JP2022164130A - Program, information processing apparatus, and method - Google Patents

Abstract

To provide a program for properly representing a state of a group or a member, an information processing apparatus and method.SOLUTION: In data analysis processing of an information processing apparatus, a program causes a computer to execute the steps of: receiving multiple input of data including subjective episode of a member with respect to the experience of the member, for each of the members belonging to a group; extracting at least one of a state of a specific member in the group and a state of the group from the multiple data; and displaying at least one of the extracted state of the specific member and the state of the group.SELECTED DRAWING: Figure 22

Description

The present disclosure relates to programs, information processing apparatuses, and methods.

A company has a service that measures employee engagement. In Non-Patent Document 1, (1) a multiple-choice questionnaire is conducted to investigate employee loyalty to the company and satisfaction level, (2) the results of the questionnaire responses are analyzed, and an engagement score displayed as a deviation value is obtained, ( 3) Engagement scores are visualized as an organizational state.

Link and Motivation Co., Ltd., [online], [searched on January 20, 2021], Internet, <URL: https://www.motivation-cloud.com/service/>MOTIVATION CLOUD (https://www .motivation-cloud.com/service/)

However, even if the engagement score is visualized in on-site communication of a group (for example, a company), there is little means for improving the subsequent specific organizational state. Improvement measures that are extremely difficult to ensure quality are presented, such as “participating in discussions”, “providing sufficient opportunities for question-and-answer sessions”, and “disclosing data, processes, etc. carefully”. be. These improvement measures are measures to increase the score of the item that the company wants to measure, and are not based on the employee's insight, which is the reason why the numerical value of the item is low. The reason why such measures are presented is that the state of the group cannot be expressed appropriately. For this reason, it is considered that appropriate means for improving the group's condition cannot be determined.

In addition, each group member (e.g., employee) has a different way of feeling about various things. Therefore, it is necessary to appropriately express the state of the members, but it is difficult to express how they feel. Therefore, there is a problem that even if the same improvement means is presented to all members, it is not necessarily an appropriate improvement means.

Therefore, the present disclosure provides a technology capable of appropriately representing the states of groups and members.

The program according to the present disclosure is a computer, for each member belonging to the group, a step of receiving multiple inputs of data including the member's subjective episode with respect to the member's experience, and from the plurality of data, in the group extracting at least one of the state of a specific member and the state of the group; and displaying at least one of the extracted state of the specific member and the state of the group. .

According to the present disclosure, it is possible to appropriately express the states of groups and members.

2 is a block diagram showing the configuration of the information processing device 10; FIG. 2 is a block diagram showing the functional configuration of the information processing device 10; FIG. It is a figure which shows the data structure of 1st data DB121. It is a figure which shows the data structure of 2nd data DB122. It is a figure which shows the data structure of 3rd data DB123. It is a figure which shows the data structure of 4th data DB124. It is a figure which shows the data structure of 5th data DB125. It is a figure which shows an example of an input screen. It is a figure which shows an example of an input screen. It is a figure which shows an example of an input screen. It is a figure which shows an example of a selection screen. It is a figure which shows an example of a specification screen. It is a figure which shows an example of a 1st state screen. It is a figure which shows an example of an image diagram. It is a figure which shows an example of an episode screen. It is a figure which shows an example of a 2nd state screen. It is a figure which shows an example of a department selection screen. It is a figure which shows an example of a 3rd state screen. 4 is a flow chart showing an example of the flow of data collection processing by the information processing apparatus 10. FIG. 4 is a flowchart showing an example of the flow of data analysis processing by the information processing apparatus 10; 4 is a flow chart showing an example of the flow of analysis result display processing by the information processing apparatus 10. FIG. 4 is a flowchart showing an example of the flow of data analysis processing by the information processing apparatus 10;

Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In the following description, the same parts are given the same reference numerals. Their names and functions are also the same. Therefore, a detailed description thereof will not be repeated.

<Summary of this disclosure>
The present disclosure collects data that includes subjective episodes of group members' experiences. A program, an information processing apparatus, and a method for extracting and displaying at least one of the state of a specific member in the group and the state of the group from collected data will be described. In addition, in this disclosure, a case where the group is a company and the members are employees will be described as an example.

<1. Configuration of Information Processing Device 10>
An information processing apparatus 10 according to the present disclosure will be described with reference to FIGS. 1 and 2. FIG. The information processing device 10 according to the present disclosure is a device for executing a process of extracting and displaying at least one of the state of a specific member in the group and the state of the group.

FIG. 1 is a diagram showing the configuration of an information processing system 1. As shown in FIG. The information processing system 1 includes an information processing device 10 , member terminals 20 , user terminals 30 and a network 40 . The information processing apparatus 10 is, for example, a laptop computer, a rack-mount type computer, a tower type computer, or the like. The information processing device 10 may be configured by a plurality of information processing devices 10 and the like. In addition, in the information processing system 1 of the present embodiment, a collection of devices (for example, member terminals 20, user terminals 30, etc.) can be configured as one "information processing device". The method of distributing a plurality of functions required for realizing the information processing system 1 can be appropriately determined in consideration of the processing capability of each piece of hardware, the specifications required for the information processing system 1, and the like.

The information processing device 10 includes a processor 11 , a memory 12 , a storage 13 , a communication IF 14 and an input/output IF 15 .

The processor 11 is hardware for executing an instruction set described in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

The memory 12 temporarily stores programs and data processed by the programs, and is a volatile memory such as a DRAM (Dynamic Random Access Memory).

The storage 13 is a storage device for storing data, and is, for example, a flash memory, HDD (Hard Disc Drive), or SSD (Solid State Drive).

The communication IF 14 is an interface for inputting/outputting signals so that the information processing device 10 communicates with an external device. The communication IF 14 is wired or wirelessly connected to a network 40 such as the Internet or a wide area Ethernet.

The input/output IF 15 functions as an interface with an input device (for example, a pointing device such as a mouse, a keyboard) for receiving input operations, and an output device (display, speaker, etc.) for presenting information.

The member terminal 20 is a terminal operated by an employee. The member terminal 20 is, for example, a smart phone, a personal computer, or the like. At least one member terminal 20 exists in the information processing system 1 . The member terminal 20 may be a terminal assigned to each employee or a terminal operated by multiple employees.

The user terminal 30 is a terminal operated by a user. Here, the user is, for example, a company manager, a personnel department, or an employee's boss, who is in a position to understand the status of an employee or a group, and to think about improvement measures. Users are not limited to these persons, and any person who wants to know the status of an employee or the status of a group may be used. The user terminal 30 is, for example, a smart phone, a personal computer, or the like.

Information processing device 10 , member terminal 20 , and user terminal 30 are configured to be able to communicate with each other via network 40 .

