JP7727963B2 - Human Resources Matching System, Human Resources Matching Method and Program - Google Patents

Description

The present invention relates to technology that is effective in human resource matching, matching experts with clients.

In recent years, in order to enhance the functions of the judicial system and meet the legal needs of the public, the number of people passing the bar exam has increased significantly, while the number of cases being accepted by new lawyers has decreased significantly. As a result, the number of cases accepted per lawyer has also decreased significantly.
Legal troubles such as power harassment, bullying, divorce issues, domestic violence, fraud, consumer harm, and internet harm are on the rise, and as a result, the number of questions people want to ask or consult with lawyers is increasing. However, for those with legal troubles, actually consulting a lawyer can be a high hurdle due to a variety of factors.

When a client wants to consult with a lawyer, they search for a lawyer using a search engine or a human resources matching service.
As an example of such a talent matching service, Patent Document 1 discloses a configuration in which replies from lawyers who meet certain conditions are displayed in chronological order in response to a consultation made by a client.

Japanese Patent Application Laid-Open No. 2017-73007

However, even if a client suddenly needs to consult with a lawyer and searches for one using a search engine, there is a problem that it is unclear which lawyer is the most suitable. In addition, consultations are by appointment only, so sudden consultations cannot be accommodated.
In addition, some traditional human resource matching services aim to have lawyers respond to free consultations and then lead to cases being accepted, but because lawyers often respond online, there is a problem that if the client's address is far from the lawyer's location, it is difficult to actually accept the case.
Therefore, the inventors came up with the idea of a talent matching system, talent matching method and program that allows clients to consult with experts in a relaxed manner and also provides experts with information on suitable locations for opening a new business.

The main purpose of the present invention is to provide a talent matching system, talent matching method, and program that allows clients to consult with experts in a relaxed manner and provides experts with information about suitable locations for opening a new business.

The present invention is a human resources matching system that matches experts with clients,
a consultation content acquisition unit that acquires consultation content from the client;
a search unit that searches for the expert who can answer the acquired consultation content;
an expert output unit that outputs the attributes, consultation method, fee, and available consultation date and time of the searched expert;
an application acquisition unit that acquires the output application for consultation with the specialist from the client;
a location learning unit that learns a correlation between the consultation content and the location of the expert to whom the consultation is requested;
a candidate site identification unit that identifies candidate sites for new business locations based on the learning results;
a candidate site generating unit that generates the identified candidate sites on a map;
a candidate site output unit that outputs the generated map of the candidate sites;
The present invention provides a human resources matching system comprising:

According to the present invention, a human resources matching system that matches experts with people seeking advice acquires consultation details from the people seeking advice, searches for experts who can respond to the acquired consultation details, outputs the attributes, consultation method, fees, and available consultation dates and times of the searched experts, acquires a consultation request from the people seeking advice from the outputted expert, learns the correlation between the consultation details and the location of the expert with whom the consultation request was made, identifies candidate sites for new business locations based on the learning results, creates a map of the identified candidate sites, and outputs the map with the candidate sites created.

This invention is categorized as a system, but methods and programs also have the same effects and benefits.

This invention allows clients to consult with experts in a relaxed atmosphere and provides experts with information on suitable locations for new business openings.

1 is a diagram illustrating an overview of a human resources matching system 1. FIG. FIG. 1 is a diagram illustrating the functional configuration of a talent matching system 1. FIG. 10 is a flowchart showing the lawyer DB creation process executed by the human resources matching system 1. FIG. 2 is a diagram illustrating an example of a lawyer DB. A figure showing a flowchart of the lawyer information output process executed by the human resources matching system 1. FIG. 4 is a diagram schematically illustrating an example of a consultation content input screen 40. 10 is a diagram showing an example of a lawyer information display screen 50. FIG. 10 is a diagram showing an example of a lawyer information display screen 50. FIG. 10 is a diagram showing an example of a lawyer information display screen 50. FIG. FIG. 10 is a flowchart showing an evaluation acquisition process executed by the talent matching system 1. FIG. 10 is a flowchart showing a point allocation process executed by the talent matching system 1. FIG. 10 is a flowchart showing a consultation process executed by the talent matching system 1. FIG. 10 is a flowchart showing a consultation result providing process executed by the talent matching system 1. FIG. 10 is a flowchart showing a consultation result learning process executed by the human resources matching system 1. FIG. 10 is a flowchart showing a response output process executed by the talent matching system 1. FIG. 10 is a flowchart showing a request process executed by the talent matching system 1. FIG. 10 is a flowchart showing a location learning process executed by the talent matching system. FIG. 10 is a flowchart showing a candidate location output process executed by the talent matching system. FIG. 1 is a diagram schematically illustrating an example of a plurality of candidate sites 61 generated on a map 60.

Hereinafter, a detailed description of an embodiment of the present invention (hereinafter, "embodiment") will be given with reference to the accompanying drawings. In the following drawings, the same elements are designated by the same numbers or symbols throughout the description of the embodiment.

[Basic concept/basic configuration]
1 is a diagram for explaining an overview of a talent matching system 1. The talent matching system 1 is a system including at least a computer 10 that matches a specialist with a person seeking advice.

In this embodiment, as an example, the expert is a lawyer, the client is a person with legal trouble, and the client consults the lawyer about the legal trouble, but the present invention is not limited to this and can also be applied to other professionals such as patent attorneys, artists such as musicians, etc. Furthermore, the client terminal 20 and the lawyer terminal 30 perform the necessary processing via a predetermined application installed in each terminal.
In addition, in this embodiment, the computer 10 is connected to a client terminal 20 carried by the client and a lawyer terminal 30 carried by the lawyer so as to be able to perform data communications.

The processing steps performed by the talent matching system 1 when matching talent are described below with reference to Figure 1.

The computer 10 acquires the consultation content from the client (step S1).
The client terminal 20 accepts input of consultation content from the client. This consultation content represents a category of the consultation, such as divorce/gender issues, traffic accidents, labor issues, fraud victims, consumer fraud, debt/debt consolidation, inheritance consultations, debt collection, international/foreigner issues, internet issues, crime/criminal cases, medical care, real estate, and other legal issues. The client terminal 20 accepts input of consultation content by accepting selections, such as tapping, for the categories displayed on the display unit. In addition to the category, input of text information indicating the specific content of the consultation may also be accepted. In this embodiment, specific content is not accepted; only the category selection is required. To maintain high-quality expert consultations in a short time and at a low price, it is necessary to minimize the free expert response time and implement clear hourly charges. While collecting specific information about the consultation content in advance naturally leads the client to expect the expert to conduct a preliminary review, charging for the expert's time for preliminary review is inappropriate for a simple matching system. On the other hand, if there is no information at all about the content of the consultation, there are too few clues to select which registered lawyer to recommend, so as described above, only a pre-defined category is selected and used as primary information to list appropriate lawyers. In this embodiment, by limiting the input to the selection of a category, the client does not expect a specialist to consider the matter in advance, which ultimately improves the client's satisfaction.
The client terminal 20 transmits the consultation content that has been input to the computer 10 .
The computer 10 receives the consultation content and thereby acquires the consultation content from the client.

