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 .
This final action is in response to Applicant’s filing dated January 30, 2026. Claims 1-20 are currently pending and have been considered, as provided in more detail below. Claims 1-3, 5, 8-12 and 15 - 20 have been amended.
*Examiner Note: Claim language is bolded. Cited References and Applicant’s arguments are italicized. Examiner interpretations are preceded with an asterisk *.
Response to Arguments
Applicant's arguments filed 1/30/26 have been fully considered but they are not persuasive. Applicant asserts that Chatterjee fails to describe or suggest, “determining a physical state of the individual before entering the vehicle based on the sensor data and creating a profile of the individual with the physical state of the individual; detecting that the individual has entered the vehicle; executing, in response to detecting entry to the vehicle, an action determined based on execution of an artificial intelligence (AI) model on the profile of the individual while the vehicle is maneuvering toward a destination, wherein execution of the action is withheld until detecting the individual has entered the vehicle." However, Applicant’s arguments are not persuasive because Chatterjee explicitly teaches obtaining and using user physical state data from wearable devices while the user is outside the vehicle. As discussed in detail below, at least para. [0084] of Chatterjee discloses receiving physiological data (pulse rate, blood pressure, activity level, etc.) from a wearable device and Chatterjee further discloses “the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle” (see at least para. [0016] of Chatterjee). Under the broadest reasonable interpretation, collecting and processing physiological data from a wearable device necessarily corresponds to determining a physical state. Since the wearable device is operated while the user is outside the vehicle, the determination occurs prior to entry. Examiner has interpreted Chatterjee’s processing of wearable derived physiological data as determining a physical state of the individual prior to entering the vehicle.
Additionally, Examiner respectfully disagrees with Applicant’s improper attack on Chatterjee in isolation. As discussed in detail below, the rejection is based on the combined teachings of Burk, Chatterjee, and Kim. Therefore, Applicant’s argument regarding Chatterjee’s lack of profile creation is not persuasive and fails. Burk is being relied upon to teach maintaining user-specific data associated with an identified individual and applying such data to customize vehicle operation (see at least para. [0011] of Burk which explicitly details “A user profile 121 may include user credentials for authenticating a user. The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100”, *Burk discloses maintaining a user profile associated with an identified individual, which stores data used to customize vehicle operation. Examiner interprets such a profile as a data structure capable of storing attributes of the individual). In view of Chatterjee’s teaching of determining the individual’s physical state, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate such information into the user profile of Burk to improve personalization of vehicle settings, as discussed below. Regarding the limitation that the action is determined base on execution of an artificial intelligence (AI) model, Kim expressly discloses inputting physiological characteristics into a pretrained deep neural network model to predict a physical state of an occupant (see at least para. [0164] of Kim which discloses “The movement characteristic M, the respiratory characteristic B, and the heart rate characteristic H may be then input into the pretrained deep neural network model 40 to predict the physical state of the occupant based on the artificial intelligence (AI) model”). Examiner interprets this an execution of an AI model on data associated with the individual. In view of Chatterjee’s teaching of using a physical state to select vehicle settings and Burk’s teaching of applying user-specific settings, the combination with Kim teaches determining an action based on execution of an AI model on the profile of the individual. With respect to execution while the vehicle is maneuvering toward a destination, Kim discloses monitoring and evaluating the occupant’s physical state while the vehicle is traveling (see at least para. [0086] of Kim which discloses “after the occupant enters the vehicle 10, the embodiment of the present disclosure is to inform others that a physical change in the occupant has occurred while the vehicle is traveling”, * Kim discloses that the physical state of the occupant may be monitored while the vehicle is traveling). Examiner interprets this as corresponding to execution of the action while the vehicle is maneuvering toward a destination.
Applicant attempts to argue that because Chatterjee describes adjusting vehicle settings based on information received from a wearable device (or indirectly via a mobile device) once the vehicle is already active and Applicant’s claim 1 teaches sensor data is obtained while the individual is still outside the vehicle and a physical state is determined before entry (not just receiving data after the person is in the vehicle, therefore this temporal ordering is not present in Chatterjee. Examiner respectfully does not agree. Chatterjee, expressly teaches that user data from wearable devices is collected regardless of whether the user is inside or outside the vehicle (see at least para. [0031] of Chatterjee which discloses “data may be gathered by wearable devices 206-210 as long as the device is worn by the user, irrespective of whether the user is inside or outside of vehicle 102”). Examiner interprets this disclosure as teaching that sensor data is obtained while the individual is located outside of the vehicle. Secondly, Chatterjee further discloses that the collected data (including physiological parameter such as pulse rate, blood pressure and other indicators of physical condition) is used to determine a suer state (see para. [0084] of Chatterjee). Chatterjee also teaches that such data may be used to adapt vehicle settings before the user enters the vehicle (see at least para. [0016] and [0064]). Examiner interprets these paragraphs as determining a physical state of the individual prior to entry based on the sensor data. Contrary to Applicant’s assertion, Chatterjee is not limited to operation after the vehicle is active. Instead, Chatterjee explicitly teaches the option of pre-entry sensing and pre-entry adaptation. Applicant is reminded that the rejection relies upon the combined teaches of Burk, Chatterjee and Kim where Chatterjee teaches outside-vehicle sensing and pre-entry state determination; Burk teaches detecting entry and executing action in response and Kim teaches AI-based determination of a user’s condition. When combined, these teachings reasonably yield the claimed sequence of obtaining data outside the vehicle, determining a physical state prior to entry, detecting entry and executing actions, thereafter. The claim does not require a rigid or exclusive temporal ordering beyond what is reasonably interpreted as broadly as recited. Therefore, Applicant’s argument regarding the absence of the claimed temporal ordering is not persuasive.
Applicant also asserts that “in Claim 1, execution of the action is withheld until entry is detected and then the AI model is executed while the vehicle is maneuvering toward a destination. That explicit deferral and event-triggered actuation is a control flow tied to a physical condition change and a physical system state transition. Chatterjee does not describe a system that withholds action until a detected entry event, nor does it use a physical entry detection event as a trigger for executing vehicle control. Instead, the system of Chatterjee generally adjusts settings based on user state once the vehicle's in-vehicle system receives input”. The Examiner respectfully disagrees. Applicant’s argument is not persuasive because it again attacks Chatterjee in isolation. However, the rejection is based on the combined teachings of Burk, Chatterjee and Kim. With respect to the limitation that execution of the action is withheld until detecting entry, Burk expressly discloses detecting the presence or identity of a user upon entering the vehicle (see at least para. [0024] of Burk) and applying control instructions in response to such detection (see at least para. [0058] of Burk). Examiner interprets this conditional execution, i.e., applying settings only after detecting the occupant as corresponding to executing an action upon entry such that prior to detection the action is not executed, thereby meeting the claimed “withheld until detecting the individual has entered the vehicle” limitation. Applicant’s characterization of this limitation as requiring a specific or complex control flow tied to a physical system state transition is not commensurate with the broadest reasonable interpretation of the claim. The claim does not require any particular implementation of control logic beyond executing an action in response to detecting entry. Event-driven execution based on a detection condition, as taught by Burk reasonably satisfies this limitation. Regarding Applicant’s position that the AI model must be executed only after entry while the vehicle is maneuvering, Examiner asserts that the rejection relies on the combined teachings of the cited references (Burk, Chatterjee and Kim). In this connection, Kim teaches executing an artificial intelligence model to determine a physical state of the occupant (see at least para. [0164] of Kim) and further teaches that such evaluation occurs while the vehicle is traveling (see at least para. [0086] of Kim). Chatterjee teaches using the determined state to select vehicle settings and Burk teaches applying such settings in response to detecting entry. Examiner interprets the combination as executing or utilizing the results of the AI model in conjunction with entry detection to determine and apply vehicle actions during vehicle operation. Therefore, the cited references collectively teach or render obvious deferring execution until entry and then performing an action based on AI-derived information while the vehicle is moving toward a destination. The combination represents using known event-driven control and data-driven decision-making techniques. Accordingly, Applicant’s arguments are not persuasive.
Finally, Applicant mischaracterizes Kim. Kim is not being relied upon for pre-entry sensing or entry trigger execution. Instead, Kim is relied upon for its explicit disclosure of using an artificial intelligence model – specifically a pretrained deep neural network to process physiological signals and determine a physical state of an individual (see at least para. [0164] of Kim). This teaching directly corresponds to the claimed limitation of determining information based on execution of an AI model. Applicant’s assertion that “Actions such as generating an alarm or informing occupants are linked directly to an in-vehicle physical condition, not sensing outside and gating actuation on a future event” is not commensurate with the scope of the claims, as amended. The claim merely requires the execution of an action is performed in response to detecting entry and is not executed prior to such detection. Under the broadest reasonable interpretation this corresponds to conditional or event-driven execution based on a detection event. The claim does not require a specific gating actuation on a future event as asserted by Applicant. Accordingly, Applicant’s argument improperly imports additional limitations into the claim that are not supported by the claim language. As discussed above, Burk teaches executing control instruction ins response to detecting the presence or identity of an occupant, which reasonably corresponds to executing an action upon entry such that execution does not occur prior to detection.
Therefore, Applicant’s arguments are not considered persuasive and the reject under 35 USC 103 is maintained as outlined below.
Response to Amendment
Regarding the rejection under 35 USC 101, Applicant has amended the claims to overcome the rejection. The rejection under 35 USC 101 has been withdrawn.
