Data acquisition device

Abstract

This data acquisition device comprises: an information acquisition unit that acquires psychological reflection information of a user; an estimation unit that estimates the psychological state of the user by using a psychological-state estimation model for which the acquired psychological reflection information of the user serves as input information; a determination unit that determines whether acquisition of a correct-answer label corresponding to the acquired psychological reflection information of the user is necessary; and a label creation unit that, when it is determined by the determination unit that acquisition of the correct-answer label is necessary, creates a correct-answer label corresponding to the psychological reflection information of the user on the basis of the user's answer to a test presented to the user.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a data acquisition device.

BACKGROUND ART

[0002] Patent Document 1 discloses a technique for confirming and managing the mental state of employees. In this technique, a trained model is used to determine whether there is a concern about mental distress of an employee based on the work status of the employee.

PRIOR ART DOCUMENTS

PATENT DOCUMENTS

[0003]

PATENT DOCUMENT 1

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0004] When a learning model for estimating a user's psychological state from user information is constructed by machine learning, learning data (teacher data) corresponding to each psychological state is required. For example, by having an information provider provide biological information and the like, and identifying the psychological state of the information provider through a predetermined test or the like, learning data in which the user's information and the psychological state are paired can be acquired. Although learning data can be acquired from information providers in each psychological state, it is difficult to similarly acquire learning data for each psychological state. In particular, people who are cooperative in providing learning data often have a good psychological state, and it is difficult to acquire learning data corresponding to a poor psychological state.

[0005] The present disclosure has been made in view of the above, and an object thereof is to provide a data acquisition device capable of acquiring necessary learning data.

MEANS FOR SOLVING THE PROBLEMS

[0006] The data acquisition device relating to this disclosure comprises a psychological state estimation model, an information acquisition unit, an estimation unit, a determination unit, a label creation unit, and a data storage unit. The psychological state estimation model is trained using psychological reflection information and correct labels indicating the corresponding psychological states as training data. The information acquisition unit acquires the user's psychological reflection information. The estimation unit uses the psychological state estimation model to estimate the user's psychological state using the acquired user's psychological reflection information as input information. The determination unit determines whether it is necessary to acquire the correct labels corresponding to the acquired user's psychological reflection information. If the determination unit determines that it is necessary to acquire the correct labels, the label creation unit creates correct labels corresponding to the user's psychological reflection information based on the user's answers to a test presented to the user. The data storage unit stores the user's psychological reflection information and the user's correct labels as a set of data.

[0007] In the data acquisition device described above, a psychological state estimation model estimates the user's psychological state using the user's psychological reflection information as input data. The data acquisition device can acquire training data by having the user take a predetermined test and creating correct labels corresponding to the user's psychological reflection information based on the test answers obtained. Since such a data acquisition device has a determination unit that determines whether or not it is necessary to acquire the correct labels corresponding to the acquired user's psychological reflection information, training data can be acquired as needed. [Effects of the Invention]

[0008] According to this disclosure, it is possible to provide a data acquisition device that can acquire the necessary training data. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 shows the configuration of a data acquisition device according to an embodiment of this disclosure. [Figure 2] Figure 2 shows an example of the configuration of a determination unit. [Figure 3] Figure 3 is a flowchart illustrating the operation of an example of a determination unit. [Figure 4] Figure 4 is a flowchart illustrating the operation of the determination unit in another example. [Figure 5] Figure 5 is a flowchart illustrating the operation of the determination unit in yet another example. [Figure 6] Figure 6 is a flowchart illustrating the operation of the determination unit in yet another example. [Figure 7] Figure 7 is a flowchart illustrating the operation of the determination unit in yet another example. [Figure 8] Figure 8 is a flowchart illustrating the operation of the determination unit in yet another example. [Figure 9] Figure 9 is a flowchart illustrating an example of the operation of a data acquisition device. [Figure 10] Figure 10 shows the hardware configuration of an example data acquisition device. [Modes for carrying out the invention]

[0010] The embodiments of the data acquisition device relating to this disclosure will be described in detail below, along with the drawings. In the description of the drawings, the same elements will be denoted by the same reference numerals, and redundant explanations will be omitted.

[0011] Figure 1 shows the configuration of a data acquisition system 1, including an example data acquisition device 10. This example data acquisition system 1 includes a terminal device 5 and the data acquisition device 10. For example, the data acquisition device 10 is configured to communicate with the terminal device 5 via a communication network N. Although only one terminal device 5 is shown in Figure 1, the data acquisition device 10 may be able to connect to multiple terminal devices 5 simultaneously.

