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 .
1. Applicant’s amendment filed 04/06/2026 is entered. Claims 1, 10, 16, and 19 are currently amended. Claims 1, 10, and 19 are independent claims. Claims 2-9 depend from claim 1, claims 11-18 depend from claim 10, and claim 20 depend from claim 19.
Claim Rejections - 35 USC § 101
2. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more, when analyzed as per MPEP 2106.
Step 1 analysis:
Claims 1-9 are to a process comprising a series of steps, clams 10-18 to a system /apparatus, and claims 19-20 are to manufacture, which are statutory (Step 1: Yes).
Step 2A Analysis:
Claim 1 recites:
1. (Currently Amended) A method for recommending vehicle groups, comprising:
(i) receiving, by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user, wherein the vehicle click data is received from an external server;
(ii) determining, by the vehicle recommendation system, one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data;
(iii) providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture; [[and]]
(iv) receiving, by the vehicle recommendation system from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups; and
(v) providing the rankings of the one or more predicted vehicle groups to the external server for causing a user device to display the rankings, wherein the rankings indicate a vehicle group with a highest probability of being selected by the user device.
Step 2A Prong 1 analysis: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
Claims 1-20 recite abstract idea.
The highlighted limitations of claim 1 in steps (ii) (iii)and (iv) comprising, “ determining one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data; providing the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture; and rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups”, under their broadest reasonable interpretation, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. Step (ii) recites detecting one or more input vehicle groups from a collected data on one or more vehicle IDs of the vehicle click data Under its broadest reasonable interpretation when read in light of the specification, the “detecting” encompasses mental observations of a collected data and making simple evaluations of grouping the vehicle IDS based on comparing the collected history of vehicle click data. Limitations in step (iii) merely recite determining data to be input and feeding the same into a mathematical model [Machine learning model including a transformer architecture] which can be performed by a human operator. Limitations in step (iv) recite processing a mathematical model land then making a simple decision of ranking the vehicle groups based on comparing their similarities. Under their broadest reasonable interpretation , the limitations in step (iv) can be performed mentally using a pen and paper using mathematical models to output ranked results of the vehicles based on their similarities. See MPEP 2106.04(a)(2), subsection III. See MPEP 2106.04(a)(2) Abstract Idea Groupings [R-07.2022] II. MENTAL PROCESSES: claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); • a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011).
Thus claim 1, with its dependent claims 2-9 recite an Abstract idea. Since the limitations of the other two independent claims10 and 19 recite limitations similar to claim 1, they are analyzed on the same basis as claim 10 with its dependent claims 11-18 and claim 19 with its dependent claim 20 recite an abstract idea. (Step 2A, Prong One: YES).
Step 2A Prong 2 analysis: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Claims 1-20: The judicial exception is not integrated into a practical application.
Claim 1 recites the additional limitations of using generic computer components as parts of a vehicle recommendation system implementing the steps: (i) receiving, by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user, wherein the vehicle click data is received from an external server; (ii) determining, by the vehicle recommendation system, one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data; (iii) providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture; (iv) receiving, by the vehicle recommendation system from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups; and v) providing the rankings of the one or more predicted vehicle groups to the external server for causing a user device to display the rankings, wherein the rankings indicate a vehicle group with a highest probability of being selected by the user device.
The limitations in steps (i), (iv) and (v) “(i) receiving, by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user, wherein the vehicle click data is received from an external server; ( (iv) receiving, by the vehicle recommendation system from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups; and v) providing the rankings of the one or more predicted vehicle groups to the external server for causing a user device to display the rankings, wherein the rankings indicate a vehicle group with a highest probability of being selected by the user device. “, are mere data receiving/gathering and output/providing/displaying recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data collecting/gathering and outputting/displaying/transmitting, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering, outputting, transmitting and displaying. See MPEP 2106.05. Further, these limitations are recited as being performed by a computer [vehicle recommendation system] recited at a high level of generality and the computer is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05.
In the limitations in steps (ii), (iii), and iv}, “ (ii) determining, by the vehicle recommendation system, one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data; (iii)providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture; (iv) receiving, by the vehicle recommendation system from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups;”, the computer [vehicle recommendation system] is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The limitations in (iii) and (iv) reciting “using the ML model comprising a transformer architecture” provides nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. The judicial exception of “using a ML model comprising a transformer architecture for the input vehicle groups data “and “ performing the mathematical calculations to provide rankings” amounts to generally apply the abstract idea without placing any limits on how the Machine learning functions. Rather, these limitations only recite the outcome of “performing a mathematical calculation” and “outputting the results in the form of rankings by comparing the similarity metrics of the vehicle groups ” and do not include any details about how these steps are accomplished. See MPEP 2106.05(f). Thus, the recitation of “using a ML model” in limitations (iii) and (iv) also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “using a ML model” limits the identified judicial exceptions , this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Even when viewed in combination, the additional elements in claim 1 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Since the limitations of the other two independent claims 10 and 19 recite similar limitations as claim 1, they are analyzed on the same basis as directed to an abstract idea.
