Tissue attribute analysis system and tissue attribute analysis device

Abstract

This invention provides an organizational attribute analysis system and apparatus that analyze organizational attributes through changes in the biological responses of users participating in online sessions conducted within an organization. [Solution] The tissue attribute analysis device 100 includes a bio-response analysis unit 12 that analyzes changes in the user's bio-response for each online session of multiple organizations in which the same user participates, based on video images obtained of the user during an online session conducted by multiple users, and a tissue attribute analysis unit 13 that analyzes the attributes of the organization based on the changes in bio-response analyzed by the bio-response analysis unit 12 for each online session of multiple organizations. The device analyzes the bio-response of participants in online sessions conducted within an organization and analyzes the attributes of the organization based on the analysis results.

Description

Technical Field

[0001] The present invention relates to an organizational attribute analysis system and an organizational attribute analysis device, and is particularly suitable for use in a system that analyzes the attributes of an organization to which a user participating in an online session belongs.

Background Art

[0002] Conventionally, there has been known a technique for visualizing organizational culture (including organizational climate, etc.) by analyzing data (such as e-mails, commands, web contents, call data, etc.) that is routinely circulated within an organization (see, for example, Patent Document 1). In Patent Document 1, as an example of organizational culture, the relatedness between departments, the relatedness between companies, negative / positive, introverted / extroverted, activity volume, and innovation / bureaucracy orientation are cited.

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the technique described in Patent Document 1 above, the culture of an organization is analyzed based on data collected within one organization. Patent Document 1 also describes data used for the analysis, including data used in web conferences. Specifically, it is disclosed that the situation of an organization such as organizational activities and organizational culture is analyzed from information identifying the terminals participating in a web conference. However, it does not disclose how to analyze organizational culture and the like from the information identifying the terminals participating in a web conference, and the analysis content is unclear.

[0005] ​​​​​​​​​​​This invention relates to the biological responses of users participating in online sessions conducted within an organization. The aim is to enable the analysis of organizational attributes through change. [Means for solving the problem]

[0006] To solve the above-mentioned problems, the organizational attribute analysis system of the present invention provides multiple users Based on video footage obtained about the user during the online session, the same user The system monitors changes in the user's biometric responses for each online session involving multiple organizations. Each was analyzed, and based on the changes in biological responses analyzed for each online session of multiple organizations... And we are trying to analyze the attributes of the organization. [Effects of the Invention]

[0007] If the same user participates in online sessions for multiple organizations, the user will be subject to organization-specific rules. Attributes may influence the changes in biometric responses for each online session of each organization. It has the potential. According to the present invention configured as described above, online meetings conducted in each organization By analyzing the changes in the biological responses of users participating in the session, we can analyze the organizational attributes. It is possible. [Brief explanation of the drawing]

[0008] [Figure 1] This block diagram shows an example of the overall configuration of the organizational attribute analysis system according to this embodiment. [Figure 2] This block diagram shows an example of the functional configuration of the tissue attribute analysis device according to this embodiment. [Modes for carrying out the invention]

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Figure 1 shows the organization according to this embodiment. It is a diagram showing an overall configuration example of an attribute analysis system. As shown in FIG. 1, the organizational attribute analysis system of the present embodiment includes an organizational attribute analysis device 100, a plurality of user terminals 200 -1 , 200 -2 , ·· ·· (hereinafter, simply referred to as the user terminal 200 when not particularly distinguished) and a session management server 300. These organizational attribute analysis device 100, user terminal 200 and session management server 300 are connected via a communication network 500 such as the Internet or a mobile phone network.

[0010] The organizational attribute analysis system of the present embodiment analyzes changes in the biological reactions of users participating in an online session held in a plurality of organizations, and analyzes the attributes of the organizations based on the analysis results. Specifically, for each online session of a plurality of organizations in which the same user participates, changes in the biological reactions of the user are analyzed respectively, and based on each analysis result, the attributes of a plurality of organizations are analyzed.

