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. Claims 1-20 filed 03/29/2024 are pending for examination.
2. Continuity: This application does not claim continuity to parent applications.
Claim Rejections - 35 USC § 101
3. 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. 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;
determining, by the vehicle recommendation system, one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data;
providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model; and
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.
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.
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; and receiving 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 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; and receiving 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”, 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 steps (iii) and (iv) recite using Machine learning model to process the determined one or more vehicle groups from the determining step and obtain results from processing a mathematical model and then making a simple decision of ranking the vehicle groups based on comparing their similarities. Under their broadest reasonable interpretation , the limitations in steps (iii) and (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; (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; 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.
The limitations in steps (i), (iii) and (iv) : (i) receiving, by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user; (iii) providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model; 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”, are mere data gathering, inputting and outputting 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 gathering/receiving/inputting and output/display/provide, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05. Further, the limitations in steps (i), (ii), (iii), and (iv) are recited as being performed by a computer of the vehicle recommendation system [See Figs 1 and 2] . The computer is recited at a high level of generality and is in steps (i), (iii) and (iv) the computer is used as a tool to perform the generic computer functions of receiving data, providing/inputting and outputting/providing/displaying data . See MPEP 2106.05(f). In limitations (ii), (iii) and (iv) the computer 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” 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 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.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
4.1. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dana et al. [US 20240273599 A1]; hereinafter Dana in view of Gupta et al. [US 20230080589 A1]; hereinafter Gupta.
Regarding claim 1, Dana teaches 1. A method for recommending vehicle groups [See Abstract [“a recommendation system may receive information related to a browser context associated with the client device. The recommendation system may generate a vehicle feature vector that includes an array of elements to represent a plurality of vehicle attributes. The recommendation system may apply a similarity model to the vehicle feature vector to determine a vehicle recommendation dataset that includes a plurality of vehicles that are each associated with a respective set of vehicle attributes.”, comprising:
receiving, by a vehicle recommendation system, browsing history of a user, the browsing history including vehicle click data of the user; determining, by the vehicle recommendation system, one or more input vehicle groups based on one or more vehicle IDs of the vehicle click data [See Abstract and para{ 0022 Abstract :” a recommendation system may receive information related to a browser context associated with the client device. The recommendation system may generate a vehicle feature vector that includes an array of elements to represent a plurality of vehicle attributes. The recommendation system may apply a similarity model to the vehicle feature vector to determine a vehicle recommendation dataset that includes a plurality of vehicles that are each associated with a respective set of vehicle attributes. The recommendation system may filter the vehicle recommendation dataset based on a subset of the information related to the browser context associated with the client device that indicates a profile of a user associated with the client device. The recommendation system may provide information related to the vehicle recommendation dataset to the client device for display in an interface of the client device.’; and para 0022 “ 0022] In some implementations, to map the browser context received from the client device to the browser-based vehicle preference dataset, the recommendation system may obtain the browsing history of a user on the user interface and determine vehicle attributes associated with one or more past or recently viewed vehicles based on subset of the browser interactions with content related to vehicles. For example, in some implementations, the recommendation system may determine the vehicle attributes associated with the N past or recently viewed vehicles, where N may generally be a positive integer value (e.g., 1, 3, 5, 10, 20, 100, or another suitable quantity). For example, in some implementations, the recommendation system may analyze the browser history or browsing habits recorded in the browser context to identify vehicles that the user viewed based on interactions with known vehicle manufacturer or dealer websites, consumer report websites, vehicle research websites, vehicle valuation websites, used car websites, or the like. Additionally, or alternatively, the vehicles viewed by the user may be determined based on interactive sessions in which the user configured or explored different combinations of vehicle attributes (e.g., configuring the same vehicle with a V4 and a V6 engine) and/or advertising impressions or advertising click-throughs (e.g., an advertising impression may indicate one or more vehicle attributes that an advertising system determined to be potentially relevant to the interests of the user, and an advertising click-through may indicate one or more vehicle attributes that are confirmed to be relevant to the interests of the user. These excerpts describe a system receiving, browsing history of a user, the browsing history of vehicles including vehicle click data of the user and from the collected vehicle browsing history including click data determining vehicle groups by attributes such as manufacturer, or a specific dealer or type of engine that V4 or V6 etc. based on similarity metrics. Dana teaches , see para 0017 that the vehicle groups can be formed based on Vehicle IDs by “ make, model, year, fuel efficiency, mileage, price, engine or motor type, fuel type, drive train, exterior color, body style, condition, and/or transmission, among other examples.”. Note: The Applicant’s Specification, see para 0025 explains that the vehicle groups are formed by Vehicle IDs such as vehicle year, make, vehicle model etc. e vehicle IDS ).