Examiner has reviewed the dependent claims 2-9, 11-18 and 20 and their limitations merely expand the scope of the limitations already discussed for the base claims 1, 10, and 19 without adding any meaningful limits on practicing the abstract idea. Claims 2, 4-5, 7, 8 9, and 11, 13-14, 16, 17, 18 and 20 recite limitations, under their broadest reasonable interpretation, cover performance I mind, as analyzed for the limitations of their base claims 1,10, and 19. Limitations of dependent claims 3, 6, 12 and 15 are directed to non-significant extra solution activity of providing/transmitting data. Claims 9, 18 and 20 also recite the limitations of encoding input data of vehicle groups into one or more numerical representations, which, under their broadest reasonable interpretation, relates to simply integer assignments identifying similarities for machine learning and such simple encoding technique can be practiced manually. The claim limitations do not provide any details reflecting any technical improvement over existing encoding techniques.
Even when viewed individually and in combination, the additional elements, as recited, in claims 1-20 do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2A=Yes. Claims 1-20 are directed to abstract ideas.
Step 2B analysis: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Since claims are as per Step 2A are directed to an abstract idea, they have to be analyzed per Step 2B, if they recite an inventive step, i.e., the claim recite additional elements or a combination of elements that amount to “Significantly More” than the judicial exception in the claim.
As discussed above with respect to Step 2A Prong Two, the additional elements in the claims 1-20 amount to no more than mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use. The same analysis applies here in 2B, i.e., mere instructions to apply the exception using a generic computer components, and generally linking the judicial exception to a particular technological environment or field of use using a generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The additional elements of using ML model for inputting the vehicle groups including encoding the vehicle groups into numerical representations, and then providing ranking results for recommendation is Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Additional elements including data receiving , providing, outputting/displaying were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering/transmitting/ outputting/ displaying/presenting/storing data . However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). ). The background of the example does not provide any indication that the computer components are anything other than a generic, off the shelf computer component and the Symantec, TLI, OIP Techs, Versata court decisions cited in MPEP 2106.05(d) (ii) indicate that mere data gathering/ transmitting/ outputting/displaying/presenting steps using a generic computer are well-understood, routine, conventional function when they are claimed in a merely generic manner (as it is here).
Accordingly, a conclusion that the receiving, acquiring, transmitting, and outputting/ displaying steps are well-understood, routine conventional activities are supported under Berkheimer Option 2. See MPEP 2106.05 (f) 2: Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
Even when considered individually and in combination, the additional elements in claims 1-20 represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Thus claims 1-20, as drafted, recite patent ineligible subject matter.
3. Prior art discussion:
The best prior art combination of Dana et al. [US 20240273599 A1]; hereinafter Dana in view of Gupta et al. [US 20230080589 A1]; hereinafter Gupta cited in the Non-Final Rejection mailed 12/05/2025 fails to disclose the limitations, as a whole, comprising “ receiving by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user, wherein the vehicle click data is received from an external server, providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture, receiving, by the vehicle recommendation system from the ML model, rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups”. [See independent claims 1, 10, and 19. Claims 2-9 depend from claim 1, claims 11-18 depend from claim 10, and claim 20 depends from claim 19.
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(i) Duan et al. [ US 20210149971 A1; see para 0002] describes a method comprising receiving user interface interaction data one or more vehicle search engines that display images of a set of vehicles including a specific part of an image of a vehicle. Based on this received data, the system using a data model trained using machine learning to process image data that depicts at least a portion of the vehicle, an array of vectors that includes one or more vectors that represent the set of vehicle characteristics of the vehicle and then assigning one or more weights to the one or more vectors based on the user interface interaction data. The method further determines and selects a subset of the images of the set of vehicles based on the set of similarity scores which are displayed via at least one of the one or more interfaces of the vehicle search engines.
(ii) Ding [US 20220405686 A1 cited in the Non-Final Rejection mailed 12/05/2025; see para 0096] describes a system for providing content recommendations, wherein a prediction-machine-learning model is utilized which encodes the features into numerical or mathematical representations.