FIG. 2 is a block diagram showing the functional configuration of the information processing device 10. As shown in FIG. As shown in FIG. 2, the information processing apparatus 10 includes a communication section 110, a storage section 120, and a control section .

The communication unit 110 performs processing for the information processing device 10 to communicate with an external device.

The storage unit 120 stores data and programs used by the information processing apparatus 10 . The storage unit 120 stores a first data DB 121, a second data DB 122, a third data DB 123, a fourth data DB 124, a fifth data DB 125, and the like.

The first data DB 121 is a database that holds employee information.

FIG. 3 is a diagram showing the data structure of the first data DB 121. As shown in FIG. A record of the first data DB 121 includes items such as the item “member ID”, the item “name”, the item “department”, and the item “date of birth”.

The item "member ID" is information for identifying an employee. The item "member ID" is the same in other databases. The member ID is, for example, information such as "M001" and "M002" as shown in FIG.

The item "name" is information indicating the name of the employee.

The item "department" is the name of the department to which the employee belongs in the company. The belonging department may include multiple departments.

The item "date of birth" is information on the date of birth of the employee.

The second data DB 122 is a database that holds detailed personnel information on employees.

FIG. 4 is a diagram showing the data structure of the second data DB 122. As shown in FIG. The record of the second data DB 122 includes the item “member ID”, the item “past company”, the item “affiliation history”, the item “year of employment”, the item “gender”, and the like.

The item "past company" is information about the company to which the employee belonged in the past. Past companies are stored as a "M001.COM" file, for example, as shown in FIG. The file contains information such as the company name of the past company, the business, the employee's duties in the past company, and the employee's affiliation period.

The item “history of affiliation” is information on departments to which the employee has belonged in the past. The affiliation history is stored as a "M001.dep" file, for example, as shown in FIG. This file contains, as an affiliation history, information on past affiliation departments and affiliation times, for example.

The item “year of employment” is information indicating the year in which the employee joined the company. Year, month, and date may be used instead of the year of joining the company.

The item "gender" is information on the gender of the employee. In addition to the conventional gender division, it stores information published by employees, information obtained by consensus of employees, and so on.

It should be noted that the second data DB 122 can also include additional information such as the item "post" and the item "history of post".

The third data DB 123 is a database that holds data on employee experiences.

FIG. 5 is a diagram showing the data structure of the third data DB 123. As shown in FIG. The records of the third data DB 123 include the item "member ID", the item "data ID", the item "episode", the item "timing", the item "first information", the item "moving image", the item "tag", and the like. .

The item "data ID" is information for identifying the episode and happiness level corresponding to the data. Data IDs are used, for example, to create links, which will be described later.

The item "episode" is text data about the employee's experience. An episode includes a title and an employee's subjective episode.

The item "time" is the time when the episode was experienced. For example, as shown in FIG. 4, the time may be the Western calendar and the month, the date, or the age of the employee.

The item "first information" is information that quantifies what the employee felt when he/she experienced the episode. The first information is, for example, the degree of happiness that indicates how happy the employee felt when he/she experienced it. As the first information, in addition to the degree of happiness, for example, the degree of various emotions such as the degree of interest and disgust felt when experienced can be adopted. In the present disclosure, a case where the first information is the degree of happiness will be described as an example. The happiness level is preferably a numeric value that can be easily input by the employee and can be expressed intuitively. In the present disclosure, a case where an integer from -10 to 10 is used for the degree of happiness will be described as an example.

The item “moving image” is data of a moving image regarding an episode. The moving image stores moving image or still image data.

The item "tag" is information indicating the type of member's experience. The tags may be the employee's personal experience, the employee's current company experience, the employee's experience in other organizations, and so on. A tag is, for example, information such as "#1" and "#2" as shown in FIG. In this case, the information processing device 10 assigns "#1" as the member's personal experience and "#2" as the company's experience.

The fourth data DB 124 is a database that holds the status of each employee.

FIG. 6 is a diagram showing the data structure of the fourth data DB 124. As shown in FIG. The records of the fourth data DB 124 include the item "member ID", the item "word", the item "importance", the item "link", and the like.

The item "word" is information indicating words extracted from all episodes of the employee. Word extraction will be described later. The word is information such as "cheerful", "friendly", etc., as shown in FIG. A word here can be a noun, an adjective, or a verb. The word may include other parts of speech (for example, adverbs, auxiliary verbs, etc.). The same applies to the fifth data DB 125 as well.

The item "importance" is information indicating how important the word is to the employee. The importance is information normalized between 0 and 1, as shown in FIG. 6, for example. In this disclosure, the closer the importance is to 1, the more important it is.

The item "link" is the link of the episode containing the word. The link includes the address of the text data of the episode of the item “episode” of the third data DB 123 . Note that the link may include links to multiple episodes.

The fifth data DB 125 is a database that holds the status of groups.

FIG. 7 is a diagram showing the data structure of the fifth data DB 125. As shown in FIG. The records of the fifth data DB 125 include the item "group", the item "word", the item "importance", the item "link", and the like.

The item "group" is information for identifying the entire company and subgroups in the company. A subgroup is a department to which the user belongs, a job title, or the like. The group is, for example, information such as "company" and "sales department" as shown in FIG. In the present disclosure, an example in which subgroups are departments to which they belong will be described.

The item "importance" is information indicating how important the word is to the employees of the item "group". The importance is information normalized between 0 and 1, as shown in FIG. 7, for example. In this disclosure, the closer the importance is to 1, the more important it is.

The item "link" is the link of the episode containing the word. The link includes the address of the text data of the episode of the item “episode” of the third data DB 123 . Note that the link may include links to multiple episodes.

The control unit 130 controls the reception control unit 131, the transmission control unit 132, the first display unit 133, the acquisition unit 134, the extraction unit 135, and the second display unit by the processor 11 of the information processing apparatus 10 performing processing according to a program. The function shown in 136 is exhibited.

The reception control unit 131 controls processing for the information processing device 10 to receive a signal from an external device according to a communication protocol.

The transmission control unit 132 controls processing for transmitting a signal from the information processing device 10 to an external device according to a communication protocol.

The first display unit 133 causes the member terminal 20 to display an input screen for allowing the employee to input information about the experience.

Specifically, the first display unit 133 displays data including subjective episodes of the employee's experience, the degree of happiness felt by the employee when the employee has experienced the experience, and tags corresponding to the type of experience. An input screen for accepting input is displayed on the member terminal 20 via communication.

8 and 9 are diagrams showing examples of input screens. As shown in FIG. 8 , the input screen 140 includes a pulldown 141 , a pulldown 142 , an image adding section 143 , a text box 144 , a textbox 145 , a pulldown 146 and a button 147 .

