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 .
DETAILED ACTION
The terminal disclaimer filed on 08/04/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of U.S. Serial No. 18/827,404 and U.S. Serial No. 18/828,857 has been reviewed and is accepted. The terminal disclaimer has been recorded.
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 1-3, 7, 10, 13, 16, 21, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over S 20240420155 A1 Banks; Anthony et al. (hereinafter Banks) in view of US 20250026144 A1 Albin; Thomas et al. (hereinafter Albin).
Re claim 1, Banks teaches
1. A computer-implemented method for assisting a user associated with a vehicle, the computer implemented method comprising: (fig. 1-3 and chatbot based assistance for driver 0004)
executing, by a vehicle computing system of the vehicle that comprises one or more processors and memory, a chatbot… in association with the vehicle; (trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
receiving, by the vehicle computing system, and via the chatbot during the conversation with the user in association with the vehicle, user input comprising natural language input; (input: and for example roadside assistance using a microphone to detect users voice 0025 and 0031 in context or topic thereof to route user and respond if the category/topic is found to further conversation in that particular domain fig. 4 e.g. element 406 and subsequent steps with 0016-0018… user submitted a query and gets a response displayed for instance, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
generating, by the vehicle computing system, and via the chatbot…, natural language output based at least in part on the user input; and (response: user submitted a query and gets a response displayed for instance, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
presenting, by the vehicle computing system, and via the chatbot, the natural language output during the conversation. (display: user submitted a query and gets a response displayed for instance with capabilities to receive user voice and output information on speakers 0031, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
However, while Banks teaches learning models trained, it fails to teach GPT including local models in a vehicle thereof:
based upon a generative Al model, wherein: (Albin in fig. 7 for example a generative model is present locally on the vehicles computing system element 720 with 724 and optionally downable new models for local use, here a user communicates related to vehicle sensors, not limited to microphones per se, see 0069 and 0139)
the generative Al model and computer-executable instructions for executing the chatbot are stored in the memory of the vehicle computing system, and (Albin in fig. 7 for example a generative model is present locally on the vehicles computing system element 720 with 724 and optionally downable new models for local use, here a user communicates related to vehicle sensors, not limited to microphones per se, see 0069 and 0139, and applicable vehicle communication i.e. chat analogous with Banks per se, see 0098 0122 0139 and 0179)
the generative Al model is trained, based upon a training dataset, to generate natural language output…(Albin in fig. 7 for example a generative model is present locally on the vehicles computing system element 720 with 724 and optionally downable new models for local use, here a user communicates related to vehicle sensors, not limited to microphones per se, see 0069 and 0139, and applicable vehicle communication i.e. chat analogous with Banks per se, see 0098 0122 0139 and 0179)
…using the generative AI model stored in the memory…(Albin in fig. 7 for example a generative model is present locally on the vehicles computing system element 720 with 724 and optionally downable new models for local use, here a user communicates related to vehicle sensors, not limited to microphones per se, see 0069 and 0139, and applicable vehicle communication i.e. chat analogous with Banks per se, see 0098 0122 0139 and 0179)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks to incorporate the above claim limitations as taught by Albin to allow for combining prior art elements according to known methods to yield predictable results such as using one or more local GPTs in the context of natural language handling, and optional downloadable isolated models to use locally, to communicate with a user i.e. chat with a driver/passenger/user and as a form of a learning model for reduced hallucinations and covering larger context windows for complex tasks, consistent performance, and safer outcomes in situations like driving or flying for instance.
Re claim 10, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 1-3 of Banks
Re claim 16, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 1-3 of Banks
Re claim 2 while Banks teaches learning models trained, it fails to teach GPT:
2. The computer-implemented method of claim 1, wherein the chatbot is a generative pre-trained transformer (GPT) model trained on the training dataset. (Albin 0069 and 00139)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks to incorporate the above claim limitations as taught by Albin to allow for combining prior art elements according to known methods to yield predictable results such as using a GPT as a form of a learning model for reduced hallucinations and covering larger context windows for complex tasks, consistent performance, and safer outcomes in situations like driving or flying for instance.
Re claim 3, Banks teaches
3. The computer-implemented method of claim 1, wherein the training dataset comprises at least one of:
policy information associated with a set of policies associated with coverage of corresponding vehicles,
collision response data indicating actions to perform in response to vehicle collisions,
vehicle data indicating attributes of vehicles, or (trained from conversation 0016-0017 using historical data such as user specifying attributes 0037)
range data indicating at least one of travel ranges or battery life ranges associated with electric vehicles.
