DIAGRAM MODIFICATION DEVICE, DIAGRAM MODIFICATION METHOD, AND DIAGRAM MODIFICATION PROGRAM

    公开(公告)号:US20230169706A1

    公开(公告)日:2023-06-01

    申请号:US17920430

    申请日:2020-04-28

    Inventor: Dai KUBOTA Riki Eto

    CPC classification number: G06T11/206 B61L27/16

    Abstract: The output means 81 outputs a diagram to a display device. The input means 82 accepts designation of a change point and a change condition for the displayed diagram. The constraint generation means 83 generates a constraint for an objective function used for optimization of the diagram based on the designation. The change proposal generation means 84 generates a change proposal for the diagram by optimizing the objective function based on the generated constraint. Then, the input means 82 accepts, for each change point, the designation of a hard constraint indicating a condition that must be satisfied, or a soft constraint indicating a condition that increases a penalty according to degree of unsatisfactory, as the designation of the change condition, the constraint generation means 83 generates the constraint according to the hard constraint or soft constraint, and the output means 81 outputs the change proposal of the diagram.

    SEARCH DEVICE, SEARCH METHOD, AND SEARCH PROGRAM

    公开(公告)号:US20240320216A1

    公开(公告)日:2024-09-26

    申请号:US18575386

    申请日:2021-07-13

    CPC classification number: G06F16/24542

    Abstract: The search means 91 searches for an optimization problem matching a specified search condition from a database that stores search information that associates first data indicating an optimization problem including an objective function and a constraint with second data indicating a feature of the optimization problem. The input means 92 accepts input of the second data as search condition.

    LEARNING DEVICE, LEARNING METHOD, AND LEARNING PROGRAM

    公开(公告)号:US20220390909A1

    公开(公告)日:2022-12-08

    申请号:US17775395

    申请日:2019-11-14

    Inventor: Dai KUBOTA Riki ETO

    Abstract: A learning unit 80 includes an input unit 81, a reward function estimation unit 82, and a temporal logic structure estimation unit 83. The input unit 81 receives input of an action history of a worker who performs multiple tasks in time series. The reward function estimation unit 82 estimates a reward function for each task in time series based on the action history. The temporal logic structure estimation unit 83 estimates a temporal logic structure between tasks based on a transition condition candidate at a point in time when each estimated reward function switched.

    ASSISTANCE APPARATUS, ASSISTANCE METHOD, AND STORAGE MEDIUM

    公开(公告)号:US20250021614A1

    公开(公告)日:2025-01-16

    申请号:US18711334

    申请日:2021-11-25

    Inventor: Dai KUBOTA

    Abstract: In order to enable facilitation of mathematical optimization, an assistance apparatus (1) includes: a reception section (11) which receives input of input data indicative of a problem to be solved as an optimization problem; and a modeling section (12) which determines or generates, in accordance with the input data, at least one of (i) a problem class used when the problem is handled as a mathematical optimization problem, (ii) an optimization model expressing the problem in the form of a mathematical optimization problem, and (iii) an optimization calculation algorithm for solving an optimization problem.

    LEARNING DEVICE, LEARNING METHOD, AND LEARNING PROGRAM

    公开(公告)号:US20230281506A1

    公开(公告)日:2023-09-07

    申请号:US17922029

    申请日:2020-05-11

    Inventor: Dai KUBOTA Riki ETO

    CPC classification number: G06N20/00

    Abstract: The first output means 81 outputs a second target, which is an optimization result for a first target using an objective function generated in advance by inverse reinforcement learning based on decision making history data indicating an actual change to the target. The second output means 82 outputs a third target indicating a target resulting from further changing of the second target based on a change instruction regarding the second target accepted from the user. The data output means 83 outputs the actual change from the second target to the third target as decision making history data. The learning means 84 learns the objective function using the decision making history data.

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