Regarding the rejection under 35 USC 103, the amendments made to the claims fail to overcome the prior art. The rejection under 35 USC 103 is maintained as outlined below.
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.
Claims 1-5, 7-12 and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Burk (US20220016999 A1) in view of Chatterjee (US2015/0127215 A1) and further in view of Kim (US 2020/0070657 A1).
Regarding amended claim 1, Burk discloses A method (Fig. 4, 400 and see at least para.
[0059] of Burk) which describes “the method 400”), comprising: receiving (see at least para. [0021] of Burk which discloses the collection of “data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”), via a vehicle (Fig. 1, 103 and see at least para. [0011] of Burk which describes “a vehicle 103” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110”), sensor data (see at least para. [0020] of Burk which discloses “A sensor 151 generates sensor data that may be processed to determine the identity and/or position of an occupant” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant”) of an individual (see at least para. [0024] of Burk which discloses “the user” and “the vehicle occupant”, *Examiner interprets these to be an individual. Further, see at least para. [0044] of Burk which discloses “The sensor data may comprise data from multiple types of sensors to determine the identity of the vehicle occupant. The sensor data may comprise a biometric scan such as a fingerprint scan or retinal scan”, *Examiner interprets this to be physical identification of an individual) and creating (see at least para. [0011] of Burk which discloses “A user account 115 may be created and maintained for an individual user. A user account 115 may include a user profile 121”) a profile (Fig. 1, 121 and see at least para. [0011] of Burk which discloses “A user profile 121 may include user credentials for authenticating a user. The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100”, *Burk discloses maintaining a user profile associated with an identified individual, which stores data used to customize vehicle operation. Examiner interprets such a profile as a data structure capable of storing attributes of the individual) of the individual (see at least para. [0011] of Burk which discloses “an individual user. A user account 115 may include a user profile 121”); detecting that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle. Burk discloses detecting that a user has entered the vehicle. Specifically, Burk teaches that upon entering the vehicle, one or more sensors identify the user (para. [0024]). Examiner interprets such identification of the user upon entry int the vehicle as detecting that the individual has entered the vehicle); executing, in response to detecting entry to the vehicle (see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”), an action (see at least para. [0026] of Burk which disclose “Upon identifying the appropriate user profile 121, the customization application 106 extracts desired user settings and generates control instructions to implement the desired user settings”, *Burk discloses detecting entry of an individual into the vehicle and executing vehicle settings in response to such detection based on a stored user profile (see at least para. [0024] of Burk), wherein execution of the action is withheld until detecting the individual has entered the vehicle (see at least para. [0008] of Burk which discloses “the vehicle's settings may be updated or otherwise controlled in response to new passengers entering the vehicle” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant. In addition, the sensor data may indicate a location or seat 142 where the occupant has been situated” and see at least para. [0039] of Burk which discloses “when the second user enters the vehicle, the system may generate control instructions that are implemented by the vehicle 103 to actuate a privacy screen”, *Burk discloses applying vehicle settings upon detecting the presence/identity of the user when the user enters the vehicle. Examiner interprets this conditional application as corresponding to executing an action in response to detecting entry, such that prior to detection the action is not executed, i.e., execution is withheld until entry is detected); and adjusting a setting of a vehicle subsystem (see at least para. [0032] of Burk which discloses “settings include temperature settings, video settings, seat adjustments, audio settings, privacy settings, or other settings for configuring a vehicle 103. Temperature settings may include fan speed, preferred temperature, and other settings that control a vehicle's heating and cooling system. Video settings may include preferred genres of content, the identification of specific content, display settings such as, for example, brightness, and other settings for controlling the presentation of video. Seat adjustments may include settings for height, recline, lumbar support, armrest height, and other settings for configuring a seat” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”, *Examiner interprets the “vehicle components” of Burk that receive control instructions as corresponding to vehicle subsystems, as they are controllable systems within the vehicle capable of adjusting operational settings in response to executed actions) based on the executed actions (see at least para. [0051] of Burk which discloses “the control instruction may include an instruction to adjust climate or temperature settings. In addition, the control instruction may indicate a zone within the vehicle to implement the instruction, where the zone corresponds to the location of the seat 142 that is occupied by the user. The control instruction may include an instruction to adjust seat settings, adjust a privacy screen, or instructions for other vehicle configurations” and see at least para. [0058] of Burk which discloses “in response to detecting the presence or identity of a new occupant, the subsequent control instruction may be an instruction to limit playback volume, control a privacy screen, adjust the seat settings, adjust the temperature to an average temperature, or other instructions to adjust a vehicle control”). Burk discloses adjusting a setting of a vehicle subsystem. Specifically, Burk discloses that vehicle settings include temperature settings, seat adjustments, video/display settings, and audio settings (see at least para. [0032]). Temperature settings control a vehicle’s heating and cooling system, and seat adjustments control positioning and support of a seat, which Examiner interprets as corresponding to settings of respective vehicle subsystems. Burk further discloses that control instructions are generated and executed to adjust such settings, including instructions to adjust climate or temperature settings, adjust seat settings, control a privacy screen and other vehicle configurations (see at least para. [0051] and [0058]). Examiner interprets such control instructions as executed actions what result in adjusting settings of vehicle subsystems based on the executed actions.
Burk does teach receiving sensor data of an individual via a vehicle.
Burk may not explicitly disclose that the sensor data is obtained while the individual is
located outside of the vehicle; nor determining a physical state of the individual before entering the vehicle based on the sensor data.
However, in the same field of endeavor, Chatterjee discloses that the sensor data (see at
least para. [0084] of Chatterjee which disclose “information regarding physical parameters of the user from the wearable device. For example, the application may retrieve information regarding a physical state of the user (nature of physical activity performed, duration of physical activity, user's heart rate, pulse rate, blood pressure, body temperature, etc.). The wearable device may include various sensors for sensing and estimating the various physical parameters of the user”. In this connection, it should be noted that Chatterjee also discloses a vehicle (Fig. 2, 102 and see at least para. [0017] of Chatterjee which discloses “a vehicle 102”), sensor data (see at least para. [0031] of Chatterjee which discloses “user data may be gathered by wearable devices 206-210 as long as the device is worn by the user, irrespective of whether the user is inside or outside of vehicle 102”, *Examiner interprets the user data to be sensor data and see at least para. [0081] of Chatterjee which discloses “sensor data from the mobile device and/or the wearable device”) of an individual (Fig. 2, 202 and see at least para. [0026] of Chatterjee which discloses a “user 202”) from one or more devices (Fig. 2, 206-210 and see at least para. [0031] of Chatterjee which discloses “Wearable devices 206-210 may include devices worn by user 202”) that are located outside of the vehicle (see at least para. [0024]of Chatterjee which discloses “wearable device 150 is located outside of vehicle 102”) is obtained from devices while the individual is located outside of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle”, *Before a user/individual enters the vehicle they must be outside the vehicle); determining a physical state of the individual (see at least para. [0005] of Chatterjee which discloses “The storage device may store instructions executable by the processor to receive aggregated input regarding a physical condition and an environment of a user from the mobile device, the input regarding the physical condition of the user”) before entering the vehicle based on the sensor data (see at least para. [0035] of Chatterjee which discloses “selecting settings before the user returns inside the vehicle. That is, in-vehicle computing system 109 may infer settings that the user is likely to choose, or would prefer, based on an assessment of the input received from the wearable device and/or mobile device of the user“ and see at least para. [0064] of Chatterjee which discloses “By assessing the input from the mobile device and wearable device to infer a user state, and adjusting vehicle settings based on the user state even before the user gets into the vehicle”, *Examiner interprets this as determining a physical state of the individual before entering the vehicle based on the sensor data).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the method of Burk to include obtaining the sensor data while the individual is located outside of the vehicle; determining a physical state of the individual before entering the vehicle based on the sensor data; as taught in Chatterjee with a reasonable expectation of success in order to enable the vehicle to prepare and apply personalized settings based on the individual’s physical condition prior to entry, thereby improving the timeliness and responsiveness of vehicle system adjustments and enhancing the occupant’s vehicle experience. See para. [0064] of Chatterjee for motivation.
Burk may not explicitly disclose the profile with the physical state of the individual.
However, Chatterjee describes a physical state (see at least para. [0003] of Chatterjee which
discloses a “physical state (e.g., heart rate), location, cognitive load, etc.”) of the individual (Chatterjee discloses determining a physical state of the user form sensor data and using that information to select or infer vehicle settings. Under the broadest reasonable interpretation, Examiner interprets this as associating the physical state information with the user, thereby corresponding to storing such state information in a user profile).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to incorporate the physical state determination of Chatterjee into the user profile system of Burk in order to improve personalization of vehicle settings based on the user’s condition.
Burk, as modified by Chatterjee may not explicitly disclose that the action is determined
based on execution of an artificial intelligence (Al) model on the profile of the individual while the vehicle is maneuvering toward a destination.