The pull-down 141 is a pull-down menu for inputting the period of experience. In the member terminal 20, the employee responds to the operation of pressing the pull-down menu 141, and a plurality of predetermined times are displayed so as to be selectable (FIG. 9). The pull-down 141 accepts an operation specified by the employee for one or more displayed periods. In the examples of FIGS. 8 and 9, the pull-down 141 uses the example of inputting the Christian era, but it may be possible to input the year/month or year/month/day. Also, the pull-down 141 may be configured to select an age.

Pull-down 142 is a pull-down menu for inputting the degree of happiness. In the member terminal 20, the employee responds to the operation of pressing the pull-down menu 142, and a plurality of predetermined happiness levels are displayed in a selectable manner (FIG. 9). The pull-down 142 accepts the operation specified by the employee for the plurality of displayed happiness levels.

The image adding unit 143 implements a function for the employee to add an image related to the experience. An image can be added by an employee's operation. Note that the image adding unit 143 may be configured to add a moving image.

A text box 144 is a text box for entering the title of the episode.

A text box 145 is a text box for entering an episode about the employee's experience. Note that the text box 145 may include a text box for inputting facts about the episode and a text box for inputting what the employee felt about the episode.

Pull-down 146 is a pull-down menu for entering tags. In the member terminal 20, a plurality of predetermined tags are displayed in a selectable manner in response to the operation of pressing the pull-down menu 141 by the employee (FIG. 9). In the example of FIG. 9, "private experience" corresponds to "employee's personal experience", "work experience" corresponds to "employee's current company experience", and "other 'experience at other organizations' corresponds to 'employee's experience at other organizations'.

The button 147 is a button for transmitting data input to the pulldown 141 , the pulldown 142 , the image addition section 143 , the text box 144 , the textbox 145 , and the pulldown 146 to the information processing apparatus 10 . The member terminal 20 transmits the data to the information processing device 10 in response to the employee pressing the button 147 on the member terminal 20 .

Further, the first display unit 133 may display an input screen for interactively inputting the data. Specifically, the input screen receives data input by a dialogue system such as a chatbot.

FIG. 10 is a diagram showing an example of an input screen. As shown in FIG. 10 , input screen 150 includes chat screen 151 , text box 152 and button 153 .

The chat screen 151 is a screen for displaying questions and answers regarding episodes in an interactive manner. The first display unit 133 displays data desired to be input to the pull-down 141, the pull-down 142, the text box 144, the text box 145, and the pull-down 146 in the form of a question on the chat screen 151. FIG.

As for the interactive format, for example, a method of sequentially displaying predetermined questions each time an answer is received, a method of using a chatbot, or the like can be adopted. Also, for the questions, objective questions and subjective questions about the episode are prepared so that the employee can easily answer them. With such a configuration, the employee can input the objective part and the subjective part of the episode separately.

A text box 152 is a text box for entering an answer.

A button 153 is a button for replying to the chatbot with the text data entered in the text box 152 . In the member terminal 20 , in response to the employee pressing the button 153 , the member terminal 20 transmits the data entered in the text box 152 to the information processing device 10 .

Employees who don't usually reflect much often find it difficult to put their experiences into words. Therefore, the first display unit 133 can input several (for example, three) experiences to be input in an interactive manner through the input screen 150 . After that, the first display unit 133 receives input from the input screen 140 . By doing so, even an employee who is not good at self-reflection will be able to self-reflect. In addition, it is possible to eliminate the annoyance of the employees. In addition, the first display unit 133 can facilitate the input of episodes by allowing the chatbot to separately input facts, which are objective parts, and feelings, which are subjective parts.

The acquisition unit 134 includes, for each employee belonging to the company, a subjective episode about the employee's experience, the degree of happiness felt by the employee when the employee has experienced the experience, and a tag corresponding to the type of experience. accept data.

Specifically, the acquisition unit 134 accepts data received from the member terminal 20 . Acquisition unit 134 stores the received data in third data DB 123 .

The extraction unit 135 extracts at least one of the state of a specific employee in a company and the state of the company from a plurality of data.

Specifically, for each employee, the extraction unit 135 generates a first graph that chronologically represents the degree of happiness for each experience of the employee as the state of the employee. For example, the first graph is a line graph, curve, or the like, with the vertical axis representing the degree of happiness and the horizontal axis representing the Western calendar, passing through the employee's episode time and happiness level.

In addition, the extracting unit 135 generates a second graph showing, as the state of the company, the statistical values of the happiness levels of each employee of the company having experience during a predetermined time in chronological order. For example, the second graph is a line graph, a curve, or the like, with the vertical axis representing the degree of happiness and the horizontal axis representing the Western calendar, passing through the average happiness level of employees who have experience in each period. Note that the extraction unit 135 may be configured to use a median value, a representative value determined by a predetermined method, or the like instead of the average happiness level.

The extracting unit 135 also generates a third graph showing, as the state of each subgroup, the statistic value of the happiness level of each employee in the subgroup having experience during a predetermined time in chronological order. For example, the third graph is a line graph, curve, or the like, with the vertical axis representing the degree of happiness and the horizontal axis representing the Western calendar, passing through the average happiness of employees belonging to each subgroup having experience in each period.

The extraction unit 135 stores the generated first graph, second graph, and third graph of each department in the storage unit 120 .

Also, the extraction unit 135 analyzes a plurality of data about a specific employee, and calculates the importance of each word for the specific employee for each word included in the plurality of data.

Specifically, the extraction unit 135 first extracts words from each episode of the employee. More specifically, the extraction unit 135 extracts word groups contained in the document by analyzing all the episodes as one document by a technique such as morphological analysis. The extraction unit 135 stores the extracted word group in the fourth data DB 124 and the fifth data DB 125 . The extraction unit 135 also associates the extracted word group with the part of speech of each word, and stores them in the storage unit 120 .

Next, the extraction unit 135 calculates the importance of each word in the extracted word group for each employee. Specifically, for each word in the word group, the extraction unit 135 calculates the frequency of the word in all episodes of the employee and a coefficient predetermined according to the happiness level of the episode containing the word. Based on this, the importance is calculated.

More specifically, for each word in the word group, the extraction unit 135 calculates a score according to the frequency of appearance of the word from the frequency of appearance of the word in the document and the general appearance rate of the word.

Next, the extraction unit 135 obtains, for each word in the word group, a coefficient corresponding to the degree of happiness in the episode in which the word appears. The coefficient according to the degree of happiness is calculated from the sum of the degrees of happiness in the episodes in which the word appears, the number of appearances for each degree of happiness, and the like. Here, the coefficient corresponding to the degree of happiness is set to a larger value as the absolute value of the degree of happiness increases. This is because words appearing in episodes in which the employee's subjective feelings are high are considered to be of high importance. For example, the coefficient is set to increase exponentially as the absolute value of happiness increases from 0 happiness.