The computer 10 searches for an expert who can answer the acquired consultation content (step S2).
The computer 10 searches for an expert by referring to a database (hereinafter, the database may also be simply referred to as a DB) that previously stores expert attributes (e.g., photo, name, name of affiliated agency, location, area of focus), consultation method (e.g., telephone, online, face-to-face), fees, and available consultation dates and times in association with each other. The computer 10 refers to the expert attributes (areas of focus) in the DB and searches for an expert whose area of focus is the consultation content acquired this time.

The computer 10 outputs the attributes, consultation methods, fees, and available consultation dates and times of the searched experts (step S3).
The computer 10 refers to the DB and transmits the attributes, consultation method, fee, and consultation date and time of the currently searched expert to the client terminal 20. If the search results in only one expert, the computer 10 transmits the attributes, consultation method, fee, and consultation date and time of this one expert, and if the search results in multiple experts, the computer 10 transmits the attributes, consultation method, fee, and consultation date and time of each expert.
The client terminal 20 receives the attributes of the expert, the consultation method, the fee, and the consultation date and time, and displays them on its own display unit.
The computer 10 outputs the attributes, consultation method, fee, and consultation available date and time of the searched expert by displaying the expert's attributes, consultation method, fee, and consultation available date and time on the client terminal 20.

The computer 10 receives the outputted consultation request from the client (step S4).
The client terminal 20 accepts input of a consultation request to the displayed expert. This consultation request includes the attributes of the expert, the consultation method, the consultation date and time, the consultation duration, etc. The client terminal 20 transmits this consultation request to the computer 10.
The computer 10 receives this consultation request and acquires the output consultation request from the client to the specialist.

Based on this consultation request, the computer 10 performs the processes necessary to make the consultation reservation, such as making the reservation, settling the fee, and notifying the client and the specialist depending on the consultation method (step S5).

The computer 10 learns the correlation between the consultation content and the location of the specialist to whom the consultation is requested (step S6).
The computer 10 refers to the DB and identifies the location of the specialist to whom the consultation is requested. The computer 10 learns the correlation between the consultation content and the location of this specialist. The learning method will be described in detail later.

The computer 10 identifies candidate locations for new business openings based on the learning results (step S7).
The lawyer terminal 30 accepts input of the field and region desired by the expert and transmits the accepted field and region to the computer 10 .
The computer 10 receives the field and region, and identifies candidate locations for opening a new business based on the learning results and the field and region. For example, the computer 10 identifies, as candidate locations, locations near the locations of experts in the field who have received many consultation requests.

The computer 10 generates the identified candidate sites on a map (step S8).
The computer 10 generates the identified candidate location on a map stored in advance in the computer 10 or on a map acquired from an external database, etc. The candidate location may be, for example, an icon, a pin, a graphic, or text.

The computer 10 outputs the map on which the candidate sites have been generated (step S9).
The computer 10 transmits this map to the lawyer terminal 30 .
The lawyer terminal 30 receives this map and displays it on its own display unit.
The computer 10 outputs the map on which the candidate sites have been generated by displaying this map on the lawyer terminal 30 .

This type of talent matching system 1 allows clients to consult with experts in a relaxed atmosphere, and also provides experts with information about suitable locations for new business openings.

[Functional configuration]
The functional configuration of the talent matching system 1 will be described with reference to FIG.
The human resources matching system 1 is a system that includes at least a computer 10, which is connected to a client terminal 20 held by the client and a lawyer terminal 30 held by the lawyer via a network 9 such as a public line network so that data communication is possible.
The talent matching system 1 may include other terminals and devices in addition to the above-mentioned computer 10, client terminal 20, and lawyer terminal 30. In this case, the talent matching system 1 executes the processing described below using one or a combination of the above-mentioned computer 10, client terminal 20, and lawyer terminal 30 as well as other terminals and devices.

The computer 10 is a computer or personal computer having a server function for matching experts with people seeking advice.
The computer 10 may be realized, for example, by a single computer, or by multiple computers, such as a cloud computer. The cloud computer in this specification may refer to either a computer that uses any computer in a scalable manner to perform a specific function, or a computer that includes multiple functional modules to realize a system and uses the functions in any combination.

The computer 10 has a control unit including a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), etc., and a communication unit including a device for enabling communication with other terminals and devices, a consultation content acquisition unit 11 for acquiring consultation content from the client, an expert output unit 12 for outputting the attributes of the expert, the consultation method, the fee, and the consultation date and time, an application acquisition unit 13 for acquiring a consultation application from the client to the expert, and a candidate site output unit 14 for outputting a map.
The computer 10 also includes a data storage unit such as a hard disk, semiconductor memory, storage medium, or memory card as a storage unit.
The computer 10 also includes, as processing units, various devices for executing various processes, a search unit 15 for searching for experts who can answer the consultation content, a location learning unit 16 for learning the correlation between the consultation content and the location of the expert to whom the consultation has been requested, a candidate site identification unit 17 for identifying candidate sites for new business openings, and a candidate site generation unit 18 for generating candidate sites on a map.

In computer 10, the control unit loads a specified program and works in cooperation with the communication unit to realize a lawyer information acquisition module, a consultation content acquisition module, a lawyer information output module, an application acquisition module, an evaluation acquisition module, a referral reception module, an extension instruction acquisition module, a provision instruction acquisition module, a provision module, an availability acquisition module, an answer output module, a request acquisition module, a preference acquisition module, and a candidate location output module.
In addition, in computer 10, the control unit reads a specified program and works in cooperation with the memory unit to realize a lawyer DB memory module, a lawyer memory module, a points memory module, a consultation result memory module, a learning result memory module, and a bulk memory module.
In addition, in computer 10, the control unit reads a specified program and works in cooperation with the processing unit to realize a lawyer DB creation module, a search module, a consultation reservation execution module, a points allocation module, a discount module, a consultation management module, an extension instruction acquisition determination module, a consultation result identification module, an availability determination module, a consultation result learning module, an answer generation module, an exchange identification module, a consultation acquisition module, a location identification module, a location learning module, a candidate location identification module, a multiple determination module, a candidate location generation module, a weighting module, and a display mode setting module.

The client terminal 20 is a terminal device such as a mobile phone, smartphone, tablet, or personal computer owned by the client, and, like the computer 10, has a CPU, GPU, RAM, ROM, etc. as a terminal control unit, a device that enables communication with the computer 10 as a communication unit, and various devices that perform input and output of a screen, data, etc. as an input/output unit.

The lawyer terminal 30 is a terminal device such as a mobile phone, smartphone, tablet terminal, or personal computer carried by the lawyer, and is equipped with a terminal control unit, communication unit, and input/output unit similar to the client terminal 20.

