JP2009075937A - Equipment operation setting device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To configure an optimum operational setting for each user who uses equipment, through user's simple input operation, according to the characteristics of the user. <P>SOLUTION: An equipment operation setting device is provided with; an operation attribute storage means 12 which stores operation data, score distribution and attribute classification; a cognitive load attribute storage means 13 which stores answer data, score distribution and attribute classification; an attribute determination means 11 which determines, by cluster analysis, the classifications of the operation attributes and the cognitive load attribute which are highly correlated with the operation data and the answer data; an approximation item extraction means 14 which extracts approximation item data, which is approximate to the classification of the operation attribute or the cognitive load attribute determined from among operation data and the answer data, and extracts, by factor analysis, one or more representative approximation item data, which are large in factor load quantity; a user-categorizing means 15 which determines, by the cluster analysis, the groups that are most similar in characteristics indicated by the representative approximation item data; and an operation-setting means 16 which sets equipment according to the characteristics of the user on the basis of the determined groups. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a device operation setting device that specifies a user characteristic based on a user's operation of a device and a user's response to the operation of the device, and performs device operation setting according to the user's characteristic.

  In recent years, with the digitization and networking of devices, users can use a wide variety of functions installed in the devices, and convenience has improved dramatically. On the other hand, the operation of the device is complicated for the user due to the multi-functionality of the device. For this reason, the user often feels resistance to the operation of the device or cannot operate correctly, which may cause the user to feel great stress. Therefore, it is desirable for the user to be able to operate according to the image and to easily operate the function to be used.

  Therefore, as a conventional technique related to the menu of equipment, there is a method of classifying for beginners / experts and setting the equipment according to the classification. However, these two types of classifications cannot sufficiently satisfy the user's requirements. In addition, as a technique for complementing this user classification method, there is a method in which the user customizes himself according to usage and preference. However, this method requires a high literacy (skill level) for the user's device and that the user is in a well-known state for each function. Furthermore, regarding various setting values of the device, it is difficult to set optimum values for operating according to the user's image unless the user has high literacy for the device.

  In Non-Patent Document 1 as a method for supplementing the conventional technology as described above, users are grouped by a questionnaire survey, the users to be targeted are narrowed down in advance, and a device suitable for the users to be narrowed down is designed. A method has been proposed.

  Further, in Patent Document 1, a user attribute database is provided in an internal server or an external server, and the user's selection is analyzed by multivariate analysis of the similarity of the user's selection with the selection of other users. A provision information determination system has been proposed that provides information that matches the user's preference.

Further, Patent Document 2 proposes a provision information determination method that customizes user input data generated by an input device to generate an adaptive input profile, and filters the user input data according to the generated adaptive input profile.
Hiroaki Tahira, Atsuko Tsuji, “Scale for classifying users based on how they interact with products: development of car navigation version”, Paper presented at Human Interface Symposium 2003, 2003 JP 2004-206256 A JP 2000-231428 A

  However, in the method of designing a device suitable for the user described in Non-Patent Document 1, when designing a device by narrowing down the target user, a user other than the target user used the device. There was a problem that no risk was assumed.

  In addition, the provided information determination system described in Patent Document 1 analyzes information similar to a plurality of users and presents information that matches the user's preference, so the user needs to continue using the device to some extent. It was. Furthermore, even if a questionnaire is conducted in the initial stage and user attributes are classified, the user may eventually have to determine a set value. For this reason, it cannot be said that it sufficiently compensates the cognitive load for the user in the initial use.

  Furthermore, in the provided information determination method described in Patent Document 2, the user input data generated by the input device is customized to generate an adaptive input profile, and the user input data is filtered according to the generated adaptive input profile. In specifying the user's characteristics, only the user input data input by the user operation is reflected. In such a method, since the user's cognitive load on the display screen displayed by the device is not taken into consideration, the characteristics of the user cannot be accurately specified.

  The present invention has been made in view of the above problems, and an apparatus operation setting device that performs an optimal apparatus operation setting according to the characteristics of each user by a simple input operation of the user who uses the apparatus. The purpose is to provide.

  In order to solve the above-mentioned object, the first feature of the device operation setting device according to the present invention is based on the user's response to the operation of the device and the operation of the device by the user. A device operation setting device that specifies a predetermined characteristic and sets the operation of the device according to the characteristic, and is divided into a plurality of device operation data based on user input and a plurality of each operation data. Operation attribute storage means for associating a score distribution indicating which class the operation data belongs to among the classes, and classifying the plurality of operation data and score distribution into a plurality of attribute classifications and storing them as operation attribute data And a plurality of response data of the user with respect to the operation of the device, and which class the response data belongs to among a plurality of classes divided for each response data A cognitive load attribute storage means for correlating the plurality of response data and the score distribution into a plurality of attribute classifications and storing them as cognitive load attribute data, and storing the stored operation attribute data and cognition respectively. Attribute determination means for reading out the dynamic load attribute data and determining, by cluster analysis, the attribute classification of the operation attribute highly correlated with the plurality of operation data and the attribute classification of the cognitive load attribute highly correlated with the plurality of response data; Approximate item data extracted by factor analysis by extracting the approximate item data that approximates the attribute classification of the operation attribute or the cognitive load attribute attribute determined by the attribute determining means from the operation data and the plurality of response data An approximate item extraction means for extracting one or more representative approximate item data having a large factor load, User categorization means that determines by cluster analysis the group with the most similar characteristics indicated by the extracted representative approximate item data among the groups classified for each specific characteristic, and the user based on the determined group And an operation setting means for setting the device according to the predetermined characteristics.

  In order to solve the above-described object, the second feature of the device operation setting device according to the present invention is to classify each operation data into a plurality of classes, and to which class the operation data belongs among the classified classes. And a scoring means for calculating a score distribution to calculate a score distribution indicating to which of the divided classes the response data belongs, as well as calculating the score distribution to be shown and dividing the response data into a plurality of classes Based on the score distribution of each of the operation data and the response data, the operation attribute and the cognitive load attribute are classified into a plurality of attribute classifications, and the operation data and the score distribution corresponding to the operation data are classified for each attribute classification of the operation attribute. The operation attribute data is stored in the operation attribute storage means in association with each other, and the response data and the score distribution corresponding to this response data are associated with each attribute classification of the cognitive load attribute. Further in that a storage control unit that only by cognitive load attribute storage means causes the stored as cognitive attribute data.

  In order to solve the above-mentioned object, the third feature of the device operation setting device according to the present invention is that the factors classified into a plurality of attribute classifications are used attribute data based on the factor analysis based on the answer data on how the user uses the device. It further comprises usage attribute storage means for storing, and the attribute determination means reads the stored operation attribute data, cognitive load attribute data, and usage attribute data, respectively, and correlates with the operation data, response data, and answer data by cluster analysis. The attribute classification of each of the high operation attribute, the cognitive load attribute, and the usage attribute is determined, and the approximate item extraction means is the operation attribute determined by the attribute determination means among the operation data, response data, and answer data, cognitive Approximate item data that approximates the attribute classification of load attribute or usage attribute is extracted and extracted by factor analysis. In that so as to extract one or more representative approximation item data is larger factor loadings of the approximation item data.

  In order to solve the above object, the fourth feature of the device operation setting device according to the present invention is further provided with factor extraction means for extracting a factor from answer data by factor analysis, and the storage control means further extracts the factor. Based on this, the usage attributes are classified into multiple attribute classifications, and for each attribute classification of this usage attribute, the factor and the factor score corresponding to this factor are associated and stored as usage attribute data in the usage attribute storage means. It is in.

  In order to solve the above-mentioned object, a fifth feature of the device operation setting device according to the present invention is to store, as preference attribute data, factors classified into a plurality of attribute categories by factor analysis based on answer data relating to user preference. A preference attribute storage unit is further provided, and the attribute determination unit reads the stored operation attribute data, cognitive load attribute data, and preference attribute data, respectively, and has a high correlation with the operation data, response data, and answer data by cluster analysis. The attribute classification of each of the operation attribute, the cognitive load attribute, and the preference attribute is determined, and the approximate item extraction unit is the operation attribute, the cognitive load attribute determined by the attribute determination unit among the operation data, the response data, and the answer data. , Or the approximate item data that approximates the attribute classification of the preference attribute, and out of the approximate item data extracted by factor analysis In that so as to extract one or more representative approximation item data large child load.

  In order to solve the above object, the sixth feature of the device operation setting device according to the present invention is further provided with factor extracting means for extracting a factor from the answer data by factor analysis, and the storage control means further includes the extracted factor. Based on this, the preference attribute is classified into a plurality of attribute classifications, and for each attribute classification of the preference attribute, the factor and the factor score corresponding to the factor are associated and stored in the preference attribute storage means as preference attribute data. It is in.

  According to the device operation setting apparatus according to the present invention, it is possible to perform optimal device operation settings corresponding to the characteristics of each user by a simple input operation of the user who uses the device.

  The best mode for carrying out the present invention will be described below with reference to the drawings.

<First Embodiment>
FIG. 1 is a configuration diagram showing a configuration of a device operation setting apparatus according to the first embodiment of the present invention.

  As shown in FIG. 1, the device operation setting device 1 is connected to an input interface unit 2 and a device control unit 3, and an input unit 4 and a detection unit 5 are further connected to the input interface unit 2. .

  The input unit 4 includes, for example, an electrostatic sensor, a pressure sensor, an operation button, and the like. The input unit 4 generates an operation setting instruction signal for requesting an operation setting of the device by a user's input operation, and sends the operation setting instruction signal to the device operation setting device 1. Supply. Further, an operation command signal is generated by a user's input operation, and operation data such as a pressurization weight, a pressurization angle, an operation angle, and an operation time are generated, and these are input to the device operation setting device via the input interface unit 2. 1 is supplied.

  The detection unit 5 includes, for example, a camera that captures the user, and detects the user's line-of-sight position by analyzing an image captured by the camera. Then, the user's line-of-sight movement area, the number of line-of-sight movements, the line-of-sight movement time, the gaze time, and the like are calculated from the detected line-of-sight position of the user, and these data are generated as response data, and the device is connected via the input interface unit Supply to the operation setting device 1.

  FIG. 2 is a diagram for explaining an example of operation data and response data generated by the input unit 4 and the detection unit 5.

  As shown in FIG. 2, at T1, for example, when a question and an answer option corresponding to the question are displayed on the screen of the display device, the user reads the question displayed on this screen and becomes the answer to this question. Search for options by moving the line of sight. Then, when the user recognizes an option as an answer to the question, it can be assumed that the user stares at the option. Therefore, the detection unit 5 detects the user's line of sight as described above, and calculates the time from the point of time T1 to the point of time T2 as the line-of-sight movement time 306 as T2.

