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 1-6 are pending in the application under prosecution and have been examined.
In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 1-6 are rejected under 35 U.S.C. 102(a1)(a2) as being anticipated by US 8,639,556 (MORIMURA et al).
With respect to claim 1, MORIMURA teaches non-transitory computer-readable storage medium storing a program that causes a computer to perform a process (hardware, software, or combination of hardware and software implementation of a data processing system having a specific program executed to control the data processing system realizing determination of optimal actions considered cumulative rewards) comprising: calculating a plurality of first evaluation values respectively corresponding to a plurality of actions, based on probability distribution information indicating a selection probability of each of the plurality of actions (calculating, with a processing device, a probability distribution of an evaluation value for a selected action wherein said evaluation value corresponds to an action candidate that can be executed in a selected state); converting, upon determining that the plurality of first evaluation values include a negative evaluation value, the plurality of first evaluation values to a plurality of second evaluation values that are non- negative , using a negative reference value (determining, with a processing device, a weighting function evaluating value measures of selected actions conforming to a preference of action with risk measure into consideration, risk representing a constraint); and updating the selection probability of each of the plurality of actions, based on the plurality of second evaluation values (state transition or probability transition parameter updated into a parameter storage unit that stores, for each of said possible states, at least one parameter indicative of said probability transition parameter obtained when transition to said state occurs as a result of executing said action in said state, i.e. probability transition parameter being attribute data representing at least one said action that can be executed in each of possible states of action with risk measure featuring transitional probability of transitioning to each target state when each of at least one said action is executed) [Abstract; Fig. 5; Col. 2, Lines 15-51; Col. 11, Lines 1-28; Col. 6, Lines 11-31; Col. 7, Line 59 to Col. 8, Line 47].
With respect to claim 2, MORIMURA teaches non-transitory computer-readable storage medium, wherein the negative reference value is less than or equal to a minimum value of the plurality of first evaluation values, and the converting includes calculating differences between each of the plurality of first evaluation values and the negative reference value (calculating a risk measure using the probability distribution of the evaluation value by determining a weighting function conforming to at least one preference by taking the risk constraint measure into consideration and comparing the value measures of the selected actions in order to determine an optimal action for the selected state) [Abstract; Col. 6, Lines 11-31; Col. 7, Line 59 to Col. 8, Line 47].
With respect to claim 3, MORIMURA teaches non-transitory computer-readable storage medium, wherein the updating includes calculating a new selection probability for each of the plurality of actions, based on the plurality of second evaluation values, and calculating, for each of the plurality of actions, a weighted average of the selection probability before the updating and the new selection probability (calculating a value measure that considers risk in conformance with predetermined preferences for each action candidate that can be executed in each state provided with advance state transition probability parameters associated with reward for an action that is executed in a certain state) [Col. 6, Lines 11-31; Col. 7, Line 59 to Col. 8, Line 47; Col. 2, Lines 15-51].
With respect to claim 4, MORIMURA teaches non-transitory computer-readable storage medium, wherein the calculating of the plurality of first evaluation values, the converting, and the updating are iteratively performed, and the updating includes changing a weight for the new selection probability with an increase in a number of iterations (value measure for an action candidate calculated with probability distribution of an evaluation value with information acquired through accesses analyzed to automatically generate and update probability values) [Col. 6, Lines 11-31; Col. 7, Line 59 to Col. 8, Line 47].
With respect to claims 5 and 6, MORIMURA teaches equilibrium solution search method comprising: calculating, by a processor, a plurality of first evaluation values respectively corresponding to a plurality of actions, based on probability distribution information indicating a selection probability of each of the plurality of actions (hardware, software, or combination of hardware and software implementation of a data processing system having a specific program executed to control the data processing system realizing determination of optimal actions, by calculating, with a processing device, a probability distribution of an evaluation value for a selected action wherein said evaluation value corresponds to an action candidate that can be executed in a selected state); converting, by the processor, upon determining that the plurality of first evaluation values include a negative evaluation value, the plurality of first evaluation values to a plurality of second evaluation values that are non-negative, using a negative reference value; and updating, by the processor, the selection probability of each of the plurality of actions, based on the plurality of second evaluation values (determining, with a processing device, a weighting function evaluating value measures of selected actions conforming to a preference of action with risk measure into consideration, risk representing a constraint, the preferred action featuring a state transition or probability transition parameter updated into a reward parameter storage unit that stores, for each of said possible states, at least one parameter indicative of said probability distribution executing the action obtained when transition to said state occurs as a result of executing said action in said state despite the risk, i.e. probability transition parameter being attribute data representing at least one said action that can be executed in each of possible states of action with risk measure featuring transitional probability of transitioning to a previous target state when each of at least one said actions is executed) [Abstract; Fig. 5; Col. 2, Lines 15-51; Col. 11, Lines 1-28; Col. 6, Lines 11-31; Col. 7, Line 59 to Col. 8, Line 47].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jeong-Hyun Kim, Jong-Hyun Park and Dong-Joong Kang, "Method to improve the performance of the AdaBoost algorithm using Gaussian probability distribution," 2008 International Conference on Control, Automation and Systems, Seoul, 2008, pp. 1749-1752.
T. Yagyu, H. Tanase, T. Moriyama and T. Matsui, "Multi-layered inference with possibility distribution for multiple micro-machines control," 1994 5th International Symposium on Micro Machine and Human Science Proceedings, Nagoya, Japan, 1994, pp. 55-.
US 20220237348 A1 (YOKOTA et al) teaching information processing apparatus has an output data acquisition unit configured to acquire an output value obtained by performing an experiment or simulation based on an input parameter of a predetermined number of dimensions.
US 20180205947 A1 (XU et al) teaching context value calculated by applying a first weighting value to the first sign value and a second weighting value to the second sign value with probability value selected based on the context value and the sign value current block then determined using the probability model.
US 20240394554 A1 (HIRAOKA) teaching learning device calculates each of a plurality of second evaluation values that include noise using a plurality of evaluation models, each of which calculates, on the basis of both a second state resulting from a first action performed by a control target in a first state, and a second action calculated from the second state using a policy model.
US 20190235033 A1 (OHZEKI) teaching determination unit for determining whether a system has been put into an equilibrium state or not, a first magnetization calculation unit for calculating magnetization of predetermined direction of the system in the equilibrium state, a magnetic field calculation unit for calculating a magnetic field of the predetermined direction, a magnetic field determination unit for determining whether the magnetic field is in a steady state or not, and a physical quantity calculation unit for calculating a physical quantity related to the system.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth LO can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PIERRE MICHEL BATAILLE/Primary Examiner, Art Unit 2136