(iii) Ramanuja et al. [US 20160364 783 A1 cited in the Non-Final Rejection mailed 12/05/2025; see paras 0009, 0113 and 0153 describe using similarity vector to rank the inventory vehicles and provides the recommendation based on the similarity scores. The system uses machine learning models.
Foreign reference:
(iv) CA 3051263 cited in the Non-Final Rejection mailed 12/05/2025 [see para 0057] describes a server 140 applying machine learning model to calculate matching scores and for ranking the vehicle recommendations based on the user’s preferences.\
NPL references:
(v) R. Alabduljabbar, M. Alghamdi and H. Alshamlan, "Personalized Car Recommendations Using Knowledge-Based Methods," 2023 Intelligent Methods, Systems, and Applications (IMSA), Giza, Egypt, 2023, pp. 539-544, retrieved from IP. Com on 11303025 and cited in the Non-Final Rejection mailed 12/05/2025describes [see Abstract and page 540] a system utilizing a variety of car features, including brand, color, year, gear type, number of seats, and price, to provide personalized recommendations for the user based on his preferences and needs, such as car capacity, fuel type, and budget, are considered to recommend cars to the user. These recommendations are generated using machine learning techniques, and distinct visualization options are available to provide users with detailed analyses based on various parameters.
(vi) S. Priyanka, N. Abinaya, S. Keerthika, S. Santhiya, P. Jayadharshini and B. Vinothini, "Product Recommendation System Using Machine Learning," 2024 2nd International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 2024, pp. 1515-1518, retrieved from IP. Com on 11303025 , and cited in the Non-Final Rejection mailed 12/05/2025describes [see Abstract and page 540] a system applying a machine learning application that suggests products that users may purchase or engage with.
Response to Arguments
5.1. Applicant's arguments filed 04/06/2026, see pages 7-15 against rejection of claims under 35 USC 101 have been fully considered but they are not persuasive for following reasons:
Examiner’s response has considered claim1 as an exemplary claim to respond to the applicant’s arguments.
Step2A, Prong One:
Applicant’s arguments on page 7 of remarks have been noted but as presented above the claim limitations do recite “Mental Processes” grouping of abstract idea, because the limitations in unamended steps (ii) and (iv) “ determining one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data, and rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups:; under their broadest reasonable interpretation, fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. That is, other than reciting “by a vehicle recommendation system” nothing in the claim elements precludes the step from practically being performed in the mind, as explained in detail above. Thus, the claim 1 does recite an abstract idea.
Step 2A, Prong Two:
Examiner has considered the Applicant’s arguments fully on pages 7-12 but respectfully disagrees for following reasons:
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim 1 recites the additional elements of a generic computer system [vehicle recommendation system] implementing the steps of (i) receiving browsing history of a user, ……wherein the vehicle click data is received from an external server; (ii) determining one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data; (iii) providing the one or more input vehicle groups to a machine learning (ML) model, wherein the ML model includes a transformer architecture; (iv) receiving rankings of one or more predicted vehicle groups based on similarities of the one or more predicted vehicle groups to the one or more input vehicle groups; and v) providing the rankings of the one or more predicted vehicle groups to the external server for causing a user device to display the rankings, …….”, which have been analyzed in paragraph 2 above under 35 USC 101 rejection are mere data receiving/gathering and output/providing/displaying recited at a high level of generality, and thus are insignificant extra-solution activity and ‘Mental Processes”. The generic computer s used in all these limitations at a high level of generality using it as a tool to perform generic computer functions and as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
Examiner has fully considered the Applicant’s arguments on page 8 but respectfully disagrees with the Applicant’s arguments that the “Ex prate Desjardins (Precedential, November 2025)” is applicable to the limitations of claim 1, because here the recitation of using machine learning model with a transformer architecture is applied to automate a manual process of determining rankings of vehicle groups based on their similarities, as detailed above in paragraph 2 under 35 USC 101 rejection and does not recite any steps or functions improving the functioning of the machine learning model with transformer architecture.
