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 .
Status of the Claims
The claims 26-45 are currently pending and have been examined. Applicant has amended claims 26, 31, 33-36, 38, 39, and 41 and 43.
Response to Arguments/Amendments
The amendment filed April 27, 2026 has been entered. Claims 26-45 are currently pending in the Application. Applicant's amendments and arguments regarding the 35 U.S.C. 101 mental process rejection have been fully considered and they are persuasive. As such, the rejection under 35 U.S.C. 101 has been withdrawn.
Applicant’s arguments with respect to claims 26-45 under 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant argues that Kim only discloses a single AI model to infer infant states and serves a different purpose than the claimed architecture by predicting the state of an already-identified infant passenger, and therefore none of the cited references teach “one or more circuits configured to implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types” (See Applicant’s remarks, page 15). The Examiner respectfully disagrees. Kim teaches an AI device including a learning processor and processor configured to utilize trained artificial neural network/deep-learning models to predict the state of the infant based on sensor information (See paragraphs [0042], [0087], [0101].). The Examiner maintains that, under broadest reasonable interpretation, these teachings reasonably correspond to trained neural networks outputting probabilities for different assistance types based on sensor data. Applicant’s argument focuses on whether Kim discloses assistance type classification of an unknown passenger and that the assistance type categories are those such as disabled, elderly, pregnant, or infant. However, the claim does not require the assistance type to be a classification of an unknown passenger, nor does it require any particular assistance type categories. Applicant’s additional argument regarding the claimed architecture addressing a specific technical challenge does not change what is set forth in the claimed limitations and the broadest reasonable interpretation of said claims.
Applicant further argues that Kim’s single AI model does not have a separate class-fusion circuit that fuses probability outputs from a plurality of neural networks and is not the specific architectural component claimed, and therefore none of the cited references teach or suggest “a class-fusion circuit configured to identify the assistance type of the passenger based on the probabilities calculated by the neural networks in the plurality of neural networks” (See Applicant’s remarks, page 16). The Examiner respectfully disagrees. Kim teaches an AI device including a learning processor and processor configured to utilize trained artificial neural network/deep-learning models to predict the state of the infant based on sensor information (See paragraphs [0042], [0087], [0101].), therefore teaching circuitry configured to perform the recited function. Applicant’s argument focuses on whether Kim discloses a specific architectural component that combines probability outputs from multiple distinct neural networks to arrive at a final classification. However, the claim does not recite the specific architectural details argued, and under the broadest reasonable interpretation the cited circuitry configured to perform the recited function reasonably corresponds to the claimed class-fusion circuit. Additionally, the rejection relies on Jin for the portion of the limitation “configured to identify the assistance type of the passenger”, which applicant has not substantively addressed.
Applicant further argues that it would not have been obvious to modify Kim’s single-model approach to arrive at the claimed multi-neural network architecture with a class-fusion circuit, stating that Kim has no need for a multi-classifier architecture because the infant is already identified (See Applicant’s remarks, page 16). The Examiner respectfully disagrees. The motivation to combine has been set forth in the rejection, and the fact that Kim determines the passenger to be an infant does not prevent a reasonable combination of the references under 35 U.S.C. 103. The applicant has not provided adequate reasoning why the motivation lacks rational underpinning to combine. Applicant lastly argues that it would not have been obvious to combine Jin, Bernd, and Kim, stating that none of the references teach or suggest the specific architecture of multiple specialized neural networks, each calculating assistance-type probabilities from sensor data, with a class-fusion circuit combining those outputs and that each reference addresses a fundamentally different problem (See Applicant’s remarks, page 17). The Examiner respectfully disagrees. The motivation to combine has been set forth in the rejection, including Bernd and Kim’s respective motivations of improving individualized vehicle functionality and accuracy of vehicle functions (See Bernd paragraph [0015].) and increasing a user’s convenience using services (See Kim paragraph [0004].). The fact that the references address related subject matter while differing in particular implementations does not preclude a reasonable combination under 35 U.S.C. 103. Applicant’s argument regarding the newly amended limitations of claims 31 and 43 are addressed by the newly applied teachings of Hicock in the rejection set forth below. Accordingly, the combination of Jin, Bernd, Kim, and Hicock teaches the limitations argued.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 26-30, 37-42, 44, 45 is/are rejected under 35 U.S.C. 103 as being unpatentable over JIN (KR 20210078071 A) in view of BERND (EP 3718797 A1) and KIM (US 20210155262 A1).
Regarding Claim 26, JIN teaches A passenger-assistance system for a vehicle, the passenger-assistance system comprising: first circuitry configured to perform one or more first-circuitry operations including identifying an assistance type of a passenger of the vehicle (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”); third circuitry configured to perform one or more third-circuitry operations including generating a modified route for a ride for the passenger at least in part by modifying an initial route for the ride based on the identified assistance type (See at least paragraph [0029], “The processor is configured to: a first route from the current location to the specific passenger boarding point, a second route from the specific passenger boarding point to the specific passenger boarding point, and from the specific passenger boarding point to the destination location. A third route may be provided.” The system generates routes for transporting a specific passenger, including routes between boarding and disembarking points selected to safely accommodate the specific passenger, which corresponds to modifying an initial route for a ride based on the identified assistance type.); and fourth circuitry configured to perform one or more fourth-circuitry operations including one or both of conducting a pre-ride safety check based on the identified assistance type and conducting a pre-exit safety check based on the identified assistance type (See at least paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.”); configured to identify an assistance type of the passenger (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”).