However, Kim discloses that the action is determined based on execution of an artificial
intelligence (Al) model (see at least para. [0164] of Kim which discloses “The movement characteristic M, the respiratory characteristic B, and the heart rate characteristic H may be then input into the pretrained deep neural network model 40 to predict the physical state of the occupant based on the artificial intelligence (AI) model” and see at least para. [0087] of Kim which discloses “artificial intelligence algorithms” and further discloses “the apparatus 100 may operate in connection with a database server providing data related to big data and speech recognition, which are used for applying various artificial intelligence algorithms. In addition, an application or a web browser may be installed in the apparatus 100, and the apparatus 100 may be remotely controlled via a web server or an application server” and see at least para. [0088] of Kim which discloses “Artificial intelligence (AI) is an area of computer engineering science and information technology that studies methods to make computers mimic intelligent human behaviors such as reasoning, learning, self-improving, and the like”, *Examiner interprets this as teaching execution of an artificial intelligence model on data associated with an individual to determine a condition of the individual) on the profile of the individual (see at least para. [0157] of Kim which discloses “the apparatus 100 may be provided with an artificial neural network and may perform machine learning-based user activity recognition by using the bio-signals of the vehicle occupants as input data”, *Examiner interprets this as evidence that the vehicle can maneuvering to a destination based on execution of an artificial intelligence (AI) model on the profile of the individual) while the vehicle is maneuvering toward a destination (see at least para. [0086] of Kim which discloses “after the occupant enters the vehicle 10, the embodiment of the present disclosure is to inform others that a physical change in the occupant has occurred while the vehicle is traveling”, * Kim discloses that the physical state of the occupant may be monitored while the vehicle is traveling. Examiner interprets this as corresponding to execution of the action while the vehicle is maneuvering toward a destination). Further, as taught by Chatterjee, the determined physical state of the individual is used to select or adjust vehicle settings (see at least para. [0035] and [0064], and Burk teaches applying vehicle settings based on a profile associated with the individual. Therefore, Examiner interprets the combination as determining an action based on execution of an AI model on the profile of the individual.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee, to determine the action based on execution of an artificial intelligence (Al) model on the profile of the individual while the vehicle is maneuvering toward a destination, as taught in Kim with a reasonable expectation of success in order to improve the accuracy and adaptability of selecting vehicle actions based on the individual’s condition by leveraging data-driven inference, thereby enabling more precise and responsive adjustment of vehicle subsystems during vehicle operation. See para. [0088] and [0164] of Kim for motivation.
Regarding claim 2, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the receiving comprises receiving the sensor data from a home network (see at least para. [0009] of Burk which discloses “The networked environment includes a computing system 101 that is made up of a combination of hardware and software. The networked environment 100 may also include mobile device(s) 102, vehicle(s) 103, and cloud services 160. The computing system 101 includes a data store 104, and a customization application 106. The computing system 101 may be connected to a network 110 such as, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, cellular networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks”) prior to detecting that the individual has entered the vehicle (see at least para. [0011] of Burk which discloses “The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100. The user account 115 may be accessible from a server or other computing system 101. In some embodiments, the user account 115 may be stored locally at a mobile device 102 or within a memory of a vehicle 103. The user account may be redundant among a fleet of vehicles 103 that store duplicate versions of the user account 115” and see at least para. [0012] which discloses “the computing system 101 may include a customization application 106, which may access the contents of the data store 104. The customization application 106 may comprise a vehicle interface 124 for communicating with a vehicle 103. The customization application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121”).
Regarding claim 3, the combination of Burk, in view of Chatterjee and further in view of Kim
Discloses wherein the detecting that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle).
Chatterjee further discloses detecting that at least one of a key (see at least para. [0042] of
Chatterjee which discloses “a fob sensor receiving commands from and optionally tracking the geographic location/proximity of a fob of the vehicle”) associated with the individual and a mobile device associated with the individual are within an interior of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle (or while the user is in the vehicle) to improve the user's in-vehicle experience” and see at least para. [0073] of Chatterjee which discloses “vehicle system settings are selected based on input received from a wearable device or mobile device of the user while the user is outside the vehicle, the vehicle system settings adjusted immediately before or as soon as the user enters the vehicle, in alternate embodiments, the adjustments may be performed while the user is in the vehicle. The user may have the wearable device on and/or may be operating an application on the mobile device while in the vehicle. Based on input received from the devices within the vehicle, the in-vehicle computing system may determine settings and transmit control instructions to target systems within the vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee and Kim to further include detecting that at least one of a key associated with the individual and a mobile device associated with the individual are within an interior of the vehicle, as further taught by Chatterjee with a reasonable expectation of success in order to facilitate easy detection of an individual as they enter the vehicle so that the chosen action may be performed.
Regarding claim 4, the combination of Burk, in view of Chatterjee and further in view of Kim
discloses wherein the receiving the sensor data comprises receiving at least one of image data and biometric data of the individual (see at least para. [0030] of Burk which discloses “The user recognition data 206 may include voice prints, finger prints, facial images, biometric data, and other information used to recognize a user. When sensor data is received, the sensor data may be analyzed with respect to user recognition data 206 to determine a user identity”).
Kim further discloses and the determining the physical state of the individual comprises
determining a physical condition of the individual based on execution of the AI model on the at least one of the image data and the biometric data of the individual (see at least para. [0143] of Kim which discloses “a normal or abnormal state may be determined via the deep neural network learned through supervised learning using biometric data (respiratory, heart rate, and movement signals) of the normal state and biometric data of the abnormal state”, *Examiner interprets this as determining an individual’s physical condition based on AI model execution on image data or biometric data of the individual).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee to include determining the physical state of the individual comprises determining a physical condition of the individual based on execution of the AI model on the at least one of the image data and the biometric data of the individual, as further taught in Kim with a reasonable expectation of success in order to improve vehicle safety for an individual based on the individual’s unique physical condition.
Regarding claim 5, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the (see at least para. [0037] of Burk which discloses “the user preferences indicate that the first user prefers a particular seat position” and see at least para. [0049] of Burk which discloses a “seat position. For example, a user may prefer particular seat adjustment for a driver seat but different seat adjustments when sitting in a passenger seat” and see at least para. [0035] of Chatterjee which discloses “the in-vehicle control system may be configured to adjust one or more vehicle settings based on the input received from the mobile device and/or the wearable device. These may include settings for one or more vehicle systems such as a vehicle climate control system (e.g., air conditioner or heater settings), in-vehicle audio system (e.g., volume level and audio source settings), driver seat (e.g., recline angle of driver seat), etc. The settings may be automatically adjusted without requiring specific input from the vehicle operator, such as by selecting settings before the user returns inside the vehicle. That is, in-vehicle computing system 109 may infer settings that the user is likely to choose, or would prefer, based on an assessment of the input received from the wearable device and/or mobile device of the user”. Burk discloses adjusting vehicle settings including seat adjustments and temperature settings (see para. [0032] of Burk), which Examiner interprets as corresponding to adjusting a seat within the vehicle and an air conditioning system of the vehicle) using the AI model (Kim discloses determining a condition of an individual using a deep neural network model (see at least par. [0164] of Kim). Examiner interprets such determination as determining an optimal setting using an AI model) and adjusting at least one of a seat within the vehicle, an air conditioning system (see at least para. [0048] of Chatterjee which discloses “Climate control system 334 may be configured to provide a comfortable environment within the cabin or passenger compartment of vehicle 301. Climate control system 334 includes components enabling controlled ventilation such as air vents, a heater, an air conditioner, an integrated heater and air-conditioner system, etc. Other components linked to the heating and air-conditioning setup may include a windshield defrosting and defogging system capable of clearing the windshield and a ventilation-air filter for cleaning outside air that enters the passenger compartment through a fresh-air inlet”) of the vehicle, and an infotainment system of the vehicle, based on the optimal setting (see at least para. [0018] of Chatterjee which discloses “an infotainment system), an audio system control panel, and an instrument cluster 110. While the example system shown in FIG. 1 includes audio system controls that may be performed via a user interface of in-vehicle computing system 109, such as touch screen 108 without a separate audio system control panel, in other embodiments, the vehicle may include an audio system control panel, which may include controls for a conventional vehicle audio system such as a radio, compact disc player, MP3 player, etc.”).
Regarding claim 7, the combination of Burk in view of Chatterjee and further in view of Kim
discloses comprising capturing additional sensor data (see at least para. [0020] of Burk which discloses “A sensor 151 generates sensor data that may be processed to determine the identity and/or position of an occupant” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant”, *Examiner interprets the sensor 151 and the mobile device 102 to be example of the one or more devices and the sensor data is obtained from these devices with the mobile device 102 being located outside the vehicle. Finally, see at least para. [0044] of Burk which discloses “The sensor data may comprise data from multiple types of sensors to determine the identity of the vehicle occupant. The sensor data may comprise a biometric scan such as a fingerprint scan or retinal scan”, *Examiner interprets this to be physical identification of an individual).
Kim further discloses comprising capturing additional sensor data of the individual while
the individual is within an interior of the vehicle (see at least para. [0006] of Kim which discloses “the driver's biometric information, for example, a drowsy state and a driving state, by using the camera installed in the vehicle”, *Examiner interprets the driver’s biometric information to be the additional sensor data of the individual within the interior of the vehicle), determining a level of fatigue (see at least para. [0051] of Kim which discloses “a drowsy state”) of the individual based on execution of the AI model on the additional sensor data (see at least para. [0078] of Kim which discloses “when a bio-signal of an occupant measured by the apparatus 100 is the bio-signal of a driver, and the measured bio-signal of the driver is determined to be “drowsy” by a pretrained deep neural network model, the apparatus 100 may determine that a physical state of the driver is a drowsy state”), generating a notification based on the level of fatigue, and displaying the notification via a display device of the vehicle (see at least para. [0084] of Kim which discloses “when the vehicle occupant is the driver and the physical state of the driver is estimated to be “drowsy,” an alarm may be generated by an alarm system installed in the vehicle or a user device of the driver” and see at least para. [0085] of Kim which discloses “a warning phrase may be displayed on a vehicle instrument panel or a dashboard, or an alarm may be generated by a speaker so that a physical state of an occupant needing a status check may be confirmed”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee, to include capturing additional sensor data of the individual while the individual is within an interior of the vehicle, determining a level of fatigue of the individual based on execution of the AI model on the additional sensor data, generating a notification based on the level of fatigue, and displaying the notification via a display device of the vehicle, as further taught in Kim with a reasonable expectation of success in order to improve vehicle safety consistent with the industry motivation to enhance driver monitoring systems using AI models.