Below, we will explain each process executed by the talent matching system 1, along with the processes executed by each of the modules mentioned above. In the following explanation, the experts are assumed to be lawyers, and the clients are assumed to be people who seek advice on legal issues. The client terminal 20 and the lawyer terminal 30 each execute processes and control the terminal using a specific application installed on them.
It goes without saying that the present invention is applicable even when the expert is not a lawyer, and the consultation content will be adapted to the type of expert.

[Lawyer DB Creation Process Executed by Computer 10]
The attorney DB creation process executed by the computer 10 will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the attorney DB creation process executed by the computer 10.

The lawyer information acquisition module acquires lawyer attributes, consultation methods, fees, available consultation dates and times, and application deadlines as lawyer information (step S10).
As described above, attributes include personal information about the lawyer, such as a photo, name, firm name, location, and areas of focus. Furthermore, as described above, consultation methods refer to the methods by which clients consult with lawyers, such as telephone, online, or face-to-face consultations. Areas of focus are registered by lawyers selecting and inputting the same items as the consultation categories described above. In the case of online consultations, there may be restrictions on the tools that can be used depending on the organization or firm to which the lawyer belongs. Therefore, lawyers are allowed to register up to three "usually used tools" from multiple tools in order of priority. Fees are charged per specified time for each consultation method, such as per 10 minutes, per 20 minutes, or per 30 minutes. Consultation dates and times are the dates and times when the lawyer is available to respond to consultations. Regarding consultation dates and times, available days and time slots can be registered in advance, or data can be linked to external schedule management tools, such as Google Calendar or Outlook Calendar, that each lawyer uses to manage their own schedule. The application deadline is the deadline by which a lawyer can accept consultation reservations up to a certain number of hours in advance. For example, by setting it to 48 hours, consultation appointments within the next 48 hours will be excluded from the list of candidates. This improves usability for lawyers who dislike sudden appointments.
The lawyer terminal 30 accepts input of lawyer information and transmits the accepted lawyer information to the computer 10.
By receiving this lawyer information, the lawyer information acquisition module acquires the lawyer's attributes, consultation method, fees, available consultation dates and times, and application deadlines as lawyer information.
The lawyer information is not limited to the examples described above and may include other information. Similarly, the attributes, consultation method, fees, available consultation dates and times, and application deadlines are not limited to the above and may include other information or may be a part of the above information.

The lawyer DB creation module creates a lawyer DB that links the acquired lawyer information (step S11).
The lawyer DB creation module links the attributes, consultation methods, fees, available consultation dates and times, and application deadlines in the acquired lawyer information to create a lawyer DB (see Figure 4). The lawyer DB creation module creates a lawyer DB for each lawyer whose lawyer information it has acquired.
The lawyer DB creation module may create a lawyer DB that compiles multiple lawyers, rather than creating a lawyer DB for each lawyer.

[Lawyer DB]
The lawyer DB created by the lawyer DB creation module will be described with reference to Fig. 4. This figure is a diagram showing a schematic example of the lawyer DB created by the lawyer DB creation module.
The lawyer DB creation module links the attributes, consultation method, fees, available consultation dates and times, and application deadlines for each acquired lawyer, and creates a lawyer DB in table format. In this embodiment, the lawyer DB creation module links the photo, name, gender, affiliated firm name, location/area available for consultation, areas of focus, consultation method/fees (telephone, online, face-to-face), and available consultation dates and times to create the lawyer DB.
The lawyer DB is not limited to the above example and may be in other formats.

Returning to FIG. 3, the lawyer DB creation process will be continued.
The lawyer DB storage module stores the created lawyer DB (step S12).

The contents of the lawyer DB can be changed, amended, or added to as appropriate. For example, if the computer 10 acquires changes, amendments, or additions to lawyer information by re-executing the lawyer DB creation process, it can change, amend, or add to the registered contents of the lawyer DB based on the contents of these changes, amendments, or additions. In particular, it is desirable for the computer 10 to change, amend, or add to the consultation date and time as appropriate by re-acquiring the lawyer information.

This completes the lawyer DB creation process.
The computer 10 executes the processing described below using the lawyer DB created and stored by the lawyer DB creation processing.

[Lawyer Information Output Process Executed by Computer 10]
The lawyer information output process executed by the computer 10 will be described with reference to Figure 5. This figure is a flowchart of the lawyer information output process executed by the computer 10. The lawyer information output process is a process performed after the lawyer DB creation process described above, and includes details of the consultation content acquisition process (step S1), expert search process (step S2), expert attribute, consultation method, fee, and available consultation date and time output process (step S3), and consultation application acquisition process (step S4).

The consultation content acquisition module acquires the consultation content from the client (step S20).
As mentioned above, the consultation content refers to the classification of consultations, and includes legal troubles such as divorce/gender-related consultations, traffic accidents, labor issues, fraud victims, consumer damage, debt/debt settlement, inheritance consultations, debt collection, international/foreigner issues, internet issues, crime/criminal cases, medical care, and real estate.
The client terminal 20 displays a consultation content input screen 40 on its display unit or the like (see FIG. 6 ) and accepts input such as tapping on icons 41 for each category of consultation content. The client terminal 20 transmits the consultation content that has been accepted and the client's identifier (e.g., ID, name, telephone number, terminal serial number, terminal MAC address, terminal IP address) to the computer 10.
The consultation content acquisition module receives the consultation content and the identifier of the client, and thereby acquires the consultation content from the client.

The search module searches for a lawyer who can answer the acquired consultation content (step S21).
The search module refers to the lawyer DB created by the lawyer DB creation process described above, and searches for a lawyer who can answer the acquired consultation content.
The search module refers to the lawyer DB and searches for lawyers who specialize in the consultation content acquired this time. For example, if the consultation content acquired this time is divorce/gender issues, the search module refers to the lawyer DB and searches for lawyers who specialize in divorce/gender issues.
The search module may search for only one lawyer or multiple lawyers. Furthermore, if the search module returns multiple lawyers as search results, it may search for all lawyers or a subset of the lawyers based on predetermined criteria (e.g., low fees, track record). The lawyers' schedule information is a schedule database entered by the lawyers, and may be created independently or obtained via data integration from an external schedule management tool, such as Google Calendar or Outlook Calendar, that each lawyer uses to manage their own schedule. If the client specifies a desired date and time, the search module may retrieve the latest schedule information for each lawyer immediately before finalizing the search results. If there are no available slots for the client's desired date and time, the lawyer may be removed from the search results.
The search module may search for lawyers taking into account personal information of the client (e.g., address, residence, age, gender) previously acquired from the client. For example, the search module may search for lawyers by adding conditions based on the client's personal information, such as the lawyer's location being within a predetermined distance from the client's address or the lawyer being of the same gender as the client, in addition to the content of the consultation.

