Matching device, personnel management device, matching method, and program

Abstract

The present invention comprises: an input unit for inputting comprehensive information, which is information relating to personal attributes, work experience, work values, and current job awareness of a first user; a facility identification unit for identifying, from among a plurality of second users, a plurality of similar users having personal attributes, work experience, and work values that are similar to the personal attributes, work experience, and work values of the first user, and identifying a high satisfaction level facility, which is a facility to which belongs a person with a high current job satisfaction level, among the plurality of similar users; and a display unit for displaying the high satisfaction level facility as a recommended facility.

Description

Matching device, human resource management device, matching method, and program

[0001] The present disclosure relates to a matching device, a human resources management device, a matching method, and a program.

[0002] Patent Literature 1 discloses a job search / recruitment support system that can present combinations that are satisfactory to both job-seeking doctors and employing medical institutions. This job search / recruitment support system includes a database that stores information on the ability of doctors and medical institutions' treatment information, a doctor evaluation means that calculates a ranking of weaknesses and strengths of the doctors' specialties, a medical institution evaluation means that calculates a ranking of weaknesses and strengths of the number of cases by medical institution's speciality, and a matching degree calculation means that calculates a weighted score based on the rankings of weaknesses and strengths evaluated by the doctor evaluation means and the rankings of weaknesses and strengths evaluated by the medical institution evaluation means, and calculates a matching degree related to specialties that serves as a measure of a preferable combination.

[0003] Japanese Patent Application Laid-Open No. 2015-132971

[0004] When workplaces are recommended by focusing only on the job seeker's ability-related characteristics, as described in Patent Document 1, there is a problem that the best workplace for the job seeker cannot necessarily be recommended. Conventional matching devices are unable to recommend workplaces that will provide high satisfaction to job seekers, which results in problems such as employees becoming stressed, experiencing health problems, or quitting their jobs at the new workplace. Furthermore, similar problems can arise when human resource management is not carried out appropriately in the workplace.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a matching device that is suitable for recommending the best workplaces to job seekers, a human resources management device that makes it possible to reduce employee turnover, and a matching method and program.

[0006] The matching device according to the present disclosure is characterized by comprising an input unit for inputting comprehensive information which is information relating to a first user's personal attributes, work experience, work values, and current job awareness; a facility identification unit for identifying, from among a plurality of second users, a plurality of similar users who have personal attributes, work experience, and work values ​​similar to the personal attributes, work experience, and work values ​​of the first user, and identifying high satisfaction facilities which are facilities to which those of the plurality of similar users who have high current job satisfaction belong; and a display unit for displaying the high satisfaction facilities as recommended facilities.

[0007] The human resources management device of the present disclosure is characterized by having an information acquisition unit that acquires information regarding professional values ​​and current job awareness for multiple medical institution employees at a predetermined medical institution, and a compatibility rate calculation unit that calculates the compatibility rate for each department of the multiple medical institution employees based on the information obtained by the information acquisition unit.

[0008] Another matching device according to the present disclosure is characterized by having an input unit for inputting comprehensive information, which is information regarding a first user's personal attributes, work experience, work values, and current job awareness, and a display unit for displaying the names of workplaces recommended to the first user as recommended facilities in accordance with the input of the comprehensive information.

[0009] Other features of the present disclosure are set forth below.

[0010] It is possible to provide a matching device suitable for recommending the best workplace for a job seeker and a human resources management device that suppresses employee turnover.

[0011] FIG. 1 is a diagram showing an example of processing by a matching device. FIG. 2 is a diagram showing an example of processing by a recommendation algorithm processing unit. FIG. 3 is a diagram showing an example of processing by a value assessment unit. FIG. 4 is a diagram showing an example of processing by a matching unit. FIG. 5 is a diagram showing an example of screen transitions of an application. FIG. 6 is a diagram showing an example of processing by a human resources management device. FIG. 7 is a diagram showing an example of displaying a relevance rate. FIG. 8 is a conceptual diagram of a system including a matching device and a human resources management device. FIG. 9 is a diagram showing an example of a hardware configuration.

[0012] A matching device, a human resources management device, a matching method, and a program according to embodiments will be described with reference to the drawings. The same or corresponding components are designated by the same reference numerals, and repeated description may be omitted.

[0013] Embodiment. FIG. 1 is a functional block diagram of a matching device according to an embodiment. Job seeker response data 10 is acquired by an input unit 12. According to one example, a job seeker launches application software (hereinafter, sometimes simply referred to as an application) on a computer and answers questions from the application, thereby providing the job seeker response data 10 to the input unit 12. According to one example, the job seeker response data 10 includes information on the personal attributes, work experience, work values, and current job awareness of a first user who is a job seeker. The information on personal attributes, work experience, work values, and current job awareness is collectively referred to as comprehensive information. Below, each of the information on personal attributes, work experience, work values, and current job awareness will be described.