  Then, the detection unit 5 calculates the line-of-sight movement area 302 and the number of line-of-sight movements 304 based on the line of sight of the user who moved between time T1 and time T2 at time T2. Here, the line-of-sight movement area 302 is an area where the line of sight of the user has moved until the user determines an operation target to be operated on the display screen. For example, the display screen is divided into a matrix shape, and is a value calculated as an integrated area of the matrix through which the user's line of sight passes. The line-of-sight movement count 304 is the number of times that the user's line of sight passes through a predetermined area on the display screen.

  In addition, the user decides this option as a question answer after time T2 when the user has searched for the option that becomes the answer to the question. At that time, it can be estimated that the user removes his line of sight from the display screen in order to perform the operation. Therefore, the detection unit 5 calculates a gaze time 308 from the time T2 when the movement of the detected user's line of sight stops to the time T3 when the detected user's line of sight deviates from the display screen.

  Further, the user performs an input operation from the input unit 4 after time T3 when the answer to the question is determined. Therefore, the detection unit 5 supplies the time data at the time T3 when the question answer is determined to the input unit 4, and the input unit 4 is input by the user from the time T3 when the question answer is determined based on the supplied time data. The time until T4 when the unit 4 is operated is calculated as the operation time 208.

  Here, the input unit 4 calculates the operation time 208 and calculates the pressurization weight 202, the pressurization angle 204, and the operation angle 206 at time T4. Here, the pressurization weight 204 is a pressurization weight to the operation button detected when the user performs a push button operation on the input unit 4. The pressurization angle 204 is an angle of the operation button detected when the user performs a push button operation on the input unit 4. The operation angle 206 is calculated based on a touch area detected when a touch operation is performed with a finger, a touch pen, or the like on the electrostatic panel or the pressure-sensitive panel provided in the input unit 4, for example. And the angle of the touch pen operated by the user.

  In this way, the input unit 4 generates operation data such as the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 by the user's input operation, and supplies the operation data to the device operation setting device 1. The detection unit 5 calculates the user's line-of-sight movement area 302, the line-of-sight movement number 304, the line-of-sight movement time 305, the gaze time 308, and the like from the detected user's line-of-sight position, and generates these data as response data And supplied to the device operation setting device 1.

  The device control unit 3 performs device operation settings according to the user based on the user group determined by the device operation setting device 1.

  The apparatus operation setting device 1 includes an attribute determination unit 11, an operation attribute storage unit 12, a cognitive load attribute storage unit 13, an approximate item extraction unit 14, a user categorization unit 15, an operation setting unit 16, and a scoring function. Means 17 and storage control means 18 are provided.

  When the operation data and response data are supplied via the input interface unit 2, the attribute determination unit 11 stores the operation attribute data stored in the operation attribute storage unit 12 described later and the cognitive load attribute storage unit 13. And the cognitive load attribute data that is highly correlated with the operation data based on the cluster analysis based on the read operation attribute data and the cognitive load attribute data. Determine the attribute classification of the attribute.

  The operation attribute storage unit 12 stores a simulation program for collecting operation data and response data. Further, the operation data of the device input by the user is associated with a score distribution indicating which class the operation data belongs to among the classes divided into a plurality for each operation data, and the operation data and the score distribution Are classified into a plurality of attribute classifications and stored as operation attribute data.

  FIG. 3 is a diagram showing an example of operation attribute data stored in the operation attribute storage unit 12.

  As shown in FIG. 3, the operation attribute data includes a user ID 201, a pressurization weight 202, a score distribution 203 of the pressurization weight 202, a pressurization angle 204, a score distribution 205 of the pressurization angle 204, and an operation. The angle 206, the score distribution 207 of the operation angle 206, the operation time 208, the score distribution 209 of the operation time 208, and the attribute classification 210 are stored in association with each other.

  Here, as described above, the pressurization weight 202 is a pressurization weight to the operation button detected when the user operates the input unit 4. The pressurization angle 204 is an operation angle to the operation button detected when, for example, a push button operation is performed on the input unit 4. The operation angle 206 is, for example, an angle operated by a user calculated based on a touch area detected when a touch operation on the electrostatic panel provided in the input unit 4 is performed. The operation time 208 is a time from when the detection unit 5 recognizes that the user's line of sight is off the screen until the operation data is received.

  The score distributions 203, 205, 207, and 209 represent the score distribution values of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 that are operation data. This is a value indicating to which class, of the classes divided into about 2 to 7, for example, every 202, pressurization angle 204, operation angle 206, and operation time 208.

  The attribute classification 210 is an attribute classification classified into a plurality of similar attributes based on the score distributions 203, 205, 207, and 209.

  The cognitive load attribute storage means 13 associates user response data to the operation of the device with a score distribution indicating which class the response data belongs to among the classes divided into a plurality for each response data, These response data and score distribution are classified into a plurality of attribute classifications and stored as cognitive load attribute data.

  FIG. 4 is a diagram illustrating an example of cognitive load attribute data stored in the cognitive load attribute storage unit 13.

  As shown in FIG. 4, the cognitive load attribute data includes a user ID 201, a gaze movement area 302, a score distribution 303 of the gaze movement area 302, a gaze movement count 304, and a score distribution 305 of the gaze movement count 304. The gaze movement time 306, the score distribution 307 of the gaze movement time 306, the gaze time 308, the score distribution 309 of the gaze time 308, and the attribute classification 310 are stored in association with each other.

  Here, as described above, the line-of-sight movement area 302 is an area where the line of sight of the user has moved until the user determines an operation target to be operated on the display screen. The line-of-sight movement count 304 is the number of times that the user's line of sight passes through a predetermined area on the display screen. The line-of-sight movement time 306 is the time from the time when the display screen is displayed to the time when the user's line-of-sight movement stops. The gaze time 307 is the time from when the user recognizes the operation target to be operated on the display screen to when the user recognizes that the user's line of sight is off the screen.

  The score distributions 303, 305, 307, and 309 represent the respective score distribution values of the line-of-sight movement area 302, the line-of-sight movement number 304, the line-of-sight movement time 306, and the gaze time 308, which are response data. For each area 302, the number of line-of-sight movements 304, the line-of-sight movement time 306, and the gaze time 308, for example, this is a value indicating which of the classes divided into about 2 to 7 belongs.

  The attribute classification 310 is an attribute classification classified into a plurality of similar attributes based on the score distributions 303, 305, 307, and 309.

  The approximate item extraction unit 14 extracts the approximate item data that approximates the attribute classification of the operation attribute or the cognitive load attribute determined by the attribute determination unit 11 from the operation data and the cognitive load data, and is extracted by factor analysis. One or a plurality of representative approximate item data having a large factor load is extracted from the approximate item data.

  The user categorizing unit 15 determines, by cluster analysis, a group having the most similar characteristic indicated by the representative approximate item data extracted by the approximate item extracting unit 14 among groups set in advance for each user characteristic.

  Based on the group determined by the user categorizing means 15, the operation setting unit 16 sets a device according to the user. Specifically, the menu switching unit 16a provided in the operation setting unit 16 generates a menu corresponding to the group to which the user belongs, or the default value setting unit 16b is a default value corresponding to the group to which the user belongs. Or setting.

  The scoring means 17 classifies each operation data received from the input unit 4 into a plurality of classes, and calculates a score distribution indicating which class the operation data belongs to among the classified classes. Further, each response data received from the detection unit 5 is divided into a plurality of classes, and a score distribution indicating which class the response data belongs to among the classified classes is calculated.

  The storage control unit 18 classifies the operation attributes into a plurality of attribute classifications based on the score distribution of the operation data calculated by the scoring unit 17, and corresponds to the operation data and the operation data for each attribute classification of the operation attributes. The score distribution is associated and stored in the operation attribute storage means 12 as operation attribute data. The storage control unit 18 classifies the cognitive load attributes into a plurality of attribute classifications based on the score distribution of the response data calculated by the scoring unit 17, and sets response data and response data for each attribute class of the cognitive load attributes. The score distribution corresponding to this response data is associated and stored in the cognitive load attribute storage means 13 as cognitive attribute data.

≪Action≫
Next, the operation of the device operation setting device 1 according to the first embodiment will be described with reference to the drawings.

  FIG. 5 is a process of generating operation attribute data stored in the operation attribute storage unit 12 of the device operation setting device 1 according to the first embodiment and cognitive load attribute data stored in the cognitive load attribute storage unit 13. It is a flowchart which shows a procedure.

  First, the device operation setting device 1 starts executing a simulation program for collecting operation data and response data (step S101). Specifically, the simulation program stored in the operation attribute storage unit 12 is read, and the read simulation program is executed. Thereby, the device operation setting device 1 displays a display screen that prompts the user to perform an operation, for example. Here, the display screen to be displayed is, for example, a screen that displays a simple question and allows the user to select one of a plurality of options displayed on the display screen. In addition, when selecting options, it is desirable to display each option in a distributed manner, such as by providing a predetermined interval between the options so that the user's line of sight moves.

  When the user operates the input unit 4 to the question output on the display screen displayed in step S101, the device operation setting device 1 transmits the operation data from the input unit 4 to the input interface unit 2. (Step S102). Specifically, the device operation setting apparatus 1 receives data of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 as operation data, and uniquely identifies these operation data. The user ID 201 is stored in the operation attribute storage unit 12 in association with it.

  Next, the scoring means 17 of the device operation setting device 1 performs scoring processing when it is determined in step S102 that a considerable number of operation data has been received (step S103). Specifically, the scoring means 17 calculates an average value for each operation data, and divides the value of each operation data into, for example, about 2 to 7 classes around the calculated average value. Then, it is determined to which stage the operation data for each user ID 201 belongs.

  Then, the storage control means 18 of the device operation setting device 1 classifies the operation attributes into a plurality of attribute classifications based on the determination result determined in step S103, that is, the score distribution. The score distribution corresponding to the data is associated and stored as operation attribute data in the operation attribute storage means 12 (step S104).

  On the other hand, in step S101, when the device operation setting device 1 starts executing the simulation program, the detection unit 5 detects and detects a user response to the question output on the display screen displayed in step S101. The response data is transmitted to the device operation setting device 1 via the input interface unit 2 (step S105). Specifically, the device operation setting device 1 receives data of the line-of-sight movement area 302, the number of line-of-sight movements 304, the line-of-sight movement time 306, and the gaze time 308 as response data, and uniquely identifies these response data. The user ID 201 is associated and stored in the cognitive load attribute storage unit 13.

  Next, the scoring means 17 of the device operation setting device 1 performs scoring processing when it is determined in step S105 that a considerable number of response data has been received (step S106). Specifically, the scoring means 17 calculates an average value for each response data, and divides the value of each response data into, for example, about 2 to 7 classes around the calculated average value. Then, it is determined to which class the response data for each user ID belongs.

  Then, the storage control means 18 of the device operation setting device 1 classifies the cognitive load attributes into a plurality of attribute classifications based on the determination result determined in step S106, that is, the score distribution, and for each attribute classification, response data and The score distribution corresponding to the response data is associated and stored as cognitive load attribute data in the cognitive load attribute storage means 13 (step S107).

