best steps for the vehicle maintenance going forward would be. This allows the user to save time/money in trying to figure out how to service their vehicle. Additionally, the claims would be obvious as they are merely Automating a manual activity, MPEP 2144.04.III. As seen with In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), where the court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art. (Emphasis added). The applicant has broadly provided a generative AI in order to generate maintenance schedules and search queries. The AI is recited as a very broad level; therefore the examiner finds it to be merely automating the manual activity of searching and determining the maintenance cycle of a vehicle. As this is a well known manual activity, see Johnson [0029], which teaches maintenance procedures are normally provided by the manufacturer, i.e. a person.
Claims 15 and 20 are substantially similar and would be rejected for the same rationale as recited above.
Regarding claim 3, Kuznetsova teaches the method of claim 1, wherein the information corresponding to the vehicle is input by the user via a search engine and communicated to the generative AI model. (Col. 6, lines 38-47; teach allowing a user to input vehicle information into an ecommerce website, which then can search for and retrieve vehicle information.)
Again Kuznetsova does not teach the use of Gen AI, but for the reasons recited above the incorporation of Johnson would remedy this.
Regarding claim 6, Kuznetsova teaches the method of claim 1, wherein the user interface also presents a description of the tasks in the maintenance schedule, a level of difficulty to perform the tasks, or instructions to perform the tasks. (Col. 4, lines 52-55; Col. 6, lines 24-25; teach the user receiving information relating to the task at hand including a description of the maintenance task to be carried out)
Regarding claim 11, Kuznetsova teaches the method of claim 1, further comprising receiving event information corresponding to an upcoming event. (Col. 7, lines 48-61; teach receiving information about an upcoming maintenance event of a vehicle based on historical vehicle usage data)
Regarding claim 12, Kuznetsova teaches the method of claim 11, further comprising, based on the event information corresponding to the upcoming event, modifying the maintenance schedule. (Col. 7, lines 48-61; teach modifying the recommended maintenance schedule by recommending parts earlier than they would otherwise be needed)
Regarding claim 13, Kuznetsova teaches method of claim 1, further comprising receiving part order information corresponding to non-maintenance parts that have been ordered for other vehicles. (Col. 7, lines 14-20; teach determining the parts ordered for other vehicles during their maintenance tasks and recommending them for the current vehicle)
Regarding claim 16, Kuznetsova teaches the one or more non-transitory computer storage media of claim 15, wherein the information corresponding to the vehicle is: input by the user via a search engine and communicated to the generative AI model, received at the search engine from sensors of the vehicle via an application programming interface and communicated to the generative AI model, or received at the search engine from an On-Board Diagnostic II scanner via an application programming interface and communicated to the generative AI model. (Col. 6, lines 38-47; teach allowing a user to input vehicle information into an ecommerce website, which then can search for and retrieve vehicle information.)
Again Kuznetsova does not teach the use of Gen AI, but for the reasons recited above the incorporation of Johnson would remedy this.
Regarding claim 18, Kuznetsova teaches the one or more non-transitory computer storage media of claim 15, wherein the operations further comprise: receiving event information corresponding to an upcoming event; (Col. 7, lines 48-61; teach receiving information about an upcoming maintenance event of a vehicle based on historical vehicle usage data) and based on the event information corresponding to the upcoming event, modifying the maintenance schedule. (Col. 7, lines 48-61; teach modifying the recommended maintenance schedule by recommending parts earlier than they would otherwise be needed)
Claim(s) 4-5, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsova and Johnson in view of Lavie (US PG Pub 2016/0133066).
Regarding claim 4, the combination of Kuznetsova and Johnson teaches the method of claim 1.
The combination of Kuznetsova and Johnson fails to teach wherein the information corresponding to the vehicle is received at a search engine from sensors of the vehicle via an application programming interface and communicated to the generative AI model.
