DETAILED ACTION
Status of Claims
This action is in reply to the response received on 22 January 2026.
Claims 1, 11, 19, and 20 have been amended.
Claims 1-20 are pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 22 January 2026 has been entered.
Allowable Subject Matter
Claims 1-20 now recite allowable subject matter. As indicated in the Office Action mailed on 16 October 2025, the claims 11-19 did recite allowable subject matter. The amended claims 1 and 20 now recite similar language recited in claim 11. The claims would be allowable if the claims were re-written to overcome the 101 rejection stated in the current Office Action below.
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.
Under step 1, it is determined whether the claims are directed to a statutory category of invention (see MPEP 2106.03(II)). In the instant case, claims 1-10 are directed to a method, claims 11-19 are also directed to a method, and claim 20 is directed to a system.
While the claims fall within statutory categories, under revised Step 2A, Prong 1 of the eligibility analysis (MPEP 2106.04), the claimed invention recites an abstract idea of identifying vehicles for users. Specifically, representative claim 1 recites the abstract idea of:
receiving, from a merchant, a plurality of interactions made by one or more users;
mapping a first user to a first user of the one or more users to a first data set, wherein the first user data set includes user information and one or more interactions, from the plurality of interactions, made by the first user;
determining a first subset of the one or more interactions based on a first period of time;
identifying, and based on the user information and the first subset of the one or more interactions, one or more vehicles for the first user at a first time, wherein the one or more vehicles are identified by:
parsing the first subset of the one or more interactions, to determine one or more trends based on one or more attributes of the first subset of the one or more interactions;
determining a first user profile based on the user information and the one or more trends, the first user profile including a user criteria score for each of one or more user criteria;
comparing the user criteria score for each of the one or more user criteria with one or more vehicles, each of the one or more vehicles including a vehicle criteria score for each of one or more vehicle criteria, each vehicle criteria corresponding to a respective user criteria; and
based on the comparing, identifying the one or more vehicles for the first user at the first time;
determining, real-time directions to one or more locations of the one or more vehicles based on the first user;
displaying to the first user, the real-time directions to the one or more locations of the one or more vehicles and for the first user to opt into receiving financial information related to the one or more vehicles;
receiving, via an input by the first user, an indication of the first user accessing the information and opting into receiving the financial information related to the one or more vehicles;
based on the indication of the first user, determining an efficacy of the one or more vehicles for the first user; and
based on the efficacy, adjusting one or more variables.
Under revised Step 2A, Prong 1 of the eligibility analysis, it is necessary to evaluate whether the claim recites a judicial exception by referring to subject matter groupings articulated in 2106.04(a) of the MPEP. Even in consideration of the analysis, the claims recite an abstract idea. Representative claim 1 recites the abstract idea of identifying vehicles for users, as noted above. This concept is considered to be a method of organizing human activity. Certain methods of organizing human activity include “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” MPEP 2106.04(a)(2)(II). In this case, the abstract idea recited in representative claims 1 is a certain method of organizing human activity because the claims recite steps that relate to sale activities or behaviors. For example, the claims recite the activities of receiving from a merchant, interactions made by one or more users, mapping a user to user data that includes interaction information by the user, parsing the first subset of interactions to determine trends based on attributes of the interactions in the subset, determining a user profile based on the user information and the trends determined, comparing the user criteria score for each of the one or more user criteria with one or more vehicles where the vehicles include a vehicle criteria score for the vehicle criteria, transmitting to the user information to opt into receiving financial information related to the one or more vehicles, receiving input by the first user an indication of the user accessing the information and opting into receiving the financial information related to the one or more vehicles, determining an efficacy of one or more vehicles for the user and adjusting variables based on the efficacy of the one or more vehicles for the user, thereby making the abstract idea related to sales activity or behavior.
Thus, representative claim 1 recites an abstract idea.
The Examiner additionally notes that that the step of determining a first subset of one or more interactions based on a first period of time would fall into the enumerated grouping of mental processes. A mental process is defined as and includes “concepts performed in the human mind (including an observation, evaluation, judgement, and opinion)” (see MPEP 2106.04(a)(2)(III)). In this case, the step of determining a first subset of one or more interactions, is considered to be a concept performed in the human mind, observations and evaluations. Thus, representative claim 1 recites an abstract idea that also falls into the grouping of mental processes.
