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 .
Introduction
This is a response to applicant’s submissions filed on February 4, 2026. Claims 1-5, 7-15, and 17-20 are pending.
Examiner' s Note
Examiner has cited particular paragraphs / columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Applicant is reminded that the Examiner is entitled to give the broadest reasonable interpretation to the language of the claims. Furthermore, the Examiner is not limited to Applicants' definition which is not specifically set forth in the disclosure.
Response to Arguments
All of applicant’s arguments filed February 4, 2026 have been considered.
Regarding applicant’s argument that the “machine learning models in the cloud” of Toyota are not specifically described as a “machine-learned large language model” (Applicant’s Response, pg. 15). The argument is moot in view of the new rejection below necessitated by applicants amendments.
Regarding applicant’s argument that Sahoo describes comparing a text segment to a local intent (Applicant’s Response, pg. 16), the examiner agrees.
Regarding applicant’s argument that Sahoo does not disclose “determining a type of the first prompt message” (Applicant’s Response, pg. 16), the examiner respectfully disagrees. Sahoo discloses determining if a text input matches a local intent (i.e., first type) and transmitting the text input to a remote service if the text input does not match a local intent (i.e., second type).
Regarding applicant’s argument that the cited references fail to teach or suggest "modifying, in response to determining the first prompt message to be the second type, the first prompt message to add conversation context information to the first prompt message, the conversation context information including a predetermined number of recent prompt messages" (Applicant’s Response, pg. 16), the examiner agrees. The argument is moot in view of the new rejection below.
Drawings
The drawings were received on February 4, 2026. These drawings are acceptable.
Specification
Amendments to the specification were received on February 4, 2026.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7, 9, 11-14, 17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Toyota (Toyota’s New In-House Intelligent Assistant Learns Voice Commands and Gets Smarter Over Time), as evidenced by ToyotaJeff Review (*It Can do WHAT??* DEMO of NEW Toyota Audio Multimedia System Voice Commands), in view of Sahoo (US 2024/0075944), Singh (US 2024/0095077), and Renard (US 2016/0098992).
Toyota and ToyotaJeff Review are both referring to the same Intelligent Assistant as Toyota states that the assistant is in the 2022 Toyota Tundra (Paragraph 3) and ToyotaJeff Review is a demo of the multimedia system in a 2022 Toyota Tundra (0:10).
Regarding claims 1, 11, and 19, Toyota, as evidenced by ToyotaJeff Review, discloses a computing system for a vehicle, the computing system comprising:
one or more processors; and
one or more tangible, non-transitory, computer readable media storing instructions for execution by the one or more processors to cause the computing system to perform operations comprising:
accessing a first voice prompt indicative of a speech-based voice query provided by a user of the vehicle (Toyota, paragraph 1 regarding telling the voice assistant I fancy some coffee);
determining, based on the first voice prompt, a first prompt message corresponding to a text-based transcription of the first voice prompt (Toyota, paragraph 5 regarding transcribing the driver's speech to text);
processing the first prompt message to generate a digital message response based on the first prompt message (Toyota, paragraph 8 regarding the transcribed command text being processed by Toyota Connected's machine learning models);
converting the digital message response to a speech-based voice response; and
providing the speech-based voice response as audio output to the user of the vehicle (ToyotaJeff Reviews, 3:05 regarding asking for an Italian restaurant and it replying saying it found 15 results and naming the first one on the list. By providing an audio response, Toyota Connect is determining a response to the message and then converting the response to a speech-based voice response.).
Toyota, does not disclose determining a type of the first prompt message as one of a first type for processing by a vehicle system onboard the vehicle or a second type for processing by a machine-learned large language model provided in a remote computing platform;
modifying, in responds to determining the first prompt message to be the second type, the first prompt message to add conversation context information to the first prompt message, the conversation context information including a predetermined number of recent prompt messages; and
processing, in response to determining the first prompt message to be the second type, the first prompt message including the conversation context information with the machine-learned large language model trained to generate a digital message response based on the first prompt message.
