Wearing comfort prediction system and wearing comfort prediction program

Abstract

To provide a wearing comfort prediction system, a wearing comfort prediction method, and a wearing comfort prediction program capable of producing an appropriate wearing comfort prediction value. [Solution] A comfort prediction system 10 includes a storage unit 11 that stores clothing comfort history data for a user, and a prediction unit 12 that predicts comfort based on the user's measured weight at the time of comfort prediction and the comfort history data. The comfort history data includes one or more data sets for each date and time. The data set includes the date on which the clothing was worn, the weight on the date of wearing, and comfort information indicating the comfort of the clothing when worn. The prediction unit 12 extracts a data set including a weight equal to or close to the measured weight from the comfort history data by a comparison process that compares the measured weight with the weight of the data set, and sets the comfort of the extracted data set as a predicted value of the clothing comfort for the user.

Description

[Technical field]

[0001] The present disclosure relates to a comfort prediction system. Hmm, Also, it relates to a wear comfort prediction program. [Background technology]

[0002] Systems that assist with clothing coordination are known (for example, Patent Document 1). The system in Patent Document 1 includes a primitive life log analysis unit and a recommendation logic unit. The primitive life log analysis unit receives a log recorded in a mobile communication means, which includes information on the location of the user's outing, the temperature, humidity, and weather of the location, and the comfort of the clothes, and converts the log into structured data. The recommendation logic unit searches for clothes that meet the user's needs. The recommendation logic unit recommends appropriate clothes in response to the user's request information by calculating the similarity between the clothing life log and the user's request information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2014-13460 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the comfort of clothes changes from day to day. It is difficult to provide comfortable clothes when wearing clothes. In this respect, there is room for improvement in a system, a method, and a program for providing comfortable clothes. [Means for solving the problem]

[0005] (1) A comfort prediction system for solving the above problem is a comfort prediction system for predicting the comfort of clothes, comprising: a storage unit for storing comfort history data of the clothes for a user; and a prediction unit for predicting the comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data, the comfort history data including one or more data sets for each date and time, the data set including the date of wearing the clothes, the weight on the date of wearing, and comfort information indicating the comfort of the clothes when worn, the prediction unit extracts the data set including a weight equal to or close to the measured weight from the comfort history data by a comparison process of comparing the measured weight with the weight of the data set, and sets the comfort of the extracted data set as the predicted comfort value of the clothes for the user. According to this configuration, the predicted comfort value is set based on the comfort when the weight is equal to or close to the measured weight, so that an appropriate comfort can be provided.

[0006] (2) In the building comfort prediction system of (1) above, when the comparison process detects only one of the data sets including a weight equal to the measured weight, the prediction unit sets the comfort of the data set detected by the comparison process as a predicted comfort value of the clothes for the user. With this configuration, it is possible to provide an appropriate comfort.

[0007] (3) In the building comfort prediction system of (1) or (2) above, when the comparison process detects a plurality of data sets including a weight equal to the measured weight, the prediction unit determines the most uncomfortable fit among the plurality of data sets as the predicted value of comfort of the clothes for the user. With this configuration, it is possible to provide an appropriate fit.

[0008] (4) In the building comfort prediction system according to any one of (1) to (3) above, when the prediction unit determines by the comparison process that there is no data set including a weight equal to the measured weight, the prediction unit sets the comfort of the data set including a weight closest to the measured weight as the predicted comfort value of the clothes for the user. With this configuration, it is possible to provide an appropriate comfort.

[0009] (5) In the building comfort prediction system according to any one of (1) to (4) above, when the prediction unit determines by the comparison process that there is no data set including a weight equal to the measured weight, and when the prediction unit determines that the weights of all of the data sets are greater than the measured weight, or when the prediction unit determines that the weights of all of the data sets are smaller than the measured weight, the prediction unit determines the comfort of the data set including the weight closest to the measured weight as the predicted comfort value of the clothes for the user. With this configuration, it is possible to provide an appropriate comfort.

[0010] (6) In the building comfort prediction system according to any one of (1) to (5) above, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the comparison process determines that the data set including a weight greater than the measured weight and a data set including a weight less than the measured weight are present in the comfort history data, and when the comfort of the data set including a weight greater than the measured weight and closest to the measured weight is equal to the comfort of the data set including a weight less than the measured weight and closest to the measured weight, the comfort of the data set including a weight greater than the measured weight and closest to the measured weight or the comfort of the data set including a weight less than the measured weight and closest to the measured weight is set as the predicted comfort value of the clothes for the user. This configuration can provide an appropriate comfort.

[0011] (7) In the building comfort prediction system according to any one of (1) to (6) above, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the comparison process determines that the comfort history data includes a data set including a weight greater than the measured weight and a data set including a weight smaller than the measured weight, and when the comfort of the data set including a weight greater than the measured weight and closest to the measured weight differs from the comfort of the data set including a weight smaller than the measured weight and closest to the measured weight, the comfort of the data set including the weight closest to the measured weight is set as the predicted comfort value of the clothes for the user. With this configuration, it is possible to provide an appropriate comfort.

[0012] (8) In the building comfort prediction system according to any one of (1) to (7) above, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the prediction unit determines by the comparison process that the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight are included in the comfort history data, the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight are included as a first data set, and the data set including a weight smaller than the measured weight and the data set including the weight smaller than the measured weight and the data set including the weight smaller than the measured weight and the data set including the weight smaller than the measured weight are included as a second data set, and when the comfort of the first data set and the comfort of the second data set are different and the difference between the weight of the first data set and the measured weight is equal to the difference between the weight of the second data set and the measured weight, the comfort of the first data set and the comfort of the second data set that is the most difficult to wear is set as the predicted comfort value of the clothes for the user. This configuration can provide an appropriate comfort.

[0013] (9) In the building comfort prediction system according to any one of (1) to (8), the data set further includes the comfort prediction value and prediction accuracy information, and the prediction accuracy information includes a match value indicating that the comfort prediction value and the comfort coincide with each other, and a mismatch value indicating that the comfort prediction value and the comfort do not coincide with each other. With this configuration, the validity of the comfort prediction value can be evaluated.

[0014] (10) The building comfort prediction system of (9) above further comprises a validity evaluation unit that evaluates the validity of the predicted comfort value that predicts the comfort, the storage unit stores a database including the comfort history data for each of a plurality of clothes, and the validity evaluation unit, when the prediction unit predicts the predicted comfort value of the clothes, judges the validity of the predicted comfort value based on whether or not there is information for which the prediction accuracy information is a mismatch value from past comfort history data within a predetermined period prior to the prediction date including the time of the comfort prediction, for one or a plurality of other clothes different from the clothes. With this configuration, the validity of the predicted comfort value can be evaluated.

[0015] (11) In the building comfort prediction system of (10) above, when the prediction unit predicts the comfort prediction value of the clothes, if there is no information in which the prediction accuracy information is a discrepancy value in the comfort history data for one or more of the other clothes within a predetermined period from the prediction date, the validity evaluation unit determines that the comfort prediction value is valid and confirms the comfort prediction value. With this configuration, it is possible to provide a highly accurate comfort prediction value.

[0016] (12) In the building comfort prediction system of (10) or (11) above, when the prediction unit predicts the comfort prediction value of the clothes, if there is information for which the prediction accuracy information is a discrepancy value from past comfort history data within a predetermined period from the prediction date for one or more of the other clothes, the validity evaluation unit determines that the comfort prediction value is invalid. With this configuration, it is possible to deal with invalid comfort prediction values.

[0017] (13) The building comfort prediction system of (12) further includes a correction unit that corrects the predicted comfort value, and when the validity evaluation unit determines that the predicted comfort value is not valid, the correction unit corrects the predicted comfort value based on a comparison between a first weight deviation and a second weight deviation, and the first weight deviation is a difference between a weight included in the data set of the other clothes used to predict the comfort of the other clothes and the weight of the user on the day the comfort of the other clothes is predicted for the other clothes related to the data set including information that the prediction accuracy information is a mismatch value, and the second weight deviation is a difference between a weight included in the data set of the clothes used to predict the comfort of the clothes and the weight of the user on the day the comfort of the clothes is predicted. According to this configuration, when the predicted comfort value is determined to be invalid, the predicted comfort value is corrected, so that a highly accurate predicted comfort value can be provided.

[0018] (14) In the system for predicting comfort of a building according to (13) above, the correction unit corrects the predicted comfort value to be more difficult to wear when the second weight deviation degree is greater than the first weight deviation degree. This configuration makes it possible to provide a highly accurate predicted comfort value.

