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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/25/2026 has been entered.
Status of claims
Claims 1,3, 10, 14 and 18 are amended.
Claim 9 is canceled.
Claims 1-8, 10-20 are pending.
The office considered claim (05/21/2026) since it filed after final with amendments.
Response to arguments
With respect to Applicant’s remarks filed on 06/25/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Applicant remarks:
Amendment should overcome 35 U.S.C. 101 rejection.
Claim 9 combined with independent claim 1 to overcome 35 U.S.C 102 rejection. Onorato does not teach machine learning model use remote server to augment data for a person stored locally.
For claim 10, Mehdi does not teach linking a first local profile to a first remote profile.
For claim 18, Ricci’s generating or presenting a group avatar is not configuring a function of the vehicle.
Office Response:
Amendment overcomes 35 U.S.C. 101 rejection.
Amended claim 1 overcomes 35 U.S.C. 102 rejection.
Please see the new mapping specifically independent claims.
Regarding claim 18, Ricci teaches in para[0383] that identification characteristics stored in the memory of a social networking site match at least one identification characteristics stored in the memory, in para[0402] based on the association (in terms of avatars, group of users, brother and sister) they have common setting of music playing in the vehicle when they may be seated in a passenger area of a vehicle, in para[0304] Further, the gestures may be made with other body parts or, for example, different expressions of a person's face and may be used to control functions in the vehicle 104.
Applicant further argues that the other independent claims which recite similar features are allowable and the dependent claims are also allowable since they depend on allowable subject and the Office respectfully disagrees. It is the Office's stance that all of the claimed subject matter has been properly rejected; therefore, the Office's respectfully disagrees with applicant’s arguments.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 10, 11, 13,14 and 17 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by US 20210284175 A1 to Mehdi et al. (herein after “Mehdi”).
Regarding claim 10, Mehdi teaches An apparatus comprising: memory storing a database(see Mehdi para[0044] “In each ECU, the microcontroller includes a … memory…”); and at least one processing device configured to: store a plurality of local profiles in the database(see Mehdi para[0023] “…and store the information in the profile of the one of the occupants, Abstract ”), each local profile including data regarding an occupant of a vehicle(See Mehdi para[0068] “At 208, control detects faces (or fingerprints, voices, mobile devices) of all occupants of the vehicle (e.g., by capturing images of their faces, or data about their fingerprints, voices, or mobile devices and matching them to the corresponding data stored in a database in the remote server).”),; and receive a first remote profile from a computing device (see Mehdi para[0068] “At 212, control perceives each occupant's preferences (as explained below) and adds them to their respective profiles if settings in the profiles allow.”)
wherein the configuration data is generated by the computing device based on linking a first local profile to a first remote profile. (see Mehdi para [0068]At 208, control detects faces (or fingerprints, voices, mobile devices) of all occupants of the vehicle (e.g., by capturing images of their faces, or data about their fingerprints, voices, or mobile devices and matching them to the corresponding data stored in a database in the remote server). At 212, control perceives each occupant's preferences (as explained below) and adds them to their respective profiles if settings in the profiles allow. At 214, control presents content to the occupants based on their detected preferences.)
Regarding claim 11, Mehdi remains applied as Claim 10. Mehdi teaches wherein the data regarding the occupant includes biometric data of the occupant (see Mehdi para[0004] A method comprises detecting identities of occupants of a vehicle using any of a camera system, an audio system, a biometric sensing system,).
Regarding claim 13, Mehdi remains applied as Claim 10. Mehdi discloses wherein each local profile is generated in response to detection of the respective occupant. (see Mehdi para[0014] “The identity detection system is configured to match the detected identities to corresponding profiles of the occupants.”).
Regarding claim 14, Mehdi remains applied as Claim 10. Mehdi discloses wherein the first remote profile stores configuration data for a first occupant. (see Mehdi para[0068] “…corresponding data stored in a database in the remote server…”)
Regarding claim 17, Mehdi remains applied as Claim 10. Mehdi discloses wherein the computing device is configured to determine a correlation between the first local profile and the first remote profile. (see Mehdi para ([0090] “Alternatively, some of the above processing can be performed at the vehicle as well. For example, profiles of identified occupants can be downloaded via the network from the remote server to the vehicle. The profiles updated with any preference data can be subsequently uploaded via the network to the remote server.”).
