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 .
Response to Amendment
All pending claims 1-20 filed December 11, 2025 are examined in this final office action necessitated by amendment.
Response to Arguments
Applicant’s arguments, see remarks filed December 11, 2025 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 Therefore, the rejection has been withdrawn.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 necessitated by amendment. The combination of Krasadakis-Christeas-Leibowitz is withdrawn in favor Edwards-Batie necessitated by amendment. All arguments are hinged on Krasadakis-Christeas-Leibowitz and are moot.
35 USC § 101
All independent claims require a trained generative artificial intelligence model to be trained on vehicle data and driver data. The instant specification has sufficient depth and application of machine learning and learning model training to render independent claims and respective dependents as a practical application under Step 2A (second prong). Execution of each independent claim and respective dependents effectively improves computing efficiency by reducing processing cycles and network traffic.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Independent claims 1, 12 and 17 contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claimed subject matter “wherein the generative AI model is trained on vehicle data to identify vehicle traits other than price …” injects a negative limitation. Any negative limitation or exclusionary proviso must have basis in the original disclosure. There is no basis in the original disclosure for “the generative AI model is trained on vehicle data to identify vehicle traits other than price…”
The following are excerpts from the instant specification as filed:
[0068] … The training data 310 may also include price data associated with vehicles with various features and in various conditions.
[0087] … The generative AI may have been further trained on price data to assess whether a contacted seller’s quoted price is fair for the vehicle.
The Applicant is attempting to define the claimed invention in terms of what it is not rather than pointing out the invention as disclosed. Excluding price from training data is not positively recited in the specification.
For Applicant’s convenience, MPEP 2173.05(i) Negative Limitations [R-07.2022] is recited below:
The current view of the courts is that there is nothing inherently ambiguous or uncertain about a negative limitation. So long as the boundaries of the patent protection sought are set forth definitely, albeit negatively, the claim complies with the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Some older cases were critical of negative limitations because they tended to define the invention in terms of what it was not, rather than pointing out the invention. Thus, the court observed that the limitation "R is an alkenyl radical other than 2-butenyl and 2,4-pentadienyl" was a negative limitation that rendered the claim indefinite because it was an attempt to claim the invention by excluding what the inventors did not invent rather than distinctly and particularly pointing out what they did invent. In re Schechter, 205 F.2d 185, 98 USPQ 144 (CCPA 1953).
A claim which recited the limitation "said homopolymer being free from the proteins, soaps, resins, and sugars present in natural Hevea rubber" in order to exclude the characteristics of the prior art product, was considered definite because each recited limitation was definite. In re Wakefield, 422 F.2d 897, 899, 904, 164 USPQ 636, 638, 641 (CCPA 1970). In addition, the court found that the negative limitation "incapable of forming a dye with said oxidized developing agent" was definite because the boundaries of the patent protection sought were clear. In re Barr, 444 F.2d 588, 170 USPQ 330 (CCPA 1971).
Any negative limitation or exclusionary proviso must have basis in the original disclosure. If alternative elements are positively recited in the specification, they may be explicitly excluded in the claims. See In re Johnson, 558 F.2d 1008, 1019, 194 USPQ 187, 196 (CCPA 1977) ("[the] specification, having described the whole, necessarily described the part remaining."). See also Ex parte Grasselli, 231 USPQ 393 (Bd. App. 1983), aff’d mem., 738 F.2d 453 (Fed. Cir. 1984). In describing alternative features, the applicant need not articulate advantages or disadvantages of each feature in order to later exclude the alternative features. See Inphi Corporation v. Netlist, Inc., 805 F.3d 1350, 1356-57, 116 USPQ2d 2006, 2010-11 (Fed. Cir. 2015). The mere absence of a positive recitation is not basis for an exclusion. However, a lack of literal basis in the specification for a negative limitation may not be sufficient to establish a prima facie case for lack of descriptive support. Ex parte Parks, 30 USPQ2d 1234, 1236 (Bd. Pat. App. & Inter. 1993). "Rather, as with positive limitations, the disclosure must only 'reasonably convey to those skilled in the art that the inventor had possession of the claimed subject matter as of the filing date.' ... While silence will not generally suffice to support a negative claim limitation, there may be circumstances in which it can be established that a skilled artisan would understand a negative limitation to necessarily be present in a disclosure." Novartis Pharms. Corp. v. Accord Healthcare, Inc., 38 F.4th 1013, 2022 USPQ2d 569 (Fed. Cir. 2022) (quoting Ariad Pharm. Inc. v. Eli Lilly & Co., 589 F.3d 1336, 1351, 94 USPQ2d 1161, 1172). Any claim containing a negative limitation which does not have basis in the original disclosure should be rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, as failing to comply with the written description requirement. See MPEP § 2163 - § 2163.07(b) for a discussion of the written description requirement of 35 U.S.C. 112(a) and pre-AIA 35 U.S.C. 112, first paragraph.