[0011] The moving images handled in the online session include the face images of users (participants in the online session) using the user terminal 200 (actually, images of body parts other than the face and backgrounds are also included) and voices. The face images and voices of the users are acquired by a camera and a microphone provided in or connected to the user terminal 200, and are transmitted to the session management server 300. Then, the face images and voices of each user transmitted to the session management server 300 are acquired by the organizational attribute analysis device 100, and are transmitted from the organizational attribute analysis device 100 to the session application of each user terminal 200. Note that the transmission from the user terminal 200 The transmitted video image is acquired by the tissue attribute analysis device 100, and this is then processed by the tissue attribute analysis device 100. Alternatively, the session may be forwarded to the session management server 300. The video is sent from 0 to both the tissue attribute analysis device 100 and the session management server 300. You may do so.

[0012] Furthermore, the video and image content includes images such as documents that are shared and viewed by multiple users. The document images that Za views are sent from any user terminal 200 to the session management server 300. The data is sent. The document images sent to the session management server 300 are then subjected to organizational attribute analysis. The data is acquired by the device 100 and sent from the tissue attribute analysis device 100 to each user terminal 200. It will be sent to the app.

[0013] Through the above operations, the facial images of multiple users are displayed on each of the multiple user terminals 200. An image or reference image is displayed on the screen, and the voices of multiple users are output from the speaker. This is done using the functionality of the session application installed on user terminal 200. The display screen switches between showing a face image and a reference image, displaying only one of them at a time. It is also possible to separate the display area and display the face image and the reference image simultaneously. Additionally, it is possible to display the image of one user in full screen, or some or all of the users' images. It is possible to split the image of "The" into smaller screens and display them there.

[0014] Furthermore, the functionality of the session application installed on the user terminal 200 allows It's also possible to switch the camera on and off, and the microphone on and off. Yes, for example, user terminal 200 -1If the camera is turned off in the user terminal 200 -1 The facial images captured by the camera are stored on the session management server 300 and the organization attribute analysis system. It is transmitted to the device 100, but is not transmitted from the organizational attribute analysis device 100 to each user terminal 200. Similarly, user terminal 200 -1 If the microphone is turned off, user terminal 200 -1 The audio collected by the microphone is sent to the session management server 300 and the organization attribute analysis device 1 It is sent to 00, but not sent from the organizational attribute analysis device 100 to each user terminal 200.

[0015] In this embodiment, the tissue attribute analysis device 100 receives data transmitted from multiple user terminals 200. The system retrieves the video footage and uses this video footage to determine the user's (online session participant's) behavior. We analyze changes in biological responses.

[0016] Figure 2 is a block diagram showing an example of the functional configuration of the tissue attribute analysis device 100 according to this embodiment. As shown in Figure 2, the tissue attribute analysis device 100 of this embodiment has the following functional configuration: video It includes an image acquisition unit 11, a biological reaction analysis unit 12, and a tissue attribute analysis unit 13. The tissue attribute analysis device 100 of the application form is equipped with a video storage unit 101 as a storage medium. .

[0017] Each of the above function blocks 10-13 is hardware, DSP (Digital Signal Processor) ), it can be configured by either software. For example, by software In this configuration, each of the above functional blocks 10-13 is actually the computer's CPU, It is configured with RAM, ROM, etc., and RAM, ROM, hard disk or semiconductor This is achieved by the operation of a program stored on a recording medium such as a Mori.

[0018] The video acquisition unit 11 acquires video transmitted from each user terminal 200 during the online session. Images (face images, audio, document images) are retrieved from session management server 300. (Video and image retrieval) The acquisition unit 11 acquires video from each user terminal 200 via the session management server 300. The video is associated with information that can identify each user (e.g., user ID) in the video storage unit 10. Stored in 1. In this embodiment, the video acquisition unit 11 is used in multiple organizations. For each online session, videos of multiple users participating in that online session. The image is acquired and stored in the video storage unit 101.

[0019] The facial images obtained from the session management server 300 are displayed on the screen of each user terminal 200. Whether or not it is set as shown (whether the camera is set to on or off) It does not matter whether it is done or not. In other words, the video acquisition unit 11 acquires the video from each user terminal 200. The session management system manages facial images, including both facial images currently displayed and hidden on the screen. It is obtained from server 300. In addition, the audio obtained from session management server 300 is each user Is it set to output from the speaker of the device 200? (Is the microphone off?) It does not matter whether it is set to ON or OFF. That is, the video acquisition unit 1 1 includes both audio being output and audio not being output from the speaker of each user terminal 200. The audio is retrieved from session management server 300.