[0012] Terminal device 5 is, for example, a communication device operated by a user. Terminal device 5 performs tasks such as receiving input, transmitting the received information to data acquisition device 10, and displaying the information received from data acquisition device 10. Terminal device 5 may be, for example, a mobile phone (smartphone), a mobile phone, or a personal digital assistant (PDA). The devices constituting terminal device 5 are not interpreted restrictively.

[0013] Terminal device 5 can transmit psychological reflection information obtained from the user to data acquisition device 10 in association with user identification information and the like stored in terminal device 5. Psychological reflection information includes various data that reflect the user's psychological state. Psychological reflection information may include the user's biometric information. Biometric information is information that indicates the state of the body and can be obtained from the body depending on the state at any given time. Biometric information can be obtained by terminal device 5. For example, terminal device 5 may include a sensor for obtaining biometric information, may be connected to an external sensor, or may obtain data obtained by an external sensor.

[0014] One example of biometric information may be at least one of audio information, video information, and other detection information. Audio information may be sound data including voices emitted by the user. In one example, audio information may be acquired by a microphone provided by the terminal device 5. Video information may be video data including images of the user. Video information may include information such as the user's facial expressions, eye movements, and gestures. Video information may be acquired by a camera provided by the terminal device 5. Other detection information as biometric information may be, for example, detection data related to the user's heart rate, pulse, blood flow, body temperature, sweating, respiration, brain waves, user movements, posture, etc.

[0015] The data acquisition device 10 has an aspect as an estimation device that estimates the psychological state of a user using the terminal device 5. Further, the data acquisition device 10 has an aspect as an acquisition device that acquires learning data usable for a machine learning model used in the estimation device. As shown in FIG. 1, the data acquisition device 10 as an example includes, as its functional units, an information acquisition unit 11, an estimation unit 12, a determination unit 13, a label creation unit 15, and a data storage unit 16.

[0016] The information acquisition unit 11 acquires user's psychological reflection information. As described above, the user's psychological reflection information can be transmitted from the terminal device 5 used by the user to the data acquisition device 10. The information acquisition unit 11 can acquire the user's psychological reflection information in association with the user's identification information. The information acquisition unit 11 as an example may acquire the user's identification information, the psychological reflection information, and the reception date and time (time information) in association with each other. Further, the information acquisition unit 11 can acquire data indicating the attribute of the user in association with the user's identification information. The attribute may be a feature for each user for classifying individuals without identifying the individual. The attribute may be, for example, gender, age group, household composition, lifestyle, temperament, attendance information, and the like. The attribute data may be stored in the terminal device 5 in advance.

[0017] The estimation unit 12 estimates the psychological state of the user using the user's psychological reflection information acquired by the information acquisition unit 11 as input information. As shown in FIG. 1, the estimation unit 12 as an example includes a first estimation engine 12a having a psychological state estimation model 12b. The psychological state estimation model 12b may be a machine learning model trained using the psychological reflection information and a correct label indicating the psychological state corresponding to the psychological reflection information as learning data. The machine learning framework may be, for example, any of regression, binary classification, anomaly detection, change detection, multi-class classification, and the like. In the present embodiment, an example in which the framework of the psychological state estimation model 12b is regression will be described.

[0018] Example learning data used for learning the mental state estimation model 12b includes a data set consisting of data indicating mental reflection information and a correct label obtained by digitizing the mental state of the provider of the mental reflection information. When the mental reflection information is voice data, video data or the like, the mental reflection information may be acquired by the terminal device 5. The correct label is acquired based on responses to a predetermined test performed on the provider, which is conducted either simultaneously with the acquisition of the mental reflection information or promptly after the acquisition of the mental reflection information.

[0019] This test may be composed of, for example, a plurality of questions for digitizing the degree of psychological stress response. That is, in this test, a measured value obtained by digitizing the subject's mental state is determined. An example question is composed of a question text directed to the subject and a plurality of options for the question text. After reading the question text, the subject can select their own answer from the options. For example, a score is assigned to each of the options, and the measured value may be determined based on the score of the option selected as the answer. The correct label constituting the learning data may be a numerical value obtained from the result of the above-described test. In the learning model of the present embodiment, a test is used in which the measured value increases as the stress level rises. The set data composed of the mental reflection information and the correct data may be stored in the data storage unit 16 in association with the attribute of the provider.