Re claim 7, Banks teaches
7. The computer-implemented method of claim 1, wherein the user is an occupant of the vehicle. (user can be any occupant including driver, submitted a query and gets a response displayed for instance with capabilities to receive user voice and output information on speakers 0031, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
Re claim 13, Banks teaches
13. The vehicle computing system of claim 10, wherein:
the user is the occupant of the vehicle, (user can be any occupant including driver, submitted a query and gets a response displayed for instance with capabilities to receive user voice and output information on speakers 0031, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
the chatbot receives the user input as at least one of text input or audio input via one or more input devices of the vehicle, and (user submitted a query and gets a response displayed for instance with capabilities to receive user voice and output information on speakers 0031, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
the chatbot presents the natural language output as at least one of text output or audio output via at least one of a dashboard screen, speakers, or other output devices of the vehicle. (user submitted a query and gets a response displayed for instance with capabilities to receive user voice and output information on speakers 0031, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
Re claim 21, Banks teaches
21. (New) The computer-implemented method of claim 1, wherein the chatbot is configured to generate the natural language output or second natural language output to steer the conversation towards one or more topics associated with the vehicle. (input: and for example roadside assistance using a microphone to detect users voice 0025 and 0031 in context or topic thereof to route user and respond if the category/topic is found to further conversation in that particular domain fig. 4 e.g. element 406 and subsequent steps with 0016-0018… user submitted a query and gets a response displayed for instance, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
22. (New) The computer-implemented method of claim 1, further comprising:
receiving, by the vehicle computing system, sensor data captured by one or more sensors of the vehicle, (input a microphone i.e. sensor in a vehicle: and for example roadside assistance using a microphone to detect users voice 0025 and 0031 in context or topic thereof to route user and respond if the category/topic is found to further conversation in that particular domain fig. 4 e.g. element 406 and subsequent steps with 0016-0018… user submitted a query and gets a response displayed for instance, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
wherein the chatbot generates the natural language output based at least in part on the sensor data. (input a microphone i.e. sensor in a vehicle: and for example roadside assistance using a microphone to detect users voice 0025 and 0031 in context or topic thereof to route user and respond if the category/topic is found to further conversation in that particular domain fig. 4 e.g. element 406 and subsequent steps with 0016-0018… user submitted a query and gets a response displayed for instance, trained chatbot model 0016-0017 for a chatbot to engage with a user 0004-0005 using natural language processing 0026, and augmented reality capable 0022)
Claims 4, 5, 9, 15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over S 20240420155 A1 Banks; Anthony et al. (hereinafter Banks) in view of US 20250026144 A1 Albin; Thomas et al. (hereinafter Albin) and further in view of US 20210293572 A1 Konrardy; Blake et al. (hereinafter Konrardy).
Re claim 4, while Banks in view of Albin teaches learned model trained vehicle chatbot communication with a user for roadside assistance, it fails to teach:
4. The computer-implemented method of claim 1, wherein:
the vehicle is an autonomous vehicle configured to perform autonomous driving operations, and (Konrardy coverage notification for autonomous vehicles 0218 and changes thereof 0215 using an AI model 0229)
the natural language output expresses coverage information indicating whether a policy covers the autonomous driving operations of the vehicle. (Konrardy coverage notification for autonomous vehicles 0218 and changes thereof 0215 using an AI model 0229)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks in view of Albin to incorporate the above claim limitations as taught by Konrardy to allow for combining prior art elements according to known methods to yield predictable results such as to enable usage-based, personalized premiums, enhance risk assessment, and accelerate claims processing by leveraging live telematics data, thereby improving the model of Banks or adding a separate model thereof.
Re claim 5, while Banks in view of Albin teaches learned model trained vehicle chatbot communication with a user for roadside assistance, it fails to teach:
5. The computer-implemented method of claim 4, further comprising:
determining, by the vehicle computing system, that the user input requests a change to the policy to cover the autonomous driving operations; and (Konrardy coverage notification for autonomous vehicles 0218 and changes thereof 0215)
initiating, by the vehicle computing system, the change to the insurance policy based upon the user input. (Konrardy coverage notification for autonomous vehicles 0218 and changes thereof 0215)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks in view of Albin to incorporate the above claim limitations as taught by Konrardy to allow for combining prior art elements according to known methods to yield predictable results such as to enable usage-based, personalized premiums, enhance risk assessment, and accelerate claims processing by leveraging live telematics data, thereby improving the model of Banks or adding a separate model thereof.