[0020] The biological response analysis unit 12 acquires images from the video image acquisition unit 11 and stores them in the video image storage unit 101. The moving image (whether or not it is a face image displayed on the screen of user terminal 200, the screen of user terminal 200) Based on (regardless of whether or not sound is being output from the speaker), for each of multiple users In this embodiment, the biological responses that occur as a result of emotional changes are analyzed. The reaction analysis unit 12 analyzes the video images acquired by the video image acquisition unit 11 to create a set of face images (frames). The image data and audio are separated, and changes in biological responses are analyzed from each.

[0021] For example, the biological reaction analysis unit 12 separates from the video acquired by the video acquisition unit 11. By analyzing the user's face image using framed images, we can determine facial expressions, gaze, pulse, and facial features. The biological response changes related to at least one movement are analyzed. In addition, the biological response analysis unit 12 By analyzing the audio separated from the video acquired by the video acquisition unit 11, We will analyze changes in biological responses related to at least one aspect of the content of the speaker's speech and their voice quality.

[0022] When a person's emotions change, it manifests in their facial expressions, gaze, pulse, facial movements, what they say, and tone of voice. This manifests as a change in bodily response. In this embodiment, this is caused by a change in the user's emotions. We analyze changes in bodily responses. Furthermore, through the analysis of changes in biological responses, we can understand the user's emotions. You may also analyze the changes in [the variable].

[0023] The analysis of changes in facial expression is performed, for example, as follows: That is, the biological response analysis unit 12 For each frame image, the facial region is identified within the frame image and pre-trained using machine learning. The image analysis model analyzes which facial expression elements each expression corresponds to. Based on the analysis results, it is determined whether or not a change in facial expression occurs between consecutive frame images. If a change is occurring, is it a positive or negative change in facial expression, and what We analyze the degree of facial expression change occurring.

[0024] Facial expression elements include, for example, neutral, calm, happy, and surprised. (surprised) / sad / angry / fearful / disgust These include joy and surprise, which are positive facial expression elements, while sadness, anger, Fear and disgust are negative facial expression elements.

[0025] The biological response analysis unit 12 analyzes the facial expression in each frame image, considering multiple facial expression elements. A score is calculated that totals 100. For example, Neutral = 10, Calm = 10, Joy = 30, surprise=20, sadness=10, anger=10, fear=5, disgust=5, and so on. A score is calculated for each facial expression element based on the likelihood of it matching the expression. Then, For example, the facial expression element with the highest score is determined to be the facial expression in that frame image. Below, we will discuss the facial expression score determined for each frame image (calculated for multiple facial expression elements). The highest score among those given is called the "facial expression score."

[0026] The biological reaction analysis unit 12 determines the facial expression elements for each frame image in this manner and Did at least one of the facial expression scores calculated for each frame image change from the previous frame? Whether or not this occurs determines whether or not a change in facial expression has occurred between consecutive frame images. Then, the biological response analysis unit 12, if there is no change in the facial expression element with the highest score, from the previous frame Even if you determine that a change in facial expression has occurred when the amount of change in the score exceeds a predetermined threshold, Good. The magnitude of the facial expression change should be determined by the amount of change in the facial expression score from the previous frame. This is possible.

[0027] Furthermore, the biological response analysis unit 12 detects that the facial expression score for positive expressions has increased from the previous frame. If this occurs, and if the expression changes from a negative expression in the previous frame to a positive expression in the current frame... If this occurs, it is determined that a positive change in facial expression has occurred. Meanwhile, the biological reaction analysis unit 1 2 is when the facial expression score for a negative expression has increased from the previous frame, and the previous frame When the expression changes from a positive expression to a negative expression in the current frame, It is determined that a change in facial expression has occurred.

[0028] Here, we have explained an example of analyzing changes in facial expressions between consecutive frame images, but You could also analyze facial expression changes at fixed time intervals (for example, every 500 milliseconds). This is used for analyzing changes in eye gaze, pulse rate, and facial movements, as described below. The same applies to this matter.

[0029] The analysis of changes in eye gaze is performed, for example, as follows: That is, the biological reaction analysis unit 12 For each frame image, identify the eye region within the frame image and determine the direction (gaze) of both eyes. Analyze the data. For example, determine whether the user is looking at the speaker's face or the shared document being displayed. It analyzes where the user is looking within the shared document being displayed, whether they are looking off-screen, and also analyzes eye movements. You can also analyze whether the movement is large or small, and whether the movement is frequent or infrequent. Changes in eye gaze are also related to the user's level of concentration.