Regarding amended claim 8, Burk discloses An apparatus (Fig. 5, 101 and see at least para.
[0059] of Burk which discloses “a computing system 101 according to various embodiments of the present disclosure. The computing system 101 may include one or more computing devices 500 used to implement the computing functionality of computing system 101 in the networked envionrment 100”) comprising: a memory (Fig. 5, 506 and see at least para. [0059] of Burk which discloses “memory 506”); and a processor (Fig. 5, 503 and see at least para. [0059] of Burk which discloses “a processor 503”) coupled to the memory (see at least para. [0059] of Burk which discloses “a processor 503 and memory 506, both of which are coupled to a local interface 509 or bus”, *Examiner interprets this as the processor and memory being indirectly coupled to each other), the processor (see at least para. [0060] of Burk which discloses “Stored in the memory 506 are both data and several components that are executable by the processor 503. In particular, stored in the memory 506 and executable by the processor 503 is a data store 104”, *Examiner interprets this as the processor is configured to receive data) configured to: receive (see at least para. [0021] of Burk which discloses the collection of “data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”), via a vehicle (Fig. 1, 103 and see at least para. [0011] of Burk which describes “a vehicle 103” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110”), sensor data (see at least para. [0020] of Burk which discloses “A sensor 151 generates sensor data that may be processed to determine the identity and/or position of an occupant” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant”) of an individual (see at least para. [0024] of Burk which discloses “the user” and “the vehicle occupant”, *Examiner interprets these to be an individual. Further, see at least para. [0044] of Burk which discloses “The sensor data may comprise data from multiple types of sensors to determine the identity of the vehicle occupant. The sensor data may comprise a biometric scan such as a fingerprint scan or retinal scan”, *Examiner interprets this to be physical identification of an individual) and create (see at least para. [0011] of Burk which discloses “A user account 115 may be created and maintained for an individual user. A user account 115 may include a user profile 121”) a profile (Fig. 1, 121 and see at least para. [0011] of Burk which discloses “A user profile 121 may include user credentials for authenticating a user. The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100”, *Burk discloses maintaining a user profile associated with an identified individual, which stores data used to customize vehicle operation. Examiner interprets such a profile as a data structure capable of storing attributes of the individual) of the individual (see at least para. [0011] of Burk which discloses “an individual user. A user account 115 may include a user profile 121”), detect that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle. Burk discloses detecting that a user has entered the vehicle. Specifically, Burk teaches that upon entering the vehicle, one or more sensors identify the user (para. [0024]). Examiner interprets such identification of the user upon entry int the vehicle as detecting that the individual has entered the vehicle), execute, in response to detecting entry to the vehicle (see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”), an action (see at least para. [0026] of Burk which disclose “Upon identifying the appropriate user profile 121, the customization application 106 extracts desired user settings and generates control instructions to implement the desired user settings”, *Burk discloses detecting entry of an individual into the vehicle and executing vehicle settings in response to such detection based on a stored user profile (see at least para. [0024] of Burk), wherein execution of the action is withheld until detecting the individual has entered the vehicle(see at least para. [0008] of Burk which discloses “the vehicle's settings may be updated or otherwise controlled in response to new passengers entering the vehicle” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant. In addition, the sensor data may indicate a location or seat 142 where the occupant has been situated” and see at least para. [0039] of Burk which discloses “when the second user enters the vehicle, the system may generate control instructions that are implemented by the vehicle 103 to actuate a privacy screen”, *Burk discloses applying vehicle settings upon detecting the presence/identity of the user when the user enters the vehicle. Examiner interprets this conditional application as corresponding to executing an action in response to detecting entry, such that prior to detection the action is not executed, i.e., execution is withheld until entry is detected); and adjust a setting of a vehicle subsystem (see at least para. [0032] of Burk which discloses “settings include temperature settings, video settings, seat adjustments, audio settings, privacy settings, or other settings for configuring a vehicle 103. Temperature settings may include fan speed, preferred temperature, and other settings that control a vehicle's heating and cooling system. Video settings may include preferred genres of content, the identification of specific content, display settings such as, for example, brightness, and other settings for controlling the presentation of video. Seat adjustments may include settings for height, recline, lumbar support, armrest height, and other settings for configuring a seat” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”, *Examiner interprets the “vehicle components” of Burk that receive control instructions as corresponding to vehicle subsystems, as they are controllable systems within the vehicle capable of adjusting operational settings in response to executed actions) based on the executed actions (see at least para. [0051] of Burk which discloses “the control instruction may include an instruction to adjust climate or temperature settings. In addition, the control instruction may indicate a zone within the vehicle to implement the instruction, where the zone corresponds to the location of the seat 142 that is occupied by the user. The control instruction may include an instruction to adjust seat settings, adjust a privacy screen, or instructions for other vehicle configurations” and see at least para. [0058] of Burk which discloses “in response to detecting the presence or identity of a new occupant, the subsequent control instruction may be an instruction to limit playback volume, control a privacy screen, adjust the seat settings, adjust the temperature to an average temperature, or other instructions to adjust a vehicle control”). Burk discloses adjusting a setting of a vehicle subsystem. Specifically, Burk discloses that vehicle settings include temperature settings, seat adjustments, video/display settings, and audio settings (see at least para. [0032]). Temperature settings control a vehicle’s heating and cooling system, and seat adjustments control positioning and support of a seat, which Examiner interprets as corresponding to settings of respective vehicle subsystems. Burk further discloses that control instructions are generated and executed to adjust such settings, including instructions to adjust climate or temperature settings, adjust seat settings, control a privacy screen and other vehicle configurations (see at least para. [0051] and [0058]). Examiner interprets such control instructions as executed actions what result in adjusting settings of vehicle subsystems based on the executed actions.
Burk does teach receiving sensor data of an individual via a vehicle.
Burk may not explicitly disclose that the sensor data is obtained while the individual is
located outside of the vehicle, nor that the processor can determine a physical state of the individual before entering the vehicle based on the sensor data.
However, in the same field of endeavor, Chatterjee discloses that the sensor data (see at
least para. [0084] of Chatterjee which disclose “information regarding physical parameters of the user from the wearable device. For example, the application may retrieve information regarding a physical state of the user (nature of physical activity performed, duration of physical activity, user's heart rate, pulse rate, blood pressure, body temperature, etc.). The wearable device may include various sensors for sensing and estimating the various physical parameters of the user”. In this connection, it should be noted that Chatterjee also discloses a vehicle (Fig. 2, 102 and see at least para. [0017] of Chatterjee which discloses “a vehicle 102”), sensor data (see at least para. [0031] of Chatterjee which discloses “user data may be gathered by wearable devices 206-210 as long as the device is worn by the user, irrespective of whether the user is inside or outside of vehicle 102”, *Examiner interprets the user data to be sensor data and see at least para. [0081] of Chatterjee which discloses “sensor data from the mobile device and/or the wearable device”) of an individual (Fig. 2, 202 and see at least para. [0026] of Chatterjee which discloses a “user 202”) from one or more devices (Fig. 2, 206-210 and see at least para. [0031] of Chatterjee which discloses “Wearable devices 206-210 may include devices worn by user 202”) that are located outside of the vehicle (see at least para. [0024]of Chatterjee which discloses “wearable device 150 is located outside of vehicle 102”) is obtained form devices while the individual is located outside of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle”, *Before a user/individual enters the vehicle they must be outside the vehicle); that the processor can determine a physical state of the individual (see at least para. [0005] of Chatterjee which discloses “The storage device may store instructions executable by the processor to receive aggregated input regarding a physical condition and an environment of a user from the mobile device, the input regarding the physical condition of the user”) before entering the vehicle based on the sensor data (see at least para. [0035] of Chatterjee which discloses “selecting settings before the user returns inside the vehicle. That is, in-vehicle computing system 109 may infer settings that the user is likely to choose, or would prefer, based on an assessment of the input received from the wearable device and/or mobile device of the user“ and see at least para. [0064] of Chatterjee which discloses “By assessing the input from the mobile device and wearable device to infer a user state, and adjusting vehicle settings based on the user state even before the user gets into the vehicle”, *Examiner interprets this as determining a physical state of the individual before entering the vehicle based on the sensor data).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the method of Burk to include obtaining the sensor data while the individual is located outside of the vehicle; that the processor can determine a physical state of the individual before entering the vehicle based on the sensor data; as taught in Chatterjee with a reasonable expectation of success in order to enable the vehicle to prepare and apply personalized settings based on the individual’s physical condition prior to entry, thereby improving the timeliness and responsiveness of vehicle system adjustments and enhancing the occupant’s vehicle experience. See para. [0064] of Chatterjee for motivation.
Burk may not explicitly disclose the profile with the physical state of the individual.