[0019] (15) The building comfort prediction system according to any one of (1) to (14) above further includes a selection unit that selects comfortable clothes from a plurality of clothes, the prediction unit predicts the predicted comfort value for the user for each of the plurality of clothes, and the selection unit selects one or more recommended clothes based on the predicted comfort value for each of the plurality of clothes. With this configuration, one or more clothes can be provided as comfortable clothes.

[0020] (16)The comfort prediction method is a comfort prediction method for predicting the comfort of clothes, and includes a preparation step of preparing comfort history data of the clothes for a user, and a prediction step of predicting the comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data, wherein the comfort history data includes one or more data sets for each date and time, and the data set includes the date on which the clothes were worn, the weight on the date of wearing, and comfort information indicating the comfort of the clothes when worn, and the prediction step includes a comparison process of comparing the measured weight with the weight of the data set to extract the data set including a weight equal to or close to the measured weight from the comfort history data, and the comfort of the extracted data set is set as a predicted value of the comfort of the clothes for the user.

[0021] (17) A comfort prediction program for solving the above-mentioned problems is a comfort prediction program for predicting the comfort of clothes, the program including: a preparation step of having a computer prepare comfort history data of the clothes for a user; and a prediction step of having the computer predict the comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data, the comfort history data including one or more data sets for each date and time, the data set including the date on which the clothes were worn, the weight on the date of wearing, and comfort information indicating the comfort of the clothes when worn, and the prediction step includes a comparison process of comparing the measured weight with the weight of the data set to extract the data set including a weight equal to or close to the measured weight from the comfort history data, and the comfort of the extracted data set is set as a predicted value of the comfort of the clothes for the user. Effect of the Invention

[0022] According to the wearing comfort prediction system, wearing comfort prediction method, and wearing comfort prediction program of the present disclosure, an appropriate wearing comfort prediction value can be provided. [Brief description of the drawings]

[0023] [Figure 1]FIG. 1 is a schematic diagram of a wearing comfort prediction system. [Diagram 2] FIG. 1 is a schematic diagram of a fitting room. [Diagram 3] FIG. 1 is a diagram illustrating an electronic closet. [Figure 4] FIG. 13 is a diagram showing an example of comfort history data. [Diagram 5] FIG. 13 is a diagram showing a set of data sets that include a history of going out wearing a device within a predetermined period from the current day to the past. [Figure 6] FIG. 2 is a diagram showing a first display screen of the display device. [Figure 7] FIG. 4 is a diagram showing a second display screen of the display device. [Figure 8] FIG. 4 is a diagram showing a third display screen of the display device. [Figure 9] FIG. 13 is a diagram showing a first example of the relationship between weight and comfort in the comfort history data, weight measured on the day, and predicted comfort value for a target garment. [Figure 10] FIG. 13 is a diagram showing a second example of the relationship between the weight and comfort of the comfort history data, the weight measured on the day, and the predicted comfort value for the target clothing. [Figure 11] FIG. 13 is a diagram showing a third example of the relationship between the weight and comfort of the comfort history data, the measured weight on the day, and the predicted comfort value for the target clothing. [Figure 12] FIG. 13 is a diagram showing a fourth example of the relationship between weight and comfort in the comfort history data, weight measured on the day, and predicted comfort value for a target garment. [Figure 13] FIG. 13 is a diagram showing a fifth example of the relationship between the weight and comfort of the comfort history data, the measured weight on the day, and the predicted comfort value for the target clothing. [Figure 14] FIG. 13 is a diagram showing a sixth example of the relationship between the weight and comfort of the comfort history data, the measured weight on the day, and the predicted comfort value for the target clothing. [Figure 15] FIG. 13 is a diagram showing the relationship between the weight and comfort of the comfort history data, the weight measured on the day, the provisional comfort prediction value, and the final comfort prediction value for a target garment. [Figure 16]This is a diagram showing the relationship between weight, comfort, and predicted comfort value in comfort history data for clothes whose prediction accuracy information is a mismatch value at the time of validity evaluation. [Figure 17] 13 is a flowchart of a prediction process. [Figure 18] 13 is a flowchart of a comfort prediction process. [Figure 19] 13 is a flowchart of a selection process based on the number of relevant data sets. [Figure 20] 13 is a flowchart of a validity evaluation process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] A wearing comfort prediction system 10 of the present embodiment will be described with reference to Figs. The wearing comfort prediction system 10 predicts the wearing comfort of the clothes 7. The wearing comfort prediction system 10 is used when a user selects the clothes 7. The wearing comfort prediction system 10 provides the user with clothes 7 that are comfortable to wear.

[0025] As shown in FIG. 1, a comfort prediction system 10 is connected to a photographing device 2, an electronic weighing scale 3, an electronic closet 4, and a display device 5 via a network N.

[0026] The fitting room 1 will be described with reference to FIG. 2. The fitting room 1 is configured to be able to use a comfort prediction system 10. The fitting room 1 is provided with a photographing device 2, an electronic scale 3, an electronic closet 4, and a display device 5. The fitting comfort prediction system 10 may be provided in the fitting room 1, or may be provided outside the fitting room 1. The fitting comfort prediction system 10 may be provided on the cloud. The photographing device 2, the electronic scale 3, the electronic closet 4, and the display device 5 are connected to the fitting comfort prediction system 10 directly or via a network N. The photographing device 2, the electronic scale 3, the electronic closet 4, and the display device 5 are configured to be able to exchange information with the fitting comfort prediction system 10.

[0027] The photographing device 2 photographs people who enter the fitting room 1. The photographing device 2 identifies people by image recognition. Users are registered in advance in the photographing device 2. The photographing device 2 identifies the user who has entered the fitting room from among the registered users. The photographing device 2 sends user information related to the identified user to the comfort prediction system 10. Based on receiving the user information, the comfort prediction system 10 prepares a database of the user's clothes 7 related to the user information.

[0028] The electronic scale 3 measures the weight of the person who steps on the electronic scale 3. The electronic scale 3 sends the measured weight as the measured weight to the wearing comfort prediction system 10. When the wearing comfort prediction system 10 receives the measured weight, it stores it as the measured weight of the user who entered the room.

[0029] The electronic closet 4 will be described with reference to FIG. 3. The electronic closet 4 stores clothes 7. The clothes 7 stored in the electronic closet 4 have a first electronic tag 7A including first identification information. The electronic closet 4 manages the clothes 7 so that each of the clothes 7 can be identified. The electronic closet 4 includes an electronic hanger 8 and a reader. The electronic hanger 8 has an alarm unit 8B including a second electronic tag 8A. The alarm unit 8B has a light-emitting device or a speaker. The reader reads the second identification information of the electronic hanger 8 and the first identification information of the clothes 7. The reader sends the second identification information and the first identification information as a pair to the comfort prediction system 10. For example, when a user hangs clothes 7 on the electronic hanger 8, the user causes the reader to read the second identification information of the electronic hanger 8 and the first identification information of the clothes 7. In the comfort prediction system 10, the electronic hanger 8 with the second identification information and the clothes 7 with the first identification information are stored as a set of data. This allows the electronic hanger 8 and the clothes 7 to be managed as a set. The electronic closet 4 generates an alarm sound or an alarm light in the electronic hanger 8 corresponding to the second identification information based on receiving the first identification information or the second identification information from the comfort prediction system 10. In this way, the comfort prediction system 10 can present a specific piece of clothes 7 from among multiple pieces of clothes 7 to the user by outputting the first identification information or the second identification information.

[0030] The display device 5 displays an image of the clothes 7 and information about the clothes 7. The display device 5 has a display unit that is equivalent to the size of a person. The display device 5 is also called a smart mirror. The display device 5 acquires an image of the clothes 7 and information about the clothes 7 from the comfort prediction system 10, and displays the acquired image of the clothes 7 and information about the clothes 7. The display device 5 accepts input from a person. In one example, the display unit of the display device 5 has a touch panel. The display device 5 replaces the image of the clothes 7 displayed on the display unit by a human operation. Specifically, the display device 5 acquires information about the next clothes 7 to be prepared in the comfort prediction system 10 by sending a signal based on the human operation to the comfort prediction system 10.

[0031] 1, the comfort prediction system 10 includes a storage unit 11 and a prediction unit 12. The comfort prediction system 10 may further include a validity evaluation unit 13. The comfort prediction system 10 may further include a correction unit 14. The comfort prediction system 10 may further include a selection unit 15.

[0032] [Storage] The storage unit 11 records the data sent from the display device 5 to the comfort prediction system 10 in the comfort history data 20. For example, the storage unit 11 records the user's evaluation of the comfort of the clothes 7 in the comfort history data 20.

[0033] The storage unit 11 stores a database. A database is provided for each registered person. For example, a database is provided for each family member. In the database, each of the multiple clothes 7 is associated with one piece of comfort history data 20. That is, the comfort history data 20 is configured for each piece of clothing 7.