[0030] The analysis of changes in pulse rate can be performed, for example, as follows: That is, for each frame image, Identify the facial region within the frame image. Then, extract the numerical value of the face's color information (G in RGB). Using a pre-trained image analysis model, we analyze the changes in G color on the face surface. By arranging them along the time axis, a waveform representing the change in color information is formed, and from this waveform... Identify your pulse rate. When a person is nervous, their pulse rate increases, and when they calm down, their pulse rate decreases.

[0031] The analysis of changes in facial movement is performed, for example, as follows: Bio-reaction analysis unit 1 Step 2 identifies the facial region within each frame image and analyzes the orientation of the face. By doing so, the system analyzes where the user is looking. For example, by looking at the face of the speaker currently displayed. Are you looking at the shared document that is currently displayed? What part of the shared document are you looking at? It analyzes things like whether the person is looking outside. It also analyzes whether the facial movements are large or small, and how frequently the movements occur. It would also be good to analyze whether there is a small or small amount of movement. The analysis could combine facial movements and eye movements. You may also do the following: For example, look directly at the speaker's face while they are being displayed, or look up or You could also analyze whether the person is looking downwards or from an angle.

[0032] The analysis of the content of the statements is performed, for example, as follows: That is, the biological reaction analysis unit 12, Publicly known speech recognition processing for audio of a specified duration (for example, approximately 30 to 150 seconds). By performing this process, the audio is converted into a string, and then morphological analysis is performed on that string to assist... Remove unnecessary words such as words of speech and articles that are not needed to express conversation. Then, use TF- Vectorized using methods such as IDF (Term Frequency - Inverse Document Frequency), Based on the characteristics of the vector, it can determine whether a positive or negative emotional change is occurring. This analyzes whether a change is occurring and to what extent the emotional change is occurring. Then, based on the features of the vector calculated according to the content of the statement, the vector features and the content of the statement Using a database that stores information associating the types of contents, we can determine what kind of output To estimate whether it is the content of the statement.

[0033] As another example, the following may be used: That is, the biological reaction analysis unit 12 specifies A dictionary was created by extracting words from the content of the speeches during the allotted time (determining whether each word is positive or negative). (The corrected version) is compared with the frequency of occurrence of positive words and negative words. By counting the current number of occurrences, we can determine whether positive emotional changes are occurring or negative emotional changes are occurring. This analyzes what kind of emotional changes are occurring and to what extent those emotional changes are occurring. ru.

[0034] Voice quality analysis is performed, for example, as follows: That is, the biological response analysis unit 12 is specified For the audio of a certain duration (for example, 30 to 150 seconds), a known speech analysis process is performed. This allows for the identification of the acoustic characteristics of speech (e.g., MFCC (Mel-frequency cepstrum coefficient)). Identify the acoustic characteristics, and then, based on the values ​​representing those characteristics, determine if a positive change in voice quality occurs. Whether or not there is a negative change in voice quality, and to what extent is there such a change in voice quality? It analyzes whether the person is awake or not. For example, similar to analyzing facial expressions, it uses pre-trained machine learning on voice data. According to the analysis model, the voice is categorized as neutral / calm / joy / surprise / sadness / anger / fear / disgust. It analyzes which emotional element it corresponds to. Then, based on the results of that analysis, for a predetermined time Whether or not emotional changes are occurring in each interval, and if so, whether they are positive. Is it a positive or negative emotional change, and to what extent is the emotional change occurring? This will be analyzed. Note that MFCC is just one example of an acoustic feature of speech, and is not limited to it. It's not that.

[0035] The tissue attribute analysis unit 13 performs online sessions of multiple tissues using the biological response analysis unit 12. Based on the changes in the user's bioresponses analyzed for each user, the attributes of the organization are analyzed. Even with the same organization, the way biological responses manifest can differ depending on the online session of the participating organization. If such an atmosphere arises, it is because it is specific to the online sessions of that organization. It may be influenced by the unique attributes that the organization possesses. The organizational attribute analysis unit 13, These organization-specific attributes are used in online sessions involving multiple organizations with the same user. We will analyze the differences in biological responses.

