CTNF 18/495,254 CTNF 79141 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 07-30-02 AIA 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. 07-34-01 Claims 7-15 are 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. The claim, broadly speaking, recites that a user’s driving patterns including acceleration is used to modulate the operation of an adaptive cruise control system. But this does not make sense to the examiner. In particular, an adaptive cruise control system is one that follows the flow of traffic and maintains such things as following distance. The adaptive cruise control system will depend on the behavior of other cars on the road, not on the user of the vehicle. If the adaptive cruise control (which controls acceleration) were to vary based on other factors then it would be out of sync with the other cars on the road and would not work properly. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-20-02-aia AIA 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. Claims 1-6 are rejected under 35 U.S.C. 103 as being obvious over Wiese et al. (US 2023/0099486). For the sake of examination, the claims are interpreted as essentially reciting that driver behavior including acceleration habits are used in thermal management and driver-specific colling of an electric car battery. So, if a user tends to have hard acceleration and braking then more cooling will be used for the battery. Re claim 1: Wiese et al. teaches at para 0048 (with emboldening by examiner): “[0048] A portion of a battery temperature prediction may be estimated via the battery temperature prediction module 310 of controller 12 using inputs from the cloud network 306, onboard sensors 304, and the model monitoring module 318, wherein the inputs may include one or more of driver behavior , predicted vehicle speed, predicted road grade, location, traffic congestion, and weather as a result of data from connected vehicles and/or connected infrastructure communicating with cloud network 306. In one example, onboard sensors 304 may correspond to feedback from one or more sensors of the vehicle, such as sensors 16 of FIG. 1. For example, the battery temperature prediction may increase if the vehicle is expected to drive in direct sunlight. As another example, the battery temperature prediction may decrease if expected driver demand is low or if an ambient temperature forecast is low. Battery temperature prediction module 310 may include a battery power prediction module 312 and a battery cell temperature prediction module 314. Data from onboard sensors 304 and cloud network 306 may be used by battery power prediction module 312 to predict a vehicle battery power along a route. In one example, a vehicle may be operated along a route along which other similar vehicles have traveled. Other similar vehicles may include one or more similarities to the vehicle, including but not limited to one or more of make, model, manufacture data, manufacture location, driver age, driver sex, number of occupants, battery age, maintenance history, and the like. Data from cloud network 306 may be based on similarities between the vehicle and other vehicles traveling the same route. The battery cell temperature prediction module 314 may then receive a power prediction from the power prediction module 312 along with feedback from the model adaptation module 320, which may include data regarding a current battery cooling .” Wiese et al. further teaches (para 0060, with emboldening by examiner): “[0060] Vehicle operator behavior (e.g., aggressiveness, interior climate preferences) may influence a usage of battery power, resulting in changes to battery temperature. Vehicle operator behavior may affect battery temperature predictions in combination with and/or separately from a distance remaining on a current route or an upcoming route. Model parameters may be adapted as a function of vehicle operator behavior. In one example, if multiple vehicle operators use the vehicle, then model parameters may be updated based on vehicle operator behavior such that each model of the models reflects each individual vehicle operator behavior. In one example, if a vehicle operator behavior includes more aggressive driving (e.g., harder accelerations ), then model parameters may be updated to adjust the power prediction 312 based on the increased power demand due to aggressive driving behaviors. In one example, aggressive driving behaviors may include hard tip-ins, late brake timing, and the like. Hard tip-ins may include where the accelerator pedal is depressed more quickly than soft tip-ins associated with less aggressive driving behaviors.” Fig 4 is shown below: PNG media_image1.png 722 498 media_image1.png Greyscale Clearly the cooling response is customized based on predicted cooling needs for the electric car battery, based on how the user drives including acceleration. Regarding claims 2-6, these are obvious variants centered around the same principle, that would follow from ordinary experimentation. 07-21-aia AIA Claim s 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Joo (US US 2024/0185643) . Re claim 16: The claim is interpreted generally as: Machine learning is used based a user’s driving behavior to judge predict distance-to-empty. Joo teaches: “[0007] Various aspects of the present disclosure are directed to providing a vehicle distance to empty (DTE) prediction apparatus and a method therefor, configured for predicting the DTE based on a linear regression model that reflects a Bayesian probability distribution based on machine learning .” “[0140] Referring to FIG. 11, the DTE prediction apparatus 100 may pre-learn a driving pattern personalization cluster model based on past driving history and a DTE prediction model based on machine learning (S101).” Regarding claims 17-20, these are obvious variants centered around the same principle, that would follow from ordinary experimentation. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL A HESS whose telephone number is (571)272-2392. The examiner can normally be reached Monday through Friday, from 9 AM to 5 PM. 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, Michael G. Lee can be reached at (571)272-2398. 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. /DANIEL A HESS/Primary Examiner, Art Unit 2876 Application/Control Number: 18/495,254 Page 2 Art Unit: 2876 Application/Control Number: 18/495,254 Page 3 Art Unit: 2876 Application/Control Number: 18/495,254 Page 4 Art Unit: 2876 Application/Control Number: 18/495,254 Page 5 Art Unit: 2876 Application/Control Number: 18/495,254 Page 6 Art Unit: 2876 Application/Control Number: 18/495,254 Page 7 Art Unit: 2876 Application/Control Number: 18/495,254 Page 8 Art Unit: 2876
Prosecution Insights