Referral Program, Referral System and Referral Method

Abstract

An object of the present invention is to provide a program that enables each user to easily specify candidates who meet the desired introduction conditions (candidate conditions). [Solution] The program of the present invention executes, in an introduction system having at least a memory unit and a processing unit, a talent storage function for storing user information, candidate information, and introducer information in the memory unit, a condition storage function for storing in the memory unit the candidate conditions desired by a user contained in the user information of each of the users, and, when a candidate who meets the candidate conditions exists and an introducer who is able to introduce the candidate has a connection with the user, an identification function for identifying the one or more candidates as one or more suitable candidates to introduce to the user.

Description

[Technical field]

[0001] The present invention relates to a referral program, a referral system, and a referral method. [Background technology]

[0002] Patent Document 1 discloses a sales support system for realizing optimization of sales activities, as shown in claim 1, Figs. 1, 9, 11, and the like. Specifically, this sales support system is equipped with the following (1) business card management function, (2) marketing strategy function, and (3) sales support function, and the data digitized by each of these functions is managed centrally as customer charts. (1) The business card management function refers to the function of inputting business card information, including the name, organization name, and organization address, written on business cards obtained by a system user, and converting the business cards into digital data by adding attribute information of the organization contained in the business card information and information about the person whose name is contained in the business card information. (2) The marketing strategy function refers to the function in which the system user analyzes and extracts targets for sales from the data created by the business card management function, and by sending e-mails to the extracted targets, converts the behavior of the targets into digital data and discovers new customers. (3) The sales support function refers to the function of converting information regarding sales activities conducted toward customers identified by the marketing strategy function into digital data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication 2018-185614 Summary of the Invention [Problem to be solved by the invention]

[0004] The sales support system disclosed in Patent Document 1 targets multiple members (employees) belonging to the same organization as its users, and aims to optimize sales activities (sales activities free of waste and errors) by centrally managing data related to target organizations; it is not intended to propose and introduce suitable candidates who meet the desired criteria for introductions requested by multiple users who are not bound to the same organization.

[0005] One of the objects of the present invention is to provide a program that enables each user to easily specify candidates who meet the desired introduction conditions (candidate conditions). [Means for solving the problem]