[0014] Examples of personal attributes include date of birth, gender, marital status, number of children, age of youngest child, whether the first user has family members for whom they provide care, the status of family members living together, licenses and qualifications held, educational background, postal code of residence, the first user's employment status, and whether the first user has a job title. In one example, the first user answers the following questions via an application, thereby acquiring personal attribute information: - Please tell us your date of birth - Please tell us your gender (female / male) - Please tell us your marital status (married / single / other) - How many children do you have - Please tell us the age of your youngest child - Is there anyone in your family for whom you are the primary caregiver (yes / no) - Please tell us about the family members living with you. Please answer by your relationship to the person [multiple choices possible] (living alone / spouse / child / father / spouse's father / mother / spouse's mother / grandparents / grandchild / siblings / other) ・Please tell us all the licenses and other qualifications you have [multiple choices possible] (nurse / midwife / public health nurse / licensed practical nurse / specialist nurse / certified nurse / certified nursing manager / graduated training in specific nursing practices / other / none that apply) ・We will ask you about your completed professional education. If you are currently enrolled or dropped out, please specify in the "other" section [multiple choices possible] (nursing university / master's program in nursing graduate school / doctoral program in nursing graduate school / 2-year nursing program / 3-year nursing program / 5-year integrated nursing school / licensed practical nurse training school / other) ・Please tell us the postal code where you live ・Are you currently employed (yes / no / on maternity leave / childcare leave / other) ・Do you hold a position such as head nurse, section chief, or department head? If you have a position, please tell us the name of that position. (This does not include facility staff or preceptors.) (Yes / I do not have a position.)

[0015] According to another example, as questions for acquiring personal attribute information, it is possible to adopt part of the above content, add new questions to the above content, or ask different questions.

[0016] Work experience refers to work history, and includes, for example, years of experience as a nurse, the number of facilities where you have worked, information about the first facility where you worked, the qualifications that served as the basis for employment, the reason for choosing the facility, and your current employment status at that facility. In one example, information about work experience is obtained by the first user answering the following questions via an application: ・How many years of experience do you currently have as a nurse? If you are not currently working or have left nursing, please answer with the number of years at the time of leaving. Also, please consider the year you first started working as a nurse as your "first year." ・How many facilities have you worked at? Please count transfers within the same organization as separate facilities (changes in hospital name, changes in management only, and one-off part-time work such as event rescue are not included in this count). ・For the first facility where you worked, what year (Gregorian calendar) did you start working there? ・For the first facility where you worked, what name did you use? ・Please select the appropriate facility classification from the list below.If you select a hospital, please also select the size, category, and function (hospital / clinic with beds / clinic without beds / nursing care facility / visiting nursing agency / nursery school, kindergarten / government agency / general company / nursing training institution / other) ・If you select a hospital, please select the size (large (over 500 beds) / medium (100-499 beds) / small (under 100 beds)) ・If you select a hospital, please select the category (highly acute / acute / recovery / chronic) ・If you select a hospital, please select the function (general hospital / specialized hospital / psychiatric specialized hospital / specialized function hospital / regional medical support hospital / not applicable) ・What qualification did you work in (nurse / midwife / public health nurse / licensed practical nurse / not working as a nurse)・Please tell us why you chose this facility [multiple selections possible] (good education and career advancement system / good employee benefits / good location for commuting etc. / well-known / recommended by family, acquaintances or teachers / good salary / convenient work schedule / seem like I'll be able to demonstrate my abilities / seem like I'll be able to do what I want / seem like the atmosphere including interpersonal relationships at work / other) ・Are you currently working at this facility (yes / no) ・If you select "no", please tell us the year (Gregorian calendar) when you left or moved to another facility within the same corporation. ・If you select "no", please tell us the reason why you left or moved to another facility within the same corporation (marriage / pregnancy / childbirth / interpersonal relationships at work / lack of fulfillment / deteriorating physical or mental condition / career advancement / interest in other facilities / other companies / further education / study abroad / retirement / change in lifestyle / other).

[0017] According to another example, the questions for obtaining information about work experience may include some of the above content, new questions may be added to the above content, or different questions may be asked.