Re claims 9, 15, and 20, while Banks in view of Albin teaches learned model trained vehicle chatbot communication with a user for roadside assistance, it fails to teach:
9. The computer-implemented method of claim 1, wherein the vehicle is an autonomous vehicle configured to perform autonomous driving operations, and the method further comprises: (Konrardy controlling an autonomous vehicle in an emergency situation to operate autonomously to a destination to a nearby hospital 0161 and fig. 7)
determining, by the vehicle computing system, that the vehicle has been in a collision; (Konrardy controlling an autonomous vehicle in an emergency situation to operate autonomously to a destination to a nearby hospital 0161 and fig. 7)
determining, by the vehicle computing system, that the vehicle is capable of performing the autonomous driving operations following the collision; and (Konrardy controlling an autonomous vehicle in an emergency situation to operate autonomously to a destination to a nearby hospital 0161 and fig. 7)
causing, by the vehicle computing system, the vehicle to autonomously drive to a hospital or other location where occupants of the vehicle may receive medical assistance. (Konrardy controlling an autonomous vehicle in an emergency situation to operate autonomously to a destination to a nearby hospital 0161 and fig. 7)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks in view of Albin to incorporate the above claim limitations as taught by Konrardy to allow for combining prior art elements according to known methods to yield predictable results such as to increase patient outcomes, emergency system efficiency, and overall road safety including when EMS are short staffed for non-critical or death-imminent without care situations.
Claims 6, 12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over S 20240420155 A1 Banks; Anthony et al. (hereinafter Banks) in view of US 20250026144 A1 Albin; Thomas et al. (hereinafter Albin) and further in view of US 20170168493 A1 MILLER; Kenneth James et al. (hereinafter MILLER).
Re claims 6, 12, and 18, while Banks in view of Albin teaches learned model trained vehicle chatbot communication with a user for roadside assistance, it fails to teach:
6. The computer-implemented method of claim 1, wherein:
the vehicle is an electric vehicle powered by a battery, and (MILLER suggest altering vehicle use to extend battery life 0055)
the natural language output expresses recommended driving actions predicted to extend at least one of a travel range of the electric vehicle or a battery life of the battery. (MILLER suggest altering vehicle use to extend battery life 0055)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks in view of Albin to incorporate the above claim limitations as taught by MILLER to allow for combining prior art elements according to known methods to yield predictable results such as to improve battery efficiency during navigation by communicating to a user and thereby using the chatbot operations of Banks with learning models thereof.
Claims 8, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over S 20240420155 A1 Banks; Anthony et al. (hereinafter Banks) in view of US 20250026144 A1 Albin; Thomas et al. (hereinafter Albin) and further in view of US 20200158518 A1 Kim; Hyo Jeong et al. (hereinafter Kim).
Re claims 8, 14, and 19, while Banks in view of Albin teaches learned model trained vehicle chatbot communication with a user for roadside assistance, it fails to teach:
8. The computer-implemented method of claim 1, wherein the user that engages in the conversation with the chatbot is a Public Safety Answering Point (PSAP) operator, and (Kim PSAP 0003 a PSAP requires an operator to handle emergencies e.g. “requesting a rescue in a vehicle to help speed up the rescue even when the driver is unconscious or cannot make a call” and “The PSAP identifies the traffic accident information received from the eCall system, and transmits accident-related information to the nearest rescue facility to the accident location”)
the method further comprises:
determining, by the vehicle computing system, that the vehicle has been in a collision; (Kim PSAP 0003 “When the traffic accident is detected, the eCall system transmits traffic accident information including an accident location, a vehicle type”, a PSAP requires an operator to handle emergencies e.g. “requesting a rescue in a vehicle to help speed up the rescue even when the driver is unconscious or cannot make a call” and “The PSAP identifies the traffic accident information received from the eCall system, and transmits accident-related information to the nearest rescue facility to the accident location”)
determining, by the vehicle computing system, that one or more occupants of the vehicle are unable to communicate with the PSAP operator; and (Kim PSAP 0003 “When the traffic accident is detected, the eCall system transmits traffic accident information including an accident location, a vehicle type”, a PSAP requires an operator to handle emergencies e.g. “requesting a rescue in a vehicle to help speed up the rescue even when the driver is unconscious or cannot make a call” and “The PSAP identifies the traffic accident information received from the eCall system, and transmits accident-related information to the nearest rescue facility to the accident location”)
initiating, by the vehicle computing system, the conversation between with the PSAP operator [[via]]and the chatbot through a cellular connection (Kim PSAP 0003 “When the traffic accident is detected, the eCall system transmits traffic accident information including an accident location, a vehicle type”, a PSAP requires an operator to handle emergencies e.g. “requesting a rescue in a vehicle to help speed up the rescue even when the driver is unconscious or cannot make a call” and “The PSAP identifies the traffic accident information received from the eCall system, and transmits accident-related information to the nearest rescue facility to the accident location”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Banks in view of Albin to incorporate the above claim limitations as taught by Kim to allow for combining prior art elements according to known methods to yield predictable results such as to improve safety by instantly connecting vehicles to emergency responders, reducing response times, and transmitting critical crash data, which saves lives and lowers mortality rates including for autonomous vehicles, such as by using the chatbot feature of Banks including model learning.
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20240294189 A1 Stählin; Ulrich et al.
Crash avoidance
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
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Michael.Colucci@uspto.gov
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