The lawyer information output module outputs the attributes, consultation method, fees, and available consultation dates and times of the lawyers found (step S22).
The lawyer information output module transmits the attributes, consultation method, fees, and available consultation dates and times of the searched lawyer to the client terminal 20 as lawyer information.
The client terminal 20 receives this lawyer information and displays the lawyer information display screen 50 on its display unit (see Figures 7-9). These lawyer information display screens 50 show the lawyer's attributes, consultation method, fees, and available consultation dates and times on a single screen. For example, by swiping vertically, more detailed information about the lawyer may be displayed. By receiving input from the client, such as a horizontal swipe, on the lawyer information display screen 50 shown in Figure 7, the client terminal 20 transitions to another lawyer information screen 50 shown in Figure 8 or Figure 9. For example, by tapping or double-tapping, the client may temporarily flag a lawyer of interest from among multiple candidate lawyers.
The lawyer information output module displays this lawyer information display screen 50 on the client terminal 20, thereby outputting the attributes, consultation method, fees, and available consultation dates and times of the searched lawyer on a single screen.
By viewing the lawyer information display screen 50, the client can grasp all the information necessary for consulting with a lawyer.

The application acquisition module acquires the outputted application for consultation with the lawyer from the client (step S23).
The client terminal 20 accepts input from the client requesting consultation with a lawyer. This consultation request is made, for example, by accepting input on the lawyer information display screen 50 described above. The client terminal 20 accepts the input of the consultation request by accepting input for the consultation method, fee, and available consultation date and time. In other words, the client terminal 20 accepts input as a consultation request specifying that the lawyer displayed on the lawyer information display screen 50 will be the consultation partner, and that the lawyer will be consulted using the consultation method, fee, and date and time that have been accepted.
The client terminal 20 transmits the consultation application that has been received to the computer 10 .
The application acquisition module receives this consultation application and acquires the outputted consultation application from the client to the lawyer.

The consultation reservation execution module executes the process required for making a consultation reservation based on the acquired consultation application (step S24).
Based on the acquired consultation application, the consultation reservation execution module executes processes necessary for making a consultation reservation, such as making a consultation reservation with the lawyer who has made the consultation request, settling the fee, and notifying the client and lawyer according to the consultation method, consultation date, etc. Furthermore, if a new consultation reservation is entered into the lawyer's schedule management tool and a web-based consultation is scheduled, the module sets the reservation date and time to the web conference system previously selected by the lawyer. The processes necessary for this consultation are not limited to these, and other processes may be performed, or only some of these processes may be performed. Furthermore, the processes related to making a consultation reservation, settling the fee, and notifying the client and lawyer may be general processes, and detailed descriptions will be omitted. While the lawyer may confirm the reservation acceptance at this stage, in this embodiment, the lawyer does not confirm the reservation at this stage. A certain amount of time is required to confirm the reservation from the individual. In this embodiment, the client's reservation can be decided on the spot without requiring the lawyer to confirm the reservation. The consultation reservation execution module notifies the lawyer terminal 30 of the reservation, registers the date and time of the scheduled consultation in the schedule management tool registered by the lawyer, and accepts the lawyer's input of reservation acceptance. The consultation reservation execution module may be configured to present another candidate to the client terminal 20 if the lawyer does not input reservation acceptance within a predetermined time, such as 72 hours, before the scheduled consultation date and time.

The lawyer storage module stores the client and the lawyer who requested the consultation in association with each other (step S25).
The lawyer storage module stores the identifier of the client and the identifier of the lawyer in association with each other, and also stores the consultation content, consultation method, consultation time, consultation start date and time, etc. in association with each other.

This completes the lawyer information output process.
In the lawyer information output process, the computer 10 may again acquire the consultation content from the client terminal 20 that has acquired the current consultation application, and when executing the process of step S21 described above, may search for a lawyer who can respond to the consultation content from among the lawyers linked to the client by the process of step S25 described above. In other words, after searching for lawyers by the process of step S21, the computer 10 may use, as the search result, the lawyer linked to the client from whom the consultation content has been acquired again, or the computer 10 may omit the search for a lawyer by the process of step S21 and instead use, as the search result, the lawyer linked to the client from whom the consultation content has been acquired again.

[Evaluation Acquisition Process Executed by Computer 10]
The evaluation acquisition process executed by the computer 10 will be described with reference to Figure 10. The figure shows a flowchart of the evaluation acquisition process executed by the computer 10. The evaluation acquisition process is a process that is performed after the lawyer information output process described above, and more specifically, is a process that is performed after the client has consulted with the lawyer.

The evaluation acquisition module acquires evaluations of the consultant from the consultant or from both the consultant and the lawyer (step S30).
The client terminal 20 accepts input of an evaluation of the lawyer with whom the client consulted regarding a legal problem. The evaluation may be, for example, a number, a symbol, or text.
The client terminal 20 transmits the evaluation of the lawyer and the lawyer's identifier that have been input to the computer 10 .
The evaluation acquisition module receives the evaluation of the lawyer and the lawyer's identifier, and thereby acquires the client's evaluation of the lawyer.
The lawyer terminal 30 accepts input of an evaluation of a client who has consulted with the lawyer about a legal problem. The evaluation may be, for example, a number, a symbol, or text.
The lawyer terminal 30 transmits the evaluation of the client and the client's identifier that have been input to the computer 10 .
The evaluation acquisition module receives the evaluation of the client and the identifier of the client, and thereby acquires the evaluation of the client from the lawyer.

The lawyer DB storage module stores the acquired evaluation of the lawyer (step S31).
The lawyer DB storage module references the lawyer DB and stores the acquired evaluation in the lawyer DB, linking it to the lawyer that matches the acquired lawyer's identifier. At this time, if an evaluation has already been linked to the lawyer and stored, it is possible to perform some processing between the newly acquired evaluation and the already stored evaluation, such as calculating the average evaluation value, and then link and store it in the lawyer DB. For example, if the evaluation is a number, it is possible to calculate the average of the acquired evaluation and the already stored evaluation, and link the calculated average evaluation value to the lawyer and store it in the lawyer DB.

In the lawyer information output process described above, computer 10 may also search for lawyers in the process of step S21 described above, taking into account the lawyer ratings stored in the process of step S31 described above. For example, the search module may be configured to search for lawyers with the highest ratings linked to the lawyer DB, lawyers with ratings above a predetermined value, or a ranking of ratings in addition to the content of the consultation, and then search for a predetermined number of lawyers from the top.

This completes the evaluation acquisition process.

[Point Assignment Process Executed by Computer 10]
The point-granting process executed by the computer 10 will be described with reference to Fig. 11. The figure shows a flowchart of the point-granting process executed by the computer 10. The point-granting process is performed either before or after the execution of the process described above or the process described below.

The introduction reception module receives introductions of other clients from clients (step S40).
The introduction reception module receives introductions of other clients using an introduction code or the like that is issued in advance for each client. Each introduction code is linked to an identifier or the like of the client.
The client terminal 20 transmits the issued referral code to the client terminal 20 held by the other client. The client terminal 20 receives this referral code and displays it on its own display. The client terminal 20 accepts input for this referral code. The client terminal 20 transmits the accepted referral code to the computer 10.
The introduction reception module receives the introduction code and identifies the client linked to the introduction code, thereby accepting introductions of other clients from the client.
The method of receiving an introduction of another client from a client is not limited to the above example, and other methods may be used.