Priority
The effective priority date is the filing date (March 6, 2024) of this non-provisional application.
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows: The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosures of the prior-filed applications, Application Nos. 63/462,101, 63/528,141 and 63/624,616 fail to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claimed subject matter “wherein the generative AI model is trained on vehicle data to identify vehicle traits other than price …” in the instant non-provisional claims as amended is not supported in any provisional application identified above.
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 17-20 are rejected under 35 USC 103 as being unpatentable over Edwards et a., US 2020/0202410 “Edwards,” in view of Batie et al., US 2024/0289859 “Batie.”
In Edwards 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,
[Edwards: 0015] The systems and methods described herein are an improvement to conventional methods of determining a new vehicle to purchase, as the systems and methods described herein use data that is unique and personalized to the driver to provide a tailored recommendation to the driver. While general surveys and quizzes exist that may match a prospective vehicle purchaser to one or more vehicles, these conventional surveys and quizzes do not rely on actual vehicle sensor data and instead rely on the driver's prioritization of desired features of a vehicle. The driver's prioritization of desired features of a prospective vehicle may not completely inform the driver's purchasing decision, as the driver may not be fully aware of a mismatch between the driver's needs and the features of the driver's current vehicle.
[Edwards: 0016] As used herein, “driver” may refer to a human driver of a vehicle or one or more processors configured to operate an autonomous or semi-autonomous vehicle. “Driver” may also be used herein to refer to the operator of the vehicle and the owner or lessee of the vehicle.
obtaining, by the one or more processors, driver data associated with the driver,
[Edwards: 0014] Disclosed herein are systems, vehicles, and methods for recommending a new vehicle to purchase. The systems and methods described herein use sensors of a current vehicle of a driver to detect data associated with the driver's capabilities and preferences. The sensor data is analyzed to determine one or more other subsequent vehicles that may better suit the driver when the driver is interested in a new vehicle.
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 Edwards and further upon the combination of Edwards-Batie.
In Edwards see at least:
[Edwards: 0038] The computing device may display in the user interface 304 a suggested vehicle based on the sensor data of the driver's current vehicle. As shown in FIG. 3B, when the driver 308 is speaking with a salesperson 306, the salesperson may view vehicle recommendation data on a user interface 310. The vehicle recommendation data may be determined by the computing device 312 based on sensor data collected by the current vehicle 314. In some embodiments, more than one vehicle may be suggested as a subsequent vehicle for the driver, and each of the multiple suggested vehicles may have a corresponding score indicating a compatibility with the driver or an increase in compatibility with the driver as compared to the driver's current vehicle. Each vehicle may have an associated ideal driver profile, and based on the sensor data, the driver may have a driver profile constructed by a computing device. The driver's profile and the ideal driver profile of each vehicle may be compared to determine a compatibility score between the driver and each vehicle.
[Edwards: 0056] The memory 418 may also store sensor data and/or driver profile data of the driver corresponding to previous vehicles owned and/or operated by the driver. This previous vehicle data of the driver may be used as additional data points of reference when determining one or more suggested subsequent vehicles for the driver.
[Edwards: 0060] The processor 414 of the remote data server 412 and/or the processor 422 of the computing device 420 may use machine learning techniques to determine trends based on the sensor data and may also use machine learning techniques to determine one or more suggested subsequent vehicles. One or more algorithms for determining trends or outliers in the sensor data may also be used to determine any suggested subsequent vehicles.
Although Edwards uses artificial intelligence (i.e. machine learning) to a) evaluate driver data collected from the driver’s current vehicle sensor data and b) use the driver profile as additional data points of reference when determining one or more suggested vehicles for the driver, Edwards does not expressly mention techniques that use generative artificial intelligence. Batie on the other hand would have taught Edwards such techniques that use generative machine learning model to make vehicle recommendations.