[0018] Career values ​​are judgments about what and what value is placed on a person's career, which are used as a standard when evaluating a career. Career values ​​are measured, for example, using scales related to intrinsic career values, extrinsic career values, social career values, and authoritative career values. In one example, information about career values ​​is obtained when the first user answers the following questions via an application. [Questions about intrinsic career values] How important are the following items to you in working as a nurse? Please select a number from 1 to 5 for each question. 5. Very important 4. Fairly important 3. Somewhat important 2. Not very important 1. Not important at all Q1. To grow as a nurse (5 / 4 / 3 / 2 / 1) Q2. To improve practical skills as a nurse (5 / 4 / 3 / 2 / 1) Q3. To learn new knowledge and skills (5 / 4 / 3 / 2 / 1) Q4. Regarding the above three items, does the facility where you work meet your values? (5: Very much so / 4: Quite so / 3: Somewhat so / 2: Not very so / 1: Not at all so) [Questions regarding extrinsic career values] How important are the following items to you when working as a nurse? Please select a number from 1 to 5 for each question. 5: Very important 4: Quite important 3: Somewhat important 2: Not very important 1: Not at all important Q1. Earning a salary above average (5 / 4 / 3 / 2 / 1) Q2. Working as a full-time employee rather than a part-time or casual employee (5 / 4 / 3 / 2 / 1) Q3. Having long-term employment guaranteed (5 / 4 / 3 / 2 / 1) Q4. Regarding the above three items, does the facility where you work meet your values? (5: Very much so / 4: Quite so / 3: Somewhat so / 2: Not so much so / 1: Not at all so) [Questions regarding social and professional values] How important are the following items to you when working as a nurse? Please choose a number from 1 to 5 for each question.5. Very important 4. Quite important 3. Somewhat important 2. Not very important 1. Not at all important Q1. To be able to help as many people as possible by working as a nurse (5 / 4 / 3 / 2 / 1) Q2. To be useful to patients or patients by working as a nurse (5 / 4 / 3 / 2 / 1) Q3. To contribute to the team or organization as a nurse (5 / 4 / 3 / 2 / 1) Q4. With regard to the above three items, does the facility where you work meet your values? (5. Very much so / 4. Quite so / 3. Somewhat so / 2. Not very so / 1. Not at all important) [Questions regarding authoritative professional values] How important are the following items to you when working as a nurse? Please choose a number from 1 to 5 for each question. 5. Very important 4. Quite important 3. Somewhat important 2. Not very important 1. Not at all important Q1. To be respected by others as a nurse (5 / 4 / 3 / 2 / 1) Q2. Being highly regarded by patients and subjects (5 / 4 / 3 / 2 / 1) Q3. Being respected by junior nurses (5 / 4 / 3 / 2 / 1) Q4. With regard to the above three items, does the facility where you work meet your values? (5: I think so very much / 4: I think so quite a bit / 3: I think so somewhat / 2: I don't think so very much / 1: I don't think so at all).

[0019] According to another example, as questions for obtaining information about occupational values, some of the above content may be adopted, new questions may be added to the above content, or different questions may be asked.

[0020] Current job perceptions are perceptions of the current work environment. Current job perceptions are measured, for example, using a scale related to workplace social capital, a scale related to work engagement, and a scale related to current job satisfaction. In one example, information on current job perceptions is obtained when the first user answers the following questions via an application. [Questions related to workplace social capital] For the following questions, please choose the answer that best applies to your current workplace. 4. True 3. Somewhat true 2. Somewhat different 1. Different Q1. In our workplace, there is an attitude of working together (4 / 3 / 2 / 1) Q2. In our workplace, we understand and acknowledge each other (4 / 3 / 2 / 1) Q3. In our workplace, work-related information is shared (4 / 3 / 2 / 1) Q4. In our workplace, there is an atmosphere of mutual support (4 / 3 / 2 / 1) Q5. In our workplace, we trust each other (4 / 3 / 2 / 1) Q6. A workplace filled with laughter and smiles (4 / 3 / 2 / 1)

[0021] Workplace social capital is social capital in the workplace. More specifically, it is composed of trust, reciprocity, and personal networks among organizational members. Increased workplace social capital promotes mutually beneficial collaboration and leads to innovation. The six questions presented here are adapted from the workplace social capital scale proposed in the following paper: PLOS ONE "Psychometric assessment of a scale to measure bonding workplace social capital" Hisashi Eguchi, Akizumi Tsutsumi, Akiomi Inoue, Yuko Odagiri (2017). Questions Q1-Q3 above are measures of the cohesive aspects of networks, while questions Q4-Q6 above are measures of cognitive aspects of reciprocity, trust, and smiles. For example, questions used to obtain information on workplace social capital could include some of the above, add new questions to the above, or ask different questions.

[0022] [Questions about work engagement] The following questions describe how you feel about your work. Read each statement carefully and decide whether you feel that way about your work. If you have never felt that way, select 0 (zero). If you have felt that way, select the number (1 to 6) that corresponds to how often. 6 Always 5 Very often 4 Often 3 Sometimes 2 Rarely 1 Almost never 0 Never Q1. When I'm working, I feel energized (6 / 5 / 4 / 3 / 2 / 1 / 0) Q2. I'm enthusiastic about my work (6 / 5 / 4 / 3 / 2 / 1 / 0) Q3. I'm absorbed in my work (6 / 5 / 4 / 3 / 2 / 1 / 0)

[0023] The work engagement scale evaluates the state of being actively engaged in work and feeling energized. In this embodiment, the 3-item version of the Utrecht Work Engagement Scale was used as the work engagement scale. Alternatively, the 17-item or 9-item version may be used, or another scale may be used. In another example, the questions used to obtain information about work engagement may include some of the above content, new questions may be added to the above content, or other questions may be asked.

[0024] [Questions regarding workplace satisfaction] Please circle the number that best describes how often the following has happened in the past six months. 5 Yes 4 Close to yes 3 Neither 2 Close to no 1 No Q1. I am satisfied with my current workplace (5 / 4 / 3 / 2 / 1) Q2. I am satisfied with the content of my current job (5 / 4 / 3 / 2 / 1) Q3. Overall, I am satisfied with my current job (5 / 4 / 3 / 2 / 1) Q4. If things go my way, I would like to still be working at my current workplace five years from now (5 / 4 / 3 / 2 / 1)

[0025] The four questions mentioned above, which were used as a measure of workplace satisfaction, are based on four items related to "overall job satisfaction" described in the following paper: "Industrial Stress Research" journal 5, 72-81 (1998), Examination of the relationship between workplace satisfaction and stress reactions, Miyuki Tanaka. According to another example, questions used to obtain information on workplace satisfaction can be part of the above content, new questions can be added to the above content, or different questions can be asked.

