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. Claim Rejections - 35 USC § 112 The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.— The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 6 recites the limitation "the plurality of front driver facing camera" in line 1. There is insufficient antecedent basis for this limitation in the claim. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claim s 1 -5, 7, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski (US 10,518,602 – provided by Applicant in the IDS) in view of Lee (US 10,011,156 – provided by Applicant in the IDS). Regarding claim 1, Ostrowski teaches a method for controlling a vehicle cabin climate (see Title, Abstract) , comprising: receiving identification data at a vehicle, wherein the identification data identifies one or more users of the vehicle (see Abstract, “collecting data relating to the Certain Identifiable Conditions while the first occupant is occupying the seating assembly” ) ; receiving aggregated data from a vehicle, wherein the aggregated data relates to a plurality of inputs (see col. 2, lines 29-67 which note the detection of multiple temperatures, further see col. 7, lines 17-25 which note the sensors that can provide the data) , wherein at least some of the data is acquired from input sources at the vehicle (see col. 2, lines 29-67) ; utilizing a machine learning network including a machine learning model at the vehicle to determine a personalized-optimal cabin climate based on the aggregate data, wherein the machine learning model is updated with a trained version of the model utilizing the aggregated data (see col. 34, lines 1-28, “Refining the Pre-established Predictive Activation Model into the New Predictive Activation Model, the Newer Predictive Activation Model, and subsequent refinements thereof will identify the preferences of the occupant, including situations when the occupant desires the activation of the temperature altering element 24 for reasons other than in-vehicle 10 temperature or ambient temperature. For example, the occupant may desire the temperature altering element 24 to impart heat during the first several minutes on a commute to work, for therapeutic reasons ….The CART analysis is thus a learning algorithm that provides a high degree of accuracy because the Certain Identifiable Conditions are considered across the entire history of the vehicle 10 . Other possible non-learning methods, such as those involving weighted averages, will not be as accurate and will not account for time/day/season dependent behavior” ) , wherein the trained version of the model predicts a desired setting for the user; and controlling one or more climate features of the user of the vehicle according to the personalized-optimal cabin climate (see col. 34, lines 1-28) . Ostrowski does not teach that some of the data is acquired from input sources located remotely from the vehicle. Lee teaches that climate settings within a vehicle may be adjusted based on vital sign readings from a wearable device from a user (Lee, col. 9, lines 42-56). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski with acquiring data remotely from the vehicle, as taught by Lee, in order to provide more accurate climate control for users within the vehicle. Regarding claim 2, Ostrowski as modified teaches the method of claim 1, wherein the machine learning network is not at a remote server (Ostrowski does not note using a remote server) . Regarding claim 3, Ostrowski as modified teaches the method of claim 1, herein the aggregated data is not sent to a remote server (Ostrowski does not note using a remote server) . Regarding claim 4, Ostrowski as modified teaches the method of claim 1, wherein the plurality of inputs includes one or more sensors configured to collect at least a cabin temperature or an ambient temperature (see col. 2, lines 29-67 which note the detection of multiple temperatures, further see col. 7, lines 17-25 which note the sensors that can provide the data) . Regarding claim 5, Ostrowski as modified teaches the method of claim 1, wherein the plurality of inputs includes a wearable device associated with the user, wherein the aggregated data includes a heart rate associated with the user as determined by the wearable device (met through the combination with Lee, see Lee, col. 9, lines 42-56) . Regarding claim 7, Ostrowski as modified teaches the method of claim 1, wherein the machine learning model is constantly updated utilizing the aggregated data in response to the vehicle ignition or vehicle battery being on (see Ostrowski, col. 34, lines 1-28) . Regarding claim 19, Ostrowski teaches the computer-implemented method of claim 18, but does not teach that the data collected from the plurality of sensors includes weather data from a remote server. Lee teaches that climate settings within a vehicle may receive data from weather services (Lee, col. 9, lines 8-30). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski with receiving weather data, as taught by Lee, in order to provide more accurate climate control for users within the vehicle. 