DETAILED ACTION
This action is responsive to the RCE filed on 07/02/2026, in which claims 1-4, 6-11, 13-18, and 20 are pending; claims 1, 6, 8, 13, 15, and 20 are amended; claims 5, 12, and 19 are cancelled.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/02/2026 has been entered.
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 1-4, 6-11, 13-18, and 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 user data for the set of users and the data for the set of historical transactions comprising a set of related data, wherein the set of data indicates a plurality of correlations, each correlation being between features of one or more vehicles selected by a given user and a vehicle purchased by the given user; 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, wherein the machine learning model is trained based on the set of related data from the data store to perform the following: determining a candidate vehicle based on the plurality of features, wherein the 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; and determining 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 2-4, and 6-7, 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 9-11,13-14,16-18, and 20 are further narrowing the abstract idea presented by corresponding independent claims. 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 1-4, 6-11, 13-18, and 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 on 07/02/2026 have been fully considered but they are not persuasive.
Applicant states “applicant's Attorney respectfully submits that the amended independent claims do not comprise an abstract idea. When viewed as a whole and in light of the specification, the amended independent claims recite a specific technical implementation for a specific improvement to the functioning of a vehicle data recommendation system, not merely mathematical concepts, mental processes, or organizing human activity in the abstract.” (Pg. 6)
As stated in the 35 U.S.C 101 analysis above, 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.
Thus claims clearly recite abstract idea; the examiner agrees that claim also includes additional limitation, however as explained in the rejections section these additional elements don’t integrate the abstract idea into a practical application. Furthermore, the amended claims merely introduce what the set of data indicates, and what data indicates clearly indicate abstract idea such as mathematical concept (correlation), and organizing human activity (vehicles purchased by user). Even if the examiner views this data more than an abstract idea, it will merely fall under insignificant extra solution activity of gathering data, that is used by the machine learning. MPEP 2106.05 (g) recites:
“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);”
In addition, applicant merely provides conclusory statement with regard to technical improvement, without discussing any technical improvement.
Applicant argues, “nonetheless, assuming in arguendo that the amended independent claims do comprise an abstract idea, Applicant's Attorney submits that the amended independent claims incorporate any such abstract idea into a practical application by improving the functioning of a vehicle recommendation system, particularly for cold-start users. This practical application is incorporated into the amended independent claims via at least: (a) the storing of user data and historical transaction data indicating correlations between selected vehicles and actual purchases, and (b) training the machine learning model on such correlations in the data to perform determining cross- make/model candidate vehicles, determining candidate vehicle features from engineered features embedded in the random forest, and binary evaluation of selected vehicle features vs. candidate vehicle features.” (Pg. 7)
The recited limitation have been addressed in the 35 U.S.C 101 section above; furthermore, it seems the applicant is stating that improvement is in the machine learning itself, by addressing the cold start problem? Please note, this entire concept described in the specification is abstract idea, and don’t pertain to cold-start problem. For example, if we want to purchase a car, and go to dealer and tell the dealer that we want to see the cars that have the specific features, or show me cars that have similar feature to Honda Accord I purchased, based on this information, the dealer can show all the potential matches available in inventory. Here, the data is available to make such determination. The cold start problem in machine learning occurs when a system lacks sufficient historical data to make accurate predictions or draw meaningful inferences.” In this instance, we have sufficient historical data, as pointed out by the claim limitation “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 user data for the set of users and the data for the set of historical transactions comprising a set of related data, wherein the set of data indicates a plurality of correlations, each correlation being between features of one or more vehicles selected by a given user and a vehicle purchased by the given user.” Furthermore, the query already includes the information, that are processed: “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.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (see office action mailed on 12/22/2025).
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/WASEEM ASHRAF/Supervisory Patent Examiner, Art Unit 3621
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