[0034] With reference to FIG. 4, the comfort history data 20 for one piece of clothing 7 selected from a plurality of pieces of clothing 7 will be described.

[0035] The comfort history data 20 includes an identification number corresponding to the clothing 7. The identification number is a number that is individually set for the clothing 7. The comfort history data 20 includes an image of the clothing 7. The image of the clothing 7 may be an image of the clothing 7 alone, or an image of the clothing 7 when being worn. The comfort history data 20 may include multiple images of one piece of clothing 7 taken from different angles.

[0036] The comfort history data 20 includes information on the clothes 7. The information on the clothes 7 may include information on the type of the clothes 7, information on the purpose of the clothes 7, information on the season suitable for wearing the clothes 7, information on the progress since the purchase of the clothes 7, information on the user's preferences for the clothes 7, and the like.

[0037] The comfort history data 20 includes owner information indicating the owner of the clothes 7. The owner information is information that is used when the comfort prediction system 10 extracts the clothes 7 of a user when the user is identified.

[0038] The comfort history data 20 includes one or more data sets 21 for each date and time. The data set 21 includes the date on which the clothes 7 were worn, the weight on the date of wearing, and comfort information 30 indicating the comfort 31 of the clothes 7 when worn. The data set 21 may also include the date on which the clothes 7 were purchased.

[0039] The weight is a value measured by the electronic scale 3. When the user enters the fitting room 1, he or she steps on the electronic scale 3. When the electronic scale 3 measures the user's weight, it sends the measured weight to the wearing comfort prediction system 10 as the measured weight. The wearing comfort prediction system 10 stores the measured weight together with the date and time in the memory unit 11. The wearing comfort prediction system 10 also links the measured weight to the user identified by the photographing device 2 at the time of entry. In this way, the measured weight is recorded in the wearing comfort history data 20 of the user.

[0040] The comfort information 30 is input by the user via the display device 5. When the user tries on the clothes 7 displayed on the display device 5, the user inputs the comfort 31 of the clothes 7 via the touch panel of the display device 5 while the clothes 7 is displayed. The display device 5 sends the input information to the comfort prediction system 10 as the comfort information 30. The comfort prediction system 10 stores the comfort information 30 about the clothes 7 displayed on the display device 5 together with the date and time in the storage unit 11. The comfort prediction system 10 also links the user identified by the image capture device 2 at the time of entry to the clothes 7 and the comfort information 30 of the clothes 7. The comfort prediction system 10 then updates the comfort history data 20 with this information.

[0041] The comfort information 30 indicates a user's comfort 31 of the clothes 7. The comfort information 30 includes information corresponding to each body position. For example, if the clothes 7 are flare pants, the comfort information 30 includes first comfort information corresponding to the waist, second comfort information corresponding to the thighs, and third comfort information corresponding to the buttocks.

[0042] In FIG. 4, a comfortable fit 31 that fits is indicated by a "C." A tight fit 31 is indicated by a "T." A loose fit 31 is indicated by an "L."

[0043] The data set 21 may further include a comfort prediction value 40 and prediction accuracy information 45. The prediction accuracy information 45 has a match value and a mismatch value. The match value indicates that the comfort prediction value 40 and the comfort 31 match. In this embodiment, the match between the comfort prediction value 40 and the comfort 31 indicates that all three pieces of information, the first comfort information, the second comfort information, and the third comfort information, match. The mismatch value indicates that the comfort prediction value 40 and the comfort 31 do not match. In this embodiment, the mismatch between the comfort prediction value 40 and the comfort 31 indicates that at least one piece of information, the first comfort information, the second comfort information, and the third comfort information, does not match. In Figs. 4 and 5, the match value is indicated by a circle, and the mismatch value is indicated by a cross.

[0044] The data set 21 may include reference information 34. The reference information 34 is information used as a prediction basis for the comfort prediction value 40 when deriving the comfort prediction value 40. Specifically, the reference information 34 includes a date of the past data set 21 (reference date in FIG. 5) used as a prediction basis for the comfort prediction value 40, and a weight of the past data set 21 (reference weight in FIG. 5).

[0045] The dataset 21 may include an outing history 33 indicating that the user went out wearing the clothes 7 .

[0046] Furthermore, the data set 21 may include period information 35 (in FIG. 4, "within a specified period from today") indicating whether or not the date of selecting the clothes 7 (in FIG. 4, today) is within a specified period in the past. The period information 35 is updated every time the comfort prediction system 10 is started. In the example of FIG. 4, the information "within 2 weeks" is added to data that is within two weeks.

[0047] FIG. 5 shows data extracted from a user's multiple clothes 7 as being within a predetermined period from the current day based on period information 35. In this example, the current day is June 10, 2023, and data on clothes 7 within two weeks in the past from that day is extracted. According to period information 35, data within a predetermined period in the past from the current day can be easily and quickly extracted.

[0048] [Display device] The operation of the display device 5 will be described with reference to FIGS. The display device 5 displays recommended clothes 7 that the user can wear comfortably when the prediction unit 12 sets the comfort prediction values ​​40 for all of the user's clothes 7. Specifically, the display device 5 displays images of the clothes 7 in the data set 21 selected by the selection unit 15 based on the comfort prediction values ​​40.

[0049] FIG. 6 is a screen of the display device 5 showing an image of the recommended clothes 7. The screen shows a "previous" button for returning to the previous image, a "next" button for moving to the next image, and a "try on" button for selecting the clothes 7 to wear. When the user touches the "try on" button, the first identification information of the clothes 7 is sent to the comfort prediction system 10. At this time, the comfort prediction system 10 commands the electronic hanger 8 linked to the first identification information to give an alert. The electronic hanger 8 of the clothes 7 stored in the electronic closet 4 gives an alert by sound or light. This allows the user to easily take out the desired clothes 7.

[0050] 7, when the user touches the "Try on" button, a notification is sent from the electronic hanger 8 and a field of predicted comfort values ​​predicted by the comfort prediction system 10 appears on the display. In this example, predicted comfort values ​​are shown for the waist, thighs, and buttocks.

[0051] As shown in FIG. 8, when the user touches the "Try on" button again, an evaluation column appears on the display. In the evaluation column, a button for evaluating comfort is displayed. After trying on the clothes 7, the user can evaluate the comfort of the waist, thighs, and buttocks. The display device 5 sends the user's evaluation to the comfort prediction system 10. The memory unit 11 of the comfort prediction system 10 records the user's evaluation of the clothes 7 in the comfort history data 20 of the clothes 7. In this way, the comfort history data 20 is updated every time the clothes 7 is worn.

[0052] FIG. 8 shows the screen when an evaluation of the clothing 7 has been input. As shown in FIG. 8, once the evaluation of the clothing 7 has been completed, a "Decide" button and a "Choose Another" button are displayed. The "Decide" button is a button for deciding to wear the clothing 7. The "Choose Another" button is a button for displaying the next recommended clothing 7.

[0053] [Prediction section] The prediction unit 12 predicts the comfort of the clothes 7 for all of the user's clothes 7 or for a predetermined range of the user's clothes 7. The predetermined range of clothes 7 is specified by the user. An example of the predetermined range is divided by season.

[0054] The prediction unit 12 predicts a comfort prediction value 40 for the user for each of the multiple clothes 7.

[0055] The prediction unit 12 predicts the comfort of each piece of clothing 7 based on the measured weight of the user at the time of comfort prediction and the comfort history data 20. In this embodiment, the comfort prediction time is the same as the time when the clothing 7 is selected. The prediction date including the comfort prediction time is the current day.

[0056] The prediction unit 12 predicts the comfort of the clothes 7 displayed on the display unit of the display device 5 (hereinafter referred to as "target clothes 7") to the user based on the comfort history data 20 about the clothes 7. The user is a person who has measured his / her weight.

[0057] The prediction unit 12 extracts a data set 21 including a weight equal to or close to the measured weight from the comfort history data 20 by a comparison process. The extracted data set 21 is data on which the predicted comfort value 40 is predicted. The prediction unit 12 sets the comfort 31 of the extracted data set 21 as the predicted comfort value 40 of the clothes 7 for the user. The data set 21 on which the prediction is based is recorded in the comfort history data 20 together with the measured weight as reference information and data for the day.

[0058] In the above-mentioned comparison process, the prediction unit 12 compares the measured body weight with the body weight in each of the data sets 21 of the comfort history data 20.

[0059] With reference to Figs. 9 to 16, several examples of deriving the comfort prediction value 40 of a given piece of clothing 7 will be described. In Figs. 9 to 16, black circles represent past data, and white circles represent data for the current day. Here, the current day refers to the day when the comfort prediction system 10 predicts the comfort of the clothing 7.