Reference limitations, “ providing, by the vehicle recommendation system, the one or more input vehicle groups to a machine learning (ML) model; and 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”, Dana teaches [see paras 0029—0031” 0029] In some implementations, the recommendation system may use the techniques described herein to generate the vehicle feature vector based on the browser-based vehicle preference dataset or based on a combination of the browser-based and user-specified vehicle preference datasets. ……[0030] For example, in some implementations, the similarity model may be a cosine similarity model. In some implementations, the cosine similarity model may be configured to minimize the cosine distance between the vehicle feature vector derived from the weighted feature dataset and a vehicle represented as a vector. In some implementations, the recommendation system may perform cosine distance calculations between the vehicle feature vector based on the user's preferences and each vector that represents a vehicle available in a vehicle inventory. In this manner, a vehicle in the vehicle inventory having a set of attributes most similar to the vehicle feature vector may be determined by the recommendation system. ……. determining one or more similarity values between the feature representations for each respective vehicle and the vehicle feature vector representing the user's preferences, and selecting one or more vehicles corresponding to a subset of the feature representations having the closest determined similarity values (e.g., the lowest cosine distances). As a result, a value between 0 and 1 may be associated with each vehicle in the vehicle inventory, where the vehicles associated with relatively greater values (closer to 1) are representative of vehicles that are more similar to the ideal vehicle represented by the vehicle feature vector derived, at least in part, from the browser context associated with the client device.[0031] ……., the vehicle recommendation dataset may generally include information related to one or more vehicles that are available in a vehicle inventory associated with one or more vehicle manufacturers, dealers, or other sellers that are potentially relevant to the user (e.g., that the user may be interested in purchasing), which are identified based on the browser-based vehicle preference data or a combination of the browser-based vehicle preference data and the user-specified vehicle preference data. In particular, vehicles that are available in a vehicle inventory accessible to the recommendation system may be ranked according to their similarity (e.g., based on a cosine distance) to the vehicle feature vector that is based on the user vehicle preferences. ……] that the recommendation system applies the similarity models to the vehicle groups identified based on make, model, year, fuel efficiency, mileage, price, engine or motor type, fuel type, drive train, exterior color, body style, condition, and/or transmission, etc., and provide recommendations for the most similar vehicles. The similarity models may be a cosine model and based cosine distance the vehicles are ranked according to their similarity. Dana fails to teach that the similarity model is a machine learning model [ML model]. Gupta in the same field of recommending vehicles based on browsing history teaches using machine learning model and ranks the recommended vehicles [See Abstract and paras 0025, and 0048 “ A machine learning model can determine respective embeddings for the vehicle attribute values and the respective embeddings can be concatenated, where the concatenated embeddings represent the user-specified vehicle in one embedding. The system can determine similarity metrics of the concatenated embeddings against reference embeddings. For example, a cosine similarity value can be determined for the concatenated embedding of the user-specified vehicle and the respective reference embeddings. Each similarity metric can represent a measure of similarity between the user-specified vehicle and a given vehicle. The vehicle recommendation system provides for display identifiers of vehicles that are ranked based on the determined similarity metrics.”; [0025] Vehicle recommendation systems described herein (e.g., vehicle recommendation system 110 or 140) determine similarities between vehicles. …… The vehicle may be automated, semiautomated, or manually operated. The vehicle recommendation systems use a machine learning model (e.g., a neural network) to determine numerical or alphanumerical representations, or “embeddings,” of vehicle attributes. The machine learning model is trained to represent a variety of vehicle attributes (e.g., various colors of vehicles) in a latent space, where the vehicle recommendation system reduces the dimension of the latent space to improve processing efficiency while minimizing a loss function to maintain accurate numerical representations of attributes. Further, the dimension reduction enables the vehicle recommendation system to achieve more accurate information about a user’s preference, where similar attributes are closer in the latent space and more likely to be preferred by a user. For example, while the colors white and gray do not match lexically, but when their embeddings are projected in lower dimensions, the embeddings will be similar. This may indicate that the colors are similar and that the user could prefer both colors. The vehicle recommendation systems use the embeddings to determine similarity metrics (e.g., vector similarities between embeddings) to determine quantitative measures by which other vehicles are similar to a particular vehicle (e.g., specified using a vehicle identification number (VIN) by the user).”; and para 0048] GUI module 240 may display identifiers of one or more vehicles ranked based on similarity metrics determined by embedding similarity module 235. ……..).