  As described above, the device operation setting device 1 according to the first embodiment stores operation attribute data necessary for executing the device operation setting process described later in the operation attribute storage unit 12 and recognizes cognitive load attribute data. Can be stored in the dynamic load attribute storage means 13.

  FIG. 6 is a flowchart illustrating a processing procedure of device operation setting processing in the device operation setting device 1 according to the first embodiment.

  First, the device operation setting device 1 determines whether or not the device is initially set when power is turned on to the device that is the operation setting target (step S201).

  If it is determined in step S201 that the device that is the operation setting target is initially set, the device is caused to perform a normal operation (step S202).

  On the other hand, when it is determined in step S201 that the device that is the operation setting target is not initially set, or the operation setting instruction signal is supplied from the input unit 4 when the operation target device is operating normally. In (Step S203), the process proceeds to Step S205.

  Next, the device operation setting device 1 starts executing a simulation program for collecting operation data and response data (step S205). Specifically, the simulation program stored in the operation attribute storage unit 12 is read, and the read simulation program is executed. Thereby, the device operation setting device 1 displays a display screen that prompts the user to perform an operation, for example. Here, the display screen to be displayed is, for example, a screen in which a simple question is displayed and the user can select one of a plurality of options displayed on the display screen, and the option is selected. At this time, it is desirable to display each option in a distributed manner, such as by providing a predetermined interval between each option so that the user's line of sight moves.

  When the user performs an operation from the input unit 4 on the question output on the display screen displayed in step S205, the device operation setting device 1 transmits the operation data from the input unit 4 to the input interface unit 2. The response data is received from the detection unit 5 via the input interface unit 2 (step S206). Specifically, the device operation setting apparatus 1 receives data of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 as operation data, and receives the line-of-sight movement area 302, the line-of-sight as response data. Data on the number of movements 304, the line-of-sight movement time 306, and the gaze time 308 are received.

  Next, when the attribute determination means 11 receives the operation data and the response data in step S206, the operation attribute data stored in the operation attribute storage means 12 and the cognitive load attribute data stored in the cognitive load attribute storage means 13 are received. And read.

  Then, the attribute determination unit 11 performs cluster analysis based on the read operation attribute data, thereby determining the attribute classification of the operation attribute highly correlated with the operation data received in step S206 (steps S207 and S208). Similarly, the attribute determining unit 11 performs cluster analysis based on the read cognitive load attribute data, thereby determining the attribute classification of the cognitive load attribute highly correlated with the response data received in step S202 (step S207). , S208).

  Next, the approximate item extraction unit 14 extracts approximate item data that approximates the attribute classification of the operation attribute determined by the attribute determination unit 11 from the operation data received in step S206. Similarly, the approximate item extracting unit 14 extracts approximate item data that approximates the attribute classification of the cognitive attribute determined by the attribute determining unit 11 from the response data. (Step S209).

  Then, the approximate item extraction unit 14 integrates the approximate item data of the operation attribute and the approximate item data of the cognitive load attribute extracted in step S209, performs factor analysis (step S210), and among the integrated approximate item data One or a plurality of representative approximate item data having a large factor loading is extracted (step S211).

  Next, the user categorizing unit 15 performs cluster analysis on the representative approximate item data extracted by the approximate item extracting unit 14 (step S212). As a result, among the groups classified in advance for each user characteristic, a group having the most similar characteristic indicated by the representative approximate item data extracted in step S211 is determined (step S213). Here, each group classified in advance for each user characteristic is characterized by an operation / cognitive load, and is classified into an arbitrary number of groups by the provider or the user of the device operation setting device 1.

  Thus, by performing cluster analysis on the representative approximate item data extracted in step S211, the group to which the user characteristics belong is determined after integrating the multi-dimensional attributes of the operation attribute and the cognitive load attribute. It becomes possible to assign users to the optimum group according to their characteristics.

  Next, based on the group determined by the user categorizing means 15, the operation setting unit 16 sets a device according to the characteristics of the user. Specifically, the menu switching means 16a generates a menu suitable for the group most similar to the user characteristics, or the default value setting means 16b is a default value suitable for the group most similar to the user characteristics. Is set (step S214).

  In this way, since the device is set according to the optimally assigned group, it is possible to perform the optimal device operation setting according to the characteristics of the user. In addition, since device operation settings are made based on user operation data and response data, even when this device is used by multiple users, the optimal device operation according to the characteristics of each user Settings can be made.

  FIG. 7 is a diagram for explaining an example of device operation setting processing when the device operation setting device 1 according to the first embodiment is applied to an HDD recorder.

  First, when it is determined that the device (HDD recorder) that is the operation setting target is not initially set, or when the operation target device is in normal operation, the device operation setting device 1 receives an input interface from the input unit 4. When the operation setting instruction signal is supplied via the unit 2, a screen is displayed to collect operation data and response data.

  Then, the device operation setting device 1 receives the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 as operation data 401 from the input unit 4 through the input interface unit 2 by a user operation. Then, the line-of-sight movement area 302, the number of line-of-sight movements 304, the line-of-sight movement time 306, and the gaze time 308 are received as response data 402 from the detection unit 5 via the input interface unit 2.

  Next, when receiving the operation data 401, the attribute determination unit 11 reads the operation attribute data stored in the operation attribute storage unit 12, and performs cluster analysis based on the read operation attribute data, thereby receiving the received operation data. In 401, the attribute classification of the operation attribute 403 having a high correlation is determined.

  In the example illustrated in FIG. 7, attribute 3 is determined as the attribute classification of the operation attribute 403 having a high correlation with the operation data 401 by cluster analysis.

  Similarly, upon receiving the response data 402, the attribute determination unit 11 reads the cognitive load attribute data stored in the cognitive load attribute storage unit 13, and performs cluster analysis based on the read cognitive load attribute data. Thus, the attribute classification of the cognitive load attribute 404 having a high correlation with the received response data 402 is determined.

  In the example illustrated in FIG. 7, attribute 1 is determined as the attribute classification of the cognitive load attribute 404 having a high correlation with the response data 402 by cluster analysis.

  Next, the approximate item extraction unit 14 approximates the item classification of the operation attribute 403 or the attribute classification of the cognitive load attribute 404 determined by the attribute determination unit 11 among the received operation data 401 and response data 402. And one or more representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by the factor analysis.

  Then, the user categorizing unit 15 determines, by cluster analysis, a group having the most similar characteristic indicated by the representative approximate item data extracted by the approximate item extracting unit 14 among the groups previously classified for each user characteristic. .

  As shown at 405 in FIG. 7, the approximate item data approximated to the attribute 3 of the operation attribute 403 is extracted from the operation data, and the approximate item data approximated to the attribute 1 of the cognitive load attribute 404 is extracted from the response data and extracted. One or a plurality of representative approximate item data having a large factor load is extracted from the approximate item data. Then, the group 5 is determined as the group having the most similar characteristics indicated by the extracted representative approximate item data.

  Thus, since the apparatus operation setting apparatus 1 determines the group similar to a user's specification in consideration of not only an operation attribute but also a cognitive load attribute, it respond | corresponded to the characteristic of the user more appropriately. A group can be determined.

  In the example illustrated in FIG. 7, the number of groups is six. However, the number is not limited to six, and an arbitrary number of groups is set in advance. Each set group is characterized by operation and cognitive load, and the designer sets a large number of assumed users in advance, "What kind of operation characteristics and cognitive users are?" This assumed user is set as a group. Here, an assumed user set as a group 5 says, “It is not very strong for devices and is not good at operating the input unit 4, and if the amount of information on the screen is small, it can understand the displayed information. "A certain user" is set.

  In this way, by performing cluster analysis on one or more representative approximate item data with a large factor load, the multi-dimensional attributes of the operation attribute and the cognitive load attribute are integrated and the user's Since the group to which the characteristic belongs is determined, it is possible to assign the user to the optimum group according to the characteristic.

  Next, the operation setting unit 16 sets a device according to the user based on the group determined by the user categorizing unit 15.

  In the example shown in FIG. 7, “display only basic menu”, “normal image quality”, and “slow cursor movement” are associated as the device operation setting 207 corresponding to group 5, so that the operation setting unit 16 The operation setting of the HDD recorder is performed according to this setting content. Note that arrows connecting the group 406 and the device operation setting 407 illustrated in FIG. 7 indicate device operation settings suitable for each group of the group 406.

  As a result, the operation setting unit 16 displays only the minimum necessary functions in the “display only basic menu” setting to make it easy to grasp the functions, and the user does not bother to set the “image quality normal” setting. It can be used, and the cognitive load of the user on the cursor displayed with the “cursor moving slow” setting can be reduced.

  In addition, according to the operations assumed in each group, the initial menu and initial setting values set in advance by the designer, and the optimum values updated with data fed back from the actual usage status of other users Can be provided to the user.

  As described above, in the device operation setting device 1 according to the first embodiment, the user answers a simple question, so that the menu and the system setting are matched to the operation of the device and the response of the device to the operation of the device. The optimum value (default value) can be obtained. At that time, the device operation setting device performs grouping and selection based on the integrated similarity judgment from multiple groups, so that the menu and system setting value (default value) that matches the user's operation and response )

  As described above, according to the device operation setting apparatus 1 according to the first embodiment, since the device is set according to the optimally assigned group, the optimal device operation setting according to the characteristics of the user is performed. It can be carried out.

<Second Embodiment>
The device operation setting device 1 according to the first embodiment specifies a predetermined characteristic for the user based on the user's operation of the device and the user's response to the operation of the device, and the device according to this characteristic. Set the operation settings.

  The device operation setting device according to the second embodiment specifies predetermined characteristics of a user based on how the device is used by the user in addition to the user's operation of the device and the user's response to the device operation. Then, the operation setting of the device according to this characteristic is performed.

  FIG. 8 is a configuration diagram showing the configuration of the device operation setting device according to the second embodiment of the present invention.

  As shown in FIG. 8, the device operation setting device 101 is connected to an input interface unit 2 and a device control unit 3, and an input unit 4 and a detection unit 5 are further connected to the input interface unit 2. .

  The input unit 4 includes, for example, an electrostatic sensor, a pressure sensor, an operation button, and the like. Supply. In addition to generating response data for questions about how to use the equipment displayed on the display device, etc. by user input operations, operation data such as pressure weight, pressure angle, operation angle, and operation time are generated. Is supplied to the device operation setting apparatus 101 via the input interface unit 2.

  Since the detection unit 5 has the same configuration as the detection unit 5 of the device operation setting device 1 according to the first embodiment of the present invention, the description thereof is omitted.

  The device control unit 3 performs device operation settings according to the user based on the user group determined by the device operation setting device 101.

  The device operation setting device 101 includes an attribute determination unit 11, an operation attribute storage unit 12, a cognitive load attribute storage unit 13, an approximate item extraction unit 14, a user categorization unit 15, an operation setting unit 16, and a scoring unit. Means 17, storage control means 18, usage attribute storage means 19, and factor extraction means 21 are provided.