[0020] The first estimation engine 12a of the estimation unit 12 inputs the user's mental reflection information into the trained mental state estimation model 12b, thereby acquiring a numerical value (measured value) indicating the user's mental state output from the mental state estimation model 12b. The first estimation engine 12a classifies the user's mental state into a first category and a second category. That is, the first estimation engine 12a classifies the user's mental state into poor (the first category) and good (the second category) by dividing the acquired numerical value with a first threshold value.

[0021] The estimation unit 12 estimates that the user's psychological state is poor when the numerical value output from the psychological state estimation model 12b is above a predetermined threshold. Conversely, the estimation unit 12 estimates that the user's psychological state is good when the numerical value output from the psychological state estimation model 12b is below a predetermined threshold. The estimation unit 12 may transmit the estimation results to the user's terminal device 5. The transmitted estimation results may consist only of a classification indicating the psychological state (good, poor), or they may include the estimated measured values. The estimation unit 12 may store the estimated measured values ​​and classifications in association with the user's identification information. That is, the estimation unit 12 may store previously estimated psychological states for each user.

[0022] The determination unit 13 determines whether or not it is necessary to obtain the correct label corresponding to the psychological reflection information obtained by the information acquisition unit 11. For example, in order to contribute to improving the accuracy of the psychological state estimation model, the determination unit 13 determines whether or not it is necessary to obtain the correct label based on the content of the psychological reflection information, the state of the psychological reflection information, the user's usage status, the state of the training data being used, etc. Details of the determination unit 13 will be described later.

[0023] The label creation unit 15 creates a correct label corresponding to the acquired user's psychological reflection information when the determination unit 13 determines that it is necessary to obtain a correct label. The correct label is created based on the user's answers to a predetermined test presented to the user. The test is presented on the display of the user's terminal device 5 when the determination unit 13 determines that it is necessary to obtain a correct label. In one example, the label creation unit 15 may send the test to the terminal device 5 of a user for whom it is necessary to obtain a correct label. This test may have the same content as the test used to generate the training data used to train the psychological state estimation model.

[0024] The data storage unit 16 stores the user's psychological reflection information acquired by the information acquisition unit 11 and the correct labels created by the label creation unit 15 as a set of data. This data may be stored in the data storage unit 16 in association with the user's attributes. The psychological state estimation model 12b used by the estimation unit 12 can be updated with the newly stored set of data in the data storage unit 16 as additional training data.

[0025] Next, the details of the determination unit 13 will be described. An example of the determination unit 13 may be any of the following examples: the first determination unit 13A, the second determination unit 13B, the third determination unit 13C, the fourth determination unit 13D, the fifth determination unit 13E, and the sixth determination unit 13F.

[0026] Figure 2 is a block diagram showing the function of the first determination unit 13A. Figure 3 is a flowchart showing the operation of the first determination unit 13A. The first determination unit 13A has a second estimation engine 13Aa which has a psychological state estimation model 13Ab. The psychological state estimation model 13Ab included in the second estimation engine 13Aa may be the same learning model as the psychological state estimation model 12b included in the estimation unit 12. In the first determination unit 13A, the second estimation engine 13Aa uses the psychological state estimation model 13Ab to obtain a second psychological state estimation result (step S11). The second psychological state estimation result has a higher proportion of judgments that the psychological state is unwell compared to the psychological state estimated by the estimation unit 12.

[0027] For example, the second estimation engine 13Aa can classify the user's psychological state into a third category and a fourth category. The third category is broader than the first category, which indicates a poor psychological state, and may encompass the first category. The fourth category is narrower than the second category, which indicates a good psychological state, and may be the second category minus the overlap with the third category. The second estimation engine 13Aa classifies the user's psychological state into a third category and a fourth category by dividing the measured values ​​estimated by the psychological state estimation model 13Ab using a second threshold different from the first threshold. In this case, the psychological reflection information showing measured values ​​near the threshold that separates the first and second categories will be classified into the third category. That is, users whose psychological state is poor according to the first estimation engine 12a, and users whose measured values ​​are high among those whose psychological state is good according to the first estimation engine 12a, will be classified into the third category. The first determination unit 13A determines whether it is necessary to obtain the correct label corresponding to the user's psychological reflection information based on the estimation result of the second estimation engine 13Aa (step S12). The first determination unit 13A determines that it is necessary to obtain the correct label when the psychological reflection information is estimated to be in category 3 by the second estimation engine 13Aa.