The point giving module gives points to a client who introduces another client (step S41).
The point giving module gives points to a client who has introduced other clients according to the number of clients introduced and the number of times introduced.

The point storage module stores the points given to the client (step S42).
The point storage module stores the points assigned to the client in association with the client's identifier. If points have been assigned to this client in the past, the points assigned this time are added to the points previously assigned, and the result of the addition is stored in association with the client's identifier. As a result, the point storage module stores the points assigned to the client.

The points stored in the point storage module are used to discount the fee when a client requests a consultation with a lawyer. Specifically, in the lawyer output process described above, when a consultation request is received from a client in the processing of step S23 described above, the discount module deducts the amount of the points from the fee for this consultation request. The discount module may deduct from the fee an amount equivalent to a preset number of points, an amount equivalent to points specified by the client, or an amount equivalent to all points owned by the client. Then, in the processing of step S24 described above, the consultation reservation execution module executes payment for the discounted amount.

This completes the point allocation process.

[Consultation Processing Executed by Computer 10]
The consultation process executed by the computer 10 will be described with reference to Figure 12. This figure shows a flowchart of the consultation process executed by the computer 10. The consultation process is a process that is performed after the lawyer information output process described above, and more specifically, is a process that is performed while the client is consulting and after the consultation is completed.

The consultation management module determines whether the current date and time has reached the consultation date and time (step S50).
The consultation management module determines whether the current date and time has reached the consultation date and time stored in step S25.
The consultation management module can also be configured to determine whether it is a predetermined time before the consultation date and time (for example, 1 minute, 3 minutes, or 5 minutes before). At this time, the computer 10 can be configured to output a notification to the client terminal 20 or the lawyer terminal 30 that the consultation date and time is approaching.
If the consultation management module determines that the current date and time is not the consultation date and time (NO in step S50), it repeats this process until the consultation date and time arrives.

On the other hand, if the consultation management module determines that the current date and time has reached the consultation date and time (YES in step S50), the consultation management module starts the consultation (step S51).
When the consultation begins, the consultation management module displays a message on the lawyer terminal 30 to confirm the lawyer's attendance, such as "It's time to start. Are you attending? Yes/No?", and prompts the lawyer to enter confirmation of attendance. If the lawyer's attendance is confirmed, regardless of whether the client actually attended, the consultation is considered to have actually started with the lawyer, and the remaining consultation time begins to be counted. Furthermore, the consultation management module may output a notification to the client terminal 20 and the lawyer terminal 30 that the consultation has started, may output content according to the consultation method, or may output the remaining time at a predetermined timing.

The extension instruction acquisition determination module determines whether an extension instruction has been acquired (step S52).
The extension instruction is acquired from the client terminal 20 by the extension instruction acquisition module. If the client requests it, the client terminal 20 accepts input of a request to extend the consultation time of the current consultation. When the extension instruction acquisition module accepts the input of the extension request, it outputs an extension request flag to the extension instruction acquisition determination module. The extension instruction acquisition determination module checks the schedule of the lawyer currently in consultation. If the lawyer's schedule has a next appointment, it displays "Next appointment available" on the client terminal 20 and rejects the extension request. If the lawyer's schedule has not had a next appointment available for a predetermined period of time, the extension instruction acquisition determination module displays on the lawyer terminal 30 that the client has requested an extension and accepts a selection of "Yes" or "No" regarding whether to accept the extension. If the lawyer responds "No" to the extension acceptance, the client terminal 20 displays "Next appointment available" as described above and rejects the extension request. If the lawyer selects "Yes," it transmits a message to the extension instruction acquisition module indicating that the extension is possible. The extension instruction acquisition module may accept, from the client terminal 20, an input for extending the consultation time by an amount of time specified by the client, or may accept an input for extending the consultation time by a predetermined amount of time (e.g., 10 minutes, 20 minutes, or 30 minutes). The client terminal 20 transmits the accepted input for extension as an extension instruction to the computer 10. In addition, the client terminal 20 may check the lawyer's latest schedule at the start of the consultation, and if the next schedule has been entered, it may not display a message accepting the input for an extension request, but may instead display a message urging the client to finish the consultation within the allotted time, such as "The lawyer has another schedule."
The extension instruction acquisition determination module determines whether the extension instruction acquisition module has received this extension instruction.

If the extension instruction acquisition determination module determines that an extension instruction has been acquired (YES in step S52), the consultation management module extends the consultation time (step S53).
The consultation management module extends the consultation time by adding the currently acquired consultation time to the remaining time from when the counting started. In addition, when the computer 10 acquires the extension of the consultation time, it performs the settlement of the fee required for the extension, outputs a notice that the consultation time has been extended, etc.
After extending the consultation time, the computer 10 executes the process of step S52 again.

On the other hand, if the extension instruction acquisition determination module determines that an extension instruction has not been acquired (step S52: NO), the consultation management module determines whether the consultation time has ended (step S54).
The consultation management module determines whether the remaining time of the consultation time being counted has reached zero, thereby determining whether the consultation time has ended.
If the consultation management module determines that the consultation time has not ended (NO in step S54), that is, if the remaining time of the consultation time being counted is not zero, the computer 10 executes the process of step S52 again.

On the other hand, if the consultation management module determines that the consultation time has ended (YES in step S54), that is, if the remaining time of the consultation time being counted is zero, the consultation ends (step S55).
When the consultation ends, the consultation management module outputs a notification that the consultation time has ended to the client terminal 20 and the lawyer terminal 30. The consultation management module transmits this notification to the client terminal 20 and the lawyer terminal 30.
The client terminal 20 and the lawyer terminal 30 receive this notification and display it on their own display units.
The consultation management module displays this notification on the client terminal 20 and the lawyer terminal 30, thereby outputting a notification that the consultation time has ended to the client terminal 20 and the lawyer terminal 30. The consultation management module may also display the remaining time on the client terminal and the lawyer terminal 30, for example, three minutes and one minute before the end of the consultation time, and may display the remaining seconds for the last 10 seconds.

The consultation result storage module stores the consultation content of the client and the response from the lawyer as the consultation result (step S56).
The consultation content in step S56 includes the classification of the consultation content, text of emails, chats, etc. sent by the client, video and audio of the client during the consultation, etc. The answer includes text of emails, chats, etc. sent by the lawyer, video and audio of the lawyer during the consultation, etc.
The computer 10 acquires the text, video and audio from the client terminal 20 and the lawyer terminal 30 at the end of the consultation.
The consultation result storage module stores the consultation content of the client and the answer from the lawyer as a result of the consultation, and further stores the date and time of the consultation, the client's identifier, the lawyer's identifier, etc., in association with the result of the consultation.
The consultation result storage module can also be configured to not store the response result and terminate processing when the consultation ends if permission to store the response result is not obtained from the client or the lawyer. Here, a display prompting input of an evaluation of the client may be displayed on the lawyer terminal 30. For example, if the lawyer believes that the client is unlikely to be a good client because they are too attached to their own illogical opinions, the lawyer may assign a low evaluation result, so that the next time the same client performs matching in this embodiment, the client will be removed from the search results and will not be matched with the same lawyer.