Claims 18-20 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by US 20170247000 A1 to Ricci (herein after “Ricci”).
Regarding claim 18, Ricci teaches A method comprising: querying a social network server to obtain first data (Ricci in para[0046], Ricci teaches in para[0240] that the profile identification module 848 may receive requests from a user or device to access a profile stored in profile data, in para[0113] the profile data may include one or more user profile, user profile may be generated based on data gathered from private information (such as user information from a social network)
determining, based on the first data, an association between a first occupant and a second occupant (Ricci teaches in para [0383] The recognition of facial features may include comparing identified facial features associated with a user 216 with one or more identification characteristics stored in a memory. The one or more identification characteristics can be stored in a memory of a social networking site, facial recognition data memory, profile data memory 252 and/or other memory location. Additionally, para[0319]); generating second data based on the association and causing a vehicle to configure at least one function of the vehicle based on the second data.
(See Ricci para[0385] The virtual personality may include one or more of an avatar, a voice output, a visual output, a tone, and a volume intensity. If the user profile includes a virtual personality, the personality module 2004 may retrieve and/or access the virtual personality from the user profile for use in the vehicle 104.), para[0402] For example, a brother and sister may be seated in a passenger area (e.g., area 2 508B) of a vehicle 104. The brother may like rock music and dark colors (e.g., stored in the brother's user profile), while the sister may like pop music and pastel colors (e.g., stored in the sister's user profile). As provided above, a group avatar may be created to appeal to both users (e.g., without alienating at least one user 216)).
Regarding claim 19, Ricci teaches wherein the association corresponds to a configuration for at least one function of a vehicle used by the first occupant or the second occupant (see Ricci para[0402] “…generating a group avatar that represents two or more users 216 in the group of two or more users (step 2220). This group avatar may include at least one common setting and/or preference shared between the avatars of the group of users 216. For example, a brother and sister may be seated in a passenger area (e.g., area 2 508B) of a vehicle 104. The brother may like rock music and dark colors (e.g., stored in the brother's user profile), while the sister may like pop music and pastel colors (e.g., stored in the sister's user profile). As provided above, a group avatar may be created to appeal to both users (e.g., without alienating at least one user 216.”).
Regarding claim 20, Ricci teaches A method comprising: querying a social network server to obtain first data (Ricci in para[0046], Ricci teaches in para[0240] that the profile identification module 848 may receive requests from a user or device to access a profile stored in profile data, in para[0113] the profile data may include one or more user profile, user profile may be generated based on data gathered from private information (such as user information from a social network) wherein is performed in response to determining that the occupant is present in the vehicle (see Mehdi para [0014] “The identity detection system is configured to match the detected identities to corresponding profiles of the occupants.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the .
Claims 1-5, 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over US 20210284175 A1 to Mehdi et al. (herein after “Mehdi”) in view of US 20070100666 A1 to Stivoric et al. (herein after “Stivoric”).
Regarding claim 1, Mehdi discloses A system comprising: at least one sensor; and at least one processing device configured to: collect, using the sensor, data from an interior of a vehicle (See Mehdi para[0004] “…detecting identities of occupants of a vehicle using any of a camera system, an audio system, a biometric sensing system, and a mobile device detection system in the vehicle.”); using the collected data as input (See Mehdi para[0041] “The systems and methods of the present disclosure can monitor driver/passenger activity during a ride such as where are they pointing (e.g., using hand or head motion), how long their eyes linger on an advertisement on a billboard or on a building, whether a person expressed interest in what is being observed, etc. to enable data collection for content in the real world similar to data collection performed for content that appears online., wherein the machine-learning model is trained using images of an occupant captured under different conditions(see Mehdi para[0014] “…by recognizing any of faces…”, para[0055] that the gaze tracking sensor used to detect where the occupant is looking based on the light source reflections between the cornea and the pupil);
; perform, based on the output, at least one action for the vehicle (see Mehdi see para[0073] , paras [0044]-[0051]“Each ECU controls subsystems and each subsystem 14 may include on or more sensors to sense data from one or more components of the subsystem. The ECU 12 may control one or more actuators of the corresponding subsystem 14 based on the data received from the one or more sensors and/or the one or more inputs from an occupant of the vehicle 10 (see para[0048]).