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. Claims 8, 10-12 , 14-15, 18, 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ostrowski (US 10,518,602 – provided by Applicant in the IDS). Regarding claim 8, Ostrowski teaches a system in a vehicle (see Title, Abstract) including: a plurality of sensors utilized to collect data at the vehicle, wherein at least one of the sensors is configured to identify a user in the vehicle (see Abstract, col. 2, lines 29-67 which note the detection of multiple temperatures, further see col. 7, lines 17-25 which note the sensors that can provide the data) ; a HVAC system in communication with the plurality of sensors and configured to regulate a temperature of a cabin in the vehicle according to one or more HVAC settings (see col. 2, lines 29-67) ; and a controller in communication with the plurality of sensors (26, Fig. 2) , the controller configured to: receive and aggregate the data collected from the plurality of sensors (46, Fig. 2) ; compare a current a heating, ventilation, and air conditioning (HVAC) setting to a predicted HVAC setting customized to the user (see col. 34, lines 1-28) , wherein the predicted HVAC setting is updated in response to a machine learning model located at the vehicle (see col. 34, lines 1-28) , wherein the machine learning model is configured to output the predicted HVAC setting utilizing at least the aggregated data; and send instructions to the HVAC system to adjust the current HVAC setting to the predicted HVAC setting (see col. 34, lines 1-28) . Regarding claim 10, Ostrowski teaches the system of claim 8, wherein the plurality of sensors include at least one temperature sensor configured to measure the temperature of the cabin or an ambient temperature of the vehicle (see col. 7, lines 17-25 which note the sensors that can provide the data) . Regarding claim 11, Ostrowski teaches the system of claim 8, wherein in response to actions by the user at the HVAC system, the machine learning model is constantly updated utilizing the aggregated data (see claim 14, claim 16 of Ostrowski) . Regarding claim 12, Ostrowski teaches the system of claim 8, wherein the predicted HVAC setting includes a machine learning model associated with a preferred setting compared to a vehicle environment (see col. 34, lines 1-28, “Refining the Pre-established Predictive Activation Model into the New Predictive Activation Model, the Newer Predictive Activation Model, and subsequent refinements thereof will identify the preferences of the occupant, including situations when the occupant desires the activation of the temperature altering element 24 for reasons other than in-vehicle 10 temperature or ambient temperature. For example, the occupant may desire the temperature altering element 24 to impart heat during the first several minutes on a commute to work, for therapeutic reasons) . Regarding claim 14, Ostrowski teaches a computer-implemented method, comprising: utilizing a plurality of sensors, collecting data at the vehicle, wherein at least one of the sensors is configured to identify a user in the vehicle (see Abstract, col. 2, lines 29-67 which note the detection of multiple temperatures, further see col. 7, lines 17-25 which note the sensors that can provide the data) ; utilizing an heating, ventilation, and air conditioning (HVAC) system in communication with the plurality of sensors (see col. 2, lines 29-67) , regulating a temperature of a cabin in the vehicle according to one or more HVAC settings (see col. 2, lines 29-67) ; and utilizing a controller in communication with the plurality of sensors (26, 46, Fig. 2) : receiving the data collected from the plurality of sensors; aggregating the data collected from the plurality of sensors; comparing a current HVAC setting to a predicted HVAC setting customized to the user (see col. 34, lines 1-28) , wherein the predicted HVAC setting is updated in response to a machine learning model located at the vehicle (see col. 34, lines 1-28) , wherein the machine learning model is configured to output the predicted HVAC setting utilizing at least the aggregated data (see col. 34, lines 1-28) ; and sending instructions to the HVAC system to adjust the current HVAC setting to the predicted HVAC setting (see col. 34, lines 1-28) . Regarding claim 15, Ostrowski teaches the computer-implemented method of claim 14, wherein the predicted HVAC setting includes a target temperature, a target fan speed, and a target vent (see col. 9, lines 1-57) . Regarding claim 18, Ostrowski teaches the computer-implemented method of claim 14, wherein the data collected from the plurality of sensors includes data indicating an ambient temperature at the vehicle and data indicating a cabin temperature (see col. 7, lines 17-25 which note the sensors that can provide the data) . Regarding claim 20, Ostrowski teaches the computer-implemented method of claim 14, wherein the machine learning model is in direct communication with a controller area network (CAN) of the vehicle (see Fig. 2) . Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski in view of Lee, further in view of FOR1 (CN111757814A). Regarding claim 6, Ostrowski as modified teaches the method of claim 1, but does not teach the plurality front driver facing camera is configured to capture an image utilized to identify one or more garments associated with the user, wherein the aggregated data includes the image utilized to identify one or more garments associated with the user . FOR1 teaches a thermal management system of a vehicle (FOR1, Title) which features a driver monitoring system which features a camera that can identify the identity of a passenger and the clothes worn on the top of the body (FOR1, Description). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski as modified with a camera that can identify the garments associated with a user, as taught by FOR1, in order to assess how this impacts the desired control of the system. The Examiner notes that Ostrowski as modified does not teach a plurality of front driver facing cameras are taught. However, it would have been obvious to of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski as modified with a plurality of driver facing cameras, as it has been held that a mere duplication of essential working parts involves routine skill in the art. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski in view of Lee, further in view of Bhambare ( US 2023/0322043 ). Regarding claim 9, Ostrowski teaches the system of claim 8, but does not teach that the controller is further configured to evaluate if the user adjusts the predicted HVAC setting within a threshold time period, and when the user adjusts the predicted HVAC setting with the threshold time period, the machine learning model updates the predicted HVAC setting utilizing at least data collected from the plurality of sensors during the threshold time period. Ostrowski does teach that the user can manually change the predicted settings (claim 16 at least). Bhambare teaches an automatic operation of an HVAC vent system ( Bhambare , Title) wherein the system may suggest cabin temperatures in response to a weather forecast, and may learn the user profile adjusts the setting after a certain period of time and perform the same adjustment when the period of time passes ( Bhambare , paragraph [0042], the certain time period noted is analogous to a threshold time). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski with adjusting the predicted setting within a threshold time, as taught by Bhambare , in order to continually update the system over time to be catered to the user. Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski in view of FOR1 (CN111757814A). Regarding claim 16, Ostrowski teaches the computer-implemented method of claim 14, but does not teach one of the sensors includes a camera configured to capture an image of the user in the vehicle, wherein the image of the user is utilized to identify whether a garment is worn by the user. FOR1 teaches a thermal management system of a vehicle (FOR1, Title) which features a driver monitoring system which features a camera that can identify the identity of a passenger and the clothes worn on the top of the body (FOR1, Description). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski as modified with a camera that can identify the garments associated with a user, as taught by FOR1, in order to assess how this impacts the desired control of the system. Claims 13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ostrowski in view of Choi (US 2023/0174014). Regarding claim 13, Ostrowski teaches the system of claim 8, but does not teach that the machine learning model is a convolutional neural network. Choi teaches a vehicle which includes a neural network model including a deep neural network, a convolutional deep neural network which extracts information regarding temperature data between a plurality of nodes which exchange data (Choi, paragraph [0120]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski using a machine learning model that is a convolutional neural network , as taught by Choi, as Choi teaches that such models are used in the art, thereby making it obvious to try in order to assess the impacts on the desired performance of the model. Regarding claim 17, Ostrowski teaches the computer-implemented method of claim 14, but does not teach that the machine learning model is a deep neural network. Choi teaches a vehicle which includes a neural network model including a deep neural network, a convolutional deep neural network which extracts information regarding temperature data between a plurality of nodes which exchange data (Choi, paragraph [0120]). It would have been obvious to one of ordinary skill in the art, prior to the effective filing date, to provide Ostrowski using a machine learning model that is a convolutional neural network , as taught by Choi, as Choi teaches that such models are used in the art, thereby making it obvious to try in order to assess the impacts on the desired performance of the model. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT NAEL N BABAA whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-3272 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT M-F, 9-5 EST . 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, FILLIN "SPE Name?" \* MERGEFORMAT Jerry-Daryl Fletcher can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571)-270-5054 . 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. /NAEL N BABAA/ Primary Examiner, Art Unit 3763