Here, an example in which the extraction unit 135 calculates a coefficient according to the degree of happiness will be described. Extraction part 135 defines the 1st coefficient for every degree of happiness. For example, the first coefficient is s ₁ when the happiness level is 1, s ₂ when the happiness level is 2, and so on. Here, s _i (i is an integer, −10≦i≦10) is a predetermined real number. For each level of happiness, the extraction unit 135 calculates a second coefficient by multiplying the number of appearances of a word in that level of happiness by the first coefficient of that level of happiness. That is, the second coefficient t _i in the degree of happiness i is expressed as t _i =e _i ×s _i . Here, ei is the number of occurrences of the word at happiness level _i . Then, the extracting unit 135 calculates the sum of the second coefficients for each happiness level of the word as a coefficient corresponding to the happiness level.

Then, the extracting unit 135 calculates the degree of importance of each word in the word group from the score corresponding to the appearance frequency of the word and the coefficient corresponding to the degree of happiness of the word. The importance is, for example, a value obtained by multiplying the score and the coefficient.

Here, an episode can be divided into a title and a text. Since the title is a summary of the episode, the words in the title can be considered more important to the employee than just the words in the main text. For this reason, when a certain word is included in the title, the extraction unit 135 sets a larger coefficient than when the word is included in the text, even if the degree of happiness is the same. That is, in a certain level of happiness, the importance can be calculated more appropriately by setting the coefficient for inclusion in the title to be a larger value than the coefficient for inclusion in the text. For example, let the first coefficient in the title be a larger value than the first coefficient in the text.

In addition, the extraction unit 135 analyzes a plurality of data about employees in a company, and calculates the importance of each word for the company for each word included in the plurality of data.

Specifically, the extraction unit 135 first extracts words from all employee episodes. More specifically, the extraction unit 135 extracts word groups contained in the document by analyzing all episodes of all employees as one document by a technique such as morphological analysis.

Next, for each word in the word group, the extraction unit 135 calculates a score corresponding to the frequency of appearance of the word from the frequency of appearance of the word in the document and the general appearance rate of the word. Next, the extraction unit 135 obtains, for each word in the word group, a coefficient corresponding to the degree of happiness in the episode in which the word appears.

Then, the extracting unit 135 calculates the degree of importance of each word in the word group from the score corresponding to the appearance frequency of the word and the coefficient corresponding to the degree of happiness of the word. The importance is, for example, a value obtained by multiplying the score and the coefficient.

In addition, the extraction unit 135 analyzes a plurality of data on employees in each subgroup, and calculates the importance of each word for the company for each word included in the plurality of data. In this case, the extraction unit 135 treats all episodes of all employees belonging to the subgroup as one document and obtains the importance of each word in the word group, as in the case of a company.

Then, the extracting unit 135 stores each calculated degree of importance in the storage unit 120 .

Note that the extraction unit 135 calculates the degree of importance for each tag to be analyzed. For example, the extraction unit 135 calculates the degree of importance for each combination of all three tags.

Further, the extracting unit 135 can calculate the above-mentioned importance using only episodes with a positive happiness level among the above-mentioned documents. When the degree of happiness is a positive value, it is considered that the employee perceives the episode as positive. Therefore, the extraction unit 135 can obtain the importance of words expressing a positive state. Similarly, the extracting unit 135 can calculate the importance using only episodes with negative happiness levels. In this case, the extraction unit 135 can obtain the importance of the word expressing the negative state.

The second display unit 136 causes the user terminal 30 to display a selection screen for accepting selection.

Specifically, the second display unit 136 causes the user terminal 30 to display a selection screen for the user to select a tag via communication.

In addition, the second display unit 136 causes the user terminal 30 to display a selection screen for selecting an object to be displayed via communication. Here, a user is a person who views the status of a particular employee, the status of a company, or the status of a particular department.

FIG. 11 is a diagram showing an example of the selection screen. As shown in FIG. 11 , the selection screen 160 includes check boxes 161 , 162 , 163 , 164 , 165 and 166 .

Check box 161 is a check box for accepting a selection for confirming the status of a particular employee. A check box 162 is a check box for confirming the state of the company. A check box 163 is a check box for confirming the state of the department.

Check boxes 164 to 166 are check boxes for selecting tags of episodes to be analyzed. Check box 164 is a check box for selecting whether or not to include the employee's personal experience. A check box 165 is a check box for selecting whether or not to include the employee's current experience at the company. Check box 166 is a check box for selecting whether or not to include the employee's experience in other organizations.
When any one of the check boxes 161 to 163 is selected, the second display unit 136 causes a transition to the corresponding screen.

When the check box 161 is selected, the second display unit 136 causes the user terminal 30 to display a designation screen for designating an employee. FIG. 12 is a diagram showing an example of the designation screen. As shown in FIG. 12, the designation screen 170 includes a text box 171, a search button 172, a list box 173, an enter button 174, and the like.

A text box 171 is a text box for entering a keyword for searching for an employee. A search button 172 is a button for searching for an employee based on the keyword entered in the text box 171 . When search button 172 is pressed by a user's operation, second display unit 136 searches for an employee from storage unit 120 .

The list box 173 is a list box for selectively displaying employee search results. A list box 173 displays a list of employees who match the keyword as a result of the search by pressing the search button 172 .

The decision button 174 is a button for accepting designation of an employee selected by a user's operation from the list of employees displayed in the list box 173 . When the decision button 174 is pressed, the second display section 136 acquires information on the selected employee from the storage section 120 . Then, the second display unit 136 causes the user terminal 30 to display the first status screen for displaying the status of the employee. Note that the second display unit 136 may be configured to cause the extraction unit 135 to generate the first graph for the selected employee at the timing when the enter button 174 is pressed.

FIG. 13 is a diagram showing an example of the first state screen. As shown in FIG. 13, the first state screen 180 includes a graph display portion 181, an episode display portion 182, a PWR display portion 183, an NWR display portion 184, and the like.

The graph display unit 181 displays the first graph. Specifically, the graph display unit 181 displays the first graph of the selected employee. Also, the second display unit 136 selectably displays an arbitrary portion of the time series on the first graph.

The episode display section 182 displays episodes of the portion of the first graph selected in the graph display section 181 . The episode display unit 182 may be configured to automatically or manually display episodes in chronological order. In the case of manual operation, the first state screen 180 may be configured to accept user operations by providing buttons for operating the front and back, for example.

The PWR display unit 183 displays a word ranking in which words are arranged in order of positive importance. The NWR display unit 184 displays word rankings in which words are arranged in descending order of negative importance. Specifically, the second display unit 136 acquires the word of the employee, the positive importance and negative importance of the word, and the word group from the storage unit 120 . Second display 136 then generates a positive importance ranking. The second display unit 136 displays the top N (N≧1) words on the PWR display unit 183 as positive word rankings. In addition, the second display unit 136 generates negative importance rankings. The second display unit 136 displays the top M (M≧1) words on the NWR display unit 184 as negative word rankings. The second display unit 136 may be configured to display positive and negative word rankings for each part of speech.