[0026] In this way, information regarding the personal attributes, work experience, work values, and current job perceptions of the first user who is a job seeker is input to the input unit 12 as job seeker response data 10. The information regarding the personal attributes, work experience, work values, and current job perceptions is collectively referred to as comprehensive information. The comprehensive information in this embodiment includes not only objective information such as the personal attributes and work experience of the first user who is a job seeker, but also subjective information such as work values ​​and current job perceptions, and therefore can be said to be multi-perspective information about the first user.

[0027] According to the above example, the comprehensive information includes information about the first user's personal attributes, work experience, work values, and current job perceptions. According to another example, the comprehensive information may additionally include "fit information" regarding the fit between the first user's values ​​and the values ​​of the first user's current workplace. According to one example, the fit information is obtained by the first user answering the following questions via an application. [Questions Regarding Fit Information] The following statements are about your job or organization. Please indicate how much each statement applies to you personally. Some of the questions may seem similar, but please answer each question individually. Please read the questions carefully, think about them, and use the following scale to answer: 7 Completely applies 6 5 Fairly applies 4 3 Not very applicable 2 1 Not applicable at all Q1. The things I value in life are very similar to the things my organization values ​​(7 / 6 / 5 / 4 / 3 / 2 / 1) Q2. My personal values ​​are aligned with the values ​​and culture of my organization (7 / 6 / 5 / 4 / 3 / 2 / 1) Q3. My organization's values ​​and culture are highly aligned with what I value in life (7 / 6 / 5 / 4 / 3 / 2 / 1) The three questions above to obtain fit information use the three "person-organization fit (PO fit)" items from the Japanese version of the Fit Scale (Cable & DeRue, 2002; Inoue et al., 2021). For other examples, questions to obtain fit information could use some of the above content, add new questions to the above content, or ask different questions.

[0028] The past input data storage unit 14 in FIG. 1 stores information about the personal attributes, work experience, work values, and current job perceptions of multiple second users. In addition, suitability information for each second user may also be stored. According to one example, answers given to questions in the application by a first user who is a job seeker are stored in the past input data storage unit 14. As the number of users of the application increases, the amount of data in the past input data storage unit 14 also increases, improving the quality of the matching device. According to one example, the past input data storage unit 14 stores information about more than 1,400 second users.

[0029] The recommendation algorithm processing unit 16 acquires data from the input unit 12 and the past input data storage unit 14 and identifies facilities to recommend to a first user who is a job seeker. FIG. 2 is a diagram showing an example of processing by the recommendation algorithm processing unit 16. Information regarding the personal attributes, work experience, and work values ​​of the first user provided to the input unit 12 is converted into a one-hot vector by the conversion unit 16a. Information regarding the personal attributes, work experience, and work values ​​of multiple second users stored in the past input data storage unit 14 is converted into a one-hot vector by the conversion unit 16b. According to another example, in addition to the above items, information regarding the work engagement of the first user can be converted into a one-hot vector, and information regarding the work engagement of multiple second users can be converted into a one-hot vector.

[0030] The similarity calculation unit 16c calculates the similarity between the information of the first user expressed in one-hot format and the information of multiple second users expressed in one-hot format. That is, the similarity between each of the multiple second users and the first user is calculated. The more similar the personal attributes, work experience, and work values ​​are, the higher the calculated similarity. According to another example, the more similar the information regarding personal attributes, work experience, work values, and work engagement is, the higher the calculated similarity is. According to one example, the similarity calculation unit 16c calculates the similarity using a trained model. The trained model calculates the similarity between users using an unsupervised learning technique called user-based collaborative filtering. The trained model can be trained based on data from the past input data storage unit 14. For example, the part of "selecting people similar to the first user" can be mainly machine-learned using personal attributes (including work history, etc.), values, value compatibility, workplace social capital, and work engagement from the past input data storage unit 14. The machine learning process involves applying algorithms such as cosine similarity, Pearson correlation coefficient, and Euclidean distance, adjusting and optimizing parameters, and extracting patterns from data. The more data there is in the past input data storage unit 14, the more accurate the calculation of similarity becomes, and as a result, a person who is "similar" to the first user can be selected with high accuracy from among multiple second users.

[0031] The similarity information calculated by the similarity calculation unit 16c is provided to the facility identification unit 16d. The facility identification unit 16d identifies, from among the multiple second users, multiple similar users who have personal attributes, work experience, and work values ​​similar to the personal attributes, work experience, and work values ​​of the first user. According to one example, the similar users can be the top 10 users when the multiple second users are sorted in order of similarity. The facility identification unit 16d then identifies high-satisfaction facilities, which are facilities to which the similar users with high current job satisfaction belong. For example, the similar users are sorted in order of current job satisfaction, and multiple facilities are identified as high-satisfaction facilities, starting with the facilities to which the most senior users belong. High-satisfaction facilities are workplaces recommended to the first user. For example, the facility to which the similar user with the highest current job satisfaction belongs is introduced as the facility most recommended to the first user, the facility to which the similar user with the second highest current job satisfaction belongs is introduced as the facility second most recommended to the first user, and the facility to which the similar user with the third highest current job satisfaction belongs is introduced as the facility third most recommended to the first user. In one example, the occupations of the first user and the plurality of second users are nurses, and the high satisfaction facilities are any of medical institutions, nursing care facilities, educational institutions, general companies, government agencies, research institutes, and child welfare facilities. In this embodiment, the high satisfaction facilities are facilities with proper nouns, such as A Hospital, B Nursing Care Facility, C University, D Corporation, E Municipal Medical Facility, F Research Institute, and G Child Welfare Facility.