[0060] The following (A1) and (A2) are examples when the comfort history data 20 contains data equal to the measured weight for the target clothing. (A3) to (A7) are examples when the comfort history data 20 does not contain data equal to the measured weight.

[0061] (A1) As shown in FIG. 9, when the prediction unit 12 detects, through a comparison process, only one dataset 21 that includes a weight equal to the measured weight, the comfort 31 of the dataset 21 detected through the comparison process is set as the predicted comfort value 40 of the clothes 7 for the user.

[0062] (A2) As shown in FIG. 10, when the prediction unit 12 detects, through a comparison process, multiple data sets 21 including a weight equal to the measured weight, the prediction unit 12 sets the most uncomfortable fit 31 among the fit values ​​31 of each of the multiple data sets 21 as the predicted comfort value 40 of the clothes 7 for the user.

[0063] In this embodiment, the degree of difficulty in wearing is defined as follows: In the order of loose, comfortable, and tight, the latter has a higher degree of difficulty in wearing. In other words, the tight fit is the most difficult to wear.

[0064] (A3) As shown in FIG. 11, when the prediction unit 12 determines through a comparison process that there is no dataset 21 containing a weight equal to the measured weight, it sets the comfort 31 of the dataset 21 containing the weight closest to the measured weight as the predicted comfort value 40 of the clothes 7 for the user.

[0065] (A4) As shown in FIG. 11, when the following X1 and X2 are satisfied, the prediction unit 12 sets the comfort 31 of the data set 21 including the weight closest to the measured weight as the comfort prediction value 40 of the clothes 7 for the user. X1 is a case where it is determined by the comparison process that there is no dataset 21 that includes a weight equal to the measured weight. X2 is a case where it is determined that the weight in all of the datasets 21 is greater than the measured weight, or it is determined that the weight in all of the datasets 21 is smaller than the measured weight. In the example of FIG. 11, the prediction unit 12 determines that the weight in all of the datasets 21 is smaller than the measured weight.

[0066] (A5) As shown in FIG. 12, when the following Y1, Y2, and Y3 are satisfied, the prediction unit 12 sets the next YA comfort 31 or YB comfort 31 as the comfort prediction value 40 of the clothes 7 for the user.

[0067] Y1 is the case where it is determined that there is no data set 21 that includes a weight equal to the measured weight. Y2 is a case where "the comparison process determines that the comfort history data 20 contains a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight." Y3 is the case where "the comfort 31 of the dataset 21 including a weight that is greater than the measured weight and closest to the measured weight is equal to the comfort 31 of the dataset 21 including a weight that is smaller than the measured weight and closest to the measured weight."

[0068] The YA fit 31 is the fit 31 of the data set 21 that includes a weight that is greater than and closest to the measured weight. The fit 31 of YB is the fit 31 of the data set 21 that includes a weight that is smaller than and closest to the measured weight.

[0069] (A6) As shown in FIG. 13, when the following Z1, Z2, and Z3 are satisfied, the prediction unit 12 sets the comfort 31 of the data set 21 including the weight closest to the measured weight as the comfort prediction value 40 of the clothes 7 for the user.

[0070] Z1 is a case where it is determined that there is no data set 21 that includes a weight equal to the measured weight. Z2 is a case where "the comparison process determines that the comfort history data 20 contains a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight." Z3 is a case where "the comfort 31 of the dataset 21 including a weight that is larger than the measured weight and closest to the measured weight is different from the comfort 31 of the dataset 21 including a weight that is smaller than the measured weight and closest to the measured weight."

[0071] (A7) As shown in FIG. 14, when the following R1, R2, and R3 are satisfied, the prediction unit 12 sets the most difficult to wear comfort 31 among the comfort 31 of the first data set and the comfort 31 of the second data set as the comfort prediction value 40 of the clothing 7 for the user.

[0072] R1 is the case where the prediction unit 12 determines that there is no data set 21 including a weight equal to the measured weight. R2 is a case where it is determined by the comparison process that the comfort history data 20 contains a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight.

[0073] R3 is a case where the fit 31 of the first data set defined next is different from the fit 31 of the second data set defined next, and the difference between the weight W1 of the first data set and the measured weight is equal to the difference between the weight W2 of the second data set and the measured weight. Here, the first data set is the data set 21 including the weight that is larger than the measured weight and closest to the measured weight. The second data set is the data set 21 including the weight that is smaller than the measured weight and closest to the measured weight.

[0074] [Validity Evaluation Department] The validity evaluation unit 13 evaluates the validity of a comfort prediction value 40 that predicts comfort. The validity is evaluated based on whether or not the predicted comfort value 40 matches the actual comfort value 31 in a recent specified period. If the predicted comfort value 40 matches the actual comfort value 31 when worn outside for all data sets 21 in a recent specified period, the current predicted comfort value 40 is also evaluated as valid.

[0075] On the other hand, if there is a data set 21 in which the predicted comfort value 40 when worn outside does not match the actual comfort value 31 for all data sets 21 in a recent predetermined period, the prediction is likely to be wrong, so the current predicted comfort value 40 is corrected. The validity evaluation unit 13 will be described in detail below.

[0076] When the prediction unit 12 evaluates the validity of the comfort prediction value 40 of the clothes 7, the validity evaluation unit 13 executes the following. The validity evaluation unit 13 determines whether or not there is information in which the prediction accuracy information 45 is a mismatch value from the past comfort history data 20 within a predetermined period prior to the prediction date (i.e., the current day) including the time of comfort prediction, for one or more other clothes 7 different from the target clothes 7 (hereinafter, reference clothes for validity evaluation) (hereinafter, information extraction process). The clothes 7 that the validity evaluation unit 13 targets for the information extraction process are clothes 7 in the same category as the target clothes 7 for validity evaluation.

[0077] The validity evaluation unit 13 judges the validity of the comfort prediction value 40 based on the judgment of whether or not there is information in which the prediction accuracy information 45 is a non-coincident value, as follows.

[0078] When the prediction unit 12 predicts the comfort prediction value 40 of the clothing 7, if there is no inconsistency information in the past comfort history data 20 within a predetermined period from the prediction date (i.e., the current day) for one or more other clothing items 7 (i.e., reference clothing for the validity evaluation), the validity evaluation unit 13 determines that the comfort prediction value 40 is valid. The inconsistency information indicates a data set 21 whose prediction accuracy information 45 is an inconsistency value. The inconsistency information is extracted from the comfort history data 20 based on the prediction accuracy information 45. In this case, the validity evaluation unit 13 determines the final value 42 of the comfort prediction value 40.

[0079] When the prediction unit 12 predicts the comfort prediction value 40 of the clothing 7, if there is inconsistent information in the past comfort history data 20 within a predetermined period from the prediction date (i.e., the current day) for one or more other clothing items 7 (i.e., clothing items referenced in the validity evaluation) (hereinafter, referred to as "if there is inconsistent information"), the validity evaluation unit 13 determines that the comfort prediction value 40 is invalid. If the comfort prediction value 40 is invalid, the correction unit 14 corrects the comfort prediction value 40.

[0080] [Correction section] The operation of the correction unit 14 and the method of correcting the comfort prediction value 40 will be described with reference to Fig. 15 and Fig. 16. Fig. 15 shows the comfort history data 20 of one of the user's clothes 7 and the measured weight of the user on the day when the one of the clothes 7 is set as the prediction target. Fig. 15 further shows the comfort prediction value 40 after correction by the correction unit 14.

[0081] Fig. 16 shows comfort history data 20 for clothing 7 that meets the following condition Q. Condition Q is that the clothing 7 is in the same category as the clothing 7 to be predicted, is clothing 7 that has been worn within a predetermined period of time from the current day, and the comfort prediction value 40 and the comfort 31 when actually worn are different. In short, Fig. 16 shows information about clothing 7 including the inconsistency information when there is the above-mentioned inconsistency information.

[0082] The correction unit 14 corrects the predicted wearing comfort value 40. When the validity evaluation unit 13 determines that the comfort prediction value 40 is invalid, the correction unit 14 corrects the comfort prediction value 40. A specific example will be described below.

[0083] When the validity evaluation unit 13 determines that the comfort prediction value 40 is invalid, the correction unit 14 corrects the comfort prediction value 40 based on a comparison between the first weight deviation degree dw1 and the second weight deviation degree dw2.