JIN does not explicitly disclose, however, BERND, in the same field of endeavor, teaches second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type (See at least paragraph [0035], “According to a yet further preferred embodiment, the control device may be configured to initiate individual and/or automatic climatization and/or seat heating and/or cooling for at least one or a plurality of passengers within the cabin. These vehicle functions may preferably be provided dependent on a detected seat occupation and/or seat position of the respective passenger and/or dependent on a detected passenger condition. This allows an efficient control of vehicle functions, and may increase the passenger comfort. The risk of discomfort due to temperatures being too high or too low may be avoided.”); a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger (See at least paragraph [0073], “The vehicle 10 shown in fig. 1 may further comprise a sensor device 18 device installed within the passenger cabin 12. The sensor device 18 may, for example, be installed at or close to a roof of the passenger cabin 12. Furthermore, the sensor device 18 may comprise at least one optical sensor unit for sensing wavelength in the visible spectrum and/or at least one temperature sensor unit for sensing wavelengths in the long-wave infrared spectrum. The different sensor units of the sensor device 18 are not shown here in detail. The sensor device 18 may be a condition sensor device for measuring a passenger condition and/or a vehicle cabin condition” and paragraph [0074], “As will become more apparent with reference to fig. 2 and 3 , the sensor device 18 may be configured for simultaneous and/or sequential detection of at least a plurality of vehicle passengers 14a to 14c within the passenger cabin 12. The sensor device 18 may particularly be configured for detection of vehicle passengers 14a to 14c on all or at least a majority of passenger accommodation places within the passenger cabin 12, such as on the seats 16a to 16c.”).
JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches one or more circuits configured to implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types (See at least paragraph [0087], “The autonomous vehicle 100b may perform the above-described operations using a learning model configured with at least one artificial neural network. For example, the autonomous vehicle 100b may recognize the surrounding environment and the object using the learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the autonomous vehicle 100b, or may be learned in an external device such as the AI server 200” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches using trained deep-learning/artificial neural network models to infer a passenger state from sensor information across multiple possible states, which corresponds to trained neural networks outputting likelihoods/probabilities for different assistance types based on the sensor data.); and a class-fusion circuit (See at least paragraph [0087], “The autonomous vehicle 100b may perform the above-described operations using a learning model configured with at least one artificial neural network. For example, the autonomous vehicle 100b may recognize the surrounding environment and the object using the learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the autonomous vehicle 100b, or may be learned in an external device such as the AI server 200” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches using trained deep-learning/artificial neural network models to process passenger sensor data and infer passenger states based on outputs of the neural network models, which corresponds to combining outputs of neural networks to support identification of an assistance type based on probabilities calculated by a plurality of neural networks.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types and a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks, as taught by KIM (See paragraph [0087], [0101].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
Regarding Claim 27, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN teaches wherein: the one or more first-circuitry operations further include obtaining a passenger profile associated with the passenger; and the identifying of the assistance type of the passenger is performed based at least in part on assistance-type data in the passenger profile, the assistance-type data indicating the assistance type of the passenger (See at least paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”).
Regarding Claim 37, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN teaches wherein: the initial route for the ride was generated from a first set of mapping data (See at least paragraph [0029], “The processor is configured to: a first route from the current location to the specific passenger boarding point, a second route from the specific passenger boarding point to the specific passenger boarding point, and from the specific passenger boarding point to the destination location. A third route may be provided.”); and generating the modified route comprises generating the modified route based at least in part on a second set of mapping data, the second set of mapping data being an accessibility- informed set of mapping data (See at least paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.” The system generates routes associated with a ride, including routes between passenger locations, boarding points, and destinations, and further determines passenger-specific boarding and disembarking points where safe access can be provided, which corresponds to generating an initial route from mapping data and modifying the route based on accessibility-informed mapping data.).
With respect to claim 44, please see the rejection above with respect to claim 37, which is commensurate in scope to claim 44, with claim 37 being drawn to a passenger-assistance system and claim 44 being drawn to a corresponding computer-readable storage medium.