Further, the second display unit 136 may display an image diagram in which the top or bottom N-ranked words are emphasized according to their importance, instead of the word ranking. FIG. 14 is a diagram showing an example of an image diagram. In the example of FIG. 14, the words with the highest degree of importance are displayed in a large size, and the lower the order, the smaller the display. Further, the second display unit 136 may color-code the words according to the degree of importance in the image diagram. In this manner, the second display unit 136 displays images, thereby improving visibility.

By displaying the word ranking, it is possible to grasp the important words in expressing the employee's status and employee characteristics.

In addition, the second display unit 136 displays each word ranking or each word of the image diagram in a selectable manner. Specifically, the second display unit 136 generates an episode screen for displaying an episode containing the word. Then, the second display unit 136 causes each word displayed in the word ranking to include a link to the episode screen. When the user selects a word in the word ranking, the screen transitions to the episode screen.

FIG. 15 is a diagram showing an example of an episode screen. As shown in FIG. 15, the episode screen 190 includes episodes containing the selected word and their happiness levels. As shown in FIG. 15, the selected word is displayed with emphasis (the shaded portion in FIG. 15), making it easy to grasp the frequency of the word and the situation in which it is used. The number of episodes displayed on the episode screen may be one or plural. Also, the episode screen may be a screen displaying a list of a plurality of episodes, and may be configured to display the content of the episode by selecting the episode or the like.

By displaying the contents of episodes that include words from the words in the word ranking, it is possible to capture how employees feel about the displayed words. It is also configured to verify the actual episode from the word ranking. As a result, the user can grasp the employee's condition and capture the characteristics of the employee. Therefore, the user can select appropriate improvement measures for each employee. In addition, since it is possible to grasp the real experience of each employee, it is possible to improve the efficiency and deepen the understanding of employees' personalities much more than numerical questionnaire data.

When the check box 162 is selected, the second display unit 136 causes the user terminal 30 to display a second status screen for displaying the status of the company. Note that the second display unit 136 may be configured to cause the extraction unit 135 to generate the second graph at the timing when the check box 162 is selected.

FIG. 16 is a diagram showing an example of the second state screen. As shown in FIG. 16, the second status screen 200 includes a graph display portion 201, a PWR display portion 202, an NWR display portion 203, and the like.

The graph display unit 201 displays a second graph.

The PWR display unit 202 displays word rankings in which words are arranged in order of positive importance for companies. The NWR display unit 203 displays a word ranking in which words are arranged in descending order of negative importance for companies. Specifically, the second display unit 136 acquires the word of the company, the importance of the word, and the word group from the storage unit 120 . Next, the second display unit 136 generates an importance ranking. The word ranking is the same as that of the first state screen 180 .

When the check box 163 is selected, the second display unit 136 causes the user terminal 30 to display a selection screen for designating a department. FIG. 17 is a diagram showing an example of the department selection screen. As shown in FIG. 17, the department selection screen 210 includes a pull-down 211, an enter button 212, and the like.

A pull-down 211 displays departments that can be selected by pressing a downward triangle button.

The decision button 212 is a button for accepting the selection of the department selected in the pulldown 211 . When decision button 212 is pressed, second display unit 136 acquires information on the selected department from storage unit 120 . Then, the second display unit 136 causes the user terminal 30 to display a third status screen for displaying the status of the department. Note that the second display unit 136 may be configured to cause the extraction unit 135 to generate a third graph for the selected department at the timing when the enter button 212 is pressed.

FIG. 18 is a diagram showing an example of the third state screen. As shown in FIG. 18, the third status screen 220 includes a graph display portion 221, a PWR display portion 222, an NWR display portion 223, and the like.

The graph display section 221 displays the third graph.

The PWR display unit 222 displays a word ranking in which words are arranged in order of positive importance for departments. The NWR display unit 223 displays a word ranking in which words are arranged in descending order of negative importance for departments. Specifically, the second display unit 136 acquires the word of the department, the importance of the word, and the word group from the storage unit 120 . Next, the second display unit 136 generates an importance ranking. The word ranking is the same as that of the first state screen 180 .

By displaying the content of episodes containing words from the words in the word ranking, it is possible to vividly capture how members actually feel about each important word in expressing the state of the company as a whole or the relevant department. can be done. In addition, positive word rankings and negative word rankings can be used to identify words that express a positive state and words that express a negative state. Site is easy to understand. Accordingly, it is possible to select an appropriate improvement measure according to the state of the company or department. In addition, since it is possible to grasp the real experience of each employee, it is possible to grasp the organizational state of the entire company and departments with a much higher resolution than numerical questionnaire data.

<2. Operation>
Processing in the information processing apparatus 10 will be described below with reference to the drawings.

<2.1. Data collection processing>
FIG. 19 is a flowchart showing an example of the flow of data collection processing by the information processing apparatus 10. As shown in FIG. The information processing apparatus 10 executes the process in response to an input screen display request or the like.

In step S101, the first display unit 133 causes the member terminal 20 to display an input screen for allowing the employee to input information about their experience.

In step S<b>102 , the acquisition unit 134 receives data received from the member terminal 20 .

In step S103, the acquisition unit 134 stores the acquired data in the third data DB 123, and terminates the process.

The information processing apparatus 10 can update the data by having the employee input the data collection process not only once per employee, but also periodically, for example. By repeating the data collection process, it is possible to improve the accuracy of the data analysis process, and it is possible to capture changes in the user's feelings, changes in the environment, and the like.

<2.2. Data Analysis Processing>
FIG. 20 is a flow chart showing an example of the flow of data analysis processing by the information processing apparatus 10 . The information processing apparatus 10 executes the process at arbitrary timing. As for the arbitrary timing, for example, a period is provided for all employees to input data on the member terminals 20, and the process is executed after the period has passed.

In step S<b>111 , the extraction unit 135 acquires each episode of the employee from the storage unit 120 .

In step S112, the extraction unit 135 extracts words from each episode of the employee.

In step S113, the extraction unit 135 calculates the importance of each word in the extracted word group for each employee.

In step S114, the extracting unit 135 stores each calculated degree of importance in the storage unit 120, and ends the process. The above flow chart applies mutatis mutandis to the data analysis processing for companies and departments.

<2.3. Analysis Result Display Processing>
FIG. 21 is a flowchart showing an example of the flow of analysis result display processing by the information processing apparatus 10 . The information processing apparatus 10 executes the process at arbitrary timing. Arbitrary timings include, for example, (1) a scene where an employee has an interview with a boss, and (2) a scene where a management analyzes the state of a company or department and considers improvement measures. The user is the boss in the case of (1), and the manager in the case of (2).

