INFORMATION PROCESSING APPARATUS, CONTROL METHOD, AND PROGRAM

    公开(公告)号:US20200250691A1

    公开(公告)日:2020-08-06

    申请号:US16651793

    申请日:2018-09-25

    申请人: NEC CORPORATION

    IPC分类号: G06Q30/02 G06F17/18

    摘要: An information processing apparatus according to the present invention divides a period in which performance data at a business facility as a prediction target is present into a plurality of partial periods. The information processing apparatus performs prediction processing using each of a plurality of prediction models for a second partial period which is a partial period other than a first partial period including a start time of a predetermined period, and compares the result of the process with the performance data in a partial period as a target of the prediction processing. The information processing apparatus decides a prediction model to be used for sales prediction for a period subsequent to the predetermined period on the basis of the result of the comparison.

    ESTIMATION RESULTS DISPLAY SYSTEM, ESTIMATION RESULTS DISPLAY METHOD, AND ESTIMATION RESULTS DISPLAY PROGRAM

    公开(公告)号:US20180330262A1

    公开(公告)日:2018-11-15

    申请号:US15731173

    申请日:2015-04-30

    申请人: NEC Corporation

    发明人: Yousuke MOTOHASHI

    IPC分类号: G06N7/00 G06N99/00 G06N5/00

    摘要: An estimation results display system that, in the case of displaying an estimation result derived using a learning model, enables persons to recognize how condition determination is performed to select the learning model is provided. Input means 91 receives input of information associating information indicating a learning model selected depending on a determination result of whether or not an attribute in estimation data including one or more types of attributes satisfies one or more types of conditions and an estimation result derived using the learning model. Display means 92 displays the estimation result, in association with the information indicating the learning model used for deriving the estimation result and a condition subjected to determination of whether or not satisfied by the attribute in the estimation data when selecting the learning model.

    VIDEO SEARCH SYSTEM, VIDEO SEARCH METHOD, AND COMPUTER PROGRAM

    公开(公告)号:US20230038454A1

    公开(公告)日:2023-02-09

    申请号:US17791376

    申请日:2020-09-30

    申请人: NEC Corporation

    摘要: A video search system includes: an object tag acquisition unit that obtains an object tag associated with an object that appears in a video; a search query acquisition unit that obtains a search query; a similarity calculation unit that calculates a similarity degree between the object tag and the search query; and a video search unit that searches for a video corresponding to the search query on the basis of the similarity degree. According to such a video search system, it is possible to properly recognize the video, for example, by using the search query using a natural language.

    LEARNING MODEL GENERATION SYSTEM, METHOD, AND PROGRAM

    公开(公告)号:US20180052804A1

    公开(公告)日:2018-02-22

    申请号:US15560622

    申请日:2015-03-26

    申请人: NEC CORPORATION

    摘要: Provided is a learning model generation system capable of preventing a decrease in prediction accuracy in a case where the trend of an actual value of a prediction target has changed. The learning model generation means 71 generates a learning model using, as learning data, time series data in which a value of each explanatory variable used in prediction of a prediction target is associated with an actual value of the prediction target. The prediction means 72 calculates a predicted value of the prediction target using the learning model once the value of each explanatory variable is given. The change point determination means 73 determines a change point which is a point in time when a trend of the actual value of the prediction target changed. The data correction means 74 corrects the time series data by adding a difference between the actual value and the predicted value of the prediction target at the change point and afterward to the actual value before the change point in the time series data when the change point is determined. The learning model generation means 71 regenerates the learning model using the time series data after the correction as the learning data once the time series data is corrected.