[0028] Figure 4 is a flowchart showing the operation of the second determination unit 13B. The second determination unit 13B determines whether it is necessary to obtain a correct label corresponding to the acquired user's psychological reflection information, based on the history of the user's psychological state. First, the second determination unit 13B acquires the history of the user's psychological state stored by the estimation unit 12 (step S21). For example, the second determination unit 13B may acquire the history of the user's psychological state acquired by the estimation unit 12 over a certain period in the past. Next, the second determination unit 13B statistically processes the user's past psychological state based on the acquired history of psychological state (step S22). Then, the second determination unit 13B determines whether it is necessary to obtain a correct label corresponding to the user's psychological reflection information, based on the result of the total processing (step S23).

[0029] For example, the second determination unit 13B may acquire the percentage of users whose psychological state is estimated to be poor based on the acquired history of psychological states. In this case, the second determination unit 13B may determine that it is necessary to acquire the correct label when the percentage estimated to be poor is above a predetermined threshold. Furthermore, the second determination unit 13B may perform a determination by the first determination unit 13A when the percentage estimated to be poor is above a predetermined threshold. That is, the second determination unit 13B may determine that it is necessary to acquire the correct label when the percentage of users whose past estimation results indicate poor state is above a predetermined percentage, and the current estimation result by the first determination unit 13A is in category 3.

[0030] Figure 5 is a flowchart showing the operation of the third determination unit 13C. The third determination unit 13C determines whether it is necessary to obtain the correct label based on the distribution of the data stored in the data storage unit 16. For example, the third determination unit 13C determines that it is necessary to obtain the correct label corresponding to the user's psychological reflection information when the proportion of a specific classification that constitutes multiple classifications in the data stored in the data storage unit 16 is below a predetermined value, and the user's psychological state estimated using a psychological state estimation model with the user's psychological reflection information as input information corresponds to a specific classification.

[0031] In one example, the third determination unit 13C first acquires the training data stored in the data storage unit 16 (step S31). Next, the third determination unit 13C statistically processes the training data (step S32) and, based on the results of the summation process, determines whether or not it is necessary to acquire the correct label corresponding to the user's psychological reflection information (step S33). For example, the third determination unit 13C calculates the percentage of bad data based on the classification (good, bad) associated with the acquired training data. It may then determine that it is necessary to acquire the correct label when the percentage of data indicating bad data is below a predetermined threshold. Furthermore, if the percentage of bad data is below a predetermined threshold, the third determination unit 13C performs a determination by the first determination unit 13A or the second determination unit 13B and determines whether or not it is necessary to acquire the correct label based on the result.

[0032] Figure 6 is a flowchart showing the operation of the fourth determination unit 13D. The fourth determination unit 13D evaluates the quality of the user's biometric information and determines whether or not it is necessary to acquire a correct label corresponding to the user's psychological reflection information based on the evaluation result. First, the fourth determination unit 13D acquires the biometric information that constitutes the user's psychological reflection information (step S41). Next, the fourth determination unit 13D determines the quality of the biometric information (step S42). The quality of the biometric information may be an indicator of whether the data constituting the biometric information can contribute to improving the learning accuracy of the psychological state estimation model. For example, the quality of the biometric information may be the signal-to-noise ratio in audio data, video data, etc. Based on the determination result of the quality of the biometric information, the fourth determination unit 13D determines whether or not it is necessary to acquire a correct label (step S43). For example, the fourth determination unit 13D determines that it is necessary to acquire a correct label when the quality of the biometric information exceeds a predetermined standard, and determines that it is unnecessary to acquire a correct label when the quality of the biometric information falls below a predetermined standard.

[0033] Figure 7 is a flowchart showing the operation of the fifth determination unit 13E. When the fifth determination unit 13E acquires user psychological reflection information, it determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information if a predetermined amount of time has elapsed since the user's previous response to a test for acquiring the correct label. For example, when the information acquisition unit 11 acquires the user's psychological reflection information, the fifth determination unit 13E saves the user's identification information and the acquisition date (step S51). The fifth determination unit 13E also saves whether or not there was a response to a test for acquiring the correct label, in relation to the acquisition date. The user's identification information and acquisition date may be saved as user history in the fifth determination unit 13E in relation to the psychological reflection information. When the fifth determination unit 13E acquires user psychological reflection information, it refers to the user history and obtains the response date of the most recent test for the user from whom the psychological reflection information was acquired (step S52). The fifth determination unit 13E then calculates the number of days elapsed from the most recent acquisition date to the acquisition date of the user's psychological reflection information (step S53), and determines whether or not to acquire the correct label according to this number of days elapsed (step S54). For example, the fifth determination unit 13E determines that it is necessary to acquire the correct label if the number of days elapsed is greater than or equal to a predetermined period, and determines that it is not necessary to acquire the correct label if the number of days elapsed is less than the predetermined period.