The above is the consultation process.
The computer 10 can also be configured to provide the stored consultation results to the client or lawyer upon request by the consultation process.

[Consultation result providing process executed by computer 10]
The consultation result providing process executed by the computer 10 will be described with reference to Fig. 13. Fig. 13 is a flowchart of the consultation result providing process executed by the computer 10. The consultation result providing process is a process that is performed after the consultation process described above.

The provision instruction acquisition module acquires an instruction to provide the consultation result (step S60).
This instruction to provide is an instruction to provide the client with the text, video, audio, etc. of the consultation results of this client stored in the process of step S56 described above.
The client terminal 20 receives an instruction to provide the consultation results from the client, and transmits the instruction and the client's identifier to the computer 10.
The providing instruction acquisition module receives the providing instruction and the client's identifier, and thereby acquires an instruction to provide the consultation result.

The consultation result specifying module specifies the consultation result to be provided (step S61).
The consultation result identification module identifies, from among the stored consultation results, a consultation result linked to the identifier of the client based on the acquired identifier of the client. Here, if the consultation result identification module has acquired, in addition to the consultation result, information that can identify the consultation result, such as the identifier of the lawyer who consulted and the date and time of the consultation, the consultation result identification module further identifies, from among the identified consultation results, a consultation result that corresponds to the acquired information.

The providing module provides the consultation result (step S62).
The providing module transmits the identified consultation result to the client terminal 20 .
The client terminal 20 receives the consultation result and displays it on its own display unit, outputs audio, etc.
The providing module provides the consultation result to the client by causing the client terminal 20 to display or output the consultation result. Next, a screen is displayed on the client terminal 20 to prompt the client to input an evaluation of the lawyer for this consultation (a simple evaluation such as a 5-point scale is preferable).

This completes the consultation result providing process.
In the above-described consultation result providing process, the provision instruction is received from the client, but the provision instruction may be received from a lawyer. In this case, the consultation result is identified based on the lawyer's identifier and provided to the lawyer.

[Consultation Result Learning Process Executed by Computer 10]
The consultation result learning process executed by the computer 10 will be described with reference to Fig. 14. The figure shows a flowchart of the consultation result learning process executed by the computer 10. The consultation result learning process is a process that is performed after the consultation process described above.

The availability acquisition module acquires from the client whether the consultation result is available or not (step S70).
The availability of consultation results means whether the consultation content and the answer can be made available to other people. If the consultation content and the answer are to be made available to other people, any content that is personal information of the person making the request will be deleted or blurred, etc., so that other people cannot distinguish it.
The client terminal 20 receives an input of whether or not the consultation result is available for use, and transmits this availability and the client's identifier to the computer 10.
The availability acquisition module acquires the availability and the client's identifier, thereby acquiring from the client whether the consultation result is available or not.

The availability determination module determines whether the consultation result is available for use (step S71).
If the availability of the acquired consultation result indicates that it is unavailable, that is, if the consultation result is unavailable (step S71 NO), the availability determination module ends this process.

On the other hand, if the availability determination module determines that the acquired consultation result is available, that is, if the consultation result is available (YES in step S71), the consultation result learning module learns the consultation result (step S72).
The consultation result learning module learns the consultation results by using the consultation results linked to the identifiers of the clients who have acquired the availability from among the consultation results stored by the processing of step S56 described above. Learning methods include machine learning such as supervised learning, unsupervised learning, and reinforcement learning, as well as deep learning using convolutional neural networks, recurrent neural networks, and long/short-term memory.
The consultation result learning module, for example, learns the stored consultation content and its response as supervised data and learns the correlation between them. If the consultation content or the response is text, the consultation result learning module recognizes the text by performing text recognition or the like and performs learning using the recognized text. Also, if the consultation content or the response is audio, the consultation result learning module recognizes the audio by performing speech recognition and performs learning using the recognized audio.

The learning result storage module stores the learning results (step S73).

The above is the consultation result learning process.
The computer 10 executes this consultation result learning process every time a consultation process is performed, thereby learning the consultation result.

[Answer Output Process Executed by Computer 10]
The answer output process executed by the computer 10 will be described with reference to Fig. 15. This figure is a flowchart showing the answer output process executed by the computer 10.
The answer output process is a process that is performed after the consultation result learning process described above, and is a process that uses the learning results. The answer output process is also a process that is performed in conjunction with the lawyer information output process described above.

The consultation content acquisition module acquires the consultation content from the client (step S80).
The consultation contents acquired by this process include, in addition to the consultation contents acquired in the process of step S20 described above, text indicating the specific contents of the consultation contents, etc.
The client terminal 20 accepts input of text or the like indicating the specific content of the consultation in addition to the consultation content in the process of step S20 described above. The client terminal 20 transmits the consultation content and the identifier of the client that have been input to the computer 10.
The consultation content acquisition module receives the consultation content and the identifier of the client, and thereby acquires the consultation content from the client.

The answer generation module generates an answer based on the learning result of the consultation (step S81).
The answer generation module generates an answer to the consultation content based on the acquired consultation content and the learning result from the consultation result learning process. The answer generation module generates an answer based on the classification and text of the consultation content in the acquired consultation content and the learning result.

The answer output module outputs the generated answer (step S82).
In the processing of step S22 described above, when the computer 10 outputs the lawyer information, the response output module also outputs the generated response.
The answer output module transmits the generated answer to the client terminal 20.
The client terminal 20 receives this response and displays it on its own display unit.
The answer output module outputs the generated answer by displaying the generated answer on the client terminal 20.

This completes the answer output process.

[Request Processing Executed by Computer 10]
The request processing executed by the computer 10 will be described with reference to Fig. 16. Fig. 16 is a flowchart of the request processing executed by the computer 10.
The request process is a process that is carried out at least after the consultation process described above.

The request acquisition module acquires a formal request from the client to the lawyer who has applied for consultation (step S90).
The client terminal 20 accepts input necessary to formally request a lawyer, and transmits the accepted content, the lawyer's identifier, and the client's identifier to the computer 10.
The request acquisition module receives the content and the client's identifier, and thereby acquires a formal request from the client to the lawyer who has applied for the consultation.

The exchange identification module identifies an exchange between the client and the lawyer (step S91).
Based on the acquired client identifier and lawyer identifier, the transaction identification module identifies the consultation result linked to the client identifier and lawyer identifier from the consultation results stored by the processing of step S56 described above. Furthermore, if the transaction identification module stores content linked to the client identifier and lawyer identifier in addition to the consultation result, it also identifies this content.