Sahoo teaches determining a type of the first prompt message as one of a first type for processing by a vehicle system onboard the vehicle or a second type for processing in a remote computing platform (Sahoo, Fig. 3 regarding determining if the converted speech input text matches a local intent and transmitting the text to a remote service if the text does not match a local intent (i.e., if the text matches it is a first type and if the text does not match it is the second type)).
Toyota and Sahoo are considered to be analogous to the claimed invention because they are in the same field of vehicle assistants. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota to incorporate transmitting the speech input to a remote service if it is not known on the local assistant, as disclosed by Sahoo, with a reasonable expectation of success because doing so would yield the predictable result of reducing the amount of computing power required by only using the remote service when needed.
Renard teaches modifying, in responds to determining the first prompt message to be the second type, the first prompt message to add conversation context information to the first prompt message, the conversation context information including a predetermined number of recent prompt messages (Renard, Fig. 5 & [0105] regarding pre-processing recognized speech based on context using a NLU, [0054] regarding context comprising dialog history, & [0065] regarding the context information being long term and middle term contexts).
Toyota and Renard are considered to be analogous to the claimed invention because they are in the same field of intelligent assistants. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota, as modified, to incorporate using processed context information to better determine what a user is requesting, as disclosed by Renard, with a reasonable expectation of success because doing so would yield the predictable result of improving the accuracy of response from the assistant.
Singh teaches using a large language model for processing text to calculate an output ([0070]).
Toyota and Singh are considered to be analogous to the claimed invention because they are in the same field of machine learning. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate using a large language model, as disclosed by Singh, to process and generate a digital message as disclosed by Toyota, as modified, with a reasonable expectation of success because doing so would yield the predictable result of using generating a message with a higher accuracy.
Toyota, as modified, teaches determining a type of the first prompt message as one of a first type for processing by a vehicle system onboard the vehicle or a second type for processing by a machine-learned large language model provided in a remote computing platform (Comparing the text to determine if it is something that can be done by the onboard voice assistant or needs to be sent to the intelligent assistant on the cloud (Toyota & Sahoo));
modifying, in responds to determining the first prompt message to be the second type, the first prompt message to add conversation context information to the first prompt message, the conversation context information including a predetermined number of recent prompt messages (Using context information to better understand what the user is requesting (Renard)); and
processing, in response to determining the first prompt message to be the second type, the first prompt message including the conversation context information with the machine-learned large language model trained to generate a digital message response based on the first prompt message (Using the LLM to process the users request after taking into account context information (Toyota & Singh)).
Regarding claims 2 and 12, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle and computer-implemented method as claimed in claim 1 and 11, respectively, further comprising:
converting the digital message response to a graphical response; and
providing the graphical response as visual output to the user of the vehicle in conjunction with providing the speech-based voice response as audio output to the user of the vehicle (ToyotaJeff Reviews, 3:05 regarding asking for an Italian restaurant and it replying saying it found 15 results and naming the first one on the list and displaying the list on the navigation screen).
Regarding claims 3 and 13, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle and computer-implemented method as claimed in claim 1 and 11, respectively, wherein:
the speech-based voice query provided by a user of the vehicle comprises a request for information associated with a point of interest category in a particular geographic area (ToyotaJeff Reviews, 3:05 regarding asking for an Italian restaurant); and
the speech-based voice response is indicative of a plurality of particular points of interest determined by the machine-learned large language model to be associated with the point of interest category and the particular geographic area (ToyotaJeff Reviews, 3:05 regarding it replying saying it found 15 results and naming the first one on the list).
Regarding claims 4 and 14, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle and computer-implemented method as claimed in claim 3 and 13, respectively, further comprising:
receiving user selection data indicative of a selected point of interest from the plurality of particular points of interest (ToyotaJeff Reviews, 4:35 regarding the user selecting a parking location from the list provided); and
generating vehicle navigation data as an output to the user of the vehicle, the vehicle navigation data indicative of navigational directions to the selected point of interest (ToyotaJeff Reviews, 4:35 regarding generating navigation data to the selected parking location).
