DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 July 22, 2026 has been entered.
Response to Amendment
All pending claims 1-20 filed July 22, 2026 are examined in this non-final office action in response to the request for continued examination.
Response to Arguments
35 USC 112(b)
Applicant’s arguments, see remarks filed July 22, 2026 with respect to claim 1-20 have been fully considered and are persuasive. Rejection under 35 USC 112(a) has been withdrawn.
35 USC 103
Applicant’s arguments, see remarks filed July 22, 2026 with respect to the rejections of claims under 35 USC 103, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made. The combination of Edwards-Batie is withdrawn in favor Jackson-Batie necessitated by amendment in response to the request for continued examination. Arguments hinged on Edwards alone and Edwards-Batie are moot. Jackson-Batie cures deficiencies identifies in Applicant’s remarks. Please see details below.
35 USC § 101
All independent claims train a generative artificial intelligence model on vehicle and driver data and configure the AI model to: associate vehicle traits with different vehicles, associate driver data with vehicle traits, analyze data associated with the driver to identify vehicle traits associated with a driver, determine, based upon the vehicle traits associated with the driver, vehicle suggestions, and generate an output including vehicle suggestions for the driver;”
The instant specification has sufficient depth and application of machine learning and learning model training and configuration to support the independent claims and respective dependents. Per Step 2A (second prong) said claims provide a practical application. Execution of each independent claim and respective dependents effectively improves computing efficiency by reducing processing cycles and network traffic.
Priority
The effective priority date is the filing date of provisional application 63/462,101 (April 63, 2023).
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-9, 11-15 and 17-19 are rejected under 35 USC 103 as being unpatentable over Jackson et al., US 2021/0166103 “Jackson,” in view of Batie et al., US 2024/0289859 “Batie.”
In Jackson see at least (underlined text is for emphasis):
Regarding claim 1: (Currently Amended) A computer-implemented method for identifying vehicle traits associated with a driver and providing vehicle suggestions to a buyer, the method comprising:
detecting, by one or more processors, a signal that the buyer is interested in purchasing a vehicle,
[Jackson: Fig. 1 (5, 5a): 0025] … the driver inputs the user-generated profile information …. Please note: Driver inputting the user-generated profile information qualifies as showing interest in purchasing a vehicle. Also see [Jackson: 0028] below for input information.
obtaining, by the one or more processors, driver data associated with the driver, wherein the driver data includes at least vehicle purchase history associated with the driver;
[Jackson: 0028] The user generated profile data 22 is inputted by a user, such as via a user device 5 presenting a user interface designed to collect such data. The user interface is configured to prompt a user to input various data types, including the personal information, driving practices information, and vehicle ownership information described above. The personal information may include such things as gender, age, ethnicity, occupation, employments status, industry, family size, pets, etc. Additionally, location and contact information may be inputted. Driving practices information is also gathered, such as route information, information relating to numbers and types of passengers, driving times, etc. FIG. 3A shows an exemplary user interface depicting several routes inputted by a driver, which include “commute” routes and “errand” routes that are repeated at a certain frequency, such as daily, weekly, or quarterly. “Vacation” and “family visit” routes are also inputted along with an estimated frequency of each route. This route information can be utilized to gather significant amounts of information, including information relating to region and location in which the individual drives, weather and traffic conditions therein, mileage and mileage type (city or highway), and the like.
[Jackson: 0029] Vehicle ownership information is also provided including current vehicle information about the vehicle or vehicles currently owned by a driver and past vehicle information about vehicles previously owned by a driver. FIG. 3B depicts on example of a user interface display showing vehicle ownership information for a driver, including current vehicle information and past vehicle information inputted by the driver. For each current and past vehicle, mileage, price, and ownership type (lease or own) are inputted by the user. Additionally, the system may request a driver to provide a purchase location, such as a dealership, and/or a vehicle service provider for that vehicle. Additionally, each current and past vehicle may be rated by the user, which may be a single overall rating or may be a rating based on various categories. These categories may have overlap with the vehicle attribute categories, or may be different. This vehicle ownership information may be utilized to create enriched driver profile data, such as to calculate information regarding the total miles driven by the user, the number of vehicles that the user typically owns at one time, and estimated miles or time remaining on each vehicle, and/or an estimated time that that driver will be purchasing a new vehicle. Additionally, the enriched driver profile data may include information regarding how many miles a driver can expect to drive on a vehicle and when they may expect to purchase a new vehicle in the future. As exemplified in FIG. 3B, such enriched driver profile data may be provided to the driver via the user interface.
inputting, by the one or more processors, the driver data associated with the driver into a generative artificial intelligence (Al) model to generate vehicle suggestions for the driver, …
Rejection is based in part upon the teachings applied to claim 1 by Jackson and further upon the combination of Jackson-Batie.