The batch storage module collectively stores the identified exchanges (step S92).
The batch storage module stores the identified transactions in a distributed manner across multiple storage units that make up the distributed management ledger.
An example of a distributed management ledger is blockchain technology. In this embodiment, all of the identified transactions are associated with each other and distributedly stored in each storage unit.
In this embodiment, a form using a blockchain has been described as an example of a distributed management ledger, but the distributed management ledger in the present invention is not limited to a blockchain.
The collective storage module can also be configured to collectively store transactions using a method other than the distributed management ledger.

The above is the request processing.
The computer 10 can also be configured to provide the collectively stored exchanges in response to a request from the client or the lawyer. In this case, in response to a request from the client terminal 20 or the lawyer terminal 30, the computer 10 transmits the collectively stored exchanges to the client terminal 20 or the lawyer terminal 30, thereby providing the collectively stored exchanges in response to a request from the client or the lawyer.

[Location Learning Process Executed by Computer 10]
The location learning process executed by the computer 10 will be described with reference to Fig. 17. This figure shows a flowchart of the location learning process executed by the computer 10. The location learning process is a process performed after the lawyer information output process described above, and is a detailed description of the process (step S6) for learning the correlation between the consultation content and the location of the expert described above.

The consultation acquisition module acquires the content of the consultation and the lawyer who has applied for the consultation (step S100).
The consultation content in the location learning process is a consultation classification, similar to the consultation content in the expert information output process described above.
The consultation acquisition module acquires the lawyer's identifier and the consultation content for which the lawyer has applied, which are stored in the process of step S25 described above.

The location identification module identifies the location of the acquired lawyer (step S101).
The location identification module refers to the lawyer information DB described above and identifies the location of the lawyer associated with the lawyer acquired this time.

The location learning module learns the correlation between the consultation content and the location of the identified lawyer (step S102).
The learning methods performed by the location learning module are similar to those of the consultation result learning process described above, and include machine learning using supervised learning, unsupervised learning, reinforcement learning, etc., as well as deep learning using convolutional neural networks, recurrent neural networks, long- and short-term memory, etc.
The location learning module learns, for example, the classification of consultation content and the location of the identified lawyer as supervised data, and learns the correlation between them.

The learning result storage module stores the learning results (step S103).

The above is the location learning process.
By executing this location learning process each time the expert information output process is performed, the computer 10 learns the correlation between the content of the consultation and the location of the lawyer who has requested the consultation.

[Candidate site output process executed by computer 10]
The candidate site output process executed by the computer 10 will be described with reference to Fig. 18. Fig. 18 is a flowchart showing the candidate site output process executed by the computer 10.
The candidate site output process is a process that is performed after the location learning process described above, and is a process that uses the learning results. The candidate site output process is also a process that is executed in response to a request from the lawyer terminal 30, etc. The candidate site output process is a detailed version of the candidate site identification process (step S7), candidate site generation process (step S8), and candidate site output process (step S9) described above.

The desire acquisition module acquires the desired field and region (step S110).
Desired fields are similar to the categories of consultation content mentioned above: divorce/gender counseling, traffic accidents, labor issues, fraud victims, consumer damage, debt/debt settlement, inheritance consultation, debt collection, international/foreigner issues, internet issues, crime/criminal cases, medical care, real estate, etc. Desired areas include the name of a region, prefecture, city, town, or village.
The lawyer terminal 30 accepts input of the desired field of consultation content and the desired region, and transmits the accepted desired field and region to the computer 10.
The desired field and region are received by the desired acquisition module, and the desired field and region are acquired.

The candidate site identification module identifies candidate sites for new business locations based on the learning results and the acquired desired field and region (step S111).
The candidate location identification module identifies a candidate location for a new law firm opening based on the learning results of the location learning process described above and the desired field and region acquired this time. The candidate location identification module, for example, identifies a candidate location near the location of a lawyer who has received many consultation requests in the acquired field and region. The candidate location identification module identifies a location within a predetermined range (e.g., within a 100-meter radius, within a 1-kilometer radius) near the location of the lawyer. It is desirable for this location to be a location suitable for opening a new law firm, such as a vacant lot or a building available for occupation.
Whether or not a location is an available building, vacant lot, etc. may be confirmed by referring to a predetermined website, database, etc., or by other methods.

The plurality determination module determines whether or not there are a plurality of identified candidate sites (step S112).
If the plurality determination module determines that there are not multiple identified candidate sites (step S112 NO), the candidate site generation module generates the identified candidate sites on a map (step S113).
The candidate location generation module generates candidate locations on a map stored in advance in the module or on a map acquired from an external database, etc. At this time, the candidate location generation module generates candidate locations on the identified points. As described above, the candidate locations generated are icons, pins, figures, text, etc.
The candidate site output module outputs the map on which the candidate sites have been generated (step S117). Details of step S117 will be described later.

Returning to the process of step S112, the candidate site output process will be continued.
If the plurality determination module determines that there are a plurality of identified candidate locations (YES in step S112), the weighting module performs weighting for each candidate location according to a predetermined condition (step S114).
The predetermined conditions may be, for example, the order of the number of consultation requests. The predetermined conditions may also be related to the candidate site. For example, the presence or absence of parking, distance from public transportation, rent, land rent, and size. The predetermined conditions may also be any one of these conditions, or a combination of multiple conditions.
The weighting module performs weighting and ranks the candidate locations, for example, in descending order of the number of consultation requests among the candidate locations.

The display mode setting module sets different display modes for each candidate location according to the weighting (step S115).
The display mode setting module sets a display mode for each candidate site, for example, by using a darker color for heavier weighting and a lighter color for lighter weighting. Alternatively, the display mode setting module sets the display mode in the order of red, yellow, green, and blue, in descending order of weighting, as in thermography. This weighting is intended to indicate that the heavier the weighting, the more suitable the site is for a new business location.

The candidate site generation module generates each candidate site on a map (step S116).
The candidate location generation module generates each candidate location on a map stored in advance in the candidate location generation module or on a map acquired from an external database, etc. At this time, the candidate location generation module generates each candidate location on each identified point in a different display mode according to the weighting. As described above, the candidate locations to be generated are icons, pins, figures, text, etc., and these are weighted using the color shading and color type described above. Specifically, in the case of icons, the display mode of each candidate location is changed by changing the color shading of each icon (see FIG. 19 ).
The candidate sites generated on a map by the candidate site generation module will be described with reference to Figure 19. This figure is a diagram schematically showing an example of candidate sites generated on a map by the candidate site generation module. In this figure, the candidate site generation module has generated four candidate sites 61 on a map 60. Each candidate site 61 has a different shade of color, with the darker the color, the heavier the weighting (higher the ranking) and the more suitable the site is as a candidate site.

Returning to FIG. 18, the candidate site output process will be continued.
The candidate site output module outputs the map on which the candidate sites have been generated (step S117).
The candidate location output module transmits to the lawyer terminal 30 the map 60 shown in Figure 19 on which the candidate locations have been generated by the processing of step S116 described above, or the map on which the candidate locations have been generated by the processing of step S113 described above.
The lawyer terminal 30 receives this map and displays it on its own display unit.
The candidate site output module displays this map on the lawyer terminal 30, thereby outputting the map on which the candidate sites have been generated.
By viewing this map, lawyers can easily identify suitable locations for opening new law firms.

