ESTIMATION METHOD, CHARGING METHOD, COMPUTER, AND PROGRAMS

• US 20200134680A1
• Filed: 10/02/2019
• Published: 04/30/2020
• Est. Priority Date: 10/29/2018
• Status: Active Application

• Current Assignee: Fronteo, Inc.
• Original Assignee: Fronteo, Inc.
• Inventors: TAMURA, Ryota, HASUKO, Kazumi, IGUCHI, Shinya
• Application Number:
  US16/590,505
• Publication Number:
  US 20200134680A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 30/0283 Price estimation or determination

Abstract

To acquire an estimated price that is more accurate and capable of acquiring a sense of consent from a client more readily than the estimated price acquired by a conventional estimation method. A computer includes a memory and a controller. The memory stores data set, and the controller executes: prediction processing for predicting time required for review work of each piece of electronic data based on a feature amount of content included in the electronic data; evaluation processing for evaluating the number of steps required for the review work of the data set based on the time predicted in the prediction processing for each piece of electronic data; and estimation processing for estimating the cost required for the review work of the data set based on the number of steps evaluated in the evaluation processing.

Claims

1. An estimation method for estimating a cost required for review work of a data set by using a computer comprising a controller and a memory storing the data set including at least one piece of electronic data, the estimation method comprising:

prediction processing executed by the controller to predict time required for the review work of each piece of electronic data based on a feature amount of a content included in the electronic data;

evaluation processing executed by the controller to evaluate the number of steps required for the review work of the data set based on the time predicted in the prediction processing for each piece of the electronic data; and

estimation processing executed by the controller to estimate the cost required for the review work of the data set based on the number of steps evaluated in the evaluation processing.

2. The estimation method according to claim 1, wherein the prediction processing is processing for predicting the time required for the review work of each piece of the electronic data by using a prediction model constructed by machine learning, the prediction model having the feature amount of the content of each piece of the electronic data as input and having the time required for the review work of the electronic data as output.

3. The estimation method according to claim 1, wherein the evaluation processing is processing for evaluating the number of steps required for the review work of the data set so as to be proportional to a sum total of the time predicted in the prediction processing regarding each piece of the electronic data.

4. The estimation method according to claim 1, wherein the estimation processing is processing for estimating the cost required for the review work of the data set so as to be proportional to the number of steps evaluated in the evaluation processing.

5. The estimation method according to claim 1, wherein the data set includes electronic data for which time required for the review work fluctuates according to the feature amount of the content.

6. The estimation method according to claim 1, wherein the prediction processing is processing for predicting the time required for the review work of each piece of the electronic data based on a feature amount group including a feature amount indicating complexity of the content included in the electronic data.

7. The estimation method according to claim 1, wherein the prediction processing is processing for predicting the time required for the review work of each piece of the electronic data based on a feature amount group including a feature amount indicating size of the content included in the electronic data.

8. The estimation method according to claim 1, wherein the prediction processing is processing for predicting the time required for the review work of each piece of the electronic data based on a feature amount group including a feature amount indicating emotionality of the content included in the electronic data.

9. The estimation method according to claim 1, further comprising, as processing executed prior to the prediction processing:

setting processing executed by the controller to set importance of each of attributes included in a predefined attribute group by taking a plurality of pieces of electronic data for which time required for the review work is actually measured in advance as samples; and

selection processing executed by the controller to select the attribute of the content to be used as the feature amount from the attribute group, and more preferentially select the attribute with higher importance set in the setting processing.

10. The estimation method according to claim 9, wherein the setting processing includes:

(1) a calculation step of calculating correlation coefficients between each of the attributes included in the attribute group and actually measured reviewing time by taking a plurality of pieces of electronic data for which time required for the review work is actually measured in advance as the samples; and

(2) a setting step of setting the importance of each of the attributes included in the attribute group according to the correlation coefficients corresponding to the attributes calculated in the calculation step.

11. The estimation method according to claim 9, wherein the setting processing includes:

(1) a creation step of creating a multiple regression equation having each of the attributes included in the attribute group as an explanatory variable and having actually measured reviewing time as an objective variable by taking a plurality of pieces of electronic data for which time required for the review work is actually measured in advance as the samples; and

(2) a setting step of setting the importance of each of the attributes included in the attribute group according to a partial regression variable corresponding to the attribute calculated in the multiple regression equation created in the creation step.

12. The estimation method according to claim 9, wherein the setting processing includes:

(1) a creation step of creating a regression tree having each of the attributes included in the attribute group as an explanatory variable and having actually measured reviewing time as an objective variable by taking a plurality of pieces of electronic data for which time required for the review work is actually measured in advance as the samples; and

(2) a setting step of setting the importance of each of the attributes included in the attribute group according to an extent of change in the output of the regression tree caused by changing a condition corresponding to the attribute in the regression tree created in the creation step.

13. The estimation method according to claim 1, further comprising, as processing executed prior to the prediction processing, switching processing for switching the feature amount the controller refers to in the prediction processing for each piece of the electronic data according to a kind of the electronic data.

14. A charging method, comprising:

the estimation processing for estimating the cost required for the review work of the data set according to the estimation method of claim 1; and

charging processing for charging a price based upon the review cost estimated in the estimation processing to a client ordering the review work.

15. A computer comprising a controller and a memory storing a data set including at least one piece of electronic data, the computer estimating a cost required for review work of the data set, wherein the controller executes:

prediction processing for predicting time required for the review work of each piece of electronic data based on a feature amount of a content included in the electronic data;

evaluation processing for evaluating the number of steps required for the review work of the data set based on the time predicted in the prediction processing for each piece of the electronic data; and

estimation processing for estimating the cost required for the review work of the data set based on the number of steps evaluated in the evaluation processing.