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
The Amendment filed 05/11/2026 has been entered. Claims 3 and 13 have been cancelled. Therefore, claims 1-2, 4-12, and 14-19 remain pending.
Response to Arguments
Applicant’s arguments, filed 05/11/2026, with respect to the 35 U.S.C. 101 rejection of the claims, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. Applicant’s arguments, with respect to the 35 U.S.C. 102 rejection of claims 1-3, 6-7, 10-13, and 16-17 under Kim (US Patent No. 12,437,755), 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 Therefore, the rejection has been withdrawn.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 in view of Zhao et al. (US Patent Application Publication No. 2019/0371307), in view of Hashizume (US Patent No. 7,333,889).
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.
Claim(s) 1-2, 4, 6-12, 14, and 16-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US Patent Application Publication No. 2019/0371307), hereinafter referred to as Zhao, in view of Hashizume (US Patent No. 7,333,889).
Regarding claim 1, Zhao discloses a device for providing information based on speech recognition, the device comprising: at least one memory storing computer-executable instructions ("The system includes a input device, an output device, a memory, and a processor operatively connected to the input device, the output device, and the memory," Zhao para [0012]);
and at least one processor, wherein the at least one processor is configured to execute the computer-executable instructions to ("The system includes a input device, an output device, a memory, and a processor operatively connected to the input device, the output device, and the memory," Zhao para [0012]): classify an utterance intent of a speech utterance of an occupant of a vehicle ("The general tasks of SLU involve intent determination and slot filling from an utterance. The intent determination task can be considered as a semantic utterance classification problem, while the slot filling task can be tackled as a sequence labeling problem of contiguous words," Zhao para [0004] and "In the memory 132, the slot and intent classifiers 138 are neural networks that recognize the slots and intents of the input sequence of text from the user," Zhao para [0028]),
extract at least one keyword corresponding to a slot of the utterance intent from the speech utterance (Zhao para [0028]),
obtain location information corresponding to the at least one keyword by applying a first deep learning model to the at least one keyword when the utterance intent is route setting (Zhao para [0028]),
wherein the first deep learning model includes: a text encoder trained to encode a training input keyword into a first vector representation (Zhao paras [0029]-[0030]);
and a location decoder trained to output a training output location corresponding to the training input keyword from the first vector representation (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose and provide the occupant with a navigation route from a current location of the vehicle occupant to the location information.
Hashizume teaches a method and a system for car navigation.
Hashizume teaches and provide the occupant with a navigation route from a current location of the vehicle occupant to the location information (Hashizume Fig. 6 reference character S90).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of providing a navigation route. Providing a navigation route, either through a verbal or user interface output, is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This is a logical response to requesting directions or navigation to a certain area or location and would have been an obvious inclusion.
Regarding claim 2, Zhao, in view of Hashizume, discloses all of the limitations of claim 1. Zhao further discloses when the utterance intent is any one of a point of interest (POI) guidance (Zhao para [0028]), wherein the at least one processor is configured to: identify location coordinates based on the at least one keyword ("The bolded text in each input represents the slot words that should be filled into one or more slots using a slot classifier, where the slots pertain to a characteristic of a restaurant such as the type, location, or price of the restaurant," Zhao para [0045]),
obtain a POI name by applying a second deep learning model to the location coordinates, and provide the POI name (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose a route description, an accident information guidance, or a congested section check.
Hashizume teaches a route description, an accident information guidance, or a congested section check ("For example, the traffic-related information includes the place where the traffic congestion occurs, the traffic congested area length, the traffic congestion information composed of a traffic congestion level and a travel time (time needed for the travel) for each of links constituting the traffic congested area, and the traffic regulation information such as traffic closure due to accident or construction work and closure of entrances and exits for highways and the like," Hashizume col. 5 lines 66-67 and col. 6 lines 1-6).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of including traffic information such as route descriptions, accident guidance, and traffic or congestion guidance. Providing a navigation route and updating that route depending on the presence of traffic or an accident is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This is a logical addition to navigation requests would have been an obvious inclusion.