Regarding claims 7, 12, and 20, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle, computer-implemented method, and one or more tangible, non-transitory, computer readable media as claimed in claim 1, 11, and 19, respectively, further comprising:
performing a validation function to the digital message response before providing the speech-based voice response as audio output to the user of the vehicle (ToyotaJeff Reviews, 3:35 regarding displaying the directions to the address before saying that it found the location. See paragraph 0045 of the specification regarding displaying on the head unit and also reading aloud by the voice assistance system being considered performing a validation function.).
Regarding claim 9, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle as claimed in claim 1, wherein the first prompt message corresponds to one or more of a full transcription, a partial transcription, or a modified transcription of the first voice prompt (Toyota, paragraph 5 regarding transcribing the driver's speech to text).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Toyota in view of Ling (CN 116147641) and Yasui (US 2023/0311656).
Regarding claims 5 and 15, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle and computer-implemented method as claimed in claim 4 and 14, respectively, but does not teach further comprising:
comparing a first address associated with the selected point of interest as determined by the machine-learned large language model with a second address associated with the selected point of interest determined from a vehicle navigation database to determine a more recently updated address associated with the selected point of interest; and
providing vehicle navigation data associated with the more recently updated address associated with the selected point of interest as the output to the user of the vehicle.
Ling teaches comparing an extracted offline map with an online map and using the online map if it is more feasible ([0061]).
Toyota and Ling are considered to be analogous to the claimed invention because they are in the same field of vehicle navigation. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota to incorporate comparing an offline map with an online map, as disclosed by Ling, with a reasonable expectation of success because doing so would yield the predictable result of ensuring the vehicle is using the most up to date information.
Yasui teaches that map information includes address information ([0048]).
Toyota and Ling are considered to be analogous to the claimed invention because they are in the same field of vehicle navigation. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota, as modified, to incorporate using address data in the comparison, as disclosed by Ling, with a reasonable expectation of success because doing so would yield the predictable result of ensuring the vehicle is using the most up to date address.
Therefore, Toyota, as modified, teaches comparing a first address associated with the selected point of interest as determined by the machine-learned large language model with a second address associated with the selected point of interest determined from a vehicle navigation database to determine a more recently updated address associated with the selected point of interest and
providing vehicle navigation data associated with the more recently updated address associated with the selected point of interest as the output to the user of the vehicle.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Toyota in view of DeLuca (US 2021/0150386).
Regarding claims 8 and 18, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle and computer-implemented method as claimed in claim 1 and 11, respectively, but does not explicitly disclose wherein determining the first prompt message corresponding to a text-based transcription of the first voice prompt is implemented by a prompt processing system having been trained using terminology from a user guide specific to the vehicle.
DeLuca teaches wherein determining the first prompt message corresponding to a text-based transcription of the first voice prompt is implemented by a prompt processing system having been trained using terminology from a user guide specific to the vehicle (DeLuca, [0016] regarding training a machine learning system using vehicle owner manuals and service manuals).
Toyota and DeLuca are considered to be analogous to the claimed invention because they are in the same field of vehicle assistants. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota to incorporate training the machine learning model using the vehicle owner manual, as disclosed by DeLuca, with a reasonable expectation of success because doing so would yield the predictable result of increasing the accuracy of actions performed by the machine learning model.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Toyota in view of Park (US 2024/0096311).
Regarding claim 10, Toyota, as evidenced by ToyotaJeff Reviews and in view of Sahoo, Singh, and Renaud, teaches the computing system for a vehicle as claimed in claim 1, but does not explicitly disclose wherein the machine-learned large language model comprises a generative pre-trained transformer model.
Park teaches wherein the machine-learned large language model comprises a generative pre-trained transformer model (Park, [0108] regarding the embedding layer having a generative pre-trained transformers, [0106] regarding the embedding layer being part of a machine reading comprehension model, & [0184] regarding answering a question using the machine reading comprehension model).
Toyota and Park are considered to be analogous to the claimed invention because they are in the same field of vehicle assistants. It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Toyota to incorporate using a generative pre-trained transformer, as disclosed by Park, with a reasonable expectation of success because doing so would yield the predictable result of generating an output from the user’s request.
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 ALEX GRIFFIN whose telephone number is (703)756-1516. The examiner can normally be reached Monday - Thursday 7:30am - 5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ERIN BISHOP can be reached at (571)270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEX B GRIFFIN/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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