[0084] The first weight deviation dw1 is the difference between the weight included in the dataset 21 of the other clothes 7 used to predict the comfort of the other clothes 7, for the other clothes 7 related to the dataset 21 including information that the prediction accuracy information 45 is a mismatch value, and the user's weight on the day the comfort of the other clothes 7 was predicted. In short, the first weight deviation dw1 indicates the difference between the weight (hereinafter, weight A) in the dataset 21 of the past day that is used as the basis for the prediction on the day when the prediction was incorrect, for the clothes 7 recently worn that were incorrectly predicted, and the measured weight (hereinafter, weight B) on the day when the prediction was incorrect. Specifically, the first weight deviation dw1 is weight B - weight A.

[0085] The second weight deviation dw2 is the difference between the weight (hereinafter, weight C) included in the dataset 21 of the clothes 7 used to predict the comfort of the clothes 7 for the target clothes, and the user's weight (hereinafter, weight D) on the day on which the comfort of the clothes 7 is predicted. The prediction day is specifically the day on which the clothes 7 is selected. In short, the second weight deviation dw2 indicates the difference between the weight in the dataset 21 of the past day that is used as the basis for the prediction on that day, and the measured weight on that day. Specifically, the second weight deviation dw2 is weight D-weight C.

[0086] When the second weight deviation degree dw2 is greater than the first weight deviation degree dw1, the correction unit 14 corrects the predicted comfort value 40 to the uncomfortable wearing side. When the second weight deviation degree dw2 is less than or equal to the first weight deviation degree dw1, the correction unit 14 does not correct the predicted comfort value 40.

[0087] [Selection] The selection unit 15 selects clothes 7 having high comfort 31 from a plurality of clothes 7. The selection unit 15 selects one or more recommended clothes 7 based on the comfort prediction value 40 of each of the multiple clothes 7. The selection unit 15 evaluates the level of comfort 31 according to the number of C. In one example, when the clothes 7 are flare pants, the selection unit 15 selects clothes 7 for which the comfort prediction value 40 is set to "C" for the comfort 31 of the waist, thighs, and buttocks.

[0088] [Prediction processing] The prediction process executed by the prediction unit 12 will be described with reference to FIG. When the prediction unit 12 acquires the measured weight from the electronic scale 3, it executes a prediction process for all of the user's clothes 7 identified by the photographing device 2. The prediction unit 12 predicts the comfort of all of the clothes 7 on the day the user selects the clothes 7.

[0089] The prediction unit 12 sequentially executes a comfort prediction process S1, a validity evaluation process S2, and a comfort prediction value output process S3.

[0090] In the comfort prediction process S1, a provisional value 41 of a comfort prediction value 40 for the clothes 7 is calculated. In the validity evaluation process S2, the validity of the provisional value 41 of the comfort prediction value 40 is evaluated. Furthermore, in the validity evaluation process S2, if the comfort prediction value 40 is valid, the provisional value 41 of the comfort prediction value 40 is finalized as the final value 42 of the comfort prediction value 40. Then, in the validity evaluation process S2, if the comfort prediction value 40 is not valid, the provisional value 41 of the comfort prediction value 40 is corrected, and the corrected comfort prediction value 40 is finalized as the final value 42 of the comfort prediction value 40.

[0091] In the comfort prediction value output process S3, the comfort prediction value 40 of the clothes 7 is output together with the first identification information of the clothes 7 to the storage unit 11. Based on receiving the comfort prediction value 40 of the clothes 7, the storage unit 11 updates the comfort history data 20 of the corresponding clothes 7. The comfort prediction process S1 and the validity evaluation process S2 will be described in detail below.

[0092] 18 and 19, the comfort prediction process S1 executed by the prediction unit 12 will be described. As described above, the prediction unit 12 executes the comfort prediction process S1 for all of the user's clothes 7. Hereinafter, the clothes 7 involved in the comfort prediction process S1 will be referred to as target clothes 7.

[0093] In step S10, when acquiring the measured weight, the prediction unit 12 determines whether or not there is a data set 21 of a weight equal to the measured weight in the comfort history data 20 of the target clothes 7.

[0094] If there is a dataset 21 with a weight equal to the measured weight, the prediction unit 12 executes "selection process based on the number of relevant datasets" (see FIG. 19) in step S11. The relevant dataset 21 is selected by the "selection process based on the number of relevant datasets". Next, in step S12, the prediction unit 12 sets the fit 31 of the relevant dataset 21 as the provisional value 41 of the fit prediction value 40. If there is no dataset 21 with a weight equal to the measured weight, the prediction unit 12 executes step S13 and subsequent steps.

[0095] In step S13, the prediction unit 12 judges whether or not both a data set 21 larger than the measured weight and a data set 21 smaller than the measured weight are present in the comfort history data 20 of the target clothes 7. If both a data set 21 larger than the measured weight and a data set 21 smaller than the measured weight are not present, that is, if only a data set 21 larger than the measured weight is present, or if only a data set 21 smaller than the measured weight is present, the prediction unit 12 executes steps S14 to S16.

[0096] In step S14, the prediction unit 12 extracts a data set 21 that is closest to the measured weight from the comfort history data 20 of the target clothing 7. Then, in step S15, the prediction unit 12 executes a "selection process based on the number of relevant data sets" (see FIG. 19). The relevant data set 21 is selected by the "selection process based on the number of relevant data sets". Then, in step S16, the prediction unit 12 sets the comfort 31 of the relevant data set 21 as the provisional value 41 of the comfort prediction value 40.

[0097] If it is determined in step S13 that there are data sets 21 larger and smaller than the measured weight, the prediction unit 12 executes steps S17 to S19.

[0098] In step S17, the prediction unit 12 extracts the fit 31 of the data set 21 that is larger than and closest to the measured weight (hereinafter, the former fit 31) and the fit 31 of the data set 21 that is smaller than and closest to the measured weight (hereinafter, the latter fit 31). Then, the prediction unit 12 judges whether the former fit 31 and the latter fit 31 are equal. When the prediction unit 12 judges that the former fit 31 and the latter fit 31 are equal, in step S18, the prediction unit 12 sets the fit 31 of the corresponding data set 21 as the provisional value 41 of the fit predicted value 40. That is, the prediction unit 12 sets the fit 31 of the former or latter data set 21 as the provisional value 41 of the fit predicted value 40. When the prediction unit 12 determines that the former and latter comforts 31 are not equal, it sets the most uncomfortable fit 31 among the former and latter as a provisional value 41 of the comfort prediction value 40.

[0099] The selection process based on the number of relevant data sets will be described with reference to FIG. In step S20, the prediction unit 12 judges whether there are a plurality of corresponding data. In step S20, when the prediction unit 12 judges that the number of the corresponding data sets 21 is not a plurality, that is, when the prediction unit 12 judges that the number of the corresponding data sets 21 is one, in step S21, the prediction unit 12 sets the wearing comfort 31 of the corresponding data set 21 as the wearing comfort prediction value 40.

[0100] In step S20, if the prediction unit 12 determines that the number of corresponding data sets 21 is plural, the prediction unit 12 executes step S22. In step S22, the prediction unit 12 judges whether the comfort 31 of the multiple data sets 21 is the same or not by comparing the comfort 31 of the multiple data sets 21. When the prediction unit 12 judges that the comfort 31 of the multiple data sets 21 is the same, it executes step S21. Specifically, the comfort 31 of any one of the multiple data sets 21 is set as the comfort prediction value 40.

[0101] In step S22, when the prediction unit 12 determines that the fit comfort 31 of the multiple data sets 21 is not the same, the prediction unit 12 executes step S23. In step S23, the prediction unit 12 sets the most uncomfortable wearing comfort 31 among the wearing comforts 31 of the multiple data sets 21 as the wearing comfort prediction value 40.

[0102] The validity evaluation process S2 executed by the prediction unit 12 and the correction unit 14 will be described with reference to Fig. 20. As described above, the validity evaluation process S2 is executed after the comfort prediction process S1. In the comfort prediction process S1, a provisional value 41 of the comfort prediction value 40 is set for the target clothes 7. In the validity evaluation process S2, the validity of the provisional value 41 of the comfort prediction value 40 for the target clothes 7 is evaluated.

[0103] In step S30, the prediction unit 12 judges whether or not the comfort prediction and the comfort 31 match in all of the data sets 21 having the wearing outing history 33 for the past two weeks, among the clothes 7 of the same category as the target clothes 7. If the comfort prediction and the comfort 31 match in all of the data sets 21 having the wearing outing history 33 for the past two weeks, the prediction unit 12 determines in step S31 that the provisional value 41 of the comfort prediction value 40 is valid, and confirms the provisional value 41 of the comfort prediction value 40 as the final value 42 of the comfort prediction value 40.

[0104] In the determination of step S30, if there is at least one data set 21 in which the comfort prediction and comfort 31 do not match, among all data sets 21 with the wearing outing history 33 for the past two weeks, the next step S32 is executed.