[0032] According to another example, the facility identification unit 16d can identify a facility classification. That is, the facility identification unit 16d recommends to the first user a facility classification rather than a facility with a proper noun. In this case, the facility identification unit 16d recommends to the first user at least one of, for example, a medical institution, a nursing care facility, an educational institution, a general company, a government institution, a research institution, and a child welfare facility.

[0033] In the example of FIG. 2 , the processes of the conversion units 16a, 16b, and the facility identification unit 16d are performed by a program, and the process of the similarity calculation unit 16c can be realized by a machine learning model. By utilizing artificial intelligence, similarity can be calculated more efficiently and accurately at lower cost than when a consulting or recruitment agency is used. Generally, a machine learning model (trained model) is a combination of a neural network structure, which is a type of AI program (or AI algorithm), and parameters that represent the strength of connections between neurons. Therefore, as described above, even if a machine learning model is used for part of the process, the entire process can be considered to be a program. Such a program can be described as a program that causes a computer to execute, for example, the steps of: identifying, from among a plurality of second users, multiple similar users who have personal attributes, work experience, and work values ​​similar to those of the first user; identifying high-satisfaction facilities, which are facilities where users with high current job satisfaction among the multiple similar users belong; and displaying the high-satisfaction facilities as recommended facilities.

[0034] The output of the facility identification unit 16d is displayed on a result display unit (sometimes simply referred to as a display unit). The display unit may be, for example, a computer screen, a smartphone screen, or the screen of another terminal. By displaying high-satisfaction facilities as recommended facilities on the display unit, the first user can learn about suitable employment opportunities or career changes for themselves. According to one example, the matching method includes: inputting comprehensive information, which is information regarding personal attributes, work experience, and career values; identifying high-satisfaction facilities, which are facilities to which users with high current job satisfaction belong, among multiple similar users who have personal attributes, work experience, and career values ​​similar to the comprehensive information; and displaying the high-satisfaction facilities on the display unit.

[0035] The values ​​diagnosis unit 18 in FIG. 1 diagnoses the values ​​of the first user. FIG. 3 is a diagram showing an example of processing by the values ​​diagnosis unit 18. The compatibility calculation unit 18a with current workplace in FIG. 3 calculates the compatibility between the first user and his / her current workplace from the comprehensive information of the first user obtained from the input unit 12. The compatibility between the first user and his / her current workplace is a better value, for example, the higher the first user's satisfaction, engagement, interpersonal relationships, and values ​​are satisfied. The calculated compatibility is converted into, for example, a numerical value, a percentage, or other format in the score storage unit 18c, and displayed on the display unit with or without passing through the matching unit.

[0036] The compatibility calculated by the compatibility calculation unit 18a is compared with a threshold value by the determination unit 18d, and if the compatibility is lower than the threshold value, a recommendation to change jobs is displayed on the display unit 24, and if the compatibility is equal to or higher than the threshold value, a recommendation to change jobs is not displayed on the display unit 24. This series of processes calculates the compatibility with the first user's current job from the comprehensive information, and displays on the display unit whether or not the change of job is recommended depending on the quality of the compatibility, and functions as a job change determination unit.

[0037] The extraction unit 18b extracts items related to values ​​from the comprehensive information provided by the input unit 12. For example, information related to occupational values ​​is extracted. This information is input into the inference model 18e, and the inference unit 18f infers the job seeker's value classification. The inference model 18e is a trained model that accurately infers the value classification from data accumulated in the past input data storage unit 14. According to one example, the job seeker's values ​​are classified into a career type, a self-growth type, a balanced type, and a private type. The career type is a type that emphasizes occupational and technical experience, the self-growth type is a type that emphasizes growth through one's own efforts, the balanced type is a type that emphasizes a balance between work and private life, and the private type is a type that relatively emphasizes private life. The inferred value classification of the job seeker is displayed on the display unit 24. In addition to the value classification, it is also possible to provide abstract or specific suggestions for workplaces suitable for people who fit that value classification. An abstract workplace proposal, for example, would be to propose "a hospital with a comprehensive career ladder and training system" or "obtaining specialist or certified nurse qualifications" as a "workplace suitable for career-type individuals." A concrete workplace proposal, for example, would be to propose "a large-scale hospital (general hospital) with a highly acute care setting," "a small-scale hospital (general hospital) with an acute care setting," or "a medium-sized hospital (general hospital) with a recovery care setting" as a "workplace recommended by AI for career-type individuals."