In Jackson see at least:
[Jackson: 0013] FIG. 4 schematically depicts an exemplary neural network for use in a vehicle recommender system according to one embodiment of the present disclosure.
[Jackson: 0023] The information is assimilated and homogenized into a pre-determined format so that appropriate comparisons and data analysis can be conducted. In certain embodiments, data clustering and matrix factorization techniques may be utilized to generate vehicle recommendations. Through experimentation and research, and based on expert knowledge relevant to the vehicle market, the inventor has also recognized that employing deep learning methods improves the vehicle recommendation outcomes and provides a flexible and adaptive system. Namely, in certain embodiments, neural network architecture is employed to learn a common latent low-dimensional space to represent the drivers registered in the system and available vehicle models. For example, a hybrid recommender model may be created using neural network architecture that performs deep matrix factorization and content based filtering using a neural network trained to map users, content, and vehicles into a common low-dimensional space with non-linear projections. Inputs to the neural network may include driver profile data, vehicle attribute data, and/or driver attribute target values. For example, a matrix input may be provided, such as a driver-vehicle matrix. Explicit and/or implicit feedback may be utilized as input to the model.
Although neural networks can be generative, Jackson does not expressly mention the neural network describe above is generative. Batie on the other hand would have taught Jackson the use of deep learning models, generative models, transfer learning models and neural network.
In Batie see at least:
[Batie: 0019] As will be described in more detail herein, in some embodiments, the systems and methods may implement a machine-learning model that is configured to learn one or more conditions and/or sequences of conditions for generating a recommendation of a vehicle for purchase or lease that corresponds to a driver's driving behavior. The machine-learning model may be a neural network model or other type of model including, for example, a deep-learning model. Once trained, the machine-learning model ingests vehicle sensor data from the vehicle sensors, driver's information (e.g., information regarding the ownership of their current vehicle such as the make and model, whether the vehicle is leased or not, whether the vehicle was pre-owned or new to the owner, demographic information, a driver's driving record, or the like), and vehicle sales market data (e.g., including vehicle specifications, vehicle performance attributes such as acceleration, braking, towing capacity, fuel efficiency, ...
[Batie: 0041] The remote server 120 is configured to implement a machine-learning model 122. The machine-learning model 122 is a system that can learn from inputs and make predictions or decisions therefrom. Machine-learning models 122 are trained using a dataset. In embodiments, the machine-learning model 122 is configured to be trained using data transmitted through the network 180 to the remote server 120. The data includes, but is not limited to the driving behavior data 107, ownership data 109 related to the ownership of the vehicle 100, and vehicle sales market data 105. The machine-learning model 122 may be a supervised learning models, unsupervised learning models, semi-supervised learning models, reinforcement learning models, deep learning models generative models, transfer learning models, neural networks or the like. In embodiments, the remote server 120 is configured to implement the machine-learning model 122 that ingests input data and generates a recommendation 262 (FIG. 5A). It should be understood that this process may be completed by any processor 124, 132, including one or more computing devices 130 communicatively coupled to the vehicle 100.
[Batie: 0042] The machine-learning model 122 can be one of a variety of models and algorithms. The following list of models is merely an example. The machine-learning model 122 implemented in the present embodiments may be a supervised learning model, an unsupervised learning model, a semi-supervised learning model, a reinforcement learning model, a deep learning model, a generative model, an adversarial network, a variational auto encoder, or the like.
One of ordinary skill in the art before the effective filing date would have recognized that applying the known techniques of Batie, which implement generative machine learning models to recommend a vehicle corresponding with a driver’s driving behavior, would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the techniques of Batie to the teachings of Jackson would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such data processing features into similar systems. Obviousness under 35 USC 103 in view of the Supreme Court decision KSR International Co. vs. Teleflex Inc.