In step S121, the second display unit 136 causes the user terminal 30 to display, via communication, a selection screen for selecting an analysis target (specific employee, company, department) and a tag. .

In step S122, the second display unit 136 accepts selection of an analysis target and a tag.

In step S123, the second display unit 136 displays the first status screen for displaying the status of the employee and the second status screen for displaying the status of the company according to the selected analysis target and tag. Alternatively, the third state screen for displaying the state of the department is displayed on the user terminal 30, and the process ends.

<3. Summary>
As described above, according to the present disclosure, for each member belonging to the group, multiple inputs of data including the member's subjective episode regarding the member's experience are received, and from the multiple data, By extracting at least one of the state of the specific member and the state of the group, and displaying at least one of the extracted state of the specific member and the state of the group, the group and the member state can be appropriately expressed. Further, by grasping the states of the group and members, it is possible to grasp the characteristics of the group and members.

Conventionally, even if a numerical value (for example, an engagement score) indicates the state of an organization, there has been a problem of not knowing what to do to actually improve the state of the organization. In addition to engagement, services that aggregate all kinds of personnel-related data and use AI to optimize placement and reduce turnover rates have also emerged. However, this approach relies on existing quantitative data within the company. For this reason, if the data accumulated by the company to date has problems such as gender-discriminatory personnel decisions, there is a risk of discrimination and categorization. In this way, conventionally, since qualitative data for making judgments for each person/individual has not been utilized, there has been the problem that it is not possible to support appropriate personnel-related problem solving.

In addition, surveys on organizational conditions such as engagement surveys are surveys on preset items that companies want to measure, and most of them are quantitative measurements. As a result, the actual experiences and emotions of employees cannot be grasped vividly, and since the resolution of grasping the actual situation of employees is low, there is a problem that the accuracy of the organization's improvement measures cannot be improved.

According to the present disclosure, input of data including qualitative data, which is the member's subjective episode regarding the member's experience, and happiness, which is quantitative data, is accepted. Based on the qualitative data and quantitative data input by each member of the group, the experience/feelings in life/work and the state/relationship of the degree of happiness are analyzed and visualized. In this way, it is possible to consider the situation of each organization or employee using qualitative data, taking quantitative data into consideration, and encourage decisions to increase happiness by changing communication management for individual employees. . Able to support organizational problem solving.

By using qualitative data in particular, we can understand the real voices of members, so we can understand countermeasures according to the conditions of members and the group as a whole. For example, you can find out how to communicate that suits the feelings of individual employees and keywords that can be positive. For this reason, it is possible to derive appropriate improvement measures for solving problems such as reducing the turnover rate of companies, improving productivity, and understanding detailed organizational conditions. In addition, in an organizational analysis of multiple people, it was also possible to determine what kind of experience the company was able to provide to its employees, and how happy employees felt from the experiences they provided and the experiences they provided. I can understand.

More specifically, by allowing users to grasp the current life and work situations of group members, it will be possible to conduct highly individualized management that matches the values and strengths of each employee. If the user is a management team, the current situation of the entire company/department can be grasped not only quantitatively but also with high resolution including detailed qualitative data. be able to. Also, by making particular use of qualitative data, the state of the tissue can be displayed with increased resolution.

Further, for example, there is a service that measures individual characteristics by aptitude tests based on questionnaire items such as "SPI" and "Strength Finder" (registered trademark). However, even if individual characteristics are measured and categorized using predetermined quantitative questionnaire items, it is difficult to capture in detail the unique conditions and characteristics of individuals.

In the present disclosure, for each member of the group, by extracting and displaying the state of a specific member or the state of the group from the member's subjective episode about the member's experience, qualitative data on the experience of the member of the group It is possible to grasp the state of a specific member or the state of a group that has been used. By displaying the status of a specific member, the member's status can be visualized and shared together with the individual characteristics derived from it.

It is also possible to display the state of a specific member together with the state of the group. Thus, by displaying the state of a particular member and the state of the organization, the state of the member can be visualized and shared together with the personal characteristics derived therefrom. Also, visualization of tissue characteristics and conditions can be achieved.

Also, by analyzing a plurality of data about a specific member, the importance of each word for the specific member is calculated for each word included in the plurality of data. In this way, the extraction unit 135 generates quantitative data of importance from qualitative data. Even if the same word is used with the same frequency, the degree of importance differs among members because the happiness value (how each individual feels) differs. Therefore, it is possible to finely express the granularity of the member status and group status. As a result, the user can prompt each member to make a decision to increase the happiness level by changing the communication management. Also, the words are displayed according to their importance. For this reason, it is possible to grasp words having a high degree of importance in expressing the features and states of the members.

Also, according to the present disclosure, one or more words are displayed according to their importance. For example, the second display unit 136 displays word rankings arranged in order of importance. As a result, the user can be more appropriately prompted to make a decision to increase the happiness level by changing the communication management for each individual member.

In addition, according to the present disclosure, the content of the member's life experience and the degree of happiness at that time are displayed in chronological order. Members' chronological life experiences and happiness levels can be visualized on the web. By sharing this with other members of the workplace, it is possible to utilize it for team building management based on mutual understanding, and it is possible to reduce the turnover rate and improve productivity. In addition, since the company owns what kind of work and experience it has given to its employees, by analyzing the level of happiness in the work experience of its members, it is possible to grasp the current situation of the entire company, each organization, and each individual at the episode level. It can help companies solve problems (for example, reduce the turnover rate).

<Modification>
In the above, data analysis such as calculation of the degree of importance is performed in advance, and the analysis result is displayed based on the analysis. Modification 1 describes a case where data analysis is performed when an analysis request is received from a user. The same reference numerals are assigned to the same functions and configurations as those described above, and the description thereof is omitted.

FIG. 22 is a flow chart showing an example of the flow of data analysis processing by the information processing apparatus 10. As shown in FIG. The information processing apparatus 10 executes the process at arbitrary timing. Arbitrary timings include, for example, (1) a scene where an employee has an interview with a boss, and (2) a scene where a management analyzes the state of a company or department and considers improvement measures. The user is the boss in the case of (1), and the manager in the case of (2).

In step S<b>131 , the extracting unit 135 acquires from the storage unit 120 each episode in the selected target corresponding to the analysis target and the tag received by the second display unit 136 in step S<b>122 .

In step S132, the extraction unit 135 extracts word groups from the acquired episodes.

In step S133, the extraction unit 135 calculates the importance of each word in the extracted word group.

As described above, according to this modification, an input of an analysis target and a tag is received, the state of the target is extracted according to the received analysis target and tag, and the extracted state of the target is By displaying, the status of groups and members can be expressed appropriately.