[0006] The first aspect of the referral program is An introduction system including at least a storage unit and a processing unit, a human resources storage function that stores in a storage unit managed by the server: (A1) user information of each user; (A2) candidate information on a plurality of candidates to be introduced to each user, the candidate information being composed of a plurality of attribute information organized for each candidate; and (A3) introducer information on a plurality of introducers who have a primary connection with at least one or more of the plurality of candidates and are capable of introducing the at least one or more candidates to each user; A condition storage function for storing candidate conditions desired by a user included in the user information of each user in the storage unit; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the command. The second aspect of the referral program is An introduction system including at least a storage unit and a processing unit, A human resources storage function that stores in the storage unit: (A1) user information of each user; (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate; and (A3) introducer information about multiple introducers who have a primary connection with at least one or more of the multiple candidates and are capable of introducing the at least one or more candidates to each user, the multiple attribute information including information obtained from the candidate by the introducer who has a primary connection with the candidate; A condition storage function for storing candidate conditions desired by a user included in the user information of each user in the storage unit; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the command. The third aspect of the referral program is An introduction program that operates in an introduction system including a plurality of terminals and a server, The introduction system, A human resources storage function that stores in a storage unit managed by the server: (A1) user information of each user who uses each of the multiple terminals; (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate; and (A3) introducer information about multiple introducers who have a primary connection with at least one or more of the multiple candidates and are capable of introducing the at least one or more candidates to each user, the multiple attribute information including information obtained from the candidate by an introducer who has a primary connection with the candidate; A condition transmission function that allows a user to input candidate conditions desired by the user into a terminal used by the user and transmits the candidate conditions from the terminal to the server; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the command. The fourth aspect of the referral program is An introduction program that operates in an introduction system including a plurality of terminals and a server, The introduction system, a human resources storage function that stores in a storage unit managed by the server: (A1) user information of each user who uses each of the multiple terminals; (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate; and (A3) introducer information about multiple introducers who have a primary connection with at least one or more of the multiple candidates and are able to introduce the at least one or more candidates to each user, the introducer information being composed of at least one or more relationship information with the at least one or more candidates organized for each introducer, the human resources storage function storing the multiple attribute information including information obtained from the candidate by an introducer who has a primary connection with the candidate; A condition transmission function that allows a user to input candidate conditions desired by the user into a terminal used by the user and transmits the candidate conditions from the terminal to the server; (B1) a judgment function for judging whether there is one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) whether an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user; a content creation function that creates at least one or more introduction contents for introducing the one user to the one or more suitable candidates as the one or more candidates by using the user information, the candidate information, and the introducer information when a result of the judgment function is a positive judgment; an introduction function for introducing the one user by sending at least one of the at least one or more introduction contents to a contact included in candidate information of at least one of the at least one or more suitable candidates; Execute the command. The fifth aspect of the referral program is In the fourth aspect of the referral program, moreover, The introduction system, an attribute generation function that performs machine learning on correlations between a plurality of pieces of attribute information obtained from the plurality of candidates contained in the candidate information as training data, and generates an attribute correlation model for predicting unknown attribute information when each candidate has unknown attribute information; Run the command, In the judgment function, the plurality of pieces of attribute information to be treated as the plurality of pieces of attribute information are generated using the attribute correlation model. The sixth aspect of the referral program is In the fourth or fifth aspect of the referral program, moreover, The introduction system, a result storage function that stores in the storage unit, each time the introduction function is executed, a set of information on at least one or more introduction contents, the one user, and the at least one or more matching candidates transmitted by the introduction function, as well as a contact result indicating whether the one user has been able to contact at least one or more matching candidates; a content generation function that performs machine learning on the correlation between the set information and the user information, candidate information, and introducer information related to the set information, and the contact result, as training data, to generate a content correlation model of the at least one or more introduced contents so that the contact result is a positive result; Run the command, The content creation function uses the content correlation model to create at least one or more introduction contents for introducing the one user to the one or more match candidates. The seventh aspect of the referral program is In the sixth aspect of the referral program, moreover, The introduction system, a result generation function that performs machine learning on correlations between user information and candidate information related to the set information and the contact results as training data for each execution of the result storage function, and generates a prediction model of contact results of all candidates for each user; a notification function that notifies a part or all of the contact results generated by the result generation function via a screen of a terminal used by each user; Execute the command. The eighth aspect of the referral program is In the introduction program according to any one of the fourth to seventh aspects, The content creation function creates two or more pieces of introduction content, moreover, The introduction system, A selection function that allows the one user to select which of the two or more introduction contents to send to the at least one suitable candidate via a terminal used by the one user; Run the command, The referral function sends the referral content selected by the selection function to the contact. One embodiment of the introduction system comprises: Multiple devices and A server capable of communicating with the plurality of terminals via a communication network and having a storage unit that stores the introduction program according to any one of the third to eighth aspects; Equipped with. The first aspect of the introduction method is as follows: An introduction method using the introduction program of the first or second aspect, said referral system executing said talent storage function; said referral system executing said condition storage function; said introduction system executing said specific function; Includes. The second method of introduction is as follows: A referral method using the referral program of the third aspect, said referral system executing said talent storage function; said introduction system executing said condition sending function; Includes. The third method of introduction is as follows: A referral method using the referral program of the fifth aspect, said referral system executing said talent storage function; said introduction system executing said condition sending function; said referral system performing said decision function; said introduction system executing said content creation function; said referral system executing said referral function; said referral system executing said attribute generation function; Includes. The fourth method of introduction is as follows: An introduction method using the introduction program of a sixth aspect that cites only the introduction program of the fifth aspect, said referral system executing said talent storage function; said introduction system executing said condition sending function; said referral system performing said decision function; said introduction system executing said content creation function; said referral system executing said referral function; said referral system executing said attribute generation function; said referral system executing said result storage function; said introduction system executing said content generation function; Includes. Effect of the Invention

[0007] The introduction programs of the first to third aspects enable each user to easily identify candidates who meet the desired conditions for introduction (candidate conditions). The introduction programs of the fourth to eighth aspects enable each user to easily realize the introduction of candidates who meet the desired conditions for introduction (candidate conditions). The introduction system of one embodiment allows each user to easily realize introductions of candidates that meet the desired conditions for introduction (candidate conditions). The introduction methods of the first to fourth aspects enable each user to easily realize the introduction of candidates who meet the desired conditions for introduction (candidate conditions). [Brief description of the drawings]

[0008] [Figure 1A] 1 is a schematic diagram of an introduction system according to an embodiment of the present invention (hereinafter, referred to as the present embodiment). [Figure 1B] FIG. 2 is a detailed diagram of a management server and one terminal according to the present embodiment. [Figure 1C] 11 is a list of examples of multiple pieces of attribute information according to the present embodiment. [Figure 1D] FIG. 11 is a flow diagram of an introduction program based on the basic functions of this embodiment. [Diagram 2] FIG. 11 is a flow diagram of an introduction program using additional function 1 of this embodiment. [Diagram 3]FIG. 11 is a flow diagram of an introduction program using additional function 2 of this embodiment. [Figure 4] FIG. 2 is a schematic diagram of an additional function 3 of the present embodiment. [Diagram 5] FIG. 2 is a schematic diagram of an additional function 6 of the present embodiment. [Figure 6] FIG. 11 is a flow diagram of an introduction program according to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] First, the contents that the inventors of the present application were considering when they started creating the present invention (hereinafter, referred to as the contents of consideration at the start of the creation) will be described. Then, the present embodiment and several modified examples will be described.