However, Chatterjee describes a physical state (see at least para. [0003] of Chatterjee which
discloses a “physical state (e.g., heart rate), location, cognitive load, etc.”) of the individual (Chatterjee discloses determining a physical state of the user form sensor data and using that information to select or infer vehicle settings. Under the broadest reasonable interpretation, Examiner interprets this as associating the physical state information with the user, thereby corresponding to storing such state information in a user profile).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to incorporate the physical state determination of Chatterjee into the user profile system of Burk in order to improve personalization of vehicle settings based on the user’s condition.
Burk, as modified by Chatterjee may not explicitly disclose that the processor is configured
to determine based on execution of an artificial intelligence (AI) model on the profile of the individual while the vehicle is maneuvering toward a destination.
However, Kim discloses that the processor is configured to determine based on execution
of an artificial intelligence (AI) model (see at least para. [0164] of Kim which discloses “The movement characteristic M, the respiratory characteristic B, and the heart rate characteristic H may be then input into the pretrained deep neural network model 40 to predict the physical state of the occupant based on the artificial intelligence (AI) model” and see at least para. [0087] of Kim which discloses “artificial intelligence algorithms” and further discloses “the apparatus 100 may operate in connection with a database server providing data related to big data and speech recognition, which are used for applying various artificial intelligence algorithms. In addition, an application or a web browser may be installed in the apparatus 100, and the apparatus 100 may be remotely controlled via a web server or an application server” and see at least para. [0088] of Kim which discloses “Artificial intelligence (AI) is an area of computer engineering science and information technology that studies methods to make computers mimic intelligent human behaviors such as reasoning, learning, self-improving, and the like”, *Examiner interprets this as teaching execution of an artificial intelligence model on data associated with an individual to determine a condition of the individual) on the profile of the individual (see at least para. [0157] of Kim which discloses “the apparatus 100 may be provided with an artificial neural network and may perform machine learning-based user activity recognition by using the bio-signals of the vehicle occupants as input data”, *Examiner interprets this as evidence that the vehicle can maneuvering to a destination based on execution of an artificial intelligence (AI) model on the profile of the individual) while the vehicle is maneuvering toward a destination (see at least para. [0086] of Kim which discloses “after the occupant enters the vehicle 10, the embodiment of the present disclosure is to inform others that a physical change in the occupant has occurred while the vehicle is traveling”, * Kim discloses that the physical state of the occupant may be monitored while the vehicle is traveling. Examiner interprets this as corresponding to execution of the action while the vehicle is maneuvering toward a destination). Further, as taught by Chatterjee, the determined physical state of the individual is used to select or adjust vehicle settings (see at least para. [0035] and [0064], and Burk teaches applying vehicle settings based on a profile associated with the individual. Therefore, Examiner interprets the combination as determining an action based on execution of an AI model on the profile of the individual.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee, to use the processor to make a determination based on execution of an artificial intelligence (AI) model on the profile of the individual while the vehicle is maneuvering toward a destination, as taught in Kim with a reasonable expectation of success in order to improve the accuracy and adaptability of selecting vehicle actions based on the individual’s condition by leveraging data-driven inference, thereby enabling more precise and responsive adjustment of vehicle subsystems during vehicle operation. See para. [0088] and [0164] of Kim for motivation.
Regarding claim 9, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the processor is configured to receive the sensor data from a home network (see at least para. [0009] of Burk which discloses “The networked environment includes a computing system 101 that is made up of a combination of hardware and software. The networked environment 100 may also include mobile device(s) 102, vehicle(s) 103, and cloud services 160. The computing system 101 includes a data store 104, and a customization application 106. The computing system 101 may be connected to a network 110 such as, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, cellular networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks”) prior to detecting that the individual has entered the vehicle (see at least para. [0011] of Burk which discloses “The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100. The user account 115 may be accessible from a server or other computing system 101. In some embodiments, the user account 115 may be stored locally at a mobile device 102 or within a memory of a vehicle 103. The user account may be redundant among a fleet of vehicles 103 that store duplicate versions of the user account 115” and see at least para. [0012] which discloses “the computing system 101 may include a customization application 106, which may access the contents of the data store 104. The customization application 106 may comprise a vehicle interface 124 for communicating with a vehicle 103. The customization application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121”).
Regarding claim 10, the combination of Burk, in view of Chatterjee and further in view of
Kim discloses detecting that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle).
Chatterjee further discloses the processor is configured to detect that at least one of a key
(see at least para. [0042] of Chatterjee which discloses “a fob sensor receiving commands from and optionally tracking the geographic location/proximity of a fob of the vehicle”) associated with the individual and a mobile device associated with the individual are within an interior of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle (or while the user is in the vehicle) to improve the user's in-vehicle experience” and see at least para. [0073] of Chatterjee which discloses “vehicle system settings are selected based on input received from a wearable device or mobile device of the user while the user is outside the vehicle, the vehicle system settings adjusted immediately before or as soon as the user enters the vehicle, in alternate embodiments, the adjustments may be performed while the user is in the vehicle. The user may have the wearable device on and/or may be operating an application on the mobile device while in the vehicle. Based on input received from the devices within the vehicle, the in-vehicle computing system may determine settings and transmit control instructions to target systems within the vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the apparatus of Burk, as modified by Chatterjee and Kim to further include the processor is configured to detect that at least one of a key associated with the individual and a mobile device associated with the individual are within an interior of the vehicle, as further taught by Chatterjee with a reasonable expectation of success in order to facilitate easy detection of an individual as they enter the vehicle so that the chosen action may be performed.
Regarding claim 11, the combination of Burk, in view of Chatterjee and further in view of
Kim discloses wherein the processor is configured to receive at least one of image data and biometric data of the individual (see at least para. [0030] of Burk which discloses “The user recognition data 206 may include voice prints, finger prints, facial images, biometric data, and other information used to recognize a user. When sensor data is received, the sensor data may be analyzed with respect to user recognition data 206 to determine a user identity”).
Kim further discloses and determine a physical condition of the individual based on execution
of the AI model on the at least one of the image data and the biometric data of the individual (see at least para. [0143] of Kim which discloses “a normal or abnormal state may be determined via the deep neural network learned through supervised learning using biometric data (respiratory, heart rate, and movement signals) of the normal state and biometric data of the abnormal state”, *Examiner interprets this as determining an individual’s physical condition based on AI model execution on image data or biometric data of the individual).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee to modify the processor to determine a physical condition of the individual based on execution of the AI model on the at least one of the image data and the biometric data of the individual, as further taught in Kim with a reasonable expectation of success in order to improve vehicle safety for an individual based on the individual’s unique physical condition.
Regarding claim 12, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the processor (Fig. 5, 503 and see at least para. [0059] of Burk which discloses “a processor 503”) is configured to determine an optimal setting (see at least para. [0037] of Burk which discloses “the user preferences indicate that the first user prefers a particular seat position” and see at least para. [0049] of Burk which discloses a “seat position. For example, a user may prefer particular seat adjustment for a driver seat but different seat adjustments when sitting in a passenger seat” and see at least para. [0035] of Chatterjee which discloses “the in-vehicle control system may be configured to adjust one or more vehicle settings based on the input received from the mobile device and/or the wearable device. These may include settings for one or more vehicle systems such as a vehicle climate control system (e.g., air conditioner or heater settings), in-vehicle audio system (e.g., volume level and audio source settings), driver seat (e.g., recline angle of driver seat), etc. The settings may be automatically adjusted without requiring specific input from the vehicle operator, such as by selecting settings before the user returns inside the vehicle. That is, in-vehicle computing system 109 may infer settings that the user is likely to choose, or would prefer, based on an assessment of the input received from the wearable device and/or mobile device of the user”. Burk discloses adjusting vehicle settings including seat adjustments and temperature settings (see para. [0032] of Burk), which Examiner interprets as corresponding to adjusting a seat within the vehicle and an air conditioning system of the vehicle) based on the AI model (Kim discloses determining a condition of an individual using a deep neural network model (see at least par. [0164] of Kim). Examiner interprets such determination as determining an optimal setting using an AI model) and modify at least one of a seat within the vehicle, an air conditioning system (see at least para. [0048] of Chatterjee which discloses “Climate control system 334 may be configured to provide a comfortable environment within the cabin or passenger compartment of vehicle 301. Climate control system 334 includes components enabling controlled ventilation such as air vents, a heater, an air conditioner, an integrated heater and air-conditioner system, etc. Other components linked to the heating and air-conditioning setup may include a windshield defrosting and defogging system capable of clearing the windshield and a ventilation-air filter for cleaning outside air that enters the passenger compartment through a fresh-air inlet”) of the vehicle, and an infotainment system of the vehicle, based on the optimal setting (see at least para. [0018] of Chatterjee which discloses “an infotainment system), an audio system control panel, and an instrument cluster 110. While the example system shown in FIG. 1 includes audio system controls that may be performed via a user interface of in-vehicle computing system 109, such as touch screen 108 without a separate audio system control panel, in other embodiments, the vehicle may include an audio system control panel, which may include controls for a conventional vehicle audio system such as a radio, compact disc player, MP3 player, etc.”).
Regarding claim 14, the combination of Burk in view of Chatterjee and further in view of Kim
discloses wherein the processor is configured to capture additional sensor data (see at least para. [0020] of Burk which discloses “A sensor 151 generates sensor data that may be processed to determine the identity and/or position of an occupant” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant”, *Examiner interprets the sensor 151 and the mobile device 102 to be example of the one or more devices and the sensor data is obtained from these devices with the mobile device 102 being located outside the vehicle. Finally, see at least para. [0044] of Burk which discloses “The sensor data may comprise data from multiple types of sensors to determine the identity of the vehicle occupant. The sensor data may comprise a biometric scan such as a fingerprint scan or retinal scan”, *Examiner interprets this to be physical identification of an individual).