  The attribute determination means 11 is provided with operation attribute data and cognitive load attribute storage means 13 stored in the operation attribute storage means 12 to be described later when operation data, response data, and answer data to a question about how to use the device are supplied. And the usage attribute data stored in the usage attribute storage means 19 are read out. Based on the read operation attribute data, cognitive load attribute data, and usage attribute data, attribute classification of operation attributes highly correlated with operation data by cluster analysis, attribute classification of cognitive load attributes highly correlated with response data And the attribute classification of the usage attribute having a high correlation with the answer data.

  The operation attribute storage unit 12 and the cognitive load attribute storage unit 13 have the same configuration as the operation attribute storage unit 12 and the cognitive load attribute storage unit 13 of the device operation setting device 1 according to the first embodiment of the present invention. Therefore, explanation is omitted.

  The approximate item extraction unit 14 approximates the attribute classification of the operation attribute, the attribute classification of the cognitive load attribute, or the attribute classification of the usage attribute determined by the attribute determination unit 11 among the operation data, the response data, and the response data. Item data is extracted, and one or a plurality of representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by the factor analysis.

  The user categorizing means 15, the operation setting unit 16, and the scoring means 17 are the same as the user categorizing means 15, the operation setting unit 16, and the scoring means 17 of the device operation setting device 1 that is the first embodiment of the present invention. Since it has the structure of, it abbreviate | omits description.

  The factor extraction means 21 extracts factors by factor analysis from the answer data to the question about how to use the equipment supplied from the input unit 4.

  The storage control unit 18 classifies the operation attributes into a plurality of attribute classifications based on the score distribution of the operation data calculated by the scoring unit 17, and corresponds to the operation data and the operation data for each attribute classification of the operation attributes. The score distribution is associated and stored in the operation attribute storage means 12 as operation attribute data. The storage control unit 18 classifies the cognitive load attributes into a plurality of attribute classifications based on the score distribution of the response data calculated by the scoring unit 17, and sets response data and response data for each attribute class of the cognitive load attributes. The score distribution corresponding to this response data is associated and stored in the cognitive load attribute storage means 13 as cognitive attribute data. Further, the storage control means 18 classifies the usage attributes into a plurality of attribute classifications based on the factors extracted by the factor extraction means 21, and associates the factors with the factor scores corresponding to the factors for each attribute classification. It is stored in the usage attribute storage means 19.

≪Action≫
Next, the operation of the device operation setting device 101 according to the second embodiment will be described with reference to the drawings.

  FIG. 9 illustrates operation attribute data stored in the operation attribute storage unit 12 of the device operation setting apparatus 101 according to the second embodiment, cognitive load attribute data stored in the cognitive load attribute storage unit 13, and usage attributes. It is a flowchart which shows the production | generation procedure of the usage attribute data memorize | stored in the memory | storage means.

  First, the device operation setting device 101 reads a question about how to use the device stored in the usage attribute storage unit 19 and displays the read question on, for example, a display device (step S301). Here, on the display screen to be displayed, for example, a question about simple usage is displayed and an option that allows one to be selected from a plurality is displayed. In addition, when the user selects an option, it is desirable to display each option in a distributed manner, such as by providing a predetermined interval between the options so that the user's line of sight moves.

  Then, when the user performs an operation from the input unit 4 on the question output on the display screen displayed in step S301, the device operation setting device 101 transmits the operation data from the input unit 4 to the input interface unit 2. (Step S302). Specifically, the device operation setting apparatus 101 receives data of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 as operation data, and uniquely identifies these operation data. The user ID 201 is stored in the operation attribute storage unit 12 in association with it.

  Next, the scoring means 17 of the device operation setting device 101 performs scoring processing when it is determined in step S302 that a considerable number of operation data has been received (step S303). Specifically, the scoring means 17 calculates an average value for each operation data, and divides the value of each operation data into, for example, about 2 to 7 classes around the calculated average value. Then, it is determined to which stage the operation data for each user ID 201 belongs.

  Then, the storage control means 18 of the device operation setting device 101 classifies the operation attributes into a plurality of attribute classifications based on the determination result determined in step S303, that is, the score distribution value. The score distribution value corresponding to the operation data is associated with and stored in the operation attribute storage means 12 as operation attribute data (step S304).

  On the other hand, in step S301, the device operation setting device 101 displays a question about how to use the device on the display device, and then the detection unit 5 responds to the question output on the display screen displayed in step S301. And the detected response data is transmitted to the device operation setting device 101 via the input interface unit 2 (step S305). Specifically, the device operation setting apparatus 101 receives data of the line-of-sight movement area 302, the number of line-of-sight movements 304, the line-of-sight movement time 306, and the gaze time 308 as response data, and uniquely identifies these response data. The user ID 201 is associated and stored in the cognitive load attribute storage unit 13.

  Next, the scoring means 17 of the device operation setting apparatus 101 performs scoring processing when it is determined in step S305 that a considerable number of response data has been received (step S306). Specifically, the scoring means 17 calculates an average value for each response data, and divides the value of each response data into, for example, about 2 to 7 classes around the calculated average value. Then, it is determined to which class the response data for each user ID belongs.

  Then, the storage control means 18 of the device operation setting device 101 classifies the cognitive load attribute into a plurality of attribute classifications based on the determination result determined in step S306, that is, the score distribution value, and response data for each attribute classification. And the score distribution value corresponding to this response data are stored in the cognitive load attribute storage means 13 as cognitive load attribute data (step S307).

  Further, in step S301, the device operation setting device 101 displays a question about how to use the device on the display device, and then the device operation setting device 101 inputs answer data for the question about how to use the device displayed in step S301. Received from the unit 4 (step S308). The answer data is, for example, one of “applicable to non-applicable” or a data format of multi-level rating of, for example, about 3 to 7 levels.

  Next, when it is determined in step S308 that a considerable number of response data has been received, the factor extraction unit 21 performs factor analysis to extract factors that are effective for attribute classification (step S309).

  Next, the factor extraction means 21 determines a representative question corresponding to each factor extracted in step S309 from the questions read in step S301, and stores the determined representative question in the usage attribute storage means 19 (step S310).

  Next, the storage control means 18 classifies the usage attributes into a plurality of attribute classifications based on the factors extracted by the factor extraction means 21. Then, the storage control means 18 associates the factor with the factor score corresponding to this factor for each attribute classification and stores it in the usage attribute storage means 19 (step S311).

  As described above, the device operation setting apparatus 101 according to the second embodiment stores the usage attribute data necessary for executing the device operation setting process described later in the usage attribute storage unit 19 and the operation attribute data as the operation attribute. The data can be stored in the storage unit 12, and the cognitive load attribute data can be stored in the cognitive load attribute storage unit 13.

  FIG. 10 is a flowchart illustrating a processing procedure of device operation setting processing in the device operation setting device 101 according to the second embodiment.

  First, the device operation setting device 101 determines whether or not the device is initially set when power is turned on to the device that is the operation setting target (step S401).

  If it is determined in step S401 that the device to be set for operation is initially set, normal operation is performed (step S402).

  On the other hand, when it is determined in step S401 that the device that is the operation setting target is not initially set, or the operation setting instruction signal is supplied from the input unit 4 when the operation target device is operating normally. In (Step S403), the process proceeds to Step S405.

  Next, the device operation setting apparatus 101 displays a usage question screen on a display unit or the like. Specifically, the device operation setting device 101 reads a representative question about how to use the device stored in the usage attribute storage unit 19 and displays the read representative question on, for example, a display device. (Step S405). Here, the display screen to be displayed displays, for example, a representative question about how to use a simple device, and answer data that can select one of a plurality of options as an answer to the representative question. In addition, when the user selects an option, it is desirable to display each option in a distributed manner, such as by providing a predetermined interval between the options so that the user's line of sight moves.

  Then, when the user performs an operation from the input unit 4 on the representative question output on the display screen displayed in step S405, the device operation setting device 101 receives the answer data and the operation data from the input unit 4. The data is received via the input interface unit 2 and the response data is received from the detection unit 5 via the input interface unit 2 (step S406). Specifically, the device operation setting apparatus 101 receives, for example, alternative data “applicable to non-applicable” or multilevel rating data of about 3 to 7 levels as response data. Also, as the operation data, the data of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 are received. Data on the gaze time 308 is received.

  Next, when receiving the operation data, response data, and answer data in step S406, the attribute determination unit 11 stores the operation attribute data stored in the operation attribute storage unit 12 and the cognitive load attribute storage unit 13. The cognitive load attribute data and the usage attribute data stored in the usage attribute storage means 19 are read out.

  Then, the attribute determination unit 11 performs cluster analysis based on the read operation attribute data, thereby determining the attribute classification of the operation attribute highly correlated with the operation data received in step S406 (steps S407 and S408). In addition, the attribute determination unit 11 performs cluster analysis based on the read cognitive load attribute data, thereby determining the attribute classification of the cognitive load attribute highly correlated with the response data received in step S402 (step S407, S408). Further, the attribute determining unit 11 performs cluster analysis based on the read usage attribute data, thereby determining the attribute classification of the usage attribute having a high correlation with the response data received in step S402 (steps S407 and S408).

  Next, the approximate item extraction unit 14 extracts the approximate item data that approximates the attribute classification of the operation attribute determined by the attribute determination unit 11 from the operation data received in step S406. Further, the approximate item extraction unit 14 extracts approximate item data that approximates the attribute classification of the cognitive attribute determined by the attribute determination unit 11 from the received response data. Further, the approximate item extracting unit 14 extracts approximate item data that approximates the attribute classification of the usage attribute determined by the attribute determining unit 11 from the received answer data. (Step S409).

  Then, the approximate item extraction unit 14 integrates the approximate item data of the operation attribute, the approximate item data of the cognitive load attribute, and the approximate item data of the usage attribute extracted in step S409, and performs factor analysis (step S410). Then, one or a plurality of representative approximate item data having a large factor load amount are extracted from the integrated approximate item data (step S411).

  Next, the user categorizing unit 15 performs cluster analysis on the representative approximate item data extracted by the approximate item extracting unit 14 (step S412). Thereby, among the groups classified in advance for each user characteristic, a group having the most similar characteristic indicated by the representative approximate item data extracted in step S411 is determined (step S413). Here, groups classified in advance for each user characteristic have characteristics in operation, cognitive load, and usage, and are classified into an arbitrary number of groups by the provider or user of the device operation setting device 101. .

  As described above, by performing cluster analysis on the representative approximate item data extracted in step S411, a group to which user characteristics belong is obtained after integrating multi-dimensional attributes such as operation attributes, cognitive load attributes, and usage attributes. Since the determination is made, it is possible to assign the user to the optimum group according to the characteristic.

  Next, based on the group determined by the user categorizing means 15, the operation setting unit 16 sets a device according to the characteristics of the user. Specifically, the menu switching means 16a generates a menu suitable for the group most similar to the user characteristics, or the default value setting means 16b is a default value suitable for the group most similar to the user characteristics. Is set (step S414).