Under Step 2A, Prong 2 of the eligibility analysis, if it is determined that the claims recite a judicial exception, it is then necessary to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of that exception. MPEP 2106.04(d). The courts have identified limitations that did not integrate a judicial exception into a practical application include limitations merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). MPEP 2106.04(d). In this case, representative claim 1 includes additional elements: a computer, electronic, database over an electronic network, by a trained machine learning model, by the trained machine learning model, on a global positioning signal of a user device, on a graphical user interface (GUI) of the user device, a link, over the electronic network, the graphical user interface (GUI), the link, and the trained machine learning model.
Although reciting such additional elements, the additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a computer as a tool to perform the abstract idea. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration. Similar to the limitations of Alice, representative claim 1 merely recites a commonplace business method (i.e., identifying vehicles for users) being applied on a general-purpose computer using general purpose computer technology. MPEP 2106.05(f). While the claims recite using a trained machine learning model, the recitations are results based in nature and do not include details as to how the machine learning is actually functioning beyond known functions. Thus, the claimed additional elements are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. Since the additional elements merely include instructions to implement the abstract idea on a generic computer or merely use a generic computer as a tool to perform an abstract idea, the abstract idea has not been integrated into a practical application.
Under Step 2B of the eligibility analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). MPEP 2106.05. In this case, as noted above, the additional elements of a computer, electronic, database over an electronic network, by a trained machine learning model, by the trained machine learning model, on a global positioning signal of a user device, on a graphical user interface (GUI) of the user device, a link, over the electronic network, the graphical user interface (GUI), the link, and the trained machine learning model recited in independent claim 1 are recited and described in a generic manner merely amount to no more than an instruction to apply the abstract idea using a generic computer or merely use a generic computer as a tool to perform an abstract idea.
Even when considered as an ordered combination, the additional elements of representative claim 1 do not add anything that is not already present when they considered individually. In Alice, the court considered the additional elements “as an ordered combination,” and determined that “the computer components…‘ad[d] nothing…that is not already present when the steps are considered separately’… [and] [v]iewed as a whole…[the] claims simply recite intermediated settlement as performed by a generic computer.” Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, (2014) (citing Mayo, 566 U.S. at 79, 101 USPQ2d at 1972). Similarly, when viewed as a whole, representative claim 1 simply conveys the abstract idea itself facilitated by generic computing components. Therefore, under Step 2B of the Alice/Mayo test, there are no meaningful limitations in representative claim 1 that transforms the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
As such, representative claim 1 is ineligible.
Independent claims 11 and 20 are similar in nature to representative claim 1 and Step 2A, Prong 1 analysis is the same as above for representative claim 1. It is noted that in independent claim 11 includes the additional elements of clicking the link and independent claim 20 includes the additional element of at least one memory storing instructions, at least one processor operatively connected to the at least one memory storing instructions and configured to execute the instructions to perform operations. The Applicant’s specification does not provide any discussion or description of the claimed additional elements in claim 11 and 20, as being anything other than generic elements. Thus, the claimed additional elements of claims 11 and 20 are merely generic elements and the implementation of the elements merely amounts to no more than an instruction to apply the abstract idea using a generic computer. As such, the additional elements of claims 11 and 20 do not integrate the judicial exception into a practical application of the abstract idea. Additionally, the additional elements of claims 11 and 20, considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer.
As such, claims 11 and 20 are ineligible.
Dependent claims 2-10 and 12-19, depending from claims 1 and 11, respectively, do not aid in the eligibility of the independent claim 1. The claims of 2-10 and 12-19 merely act to provide further limitations of the abstract idea and are ineligible subject matter.
It is noted that dependent claims includes the additional element of the GUI (claims 6 and 16) Applicant’s specification does not provide any discussion or description of the claimed additional elements as being anything other than a generic element. The claimed additional elements, individually and in combination do not integrate into a practical application and do not provide an inventive concept because they are merely being used to apply the abstract idea using a generic computer (see MPEP 2106.05(f)). Accordingly, claims 6 and 16 are directed towards an abstract idea. Additionally, the additional elements of claims 6 and 16considered individually and in combination, do not provide an inventive concept because they merely amount to no more than an instruction to apply the abstract idea using a generic computer. It is further noted that the remaining dependent claims 2-5, 7-10, 12-15, and 17-19 do not recite any further additional elements to consider in the analysis, and therefore would not provide additional elements that would integrate the abstract idea into a practical application and would not provide an inventive concept.