[0034] Figure 8 is a flowchart showing the operation of the sixth determination unit 13F. The sixth determination unit 13F determines whether it is necessary to obtain the correct label based on the distribution of data already stored in the data storage unit 16. For example, the sixth determination unit 13F determines that it is necessary to obtain the correct label corresponding to the user's psychological reflection information when the user's attribute is rare. First, the sixth determination unit 13F obtains the user's attributes based on the user's identification information (step S61). The attributes may be stored in the data storage unit 16 in association with the identification information. Next, the sixth determination unit 13F calculates the proportion of each attribute in the training data (including additional training data) stored in the data storage unit 16 (step S62). Next, the sixth determination unit 13F obtains the proportion of the user's attribute in the training data based on the calculated proportion of each attribute (step S63). The sixth determination unit 13F determines whether it is necessary to obtain the correct label according to the proportion of the user's attribute (step S64). For example, the sixth determination unit 13F may determine that it is necessary to obtain the correct label when the proportion of the user's attributes is less than a predetermined threshold, and determine that it is unnecessary to obtain the correct label when the proportion of the user's attributes is equal to or greater than the predetermined threshold.

[0035] Figure 9 is a flowchart illustrating an example of the operation of the data acquisition system 1. As shown in Figure 9, first, psychological reflection information is output from the terminal device 5 used by the user to the data acquisition device 10 (step S101). Next, the information acquisition unit 11 acquires the psychological reflection information output from the terminal device 5 along with the user's identification information (step S102). The estimation unit 12 inputs the psychological reflection information acquired by the information acquisition unit 11 into the psychological state estimation model 12b to obtain the estimation result of the user's psychological state (step S103). The estimation unit 12 outputs the acquired estimation result to the terminal device 5 (step S104). The terminal device 5 acquires the estimation result output from the estimation unit 12 and displays the estimation result on a screen or the like (step S105). Steps S104 and S105 may be omitted.

[0036] The determination unit 13 determines whether it is necessary to obtain the correct label for the psychological reflection information transmitted by the user (steps S106, S107). The determination unit 13 may be any or a combination of the determination units 13A to 13F described above. If it is determined that obtaining the correct label is unnecessary, the data processing device may terminate its processing. On the other hand, if it is determined that obtaining the correct label is necessary, the label creation unit 15 outputs the test to the terminal device 5 (step S108). If information related to the test is already stored in the terminal device 5, the label creation unit 15 may output a command to the terminal device 5 to display the test.

[0037] Next, when the terminal device 5 displays the test on the screen (step S109), the user can answer the test. Once the user answers the test, the terminal device 5 outputs the answer results to the data acquisition device 10 (step S110). The information acquisition unit 11 acquires the answer results (step S111), and based on these answer results, the label creation unit 15 creates a correct answer label (step S112). The correct answer label is a numerical value that quantifies the degree of psychological stress response. The correct answer label is determined based on the test answers. The created correct answer label is stored in the data storage unit 16 as training data along with the user's psychological reflection information and user attributes (step S113). The newly stored training data in the data storage unit 16 can be used to update (additional training) the psychological state estimation model. In addition, the user's psychological state (good, bad) based on the created correct answer label may be transmitted to the terminal device 5.

[0038] As described above, one example of a data acquisition device 10 includes a psychological state estimation model 12b that has been trained using psychological reflection information and correct labels indicating the psychological state corresponding to the psychological reflection information as training data; an information acquisition unit 11 that acquires the user's psychological reflection information; an estimation unit 12 that uses the acquired user's psychological reflection information as input information and estimates the user's psychological state using the psychological state estimation model 12b; a determination unit 13 that determines whether or not it is necessary to acquire the correct labels corresponding to the acquired user's psychological reflection information; a label creation unit 15 that, if the determination unit 13 determines that it is necessary to acquire the correct labels, creates correct labels corresponding to the user's psychological reflection information based on the user's answers to a test presented to the user; and a data storage unit 16 that stores the user's psychological reflection information and the user's correct labels as a set of data.

[0039] When building a machine learning model to estimate a user's psychological state, training data corresponding to the psychological state is necessary. In one example, training data generated with the cooperation of providers is used to build the model. While training data can be obtained from providers in each psychological state, it is difficult to obtain training data similarly for each psychological state. In particular, since providers who cooperate in generating training data are rarely in a poor psychological state, it is difficult to obtain training data corresponding to poor psychological states. In this case, a bias may occur between the number of training data corresponding to good psychological states and the number of training data corresponding to poor psychological states, which may make it difficult to estimate psychological states accurately.