<Other Modifications>
Although the disclosed embodiments have been described above, they can be implemented in various other forms, and can be implemented with various omissions, substitutions, and modifications. These embodiments, modifications, omissions, substitutions and changes are included in the technical scope of the claims and their equivalents.

For example, in the above description, the second display unit 136 is configured to separately display the state of a specific member, the state of a group, and the state of a subgroup, but the present invention is not limited to this. For example, the second display unit 136 simultaneously displays the status of a plurality of members, simultaneously displays the status of a plurality of subgroups, simultaneously displays the status of a group, the status of a member, and the status of a subgroup. A plurality of them may be combined and displayed.

In addition, a configuration may be adopted in which the state of a specific member, the state of a group, or the state of a subgroup is simultaneously displayed for each tag. The importance of a word in each tag makes it possible to grasp how members, groups, or subgroups feel about the same word, and what state they are in. For example, if a certain member's word is of low importance in the group, but high in the experience of other groups, understand how the member perceives it and the issues in the group's efforts related to the word. can do.

Alternatively, the third data DB 123 may be searched for collected episodes based on the input keyword, and the episodes corresponding to the keyword may be output. In this case, it is possible to confirm how the person, thing, event, or the like indicated by the input keyword is perceived by a specific member, group, or subgroup. In the case of specific members or subgroups, the search range in the third data DB 123 may be limited to specific members or subgroups. For example, in a company, when the president searches for episodes using his name as a keyword, the president can know what he thinks of himself from the output episodes.

Furthermore, the extraction unit 135 may perform the above data analysis on one or more episodes obtained by searching using the keyword. Specifically, the extraction unit 135 extracts word groups contained in the document by analyzing one or more episodes obtained by searching with the keyword as one document by a technique such as morphological analysis. do. Next, the extraction unit 135 calculates the importance of each word in the extracted word group for each employee. Then, the second display unit 136 displays the word ranking. With such a configuration, it is possible to grasp the state of a specific member, the state of a group, etc. with respect to a keyword. If the keyword is a policy for a group, it is possible to take countermeasures by grasping how the policy is perceived. In addition, by analyzing at multiple points in time, it is possible to grasp changes in how specific members and groups perceive the policy. As a result, it is possible to determine whether measures are good or bad, formulate improvement plans based on the words that appear in the word ranking, and the like.

In addition, in the above disclosure, text boxes, pull-downs, check boxes, and the like are used as input methods, but the method is not limited to these. Other methods may be used. Moreover, it is good also as a structure which uses a voice input for the input method.

Further, the screen displayed by the second display unit 136 may be configured to receive designation or selection of time or period. For example, there may be a change in each employee between the period from 4 years ago to 2 years ago and the period from 2 years ago to the present. In addition, the state of organizations such as companies and departments also differs. Therefore, the user can perform various analyzes by using the analysis results for multiple periods.

Moreover, when the user is a personnel department, it is also possible to apply the method to determine assignment destinations based on the status of individual employees and the status of departments that can be read from the analysis results.

Also, in the above description, the group is a company and the members are employees. For example, it can be applied when the group is a university and the members are university students. In this case, subgroups may be departments, circles, and the like. In this way, it is possible to apply the technology of the present disclosure to various groups.

Also, in the above description, the case where the first information is the degree of happiness has been described as an example, but the present invention is not limited to this. For example, the first information may be the member's interest in the experience. For example, the degree of interest in the words childbirth and childcare leave can be used to improve policies and childcare leave systems in companies. Since the themes differ in how they feel depending on gender and age, it is possible to grasp the conditions of the participants and capture the characteristics of the theme.

Further, the information processing device 10 may be configured as a server on an in-house LAN.

Also, each function of the information processing device 10 may be configured in another device. For example, each DB of the storage unit 120 may be constructed as an external database.

Moreover, it is good also as a structure which performs the said analysis result display process about the said specific employee, after performing a data collection process about several experiences of a specific employee. For example, if an employee has an interview with a supervisor, have the employee enter data for this disclosure. In this case, the extraction unit 135 generates the first graph of the employee, and the second display unit 136 displays the first graph of the employee and the episode. As a result, it is possible to increase the productivity of each employee by having the superior understand how each employee feels and performing specialized management for each employee.

The technology of the present disclosure can also be used for training. The training includes, for example, mutual understanding training for new employees, training for team building, training for improving management skills, career design training, and the like. Since the technology of the present disclosure can be applied to environments via communication as described above, it is highly compatible with online training. In addition, by storing data in each DB, it is possible to continue using data after training and add data, so that training is not completed one-off, and is linked to actual work and post-training productivity. It is possible to implement highly effective training. An example of utilization in new employee training will be described below. Below, the first graph and episode are referred to as human hints.

(Advance preparation to training day)
During the training period, human hints are generated according to the flow of (1) to (4) below.
(1) A first graph is generated by inputting an episode and a degree of happiness into the information processing apparatus 10 by one training participant in advance.
(2) On the day of the training, the participants of the training form a pair and listen to each other's episodes and happiness while looking at each other's human hints and having a conversation. At this time, the training participant asks a question such as adding an episode at a point in time when there is no episode in the first graph, or a question such as digging into the inputted episode. For example, the other party is asked questions such as "How was it when you were a junior high school student?", "What did you think at that time?" The training participants reflect the content of the dialogue with the other party and input episodes and happiness levels into the information processing device 10 to update human hints and enrich the content.
(3) All the training participants present and share the contents of the human hints one by one. By doing so, it is possible to deepen mutual understanding of personalities.

(After training: team building)
Share rookie human tips with the team or manager to which the rookie is assigned. In addition, all team members on the receiving side will also hold a workshop to create and share human hints.

(After training: management)
Based on monthly review timings, the newcomers input not only the results of their work, but also episodes of what kind of events they felt and how they felt. Managers manage newcomers not only based on what they can see in their work, but also based on human hints about their life and work so far.

In this way, using the technology of the present disclosure, human hints are created from qualitative data, which are subjective episodes of members, and happiness levels, which are quantitative data. can be properly expressed. From this, it is possible to judge appropriate measures for necessary improvements in management.

<Appendix>
The items described in each of the above embodiments will be added below.
(Supplementary Note 1) A step (S102) of receiving, in a computer (for example, information processing device 10), a plurality of inputs of data including the member's subjective episode regarding the member's experience for each member belonging to the group; a step of extracting at least one of the state of a specific member in the group and the state of the group from the data of; extracting at least one of the extracted state of the specific member and the state of the group; A program for executing a displaying step (S123).

