DETAILED ACTION
This action is responsive to remarks filed on 03/23/2026, in which claims 1-20 are pending. No claims have been amended.
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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 as representative:
Step 1: The claim recites a system; therefore, it is a machine.
Step 2A, Prong One: The invention as claimed comprises:
A system, comprising: a vehicle data system comprising: a data store storing user data for a set of users and a set of historical transaction data comprising data on a set of sales of vehicles, the data for the set of users and the data for the set of historical transactions comprising a set of related data; a non-transitory computer readable medium, comprising instructions for: receiving a query from a user, the query indicating a vehicle selection, wherein the vehicle selection indicates a make and a model for a selected vehicle; processing the query to determine a plurality of features from the vehicle selection; inputting the plurality of features to a machine learning model, the machine learning model comprising a random forest; determining a candidate vehicle by the random forest, wherein a candidate vehicle is a different make and different model than the selected vehicle; determining a plurality of candidate vehicle features based on a plurality of engineered features embedded in the random forest; determining, by the random forest, a binary value for each feature of the plurality of features, wherein the determining comprises evaluating each feature of the plurality of features with respect to a respective candidate vehicle feature of the plurality of candidate features; determining a positive count of binary values indicating a binary positive value; and when the positive count exceeds a threshold count, transmitting the candidate vehicle as a vehicle recommendation to the user.
All the limitations with exception of bolded and underline fall within abstract idea. Claim as drafted, focuses on facilitating a commercial transaction of purchasing a car and product recommendation, which falls under:
Mathematical concepts: The claim expressly uses a machine learning model (random forest), engineered features, binary evaluation, counting, and thresholding. These are mathematical operations or relationships.
Mental processes: Evaluating features to assign binary values, counting positives, and recommending an option based on a threshold are steps that could, at a high level, be performed mentally or with pen and paper.
Certain methods of organizing human activity: Product recommendations can be characterized as marketing/sales advisement, which sometimes falls within organizing human activity.
Step 2A Prong Two: The claim recites additional elements (bolded and underlined above), the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea:
““non-transitory computer readable medium,” “a vehicle data system” are generic computer components and conventional data operations.
Use of a random forest model with engineered features does not, by itself, indicate an improvement to the functioning of the computer or another technology, as it merely applying the random forest model (using machine learning as a tool).
Step 2B: As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component.
The same conclusion is reached in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Even if one treats use of Random Forest as more than merely applying, the Random Forest algorithm is well understood, routine and conventional as it have been known and used for a long time, for example see history of Random Forest on Wikipedia.
The Federal Circuit in Recentive Analytics v. Fox Corp. (2025) held that "claims that do no more than apply established methods of machine learning to a new data environment, without disclosing improvements to the machine learning models to be applied, are patent ineligible". The claim here appears to apply a standard random forest to vehicle recommendation data without reciting model improvements.
Also, if receiving/transmitting is treated as additional limitation, it is merely data gathering.
“iv. Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);”
Claims 2-7, further narrow the recited abstract idea above, and are rejected under same rational as claim 1.
Claim 8 is a method claim (process), and claim 15 is a CRM claim (article of manufacture), corresponding to system claim of 1; and it is rejected based on same rational as claim 1.
Dependent claims 9-14, and 16-20 are further narrowing the abstract idea. Note: all the hardware elements recited in claims are interpreted to additional elements, and as discussed with regard to claim 1, are no more than mere instructions to apply the exception using a generic computer component (computer).
Allowable Subject Matter
Claims 1-20 are allowed over the prior art. The extensive search found multiple references that teach vehicle recommendation based on feature comparison, and the use of threshold to suggest candidate vehicles. However, the references don’t explicitly teach “using Random Forest model for recommendation;
determining a positive count of binary values indicating a binary positive value; and when the positive count exceeds a threshold count, transmitting the candidate vehicle as a vehicle recommendation to the user” in the context of the claimed invention.
Miao et al. (US 20180322122 A1) teaches “[0041] The group learning module 245 extracts feature values from the groups of the training set, the features being variables deemed potentially relevant to the likelihood that the target user will join the group if presented with a recommendation to join the group. Specifically, the feature values extracted by the group learning module 245 may include values representing: the number of interactions the target user carried out with groups having at least a threshold number of characteristics matching or similar to the candidate group (hereinafter referred to as “similar groups”); the number of interactions the target user carried out with content items associated with similar groups; and the number of interactions the target user carried out with content items having at least a threshold number of characteristics matching or similar to the candidate group (hereinafter referred to as “similar content items”)….”