[0036] For example, the organizational attribute analysis unit 13 analyzes the performance of the organization and the organization's online By analyzing the correlation with changes in the user's biological responses analyzed for each session, Analyze organizational attributes that are presumed to be factors in an organization's good or bad performance. ru.

[0037] As an example, the organizational attribute analysis unit 13 analyzes organizational performance indicators (for example, the company's sales and profits). Set the target variable as ) and the changes in the user's biological responses as explanatory variables, and perform statistical processing or Machine learning (linear regression analysis, logistic regression analysis, clustering, deep learning) The correlation between performance indicators and changes in biological responses will be analyzed using methods such as [specific methods].

[0038] Here, as an organizational performance indicator, for example, the sales value that has increased compared to the previous period (meaning the organization is doing well) The metrics used are those that indicate high performance, as well as changes in the user's biological responses. For example, regarding facial expressions, we can use the facial expression scores of the facial expression elements calculated as described above. It is possible to do so. In this case, the organization's good performance and the users participating in the online session It is possible to analyze the correlation with facial expressions.

[0039] For example, for each of the multiple users participating in an online session, each frame image Alternatively, from the facial expression score of any facial expression element calculated for each predetermined time interval, Then, the average facial expression score is calculated for each user. From the average facial expression score of each element, the cross-user average is further calculated, and the cross-user average for each facial expression element is calculated. By using the average facial expression score as an explanatory variable in statistical processing or machine learning, performance indicators can be used. The correlation between each facial expression element and the average facial expression score across users will be analyzed. This analysis will then determine the online Facial expressions that can appear in a user during an in-session include neutral, calm, joy, surprise, and sadness. Identifying facial expressions that show a strong correlation with organizational performance, and those that show a weak correlation, among anger, fear, and disgust. It becomes possible to grip it.

[0040] Here, we have explained an example of calculating the average facial expression score for each facial expression element, In addition, you may calculate the variance. Alternatively, a series of online sessions Alternatively, the duration for which the facial expression score for each facial expression element is obtained can be calculated. Here, each facial expression element is calculated in a single frame image or a predetermined time interval. Using the facial expression score, which is the maximum value among the scores, throughout a series of online sessions... We have explained an example of calculating the average value for each facial expression element, but using the score for each facial expression element to calculate the table Alternatively, you could calculate the average value of the information elements.

[0041] The organizational attribute analysis unit 13 further analyzes the correlations obtained in the above manner and determines that the organization is a user We will analyze the attributes that influence their communication style. For example, There is a strong correlation between positive facial expression elements such as joy or surprise and good organizational performance, and negative ones. There is little correlation between the vital facial expression elements of sadness, anger, fear, or disgust and good organizational performance. If the analysis results indicate that the organization performed positively in online sessions, then the organization will have a positive outcome. They have a communication style that easily produces facial expressions, and they communicate It is possible to determine that certain organizational attributes may be contributing to the organization's strong performance. .

[0042] This type of analysis is performed for each online session within the organization, and the results of each analysis are compared. By doing so, we can identify the unique organizational attributes that are presumed to be factors contributing to the strong performance of each organization. It is possible to analyze this. As an organizational performance indicator, for example, sales that have decreased compared to the previous period. Performing a similar analysis using the values ​​(metric values ​​when the organization is performing poorly) This allows us to analyze the unique organizational attributes that are presumed to be contributing factors to the underperformance in each organization. This is possible.

[0043] In this context, the indicators used to represent organizational performance include company sales, profits, and other performance metrics. I've explained examples of using indicators, but they are not the only ones. For example, new projects You may also use metrics other than performance, such as the number of proposals.

[0044] Furthermore, here, changes in the user's biological responses are represented by scores for each facial expression element or I explained an example using the maximum value in the expression score, but this is not the only way to do so. No. For example, regarding sound quality, the score for each emotional element calculated as described above, or within that... You may also use the sound quality score, which is the maximum value of the score. Alternatively, you can use changes in eye movements or facial movements as a basis. A score representing the level of concentration during an online session may be calculated. Additionally, changes in pulse rate may be measured. Based on this, a score representing the degree of calmness among the emotional elements may be calculated. Also, A score representing positivity or negativity is generated based on facial expressions, content of speech, or tone of voice. You may calculate it.