[0040] In the data acquisition device 10 described above, a psychological state estimation model estimates the user's psychological state using the user's psychological reflection information as input data. The data acquisition device 10 can acquire training data by having the user take a predetermined test and creating correct labels corresponding to the user's psychological reflection information based on the test answers obtained. Since such a data acquisition device 10 has a determination unit 13 that determines whether or not it is necessary to acquire the correct labels corresponding to the acquired user's psychological reflection information, training data can be acquired as needed. In other words, if it is determined that it is necessary to acquire the correct labels when the estimated psychological state is poor, training data corresponding to poor states, which are difficult to acquire, can be acquired efficiently. Furthermore, if it is not necessary to acquire the correct labels, it is not necessary to have the user take a test, thus preventing an excessive burden on the user.

[0041] One example of a psychological state estimation model may be updated using data stored in the data storage unit 16 as training data. In this configuration, the training data containing psychological reflection information determined to be necessary by the determination unit 13 is reflected in the psychological state estimation model, allowing the psychological state estimation model to be updated according to the determination criteria of the determination unit 13.

[0042] In one example, the determination unit 13 may determine whether or not it is necessary to obtain the correct label based on the distribution of the data already stored in the data storage unit 16. In this configuration, depending on the distribution of the training data stored in the data storage unit 16, it is possible to efficiently obtain, for example, relatively insufficient training data.

[0043] In one example, the correct labels constituting the training data may be classifiable into multiple categories, and the determination unit 13 may determine that it is necessary to obtain the correct labels corresponding to the user's psychological reflection information when the proportion of a particular category constituting multiple categories in the data already stored in the data storage unit 16 is less than or equal to a predetermined value, and the user's psychological state estimated using the psychological state estimation model with the user's psychological reflection information as input information corresponds to a particular category. In this configuration, when there are categories for which data collection is expected to be difficult, the training data corresponding to those categories can be efficiently obtained.

[0044] In one example, the information acquisition unit 11 may acquire user attributes, the data storage unit 16 may store attributes together with the data, and the determination unit 13 may calculate the proportion of each attribute in the data stored in the data storage unit 16, and determine that it is necessary to acquire correct labels corresponding to the user's psychological reflection information when the proportion of user attributes acquired by the information acquisition unit 11 is less than or equal to a predetermined proportion. In this configuration, learning data corresponding to users with relatively few attributes can be efficiently acquired. The burden on users with attributes for which sufficient learning data has been accumulated can be reduced.

[0045] In one example, the estimation unit 12 may classify the user's psychological state into a first category and a second category, and the determination unit 13 may classify the user's state into a third category that broadly encompasses the first category and a fourth category obtained by subtracting the third category from the second category, and determine that it is necessary to obtain a correct label corresponding to the user's psychological reflection information when the user's state is classified into the third category. In this configuration, psychological reflection information that has been classified into the second category but potentially could be classified into the first category can be efficiently obtained.

[0046] For example, the estimation unit 12 may store previously estimated psychological states for each user, and the determination unit 13 may determine whether it is necessary to obtain a correct label corresponding to the acquired user's psychological reflection information based on the history of the user's psychological state stored by the estimation unit 12. For example, if it is found that the percentage of users who are unwell is high as a result of referring to the history of the user's psychological state, reliable data can be obtained by acquiring psychological reflection information from when the user's psychological state was estimated to be unwell.

[0047] In one example, the information acquisition unit 11 may acquire the user's biometric information as psychological reflection information, and the determination unit 13 may evaluate the quality of the user's biometric information and determine that it is unnecessary to acquire the correct label corresponding to the user's psychological reflection information if the quality of the user's biometric information falls below a predetermined standard. In this configuration, since low-quality data is not acquired, the decrease in the estimation accuracy of the psychological state estimation model is suppressed.

[0048] One example of a determination unit 13 is that when user psychological reflection information is acquired, if a predetermined amount of time has elapsed since the user's previous response to a test for acquiring the correct label, it may determine that it is necessary to acquire the correct label corresponding to the user's psychological reflection information. If a long period of time has passed since the previous response to a test for acquiring the correct label, the reliability of the correct label may not necessarily be high. This configuration can suppress the decrease in the reliability of the correct label. In addition, since the necessity of updating the correct label is determined based on the time elapsed since the previous acquisition date of the correct label, the burden of responses for users for whom a certain period of time has not elapsed since the previous acquisition date of the correct label can be reduced.