Reference claims 2 and 4, the limitations, “The method of claim 1, further comprising determining, by the vehicle recommendation system, a first predicted vehicle group of the one or more predicted vehicle groups, the first predicted vehicle group being ranked first of the one or more predicted vehicle groups.”, and “ The method of claim 2, further comprising determining, by the vehicle recommendation system a second predicted vehicle group of the one or more predicted vehicle groups, the second predicted vehicle group having a rank other than first of the one or more predicted vehicle groups”, are already covered in the analysis of claim 1, wherein the vehicle groups are ranked in order of similarity metrics wherein the highest similarity group will be ranked first, then the second , third and so on.
Reference claim 3, Dana teaches that the method of claim 2, further comprising providing, by the vehicle recommendation system, a recommendation to a device of the user including a recommended vehicle of the first predicted vehicle group [See Dama Fig.1A, “110” client device to whom the recommendations are provided.].
Reference claim 5, the limitations “ The method of claim 4, wherein the first predicted vehicle group is determined based on a distance between a vector representation of the first predicted vehicle group and the one or more input vehicle groups, the distance calculated based on vehicle attributes.”, are already covered in the analysis of claim 1 wherein the cosine distance and embeddings [vectors[ are used to calculate the similarity metrics between the vehicle groups [See Dana paras 0029-0031, cited above and Gupta [see Abstract and paras 0051—0052].
6. The method of claim 5, further comprising providing, by the vehicle recommendation system, a recommendation to a device of the user including a recommended vehicle of the second predicted vehicle group [See Dama Fig.1A, “110” client device to whom the recommendations are provided.].
.
Regarding claim 7, the limitations, “ The method of claim 4, wherein the second predicted vehicle group is determined further based on an inventory of available vehicles”, are already covered in the analysis of claim 1, see Dana paras 0029-0031 cited above.
Regarding claim 8, the limitations, “ The method of claim 1, wherein vehicles are grouped into the one or more input vehicle groups based on one or more vehicle attributes, the one or more vehicle attributes including one or more of: year; make; vehicle model; fuel type; truck cab; body style; drive train; truck bed size; or doors”, are already covered in the analysis of claim 1, see Dana para 0017.
Regarding claim 9, the combined teachings of Dana and Gupta teach and render obvious all the limitations of claim 1 including using a ML model to recommend and rank the recommended vehicles/vehicle groups based on the user’s browsing history, as analyzed above. Further, the limitations, “ The method of claim 1, wherein the ML model is configured to perform: encoding the one or more input vehicle groups into one or more numerical representations; and determining the one or more predicted vehicle groups based on a calculated distance between the one or more input vehicle groups and the one or more predicted vehicle groups”, are already covered in the analysis of claim 1 in view of the teachings of Gupta, see para 0025 cited above, which teaches that the ML model is configured to encode the vehicle groups into numerical representations such as embedding vectors based on which similarity between the vehicle groups is decided and the vehicle croups are predicted and recommended.
Regarding claims 10 and 19, since their limitations are similar to the limitations of claim1, they are analyzed as being unpatentable over Dana in view of Gupta based on the same terms.
Regarding claims 11-17, since their limitations are similar to the limitations of claims 2-8, they are analyzed as being unpatentable over Dana in view of Gupta based on the same terms.
Regarding claims 18 and 20, since their limitations are similar to the limitations of claim 9, they are analyzed as being unpatentable over Dana in view of Gupta based on the same terms.
Conclusion
5. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
(i) Ding [US 20220405686 A1; 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.
(ii) Ramanuja et al. [US 20160364 783 A1; 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:
(iii) CA 3051263 [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:
(iv) 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 describes [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.
(v) 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 describes [see Abstract and page 540] a system applying a machine learning application that suggests products that users may purchase or engage with.
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/YOGESH C GARG/ Primary Examiner, Art Unit 3688
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