… wherein the generative Al model is trained on vehicle data to identify vehicle traits and is configured to:
In Jackson-Batie see at least:
associate vehicle traits with different vehicles, associate driver data with vehicle traits, analyze data associated with the driver to identify vehicle traits associated with a driver, determine, based upon the desired vehicle traits associated with the driver, vehicle suggestions, and
[Jackson: 0026] FIG. 2 schematically depicts one embodiment of a vehicle recommender system. The left-most column represents various data types and informational inputs provided to and utilized by the recommender system 10. A set of vehicle attributes 26 is established, which is a list of categories—e.g., capacity, cargo, cost, efficiency, performance, safety, style, utility, and telematics. The categories define the structure for the vehicle and driver datasets, where vehicles are rated and drivers and vehicles are matched based thereon. In the depicted example, the vehicle attributes include capacity, cargo, cost, efficiency, performance, safety, style, utility, and telematics. Each vehicle will be given a value for each vehicle attribute based on the vehicle data. Additionally, a target value will be determined for each vehicle attribute category for each driver, which is referred to herein as driver attribute target values. Namely, a driver attribute target value is calculated for each vehicle attribute category 26 based on the driver profile data, which includes the user-generated driver profile data 22 as well as the enriched profile data.
[Jackson: 0027] Formulas may be established for each vehicle attribute category 26 in order to calculate the vehicle attribute data based on the vehicle data 28 and to calculate the driver attribute target data based on the driver profile data. The vehicle attribute categories 26 and their respective formulas may be set by a system administrator and define the way by which the drivers and vehicles are compared to one another. For example, the vehicle attribute data may comprise a vehicle attribute value as a rating between 0 and 10 for each of the vehicle attribute categories 26. Similarly, the driver attribute target data may include a value between 1 and 10 for each vehicle attribute category 26, which is the level of import that the attribute category holds in the matching determination.
generate an output including vehicle suggestions for the driver; and
[Jackson: 0041] A vehicle recommendation is generated at step 114 based on at least the vehicle attribute data and the driver attribute target values. The vehicle recommendations are compared to inventory and dealership information at step 116. For example, the driver location information stored in the driver profile data may be utilized to identify dealerships or other sales locations where the recommended vehicle or vehicles are being sold. As described above, the vehicle recommender system 10 may have an inventory database storing information regarding available vehicle inventory. The recommended vehicle information, such as vehicle class IDs outputted by the recommender neural network 50, can be compared against the available vehicle inventory within a threshold mileage of the driver's home location, for example. Moreover, the vehicle inventory information may include price and available purchase methods (e.g. financing rates and/or lease rates) etc. Such information can be compared against the driver profile data, and particularly the driver profile data regarding past and current vehicle financing and/or purchase. Purchase recommendations are then generated at step 118.
presenting, by the one or more processors, the vehicle suggestions to the buyer.
In Jackson-Batie see at least:
[Jackson: 0042] FIG. 6 shows one exemplary user interface visually depicting driver profile data, vehicle recommendations 80, and vehicle purchase recommendations 82 for a particular driver.
Regarding claim 2: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding driver behavior/habits, see [Jackson: 0024] driving patterns/past driving behavior; [Batie: 0018] driving behavior scores.
Regarding claim 3: Rejection is based upon the teachings and rationale applied to claim 2 by Jackson-Batie regarding location, speed or acceleration data, see [Jackson: 0032] user-generated profile data includes location/geographical information.
Regarding claims 4 and 5: Rejections are based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding additional user input, see [Jackson: Fig. 2 (22)].
Regarding claim 6: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding reviews, see:
[Batie: 0043] … The vehicle sales marketplace may include vehicle sales market data 105. In embodiments, this data includes vehicle information, owner information, pricing information, images, reviews, ratings, inventory, location, vehicle history reports, and the like. Further, the vehicle marketplace 110 may include data on vehicle insurance, warranty plans, financing and more. The vehicle sales market data 105 may be supplied as an input to the machine-learning model 122 through the network 180.