[0105] In step S32, the prediction unit 12 calculates a first weight deviation degree dw1 in the inconsistent data set 21 for a data set 21 in which the comfort prediction and the comfort 31 do not match. The prediction unit 12 also calculates a second weight deviation degree dw2 in the prediction for the day. The first weight deviation degree dw1 and the second weight deviation degree dw2 are as defined above.

[0106] Next, in step S33, the correction unit 14 corrects the provisional value 41 of the predicted comfort value 40 based on the first weight deviation degree dw1 and the second weight deviation degree dw2.

[0107] Specifically, when the second body weight deviation dw2 is greater than the first body weight deviation dw1 (hereinafter, condition A), the correction unit 14 corrects the provisional value 41 of the predicted comfort value 40 so that the comfort becomes more difficult to wear. Whether the first body weight deviation dw1 and the second body weight deviation dw2 are positive or negative, the correction is performed if condition A is met. When the second body weight deviation dw2 is less than or equal to the first body weight deviation dw1, the provisional value 41 of the predicted comfort value 40 is not corrected.

[0108] Next, in step S34, the prediction unit 12 determines the comfort prediction value 40 after the process of step S33 as a determined value 42 of the comfort prediction value 40.

[0109] [Operation of this embodiment] The operation of this embodiment will now be described. In the comfort prediction system 10, the prediction unit 12 compares the measured weight with the weight in the data set 21. Then, the prediction unit 12 extracts the data set 21 including a weight equal to or close to the measured weight from the comfort history data 20. The prediction unit 12 sets the comfort 31 of the extracted data set 21 as the predicted comfort value 40 of the clothes 7 for the user. In this way, the comfort prediction system 10 uses the data set 21 in which the weight and the comfort 31 are linked as a reference for prediction. Therefore, the comfort 31 of the clothes 7 on the day can be accurately derived.

[0110] [Effects of this embodiment] The effects of this embodiment will be described. (1) In the comfort prediction system 10, the prediction unit 12 extracts a data set 21 including a weight equal to or close to the measured weight from the comfort history data 20 by a comparison process. Then, the prediction unit 12 sets the comfort 31 of the extracted data set 21 as the predicted comfort value 40 of the clothes 7 for the user. According to this configuration, the predicted comfort value 40 is set based on the comfort 31 when the weight is equal to or close to the measured weight, so that an appropriate comfort can be provided.

[0111] (2) When the prediction unit 12 detects only one data set 21 including a weight equal to the measured weight by the comparison process, the comfort 31 of the data set 21 detected by the comparison process is set as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate comfort.

[0112] (3) When the prediction unit 12 detects a plurality of data sets 21 including a weight equal to the measured weight by the comparison process, the prediction unit 12 determines the most uncomfortable fit 31 among the comforts 31 of the plurality of data sets 21 as the predicted comfort value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate fit.

[0113] (4) When the prediction unit 12 determines by the comparison process that there is no data set 21 including a weight equal to the measured weight, the prediction unit 12 sets the comfort 31 of the data set 21 including a weight closest to the measured weight as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate comfort.

[0114] (5) When the following X1 and X2 are satisfied by the comparison process, the prediction unit 12 sets the comfort 31 of the data set 21 including the weight closest to the measured weight as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate comfort.

[0115] X1 is a case where it is determined that there is no dataset 21 that includes a weight equal to the measured weight, and X2 is a case where it is determined that the weights of all of the datasets 21 are greater than the measured weight, or that the weights of all of the datasets 21 are smaller than the measured weight.

[0116] (6) When the following Y1, Y2, and Y3 are satisfied, the prediction unit 12 sets the YA comfort 31 or the VB comfort 31 as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate comfort.

[0117] Y1 is a case where it is determined that there is no data set 21 including a weight equal to the measured weight. Y2 is a case where it is determined by the comparison process that there is a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight in the comfort history data 20. Y3 is a case where the comfort 31 of the data set 21 including a weight greater than the measured weight and closest to the measured weight is equal to the comfort 31 of the data set 21 including a weight less than the measured weight and closest to the measured weight. YA is the comfort 31 of the data set 21 including a weight greater than the measured weight and closest to the measured weight. YB is the comfort 31 of the data set 21 including a weight less than the measured weight and closest to the measured weight.

[0118] (7) When the following Z1, Z2, and Z3 are satisfied, the prediction unit 12 sets the comfort 31 of the data set 21 including the weight closest to the measured weight as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate comfort.

[0119] Z1 is a case where it is determined that there is no data set 21 including a weight equal to the measured weight. Z2 is a case where it is determined by the comparison process that there is a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight in the comfort history data 20. Z3 is a case where the comfort 31 of the data set 21 including a weight greater than and closest to the measured weight is different from the comfort 31 of the data set 21 including a weight less than and closest to the measured weight.

[0120] (8) When R1, R2, and R3 are satisfied, the prediction unit 12 sets the most uncomfortable fit 31 among the fit 31 of the first data set and the fit 31 of the second data set as the comfort prediction value 40 of the clothes 7 for the user. With this configuration, it is possible to provide an appropriate fit.

[0121] R1 is a case where it is determined that there is no data set 21 including a weight equal to the measured weight. R2 is a case where it is determined by the comparison process that the wearing comfort history data 20 includes a data set 21 including a weight greater than the measured weight and a data set 21 including a weight less than the measured weight. R3 is a case where the wearing comfort 31 of the first data set is different from the wearing comfort 31 of the second data set, and the difference between the weight W1 of the first data set and the measured weight is equal to the difference between the weight W2 of the second data set and the measured weight. Here, the first data set is a data set 21 including a weight greater than and closest to the measured weight. The second data set is a data set 21 including a weight less than and closest to the measured weight.

[0122] (9) In the comfort prediction system 10, the data set 21 includes a comfort prediction value 40 and prediction accuracy information 45. The prediction accuracy information 45 has, as information, a match value indicating that the comfort prediction value 40 and the comfort 31 match, and a mismatch value indicating that the comfort prediction value 40 and the comfort 31 do not match. With this configuration, the validity of the comfort prediction value 40 can be evaluated.

[0123] (10) The comfort prediction system 10 further includes a validity evaluation unit 13. When the prediction unit 12 predicts the comfort prediction value 40 of the clothing 7, the validity evaluation unit 13 judges the validity of the comfort prediction value 40 based on whether or not there is information for which the prediction accuracy information 45 is a mismatch value in the past comfort history data 20 within a predetermined period prior to the prediction date including the time of comfort prediction, for one or more other clothing 7 different from the clothing 7. With this configuration, the validity of the comfort prediction value 40 can be evaluated.

[0124] (11) When the prediction unit 12 predicts the comfort prediction value 40 of the clothing 7, if there is no information in which the prediction accuracy information 45 for one or more other clothing items 7 is a discrepant value in the past comfort history data 20 within a predetermined period from the prediction date, the validity evaluation unit 13 determines that the comfort prediction value 40 is valid and confirms the comfort prediction value 40 as the confirmed value 42 of the comfort prediction value 40. With this configuration, it is possible to provide a highly accurate comfort prediction value 40.

[0125] (12) When the prediction unit 12 predicts the comfort prediction value 40 of the clothing 7, if there is information for one or more other clothing 7 in which the prediction accuracy information 45 is a discrepant value from the past comfort history data 20 within a predetermined period from the prediction date, the validity evaluation unit 13 determines that the comfort prediction value 40 is invalid. With this configuration, it is possible to deal with an invalid comfort prediction value 40.

[0126] (13) The comfort prediction system 10 further includes a correction unit 14 that corrects the comfort prediction value 40, and when the validity evaluation unit 13 determines that the comfort prediction value 40 is invalid, the correction unit 14 corrects the comfort prediction value 40 based on a comparison between the first weight deviation degree dw1 and the second weight deviation degree dw2. According to this configuration, when the comfort prediction value 40 is determined to be invalid, the comfort prediction value 40 is corrected, so that a highly accurate comfort prediction value 40 can be provided.

[0127] (14) When the second weight deviation degree dw2 is greater than the first weight deviation degree dw1, the correction unit 14 corrects the comfort prediction value 40 to the uncomfortable wearing side. According to this configuration, it is possible to provide a highly accurate comfort prediction value 40.

[0128] (15) The wearing comfort prediction system 10 further includes a selection unit 15 that selects clothes 7 with high wearing comfort 31 from the plurality of clothes 7. The selection unit 15 selects one or more recommended clothes 7 based on the wearing comfort prediction value 40 of each of the plurality of clothes 7. According to this configuration, one or more clothes 7 can be provided as clothes 7 with high wearing comfort.