Kim further discloses comprising capturing additional sensor data of the individual while
the individual is within an interior of the vehicle (see at least para. [0006] of Kim which discloses “the driver's biometric information, for example, a drowsy state and a driving state, by using the camera installed in the vehicle”, *Examiner interprets the driver’s biometric information to be the additional sensor data of the individual within the interior of the vehicle), determine a level of fatigue (see at least para. [0051] of Kim which discloses “a drowsy state”) of the individual based on execution of the AI model on the additional sensor data (see at least para. [0078] of Kim which discloses “when a bio-signal of an occupant measured by the apparatus 100 is the bio-signal of a driver, and the measured bio-signal of the driver is determined to be “drowsy” by a pretrained deep neural network model, the apparatus 100 may determine that a physical state of the driver is a drowsy state”), generate a notification based on the level of fatigue, and display the notification via a display device of the vehicle (see at least para. [0084] of Kim which discloses “when the vehicle occupant is the driver and the physical state of the driver is estimated to be “drowsy,” an alarm may be generated by an alarm system installed in the vehicle or a user device of the driver” and see at least para. [0085] of Kim which discloses “a warning phrase may be displayed on a vehicle instrument panel or a dashboard, or an alarm may be generated by a speaker so that a physical state of an occupant needing a status check may be confirmed”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the apparatus of Burk, as modified by Chatterjee, to include capturing additional sensor data of the individual while the individual is within an interior of the vehicle, determining a level of fatigue of the individual based on execution of the AI model on the additional sensor data, generating a notification based on the level of fatigue, and displaying the notification via a display device of the vehicle, as further taught in Kim with a reasonable expectation of success in order to improve vehicle safety consistent with the industry motivation to enhance driver monitoring systems using AI models.
Regarding amended claim 15, Burk discloses A non-transitory (see at least para. [0068] of
Burk which discloses “any non-transitory computer-readable medium for use by or in connection with an instruction execution system”) computer-readable storage medium comprising instructions stored therein which when executed (see at least para. [0068] of Burk which discloses “the instruction execution system. In the context of the present disclosure, a “computer-readable medium” may be any medium that may contain, store, or maintain the logic”) by a processor (Fig. 5, 503 and see at least para. [0059] of Burk which discloses “a processor 503” and see at least para. [0060] of Burk which discloses “Stored in the memory 506 are both data and several components that are executable by the processor 503. In particular, stored in the memory 506 and executable by the processor 503 is a data store 104”) cause the processor (see at least para. [0060] of Burk which discloses “Stored in the memory 506 are both data and several components that are executable by the processor 503. In particular, stored in the memory 506 and executable by the processor 503 is a data store 104”, *Examiner interprets this as the processor is configured to receive data) to perform: receiving (see at least para. [0021] of Burk which discloses the collection of “data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”), via a vehicle (Fig. 1, 103 and see at least para. [0011] of Burk which describes “a vehicle 103” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110”), sensor data (see at least para. [0020] of Burk which discloses “A sensor 151 generates sensor data that may be processed to determine the identity and/or position of an occupant” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant”) of an individual (see at least para. [0024] of Burk which discloses “the user” and “the vehicle occupant”, *Examiner interprets these to be an individual. Further, see at least para. [0044] of Burk which discloses “The sensor data may comprise data from multiple types of sensors to determine the identity of the vehicle occupant. The sensor data may comprise a biometric scan such as a fingerprint scan or retinal scan”, *Examiner interprets this to be physical identification of an individual) and creating (see at least para. [0011] of Burk which discloses “A user account 115 may be created and maintained for an individual user. A user account 115 may include a user profile 121”) a profile (Fig. 1, 121 and see at least para. [0011] of Burk which discloses “A user profile 121 may include user credentials for authenticating a user. The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100”, *Burk discloses maintaining a user profile associated with an identified individual, which stores data used to customize vehicle operation. Examiner interprets such a profile as a data structure capable of storing attributes of the individual) of the individual (see at least para. [0011] of Burk which discloses “an individual user. A user account 115 may include a user profile 121”); detecting that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle. Burk discloses detecting that a user has entered the vehicle. Specifically, Burk teaches that upon entering the vehicle, one or more sensors identify the user (para. [0024]). Examiner interprets such identification of the user upon entry int the vehicle as detecting that the individual has entered the vehicle); executing, in response to detecting entry to the vehicle (see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user”), an action (see at least para. [0026] of Burk which disclose “Upon identifying the appropriate user profile 121, the customization application 106 extracts desired user settings and generates control instructions to implement the desired user settings”, *Burk discloses detecting entry of an individual into the vehicle and executing vehicle settings in response to such detection based on a stored user profile (see at least para. [0024] of Burk), wherein execution of the action is withheld until detecting the individual has entered the vehicle (see at least para. [0008] of Burk which discloses “the vehicle's settings may be updated or otherwise controlled in response to new passengers entering the vehicle” and see at least para. [0024] of Burk which discloses “Upon entering a vehicle 103, one or more sensors 151 may identify the user. For example, sensors 151 may be configured to transmit sensor data over the network 110 to the customization application 106. The customization application 106 may perform an analysis on the sensor data to identify the vehicle occupant. In addition, the sensor data may indicate a location or seat 142 where the occupant has been situated” and see at least para. [0039] of Burk which discloses “when the second user enters the vehicle, the system may generate control instructions that are implemented by the vehicle 103 to actuate a privacy screen”, *Burk discloses applying vehicle settings upon detecting the presence/identity of the user when the user enters the vehicle. Examiner interprets this conditional application as corresponding to executing an action in response to detecting entry, such that prior to detection the action is not executed, i.e., execution is withheld until entry is detected); adjusting a setting of a vehicle subsystem (see at least para. [0032] of Burk which discloses “settings include temperature settings, video settings, seat adjustments, audio settings, privacy settings, or other settings for configuring a vehicle 103. Temperature settings may include fan speed, preferred temperature, and other settings that control a vehicle's heating and cooling system. Video settings may include preferred genres of content, the identification of specific content, display settings such as, for example, brightness, and other settings for controlling the presentation of video. Seat adjustments may include settings for height, recline, lumbar support, armrest height, and other settings for configuring a seat” and see at least para. [0021] of Burk which discloses “The communication interface 136 may collect data received at the vehicle 103 and transmit it over the network 110. In addition, the communication interface 136 may receive data or control instructions over the network 110 and transmit them to the vehicle 103 and related vehicle components”, *Examiner interprets the “vehicle components” of Burk that receive control instructions as corresponding to vehicle subsystems, as they are controllable systems within the vehicle capable of adjusting operational settings in response to executed actions) based on the executed actions (see at least para. [0051] of Burk which discloses “the control instruction may include an instruction to adjust climate or temperature settings. In addition, the control instruction may indicate a zone within the vehicle to implement the instruction, where the zone corresponds to the location of the seat 142 that is occupied by the user. The control instruction may include an instruction to adjust seat settings, adjust a privacy screen, or instructions for other vehicle configurations” and see at least para. [0058] of Burk which discloses “in response to detecting the presence or identity of a new occupant, the subsequent control instruction may be an instruction to limit playback volume, control a privacy screen, adjust the seat settings, adjust the temperature to an average temperature, or other instructions to adjust a vehicle control”). Burk discloses adjusting a setting of a vehicle subsystem. Specifically, Burk discloses that vehicle settings include temperature settings, seat adjustments, video/display settings, and audio settings (see at least para. [0032]). Temperature settings control a vehicle’s heating and cooling system, and seat adjustments control positioning and support of a seat, which Examiner interprets as corresponding to settings of respective vehicle subsystems. Burk further discloses that control instructions are generated and executed to adjust such settings, including instructions to adjust climate or temperature settings, adjust seat settings, control a privacy screen and other vehicle configurations (see at least para. [0051] and [0058]). Examiner interprets such control instructions as executed actions what result in adjusting settings of vehicle subsystems based on the executed actions.
Burk does teach receiving sensor data of an individual via a vehicle.
Burk may not explicitly disclose that the sensor data is obtained while the individual is
located outside of the vehicle; nor determining a physical state of the individual before entering the vehicle based on the sensor data.
