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 .
Response to Arguments
Applicant’s arguments with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1-16 and 21-24 are rejected under 35 U.S.C. 103 as being unpatentable over Alt et al. (US 2021/0179118 A1) in view of Simon et al. (DE102019216232 A1).
As to claims 1/12, Alt discloses a system/method comprising: a memory (para. 0042) configured to store machine readable instructions; and one or more processors (para. 0042) that are configured to execute the machine-readable instructions stored in the memory for performing a method comprising: receive first vehicle following data (para. 0021) and associated first environmental data (para. 0056) of a plurality of vehicles; classify the first vehicle following data and the first environmental data into a plurality of driver type classifications (Fig. 2, step 230, para. 0054); train a cruise control policy model for each driver type classification based on the first vehicle following data and the first environmental data (para. 0023-0027), wherein the control policy model is trained using a machine learning technique (para. 0023-0031, parameters optimization; receive a real-time classification of a target vehicle based on second vehicle following data and associated second environmental data of a target vehicle (para. 0027, observing a driving behavior of a driver in a driving operation of the motor vehicle; identifying a driver type-specific cluster from the set of driver type- specific clusters, based on the observed driving behavior - observing a driving behavior is real-time data, because Fig. 3 illustrate the method for controlling a motor vehicle using a control system in real-time with surroundings sensors in para 0041; and output a trained cruise control policy model to the target vehicle based on the real-time classification of the target vehicle (Fig. 3, para. 00027-0031, parameters optimization is a training process in machine learning, para. 0046-0051, machine learning), wherein a following distance between the target vehicle and a lead vehicle is controlled according to the trained cruise control policy model (para. 0019-0021, 0044). Alt does not explicitly disclose the first vehicle operating data comprises at least one of a gradient of weight or a state visitation frequency of at least one of the plurality of vehicles. However, Simon teaches the use of IRL with gradient of weight (maximum entropy) to determine an optimal drive strategy (Translation, para. 0011, 0050-0054). Therefore, given the teaching of Simon, it would have been obvious to one skilled in the art before the effective filling date of the claimed invention, to have readily recognized the desirability and advantages of modifying method of Alt, by employing the well-known or conventional features of the use of IRL with gradient of weight (maximum entropy) to determine an optimal drive strategy/policy.
As to claims 2-3 and 13, Alt further discloses wherein the first vehicle operating data comprises one or more of vehicle speed, lead vehicle speed, and a following distance between the target vehicle and the lead vehicle (para. 0021, 0044), and wherein the first environmental data comprises one or more of weather information, time of day information, road type information, road surface condition information, vehicle type information, and a degree of traffic information (para. 0056).
As to claims 4 and 21, Alt further discloses comprising: receiving, from each vehicle of the plurality of vehicles, a subset of the first vehicle operating data and an associated subset of the first environmental data, wherein each subset of the first vehicle operating data and associated subset of the first environmental data correspond in time (para. 0056).
As to claims 5 and 14, Alt further discloses comprising: identifying the plurality of driver type classifications by executing unsupervised learning on the first vehicle operating data and the associated first environmental data (para. 0017, 0021, 0029, 0031).
As to claims 6 and 15, Alt further discloses comprises: for each driver type classification, applying inverse reinforcement learning (IRL) to the first vehicle operating data and the associated first environmental data classified into the respective driver type classification, wherein training the control policy model is based on the application of the IRL (para. 0017, 0029, 0031, 0053, 0069).
As to claims 7 and 22, Alt further discloses wherein the IRL infers a reward function based on observed demonstrations, wherein the first vehicle operating data and the associated first environmental data classified into the respective driver type classification is the observed demonstrations and the reward function is the control policy model (para. 0016-0021, 0028, 0031, 0052-0060, 0067).
As to claims 8 and 23, Alt further discloses wherein the second vehicle operating data comprises one or more of target vehicle speed, a lead vehicle speed, and a following distance between the target vehicle and the lead vehicle (Fig. 3, para. 0044).
As to claims 9 and 24, Alt further discloses wherein the second environmental data comprises one or more of weather information, time of day information, road type information, road surface condition information, vehicle type information, and a degree of traffic information (para. 0056).
As to claims 10 and 16, Alt further discloses comprising: receiving the second vehicle operating data and the associated second environmental data of the target vehicle; and classifying the second vehicle operating data and the associated second environmental data into one of the plurality of driver type classifications (Fig. 3).
As to claim 11, Alt further discloses wherein the first vehicle operating data is first vehicle following data of the plurality of vehicles following a plurality of lead vehicles and the second vehicle operating data is second vehicle following data of the target vehicle following a lead vehicle (para. 0044).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ce Li Li whose telephone number is (571)270-5564. The examiner can normally be reached M-F, 10AM-7PM.
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, Peter D Nolan can be reached on 571-270-7016. 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.
CE LI . LI
Examiner
Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
Prosecution Insights