[0129] <Modification> The above embodiment is an example of a form that the wearing comfort prediction system 10 can take, and is not intended to limit the form. The wearing comfort prediction system 10 can take a form different from the form exemplified in the above embodiment. Examples of such a form include a form in which a part of the configuration of the embodiment is replaced, changed, or omitted, or a form in which a new configuration is added to the embodiment. Modified examples of the embodiment are shown below.

[0130] The present system technology may be configured as the following method technology. The comfort prediction method is a method for predicting the comfort of the clothes 7. The comfort prediction method includes a preparation step and a prediction step. In the preparation step, comfort history data 20 of the clothes 7 for the user is prepared. The comfort history data 20 includes one or more data sets 21 for each date and time. The data set 21 includes the date on which the clothes 7 were worn, the weight on the date on which the clothes 7 were worn, and comfort information 30 indicating how the clothes 7 felt when worn.

[0131] In the prediction step, the comfort is predicted based on the measured weight of the user at the time of comfort prediction and the comfort history data 20. In the prediction step, the measured weight is compared with the weight in the dataset 21 to extract the dataset 21 including the weight equal to or close to the measured weight from the comfort history data 20. The comfort 31 of the extracted dataset 21 is then set as the comfort prediction value 40 of the clothes 7 for the user.

[0132] This system technology may be configured as the following program technology. The wearing comfort prediction program is a program for predicting the wearing comfort of the clothes 7. The wearing comfort prediction program includes a preparation step and a prediction step. In the preparation step, the computer prepares comfort history data 20 of the clothes 7 for the user. The comfort history data 20 includes one or more data sets 21 for each date and time. The data set 21 includes the date on which the clothes 7 were worn, the weight on the date on which the clothes 7 were worn, and comfort information 30 indicating how the clothes 7 felt when worn. In the prediction step, the computer predicts the comfort of the garment based on the measured weight of the user at the time of prediction of the comfort of the garment and the comfort history data 20. In the prediction step, a data set 21 including a weight equal to or close to the measured weight is extracted from the comfort history data 20 by a comparison process of comparing the measured weight with the weight in the data set 21. Then, in the prediction step, the comfort 31 of the extracted data set 21 is set as a comfort prediction value 40 of the clothes 7 for the user.

[0133] This specification discloses the following techniques. [Appendix 1] The technology related to Appendix 1 is a comfort prediction system that predicts how clothes will feel when worn. The comfort prediction system includes a memory unit that stores comfort history data of the clothes for a user, and a prediction unit that predicts comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data. The comfort history data includes one or more data sets for each date and time, each data set including the date on which the garment was worn, the weight on the date of wearing, and comfort information indicating how the garment felt when worn. The prediction unit extracts the dataset from the comfort history data including a weight equal to or close to the measured weight by a comparison process comparing the measured weight with the weight in the dataset, and sets the comfort of the extracted dataset as a predicted value of the comfort of the clothes for the user.

[0134] [Appendix 2] In the comfort prediction system described in Appendix 1, when the comparison process detects only one data set including a weight equal to the measured weight, the prediction unit sets the comfort of the data set detected by the comparison process as a predicted value of the comfort of the clothes for the user.

[0135] [Appendix 3] In the comfort prediction system described in Appendix 2, when the prediction unit detects, by the comparison process, a plurality of data sets each including a weight equal to the measured weight, the most uncomfortable fit among the respective comforts of the plurality of data sets is set as a predicted value of the comfort of the clothes for the user.

[0136] [Appendix 4] In the comfort prediction system described in Appendix 1, when the prediction unit determines through the comparison process that there is no data set including a weight equal to the measured weight, the comfort of the data set including a weight closest to the measured weight is set as the predicted comfort value of the clothes for the user.

[0137] [Appendix 5] In the comfort prediction system described in Appendix 1, when the prediction unit determines through the comparison process that there is no data set including a weight equal to the measured weight and when the prediction unit determines that the weight of the data set for all of the data sets is greater than the measured weight, or when the prediction unit determines that the weight of the data set for all of the data sets is smaller than the measured weight, the prediction unit sets the comfort of the data set including a weight closest to the measured weight as the predicted comfort value of the clothes for the user.

[0138] [Appendix 6] In the wearing comfort prediction system described in Appendix 1, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the comparison process determines that the wearing comfort history data includes the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight, and when the wearing comfort of the data set including a weight greater than the measured weight and closest to the measured weight is equal to the wearing comfort of the data set including a weight smaller than the measured weight and closest to the measured weight, the prediction unit sets the wearing comfort of the data set including a weight greater than the measured weight and closest to the measured weight or the wearing comfort of the data set including a weight smaller than the measured weight and closest to the measured weight as a wearing comfort predicted value of the clothes for the user.

[0139] [Appendix 7] In the comfort prediction system described in Appendix 1, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the comparison process determines that the comfort history data includes the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight, and when the comfort of the data set including a weight greater than the measured weight and closest to the measured weight differs from the comfort of the data set including a weight smaller than the measured weight and closest to the measured weight, the prediction unit sets the comfort of the data set including the weight closest to the measured weight as a predicted value of the comfort of the clothes for the user.

[0140] [Appendix 8] In the wearing comfort prediction system described in Supplementary Note 1, when the prediction unit determines that there is no data set including a weight equal to the measured weight, and when the comparison process determines that the wearing comfort history data includes the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight, the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight is set as a first data set, and the data set including a weight smaller than the measured weight and the data set including the weight smaller than the measured weight and the data set including the weight smaller than the measured weight and the data set including the measured weight is set as a second data set, if the wearing comfort of the first data set and the wearing comfort of the second data set are different and the difference between the weight of the first data set and the measured weight is equal to the difference between the weight of the second data set and the measured weight, The most uncomfortable fit among the fit of the first data set and the fit of the second data set is set as a predicted value of the fit of the clothes for the user.

[0141] [Appendix 9] In the comfort prediction system described in Supplementary Note 1, the data set further includes the comfort prediction value and prediction accuracy information. The prediction accuracy information includes a match value indicating that the comfort prediction value and the comfort match, and a mismatch value indicating that the comfort prediction value and the comfort do not match.

[0142] [Appendix 10] The comfort prediction system described in Supplementary Note 9 further includes a validity evaluation unit that evaluates validity of a comfort prediction value that predicts the comfort. The storage unit stores a database including the comfort history data for each of a plurality of clothes. When the prediction unit predicts the comfort prediction value of the clothes, the validity evaluation unit judges the validity of the comfort prediction value based on whether or not there is information in which the prediction accuracy information is a discrepancy value from past comfort history data within a predetermined period prior to the prediction date including the time of the comfort prediction, for one or a plurality of other clothes different from the clothes.

[0143] [Appendix 11] In the comfort prediction system described in Appendix 10, when the prediction unit predicts the comfort prediction value of the clothing, if there is no information in which the prediction accuracy information is a discrepant value in past comfort history data within a predetermined period from the prediction date for one or more of the other clothing, the validity evaluation unit determines that the comfort prediction value is valid and confirms the comfort prediction value.

[0144] [Appendix 12] In the comfort prediction system described in Appendix 10, when the prediction unit predicts the comfort prediction value of the clothing, if there is information for one or more of the other clothing items that shows an inconsistent prediction accuracy information from past comfort history data within a predetermined period from the prediction date, the validity evaluation unit determines that the comfort prediction value is not valid.

[0145] [Appendix 13] The comfort prediction system according to claim 12 further includes a correction unit that corrects the comfort prediction value. When the validity evaluation unit determines that the comfort prediction value is not valid, the correction unit corrects the comfort prediction value based on a comparison between a first weight deviation degree and a second weight deviation degree. The first weight deviation degree is a difference between a weight included in the data set of the other clothes used to predict the comfort of the other clothes and the weight of the user on the day the comfort of the other clothes is predicted, for the other clothes related to the data set including information that the prediction accuracy information is a mismatch value. The second weight deviation degree is a difference between a weight included in the data set of the clothes used to predict the comfort of the clothes and the weight of the user on the day the comfort of the clothes is predicted.

[0146] [Appendix 14] In the wearing comfort prediction system described in Supplementary Note 13, when the second weight deviation degree is larger than the first weight deviation degree, the correction unit corrects the wearing comfort prediction value to the uncomfortable wearing side.

[0147] [Appendix 15] The comfort prediction system according to any one of appendices 1 to 14 further includes a selection unit that selects clothes with high comfort from a plurality of clothes. The prediction unit predicts the predicted comfort value for the user for each of the plurality of clothes. The selection unit selects one or more recommended clothes based on the predicted comfort value for each of the plurality of clothes.