The reference further teaches “[0043] …... Different machine learning techniques—such as linear support vector machine (linear SVM), boosting for other algorithms (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps—may be used in different embodiments…..”
However, any combination of arts don’t provide specific details such as “determining a positive count of binary values indicating a binary positive value; and when the positive count exceeds a threshold count, transmitting the candidate vehicle as a vehicle recommendation to the user” in the context of vehicle recommendation.
Ramanuja et al. (US 20160364783 A1) substantially teaches the claimed invention including comparing the total similarity with threshold for recommendation (para 0121), however Ramanuja adds the weight for each feature (which is not binary) rather than “determining a positive count of binary values indicating a binary positive value.”
Response to Arguments
Applicant's arguments filed 03/23/2026 have been fully considered but they are not persuasive.
Applicant argues, “accordingly, as noted by both Director Squires and the Federal Circuit (see e.g., Koninklijke KPN N.V. v. Gemalto M2M GmbH (2019)) , as embodiments provide a solution to a technical problem (e.g., the ability to apply models to provide a recommendation in the absence of certain kinds of relevant data) that is based on the (e.g., training) of a particular type of machine learning model and those disclosed improvements are reflected in the currently pending claims, the claims
at issue here are patent eligible.” (Pg. 2)
The applicant is trying to match case facts with Desjardins that don’t exist in the claimed invention. In instant invention, “recommendation in the absence of certain kinds of relevant data”, does not equate to improvement presented in the Desjardins. In Desjardins, “specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).”
In instant case, the input information are already available, that are fed to the model. The claim recites, “receiving a query from a user, the query indicating a vehicle selection, wherein the vehicle selection indicates a make and a model for a selected vehicle; processing the query to determine a plurality of features from the vehicle selection; inputting the plurality of features to a machine learning model, the machine learning model comprising a random forest.
The claim even disclose what specific model is being applied (random forest).
Applicant argues, “Applicant respectfully submits that claim 1 is not directed to binary classification mathematics itself, nor to the random forest algorithm as an abstract construct. Claim 1 is directed to a vehicle recommendation system that uses a machine learning model as one component of a larger technical architecture. The focus of claim 1 is determining a cross-make, cross-model vehicle recommendation from a live dealer inventory in the absence of user purchase history, which is not a mathematical concept. It is a technical result achieved through a specific system architecture. The mathematical operations recited (binary evaluation, counting, thresholding) are means to a technical end, not the end itself. The use of mathematical rules as part of a claimed process does not render the claim directed to mathematics where the focus of the claim is on a specific technical implementation producing a concrete result. See McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016).” (Pg. 3)
The applicant is merely providing conclusory statement, a binary value is being determined for each feature of the plurality of features, positive counts are determined, and when the positive counts exceed threshold, based on that candidate vehicle is recommended. It is very clear that entire recommendation of vehicle is based on the binary count and comparison with threshold, which is both mathematical concept, and also can be done via paper and pen, thus mental process. Furthermore, the applicant already have admitted in the argument, that “the focus of claim 1 is determining a cross-make, cross-model vehicle recommendation from a live dealer inventory in the absence of user purchase history” which is organizing human activity. Also note, when a user goes to live dealer, the user does not always have previous purchase history at dealer; rather, the user goes and tells, I want the cars with following features. The dealer, provides all the candidate cars which have required/preferred features. This is straight forward organizing human activity concept, and no technical improvement is being presented. All the information are already available, and machine learning model is being used to check if features are present or not. There is no missing information; the missing information are users purchase history, however, the user’s purchase history is never used for the recommendation, so that is irrelevant.
The reply to applicant’s remaining arguments is summarized in the response above; the applicant also states that “a specific architectural improvement to the ML training methodology as the reframing of a ranking problem as a binary classification problem specifically to improve random forest performance.” First of all, one can’t show an improvement to abstract idea using the abstract idea itself. Also, note, the claim states “determining a plurality of candidate vehicle features based on a plurality of engineered features embedded in the random forest.” The claims or specification don’t provide any details with regard to any improvement into the feature embedding technology. In fact, the specification seems to be completely silent with regard to embedding.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621