  In this way, since the device is set according to the optimally assigned group, it is possible to perform the optimal device operation setting according to the characteristics of the user. In addition, since the operation setting of the device is performed based on the operation data by the user, the response data, and the answer data to the question, even when a plurality of users use this device, depending on the characteristics of each user Optimal device operation settings can be made.

  FIG. 11 is a diagram for explaining an example of device operation setting processing when the device operation setting device 101 according to the second embodiment is applied to an HDD recorder.

  First, when it is determined that the device (HDD recorder) that is the operation setting target is not initially set, or when the operation target device is in normal operation, the device operation setting device 101 receives an input interface from the input unit 4. When the operation setting instruction signal is supplied via the unit 2, a representative question about how to use is displayed.

  In the example shown in FIG. 11, the device operation setting device 101 has, as representative questions 503 regarding usage, “Would you like to use without troublesome settings?”, “Do you want to use various functions easily?”, “ Representative questions such as “Would you like to use it while setting it yourself?” And “Do you want to use it so that you can take the most beautiful pictures?” Are displayed.

  The device operation setting device 101 receives answer data for the representative question 503 regarding the usage from the input unit 4 via the input interface unit 2 by the user's operation, and pressurization weight 202, pressurization as the operation data 501 An angle 204, an operation angle 206, and an operation time 208 are received. Further, the line-of-sight movement area 302, the line-of-sight movement number 304, the line-of-sight movement time 306, and the gaze time 308 are received as response data 502 from the detection unit 5 through the input interface unit 2.

  Next, when the attribute determination unit 11 receives the answer data, the attribute determination unit 11 reads the usage attribute data stored in the usage attribute storage unit 19 and performs a cluster analysis based on the read usage attribute data, thereby obtaining the received answer data. The attribute classification of the usage attribute 506 having a high correlation is determined.

  In the example shown in FIG. 11, attribute 1 is determined as the attribute classification of the usage attribute 506 having a high correlation with the answer data for the representative question 503 regarding usage by cluster analysis.

  Similarly, when receiving the operation data 501, the attribute determining unit 11 reads the operation attribute data stored in the operation attribute storage unit 12, and performs cluster analysis based on the read operation attribute data, thereby receiving the received operation data. In 501, the attribute classification of the operation attribute 504 having a high correlation is determined.

  In the example illustrated in FIG. 11, attribute 1 is determined as the attribute classification of the operation attribute 504 having a high correlation with the operation data 501 by cluster analysis.

  Similarly, when the response determination unit 11 receives the response data 502, the attribute determination unit 11 reads the cognitive load attribute data stored in the cognitive load attribute storage unit 13, and performs cluster analysis based on the read cognitive load attribute data. Thus, the attribute classification of the cognitive load attribute 505 having a high correlation with the received response data 502 is determined.

  In the example illustrated in FIG. 11, attribute 2 is determined as the attribute classification of the cognitive load attribute 505 having a high correlation with the response data 502 by cluster analysis.

  Next, the approximate item extraction unit 14 includes the attribute classification of the operation attribute 504, the attribute classification of the cognitive load attribute 505, and the usage attribute determined by the attribute determination unit 11 among the received answer data, operation data, and response data. Approximate item data that approximates the attribute classification 506 is extracted, and one or more representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by factor analysis.

  Then, the user categorizing unit 15 determines, by cluster analysis, a group having the most similar characteristic indicated by the representative approximate item data extracted by the approximate item extracting unit 14 among the groups previously classified for each user characteristic. .

  As shown by 507 in FIG. 11, approximate item data that approximates the attribute 1 of the operation attribute 504 from the operation data, approximate item data that approximates the attribute 2 of the cognitive load attribute 505 from the response data, and a usage attribute from the answer data Approximate item data that approximates the attribute 1 of 506 is extracted. Then, one or a plurality of representative approximate item data having a large factor load is extracted from the extracted approximate item data, and group 2 is determined as a group having the most similar characteristics indicated by the extracted representative approximate item data.

  In the example shown in FIG. 11, the number of groups is six. However, the number is not limited to six, and an arbitrary number of groups is set in advance. Each set group has its own characteristics in operation, cognitive load, and usage. On the designer side, “What kind of operation characteristics and cognitive characteristics are users and how users use the equipment? A large number of assumed users “is there?” Are set in advance, and the assumed users are set as a group. Here, the assumed user set as Group 2 is “a user who has a strong feeling of wanting to use because it is bright in equipment, and can quickly find necessary information from a lot of information. “Group of users to record” is set.

  In this way, by performing cluster analysis on one or more representative approximate item data with a large factor load, the multi-dimensional attributes such as operation attributes, cognitive load attributes, and usage attributes are integrated. Since the group to which the user's characteristic belongs is determined, the user can be assigned to the optimum group according to the characteristic.

  Next, the operation setting unit 16 sets a device according to the user based on the group determined by the user categorizing unit 15.

  In the example shown in FIG. 11, “Display only all menus”, “Set weekly recording”, and “Set recording image quality arbitrarily” are associated as the device operation settings 509 corresponding to the group 2. The unit 16 sets the operation of the HDD recorder according to the setting contents. Note that the arrows connecting the group 508 and the device operation setting 509 illustrated in FIG. 11 indicate device operation settings suitable for each group of the group 508.

  As a result, the operation setting unit 16 displays all functions with the “display all menus only” setting so that they can be used, automatically sets “weekly recording”, and sets the image quality setting to “arbitrarily set”. Thus, the user can store the drama weekly while the user adjusts the recording image quality in consideration of the remaining amount of the storage medium.

  In addition, according to the operations assumed in each group, the initial menu and initial setting values set in advance by the designer, and the optimum values updated with data fed back from the actual usage status of other users Can be provided to the user.

  As described above, in the device operation setting apparatus 101 according to the second embodiment, the user answers a simple usage question, so that the user can use the device, operate the device, and respond to the device operation. The optimal menu and system setting value (default value) can be obtained. At that time, the device operation setting device 101 performs grouping and selection by integrated similarity determination from a plurality of grouped groups, so that menus and system setting values (default) that match the user's operation and response are made. Value) can be obtained optimally.

  As described above, according to the device operation setting apparatus 101 according to the second embodiment, since the device is set according to the optimally assigned group, the optimal device operation setting according to the characteristics of the user is performed. It can be carried out.

<Third Embodiment>
The device operation setting device 1 according to the first embodiment specifies a predetermined characteristic for the user based on the user's operation of the device and the user's response to the operation of the device, and the device according to this characteristic. Set the operation settings.

  The device operation setting device according to the third embodiment specifies a predetermined characteristic for the user based on the user's preference in addition to the user's operation of the device and the user's response to the operation of the device, Device operation settings are made according to this characteristic.

  FIG. 12 is a configuration diagram showing the configuration of the device operation setting device according to the third embodiment of the present invention.

  As illustrated in FIG. 12, the device operation setting device 102 is connected to the input interface unit 2 and the device control unit 3, and the input unit 4 and the detection unit 5 are further connected to the input interface unit 2. .

  The input unit 4 includes, for example, an electrostatic sensor, a pressure sensor, an operation button, and the like. Supply. In addition, it generates response data for questions relating to user preferences displayed on the display device or the like by user input operations, and generates operation data such as pressure weight, pressure angle, operation angle, operation time, These are supplied to the device operation setting device 102 via the input interface unit 2.

  Since the detection unit 5 has the same configuration as the detection unit 5 of the device operation setting device 1 according to the first embodiment of the present invention, the description thereof is omitted.

  The device control unit 3 performs device operation settings according to the user based on the user group determined by the device operation setting device 102.

  The apparatus operation setting device 102 includes an attribute determination unit 11, an operation attribute storage unit 12, a cognitive load attribute storage unit 13, an approximate item extraction unit 14, a user categorization unit 15, an operation setting unit 16, and a scoring unit. Means 17, storage control means 18, preference attribute storage means 20, and factor extraction means 21 are provided.

  The attribute determination unit 11 stores operation attribute data stored in the operation attribute storage unit 12 described later and the cognitive load attribute storage unit 13 when the operation data, the response data, and the answer data to the question about the preference are supplied. The read cognitive load attribute data and the preference attribute data stored in the preference attribute storage means 20 are read, and based on the read operation attribute data, cognitive load attribute data, and preference attribute data, operation data is obtained by cluster analysis. The attribute classification of the operation attribute having a high correlation, the attribute classification of the cognitive load attribute having a high correlation with the response data, and the attribute classification of the preference attribute having a high correlation with the response data are determined.

  The operation attribute storage unit 12 and the cognitive load attribute storage unit 13 have the same configuration as the operation attribute storage unit 12 and the cognitive load attribute storage unit 13 of the device operation setting device 1 according to the first embodiment of the present invention. Therefore, explanation is omitted.

  The approximate item extraction unit 14 approximates the attribute classification of the operation attribute, the attribute class of the cognitive load attribute, or the attribute classification of the preference attribute determined by the attribute determination unit 11 among the operation data, the response data, and the answer data. Item data is extracted, and one or a plurality of representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by the factor analysis.

  The user categorizing means 15, the operation setting unit 16, and the scoring means 17 are the same as the user categorizing means 15, the operation setting unit 16, and the scoring means 17 of the device operation setting device 1 that is the first embodiment of the present invention. Since it has the structure of, it abbreviate | omits description.

  The factor extraction means 21 extracts factors by factor analysis from the answer data to the question about the user's preference supplied from the input unit 4.

  The storage control unit 18 classifies the operation attributes into a plurality of attribute classifications based on the score distribution of the operation data calculated by the scoring unit 17, and corresponds to the operation data and the operation data for each attribute classification of the operation attributes. The score distribution is associated and stored in the operation attribute storage means 12 as operation attribute data. The storage control unit 18 classifies the cognitive load attributes into a plurality of attribute classifications based on the score distribution of the response data calculated by the scoring unit 17, and sets response data and response data for each attribute class of the cognitive load attributes. The score distribution corresponding to this response data is associated and stored in the cognitive load attribute storage means 13 as cognitive attribute data. Furthermore, the storage control means 18 classifies the preference attributes into a plurality of attribute classifications based on the factors extracted by the factor extraction means 21, and associates the factors with the factor scores corresponding to the factors for each attribute classification. And stored in the preference attribute storage means 20.

≪Action≫
Next, the operation of the device operation setting device 102 according to the third embodiment will be described with reference to the drawings.

  FIG. 13 illustrates operation attribute data stored in the operation attribute storage unit 12 of the device operation setting apparatus 102 according to the third embodiment, cognitive load attribute data stored in the cognitive load attribute storage unit 13, and preference attributes. 4 is a flowchart showing a procedure for generating preference attribute data stored in a storage unit 20.

  First, the device operation setting device 102 reads out a question about the user's preference stored in the preference attribute storage unit 20 and displays the read question on, for example, a display device (step S501). Here, the display screen to be displayed displays, for example, a question about a simple preference and an option that allows one to be selected from a plurality. In addition, when the user selects an option, it is preferable to disperse and display each option on the display screen, such as by providing a predetermined interval between the options so that the user's line of sight moves.