Regarding Claim 39, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN teaches wherein: the first circuitry identifies that the passenger is associated with multiple assistance types (See at least paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”); the controlling of the one or more passenger-comfort controls of the vehicle is based on the multiple assistance types (See at least paragraph [0048], “The navigation device 500 may be a navigation device installed (or built-in) in a mobile vehicle. For example, the navigation device 500 may include a transmission/reception system unit, a memory unit, a navigation unit, an input/output unit, a control unit, and a battery. The transmission/reception system unit may include a GPS module and a wireless modem to determine the location and speed of the moving vehicle, and collect various information about the moving vehicle. The memory unit may store map and guidance voice data. The navigation unit may set various routes from the origin to the destination and search for an optimal route. The input/output unit may output the determined path through a screen and a voice and recognize a user's input. The controller may control various software embedded in the navigation device 500 . A battery may provide power” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.” The system provides passenger-specific service based on passenger information including multiple assistance-type characteristics, which corresponds to controlling passenger-comfort controls based on multiple assistance types); the generating of the modified route for the ride is based on the multiple assistance types (See at least paragraph [0029], “The processor is configured to: a first route from the current location to the specific passenger boarding point, a second route from the specific passenger boarding point to the specific passenger boarding point, and from the specific passenger boarding point to the destination location. A third route may be provided” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.” The system generates routes for transporting a specific passenger, and the passenger is identified as having multiple assistance-type characteristics, which corresponds to generating the modified route for the ride based on multiple assistance types.); and one or both of the conducting of the pre-ride safety check and the conducting of the pre- exit safety check is based on the multiple assistance types (See at least paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do” and paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.” The system determines safe boarding and disembarking points for a passenger identified as having multiple assistance-type characteristics, which corresponds to conducting pre-ride and pre-exit safety checks based on multiple assistance types.).
With respect to claim 45, please see the rejection above with respect to claim 39, which is commensurate in scope to claim 45, with claim 39 being drawn to a passenger-assistance system and claim 45 being drawn to a corresponding computer-readable storage medium.
Regarding Claim 40, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN teaches wherein the modifying of the initial route for the ride based on the identified assistance type comprises selecting a different drop-off location at a destination of the ride based on the identified assistance type (See at least paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.” The system determines a specific passenger-only disembarking point where disembarking can be safely performed for the passenger, which corresponds to selecting a different drop-off location at a destination of the ride based on the identified assistance type.).
Regarding Claim 41, JIN teaches At least one non-transitory computer-readable storage medium containing instructions that, when executed by at least one hardware processor of a computer system, cause the computer system to perform operations comprising: receiving booking information for a ride for a passenger of a vehicle (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”); conducting a pre-ride safety check for the ride based at least on the booking information (See at least paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.”); determining that the passenger is an assistance passenger of at least one identified assistance type from among a plurality of assistance types (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”); generating a modified route for the ride at least in part by modifying an initial route for the ride based on the at least one identified assistance type (See at least paragraph [0029], “The processor is configured to: a first route from the current location to the specific passenger boarding point, a second route from the specific passenger boarding point to the specific passenger boarding point, and from the specific passenger boarding point to the destination location. A third route may be provided.” The system generates routes for transporting a specific passenger, including routes between boarding and disembarking points selected to safely accommodate the specific passenger, which corresponds to modifying an initial route for a ride based on the identified assistance type.); and conducting a pre-exit safety check based on the at least one identified assistance type (See at least paragraph [0071], “The map management server 370 may determine a specific passenger-only boarding and disembarking point at which boarding and disembarking of a specific passenger can be safely performed according to the specific passenger's current location and destination location in response to the request of the dispatch server 310. The specific passenger-only boarding and disembarking point may be a point located around the current location of a specific passenger and around the destination location among a plurality of points stored in the map database, and where boarding and disembarking of a specific passenger can be safely performed.”); to identify the at least one identified assistance type of the passenger (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0055], “The dispatch server 310 may receive a dispatch request of a specific passenger transmitted from the user device 100 . The dispatch request may include at least one of information about a specific passenger, a current location, and a destination location. Information about a specific passenger includes various information about a specific passenger and the user device 100, such as information on whether the specific passenger is one of the disabled, the elderly, pregnant women, and female passengers, and a unique number of the user device 100 can do.”).
JIN does not explicitly disclose, however, BERND, in the same field of endeavor, teaches customizing an in-vehicle experience for the assistance passenger, including controlling one or more passenger-comfort controls of the vehicle based on the at least one identified assistance type (See at least paragraph [0035], “According to a yet further preferred embodiment, the control device may be configured to initiate individual and/or automatic climatization and/or seat heating and/or cooling for at least one or a plurality of passengers within the cabin. These vehicle functions may preferably be provided dependent on a detected seat occupation and/or seat position of the respective passenger and/or dependent on a detected passenger condition. This allows an efficient control of vehicle functions, and may increase the passenger comfort. The risk of discomfort due to temperatures being too high or too low may be avoided.”); using a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger (See at least paragraph [0073], “The vehicle 10 shown in fig. 1 may further comprise a sensor device 18 device installed within the passenger cabin 12. The sensor device 18 may, for example, be installed at or close to a roof of the passenger cabin 12. Furthermore, the sensor device 18 may comprise at least one optical sensor unit for sensing wavelength in the visible spectrum and/or at least one temperature sensor unit for sensing wavelengths in the long-wave infrared spectrum. The different sensor units of the sensor device 18 are not shown here in detail. The sensor device 18 may be a condition sensor device for measuring a passenger condition and/or a vehicle cabin condition” and paragraph [0074], “As will become more apparent with reference to fig. 2 and 3 , the sensor device 18 may be configured for simultaneous and/or sequential detection of at least a plurality of vehicle passengers 14a to 14c within the passenger cabin 12. The sensor device 18 may particularly be configured for detection of vehicle passengers 14a to 14c on all or at least a majority of passenger accommodation places within the passenger cabin 12, such as on the seats 16a to 16c.”).
JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches using one or more circuits that implement a plurality of neural networks to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types (See at least paragraph [0087], “The autonomous vehicle 100b may perform the above-described operations using a learning model configured with at least one artificial neural network. For example, the autonomous vehicle 100b may recognize the surrounding environment and the object using the learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the autonomous vehicle 100b, or may be learned in an external device such as the AI server 200” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches using trained deep-learning/artificial neural network models to infer a passenger state from sensor information across multiple possible states, which corresponds to trained neural networks outputting likelihoods/probabilities for different assistance types based on the sensor data.); using a class-fusion circuit (See at least paragraph [0087], “The autonomous vehicle 100b may perform the above-described operations using a learning model configured with at least one artificial neural network. For example, the autonomous vehicle 100b may recognize the surrounding environment and the object using the learning model, and may determine a driving line using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the autonomous vehicle 100b, or may be learned in an external device such as the AI server 200” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches using trained deep-learning/artificial neural network models to process passenger sensor data and infer passenger states based on outputs of the neural network models, which corresponds to combining outputs of neural networks to support identification of an assistance type based on probabilities calculated by a plurality of neural networks.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types and a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks, as taught by KIM (See paragraph [0087], [0101].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
Regarding Claim 28, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches further comprising fifth circuitry configured to perform one or more fifth-circuitry operations including collecting passenger feedback from the passenger during at least part of the ride, the one or more fifth-circuitry operations further including modifying the controlling of the one or more passenger-comfort controls based on the collected passenger feedback (See at least paragraph [0127], “An electronic apparatus 900 may determine an operation scheme of a car seat 920 in which an infant 920 is seated based on a state of the infant 910. In one example embodiment, the electronic apparatus 900 may determine an inclination angle, a height, or a position of the car seat 920 suitable for taking care of the infant 910 for each state of the infant 910. The electronic apparatus 900 may transmit a control signal to the car seat 920 based on the determined operation scheme and control an operation of the car seat 920. The electronic apparatus 900 may control the car seat 920 through, for example, controller area network (CAN) communication” and paragraph [0128], “For example, when the infant 910 is sleeping or a diaper change is needed, the electronic apparatus 900 may tilt the car seat 920 backward by adjusting an inclination of the car seat 920 in which the infant 920 is seated by 90 degrees (°). When the infant 910 needs to burp, the electronic apparatus 900 may control the car seat 920 to operate in a vibration mode for burping the infant 910. When the infant 910 is eating, the electronic apparatus 900 may tilt the car seat 920 backward by adjusting the inclination of the car seat 920 by an angle of 30° to 45° for ease of the eating of the infant 910. When the infant 910 is nervous, the electronic apparatus 900 may control the inclination of the car seat 920 to be repetitively changed within a predetermined degree of angle to comfort the infant 910.” The system determines a passenger state during operation and controls in-vehicle comfort elements, including seat inclination and vibration, based on the determined state, which corresponds to collecting passenger feedback during part of the ride and modifying the controlling of passenger-comfort controls based on the collected feedback.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; and fifth circuitry configured to perform one or more fifth-circuitry operations including collecting passenger feedback from the passenger during at least part of the ride, the one or more fifth-circuitry operations further including modifying the controlling of the one or more passenger-comfort controls based on the collected passenger feedback, as taught by KIM (See paragraph [0087], [0101], [00127], [0128].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
With respect to claim 42, please see the rejection above with respect to claim 28, which is commensurate in scope to claim 42, with claim 28 being drawn to a passenger-assistance system and claim 42 being drawn to a corresponding computer-readable storage medium.