However, in the same field of endeavor, Chatterjee discloses that the sensor data (see at
least para. [0084] of Chatterjee which disclose “information regarding physical parameters of the user from the wearable device. For example, the application may retrieve information regarding a physical state of the user (nature of physical activity performed, duration of physical activity, user's heart rate, pulse rate, blood pressure, body temperature, etc.). The wearable device may include various sensors for sensing and estimating the various physical parameters of the user”. In this connection, it should be noted that Chatterjee also discloses a vehicle (Fig. 2, 102 and see at least para. [0017] of Chatterjee which discloses “a vehicle 102”), sensor data (see at least para. [0031] of Chatterjee which discloses “user data may be gathered by wearable devices 206-210 as long as the device is worn by the user, irrespective of whether the user is inside or outside of vehicle 102”, *Examiner interprets the user data to be sensor data and see at least para. [0081] of Chatterjee which discloses “sensor data from the mobile device and/or the wearable device”) of an individual (Fig. 2, 202 and see at least para. [0026] of Chatterjee which discloses a “user 202”) from one or more devices (Fig. 2, 206-210 and see at least para. [0031] of Chatterjee which discloses “Wearable devices 206-210 may include devices worn by user 202”) that are located outside of the vehicle (see at least para. [0024]of Chatterjee which discloses “wearable device 150 is located outside of vehicle 102”) is obtained from devices while the individual is located outside of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle”, *Before a user/individual enters the vehicle they must be outside the vehicle); determining a physical state of the individual (see at least para. [0005] of Chatterjee which discloses “The storage device may store instructions executable by the processor to receive aggregated input regarding a physical condition and an environment of a user from the mobile device, the input regarding the physical condition of the user”) before entering the vehicle based on the sensor data (see at least para. [0035] of Chatterjee which discloses “selecting settings before the user returns inside the vehicle. That is, in-vehicle computing system 109 may infer settings that the user is likely to choose, or would prefer, based on an assessment of the input received from the wearable device and/or mobile device of the user“ and see at least para. [0064] of Chatterjee which discloses “By assessing the input from the mobile device and wearable device to infer a user state, and adjusting vehicle settings based on the user state even before the user gets into the vehicle”, *Examiner interprets this as determining a physical state of the individual before entering the vehicle based on the sensor data).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the method of Burk to include obtaining the sensor data while the individual is located outside of the vehicle; determining a physical state of the individual before entering the vehicle based on the sensor data; as taught in Chatterjee with a reasonable expectation of success in order to enable the vehicle to prepare and apply personalized settings based on the individual’s physical condition prior to entry, thereby improving the timeliness and responsiveness of vehicle system adjustments and enhancing the occupant’s vehicle experience. See para. [0064] of Chatterjee for motivation.
Burk may not explicitly disclose the profile with the physical state of the individual.
However, Chatterjee describes a physical state (see at least para. [0003] of Chatterjee which
discloses a “physical state (e.g., heart rate), location, cognitive load, etc.”) of the individual (Chatterjee discloses determining a physical state of the user form sensor data and using that information to select or infer vehicle settings. Under the broadest reasonable interpretation, Examiner interprets this as associating the physical state information with the user, thereby corresponding to storing such state information in a user profile).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to incorporate the physical state determination of Chatterjee into the user profile system of Burk in order to improve personalization of vehicle settings based on the user’s condition.
Burk, as modified by Chatterjee may not explicitly disclose that the action is determined
based on execution of an artificial intelligence (Al) model on the profile of the individual while the vehicle is maneuvering toward a destination.
However, Kim discloses that the action is determined based on execution of an artificial
intelligence (Al) model (see at least para. [0164] of Kim which discloses “The movement characteristic M, the respiratory characteristic B, and the heart rate characteristic H may be then input into the pretrained deep neural network model 40 to predict the physical state of the occupant based on the artificial intelligence (AI) model” and see at least para. [0087] of Kim which discloses “artificial intelligence algorithms” and further discloses “the apparatus 100 may operate in connection with a database server providing data related to big data and speech recognition, which are used for applying various artificial intelligence algorithms. In addition, an application or a web browser may be installed in the apparatus 100, and the apparatus 100 may be remotely controlled via a web server or an application server” and see at least para. [0088] of Kim which discloses “Artificial intelligence (AI) is an area of computer engineering science and information technology that studies methods to make computers mimic intelligent human behaviors such as reasoning, learning, self-improving, and the like”, *Examiner interprets this as teaching execution of an artificial intelligence model on data associated with an individual to determine a condition of the individual) on the profile of the individual (see at least para. [0157] of Kim which discloses “the apparatus 100 may be provided with an artificial neural network and may perform machine learning-based user activity recognition by using the bio-signals of the vehicle occupants as input data”, *Examiner interprets this as evidence that the vehicle can maneuvering to a destination based on execution of an artificial intelligence (AI) model on the profile of the individual) while the vehicle is maneuvering toward a destination (see at least para. [0086] of Kim which discloses “after the occupant enters the vehicle 10, the embodiment of the present disclosure is to inform others that a physical change in the occupant has occurred while the vehicle is traveling”, * Kim discloses that the physical state of the occupant may be monitored while the vehicle is traveling. Examiner interprets this as corresponding to execution of the action while the vehicle is maneuvering toward a destination). Further, as taught by Chatterjee, the determined physical state of the individual is used to select or adjust vehicle settings (see at least para. [0035] and [0064], and Burk teaches applying vehicle settings based on a profile associated with the individual. Therefore, Examiner interprets the combination as determining an action based on execution of an AI model on the profile of the individual.
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the method of Burk, as modified by Chatterjee, to determine the action based on execution of an artificial intelligence (Al) model on the profile of the individual while the vehicle is maneuvering toward a destination, as taught in Kim with a reasonable expectation of success in order to improve the accuracy and adaptability of selecting vehicle actions based on the individual’s condition by leveraging data-driven inference, thereby enabling more precise and responsive adjustment of vehicle subsystems during vehicle operation. See para. [0088] and [0164] of Kim for motivation.
Regarding claim 16, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the receiving comprises receiving the sensor data from a home network (see at least para. [0009] of Burk which discloses “The networked environment includes a computing system 101 that is made up of a combination of hardware and software. The networked environment 100 may also include mobile device(s) 102, vehicle(s) 103, and cloud services 160. The computing system 101 includes a data store 104, and a customization application 106. The computing system 101 may be connected to a network 110 such as, for example, the Internet, intranets, extranets, wide area networks (WANs), local area networks (LANs), wired networks, cellular networks, wireless networks, or other suitable networks, etc., or any combination of two or more such networks”) prior to detecting that the individual has entered the vehicle (see at least para. [0011] of Burk which discloses “The user profile 121 may also include desired user settings or configurations that are manually provided by a user or automatically generated as the user interacts with component in the networked environment 100. The user account 115 may be accessible from a server or other computing system 101. In some embodiments, the user account 115 may be stored locally at a mobile device 102 or within a memory of a vehicle 103. The user account may be redundant among a fleet of vehicles 103 that store duplicate versions of the user account 115” and see at least para. [0012] which discloses “the computing system 101 may include a customization application 106, which may access the contents of the data store 104. The customization application 106 may comprise a vehicle interface 124 for communicating with a vehicle 103. The customization application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121”).
Regarding claim 17, the combination of Burk, in view of Chatterjee and further in view of
Kim discloses detecting that the individual has entered the vehicle (see at least para. [0012] of Burk which discloses “application 106 tracks users who enter vehicles 103, tracks their identities and positions within a vehicle 103, maintains a user profile 121 for each user, and manages vehicle settings for users according to their user profiles 121” and see at least para. [0008] of Burk which discloses “the seat or position of the occupant may be determined. For example, through the use of one or more sensors, it may be determined that an occupant is sitting in the driver seat, front passenger seat, rear left seat, rear right seat, etc.”, *Examiner interprets that since the position of occupant is determined then the occupant/individual is detected as entering the vehicle).
Chatterjee further discloses detecting that at least one of a key (see at least para. [0042] of
Chatterjee which discloses “a fob sensor receiving commands from and optionally tracking the geographic location/proximity of a fob of the vehicle”) associated with the individual and a mobile device associated with the individual are within an interior of the vehicle (see at least para. [0016] of Chatterjee which discloses “Using the data collected by such devices, an in-vehicle computing system may select vehicle settings (e.g., climate control setting, audio system settings, etc.) that would improve the vehicle ambience. In other words, the sensors of the various devices can be leveraged to adapt vehicle settings before a user enters the vehicle (or while the user is in the vehicle) to improve the user's in-vehicle experience” and see at least para. [0073] of Chatterjee which discloses “vehicle system settings are selected based on input received from a wearable device or mobile device of the user while the user is outside the vehicle, the vehicle system settings adjusted immediately before or as soon as the user enters the vehicle, in alternate embodiments, the adjustments may be performed while the user is in the vehicle. The user may have the wearable device on and/or may be operating an application on the mobile device while in the vehicle. Based on input received from the devices within the vehicle, the in-vehicle computing system may determine settings and transmit control instructions to target systems within the vehicle”).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Burk, as modified by Chatterjee and Kim to further include detecting that at least one of a key associated with the individual and a mobile device associated with the individual are within an interior of the vehicle, as further taught by Chatterjee with a reasonable expectation of success in order to facilitate easy detection of an individual as they enter the vehicle so that the chosen action may be performed.
Regarding claim 18, the combination of Burk, in view of Chatterjee and further in view of
Kim discloses wherein the receiving the sensor data comprises receiving at least one of image data and biometric data of the individual (see at least para. [0030] of Burk which discloses “The user recognition data 206 may include voice prints, finger prints, facial images, biometric data, and other information used to recognize a user. When sensor data is received, the sensor data may be analyzed with respect to user recognition data 206 to determine a user identity”).
Kim further discloses and the determining the physical state of the individual comprises
determining a physical condition of the individual based on execution of the AI model on the at least one of the image data and the biometric data of the individual (see at least para. [0143] of Kim which discloses “a normal or abnormal state may be determined via the deep neural network learned through supervised learning using biometric data (respiratory, heart rate, and movement signals) of the normal state and biometric data of the abnormal state”, *Examiner interprets this as determining an individual’s physical condition based on AI model execution on image data or biometric data of the individual).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the system of Burk, as modified by Chatterjee to include determining the physical state of the individual comprises determining a physical condition of the individual based on execution of the AI model on the at least one of the image data and the biometric data of the individual, as further taught in Kim with a reasonable expectation of success in order to improve vehicle safety for an individual based on the individual’s unique physical condition.