(Appendix 2) The data includes first information that quantifies what the member felt when he/she experienced the experience, and in the extracting step, the specific member's experience A first graph representing the first information in time series for each group, and a second graph representing the statistical value of the first information of each member of the group having experience at a predetermined time as the state of the group in time series. The program according to Appendix 1, which generates at least one of the graphs.

(Appendix 3) The data includes second information according to the type of experience of the member, and in the extracting step, for each of the second information, the state of the specific member and the state of the group The step of extracting and displaying at least one of the above includes displaying at least one of the state of the specific member and the state of the group according to the selected second information, and displaying the second information a step of selectably displaying (S121);
The program according to (Appendix 1) or (Appendix 2), causing a step (S122) of receiving the selection of the second information to be executed.

(Appendix 4) The data includes second information corresponding to the type of experience of the member, the step of displaying the second information in a selectable manner, the step of receiving selection of the second information, and the extracting The program according to (Appendix 1) or (Appendix 2), wherein the step extracts at least one of the state of the specific member and the state of the group for the selected second information.

(Appendix 5) The program according to (Appendix 3) or (Appendix 4), wherein the type of experience is a personal experience of the member, an experience in the group, or a subgroup experience in a group other than the group.

(Additional remark 6) By analyzing the plurality of data about the specific member, for each word contained in the plurality of data, the step of calculating the importance of the word for the specific member (S113 ), and a step of displaying one or more words according to the degree of importance (S123).

(Appendix 7) A step of obtaining the importance of each word in the group from the importance of each of the words for each member belonging to the group; The program according to either (Appendix 5) or (Appendix 6) for displaying the above words.

(Appendix 8) The program according to (Appendix 6) or (Appendix 7), wherein in the calculating step, the importance is weighted based on the first information.

(Appendix 9) Execute a step of displaying a screen for inputting the data in an interactive manner, and in the receiving step, receive the input of the episode input on the screen (Appendix 1) to (Appendix 8) program as described.

(Appendix 10) In the step of displaying the screen, the screen is displayed so that the member can separately input an objective part and a subjective part about the episode (Appendix 9) any of the programs described.

(Appendix 11) Means (134) for receiving a plurality of inputs of data including subjective episodes of the members with respect to the experiences of the members for each of the members belonging to the group; and means (135) for extracting at least one of the state of and the state of the group; means (136) for displaying at least one of the extracted state of the specific member and the state of the group; An information processing device (10) comprising:

(Supplementary Note 12) A step (S102) in which the computer (for example, the information processing device 10) receives multiple inputs of data including subjective episodes of the member's experience for each member belonging to the group (S102); a step of extracting at least one of the state of a specific member in the group and the state of the group from the data of; extracting at least one of the extracted state of the specific member and the state of the group; a displaying step (S123);

(Appendix 13) A computer (for example, the information processing device 10) stores, for each member belonging to the group, the member's subjective episode regarding the member's experience and the degree of happiness that the member felt when the member had the experience. A step (S102) of accepting multiple inputs of data including and, from the plurality of data, a first graph representing the happiness level for each experience of a specific member in the group in chronological order, and a member in the group The step of generating at least one of a second graph representing the degree of happiness for each experience in chronological order, and the step of displaying at least one of the generated first graph and the second graph (S123) are executed. , wherein, in the displaying step, when displaying the first graph, the episode of the specific member is displayed together with the first graph.

1 information processing system 10 information processing device 11 processor 12 memory 13 storage 14 communication IF 15 input/output IF 20 member terminal 30 user terminal 40 network 110 communication unit 120 storage unit 121 first data DB, 122 second data DB, 123 third data DB, 124 fourth data DB, 125 fifth data DB, 130 control section, 131 reception control section, 132 transmission control section, 133 first display section, 134 acquisition section , 135 extraction unit, 136 second display unit.

Claims (13)

to the computer,
For each member belonging to the group, receiving multiple inputs of data including subjective episodes of the member's experience;
Extracting at least one of the state of a specific member in the group and the state of the group from the plurality of data;
displaying at least one of the extracted state of the specific member and the state of the group;
program to run. The data includes first information that quantifies what the member felt when experiencing the experience,
In the extracting step, as the state of the specific member, a first graph representing the first information for each experience of the specific member in chronological order, and as the state of the group, experience at a predetermined time. 2. The program according to claim 1, which generates at least one of a second graph representing the statistical value of the first information of each member of the group in chronological order. The data includes second information according to the type of experience of the member,
extracting at least one of the state of the specific member and the state of the group for each of the second information in the extracting step;
The displaying step displays at least one of the state of the specific member and the state of the group according to the selected second information;
selectably displaying the second information;
receiving a selection of said second information;
3. The program according to claim 1 or 2, which causes the program to be executed. The data includes second information according to the type of experience of the member,
selectably displaying the second information;
receiving a selection of said second information;
In the extracting step, extracting at least one of the state of the specific member and the state of the group for the selected second information;
3. A program according to claim 1 or 2. 5. The program according to claim 3 or 4, wherein the type of experience is the member's personal experience, experience in the group, or experience in a group other than the group. By analyzing a plurality of the data about the specific member, for each word contained in the plurality of the data, calculating the importance of the word for the specific member;
displaying one or more of the words according to the importance;
3. The program according to claim 2, causing the execution of a step of calculating, for each word included in the data, the degree of importance of the word for the group by analyzing a plurality of the data for each member belonging to the group;
displaying one or more of the words according to the importance;
3. The program according to claim 2, causing the execution of 8. The program according to claim 6, wherein in said calculating step, said importance is weighted based on said first information. displaying a screen for interactively inputting the data;
9. The program according to any one of claims 1 to 8, wherein, in said accepting step, an episode input through said screen is accepted. 10. The program according to claim 9, wherein in the step of displaying the screen, the screen is displayed so that the member can separately input an objective part and a subjective part about the episode. Means for receiving a plurality of inputs of data including subjective episodes of the member's experience for each member belonging to the group;
means for extracting at least one of the state of a specific member in the group and the state of the group from the plurality of data;
means for displaying at least one of the extracted state of the specific member and the state of the group;
Information processing equipment including. the computer
For each member belonging to the group, receiving multiple inputs of data including subjective episodes of the member's experience;
Extracting at least one of the state of a specific member in the group and the state of the group from the plurality of data;
displaying at least one of the extracted state of the specific member and the state of the group;
how to run. to the computer,
a step of receiving multiple inputs of data including, for each member belonging to the group, the member's subjective episode regarding the member's experience and the degree of happiness felt by the member when the member experiences the experience;
A first graph representing time-series happiness for each experience of a specific member in the group from the plurality of data, and a time-series statistical value of the happiness of each member of the group having an experience at a predetermined time. generating at least one of the second graphs represented by
displaying at least one of the generated first graph and the second graph;
and
In the displaying step, when displaying the first graph, the episode of the specific member is displayed together with the first graph;
program.

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