Regarding Claim 29, JIN, BERND, and KIM teach The passenger-assistance system of claim 28, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches the one or more fifth-circuitry operations further including collecting assistance-type-detection feedback from the passenger regarding an accuracy of the identified assistance type of the passenger, the one or more first- circuitry operations further including conducting an identification of an assistance type of at least one subsequent passenger of the vehicle based at least in part on the collected assistance-type- detection feedback (See at least paragraph [0062], “The processor 180 may collect history information including, for example, the content of an operation of the AI device 100 or feedback of the user with respect to an operation, and may store the collected information in the memory 170 or the learning processor 130, or may transmit the collected information to an external device such as the AI server 200. The collected history information may be used to update a learning model” and paragraph [0071], “The processor 260 may deduce a result value for newly input data using the learning model, and may generate a response or a control instruction based on the deduced result value.” The system collects user feedback regarding system operation and uses such feedback to update a learning model that is applied to newly input data corresponds to collecting assistance-type-detection feedback regarding accuracy and identifying an assistance type of at least one subsequent passenger based at least in part on the collected feedback.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; fifth circuitry configured to perform one or more fifth-circuitry operations including collecting passenger feedback from the passenger during at least part of the ride, the one or more fifth-circuitry operations further including modifying the controlling of the one or more passenger-comfort controls based on the collected passenger feedback; and the one or more fifth-circuitry operations further including collecting assistance-type-detection feedback from the passenger regarding an accuracy of the identified assistance type of the passenger, the one or more first-circuitry operations further including conducting an identification of an assistance type of at least one subsequent passenger of the vehicle based at least in part on the collected assistance-type-detection feedback, as taught by KIM (See paragraph [0062], [0071], [0087], [0101], [00127], [0128].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
Regarding Claim 30, JIN, BERND, and KIM teach The passenger-assistance system of claim 28, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches the one or more fifth-circuitry operations further including collecting trip-planning feedback from the passenger regarding the generated modified route for the ride, the one or more third-circuitry operations further including generating a modified route for at least one subsequent ride for at least one subsequent passenger based on the collected trip-planning feedback (See at least paragraph [0134], “In one example embodiment, the electronic apparatus 400 may generate a model representing a preference of an infant with respect to a driving environment of a vehicle. The electronic apparatus 400 may acquire first information associated with at least one of a driving route, a road condition around the vehicle, and a driving speed of the vehicle as input information, and then acquire second information associated with a state of the infant as target information of the first information. For example, the electronic apparatus 400 may acquire the first information from a sensor or a navigator in the vehicle and acquire the second information from a camera or a microphone in the vehicle. Thereafter, the electronic apparatus 400 may train an AI model based on the acquired input information and target information. Through this, the electronic apparatus 400 may generate a trained AI model” and paragraph [0135], “For example, the electronic apparatus 400 may train an AI model based on information associated with the driving speed of the vehicle, which is the first information, and information associated with a reaction of the infant for each speed level of the vehicle, which is the second information. Through this, the electronic apparatus 400 may generate a model representing a preference of the infant with respect to the driving speed of the vehicle. Also, the electronic apparatus 400 may train an AI model based on information associated with the driving route of the vehicle, which is the first information, and information associated with a reaction of the infant for each driving route of the vehicle. Through this, the electronic apparatus 400 may generate a model representing a preference of the infant with respect to the driving route of the vehicle.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; fifth circuitry configured to perform one or more fifth-circuitry operations including collecting passenger feedback from the passenger during at least part of the ride, the one or more fifth-circuitry operations further including modifying the controlling of the one or more passenger-comfort controls based on the collected passenger feedback; and the one or more fifth-circuitry operations further including collecting trip-planning feedback from the passenger regarding the generated modified route for the ride, the one or more third-circuitry operations further including generating a modified route for at least one subsequent ride for at least one subsequent passenger based on the collected trip-planning feedback, as taught by KIM (See paragraph [0087], [0101], [00127], [0128], [0134], [0135].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
Regarding Claim 38, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches wherein: the first circuitry identifies that the assistance type of the passenger of the vehicle is that the passenger is an infant (See at least paragraph [0097], “In operation S510, the electronic apparatus 400 may recognize a state of an infant in a vehicle based on sensing information associated with the infant. Specifically, the electronic apparatus 400 may acquire sensing information associated with the infant and recognize a state of the infant based on the acquired sensing information. The term “infant” may refer to a small and/or little child. In one example, the infant may be a baby who is not yet able to speak. In another example, the infant may be a toddler able to stand and walk with help or alone. In another example, the infant may be a preschooler 1 to 6 years old after birth.”); and the second circuitry uses reinforcement learning and analysis of non-verbal indications of a comfort level of the infant in controlling one or more passenger-comfort controls with respect to the comfort level of the infant (See at least paragraph [0094], “The electronic apparatus 400 may recognize a state of an infant 410 in a vehicle. In one example embodiment, the electronic apparatus 400 may recognize a state of the infant 410 based on sensing information associated with the infant 410. For example, the electronic apparatus 400 may recognize a hungry state, a sleeping state, or an eating state of the infant 410 based on sensing information associated with at least one of an appearance, a sound, and a gesture of the infant 410. In another example embodiment, the electronic apparatus 400 may recognize a state of the infant 410 based on information associated with an environment around the infant 410. For example, the electronic apparatus 400 may recognize a sleeping state of the infant 410 based on current time information” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches using sensor data indicating non-verbal infant behaviors as input to an artificial intelligence model to adaptively control vehicle comfort functions over time, and further teaches utilizing centrally stored data in a server-based system to improve such control across multiple vehicles, which corresponds to reinforcement learning based control using aggregated infant-comfort-related data.); wherein the second circuitry also uses, in controlling one or more passenger-comfort controls with respect to the comfort level of the infant, aggregated infant-comfort-related data from a cloud-based management system of a plurality of vehicles that includes the vehicle (See at least paragraph [0073], “Referring to FIG. 3, in the AI system 1, at least one of the AI server 200, a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, and a home appliance 100e is connected to a cloud network 10. Here, the robot 100a, the autonomous vehicle 100b, the XR device 100c, the smartphone 100d, and the home appliance 100e, to which AI technologies are applied, may be referred to as AI devices 100a to 100e.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND and KIM such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), and one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; and the first circuitry identifies that the assistance type of a passenger of the vehicle is that the passenger is an infant; and the second circuitry uses reinforcement learning and analysis of non-verbal indications of a comfort level of the infant in controlling one or more passenger-comfort controls with respect to the comfort level of the infant, and wherein the second circuitry also uses, in controlling one or more passenger-comfort controls with respect to the comfort level of the infant, aggregated infant-comfort-related data from a cloud-based management system of a plurality of vehicles that includes the vehicle, as taught by KIM (See paragraph [0073], [0087], [0094], [0097], [0101].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].).