[0148] [Appendix 16] The comfort prediction method according to appended claim 16 is a method for predicting comfort of clothes. The comfort prediction method includes a preparation step of preparing comfort history data of the clothes for a user, and a prediction step of predicting comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data. The comfort history data includes one or more data sets for each date and time. The data set includes the date on which the clothes were worn, the weight on the date of wearing, and comfort information indicating the comfort of the clothes when worn. In the prediction step, the data set including a weight equal to or close to the measured weight is extracted from the comfort history data by a comparison process that compares the measured weight with the weight of the data set, and the comfort of the extracted data set is set as the predicted comfort value of the clothes for the user.

[0149] [Appendix 17] The comfort prediction program according to appendix 17 is a comfort prediction program for predicting the comfort of clothes. The comfort prediction program includes a preparation step of having a computer prepare comfort history data of the clothes for a user, and a prediction step of having the computer predict the comfort based on the measured weight of the user at the time of comfort prediction and the comfort history data. The comfort history data includes one or more data sets for each date and time. The data set includes the date of wearing the clothes, the weight on the date of wearing, and comfort information indicating the comfort of the clothes when worn. In the prediction step, the data set including a weight equal to or close to the measured weight is extracted from the comfort history data by a comparison process that compares the measured weight with the weight of the data set, and the comfort of the extracted data set is set as the predicted comfort value of the clothes for the user. [Explanation of symbols]

[0150] dw1...first weight deviation degree, dw2...second weight deviation degree, 1..., 7...clothing, 10...comfort prediction system, 11...memory unit, 12...prediction unit, 13...validity evaluation unit, 14...correction unit, 15...selection unit, 20...comfort history data, 21...data set, 30...comfort information, 31...comfort, 40...comfort prediction value, 45...prediction accuracy information.

Claims

1. A comfort prediction system for predicting comfort of clothes, comprising: A storage unit that stores historical data of the comfort of the clothes for a user; A prediction unit that predicts the wearing comfort based on the measured weight of the user at the time of predicting the wearing comfort and the wearing comfort history data, The comfort history data includes one or more data sets by date and time, The data set includes a date on which the clothing was worn, a weight on the date on which the clothing was worn, and comfort information indicating how the clothing felt when worn, The prediction unit is extracting the data set including a weight equal to or close to the measured weight from the comfort history data by a comparison process of comparing the measured weight with the weight of the data set, and setting the comfort of the extracted data set as a predicted value of comfort of the clothes for the user. Comfort prediction system.

2. The prediction unit is When the comparison process detects only one of the data sets including a weight equal to the measured weight, the comfort of the data set detected by the comparison process is set as a predicted value of comfort of the clothes for the user. The wearing comfort prediction system according to claim 1 .

3. The prediction unit is When the comparison process detects a plurality of data sets including a weight equal to the measured weight, The most uncomfortable feeling among the comforts of each of the plurality of data sets is set as a predicted value of the comfort of the clothes for the user. The wearing comfort prediction system according to claim 2.

4. The prediction unit is If the comparison process determines that there is no data set that includes a weight equal to the measured weight, The comfort of the data set including the weight closest to the measured weight is set as a predicted value of the comfort of the clothes for the user. The wearing comfort prediction system according to claim 1 .

5. The prediction unit is If the comparison process determines that there is no data set that includes a weight equal to the measured weight, and If it is determined that the weight of the data set is greater than the measured weight for all of the data sets, or if it is determined that the weight of the data set is less than the measured weight for all of the data sets, The comfort of the data set including the weight closest to the measured weight is set as a predicted value of the comfort of the clothes for the user. The wearing comfort prediction system according to claim 1 .

6. The prediction unit is If it is determined that there is no data set that includes a weight equal to the measured weight, and When it is determined by the comparison process that the wearing comfort history data includes the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight, If the comfort of the data set including a weight that is greater than the measured weight and closest to the measured weight is equal to the comfort of the data set including a weight that is smaller than the measured weight and closest to the measured weight, A comfort value of the data set including a weight that is larger than the measured weight and closest to the measured weight, or a comfort value of the data set including a weight that is smaller than the measured weight and closest to the measured weight, is set as a comfort prediction value of the clothes for the user. The wearing comfort prediction system according to claim 1 .

7. The prediction unit is If it is determined that there is no data set that includes a weight equal to the measured weight, and When it is determined by the comparison process that the wearing comfort history data includes the data set including a weight greater than the measured weight and the data set including a weight smaller than the measured weight, When the comfort of the data set including a weight that is larger than the measured weight and closest to the measured weight is different from the comfort of the data set including a weight that is smaller than the measured weight and closest to the measured weight, The comfort of the data set including the weight closest to the measured weight is set as a predicted value of the comfort of the clothes for the user. The wearing comfort prediction system according to claim 1 .

8. The prediction unit is When it is determined that there is no data set including a weight equal to the measured weight, it is determined by the comparison process that the comfort history data includes a data set including a weight greater than the measured weight and a data set including a weight smaller than the measured weight, and The data set including a weight that is larger than the measured weight and closest to the measured weight is a first data set, and the data set including a weight that is smaller than the measured weight and closest to the measured weight is a second data set, the comfort of the first data set is different from the comfort of the second data set, and If the difference between the weight in the first data set and the measured weight is equal to the difference between the weight in the second data set and the measured weight, The most uncomfortable fit among the fit of the first data set and the fit of the second data set is determined as a predicted fit value of the clothes for the user. The wearing comfort prediction system according to claim 1 .

9. The data set further includes the comfort prediction value and prediction accuracy information, The prediction accuracy information includes a match value indicating that the comfort prediction value and the comfort coincide with each other, and a mismatch value indicating that the comfort prediction value and the comfort do not coincide with each other. The wearing comfort prediction system according to claim 1 .

10. Further, a validity evaluation unit is provided for evaluating validity of a fitting comfort prediction value that predicts the fitting comfort, The storage unit stores a database including the comfort history data of each of a plurality of clothes, the validity evaluation unit, when the prediction unit predicts the comfort prediction value of the clothing, judges the validity of the comfort prediction value based on whether or not there is information in which the prediction accuracy information is a discrepancy value from past comfort history data within a predetermined period prior to the prediction date including the time of the comfort prediction, for one or more other clothing different from the clothing; The wearing comfort prediction system according to claim 9.

11. the validity evaluation unit, when the prediction unit predicts the comfort prediction value of the clothing, if there is no information in which the prediction accuracy information is a discrepancy value in past comfort history data within a predetermined period from the prediction date for one or more of the other clothing, determines that the comfort prediction value is valid and confirms the comfort prediction value; The wearing comfort prediction system according to claim 10.

12. the validity evaluation unit, when the prediction unit predicts the comfort prediction value of the clothing, determines that the comfort prediction value is not valid if there is information about one or more of the other clothing items in which the prediction accuracy information is a discrepancy value from past comfort history data within a predetermined period from the prediction date; The wearing comfort prediction system according to claim 10.

13. A correction unit that corrects the predicted comfort value is further provided, When the validity evaluation unit determines that the comfort prediction value is not valid, the correction unit corrects the comfort prediction value based on a comparison between a first weight deviation degree and a second weight deviation degree; the first weight deviation is a difference between a weight included in the data set of the other clothes used to predict the comfort of the other clothes related to the data set including information that the prediction accuracy information is a discrepancy value and the weight of the user on a day when the comfort of the other clothes is predicted, and The second weight deviation is a difference between a weight included in the data set of the clothes used to predict the comfort of the clothes and the weight of the user on the day on which the comfort of the clothes is predicted. The wearing comfort prediction system according to claim 12.

14. The correction unit corrects the predicted comfort value to the uncomfortable wearing side when the second weight deviation degree is greater than the first weight deviation degree. The wearing comfort prediction system according to claim 13.

15. A selection unit is further provided for selecting comfortable clothes from a plurality of clothes, The prediction unit predicts the wearing comfort prediction value for the user for each of a plurality of clothes, The selection unit selects one or more of the recommended clothes based on the comfort prediction value of each of the plurality of clothes. The wearing comfort prediction system according to any one of claims 1 to 14.

16. A wearing comfort prediction program for predicting the wearing comfort of clothes, A preparation step of having a computer prepare historical data of the clothing comfort for a user; A prediction step of having the computer predict the wearing comfort based on the measured weight of the user at the time of predicting the wearing comfort and the wearing comfort history data, The comfort history data includes one or more data sets by date and time, The data set includes a date on which the clothing was worn, a weight on the date on which the clothing was worn, and comfort information indicating how the clothing felt when worn, In the prediction step, the data set including a weight equal to or close to the measured weight is extracted from the comfort history data by a comparison process of comparing the measured weight with the weight of the data set; The comfort of the extracted data set is set as a predicted value of comfort of the clothes for the user. Wearing comfort prediction program.

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