  Then, the device operation setting device 102 stores the operation attribute data in the operation attribute storage unit 12 and stores the cognitive load attribute data in the cognitive load attribute storage unit 13 by the processes of steps S502 to S507. The processing in steps S502 to S507 is the same as the processing in steps S302 to S307 shown in FIG.

  In step S301, the device operation setting device 102 causes the display device to display a question relating to the device preference, and then the device operation setting device 102 inputs the answer data of the question related to the user preference displayed in step S301. 4 (step S508). The answer data is, for example, one of “applicable to non-applicable” or a data format of multi-level rating of, for example, about 3 to 7 levels.

  Next, when it is determined in step S508 that a considerable number of response data has been received, the factor extraction unit 21 performs factor analysis to extract factors that are effective for attribute classification (step S509).

  Next, the factor extraction means 21 determines a representative question corresponding to each factor extracted in step S509 from the questions read in step S501, and stores the determined representative question in the preference attribute storage means 20 (step S510).

  Next, the storage control unit 18 classifies the preference attributes into a plurality of attribute classifications based on the factors extracted by the factor extraction unit 21. Then, the storage control means 18 associates the factor with the factor score corresponding to this factor for each attribute classification and stores it in the preference attribute storage means 20 (step S511).

  As described above, the device operation setting device 102 according to the third embodiment stores the preference attribute data necessary for executing the device operation setting process described later in the preference attribute storage unit 20 and the operation attribute data as the operation attribute. The data can be stored in the storage unit 12, and the cognitive load attribute data can be stored in the cognitive load attribute storage unit 13.

  FIG. 14 is a flowchart illustrating a processing procedure of device operation setting processing in the device operation setting device 102 according to the third embodiment.

  First, the device operation setting device 102 determines whether or not the device is initially set when power is turned on to the device that is the operation setting target (step S601).

  If it is determined in step S601 that the device to be set for operation is initially set, normal operation is performed (step S602).

  On the other hand, when it is determined in step S601 that the device that is the operation setting target is not initially set, or when the operation setting instruction signal is supplied from the input unit 4 when the operation target device is operating normally. In (Step S603), the process proceeds to Step S605.

  Next, the device operation setting device 102 displays a preference question screen on a display unit or the like. Specifically, the device operation setting device 102 reads a representative question related to the user's preference stored in the preference attribute storage unit 20 and displays the read representative question on a display device or the like, for example. (Step S605). Here, the display screen to be displayed displays, for example, a representative question related to a simple user's preference and answer data that can select one of a plurality of options as an answer to the representative question. . In addition, when the user selects an option, it is desirable to display each option in a distributed manner, such as by providing a predetermined interval between the options so that the user's line of sight moves.

  When the user performs an operation from the input unit 4 on the representative question output on the display screen displayed in step S605, the device operation setting device 102 receives the answer data and the operation data from the input unit 4. The response data is received via the input interface unit 2 and the response data is received from the detection unit 5 via the input interface unit 2 (step S606). Specifically, the device operation setting device 102 receives, as answer data, for example, “applicable to not applicable” or multilevel rating data of about 3 to 7 levels. Also, as the operation data, the data of the pressurization weight 202, the pressurization angle 204, the operation angle 206, and the operation time 208 are received. Data on the gaze time 308 is received.

  Next, when receiving the operation data, the response data, and the answer data in step S606, the attribute determination unit 11 receives the operation attribute data stored in the operation attribute storage unit 12 and the cognition stored in the cognitive load attribute storage unit 13. Load attribute data and preference attribute data stored in the preference attribute storage means 20 are read out.

  Then, the attribute determination unit 11 performs cluster analysis based on the read operation attribute data, thereby determining the attribute classification of the operation attribute highly correlated with the operation data received in step S606 (steps S607 and S608). In addition, the attribute determination unit 11 performs cluster analysis based on the read cognitive load attribute data to determine the attribute classification of the cognitive load attribute highly correlated with the response data received in step S602 (step S607, S608). Further, the attribute determining unit 11 performs a cluster analysis based on the read preference attribute data, thereby determining the attribute classification of the preference attribute highly correlated with the answer data received in step S602 (steps S607 and S608).

  Next, the approximate item extraction unit 14 extracts the approximate item data that approximates the attribute classification of the operation attribute determined by the attribute determination unit 11 from the operation data received in step S606. Further, the approximate item extraction unit 14 extracts approximate item data that approximates the attribute classification of the cognitive attribute determined by the attribute determination unit 11 from the received response data. Furthermore, the approximate item extraction unit 14 extracts approximate item data that approximates the attribute classification of the preference attribute determined by the attribute determination unit 11 from the received answer data. (Step S609).

  Then, the approximate item extraction unit 14 integrates the approximate item data of the operation attribute, the approximate item data of the cognitive load attribute, and the approximate item data of the preference attribute extracted in step S609, and performs factor analysis (step S610). Then, one or more representative approximate item data having a large factor load amount are extracted from the integrated approximate item data (step S611).

  Next, the user categorizing unit 15 performs cluster analysis on the representative approximate item data extracted by the approximate item extracting unit 14 (step S612). As a result, among the groups classified in advance for each user characteristic, the group having the most similar characteristic indicated by the representative approximate item data extracted in step S611 is determined (step S613). Here, groups classified in advance for each user characteristic are characterized by operation, cognitive load, and preference, respectively, and are classified into an arbitrary number of groups by the provider or user of the device operation setting device 102. .

  In this way, by performing cluster analysis on the representative approximate item data extracted in step S611, a group to which the user characteristics belong after integrating multi-dimensional attributes such as operation attributes, cognitive load attributes, and preference attributes Therefore, the user can be assigned to an optimum group according to the characteristic.

  Next, based on the group determined by the user categorizing means 15, the operation setting unit 16 sets a device according to the characteristics of the user. Specifically, the menu switching means 16a generates a menu suitable for the group most similar to the user characteristics, or the default value setting means 16b is a default value suitable for the group most similar to the user characteristics. Is set (step S414).

  In this way, since the device is set according to the optimally assigned group, it is possible to perform the optimal device operation setting according to the characteristics of the user. In addition, since device operation settings are made based on user operation data, response data, and answer data to representative questions, even when multiple users use this device, it depends on the characteristics of each user. Optimal device operation settings.

  FIG. 15 is a diagram for explaining an example of device operation setting processing when the device operation setting device 102 according to the third embodiment is applied to an audio player.

  First, the device operation setting device 102 receives an input interface from the input unit 4 when it is determined that an operation setting target device (audio player) is not initially set or when the operation target device is in normal operation. When an operation setting instruction signal is supplied via the unit 2, a representative question regarding preference is displayed.

  In the example illustrated in FIG. 15, the device operation setting device 102 has, as representative questions 503 relating to preferences, “Do you like simple things anyway?”, “Do you like things with full functions?”, “ Representative questions such as "Is music software collected?"

  Then, the device operation setting device 102 receives the answer data for the representative question 603 regarding the preference from the input unit 4 via the input interface unit 2 by the user's operation, and also adds the pressurized weight 202 as the operation data 601. The pressure angle 204, the operation angle 206, and the operation time 208 are received. Further, the line-of-sight movement area 302, the line-of-sight movement number 304, the line-of-sight movement time 306, and the gaze time 308 are received as response data 602 from the detection unit 5 via the input interface unit 2.

  Next, when receiving the answer data, the attribute determination unit 11 reads the preference attribute data stored in the preference attribute storage unit 20, and performs cluster analysis based on the read preference attribute data, thereby obtaining the received answer data. The attribute classification of the preference attribute 606 having a high correlation is determined.

  In the example illustrated in FIG. 15, attribute 1 is determined as the attribute classification of the preference attribute 606 having a high correlation with the answer data for the representative question 603 regarding the preference by cluster analysis.

  Similarly, upon receiving the operation data 601, the attribute determination unit 11 reads the operation attribute data stored in the operation attribute storage unit 12, and performs cluster analysis based on the read operation attribute data, thereby receiving the received operation data. In 601, the attribute classification of the operation attribute 604 having a high correlation is determined.

  In the example illustrated in FIG. 15, attribute 2 is determined as the attribute classification of the operation attribute 604 having a high correlation with the operation data 601 by cluster analysis.

  Similarly, when receiving the response data 602, the attribute determination unit 11 reads the cognitive load attribute data stored in the cognitive load attribute storage unit 13, and performs cluster analysis based on the read cognitive load attribute data. Thus, the attribute classification of the cognitive load attribute 605 having a high correlation with the received response data 602 is determined.

  In the example illustrated in FIG. 15, attribute 1 is determined as the attribute classification of the cognitive load attribute 605 having a high correlation with the response data 602 by cluster analysis.

  Next, the approximate item extraction unit 14 includes the attribute classification of the operation attribute 604 determined by the attribute determination unit 11 among the received answer data, operation data, and response data, the attribute classification of the cognitive load attribute 605, and the preference attribute. Approximate item data that approximates the attribute classification 606 is extracted, and one or more representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by factor analysis.

  Then, the user categorizing unit 15 determines, by cluster analysis, a group having the most similar characteristic indicated by the representative approximate item data extracted by the approximate item extracting unit 14 among the groups previously classified for each user characteristic. .

  As shown at 607 in FIG. 15, approximate item data that approximates the attribute 2 of the operation attribute 604 from the operation data, approximate item data that approximates the attribute 1 of the cognitive load attribute 605 from the response data, and the preference attribute from the answer data Approximate item data that approximates attribute 1 of 606 is extracted. Then, one or a plurality of representative approximate item data having a large factor load is extracted from the extracted approximate item data, and group 3 is determined as a group having the most similar characteristics indicated by the extracted representative approximate item data.

  In the example illustrated in FIG. 15, the number of groups is six. However, the number is not limited to six, and an arbitrary number of groups is set in advance. Each set group has its own characteristics in operation, cognitive load, and usage. On the designer side, “What kind of operation characteristics and cognitive characteristics are users with what kind of preferences? A large number of assumed users “is there?” Are set in advance, and the assumed users are set as a group. Here, the assumed user set as the group 3 says, “It is not very strong for the device and is not good at the operation of the input unit 4. If the amount of information on the screen is small, the displayed information can be understood. A group of users who want to use a device as easily as possible, although they are a certain user and use a device to find new discoveries and are particular about design.

  In this way, by performing cluster analysis on one or more representative approximate item data with a large factor loading, the multi-dimensional attributes such as operation attributes, cognitive load attributes, and preference attributes are integrated. Since the group to which the user's characteristic belongs is determined, the user can be assigned to the optimum group according to the characteristic.

  Next, the operation setting unit 16 sets a device according to the user based on the group determined by the user categorizing unit 15.