[0038] The value diagnosis unit 18 infers a value classification by implementing an inference model for the classification task using only the value items, as described above. According to another example, similar to the recommendation algorithm processing unit 16, the data of the "first user" and the value classification of "similar people" can be output as the value classification of the first user. In this case, similar to the recommendation algorithm processing unit, processing is provided using a machine learning program (user-based collaborative filtering). In both examples, an inference unit is provided that infers the value classification of the first user from the comprehensive information using an inference model, and the results are displayed on a display unit. By using a trained model, value classification can be calculated efficiently and accurately at low cost compared to using a consulting or recruitment agency.

[0039] 1 determines whether the first user, who is a job seeker, wishes to be matched with specific job information. If the first user does not wish to be matched with specific job information, the facility or facility classification information output by the recommendation algorithm processing unit 16 and the value classification information output by the value diagnosis unit 18 are displayed on the display unit. In other words, only the diagnosis result is displayed on the display unit 24.

[0040] If the first user wishes to be matched with specific job information, the matching unit 22 proceeds to processing. FIG. 4 illustrates an example of processing by the matching unit 22. The information acquisition unit 22a acquires information on facility classifications recommended by the recommendation algorithm processing unit 16. The extraction unit 22b extracts job information of facility classifications matching the recommended facility classifications from the job information database 25. For example, if the recommended facility classification is medical institutions among medical institutions, nursing care facilities, educational institutions, general companies, government agencies, research institutions, and child welfare facilities, only job information for medical institutions is extracted from the job information database 25. In one example, the extracted job information is displayed on the display unit 24a as recommended workplaces. In another example, the information acquisition unit 22a acquires information on "high satisfaction facilities" identified by the facility identification unit 16d of the recommendation algorithm processing unit 16, and the extraction unit 22b extracts job information for high satisfaction facilities and displays the extraction result on the display unit 24a. In one example, the distance between the first user's home and the extracted facility can be taken into consideration when determining the extracted facility, i.e., the job information. For example, the distance between the first user's home and the facility can be estimated from the postal code information entered by the first user, and facilities that are close in distance among multiple facilities can be displayed preferentially, the distance information can be displayed on the display unit, or only facilities whose distance is shorter than a predetermined value can be extracted (or displayed).

[0041] Furthermore, the information on the facilities extracted by the extraction unit 22b is provided to the human resource item extraction unit 22c. The human resource item extraction unit 22c extracts information on the type of human resource sought by the facility extracted by the extraction unit 18b. This type of human resource sought by the facility is provided to the score calculation unit 22e. Furthermore, the acquisition unit 22d acquires the information on the job seeker's value classification and comprehensive information provided by the value assessment unit 18 and provides them to the score calculation unit 22e. The score calculation unit 22e compares this information and calculates a score indicating the degree to which the first user satisfies the human resource items that express the type of human resource sought by the facility. In calculating the score, weighting can be performed by emphasizing or deemphasizing any of the human resource items. In this way, a score is calculated for each facility extracted by the extraction unit 22b. This information is provided to the ranking unit 22f, which ranks these facilities in descending order of score. The facility with the highest score or multiple facilities extracted in descending order of score are then displayed on the display unit 24b. The display unit 24b displays one or more specific facilities recommended to the first user.

[0042] On the other hand, the information on the value classification output by the value diagnosis unit 18 and the comprehensive information can be provided to the storage unit 22g and displayed on the display unit 24c.

[0043] FIG. 5 illustrates an example of screen transitions on a terminal running an application. First, after launching the application and logging in through login screen D0, top page D1 appears on the terminal's display. For example, the login screen D0 or top page D1 displays text such as "We'll suggest a workplace that matches your values!", "Values ​​type diagnosis," and "Start diagnosis." When the user presses the diagnosis start button, input screens D2-D6 are sequentially displayed on the display. For example, input screen D2 prompts the user to enter basic attributes and skills, input screen D3 prompts the user to enter work history, input screen D4 prompts the user to enter details of work history for each facility, input screen D5 prompts the user to enter answers to questions measuring career values, and input screen D6 prompts the user to enter answers to "other measures," such as the aforementioned perception of current job and sense of fit. Once input through these input screens is complete, the first user's comprehensive information is provided to the matching device.

[0044] Once the input work up to the input screen D6 is completed, the matching device performs the above-mentioned processes. Specifically, the recommendation algorithm processing unit 16 identifies the facility or facility classification to be recommended to the first user, and the value diagnosis unit 18 outputs compatibility with the workplace, recommendation / non-recommendation of job change, and the job seeker's value classification. The results of these processes are displayed on the diagnosis result display screen D7. Furthermore, if the first user wishes to be matched with a job database, the matching unit 22 also performs matching processing. The results of the matching process are displayed on a display screen D8 for specific job information.

[0045] In this way, comprehensive information, which is information regarding the first user's personal attributes, work experience, work values, and current job awareness, is input into the matching device, and, in response to the input of the comprehensive information, for example, the names of workplaces recommended to the first user are displayed on the display unit as recommended facilities. According to one example, the display unit displays a plurality of recommended facilities, and displays the degree of match with the first user for each recommended facility. The degree of match can be displayed, for example, as a percentage display, with 100% being the best match, or as a display showing which of multiple predetermined levels the first user falls into. If the first user is an aspiring nurse, the recommended facilities can be any of medical institutions, nursing care facilities, educational institutions, general companies, government agencies, research institutions, and child welfare facilities.