[0045] Furthermore, as a change in the user's biological response, the amount of the user's biological response (e.g., the amount of speech) is used. It is acceptable to have them present. By using the volume of participation, the organization can contribute to the online session. Whether or not one possesses attributes that make it easy to speak up (an environment conducive to communication) It is possible to analyze whether or not this is the case.

[0046] For example, the organizational attribute analysis unit 13 analyzes online sessions of multiple organizations for the same user. The amount of speech analyzed by the bioreaction analysis unit 12 for each segment is standardized. Standardization is a statistical method. This is a commonly used calculation, and it involves the amount of speech per online session from multiple organizations. This refers to a transformation process that is performed so that the mean is "0" and the variance is "1". The organizational attribute analysis unit 13 Furthermore, the mean and variance of standardized speech volume values ​​for each user are calculated across users. Furthermore, the organizational attribute analysis unit 13 analyzes, for example, the average calculated across users is high and the variance is low. If the average is low, it is determined that the organization has organizational attributes that facilitate communication. A high degree of dispersion indicates that the organization possesses organizational attributes that make communication difficult.

[0047] In addition to the amount of speech, the quality of the voice (pitch, length or amount of pauses between words, volume, etc.) is also considered. It may be possible to perform the analysis by considering multiple factors. Considering multiple factors means, for example, voice quality The sound quality values, like the volume of speech, were analyzed for each online session of multiple organizations. After standardizing the sound quality values, the mean and variance of the standardized values ​​for each user are calculated across users. The mean and variance calculated for the amount of speech, and the mean and variance calculated for sound quality were calculated. Based on distribution and other factors, it is determined whether or not the organization possesses organizational attributes that facilitate communication. It means to determine or set something.

[0048] In the above embodiment, the organizational attribute analysis device 100 and the session management server 300 are Although examples of other configurations have been described, the present invention is not limited thereto. For example, organization The attribute analysis device 100 is configured to have the functions of a session management server 300, or The management server 300 may also be configured to include the functions of the organizational attribute analysis device 100.

[0049] Furthermore, the above embodiments all represent examples of how the present invention can be implemented. This is merely a matter of fact, and the technical scope of the present invention should not be interpreted as being limited by this. Yes. That is, the present invention, without departing from its gist or its main features, can be used in various ways. It can be implemented in this way. [Explanation of Symbols]

[0050] 11. Video Acquisition Unit 12. Department for Analyzing Biological Reactions 13 Organizational Attribute Analysis Department 100 Tissue attribute analysis device

Claims

1. In an environment where online sessions are conducted with multiple users, the biometric reactions of the above users An organizational attribute analysis system that analyzes organizational attributes through response analysis, During the above online session, acquire video and image data transmitted from the user's terminal. The moving image acquisition unit, Based on the video footage acquired by the above video footage acquisition unit, the same user can participate in multiple groups. The bioreaction analyzes the changes in the user's biological responses for each online session of the weaving process. The analysis unit and, The above-mentioned bioreaction analysis unit analyzes each of the above-mentioned multiple tissues in an online session. It comprises a tissue attribute analysis unit that analyzes the attributes of the above-mentioned tissue based on changes in the recorded biological response. A system for analyzing organizational attributes characterized by the following features.

2. The above-mentioned organizational attribute analysis unit analyzes the performance of the above-mentioned organization and the operations of the above-mentioned multiple organizations. For each online session, the user's biological response was analyzed by the above-mentioned biological response analysis unit. By analyzing the correlation with change, the organizational attributes estimated to be factors in the above performance can be identified. The tissue attribute analysis system according to claim 1, characterized by analyzing the following.

3. The above-mentioned organizational attribute analysis unit analyzes the amount of the above-mentioned user's biological response and determines that the above-mentioned organization is communicative. The organization according to claim 1, characterized in that it analyzes whether or not the environment is conducive to communication. Attribute analysis system.

4. Videos obtained about the above users during an online session conducted by multiple users. Based on the image, the above user per online session of multiple organizations in which the same user participates A biological reaction analysis unit analyzes the changes in the biological reactions of each of the following: The above-mentioned bioreaction analysis unit analyzes each of the above-mentioned multiple tissues in an online session. It comprises a tissue attribute analysis unit that analyzes the attributes of the above-mentioned tissue based on changes in the recorded biological response. A tissue attribute analysis device characterized by the following features.

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