Claim(s) 31-36, 43 is/are rejected under 35 U.S.C. 103 as being unpatentable over JIN (KR 20210078071 A) in view of BERND (EP 3718797 A1) and KIM (US 20210155262 A1) and HICOK (US 20190265703 A1).
Regarding Claim 31, JIN, BERND, and KIM teach The passenger-assistance system of claim 26, as set forth in the obviousness rejection above. JIN, BERND, and KIM do not explicitly disclose, however, HICOK, in the same field of endeavor, teaches wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types (See at least paragraph [0128], “A deep neural network accelerated by a deep learning accelerator hardware within a GPU may be used to perform such situational awareness in real time in order to ensure high levels of safety and accurate detection. Thus, proceeding, detecting and other operations may be performed very rapidly using advanced massively-parallel computing architectures including multiple GPUs and multiple CPUs to provide time-efficient operations and real time decision making that provides high levels of safety. Such deep learning can, for example, provide functionality that meets the expectations of passengers and others who are used to interacting with human-driven buses, taxi cabs, limousines, shuttles and other passenger vehicles. If the vehicle detects a special needs person, the vehicle may automatically lower the door entrance and if necessary, to deploy a ramp (e.g., to accommodate a wheelchair). If the vehicle detects a bicycle rider, it may unlatch a bicycle rack on the front or rear of the vehicle to accept the bicycle. When this particular passenger gets ready to disembark, the vehicle can automatically again unlatch the bicycle rack to allow the passenger to unload the bicycle. If a passenger has luggage, the vehicle can detect this and open a luggage compartment into which the passenger may deposit luggage. Once the luggage has been safely stowed, the vehicle can detect this and close the luggage compartment. The vehicle can also track who collects which luggage to ensure there are no mixups. The vehicle can provide personalized service to each individual passenger” and paragraph [0149], “Such functionality can be provided using computer vision and deep learning using deep neural networks. Such functionality can be accelerated using a hardware-based computer vision accelerator and/or a deep learning accelerator provided on the controller as part of deep learning support. Communications between the vehicle and other computing resources located remotely from the vehicle (e.g., in the cloud, over a network, ATC) may be used to support such functionality to provide additional computation power and more timely results. In one implementation, a first neural network might classify or identify the object as being a person and a second neural network may be used to recognize the face of the person. Body pose recognition can be performed by a neural network that models bodies based on “bones” (i.e., maps, joint positions within a constellation and uses the set of changing constellations to recognize current body pose and, by extension, gestures such as full body gestures). A classifier may be used to classify what pose or gesture has been detected.” The system utilizes multiple deep-learning neural networks and classifiers to make class predictions using deep learning in a parallel computing architecture, the class predictions corresponding to different passenger assistance types, which corresponds to a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND, KIM, and HICOK such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types and a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks, as taught by KIM (See paragraph [0087], [0101].), and wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types, as taught by HICOK (See paragraph [0128], [0149].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].). The motivation for doing so would be improving autonomous passenger identification and vehicle operation using deep-learning-based perception to safely and efficiently operate autonomous passenger vehicles, as taught by HICOK (See paragraph [0026].).
With respect to claim 43, please see the rejection above with respect to claim 31, which is commensurate in scope to claim 43, with claim 31 being drawn to a passenger-assistance system and claim 43 being drawn to a corresponding computer-readable storage medium.
Regarding Claim 32, JIN, BERND, KIM, and HICOK teach The passenger-assistance system of claim 31, as set forth in the obviousness rejection above. JIN teaches the plurality of assistance types including an assistance type associated with not needing assistance (See at least paragraph [0038], “The transport service system 10 may provide a specific passenger-only transport service for transporting a specific passenger” and paragraph [0042], “The user device 100 may transmit a request for dispatch of a specific passenger to the transport service server 300 . The specific passenger may be a specific person who uses a mobility aid when moving due to an unhealthy body, such as a passenger with a disability, an elderly person, a pregnant woman, or a female passenger. The mobility assistance device is a device for assisting the movement of a specific passenger, and may be a foldable mobility aid device and a non-foldable mobility aid device. The folding mobility assistance device may be various devices in which the size of the mobility assistance device such as a light wheelchair can be reduced. The non-foldable mobility assistance device may be various devices in which the size of the mobility assistance device, such as an electric wheelchair, cannot be reduced.”).