As such, dependent claims 2-10 and 12-19 are ineligible.
Reasons for Allowable Subject Matter
Prior Art Considerations:
Upon review of the evidence at hand, it is concluded that the totality of evidence in combination, neither anticipates, reasonably teaches, nor renders obvious the below noted features of the Applicant’s invention.
Regarding the independent claims 1, 11, and 20, the features are as follows:
determining, by the trained machine learning model, real-time directions to one or more locations of the one or more vehicles based on a global positioning signal of a user device of the first user;
displaying, on a graphical user interface (GUI) of the user device of the first user, the real-time directions to the one or more locations of the one or more vehicles
The most apposite prior art of record includes Wang, C., et al. (PGP No. US 2022/0222688 A1), in view of Schoeny, C. (PGP No. US 2020/0394698 A1), Maeng, J., et al. (Patent No. US 11,328,354 B1), Brannan, J., et al. (PGP No. US 2023/0153917 A1), and Chaganti, S., et al. (PGP No. US 2019/0188772 A1), to teach a method for identifying vehicles for a user.
The reference of Wang describes a method for obtaining user data that includes interaction data of each user, where the user interaction data can be mapped or associated with a specific user ID of a user profile (Wang, see: paragraphs [0041], [0044]). Wang goes on to disclose that the method can parse or extract the relevant user history, such as past interactions and actions within specific time frames (Wang, see: paragraphs [0041] and [0047]). A trained predictive model is used to generate predictions based off of the user’s previous actions and predict the probability that the specific user will perform an action, such as predicting the purchase of a vehicle (Wang, see: paragraph [0046]). The method of Wang uses the profile history information for a particular user to determine trends, such as statistical behaviors of each user, which also considers the preferences of a user, such as price ranges preferred, etc., and uses this information to further predict the action of the user (Wang, see: paragraphs [0041]-[0042], [0046], and [0048]). The method also includes comparing data and utilizing that information to further predict the user actions, such as comparing metrics that include average sales prices of vehicles associated with the past user actions, and preferences of a user (Wang, see: paragraph [0048]). Wang goes on to describe that a report is then generated based on the predictions of the user actions, which includes the comparison metric data (Wang, see: paragraph [0049]). Wang also describes that the user device belonging to the potential consumer also contains a GPS unit that generates location data of the specific user device (Wang, see: paragraph [0029] disclosing “may provide information regarding the location or movement of the client computing device 110”). Wang does not specifically include details of any type of criteria score, vehicle score, or identifying vehicles for the user based on the comparisons, or receiving any type of financial information related to the vehicles.
The reference of Schoeny describes methods for providing financial information for purchasing a vehicle, involving tracking the consumer’s interactions with an online app, and scoring the performance of specific weights according to levels of interest in a specific vehicle (Schoeny, see: paragraph [0033] and [0046]). Based on a comparison of the different sets of weights, resulting in a score, is then used to indicate a best performance for a vehicle and that can be selected (Schoeny, see: paragraph [0068]). The selected vehicle is used to find the best match for a user, displaying the results to the user of the top ranked vehicles based on their interactions with the app (Schoeny, see: paragraph [0068]). The user can also find and apply for financing to purchase the vehicle of interest, where the system can provide the user with financial institutions or banks to provide the proper financing for the vehicle (Schoeny, see: paragraphs [0024], [0028], and [0035]). Neither Wang, nor Schoeny specifically teach any type of feature of providing a link to the user to opt into receiving the financial information or generating any type of lead.
The reference of Maeng describes that a user that is interested in purchasing a specific item, can use a GUI via an application, to select a preapproval, by clicking an icon on the user device, to then contact electronically, a financial institution in order to complete the purchase (Maeng, see: Col. 23, ln. 50-57). Once the user has selected the icon, a request for preapproval is received by the financial institution, where the preapproval token for a potential purchaser is generated, indicating an interest in purchasing the item through financing options (Maeng, see: Col. 23, ln. 30-31, 45-46, and 63-67). Neither Wang, Schoeny, nor Maeng describe any type of ability to share the vehicles to another user, nor do they describe a location of an item of interest, and do not mention any type of real-time directions to the locations where the inventory is available.