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 Edwards 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 other than price and is configured to:
In Edwards-Batie see at least:
[Edwards: 0055] The memory 418 may be a non-transitory memory configured to store sensor data of the vehicle 402. The memory 418 may also store data associated with the design and features of the vehicle 402 in addition to the design and features of other vehicles that may be suggested to the driver. In an example embodiment, the memory 418 may store an ideal driver profile associated with each vehicle (and each trim level and each options package of each vehicle). The driver of the vehicle 402 may have a driver profile that is determined based on the sensor data of the vehicle 402. The driver profile of the driver of the vehicle 402 may be compared with each ideal driver profile of the many trim levels and options packages of each vehicle. Based on this comparison, an identification of one or more compatible vehicles may be made. The memory 418 may be a sorted collection of the sensor data received by the vehicle 402. The memory 418 may sort the data in any way that increases the processor's ability to efficiently access the data. The transceiver 416 may be configured to transmit and receive data, similar to transceiver 408. Please note: No mention of price being factored into user/driver data.
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
[Edwards: 0023] The suggested subsequent vehicle may have smaller dimensions or dimensions similar to a previous vehicle that the driver was able to operate without any collisions. In another example, if the sensors indicate that the driver of the vehicle 102 frequently has occupants at maximum capacity, a larger vehicle may be suggested. In another example, if the sensors indicate that the driver of the vehicle 102 frequently leaves many seats unoccupied, a smaller vehicle may be suggested. In yet another example, if the sensors indicate that the driver of the vehicle 102 frequently carries large amounts of cargo, a truck or sports utility vehicle may be suggested instead of the coupe that is currently being driven.
generate an output including vehicle suggestions for the driver; and
[Edwards: 0032] FIG. 3A illustrates a user interface 304 displayed by a display screen of a device 302. The user interface 304 may be generated by computer software executed by a computing device that is specially programmed and specially constructed to facilitate vehicle sales.
presenting, by the one or more processors, the vehicle suggestions to the buyer.
In Edwards-Batie see at least:
[Edwards: 0057] The remote data server 412 may be communicatively coupled to a computing device 420 used for displaying and/or determining a suggested subsequent vehicle for the driver of the vehicle 402. The remote data server 412 may be directly connected to the computing device 420 via a data cable or may be connected to the computing device 420 via a network, such as a local area network or the Internet.
Regarding claim 2: Rejection is based upon the teachings and rationale applied to claim 1 by Edwards-Batie regarding driver behavior/habits.
Regarding claim 3: Rejection is based upon the teachings and rationale applied to claim 2 by Edwards-Batie regarding location, speed or acceleration data, see [Edwards: 0027, 0028 & 0029].
Regarding claims 4 and 5: Rejections are based upon the teachings and rationale applied to claim 1 by Edwards-Batie regarding additional user input.
Regarding claim 6: Rejection is based upon the teachings and rationale applied to claim 1 by Edwards-Batie regarding reviews, see:
[Edwards: 0002] A prospective vehicle purchaser may perform research on a new vehicle in many different ways. The prospective vehicle purchaser may conduct research online by reading reviews from vehicle experts, the prospective vehicle purchaser may ask friends or family about their opinions of their respective vehicles, or the prospective vehicle purchaser may simply go to a vehicle dealership and ask the advice of a salesperson.
It would have been obvious to one of ordinary skill in the art before the effective filing date to ascertain the use of vehicle reviews as an additional reference point when making a vehicle recommendation.
Regarding claim 7: Rejection is based upon the teachings and rationale applied to claim 1 by Edwards-Batie regarding the output of suggested vehicles is in the form or one or more text or image, see Edwards: Fig. 3A.
Regarding claim 8: Rejection is based upon the teachings and rationale applied to claim 1 by Edwards-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 Edwards-Batie and further upon the combination of Edwards-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 Edwards-Batie and further upon the combination of Edwards-Batie regarding machine learning.
Regarding claim 12: Rejection is based upon the teachings and rationale applied to claim 1 by Edwards-Batie regarding system level elements, see Edwards Fig. 4 (400) memory, processor, network, sensors, ECU & display.
Regarding claims 13-15: Rejections are based upon the teachings and rationale applied to claims 1 and 12 by Edwards-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 Edwards-Batie regarding system level elements, see Edwards Fig. 4 (400) memory, processor, network, sensors, ECU & display.
Regarding claims 18 and 19: Rejections are based upon the teachings and rationale applied to claims 1 and 17 by Edwards-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 Edwards, US 2020/0202410, 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 Edwards-Batie and further upon the combination of Edwards-Batie-Schnitt. Although Edwards-Batie support image and text as forms of communication between buyers and sellers, Edwards-Batie do not expressly mention text-to-speech and voice-to-text communications. Schnitt on the other hand would have taught Edwards-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 Edwards-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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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, Jeffrey Smith can be reached at 571-272-6763. 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 March 19, 2026