[0049] Although one embodiment has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment. For example, the determination unit 13 may be a combination of the determination units 13 described above. For example, the determination unit 13 may be a combination of any of the first determination unit 13A, the second determination unit 13B, and the third determination unit 13C, and at least one of the fourth determination unit 13D, the fifth determination unit 13E, and the sixth determination unit 13F. In this case, the determination unit 13 may determine that obtaining the correct model is necessary if all of the combined determination units 13 determine that obtaining the correct model is necessary.

[0050] While an example was shown where the estimation unit classifies the user's psychological state into two categories, the user's psychological state may be classified into more categories.

[0051] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may be realized by combining the above one device or the above multiple devices with software.

[0052] Functions include, but are not limited to, judgment, decision, judgment, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. As mentioned above, the method of implementation is not particularly limited.

[0053] For example, the data acquisition device 10 in one embodiment of the present disclosure may function as a computer that performs the psychological state estimation process and data acquisition process of the present disclosure. Figure 10 is a diagram showing an example of the hardware configuration of the data acquisition device according to one embodiment of the present disclosure. The data acquisition device 10 described above may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0054] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the information acquisition unit 11, estimation unit 12, determination unit 13, label creation unit 15, and data storage unit 16 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0055] Each function in the data acquisition device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

[0056] The processor 1001 controls the entire computer, for example, by running the operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control units, arithmetic units, registers, etc. For example, the estimation unit 12, determination unit 13, label creation unit 15, etc., described above may be implemented by the processor 1001.

[0057] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the estimation unit 12, the determination unit 13, the label creation unit 15, etc., may be stored in the memory 1002 and implemented by a control program that runs on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0058] Memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 1002 may also be called a register, cache, main memory, etc. Memory 1002 can store executable programs (program code), software modules, etc., for carrying out a data acquisition method according to one embodiment of this disclosure.

[0059] Storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. Storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of memory 1002 and storage 1003.

[0060] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include high-frequency switches, duplexers, filters, frequency synthesizers, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the information acquisition unit 11 described above may be implemented by the communication device 1004.

[0061] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0062] Furthermore, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0063] Furthermore, the data acquisition device may include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array), and some or all of the functional blocks may be implemented by such hardware. For example, processor 1001 may be implemented using at least one of these hardware components.

[0064] Information notification is not limited to the embodiments described herein and may be carried out by other means. For example, information notification may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0065] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described herein may be reordered, provided they are consistent with each other. For example, the methods described herein present various step elements in an exemplary order and are not limited to that specific order.

[0066] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0067] The determination may be made by a value represented by 1 bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0068] Each aspect / embodiment described herein may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0069] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Accordingly, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0070] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0071] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technologies (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0072] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0073] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms that have the same or similar meaning.

[0074] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0075] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include, for example, receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0076] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0077] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0078] Any reference to elements using designations such as “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to first and second elements do not imply that only two elements may be adopted, or that the first element must precede the second element in any way.

[0079] Where the terms “include,” “including,” and their variations are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0080] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0081] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0082] The data acquisition device disclosed herein has the following configuration. [1] A psychological state estimation model trained using psychological reflection information and correct labels indicating the corresponding psychological state as training data, An information acquisition unit that acquires the user's aforementioned psychological reflection information, An estimation unit that uses the psychological state estimation model to estimate the psychological state of the user, using the user's psychological reflection information acquired by the information acquisition unit as input information, A determination unit that determines whether it is necessary to obtain the correct label corresponding to the user's psychological reflection information obtained by the information acquisition unit, If the determination unit determines that it is necessary to obtain the correct answer label, a label creation unit creates the correct answer label corresponding to the user's psychological reflection information based on the user's answers to the test presented to the user, A data acquisition device comprising a data storage unit that stores the user's psychological reflection information and the user's correct answer label as a set of data. [2] The data acquisition device according to [1], wherein the psychological state estimation model is updated with the data stored in the data storage unit as the learning data. [3] The data acquisition device according to [1] or [2], wherein the determination unit determines whether or not it is necessary to acquire the correct label based on the distribution of the data already stored in the data storage unit. [4] The correct labels that constitute the training data can be classified into multiple categories. The data acquisition device according to [3], wherein the determination unit determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the proportion of a specific category constituting the plurality of categories in the data already stored in the data storage unit is less than or equal to a predetermined value, and the user's psychological state estimated using the psychological state estimation model with the user's psychological reflection information as input information corresponds to the specific category. [5] The information acquisition unit acquires the user's attributes, The data storage unit stores the attributes along with the data, The data acquisition device according to [3], wherein the determination unit calculates the proportion of each attribute of the data stored in the data storage unit, and determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the proportion of the user's attributes acquired by the information acquisition unit is less than or equal to a predetermined proportion. [6] The estimation unit classifies the user's psychological state into a first category and a second category, The determination unit classifies the user's state into a third category that broadly encompasses the first category and a fourth category obtained by subtracting the third category from the second category, and determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the user's state is classified into the third category, according to any one of [1] to [5]. [7] The estimation unit stores the previously estimated psychological state for each user. The data acquisition device according to any one of [1] to [6], wherein the determination unit determines whether it is necessary to acquire the correct label corresponding to the acquired psychological reflection information of the user, based on the history of the user's psychological state stored by the estimation unit. [8] The information acquisition unit acquires the user's biometric information as the psychological reflection information. The data acquisition device according to any one of [1] to [7], wherein the determination unit evaluates the quality of the user's biometric information and determines that it is unnecessary to acquire the correct label corresponding to the user's psychological reflection information if the quality of the user's biometric information falls below a predetermined standard. [9] The data acquisition device according to any one of [1] to [8], wherein the determination unit determines, when the user's psychological reflection information is acquired, that it is necessary to acquire the correct answer label corresponding to the user's psychological reflection information when a predetermined amount of time has elapsed since the acquisition of the user's previous answer to the test. [Explanation of Symbols]