[0046] Here, the input unit for inputting the comprehensive information corresponds to, for example, a touch panel of a smartphone, a keyboard and a mouse of a personal computer, etc. The display unit corresponds to, for example, a display device.

[0047] As described above, the past input data storage unit 14 stores information regarding the personal attributes, work experience, work values, and current job awareness of multiple second users. The larger the amount of data in the past input data storage unit 14, the higher the accuracy of matching by the matching device. Therefore, an update and add unit may be provided as part of the past input data storage unit 14 or as part of the matching device, and the update and add unit may add information about second users to the past input data storage unit 14 as needed. According to one example, the update and add unit updates information about multiple second users or adds comprehensive information about a new user to the information about the multiple second users. For example, when comprehensive information is provided by a first user, the update and add unit adds the information to the past input data storage unit 14. This makes it possible to keep the data in the past input data storage unit 14 up to date and to increase the amount of data.

[0048] The explanation so far has been based on the assumption that the first user, a job seeker, is a nurse. There is a chronic shortage of nurses, and this shortage is expected to continue in the future. Because of the diversity of working styles and facilities, there is a particularly high need to improve the accuracy of matching. Therefore, by using the matching device described in this embodiment to perform matching from multiple perspectives, including professional values, it is possible to increase the satisfaction of nurses and contribute to solving industry issues. However, applying the technology of such a matching device to occupations other than nursing can also improve matching accuracy.

[0049] 6 is a diagram illustrating the processing of a human resource management device. According to one example, this human resource management device is used for human resource management at a specific medical institution. The human resource management device includes an information acquisition unit 52 that acquires information about occupational values ​​and current job perceptions for multiple medical institution staff at a predetermined medical institution. Response data 50 of medical institution staff is acquired by the information acquisition unit 52.

[0050] According to one example, the medical institution database 56 stores information about the professional values ​​and current job perceptions of multiple employees of the medical institution, categorized by department. The information acquired by the information acquisition unit 52 and the information in the medical institution database 56 are provided to the relevance calculation unit 54. The relevance calculation unit 54 calculates the relevance rate for each department of the multiple medical institution employees provided to the information acquisition unit 52. According to one example, a prediction model is used to calculate the relevance rate of an employee for each department of the medical institution. For example, a regression model is used to predict the satisfaction level of a certain employee in each department, and the relevance rate is displayed as a percentage based on the average satisfaction level of the entire facility. If an employee is assigned to a department with a high relevance rate, the employee's professional values ​​are more likely to be fulfilled, and it is expected that their satisfaction with their workplace will also increase.

[0051] 7 is a diagram showing an example of display of the suitability rate by department. For Mr. B, an employee at Hospital A, the suitability rate for gastroenterology is 70%, the suitability rate for thoracic surgery is 30%, the suitability rate for cardiology is 90%, and the suitability rate for psychiatry is 50%. Such suitability rates are calculated and displayed not only for Mr. B but also for all employees who have provided information to the information acquisition unit 52.

[0052] Furthermore, an optional function is a proposal unit 58 shown in FIG. 6 . Based on the information obtained by the information acquisition unit 52, the proposal unit 58 displays, on the display unit, a proposal for the placement of multiple medical institution staff, a proposal for the working patterns of multiple medical institution staff, a proposal for training multiple medical institution staff, or a proposal for skill development for multiple medical institution staff. According to one example, each proposal is made using an inference model. According to another example, each proposal may be made based on predetermined rules. According to yet another example, each proposal may be made using a combination of these methods.

[0053] A system can be constructed by incorporating such a human resources management device into the aforementioned matching device. Figure 8 is a conceptual diagram of a system incorporating a human resources management device into a matching device. Nurses 60 provide comprehensive information to an AI application 62 and receive information such as recommended facilities, value classifications, and matching. Medical institutions 64 provide job information and the like to the AI ​​application 62, and as a result of matching, receive personnel referrals from the AI ​​application. This system also includes a human resources management service provided by the AI ​​application 62 to the medical institution 64. According to one example, providing the human resources management service involves obtaining comprehensive information about staff at the medical institution 64 and storing this comprehensive information as comprehensive information for multiple second users in the matching device, thereby improving the accuracy of the matching device. This system can benefit all nurses, whether employed, seeking work, or on leave, and can be used by nurses throughout their careers. Meanwhile, application providers can obtain nursing information over the long term, further improving the accuracy of various services.

[0054] FIG. 9A is a diagram showing an example of the configuration of a controller for executing processing by an application. The controller 110 includes a receiving device 110A, a processing circuit 110B, and a transmitting device 110C. Information provided by a first user and information on multiple second users are transmitted to the receiving device 110A. The above-mentioned functions are realized by the processing circuit 110B of the controller 110. That is, the processing circuit calculates information regarding recommended facilities, recommended facility classifications, compatibility with the workplace, job change recommendation / non-recommendation, value classification, and matching. It may also calculate information regarding human resource management. The processing circuit 110B may be dedicated hardware or a CPU (also referred to as a central processing unit, processing device, arithmetic unit, microprocessor, microcomputer, processor, or DSP) that executes a program stored in memory. When the processing circuit is dedicated hardware, the processing circuit may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. The above-described functions may be realized by separate processing circuits, or may be realized together by a processing circuit. The processing by the processing circuit may include inference processing using a trained model.