Regarding claim 7: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding the output of suggested vehicles is in the form or one or more text or image, see [Jackson: Fig. 6 (80)]
Regarding claim 8: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding an inquiry into purchasing the recommended vehicle, see:
[Batie: 0043] … The marketplace typically provides a user-friendly interface for browsing and searching for vehicles, as well as tools for listing vehicles for sale, managing and communicating with buyers, and completing transactions.
[Batie: 0062] FIG. 6B shows a display of the vehicle valuation 420 of FIG. 4 and a plurality of recommendations 262 on a user device 140. As described above, the user may use the vehicle valuation 420 to make a decision in regards to the provided recommendations 262. In embodiments, the real-time vehicle valuation 420 of the vehicle 100 may show changes if the user accepts a recommendation 262 to add a service to the vehicle 100, drive easier, or trade in the vehicle 100 for a purchase of a new or used vehicle.
Regarding claim 9: Rejection is based upon the teachings and rationale applied to claim 8 by Jackson-Batie and further upon the combination of Jackson-Batie regarding the AI model further trained with price data associated with vehicle, see:
[Batie:0043] … The vehicle sales marketplace may include vehicle sales market data 105. In embodiments, this data includes vehicle information, owner information, pricing information, images, reviews, ratings, inventory, location, vehicle history reports, and the like.
Regarding claim 11: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie and further upon the combination of Jackson-Batie regarding machine learning.
Regarding claim 12: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding system level elements, see at least [Batie: Fig. 1: 0021] memory, processor, network, sensor, display.
Regarding claims 13-15: Rejections are based upon the teachings and rationale applied to claims 1 and 12 by Jackson-Batie and dependents of claim 1 reciting similar subject matter.
Regarding claim 17: Rejection is based upon the teachings and rationale applied to claim 1 by Jackson-Batie regarding system level elements, see at least [Batie: Fig. 1: 0021] memory, processor, network, sensors, display.
Regarding claims 18 and 19: Rejections are based upon the teachings and rationale applied to claims 1 and 17 by Jackson-Batie and dependents of claim 1 reciting similar subject matter.
Claims 10, 16 and 20 are rejected under 35 USC 103 as being unpatentable over Jackson, US 2021/0166103, and Batie, US 2024/0289859, as applied to claims 1, 12 and 17, further in view of Schnitt et al., US 12,211,014 “Schnitt.”
Rejections are based upon the teachings and rationale applied to claims 1, 12 and 17 by Jackson-Batie and further upon the combination of Jackson-Batie-Schnitt. Although Jackson-Batie support image and text as forms of communication between buyers and sellers, Jackson-Batie do not expressly mention text-to-speech and voice-to-text communications. Schnitt on the other hand would have taught Jackson-Batie such techniques.
In Schnitt see at least:
(Schnitt: D221: col. 58, lines 16-25) In embodiments where the platform 1600 supports audible conversations, the conversation system 1606 may include voice-to-text and text-to-speech functionality as well. In these embodiments, the conversation system 1606 may receive audio signals containing the speech of a contact and may convert the contact's speech to a text or tokenized representation of the uttered speech. Upon formulating a response to the contact, the conversation system 1606 may convert the text of the response to an audio signal, which is transmitted to a contact user device 1680.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system and methods of Jackson-Batie to offer voice-to-text and text-to-speech functionality as taught by Schnitt, in order to output a response to one or more contacts.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2022/0044289 (Awoyemi et al.) “Document Term Recognition and Analytics,” discloses: [Abstract] A device receives image data of a contractual document that includes an offer including terms of a proposed transaction, converts the image data to text data that identifies text within the contractual document, and receives preferences information for a recipient of the offer. The device identifies key terms within the contractual document by using term identification to analyze the text. The key terms may include a first key term that identifies subject matter of the proposed transaction and other key terms that are part of the offer. The device determines term scores that correspond to likelihoods of the other key terms being favorable to the recipient by using a data model to analyze the key terms and the preferences information. The device, based on the term scores, generates and provides another device with a recommendation to be used in determining whether the accept the offer.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT M POND whose telephone number is (571)272-6760. The examiner can normally be reached M-F, 8:30 AM-6:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Maria-Teresa (Marissa) Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT M POND/Primary Examiner, Art Unit 3688 August 8, 2026
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