Regarding claim 19, Burk, as modified by Chatterjee and further modified by Kim discloses
wherein the executing comprises modifying at least one of a seat within the vehicle based on an optimal seat position (see at least para. [0035] of Chatterjee which discloses “adjust one or more vehicle settings based on the input received from the mobile device and/or the wearable device. These may include settings for one or more vehicle systems such as a vehicle climate control system (e.g., air conditioner or heater settings), in-vehicle audio system (e.g., volume level and audio source settings), driver seat (e.g., recline angle of driver seat), etc.”), modifying an air conditioning system of the vehicle based on an optimal interior temperature (see at least para. [0048] of Chatterjee which discloses “Climate control system 334 may be configured to provide a comfortable environment within the cabin or passenger compartment of vehicle 301. Climate control system 334 includes components enabling controlled ventilation such as air vents, a heater, an air conditioner, an integrated heater and air-conditioner system, etc. Other components linked to the heating and air-conditioning setup may include a windshield defrosting and defogging system capable of clearing the windshield and a ventilation-air filter for cleaning outside air that enters the passenger compartment through a fresh-air inlet”), and modifying an infotainment system of the vehicle based on the optimal noise level (see at least para. [0018] of Chatterjee which discloses “an infotainment system), an audio system control panel, and an instrument cluster 110. While the example system shown in FIG. 1 includes audio system controls that may be performed via a user interface of in-vehicle computing system 109, such as touch screen 108 without a separate audio system control panel, in other embodiments, the vehicle may include an audio system control panel, which may include controls for a conventional vehicle audio system such as a radio, compact disc player, MP3 player, etc.”).
Claims 6, 13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Burk (US20220016999 A1) in view of Chatterjee (US2015/0127215 A1) and further in view of Kim (US 2020/0070657 A1) and further in view of Edgington (US 2017/0057492 A1).
Regarding claim 6, Burk, as modified by Chatterjee and Kim, discloses a node (see at least
para. [0156] of Kim which discloses “the artificial neural network can be trained by adjusting connection weights between nodes (if necessary, adjusting bias values as well) so as to produce desired output from given input. Also, the artificial neural network can continuously update the weight values through learning”).
Burk, as modified by Chatterjee and Kim, may not explicitly disclose identifying a computing
node located at the destination and transmitting the physical state of the individual to the computing node located at the destination, prior to the vehicle arriving at the destination.
However, in the same field of endeavor Edgington discloses identifying a computing node
(see at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network”) located at the destination and transmitting the physical state of the individual (see at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network. In some embodiments of the present invention, driver profile storage 620 exists in various locations with synchronization capabilities”) to the computing node located at the destination, prior to the vehicle arriving at the destination (see at least para. [0068] of Edgington which discloses “historical data storage 622 and driver analysis services 602 are each cloud computing nodes connected over a network” and see at least para. [0069] of Edgington which discloses “Correlation service 610 correlates the enriched data from data enrichment service 608 to the driver identified by identification service 606” and “correlation service 610 determines the data most useful in predicting future outcomes for the driver”, *Examiner interprets future outcomes to also include locations of nodes at destinations prior to vehicle arrival).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to have modified the method of Burk, as modified by Chatterjee and Kim, to include identifying a computing node located at the destination and transmitting the physical state of the individual to the computing node located at the destination, prior to the vehicle arriving at the destination, as taught in Edgington with a reasonable expectation of success in order to effectively transmit user information on an individual’s physical state to the computing node located at the destination, prior to the vehicle arriving at the destination to increase vehicle safety and efficiency. See para. [0037], and [0046], [0066] of Edgington for motivation.
Regarding claim 13, Burk, as modified by Chatterjee and Kim, discloses a processor (Fig. 5,
503 and see at least para. [0059] of Burk which discloses “a processor 503”) and a node (see at least
para. [0156] of Kim which discloses “the artificial neural network can be trained by adjusting connection weights between nodes (if necessary, adjusting bias values as well) so as to produce desired output from given input. Also, the artificial neural network can continuously update the weight values through learning”).
Burk, as modified by Chatterjee and Kim, may not explicitly disclose identify a computing
node located at the destination and transmit the physical state of the individual to the computing node located at the destination, prior to the vehicle arriving at the destination.
However, in the same field of endeavor Edgington discloses identify a computing node (see
at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network”) located at the destination and transmit the physical state of the individual (see at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network. In some embodiments of the present invention, driver profile storage 620 exists in various locations with synchronization capabilities”) to the computing node located at the destination, prior to the vehicle arriving at the destination (see at least para. [0068] of Edgington which discloses “historical data storage 622 and driver analysis services 602 are each cloud computing nodes connected over a network” and see at least para. [0069] of Edgington which discloses “Correlation service 610 correlates the enriched data from data enrichment service 608 to the driver identified by identification service 606” and “correlation service 610 determines the data most useful in predicting future outcomes for the driver”, *Examiner interprets future outcomes to also include locations of nodes at destinations prior to vehicle arrival).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to have modified the apparatus of Burk, as modified by Chatterjee and Kim, to include wherein the processor is further configured to identify a computing node located at the destination and transmit the physical state of the individual to the computing node located at the destination, prior to the vehicle arriving at the destination, as taught in Edgington with a reasonable expectation of success in order to effectively transmit user information on an individual’s physical state to the computing node located at the destination, prior to the vehicle arriving at the destination to increase vehicle safety and efficiency. See para. [0037], and [0046], [0066] of Edgington for motivation.
Regarding claim 20, Burk, as modified by Chatterjee and Kim, discloses a processor (Fig. 5,
503 and see at least para. [0059] of Burk which discloses “a processor 503”) and a node (see at least
para. [0156] of Kim which discloses “the artificial neural network can be trained by adjusting connection weights between nodes (if necessary, adjusting bias values as well) so as to produce desired output from given input. Also, the artificial neural network can continuously update the weight values through learning”).
Burk, as modified by Chatterjee and Kim, may not explicitly disclose identifying a computing
node (see at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network”) located at the destination, and transmitting the physical state of the individual (see at least para. [0066] of Edgington which discloses “driver profile storage 620 and driver analysis services 602 are each cloud computing nodes connected over a network. In some embodiments of the present invention, driver profile storage 620 exists in various locations with synchronization capabilities”) to the computing node located at the destination, prior to the vehicle arriving at the destination (see at least para. [0068] of Edgington which discloses “historical data storage 622 and driver analysis services 602 are each cloud computing nodes connected over a network” and see at least para. [0069] of Edgington which discloses “Correlation service 610 correlates the enriched data from data enrichment service 608 to the driver identified by identification service 606” and “correlation service 610 determines the data most useful in predicting future outcomes for the driver”, *Examiner interprets future outcomes to also include locations of nodes at destinations prior to vehicle arrival).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to have modified the system of Burk, as modified by Chatterjee and Kim, to include identifying a computing node located at the destination, and transmitting the physical state of the individual to the computing node located at the destination, prior to the vehicle arriving at the destination, as taught in Edgington with a reasonable expectation of success in order to effectively transmit user information on an individual’s physical state to the computing node located at the destination, prior to the vehicle arriving at the destination to increase vehicle safety and efficiency. See para. [0037], and [0046], [0066] of Edgington for motivation.
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Kaliouby (US2020/0311475 A1) discloses vehicular in-cabin sensing is performed using machine learning. In-cabin sensor data of a vehicle interior is collected. The in-cabin sensor data includes images of the vehicle interior. An occupant is detected within the vehicle interior. The detecting is based on identifying an upper torso of the occupant, using the in-cabin sensor data. The imaging is accomplished using a plurality of imaging devices within a vehicle interior. The occupant is located within the vehicle interior, based on the in-cabin sensor data. An additional occupant within the vehicle interior is detected. A human perception metric for the occupant is analyzed, based on the in-cabin sensor data. The detecting, the locating, and/or the analyzing are performed using machine learning. The human perception metric is promoted to a using application. The human perception metric includes a mood for the occupant and a mood for the vehicle. The promoting includes input to an autonomous vehicle. Penilla (US 2020/0152197 A1) discloses a method for determining a mood of a human driver of a vehicle and using the mood for generating a vehicle response is provided. One example method includes capturing, by a camera of the vehicle, a face of the human driver. The capturing is configured to capture a plurality of images over a period of time, and the plurality of images are analyzed to identify a facial expression and changes in the facial expression of the human driver over the period of time. The method further includes capturing, by a microphone of the vehicle, voice input of the human driver. The voice input is captured over the period of time. The voice input is analyzed to identify a voice profile and changes in the voice profile of the human driver over the period of time. The method processes, by a processor of the vehicle, a combination of the facial expression and the voice profile captured during the period of time to predict the mood of the human driver. The method generates the vehicle response that is responsive to the mood of the human driver. The vehicle response is configured to make at least one adjustment to a setting of the vehicle. The adjustment is selected based on the mood of the human driver. The vehicle response can be used to make the driver calmer and/or assist in reducing distracted driving.
Conclusion
THIS ACTION IS MADE FINAL. 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 DANA IVEY whose telephone number is (313)446-4896. The examiner can normally be reached 9-5:30 EST Monday-Friday.
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, Jelani Smith can be reached at 571-270-3969. 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.
/DANA D IVEY/Examiner, Art Unit 3662
/D.D.I/April 27, 2026
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662
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