However, Lavie teaches “wherein the information corresponding to the vehicle is received at a search engine from sensors of the vehicle via an application programming interface and communicated to the generative AI model.” ([0049]-[0050] and [0059]-[0074] teach the usage of sensor data input into a system to determine maintenance needs)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kuznetsova and Johnson with Lavie; and have a reasonable expectation of success. All relate to vehicle maintenance programs with the ability to determine the next maintenance step for a vehicle. As Lavie teaches in [0045] the usage of an OBD II and receiving information from the sensors of a vehicle allows the system to monitor the parts of a vehicle and ensure that the vehicle is operating efficiently. Using this data over time the system can predict the failure and maintenance needs of a vehicle, [0051].
Regarding claim 5, the combination Kuznetsova and Johnson teaches the method of claim 1.
The combination of Kuznetsova and Johnson fails to teach wherein the information corresponding to the vehicle is received at a search engine from an On-Board Diagnostic II scanner via an application programming interface and communicated to the generative AI model.
However, Lavie teaches “wherein the information corresponding to the vehicle is received at a search engine from an On-Board Diagnostic II scanner via an application programming interface and communicated to the generative AI model.” ([0045] teaches the use of OBDII sensors in order to receive vehicle data)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kuznetsova and Johnson with Lavie; and have a reasonable expectation of success. All relate to vehicle maintenance programs with the ability to determine the next maintenance step for a vehicle. As Lavie teaches in [0045] the usage of an OBD II and receiving information from the sensors of a vehicle allows the system to monitor the parts of a vehicle and ensure that the vehicle is operating efficiently. Using this data over time the system can predict the failure and maintenance needs of a vehicle, [0051].
Regarding claim 14, the combination of Kuznetsova and Johnson teaches the method of claim 13.
The combination of Kuznetsova and Johnson fails to teach further comprising utilizing the part order information to predict non-maintenance parts of the vehicle that are likely to fail.
However, Lavie teaches “further comprising utilizing the part order information to predict non-maintenance parts of the vehicle that are likely to fail.” ([0051] and [0130] teach the system of determining a prediction of maintenance, and when it is needed which would be analogous to predicting the failure of a component)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kuznetsova and Johnson with Lavie; and have a reasonable expectation of success. All relate to vehicle maintenance programs with the ability to determine the next maintenance step for a vehicle. As Lavie teaches in [0045] the usage of an OBD II and receiving information from the sensors of a vehicle allows the system to monitor the parts of a vehicle and ensure that the vehicle is operating efficiently. Using this data over time the system can predict the failure and maintenance needs of a vehicle, [0051].
Regarding claim 19, the combination of Kuznetsova and Johnson teaches the one or more non-transitory computer storage media of claim 15.
The combination of Kuznetsova and Johnson fails to teach further comprising utilizing part order information corresponding to non-maintenance parts that have been ordered for other vehicles to predict non-maintenance parts of the vehicle that are likely to fail.
However, Lavie teaches “further comprising utilizing part order information corresponding to non-maintenance parts that have been ordered for other vehicles to predict non-maintenance parts of the vehicle that are likely to fail.” ([0051] and [0130] teach the system of determining a prediction of maintenance, and when it is needed which would be analogous to predicting the failure of a component)
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the teachings of Kuznetsova and Johnson with Lavie; and have a reasonable expectation of success. All relate to vehicle maintenance programs with the ability to determine the next maintenance step for a vehicle. As Lavie teaches in [0045] the usage of an OBD II and receiving information from the sensors of a vehicle allows the system to monitor the parts of a vehicle and ensure that the vehicle is operating efficiently. Using this data over time the system can predict the failure and maintenance needs of a vehicle, [0051].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yates (US PG Pub 2025/0094513) teaches methods and systems provide for dynamically optimized recommendations in generative media. In one embodiment, the system receives, through a conversational interface, input submissions from a user engaging in a conversation with a generative artificial intelligence (AI) system; generates, via the generative AI system, a search query for a search engine backend of the platform; sends the search query to the search engine backend of the platform to retrieve at least a subset of a prompt as input to the generative AI system, the subset of the prompt including a sorted list of search results from the search engine backend; processes the prompt to generate a set of personalized recommendations for the user; and presents, within the platform presented at the client device, the set of personalized recommendations for the user, the presentation incorporating media content representing at least a portion of the search result items.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, 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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS STRYKER whose telephone number is (571)272-4659. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christian Chace can be reached at (571) 272-4190. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.S./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665