The reference of Brannan describes a method for sharing specific information, such as a listing of an item for sale, to another user via an input field for “sharing” a message including the listing page for the specific item with a second user (Brannan, see: paragraph [0139]). Brannan does not mention any type of real-time directions to a location of a vehicle where the vehicle is in stock.
Next, the reference of Chaganti does describe a real-time navigational guidance plan that can be displayed on a user device, based on the location of the user and each merchant that the item of interest is located, where the navigation provides a plan to optimize the route in terms of travel time, etc. (Chaganti, see: paragraphs [0005], [0024], and [0057]). However, Chaganti does not specifically relate determining by a machine learning model, real-time directions to a vehicle of interest, or locations of vehicles that are available for purchase, or displaying the real-time directions to a user on the GUI.
The Examiner further emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for further modification of the evidence at hand to arrive at the claimed invention. Moreover, the combination of features of independent claims, would not have been obvious to one of ordinary skill in the art because any combination of evidence at hand to reach the combination of features as claimed would require substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias and resulting in an inappropriate combination.
It is hereby asserted by the Examiner, that in light of the above and in further deliberation over all of the evidence at hand, that the claims recite allowable subject matter as the evidence at hand does not anticipate the claims and does not render obvious any further modification of the references to a person of ordinary skill in the art.
Response to Arguments
With respect to the rejections made under 35 USC § 101, the Applicant’s arguments filed on 22 January 2026, have been fully considered but are not considered persuasive.
In response to the Applicant’s arguments found on page 12 of the remarks stating “Even if, arguendo, one or more specific limitations in the claims fall within one or more of the subject matter groupings under Prong One of Step 2A, the claims are not directed to an abstract idea,” the Examiner respectfully disagrees. Under Step 2A, Prong 1 of the eligibility analysis, even when considering the amendments to the claims, the claims are directed to the abstract idea of identifying vehicles for users, falls into the enumerated grouping of a certain method of organizing human activity, where the steps that are recited in the claims are further related to sales activities or behaviors. For example, the claims recite receiving from a merchant, interactions made by one or more users, mapping a user to user data that includes interaction information by the user, parsing the first subset of interactions to determine trends based on attributes of the interactions in the subset, determining a user profile based on the user information and the trends determined, comparing the user criteria score for each of the one or more user criteria with one or more vehicles where the vehicles include a vehicle criteria score for the vehicle criteria, transmitting to the user information to opt into receiving financial information related to the one or more vehicles, receiving input by the first user an indication of the user accessing the information and opting into receiving the financial information related to the one or more vehicles, determining an efficacy of one or more vehicles for the user and adjusting variables based on the efficacy of the one or more vehicles for the user, thereby making the abstract idea related to sales activity or behavior. Therefore, under Step 2A, Prong 1 of the eligibility analysis, the Examiner maintains that the abstract idea falls into a grouping of a certain method of organizing human activity, such as sales activities or behaviors, and also are considered mental processes.
In response to the Applicant’s arguments found on pages 12-17 of the of the remarks stating “the claims as a whole integrate the abstract idea into a practical application under Step 2A,” and the “additional elements beyond the abstract idea that, when evaluated together and in further combination with remaining elements of the claim, reflect a technology improvement,” and “Similar to the claim at issue in Ex Parte Desjardins, the independent claims as amended herein include additional elements that recite a specific and particular manner to optimize the performance of the trained model based on received signals indicating subsequent interactions or engagement related to the vehicles output by the model” and “thus integrate the alleged abstract idea into a practical application,” the Examiner respectfully disagrees. Even when considering the amendments to the claims, under Step 2A, Prong 2 of the eligibility analysis, the claims as a whole, do not integrate the abstract idea into a practical application. The claims recite additional elements that when considered individually and in combination, are not sufficient to integrate the abstract idea into a practical application, because they are still recited in a generic manner. The additional elements recited in the amended claims of a computer, electronic, database over an electronic network, by a trained machine learning model, by the trained machine learning model, on a global positioning signal of a user device, on a graphical user interface (GUI) of the user device, a link, over the electronic network, the graphical user interface (GUI), the link, and the trained machine learning model, are still generically recited and are merely being used to apply the abstract idea with the generic computer and computing components. Although the claims do recite a trained machine learning model, the model is recited at a high-level of generality, and the model does not include specific details as to how the machine learning is actually functioning beyond known functions of a machine learning model. Further, the MPEP (2106.05(a)) provides further guidance on how to evaluate whether claims recite an improvement in the functioning of a computer or an improvement to other technology or technical field. For example, as indicated in 2106.05(d)(1) of the MPEP “the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement,” and that “[t]he specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art.” Looking to the specification is a standard that the courts have employed when analyzing claims as it relates to improvements in technology. For example, in Enfish, the specification provided teaching that the claimed invention achieves benefits over conventional databases, such as increased flexibility, faster search times, and smaller memory requirements. Enfish LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016). Additionally, in Core Wireless the specification noted deficiencies in prior art interfaces relating to efficient functioning of the computer. Core Wireless Licensing v. LG Elecs. Inc., 880 F.3d 1356 (Fed Cir. 2018). With respect to McRO, the claimed improvement, as confirmed by the originally filed specification, was “…allowing computers to produce ‘accurate and realistic lip synchronization and facial expressions in animated characters…’” and it was “…the incorporation of the claimed rules, not the use of the computer, that “improved [the] existing technological process” by allowing the automation of further tasks”. McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, (Fed. Cir. 2016).