However, Mehdi does not expressly mention or otherwise teach generate an output from a machine-learning model and the machine-learning is configured to use data from a remote server to augment data for a person stored locally. Nevertheless, Stivoric same field of endeavor teaches generate an output from a machine-learning model (See Stivoric para[0269] various machine learning techniques to generate the algorithms from the collected data.)
and the machine-learning is configured to use data from a remote server to augment data for a person stored locally; (See Stivoric para[0113] Further, with conventional machine learning techniques such, a model can be trained from the collected data that could be used to predict the parameter or activity of a user. The output of the device can also show other parameters, activities, body states, events, etc. as well, including for example, resting, walking, cycling, respiration rate, energy expenditure, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Stivoric’s machine learning model in order to allow to predict the parameter or activity of a user. (see para[0113]).
Regarding claim 2, Mehdi and Stivoric remain applied as claim 1. Mehdi discloses wherein the output is used to identify the occupant, and performing the at least one action comprises controlling operation of the vehicle. (see Mehdi see para[0073] control/detect where the person is looking; para[0074] looking based on the vehicles GPS data or by detecting the person’s gaze; para[0076] control optionally estimates the person's reaction/emotion indicating the person's liking/disliking for the content being viewed. Control estimates the person's reaction/emotion's based on audiovisual data of the person collected using microphone/camera while the person views the content, paras [0044]-[0051]) .
Regarding claim 3, Mehdi and Stivoric remain applied as claim 1. Nevertheless, same field of endeavor, Stivoric teach wherein the machine-learning model (See Stivoric’s machine learning model para[0113]) is trained using images of the occupant captured under different conditions, and the different conditions include at least one of different lighting conditions or different clothing of the occupant (See Stivoric para[0145] displayed directly on the clothing of the person being monitored or on the caregiver's clothing, displayed on household appliances such as a refrigerator, a microwave oven or conventional oven, be reflected qualitatively in controllable ambient conditions such as the temperature of a room, the lighting of the room, ).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Stivoric’s machine learning model in order to allow to predict the parameter or activity of a user. (see para[0113]).
Regarding claim 4, Mehdi and Stivoric remain applied as claim 1. Mehdi discloses wherein the output indicates whether the occupant is a driver, and the action includes selection of a manner of driving (see Mehdi para [0039] The collected data can be appended to user profiles. Inferences of driver's/passengers' actions drawn based on the collected data can be used to determine their likings and dis-likings (i.e., preferences).).
Regarding claim 5, Mehdi and Stivoric remain applied as claim 1. Mehdi discloses wherein the machine-learning model (see Mehdi “collecting data about an occupant and learning their preferences” (i.e. a machine-learned model):) is trained using at least one of images of the occupant gathered by the sensor (see Mehdi para[0014] “…by recognizing any of faces…; para[0054] For example, the cameras may be focused on headrests of each seat in the vehicle 10 to capture the facial images and other gestures of occupants of the vehicle 10.), or voice recordings of the occupant collected by the sensor (see Mehdi para[0014] The identity detection system is configured to detect identities occupants of the vehicle by recognizing any of faces, voices, fingerprints, or mobile devices of the occupants.).
Regarding claim 7, Mehdi and Stivoric remain applied as claim 1. Mehdi discloses wherein the machine-learning model is configured to determine whether an occupant is present in the vehicle based on collected data (see Mehdi [0004] “The method comprises matching the detected identities to corresponding profiles of the occupants.”);
Regarding claim 8, Mehdi and Stivoric remain applied as claim 1. Mehdi discloses wherein the sensor includes a camera (see Mehdi para[0004] “…detecting identities of occupants of a vehicle using any of a camera system, an audio system, a biometric sensing system, and a mobile device detection system in the vehicle.”).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Mehdi in view of Stivoric and US 20090082926 A1 to Klein (herein after “Klein”).