[0083] 10...Data acquisition device, 11...Information acquisition unit, 12...Estimation unit, 13...Determination unit, 15...Label creation unit, 16...Data storage unit, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. A psychological state estimation model trained using psychological reflection information and correct labels indicating the corresponding psychological state as training data, An information acquisition unit that acquires the user's aforementioned psychological reflection information, An estimation unit that uses the psychological state estimation model to estimate the psychological state of the user, using the user's psychological reflection information acquired by the information acquisition unit as input information, A determination unit that determines whether it is necessary to obtain the correct label corresponding to the user's psychological reflection information obtained by the information acquisition unit, If the determination unit determines that it is necessary to obtain the correct answer label, a label creation unit creates the correct answer label corresponding to the user's psychological reflection information based on the user's answers to the test presented to the user, A data acquisition device comprising a data storage unit that stores the user's psychological reflection information and the user's correct answer label as a set of data.

2. The data acquisition device according to claim 1, wherein the psychological state estimation model is updated with the data stored in the data storage unit as the learning data.

3. The data acquisition device according to claim 1, wherein the determination unit determines whether or not it is necessary to acquire the correct label based on the distribution of the data already stored in the data storage unit.

4. The correct labels that constitute the training data can be classified into multiple categories. The data acquisition device according to claim 3, wherein the determination unit determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the proportion of a specific category constituting the plurality of categories in the data already stored in the data storage unit is less than or equal to a predetermined value, and the user's psychological state estimated using the psychological state estimation model with the user's psychological reflection information as input information corresponds to the specific category.

5. The information acquisition unit acquires the user's attributes, The data storage unit stores the attributes along with the data, The data acquisition device according to claim 3, wherein the determination unit calculates the proportion of each attribute of the data stored in the data storage unit, and determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the proportion of the user's attributes acquired by the information acquisition unit is less than or equal to a predetermined proportion.

6. The estimation unit classifies the user's psychological state into a first category and a second category, The data acquisition device according to claim 1, wherein the determination unit classifies the user's state into a third category that broadly encompasses the first category and a fourth category obtained by subtracting the third category from the second category, and determines that it is necessary to acquire the correct label corresponding to the user's psychological reflection information when the user's state is classified into the third category.

7. The estimation unit stores the previously estimated psychological state for each user. The data acquisition device according to claim 1, wherein the determination unit determines whether or not it is necessary to acquire the correct label corresponding to the acquired psychological reflection information of the user, based on the history of the user's psychological state stored by the estimation unit.

8. The information acquisition unit acquires the user's biometric information as the psychological reflection information. The data acquisition device according to claim 1, wherein the determination unit evaluates the quality of the user's biometric information and determines that it is unnecessary to acquire the correct label corresponding to the user's psychological reflection information if the quality of the user's biometric information falls below a predetermined standard.

9. The data acquisition device according to claim 1, wherein the determination unit determines, when the user's psychological reflection information is acquired, that it is necessary to acquire the correct answer label corresponding to the user's psychological reflection information when a predetermined amount of time has elapsed since the acquisition of the user's previous answer to the test.

Images