In this case, Applicant’s specification provides no explanation of an improvement to the functioning of a computer or other technology. Rather, the claims focus “on a process that qualifies as an ‘abstract idea’ for which computers are invoked merely as a tool”. Id citing Enfish at 1327, 1336. This is reflected in paragraph [0003]-[0004] of Applicant’s specification, which describe Applicant’s claimed invention is directed toward solving problems related to customers that are overwhelmed “by the numerous options available and a complex set of criteria to consider, such as price, performance, fuel efficiency, safety, and other bells and whistles” and that “many customers lack the expertise to make an informed decision and may end up committing a large amount of financial resources for a vehicle that does not fully meet their needs and preferences,”. Although the claims include computer technology such as a computer, electronic, database over an electronic network, by a trained machine learning model, by the trained machine learning model, on a global positioning signal of a user device, on a graphical user interface (GUI) of the user device, a link, over the electronic network, the graphical user interface (GUI), the link, and the trained machine learning model, such elements are merely peripherally incorporated in order to implement the abstract idea. This is unlike the improvements recognized by the courts in cases such as Enfish, Core Wireless, and McRO. Unlike precedential cases, neither the specification nor the claims of the instant invention identify such a specific improvement to computer capabilities. The instant claims are not directed to improving the existing technological process but are directed to improving the commercial task of identifying vehicles for a user. The claimed process, while arguably resulting in improvements in identifying vehicles for a user to purchase, is not providing any improvement to another technology or technical field as the claimed process is not, for example, improving the processor and computer components that operate the system, and is not providing an improvement to the machine learning models. Rather, the claimed process is utilizing different data while still employing the same processor and/or computer components used in conventional systems to improve the identifying vehicles for a user, e.g. commercial process. Further, the claims do not recite similar features as the claim in the Ex Parte Desjardins decision. In that decision, it was determined that the claims and specification did in fact support the disclosed improvement. It was also determined that the specification supported the improvement to “effectively learn new tasks in succession whilst protecting knowledge about previous tasks” and also provided support that “the claimed improvement allows artificial intelligence (AI) systems to ‘us[e] less of their storage capacity’ and enables ‘reduced system complexity’” such that when evaluating the claim language, the independent claim 1 reflected that improvement (see Ex Parte Desjardins et al Rehearing Decision). In this case, the additional elements, including the machine learning model, are not providing sufficient directed to the technology itself. The additional elements in this case are merely being used to apply the abstract idea with generically recited computing components, as stated above. The improvements in this case are directed to providing improvements to the abstract idea, which is a commercial task. Therefore, the claims do not integrate the abstract idea into a practical application, and do not recite an improvement to the technology, nor an improvement to the technical field of machine learning models, and thus, the Examiner maintains the 101 rejection.
With respect to the rejections made under 35 USC § 103, the Applicant’s arguments filed on 22 January 2026, have been fully considered and are considered to be persuasive. In light of the amendments to claims 1 and 20, the claims now recite similar language recited in claim 11, which was indicated to recite allowable subject matter in the Office Action mailed on 16 October 2025. Therefore, the claims 1-20 now recite allowable subject matter, and thus, the 103 rejection has been withdrawn.
Conclusion
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/ASHLEY D PRESTON/Primary Examiner, Art Unit 3688