Regarding Claim 33, JIN, BERND, KIM, and HICOK teach The passenger-assistance system of claim 31, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches wherein: the plurality of neural networks includes a first neural network configured to calculate the plurality of probabilities based at least in part on an assistance-prompt subset of the sensor data (See at least paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.”); and the assistance-prompt subset of the sensor data indicates a response or lack of response from the passenger to at least one assistance prompt presented to the passenger via a user interface in the vehicle (See at least paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.” The system teaches inferring a passenger state using an AI model based on sensor data including appearance, sound, and gesture, which corresponds to using sensor data indicating a response or lack or response from a passenger to an assistance prompt presented via user interface as input to the probability-calculating neural network.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND, KIM, and HICOK such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; and the plurality of neural networks includes a first neural network configured to calculate the plurality of probabilities based at least in part on an assistance-prompt subset of the sensor data, and the assistance-prompt subset of the sensor data indicates a response or lack of response from the passenger to at least one assistance prompt presented to the passenger via a user interface in the vehicle, as taught by KIM (See paragraph [0087], [0101].), and wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types, as taught by HICOK (See paragraph [0128], [0149].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].). The motivation for doing so would be improving autonomous passenger identification and vehicle operation using deep-learning-based perception to safely and efficiently operate autonomous passenger vehicles, as taught by HICOK (See paragraph [0026].).
Regarding Claim 34, JIN, BERND, KIM, and HICOK teach The passenger-assistance system of claim 31, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches wherein: the plurality of neural networks includes a second neural network configured to calculate the plurality of probabilities based at least in part on a stimulated-response subset of the sensor data (See at least paragraph [0117], “The electronic apparatus 400 may determine a validity of the acquired sensing information. Specifically, the electronic apparatus 400 may determine a validity of the sensing information acquired in operation S620 based on the model acquired in S610. When the sensing information acquired in operation S620 is a different type of information from information for training the model acquired in operation S610, the electronic apparatus 400 may determine that the acquired sensing information is invalid. For example, when the sensing information is sensing information associated with a reaction of the infant in a special circumstance, the electronic apparatus 400 may determine that the sensing information is invalid.”); and the stimulated-response subset of the sensor data indicates a reaction or a lack of reaction by the passenger to one or more sensory stimuli presented in a defined area around the passenger (See at least paragraph [0117], “The electronic apparatus 400 may determine a validity of the acquired sensing information. Specifically, the electronic apparatus 400 may determine a validity of the sensing information acquired in operation S620 based on the model acquired in S610. When the sensing information acquired in operation S620 is a different type of information from information for training the model acquired in operation S610, the electronic apparatus 400 may determine that the acquired sensing information is invalid. For example, when the sensing information is sensing information associated with a reaction of the infant in a special circumstance, the electronic apparatus 400 may determine that the sensing information is invalid.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND, KIM, and HICOK such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; and the plurality of neural networks includes a second neural network configured to calculate the plurality of probabilities based at least in part on a stimulated-response subset of the sensor data, and the stimulated-response subset of the sensor data indicates a reaction or a lack of reaction by the passenger to one or more sensory stimuli presented in a defined area around the passenger, as taught by KIM (See paragraph [0087], [0101], [0117].), and wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types, as taught by HICOK (See paragraph [0128], [0149].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].). The motivation for doing so would be improving autonomous passenger identification and vehicle operation using deep-learning-based perception to safely and efficiently operate autonomous passenger vehicles, as taught by HICOK (See paragraph [0026].).