Regarding claim 4, Zhao, in view of Hashizume, discloses all of the limitations of claim 2. Zhao further discloses wherein the second deep learning model includes: a location encoder trained to encode a training input location into a second vector representation ("The attention mechanism allows optimized selection of input sequence for decoding for both content and location information," Zhao para [0005] and Zhao paras [0029]-[0030]);
and a text decoder trained to output a training output keyword corresponding to the training input location from the second vector representation (Zhao paras [0034]-[0035]).
Regarding claim 6, Zhao, in view of Hashizume, discloses all of the limitations of claim 2. Zhao further discloses wherein when the utterance intent is the POI guidance, the at least one processor is configured to: obtain first location around a target location according to the at least one keyword ("The bolded text in each input represents the slot words that should be filled into one or more slots using a slot classifier, where the slots pertain to a characteristic of a restaurant such as the type, location, or price of the restaurant," Zhao para [0045]),
obtain first POI names by applying the second deep learning model to the first location coordinates, and provide the first POI names (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose obtain first location coordinates around a target location.
Hashizume teaches obtain first location coordinates around a target location ("The detail data stores detailed data about a facility composed of a name, coordinates (latitude and longitude), an address, a telephone number, coordinate accuracy, and the like. The detail data is referenced from the index data according to a different retrieval method and is therefore constructed independently of retrieval methods," Hashizume col. 5 lines 4-9).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of obtaining coordinates concerning a point of interest (POI). Providing POI coordinates is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This is a logical addition to navigation requests would have been an obvious inclusion.
Regarding claim 7, Zhao, in view of Hashizume, discloses all of the limitations of claim 2. Zhao further discloses wherein when the utterance intent is the route description, the at least one processor is configured to: obtain second location within the navigation route according to the at least one keyword ("The bolded text in each input represents the slot words that should be filled into one or more slots using a slot classifier, where the slots pertain to a characteristic of a restaurant such as the type, location, or price of the restaurant," Zhao para [0045]),
obtain second POI names by applying the second deep learning model to the second location coordinates, and provide the second POI names (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose obtain second location coordinates within the navigation route.
Hashizume teaches obtain second location coordinates within the navigation route ("The detail data stores detailed data about a facility composed of a name, coordinates (latitude and longitude), an address, a telephone number, coordinate accuracy, and the like. The detail data is referenced from the index data according to a different retrieval method and is therefore constructed independently of retrieval methods," Hashizume col. 5 lines 4-9).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of obtaining coordinates concerning a navigation route. Providing navigation coordinates is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This is a logical addition to navigation requests would have been an obvious inclusion.
Regarding claim 8, Zhao, in view of Hashizume, discloses all of the limitations of claim 2. Zhao further discloses wherein when the utterance intent is the accident information guidance, the at least one processor is configured to: identify third location according to the at least one keyword ("The bolded text in each input represents the slot words that should be filled into one or more slots using a slot classifier, where the slots pertain to a characteristic of a restaurant such as the type, location, or price of the restaurant," Zhao para [0045]),
obtain a third POI name by applying the second deep learning model to the third location coordinates, and provide the third POI name (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose identify third location coordinates for an accident point within a spatial range based on accident information.
Hashizume teaches identify third location coordinates for an accident point within a spatial range based on accident information ("For example, the traffic-related information includes the place where the traffic congestion occurs, the traffic congested area length, the traffic congestion information composed of a traffic congestion level and a travel time (time needed for the travel) for each of links constituting the traffic congested area, and the traffic regulation information such as traffic closure due to accident or construction work and closure of entrances and exits for highways and the like," Hashizume col. 5 lines 66-67 and col. 6 lines 1-6).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of obtaining coordinates concerning an accident on a navigational route. Providing traffic information, such as accident or congestion factors, for rerouting or general navigational purposes is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This would help to reduce drive times and therefore would be a logical addition and an obvious inclusion.
Regarding claim 9, Zhao, in view of Hashizume, discloses all of the limitations of claim 2. Zhao further discloses wherein when the utterance intent is the congested section check, the at least one processor is configured to: identify fourth location according to the at least one keyword ("The bolded text in each input represents the slot words that should be filled into one or more slots using a slot classifier, where the slots pertain to a characteristic of a restaurant such as the type, location, or price of the restaurant," Zhao para [0045]),
obtain a fourth POI name by applying the second deep learning model to the fourth location coordinates, and provide the fourth POI name (Zhao paras [0034]-[0035]).
