CTNF 19/098,754 CTNF 77421 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. This application has been examined. Claims 1-20 are pending. 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 1, 8, and 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. As per claims 1, 8, and 15, it is unclear the following: In section (a) and (b) of claims 1, 8, and 15, it is unclear below: What is “robot-oriented expected return value”, what value is expected to return of the robot? What is “robot-oriented reward”. What difference between “robot-oriented expected return value” and “robot-oriented reward”. How to determine or calculate those value? What is “human-oriented expected return value”, what value is expected to return of human? What is “human-oriented reward”, and what difference between “human-oriented expected return value” and “human-oriented reward”. How to determine or calculate those value? Clarification is required. 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. 07-21-aia AIA Claim s 1-20, are rejected under 35 U.S.C. 103 as being unpatentable over Moskovitz et al. (US 2024/0265263 A1) in view of Guerin et al. (US 2016/0129590 A1) . As per claim 1, Moskovitz et al. disclose a system, comprising: a processor in communication with a memory, the memory including instructions executable by the processor to: access task information about a task to be completed by a computer-implemented agent (see at least the abstract; and para. [0018-0020], all disclose access task information to be completed by a robot); determine, based on the task information and by iterative modification of one or more policy parameters of a policy descriptive of one or more task-oriented actions, one or more updated policy parameters of an updated policy descriptive of an updated set of task-oriented actions for completion of the task by the computer-implemented agent (see at least [0007-0009] disclose task policy model; and para. [0036-0038] disclose policy model update unit) that: (a) satisfies a task constraint associated with the task and a safety constraint on a robot-oriented expected return value associated with a robot-oriented reward for the updated set of task-oriented actions (see at least [0038-0040] disclose the policy model update unit required to train the policy model 122 to control the agent to perform the tasks subject to one or more constraints; and para. [0092-0094]); and generate a control output for execution of the one or more task-oriented actions by the computer-implemented agent based on the updated policy with respect to the task information (see at least [0028-0035] disclose the action selection unit 126 may select the action 108 based on the numerical value, e.g., by selecting the action with the highest numerical value, or by treating the numerical values in the policy output as a defining a probability distribution over the set of actions, and sampling and action in accordance with the probability distribution). Moskovitz et al. do not explicitly disclose human-oriented reward. However, Guerin et al. disclose (b) maximizes a human-oriented expected return value associated with a human-oriented reward for the updated set of task-oriented actions; and generate a control output for execution of the one or more task-oriented actions by the computer-implemented agent based on the updated policy with respect to the task information (see at least [0047-0050] disclose the user can parameterize at least one tool behavior constraint associated with the tool to give the robot a notion of how the tool can be used to perform the task; also para. [00550059] disclose the user and robot collaborative system). It 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 to modify the teach of Makovitz et al. by combining maximizes a human-oriented expected return value associated with a human-oriented reward for the updated set of task-oriented actions for enabling human-robot collaborative behaviors. As per claim 2, Makovitz et al. disclose access perception data captured by the computer-implemented agent with respect to the task (see at least [0022], and [0096-0100] disclose the observations data include sensed electronic signals such as motor current or a temperature signal, and/or image or video data), and evaluate the policy with respect to the safety constraint and the task constraint based on the perception data (see at least [0023-0026], and [0036-0039], all para. disclose policy output and policy update unit to control the agent to perform the tasks subject to one or more constraints). As per claim 3, Makovitz et al. disclose sample, for an iteration of a plurality of iterations, a set of trajectories from a probability distribution associated with the policy; estimate one or more optimization parameter values associated with the set of trajectories (see at least [0028-0032] disclose defining a probability distribution over the set of actions); and determine the updated policy parameters of the updated policy based on the one or more optimization parameter values and a dual optimization function, the updated policy parameters corresponding with a most expected trajectory of the computer-implemented agent that satisfies the task constraint and the safety constraint (see at least [0036-0039] disclose a policy model update unit). As per claim 4, Makovitz et al. disclose the set of trajectories corresponding with one or more task-oriented actions of the policy (see at least [0030-0033] disclose the set of trajectories corresponding with the actions of the policy). As per claim 5, Makovitz et al. disclose evaluate a feasibility of the policy based on the set of trajectories with respect to the task constraint, the safety constraint, and perception data captured by the computer-implemented agent with respect to the task (see at least [0036-0038] disclose a policy model update unit 150 of the training system 190 trains (i.e., iteratively modifies) the policy model 122 based on the reward values 130, e.g., such that, while performing any given task episode, the system 100 selects actions which tend to increase the rewards 130). As per claim 6, Makovitz et al. disclose the policy being a feasible policy with respect to the task constraint and the safety constraint, the dual optimization function being a first dual optimization function, and a solution including a Lagrangian of a trust region constraint of the first dual optimization function and a Lagrangian of a linear constraint of the first dual optimization function (see at least [0040-0044] disclose Lagrangian of a linear constraint of the first dual optimization function). As per claim 7, Makovitz et al. disclose the policy being an infeasible policy with respect to the task information and the safety constraint, the dual optimization function being a second dual optimization function that aims to reduce a violation of the task constraint or the safety constraint, and a solution including a Lagrangian of a constraint of the second dual optimization function (see at least [0044-0049]). Claims 8-14, are method claims corresponding to system claims 1-7 above. Therefore, they are rejected for the same rationales set forth as above. Claims 15-19, and 20, are non-transitory computer readable medium corresponding to claims 1-5, and 7 above. Therefore, they are rejected for the same rationales set forth as above . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure ; . Bodnar et al. (11571809) . Guo et al. (US 2022/0105625 A1) . Voorhies et al. (US 2020/0338741 A1) . Johnson et al. (US 2020/0246978 A1) Any inquiry concerning this communication or earlier communications from the examiner should be directed to DALENA TRAN whose telephone number is (571)272-6968. The examiner can normally be reached M-F 7AM-5PM. 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, ADAM MOTT can be reached at 571-270-5376 . 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. /DALENA TRAN/Primary Examiner, Art Unit 3657 Application/Control Number: 19/098,754 Page 2 Art Unit: 3657 Application/Control Number: 19/098,754 Page 3 Art Unit: 3657 Application/Control Number: 19/098,754 Page 4 Art Unit: 3657 Application/Control Number: 19/098,754 Page 5 Art Unit: 3657 Application/Control Number: 19/098,754 Page 6 Art Unit: 3657 Application/Control Number: 19/098,754 Page 7 Art Unit: 3657 Application/Control Number: 19/098,754 Page 8 Art Unit: 3657 Application/Control Number: 19/098,754 Page 9 Art Unit: 3657
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