[0055] FIG. 9B shows an example configuration of the controller 110 when the processing circuit is a CPU. In this case, each function of the controller 110 is realized by software or a combination of software and firmware. The software or firmware is written as a program and stored in memory 110F. The processor 110E realizes each function by reading and executing the program stored in memory 110F. That is, memory 110F is provided to store programs that, when executed by the processing circuit of FIG. 9A, result in the execution of each process described with reference to FIGS. 1-8. These programs can also be said to cause a computer to execute the procedures and methods described in FIGS. 1-8 and their accompanying descriptions. Here, memory refers to, for example, non-volatile or volatile semiconductor memory such as RAM, ROM, flash memory, EPROM, EEPROM, magnetic disk, flexible disk, optical disk, compact disk, minidisk, or DVD. Naturally, some of the above functions may be realized by hardware, and some by software or firmware. Note that, for convenience of explanation, AI processing is included in software processing.

[0056] 16 Recommendation algorithm processing unit, 18 Value diagnosis unit, 22 Matching unit, 54 Precision calculation unit, 58 Proposal unit

Claims

1. A matching device comprising: an input unit for inputting comprehensive information relating to a first user's personal attributes, work experience, professional values, and current job awareness; a facility identification unit for identifying a plurality of similar users from a plurality of second users who have personal attributes, work experience, and professional values ​​similar to the personal attributes, work experience, and professional values ​​of the first user, and for identifying high satisfaction facilities which are facilities to which those of the plurality of similar users who have high current job satisfaction belong; and a display unit for displaying the high satisfaction facilities as recommended facilities.

2. The matching device according to claim 1, wherein the current job recognition includes a scale relating to workplace social capital, a scale relating to work engagement, and a scale relating to current job satisfaction.

3. The matching device according to claim 1, wherein the career values ​​include measures of intrinsic career values, extrinsic career values, social career values ​​and authoritative career values.

4. A matching device according to claim 1, wherein the comprehensive information includes compatibility information regarding compatibility between the values ​​of the first user and the values ​​of the first user's current workplace.

5. A matching device as described in claim 4, further comprising a job change determination unit that calculates compatibility with the first user's current job from the comprehensive information and displays on the display unit whether the job change is recommended or not recommended depending on the good or bad compatibility.

6. A matching device as described in claim 1, further comprising an inference unit that infers a value classification of the first user from the comprehensive information using an inference model, and the display unit displays the value classification inferred by the inference unit.

7. The matching device according to claim 1, further comprising a matching unit that extracts job information of the high satisfaction facilities and displays it on the display unit.

8. The matching device of claim 1, wherein the first user and the plurality of second users have a nursing occupation, and the high satisfaction facility is one of a medical institution, a nursing care and welfare facility, an educational institution, a general company, a government agency, a research institute, and a child welfare facility.

9. A matching device according to any one of claims 1 to 8, further comprising an update / add unit that updates information about the plurality of second users and adds a new user as one of the plurality of second users.

10. A human resources management device comprising: an information acquisition unit that acquires information regarding professional values ​​and current job perceptions for a plurality of medical institution staff at a predetermined medical institution; and a compatibility rate calculation unit that calculates the compatibility rate of the plurality of medical institution staff by department based on the information acquired by the information acquisition unit.

11. A human resources management device as described in claim 10, further comprising a proposal unit that displays on a display unit, based on information obtained by the information acquisition unit, proposals for the placement of the plurality of medical institution staff, proposals for work patterns of the plurality of medical institution staff, proposals for education of the plurality of medical institution staff, or proposals for skill improvement of the plurality of medical institution staff.

12. A matching device comprising: an input unit for inputting comprehensive information which is information regarding a first user's personal attributes, work experience, work values, and current job perception; and a display unit for displaying, as recommended facilities, the names of workplaces recommended to the first user in accordance with the input of the comprehensive information.

13. The matching device according to claim 12, wherein the display unit displays a plurality of the recommended facilities, and displays a degree of matching with the first user for each recommended facility.

14. A matching device according to claim 12 or 13, wherein the recommended facilities are any one of medical institutions, nursing care facilities, educational institutions, general companies, government agencies, research institutes, and child welfare facilities.

15. A matching method comprising: inputting comprehensive information which is information relating to personal attributes, work experience, and professional values; identifying high satisfaction facilities which are facilities to which users with high current job satisfaction belong among a plurality of similar users who have personal attributes, work experience, and professional values ​​similar to the comprehensive information; and displaying the high satisfaction facilities on a display unit.

16. A program that causes a computer to execute the steps of: identifying, from among a plurality of second users, a plurality of similar users who have personal attributes, work experience, and work values ​​similar to the personal attributes, work experience, and work values ​​of the first user; identifying high satisfaction facilities, which are facilities to which those of the plurality of similar users who have high satisfaction with their current jobs belong, among the plurality of similar users; and displaying the high satisfaction facilities as recommended facilities.

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