  In the example shown in FIG. 15, “Display basic menu only”, “Set screen display to modern”, and “Set to shuffle playback” are associated as the device operation settings 609 corresponding to group 2. The setting unit 16 sets the operation of the audio player according to the setting contents. Note that arrows connecting the group 608 and the device operation setting 609 shown in FIG. 15 indicate device operation settings suitable for each group of the group 608.

  As a result, the operation setting unit 16 makes it easy to grasp the functions by displaying only the minimum necessary functions in the “display only basic menu” setting, and emphasizes the design with the “setting screen display modern” setting. In addition, it is possible to make it easier to meet music that is usually not selected and listened to by setting “set to shuffle playback”.

  In addition, according to the operations assumed in each group, the initial menu and initial setting values set in advance by the designer, and the optimum values updated with data fed back from the actual usage status of other users Can be provided to the user.

  As described above, in the device operation setting device 102 according to the third embodiment, the user answers a simple preference question, so that the user's preference, the operation on the device, and the operation of the device It is possible to obtain an optimal menu and system setting value (default value) that is in response. At that time, the device operation setting device 102 performs grouping and selection by integrated similarity determination from a plurality of grouped groups, so that menus and system setting values (defaults) that match the user's operation and response are selected. Value) can be obtained optimally.

  As described above, according to the device operation setting device 102 according to the third embodiment, since the device is set according to the optimally assigned group, the optimal device operation setting according to the characteristics of the user is performed. It can be carried out.

<Fourth Embodiment>
FIG. 16 is a configuration diagram showing a configuration of a device operation setting apparatus according to the fourth embodiment of the present invention.

  As shown in FIG. 16, the device operation setting device 103 is connected to the input interface unit 2 and the device control unit 3, and the input unit 4 and the detection unit 5 are further connected to the input interface unit 2. .

  The input unit 4 includes, for example, an electrostatic sensor, a pressure sensor, an operation button, and the like. Supply. In addition, it generates response data for questions about how to use the device and user preferences displayed on the display device by user input operations, as well as operation data such as pressure weight, pressure angle, operation angle, and operation time. Are supplied to the device operation setting device 103.

  Since the detection unit 5 has the same configuration as the detection unit 5 of the device operation setting device 1 according to the first embodiment of the present invention, the description thereof is omitted.

  The device control unit 3 performs device operation settings according to the user based on the user group determined by the device operation setting device 103.

  The device operation setting device 103 includes an attribute determination unit 11, an operation attribute storage unit 12, a cognitive load attribute storage unit 13, an approximate item extraction unit 14, a user categorization unit 15, an operation setting unit 16, and a scoring unit. Means 17, storage control means 18, usage attribute storage means 19, preference attribute storage means 20, and factor extraction means 21 are provided.

  The attribute determination means 11 is provided with operation attribute data stored in the operation attribute storage means 12 to be described later, and cognitive load attribute storage means when operation data, response data, and answer data for a question about usage and preference are supplied. The cognitive load attribute data stored in 13, the usage attribute data stored in the usage attribute storage means 19, and the preference attribute data stored in the preference attribute storage means 20 are read out. Based on the read operation attribute data, cognitive load attribute data, usage attribute data, and preference attribute data, the attribute classification of the operation attribute highly correlated with the operation data by cluster analysis, the cognitive load highly correlated with the response data The attribute classification of the attribute, the attribute classification of the usage attribute highly correlated with the answer data, and the attribute classification of the preference attribute highly correlated with the answer data are determined.

  The operation attribute storage unit 12 and the cognitive load attribute storage unit 13 have the same configuration as the operation attribute storage unit 12 and the cognitive load attribute storage unit 13 of the device operation setting device 1 according to the first embodiment of the present invention. Therefore, explanation is omitted.

  The approximate item extraction unit 14 includes the attribute classification of the operation attribute, the attribute classification of the cognitive load attribute, the attribute classification of the usage attribute, or the preference attribute determined by the attribute determination unit 11 among the operation data, the response data, and the response data. Approximate item data that approximates the attribute classification is extracted, and one or more representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by factor analysis.

  The user categorizing means 15, the operation setting unit 16, and the scoring means 17 are the same as the user categorizing means 15, the operation setting unit 16, and the scoring means 17 of the device operation setting device 1 that is the first embodiment of the present invention. Since it has the structure of, it abbreviate | omits description.

  The factor extraction means 21 extracts factors by factor analysis from the answer data to the question about how to use the device and the question about the user's preference supplied from the input unit 4.

  The usage attribute storage unit 19 stores question data related to usage and representative question data related to usage to be displayed on the display device or the like, selected from the question data. Moreover, the factor classified for every some attribute classification by the factor analysis based on the answer data regarding a user's usage is memorize | stored as usage attribute data.

  The preference attribute storage means 20 stores the question data related to the preference and representative question data related to the preference selected from the question data and displayed on the display device or the like. Moreover, the factor classified for every some attribute classification | category by the factor analysis based on the reply data regarding a user's preference is memorize | stored as preference attribute data.

  The storage control unit 18 classifies the operation attributes into a plurality of attribute classifications based on the score distribution of the operation data calculated by the scoring unit 17, and corresponds to the operation data and the operation data for each attribute classification of the operation attributes. The score distribution is associated and stored in the operation attribute storage means 12 as operation attribute data. The storage control unit 18 classifies the cognitive load attributes into a plurality of attribute classifications based on the score distribution of the response data calculated by the scoring unit 17, and sets response data and response data for each attribute class of the cognitive load attributes. The score distribution corresponding to this response data is associated and stored in the cognitive load attribute storage means 13 as cognitive attribute data. Furthermore, based on the factor extracted by the factor extraction means 21, the usage attribute and the preference attribute are classified into a plurality of attribute classifications, and the usage attribute is associated with the factor and the factor score corresponding to the factor for each attribute classification. The information is stored in the storage unit 19 and the preference attribute storage unit 20.

  FIG. 17 is a diagram for explaining an example of device operation setting processing when the device operation setting device 103 according to the fourth embodiment is applied to an HDD recorder.

  First, the device operation setting device 103 determines that the operation target device is not initially set, or when the operation target device is in normal operation from the input unit 4 via the input interface unit 2. When the operation setting instruction signal is supplied, a representative question regarding usage and preference is displayed.

  In the example shown in FIG. 17, the device operation setting device 103 has, as representative questions 701 regarding usage, “Would you like to use without troublesome settings?”, “Do you want to use various functions easily?”, “ Representative questions such as “Would you like to use it while setting it yourself?” And “Do you want to use it so that you can take the most beautiful pictures?” Are displayed. Further, as representative questions 702 regarding preferences, “Do you like simple things anyway?”, “Do you like things with full functions?”, “Do you collect videos as a hobby?”, “Image quality? Display a representative question such as "Do you want to break?"

  Then, the device operation setting device 103 receives, from the input unit 4 via the input interface unit 2, the answer data for the representative question 701 regarding how to use and the answer data for the representative question 702 regarding the preference according to the user's operation. The pressurization weight 202, pressurization angle 204, operation angle 206, and operation time 208 are received as data 703. Further, the line-of-sight movement area 302, the line-of-sight movement number 304, the line-of-sight movement time 306, and the gaze time 308 are received as response data 704 from the detection unit 5 through the input interface unit 2.

  Next, when the attribute determination unit 11 receives the answer data, the attribute determination unit 11 reads the usage attribute data stored in the usage attribute storage unit 19 and performs a cluster analysis based on the read usage attribute data, thereby obtaining the received answer data. The attribute classification of the usage attribute 705 having a high correlation is determined.

  In the example shown in FIG. 17, attribute 3 is determined as the attribute classification of the usage attribute 705 having a high correlation with the answer data for the representative question 701 regarding usage by cluster analysis.

  Similarly, upon receiving the answer data, the attribute determining means 11 reads the preference attribute data stored in the preference attribute storage means 20, and performs cluster analysis based on the read preference attribute data, thereby obtaining the received answer data. The attribute classification of the preference attribute 706 having a high correlation is determined.

  In the example illustrated in FIG. 17, attribute 1 is determined as the attribute classification of the preference attribute 706 having a high correlation with the answer data for the representative question 702 regarding preference by cluster analysis.

  Further, when receiving the operation data 703, the attribute determining unit 11 reads the operation attribute data stored in the operation attribute storage unit 12, and performs cluster analysis based on the read operation attribute data, thereby receiving the received operation data 703. The attribute classification of the operation attribute 707 having a high correlation is determined.

  In the example illustrated in FIG. 17, attribute 2 is determined as the attribute classification of the operation attribute 707 having a high correlation with the operation data 703 by cluster analysis.

  Similarly, when the attribute determination unit 11 receives the response data 704, the attribute determination unit 11 reads the cognitive load attribute data stored in the cognitive load attribute storage unit 13, and performs cluster analysis based on the read cognitive load attribute data. Thus, the attribute classification of the cognitive load attribute 708 having a high correlation with the received response data 704 is determined.

  In the example illustrated in FIG. 17, attribute 3 is determined as the attribute classification of the cognitive load attribute 708 highly correlated with the response data 704 by cluster analysis.

  Next, the approximate item extraction unit 14 includes the attribute classification of the usage attribute 705, the attribute classification of the preference attribute 706, and the attribute of the operation attribute 707 determined by the attribute determination unit 11 among the received answer data, operation data, and response data. The approximate item data that approximates the classification and the attribute classification of the cognitive load attribute 708 is extracted, and one or more representative approximate item data having a large factor load amount are extracted from the approximate item data extracted by the factor analysis.

  Next, the approximate item extraction unit 14 extracts approximate item data that approximates the attribute classification of the usage attribute 705 determined by the attribute determination unit 11 from the answer data to the representative question regarding the received usage, and receives the received preference. Approximation item data that approximates the attribute classification of the preference attribute 706 determined by the attribute determination means 11 is extracted from the answer data to the representative question about the operation attribute determined by the attribute determination means 11 from the received operation data Approximate approximate item data that approximates the attribute classification of 707 is extracted, and approximate item data that approximates the attribute classification of the cognitive load attribute 708 determined by the attribute determining means 11 is extracted from the received response data. Then, the approximate item extraction unit 14 extracts one or a plurality of representative approximate item data having a large factor load among these approximate item data extracted by the factor analysis.

  Then, the user categorizing unit 15 determines, by cluster analysis, a group having the most similar characteristic indicated by the representative approximate item data extracted by the approximate item extracting unit 14 among the groups previously classified for each user characteristic. .

  As shown in 709 of FIG. 17, approximate item data that approximates attribute 3 of the usage attribute 705 from the answer data to the question about usage, and approximate item data that approximates the attribute 1 of the preference attribute 706 from the answer data to the question about preference Then, the approximate item data that approximates the attribute 2 of the operation attribute 707 is extracted from the operation data, and the approximate item data that approximates the attribute 3 of the cognitive load attribute 708 is extracted from the response data. Then, one or a plurality of representative approximate item data having a large factor load is extracted from the extracted approximate item data, and group 5 is determined as a group having the most similar characteristics indicated by the extracted representative approximate item data.