However, Zhao fails to disclose identify fourth location coordinates for a congested section within a spatial range based on traffic information.
Hashizume teaches identify fourth location coordinates for a congested section within a spatial range based on traffic information ("For example, the traffic-related information includes the place where the traffic congestion occurs, the traffic congested area length, the traffic congestion information composed of a traffic congestion level and a travel time (time needed for the travel) for each of links constituting the traffic congested area, and the traffic regulation information such as traffic closure due to accident or construction work and closure of entrances and exits for highways and the like," Hashizume col. 5 lines 66-67 and col. 6 lines 1-6).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Hashizume’s teaching of obtaining coordinates concerning traffic or congestion on a navigational route. Providing traffic information, such as accident or congestion factors, for rerouting or general navigational purposes is a well-known, conventional, and routine technique within the art of vehicle guidance systems. This would help to reduce drive times and therefore would be a logical addition and an obvious inclusion.
Regarding claim 10, Zhao, in view of Hashizume, discloses all of the limitations of claim 1. Zhao further discloses a vehicle comprising the device of claim 1 ("Examples of hardware embodiments that implement the system 100 include, for example, an in-vehicle information system, personal computer, mobile electronic device such as a smartphone or wearable computing device, and the like," Zhao para [0022]).
As to claims 11-12, 14, and 16-19, method claims 11-12, 14, and 16-19 and system claims 1-2, 4, and 6-9 are related as system and method of using same, with each claimed element’s function corresponding to the system step, respectively. Accordingly, claims 11-12, 14, and 16-19 are similarly rejected under the same rationale as applied above with respect to the system claims.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhao, in view of Hashizume, and further in view of Sharifi (US Patent Application Publication No. 2025/0237511).
Regarding claim 5, Zhao, in view of Hashizume, discloses all of the limitations of claim 4. However, Zhao fails to disclose wherein the text encoder and the location encoder have trained to reduce a difference between the first vector representation and the second vector representation when the training input keyword corresponds to the training input location.
Sharifi teaches a system and method for navigation.
Sharifi teaches wherein the text encoder and the location encoder have trained to reduce a difference between the first vector representation and the second vector representation when the training input keyword corresponds to the training input location ("More specifically, in these aspects, the NLP model 109a3 may be or include a machine learning model (e.g., a large language model (LLM)) trained by the ML module 109b using one or more training data sets of text in order to output one or more training intents and one or more training destinations, as described further herein. For example, artificial neural networks, recurrent neural networks, deep learning neural networks, a Bayesian model, and/or any other suitable ML model 109b1 may be used to train and/or otherwise implement the NLP model(s) 109a3," Sharifi para [0038] and "The weights may be modified as the network is iteratively trained, by using one of several gradient descent algorithms, to reduce loss and to cause the values output by the network to converge to expected, or “learned”, values," Sharifi para [0039]).
It would have been obvious to one of ordinary skill within the art before the effective filing date of the claimed invention to have modified Zhao’s teaching of classifying an utterance intent of an individual in a vehicle and using a deep learning model to obtain location information by including Sharifi’s teaching of utilizing a loss function to cause the model to converge to a correct value. Loss functions are a well-known, conventional, and routine technique within the art of machine learning, deep learning, and artificial intelligence. They allow the model to effectively learn from the training sets, optimizing their accuracy and ensuring that they are performing correctly. This would have been an obvious inclusion.
As to claim 15, method claim 15 and system claim 5 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly, claim 15 is similarly rejection under the same rationale as applied above with respect to the system claim.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US Patent No. 10,885,897
Zheng et al., Intent Detection and Semantic Parsing for Navigation Dialogue Language Processing, 10/19/2017
Zhang et al., Multi-vehicle routing problems with soft time windows: A multi-agent reinforcement learning approach, 11/25/2020
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/ADAM MICHAEL WEAVER/ Examiner, Art Unit 2658
/RICHEMOND DORVIL/ Supervisory Patent Examiner, Art Unit 2658
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