Examiner has fully considered the Applicant’s arguments on pages 9-11 but respectfully disagrees with the Applicant’s arguments that the limitations recite, “ Specific Improvement to Distributed System Interaction Through External Server Integration”, “Technical Improvement Through Machine-Learning Model Architecture Producing Probability-Based Rankings”, and “ Bidirectional Data Exchange Between Vehicle Recommendation System and External Server “.because , as discussed above, even when viewed in combination, the additional elements do not integrate the recited judicial exception into a practical application because they do not add any meaningful limits on practicing the abstract idea. (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). The use of Machine learning model with a transformer architecture merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. Further, using Machine learning model with a transformer architecture to provide rankings is merely amounts to applying ab abstract idea to a computer and the claim limitations are not directed to an improvement in the functioning machine learning model with the transformer architecture or to any other technical improvement in the functions of a computer except for reciting generic computer functions, as detailed above. Further, the additional elements, as discussed above in detail, of receiving data related to vehicle clicks and providing ranking or providing inputs to a model are generic computer functions comprising insignificant extra-solution activity, and automating manual activity. The recitation of using a machine learning model with a transformer architecture is nominal amounting to merely apply an abstract idea of providing rankings for a vehicle group without providing details of technical improvement in a computer functioning or in an improvement in the implementation of the machine learning model.
Examiner respectfully disagrees with the Applicant’s comparison with DDR Holdings case, because the claims in DDR Holdings dealt with a problem unique to the Internet: Internet users visiting one web site might be interested in viewing products sold on a different web site, but the owners of the first web site did not want to constantly redirect users away from their web site to a different web site. The claimed solution used a series of steps that created a hybrid web page incorporating “look and feel” elements from the host web site with commerce objects from the third-party web site. Id. The patent at issue in DDR provided an Internet-based solution to solve a problem unique to the Internet that (1) did not foreclose other ways of solving the problem, and (2) recited a specific series of steps that resulted in a departure from the routine and conventional sequence of events after the click of a hyperlink advertisement. The patent claims here do not address problems unique to the Internet, so DDR has no applicability, but instead they recite limitations directed to “Mental Processes” and resulting in marketing/sales l related benefits in providing rankings for a vehicle group with the highest probability of being selected by a user.
Examiner has fully considered the Applicant’s arguments on pages 11-12 but respectfully disagrees with the Applicant’s response to Mental process analysis, because the applicant’s arguments relate to receiving data, which Examiner has not considered a mental process. Merely providing an input to a model can be done manually by a human . Further, the Applicant’s arguments, “ A human cannot implement a transformer architecture with millions of learned parameters that encode data into high-dimensional vector spaces.”, are not relevant because the Examiner, in his analysis, does not state that performing a machine learning model with a transformer architecture is a mental process. Instead, Examiner has considered the implementation of the machine learning model with a transformer architecture as an additional element in the analysis of Step 2A, Prong Two as a generic function merely applying it to the abstract idea of providing a ranking of one or more predicted vehicle groups because the limitations do not recite details on any technical improvement in the computer functioning or in the functioning of the model. Using machine learning model with a transformer architecture for analyzing data is a known practice before the effective date of the claimed invention and the claim limitations are not directed to an improvement in the in the computer functioning or in the functioning of the model.
Step 2B:
Examiner has fully considered the Applicant’s arguments on pages 12-13 and respectfully disagrees with them, because the Bascom claims do not apply here. In Bascom, although the claims were directed to the abstract idea of filtering content, the inventive concept inquiry requires more than recognizing that each claim element, by itself, was known in the art. In Bascom the claims contained ‘significantly more” than an abstract idea of ‘filtering content because they recited a separate point of novelty that is : installation of filtering tool at a specific location on Internet of the content, remote from end users, with customizable filtering features specific to each end user. Bascom does not apply in this case. In contrast the claims in the instant application merely implement mental processes of analyzing vehicle click data and analyzing them to predict rankings having a high probability of being selected by a user known machine learning model without reciting any technical improvement to the computer functioning or in the implementation of the machine learning model. Simply appending well-understood, routine, conventional activities, such as machine learning model with a transformer architecture, previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)).
Even when considered in combination, the additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Applicant has not filed separate arguments against other claims and as such the balance claims 2-10 rise and fall with the analysis of claim 1. In view of the foregoing, the rejection of all pending claims 1-20 under 35 USC 101 is sustainable and maintained.
5.2. Rejection of claims under 35 USC 103:
Applicant’s arguments, see pages 13-15, filed 04/06/2026, with respect to rejection of claims 1-20 have been fully considered and are persuasive in view of the current amendments to the independent claims 1, 10, and 19. The rejection of claims 1-20 under 35 USC 103 has been withdrawn.
6. Note: Allowability: If the independent claims 1, 10, and 19 are amended to overcome 35 USC 101 rejection, the claims 1-20 can be placed in condition for allowance. All amendments will be subject to reconsideration and search.
Conclusion
7. Final Rejection:
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 YOGESH C GARG whose telephone number is (571)272-6756. The examiner can normally be reached Max-Flex.
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/YOGESH C GARG/Primary Examiner, Art Unit 3688
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