Regarding Claim 35, JIN, BERND, KIM, and HICOK teach The passenger-assistance system of claim 31, as set forth in the obviousness rejection above. JIN and BERND do not explicitly disclose, however, KIM, in the same field of endeavor, teaches wherein the plurality of neural networks includes a third neural network configured to use the sensor data to: calculate an estimated age of the passenger (See at least paragraph [0097], “In operation S510, the electronic apparatus 400 may recognize a state of an infant in a vehicle based on sensing information associated with the infant. Specifically, the electronic apparatus 400 may acquire sensing information associated with the infant and recognize a state of the infant based on the acquired sensing information. The term “infant” may refer to a small and/or little child. In one example, the infant may be a baby who is not yet able to speak. In another example, the infant may be a toddler able to stand and walk with help or alone. In another example, the infant may be a preschooler 1 to 6 years old after birth” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.”); and calculate the plurality of probabilities based at least in part on the calculated estimated age of the passenger (See at least paragraph [0097], “In operation S510, the electronic apparatus 400 may recognize a state of an infant in a vehicle based on sensing information associated with the infant. Specifically, the electronic apparatus 400 may acquire sensing information associated with the infant and recognize a state of the infant based on the acquired sensing information. The term “infant” may refer to a small and/or little child. In one example, the infant may be a baby who is not yet able to speak. In another example, the infant may be a toddler able to stand and walk with help or alone. In another example, the infant may be a preschooler 1 to 6 years old after birth” and paragraph [0101], “In one example embodiment, the model for predicting a state of the infant may be a model representing a correlation between first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant, the second information corresponding to the first information. The model for predicting a state of the infant may be an AI model. For example, the model for predicting a state of the infant may be a deep-learning model trained based on first information associated with at least one of an appearance, a sound, and a gesture and second information associated with a state of the infant. In this example, the second information may be target information of the first information. Thus, the electronic apparatus 400 may recognize a state of the infant based on information inferred as a result of inputting the sensing information associated with the infant to the AI model for predicting a state of the infant. For example, the electronic apparatus 400 may recognize a hungry state of the infant based on information inferred as a result of inputting sensing information associated with a sound of the infant to the AI model.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND, KIM, and HICOK such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger, as taught by BERND (See paragraph [0035], [0073], [0074].), one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types; a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks; and the plurality of neural networks includes a third neural network configured to use the sensor data to: calculate an estimated age of the passenger, and calculate the plurality of probabilities based at least in part on the calculated estimated age of the passenger, as taught by KIM (See paragraph [0087], [0097], [0101].), and wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types, as taught by HICOK (See paragraph [0128], [0149].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].). The motivation for doing so would be improving autonomous passenger identification and vehicle operation using deep-learning-based perception to safely and efficiently operate autonomous passenger vehicles, as taught by HICOK (See paragraph [0026].).
Regarding Claim 36, JIN, BERND, KIM, and HICOK teach The passenger-assistance system of claim 31, as set forth in the obviousness rejection above. JIN does not explicitly disclose, however, BERND, in the same field of endeavor, teaches wherein the plurality of neural networks includes a fourth neural network configured to use the sensor data to: identify whether the passenger has one or more assistance objects from among a plurality of assistance objects (See at least paragraph [0042], “According to a yet further preferred embodiment, the sensor device and/or the control device may form a safety and/or comfort system for the detection of a disabled passenger, for example by detecting a wheelchair, walking frame and/or walking stick, and/or for initiating a service function for disabled passengers. By detecting a disabled passenger, particularly in the door area of the cabin, a service signal may be provided to a service personnel, which may then provide personal assistance to the detected disabled person. The same may apply to service assistance in the seating requirements of a disabled passenger.”); and calculate the plurality of probabilities based at least in part on a lack of or presence of any identified assistance objects from among the plurality of assistance objects (See at least paragraph [0042], “According to a yet further preferred embodiment, the sensor device and/or the control device may form a safety and/or comfort system for the detection of a disabled passenger, for example by detecting a wheelchair, walking frame and/or walking stick, and/or for initiating a service function for disabled passengers. By detecting a disabled passenger, particularly in the door area of the cabin, a service signal may be provided to a service personnel, which may then provide personal assistance to the detected disabled person. The same may apply to service assistance in the seating requirements of a disabled passenger.”).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine the invention of JIN with the teachings of BERND, KIM, and HICOK such that the transportation service system of JIN is further configured to utilize second circuitry configured to perform one or more second-circuitry operations including controlling one or more passenger-comfort controls of the vehicle based on the identified assistance type and a sensor array comprising at least one sensor configured to collect sensor data corresponding to the passenger and the plurality of neural networks includes a fourth neural network configured to use the sensor data to: identify whether the passenger has one or more assistance objects from among a plurality of assistance objects; and calculate the plurality of probabilities based at least in part on a lack of or presence of any identified assistance objects from among the plurality of assistance objects, as taught by BERND (See paragraph [0035], [0042], [0073], [0074].), one or more circuits that implement a plurality of neural networks that have each been trained to calculate, based on the sensor data, a plurality of probabilities that each correspond to the passenger having a different particular assistance type in a plurality of assistance types and a class-fusion circuit based on the probabilities calculated by the neural networks in the plurality of neural networks, as taught by KIM (See paragraph [0087], [0101].), and wherein the plurality of neural networks comprises a parallelized set of deep learning classifiers, each deep learning classifier making a class prediction with a focus on a selected subset of individual assistance types from among the plurality of assistance types, as taught by HICOK (See paragraph [0128], [0149].), with a reasonable expectation of success. The motivation for doing so would be improving individualized vehicle functionality and accuracy of vehicle functions, as taught by BERND (See paragraph [0015].). The motivation for doing so would be increasing a user’s convenience using services, as taught by KIM (See paragraph [0004].). The motivation for doing so would be improving autonomous passenger identification and vehicle operation using deep-learning-based perception to safely and efficiently operate autonomous passenger vehicles, as taught by HICOK (See paragraph [0026].).
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 JEWEL ASHLEY KUNTZ whose telephone number is (571)270-5542. The examiner can normally be reached M-F 8:30am-5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JEWEL A KUNTZ/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
Prosecution Insights