  In the example shown in FIG. 17, the number of groups is nine. However, the number is not limited to nine, and an arbitrary number of groups is set in advance. Each set group has its own characteristics in operation, cognitive load, and usage. On the designer side, "What kind of operation characteristics and cognitive characteristics are users, how to use the equipment, how A large number of assumed users are set in advance, and are set as a group. Here, an assumed user set as a group 5 says, “It is not very strong for devices and is not good at operating the input unit 4, and if the amount of information on the screen is small, it can understand the displayed information. It is set as “a group of users who are hoping to collect a lot of moving pictures and have a lot of opportunities to take pictures of landscapes rather than a practical usage of storing moving pictures with high image quality.

  In this way, by performing cluster analysis on one or more representative approximate item data with a large factor loading, multi-dimensional attributes such as operation attributes, cognitive load attributes, usage attributes, and preference attributes are obtained. Since the group to which the user's characteristic belongs is determined after the integration, the user can be assigned to the optimum group according to the characteristic.

  Next, the operation setting unit 16 sets a device according to the user based on the group determined by the user categorizing unit 15.

  In the example shown in FIG. 17, as the device operation setting 711 corresponding to the group 5, “display only basic menu”, “cursor moving speed“ slow ””, “set image quality normal” and “set digital zoom 100 times” are set. Since they are associated with each other, the operation setting unit 16 sets the operation of the HDD recorder according to the setting contents. Note that arrows connecting the group 710 and the device operation setting 711 shown in FIG. 17 indicate device operation settings suitable for each group of the group 710.

  As a result, the operation setting unit 16 displays only the minimum necessary functions in the “display only basic menu” setting so that the functions can be easily grasped, and the cursor moving speed is decreased by “cursor moving speed“ slow ””. This setting increases the certainty of the operation of the input unit 4, and “Set to normal image quality” allows memorized images to be stored with a margin without worrying about the storage capacity, and “Set digital zoom to 100 times” makes it abrupt It is possible to perform zoom shooting at a high magnification even when shooting a distant view.

  In addition, according to the operations assumed in each group, the initial menu and initial setting values set in advance by the designer, and the optimum values updated with data fed back from the actual usage status of other users Can be provided to the user.

  As described above, in the device operation setting device 103 according to the fourth embodiment, the user answers the simple usage and preference questions, so that the usage of the device, the preference of the user, the operation on his / her device, and the device It is possible to obtain an optimum menu and system setting value (default value) that suits the user's own response to the operation. At that time, the device operation setting device 103 performs grouping and selection by integrated similarity determination from a plurality of grouped groups, so that menus and system setting values (default) that match the user's operation and response are made. Value) can be obtained optimally.

  As described above, according to the device operation setting apparatus 103 according to the fourth embodiment, since the device is set according to the optimally assigned group, the optimal device operation setting according to the characteristics of the user is performed. It can be carried out.

It is the block diagram which showed the structure of the apparatus operation setting apparatus 1 which is the 1st Embodiment of this invention. It is a figure for demonstrating an example of the operation data and response data which are produced | generated by the input part 4 and the detection part 5 of the apparatus operation setting apparatus 1 which is the 1st Embodiment of this invention. It is the figure which showed an example of the operation attribute data which the operation attribute memory | storage means 12 of the apparatus operation | movement setting apparatus 1 which is the 1st Embodiment of this invention memorize | stores. It is the figure which showed an example of the cognitive load attribute data which the cognitive load attribute memory | storage means 13 of the apparatus operation | movement setting apparatus 1 which is the 1st Embodiment of this invention memorize | stores. Procedure for generating operation attribute data stored in the operation attribute storage unit 12 of the device operation setting device 1 and the cognitive load attribute data stored in the cognitive load attribute storage unit 13 according to the first embodiment of the present invention. It is a flowchart which shows. It is a flowchart which shows the process sequence of the apparatus operation setting process in the apparatus operation setting apparatus 1 which is the 1st Embodiment of this invention. It is a figure for demonstrating an example of the apparatus operation setting process at the time of applying the apparatus operation setting apparatus 1 which is the 1st Embodiment of this invention to a HDD recorder. It is the block diagram which showed the structure of the apparatus operation setting apparatus 101 which is the 2nd Embodiment of this invention. Operation attribute data stored in the operation attribute storage unit 12 of the device operation setting apparatus 101 according to the second embodiment of the present invention, cognitive load attribute data stored in the cognitive load attribute storage unit 13, and usage attribute storage 10 is a flowchart showing a procedure for generating usage attribute data stored in means 19. It is a flowchart which shows the process sequence of the apparatus operation setting process in the apparatus operation setting apparatus 101 which is the 2nd Embodiment of this invention. It is a figure for demonstrating an example of the apparatus operation setting process at the time of applying the apparatus operation setting apparatus 101 which is the 2nd Embodiment of this invention to a HDD recorder. It is the block diagram which showed the structure of the apparatus operation setting apparatus 102 which is the 3rd Embodiment of this invention. Operation attribute data stored in the operation attribute storage unit 12 of the device operation setting device 102 according to the third embodiment of the present invention, cognitive load attribute data stored in the cognitive load attribute storage unit 13, and preference attribute storage 10 is a flowchart showing a processing procedure for generating preference attribute data stored in means 20. It is a flowchart which shows the process sequence of the apparatus operation setting process in the apparatus operation setting apparatus 102 which is the 3rd Embodiment of this invention. It is a figure for demonstrating an example of the apparatus operation setting process at the time of applying the apparatus operation setting apparatus 102 which is the 3rd Embodiment of this invention to an audio player. It is a block diagram which showed the structure of the apparatus operation setting apparatus 103 which is the 4th Embodiment of this invention. It is a figure for demonstrating an example of the apparatus operation setting process at the time of applying the apparatus operation setting apparatus 103 which is the 4th Embodiment of this invention to a HDD recorder.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1,101,102,103 ... Equipment operation setting apparatus 2 ... Input interface part 3 ... Equipment control part 4 ... Input part 5 ... Detection part 11 ... Attribute determination means 12 ... Operation attribute memory | storage means 13 ... Cognitive load attribute means 13 ... Cognitive load attribute storage means 14 ... approximate item extraction means 15 ... user categorization means 16 ... action setting unit (action setting means)
16a ... Menu switching means 16b ... Default value setting means 17 ... Scoring means 18 ... Storage control means 19 ... Usage attribute storage means 20 ... Preference attribute storage means 21 ... Factor extraction means

Claims (6)

A device that specifies predetermined characteristics of the user by factor analysis and cluster analysis based on the user's operation of the device and the user's response to the operation of the device, and sets the operation of the device according to the characteristic An operation setting device,
And associating a plurality of operation data of the device based on the input of the user with a score distribution indicating which class the operation data belongs to among a plurality of classes classified for each operation data, and Operation attribute storage means for classifying a plurality of operation data and the score distribution into a plurality of attribute classifications and storing them as operation attribute data;
The plurality of response data of the user with respect to the operation of the device is associated with a score distribution indicating to which class the response data belongs among a plurality of classes divided for each response data. Cognitive load attribute storage means for classifying the response data and the score distribution into a plurality of attribute classifications and storing them as cognitive load attribute data;
The operation attribute data and the cognitive load attribute data stored respectively are read out, and by cluster analysis, the attribute classification of the operation attribute highly correlated with the plurality of operation data and the cognitive load attribute highly correlated with the plurality of response data. Attribute determination means for determining the attribute classification;
Of the plurality of operation data and the plurality of response data, extract approximate item data that approximates the attribute classification of the operation attribute or the cognitive load attribute determined by the attribute determination means, and the extraction is performed by factor analysis. An approximate item extracting means for extracting one or a plurality of representative approximate item data having a large factor load among the approximated item data,
User categorizing means for determining, by cluster analysis, a group whose characteristics indicated by the extracted representative approximate item data are the most similar among groups classified in advance for each predetermined characteristic for the user;
Based on the determined group, operation setting means for setting a device according to a predetermined characteristic for the user;
A device operation setting device comprising: Dividing into a plurality of classes for each operation data, calculating a score distribution indicating which class the operation data belongs to among the classified classes, and classifying into a plurality of classes for each response data, Scoring means for calculating a score distribution indicating which class the response data belongs to among the classified classes;
Based on the score distribution of each of the calculated operation data and response data, the operation attribute and the cognitive load attribute are classified into a plurality of attribute classifications, and the operation data and the operation data are classified for each attribute classification of the operation attribute. Corresponding score distributions are associated with each other and stored in the operation attribute storage means as operation attribute data, and the response data and the score distribution corresponding to the response data are associated and cognitive for each attribute classification of the cognitive load attribute. Storage control means for storing in the cognitive load attribute storage means as attribute data;
The apparatus operation setting device according to claim 1, further comprising: Usage factor storage means for storing factors classified into a plurality of attribute classifications as usage attribute data by factor analysis based on answer data on how to use the device by the user,
The attribute determining means includes
The stored operation attribute data, cognitive load attribute data, and usage attribute data are read, and the operation attribute, the cognitive load attribute, and the response data highly correlated to the operation data, the response data, and the response data by cluster analysis, and Determine the attribute classification of each usage attribute,
The approximate item extraction means includes:
Extracting the operation data, the response data, and the approximate item data that approximates the attribute classification of the operation attribute determined by the attribute determination means, the cognitive load attribute, or the usage attribute among the answer data, and by factor analysis 3. The apparatus operation setting device according to claim 1, wherein one or a plurality of representative approximate item data having a large factor load amount are extracted from the extracted approximate item data. It further comprises factor extracting means for extracting a factor from the answer data by factor analysis,
The storage control means further classifies the usage attribute into a plurality of attribute classifications based on the extracted factor, and associates the factor with a factor score corresponding to the factor for each attribute classification of the usage attribute. 4. The apparatus operation setting apparatus according to claim 3, wherein the usage attribute storage means stores the attribute data as attribute data. Preference attribute storage means for storing, as preference attribute data, factors classified into a plurality of attribute classifications by factor analysis based on answer data relating to the user's preference,
The attribute determining means includes
The stored operation attribute data, the cognitive load attribute data, and the preference attribute data are read, and the operation attribute, the cognitive load attribute, and the response data highly correlated with the operation data, the response data, and the response data by cluster analysis, and Determine the attribute classification of each preference attribute,
The approximate item extraction means includes:
Extracting the operation data, the response data, and the approximate item data that approximates the attribute classification of the cognitive load attribute or the preference attribute determined by the attribute determination means from the response data, and by factor analysis 3. The apparatus operation setting device according to claim 1, wherein one or a plurality of representative approximate item data having a large factor load amount are extracted from the extracted approximate item data. It further comprises factor extracting means for extracting a factor from the answer data by factor analysis,
The storage control means further classifies the preference attribute into a plurality of attribute classifications based on the extracted factor, and associates the factor with a factor score corresponding to the factor for each attribute classification of the preference attribute. 6. The apparatus operation setting device according to claim 5, wherein the preference attribute storage means stores the attribute data as attribute data.

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