Regarding claim 6, Mehdi and Stivoric remain applied as claim 1. However, Mehdi does not expressly disclose or otherwise teach wherein the machine-learning model is configured to identify a first occupant and a second occupant as being in conflict regarding restrictions on the first or second occupant. Nevertheless, in a related field of invention, Klein teaches wherein the machine-learning model is configured to identify a first occupant and a second occupant as being in conflict regarding restrictions on the first or second occupant (See Klein [abstract] “A automotive vehicle safety system for seat belt securement that prevents the transmission from being engaged until all seated occupants have buckled their seat belts includes a weight sensing pad for each seat cushion interconnected in circuit to a buckle switch in each seat belt buckle with the buckle switches electrically interconnected to a solenoid that allows or disallows transmission engagement such that if each weight sensing pad detects no weight upon the seat the corresponding buckle switch stays open but when any weight sensing pad detects a predetermined weight (the weight of the occupant upon the seat) that weight sensing pad opens the circuit thereby causing the corresponding buckle switch to close only after that seat belt is buckled thus causing the solenoid interconnected to the transmission to close and enabling access to the transmission so that the vehicle can be shifted out of the parking gear).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Klein’s a first occupant and a second occupant as being in conflict regarding restrictions on the first or second occupant in order to allow to the transmission to close and enabling access to the transmission
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Mehdi in view of US 20090082926 A1 to Klein (herein after “Klein”).
Regarding claim 12, Mehdi remains applied as Claim 10. However, Mehdi does not expressly disclose or otherwise teach wherein the data regarding the occupant includes a classification of the occupant. Nevertheless, Klein same field of endeavor teaches wherein the data regarding the occupant includes a classification of the occupant ( see Klein [abstract] “A automotive vehicle safety system for seat belt securement that prevents the transmission from being engaged until all seated occupants have buckled their seat belts includes a weight sensing pad for each seat cushion interconnected in circuit to a buckle switch in each seat belt buckle with the buckle switches electrically interconnected to a solenoid that allows or disallows transmission engagement such that if each weight sensing pad detects no weight upon the seat the corresponding buckle switch stays open but when any weight sensing pad detects a predetermined weight (the weight of the occupant upon the seat) that weight sensing pad opens the circuit thereby causing the corresponding buckle switch to close only after that seat belt is buckled thus causing the solenoid interconnected to the transmission to close and enabling access to the transmission so that the vehicle can be shifted out of the parking gear.”; the occupant in the driver’s seat of Klein is a first occupant, and the state of an occupant’s seat belt is their classification.).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Klein’s a first occupant and a second occupant as being in conflict regarding restrictions on the first or second occupant in order to allow to the transmission to close and enabling access to the transmission so that the vehicle can be shifted out of the parking gear (see Klein abstract).
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Mehdi in view of US 20170247000 A1 to Ricci (herein after “Ricci”).
Regarding claim 15, Mehdi remains applied as Claim 10. However, Mehdi does not expressly disclose or otherwise teach wherein the configuration data is generated by the computing device based on an association between the first occupant and a second occupant. Nevertheless, Ricci same field of endeavor teaches wherein the configuration data is generated by the computing device based on an association between the first occupant and a second occupant (see Ricci para[0402] “…generating a group avatar that represents two or more users 216 in the group of two or more users (step 2220). This group avatar may include at least one common setting and/or preference shared between the avatars of the group of users 216. For example, a brother and sister may be seated in a passenger area (e.g., area 2 508B) of a vehicle 104. The brother may like rock music and dark colors (e.g., stored in the brother's user profile), while the sister may like pop music and pastel colors (e.g., stored in the sister's user profile). As provided above, a group avatar may be created to appeal to both users (e.g., without alienating at least one user 216.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Ricci’s different conditions include at least one of different lighting conditions and clothing in order to allow to address the deviation is provided by the vehicle (see Ricci para[0016]).
Regarding claim 16, Mehdi remains applied as Claim 10. However, Mehdi does not expressly disclose or otherwise teach wherein the processing device is further configured to perform, based on the configuration data, at least one action for the vehicle. Nevertheless, Ricci same field of endeavor teaches wherein the processing device is further configured to perform, based on the configuration data, at least one action for the vehicle (see Ricci[0459] “In some cases, the adjustment of one or more settings associated with the infotainment system 870 may be allowed, denied, and/or limited based on one or more access priorities. Users 216 may have an access priority assigned by the infotainment system 870.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to combine Mehdi’s detecting system identities of occupants of a vehicle using any of a camera system with Ricci’s different conditions include at least one of different lighting conditions and clothing in order to allow to address the deviation is provided by the vehicle (see Ricci para[0016]).
Conclusion
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/NAZIA AFRIN/Examiner, Art Unit 3666
/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c
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