This completes the candidate site output process.

Although each of the above processes is described as a separate process, the computer 10 can also be configured to execute a combination of some or all of the above processes. Furthermore, the computer 10 can also be configured to execute each process at a timing other than that described.

The above-mentioned means and functions are realized by a computer (including a CPU, information processing device, and various terminals) loading and executing a specified program. The program may be provided, for example, from the computer via a network (Software as a Service (SaaS)) or as a cloud service. The program may also be provided in a form recorded on a computer-readable recording medium. In this case, the computer reads the program from the recording medium, transfers it to an internal or external recording device, records it, and executes it. The program may also be pre-recorded on a recording device (recording medium) and provided from the recording device to the computer via a communication line.

Although the embodiments of the present invention have been described above, the present invention is not limited to these above-mentioned embodiments. Furthermore, the effects described in the embodiments of the present invention are merely a list of the most favorable effects resulting from the present invention, and the effects of the present invention are not limited to those described in the embodiments of the present invention.

(1) A human resources matching system that matches experts (e.g., lawyers) with clients,
a consultation content acquisition unit (for example, a consultation content acquisition unit 11, a consultation content acquisition module) that acquires consultation content from the client;
a search unit (e.g., search unit 15, search module) that searches for the expert who can answer the acquired consultation content;
an expert output unit (e.g., expert output unit 12, lawyer information output module) that outputs the attributes of the searched experts (e.g., photo, name, name of affiliated firm, location, area of focus), consultation method (e.g., telephone, online, face-to-face), fee, and available consultation date and time;
an application acquisition unit (e.g., application acquisition unit 13, application acquisition module) that acquires the application for consultation with the expert output from the client;
a location learning unit (e.g., location learning unit 16, location learning module) that learns a correlation between the consultation content and the location of the expert to whom the consultation is requested;
a candidate site identification unit (e.g., candidate site identification unit 17, candidate site identification module) that identifies a candidate site for a new business opening based on the learning result;
a candidate site generation unit (e.g., candidate site generation unit 18, candidate site generation module) that generates the identified candidate sites on a map;
a candidate site output unit (e.g., candidate site output unit 14, candidate site output module) that outputs the map generated by the candidate site;
A talent matching system equipped with:

The invention (1) allows clients to consult with experts in a relaxed manner, and also enables experts to be provided with information on suitable locations for new business openings.

(2) a weighting unit (e.g., a weighting module) that, when the candidate site identification unit identifies a plurality of candidate sites, assigns weights to each of the identified candidate sites according to predetermined conditions;
Further provided with
the candidate site generation unit generates each of the plurality of candidate sites on a map in a different display mode according to the weighting.
A human resources matching system as described in (1).

The invention (2) makes it possible to provide experts with information about suitable locations for new business openings.

(3) A computer-implemented human resource matching method for matching experts with people seeking advice, comprising:
A step of acquiring consultation content from the client (e.g., step S20);
A step of searching for the expert who will answer the acquired consultation content (for example, step S21);
a step of outputting the attributes, consultation method, fee, and available consultation date and time of the searched expert (e.g., step S22);
A step (e.g., step S23) of acquiring the outputted consultation application to the expert from the client;
A step of learning a correlation between the consultation content and the location of the specialist to whom the consultation is requested (e.g., step S102);
A step of identifying a candidate site for a new business opening based on the learning result (e.g., step S111);
A step of generating the identified candidate site on a map (e.g., steps S113 and S116);
a step of outputting the map generated from the candidate site (e.g., step S117);
A human resources matching method comprising:

(4) A computer that matches experts with people seeking advice.
A step of acquiring consultation content from the client (e.g., step S20);
A step of searching for the expert who will answer the acquired consultation content (for example, step S21);
a step of outputting the attributes, consultation method, fee, and available consultation date and time of the searched expert (e.g., step S22);
A step of acquiring the outputted consultation application to the expert from the client (e.g., step S23);
learning a correlation between the consultation content and the location of the specialist to whom the consultation is requested (e.g., step S102);
A step of identifying a candidate site for a new business opening based on the learning result (e.g., step S111);
A step of generating the identified candidate site on a map (e.g., steps S113 and S116);
a step of outputting the map on which the candidate site is generated (e.g., step S117);
A computer-readable program for executing the program.

1 Human Resource Matching System 10 Computer 11 Consultation Content Acquisition Unit 12 Expert Output Unit 13 Application Acquisition Unit 14 Search Unit 20 Client Terminal 30 Lawyer Terminal 40 Discussion Content Input Screen 41 Icon 50 Lawyer Information Display Screen 60 Map 61 Candidate Location

Claims (4)

A human resources matching system that matches experts with people seeking advice,
a consultation content acquisition unit that acquires consultation content from the client;
a search unit that searches for the expert who can answer the acquired consultation content;
an expert output unit that outputs the attributes, consultation method, fee, and available consultation date and time of the searched expert;
an application acquisition unit that acquires the output application for consultation with the specialist from the client;
a location learning unit that learns a correlation between the consultation content and the location of the expert to whom the consultation is requested;
a candidate site identification unit that identifies candidate sites for new business locations based on the learning results;
a candidate site generating unit that generates the identified candidate sites on a map;
a candidate site output unit that outputs the generated map of the candidate sites;
A talent matching system equipped with: a weighting unit that, when the candidate site identification unit identifies a plurality of candidate sites, weights each of the identified candidate sites in accordance with predetermined conditions;
Further provided with
the candidate site generation unit generates each of the plurality of candidate sites on a map in a different display mode according to the weighting.
The personnel matching system according to claim 1 . A computer-implemented human resource matching method for matching experts with clients, comprising:
acquiring consultation details from the person seeking advice;
A step of searching for the expert who can answer the acquired consultation content;
a step of outputting the attributes, consultation method, fee, and available consultation date and time of the searched expert;
receiving the outputted request for consultation with the specialist from the person seeking advice;
learning a correlation between the consultation content and the location of the specialist to whom the consultation is requested;
A step of identifying candidate locations for new business openings based on the learning results;
generating the identified candidate sites on a map;
outputting the map on which the candidate sites are generated;
A human resources matching method comprising: A computer that matches experts with people seeking advice
acquiring consultation details from the person seeking advice;
a step of searching for the expert who can answer the acquired consultation content;
a step of outputting the attributes, consultation method, fee, and available consultation date and time of the searched expert;
a step of receiving the outputted request for consultation with the specialist from the person seeking advice;
learning a correlation between the consultation content and the location of the specialist to whom the consultation is requested;
A step of identifying candidate locations for new business openings based on the learning results;
generating the identified candidate sites on a map;
outputting the map on which the candidate sites are generated;
A computer-readable program for executing the program.