[0010] <Start date considerations> For example, in many companies' sales activities, salespeople exchange many business cards with customers. The printed information on these business cards is generally digitized and managed by business card management applications, CRM systems (customer relationship management systems), etc. However, most of the information digitized from printed business card information currently remains as a simple contact database, and it is difficult to say that it is being used effectively. "Problem 1: Digitalized business card information is not being used effectively" In addition, there are sales activities that are based on "referrals" (hereinafter referred to as "referral sales") and those that are based "other than referrals" (hereinafter referred to as "non-referral sales"), but it is known that the success rate of business negotiations through referral sales is significantly higher. However, the current general referral sales method involves inefficient tasks, such as the following: 1. Obtain a letter of introduction from the business partner who will be the source of the introduction 2. Contact the referral destination in advance and obtain their consent 3. Multiple negotiations to schedule business meetings 4. Consideration of the relationship between the referral source and the referral recipient 5. Follow-up after referral Currently, these tasks must be carried out by sales representatives individually by phone or email, which requires a great deal of time and effort. Furthermore, referral sales are heavily dependent on the sales representative's personal network, which is a significant barrier for new sales representatives, sales representatives who have just been transferred, and those who have recently started a business or started a new company. "Problem 2: The current inefficient work involved in referral sales" "Problem 3: Reliance on salespeople's personal networks" In response to this situation, some customer management systems offer a function to manage a company's leads in conjunction with a business card management function, but these are limited to simply sharing contact information and do not automate or streamline the actual referral process. The inventors of the present application have investigated ways to solve the above problems and to make further improvements, and have completed the present invention.

[0011] <<Present embodiment>> Next, the introduction system CS (see FIGS. 1A and 1B) of this embodiment will be described. First, basic functions, configurations, operations, and effects (hereinafter referred to as basic aspects) will be described, and then additional functions, configurations, operations, and effects (hereinafter referred to as additional aspects) will be described. Please note that for elements in the additional forms that are equivalent or nearly equivalent to the elements used in the description of the basic form, the names and symbols of the basic form shall apply mutatis mutandis.

[0012] <Basic form of referral system> [Features and configuration of the referral system] The introduction system CS has a function of introducing at least one or more candidates C1, C2, ... (see Fig. 1A) who meet the desired conditions (candidate conditions) of each of multiple users U1, U2, U3, ... (see Fig. 1A). This function is realized by the introduction system CS executing an introduction program PG1 (see Fig. 1D) described later. Here, the multiple users U1, U2, U3, ... are people who wish to be introduced to their desired candidates, and as an example, they are sales representatives. In the following explanation, multiple users U1, U2, U3, ... will be referred to as multiple users U, multiple introducers I1, I2, ... described below will be referred to as multiple introducers I, and multiple candidates C1, C2, ... will be referred to as multiple candidates C.

[0013] As shown in FIG. 1A, the introduction system CS includes a plurality of terminals 10U, 10I, and 10C, a management server 20 (an example of a server), a communication network 30 (the Internet is an example of this), and a storage server 40A and a storage server 40B. The multiple terminals 10U, 10I, and 10C and the management server 20 are connected via a communication network 30 so as to be able to communicate with each other.

[0014] [Multiple devices] As shown in FIG. 1A, the multiple terminals 10U, 10I, and 10C are multiple user terminals 10U, multiple introducer terminals 10I, and multiple candidate terminals 10C, respectively. Any of the terminals may be, for example, a smartphone, a smart watch, a mobile phone, a tablet, a personal computer, or other information and communication terminal that can be connected to the communication network 30.

[0015] <Multiple user terminals> The multiple user terminals 10U are a user terminal 10U1 used by a user U1, a user terminal 10U2 used by a user U2, a user terminal 10U3 used by a user U3, etc. Each of the multiple user terminals 10U, as an example, runs an introduction program PG1 using an application AP1 (an installed dedicated application, see FIG. 1B).

[0016] 1B is a detailed diagram of the management server 20 and one terminal (user terminal 10U1 as an example). In this paragraph, the user terminal 10U1 will be described, but the following description also applies to the other terminals 10U, 10I, and 10C. The user terminal 10U1 has a processing unit 12 (an example of which is a CPU (Central Processing Unit)) and a storage unit 14 (an example of which is a RAM (Random Access Memory)). The processing unit 12 reads out data stored in the storage unit 14 and performs arithmetic processing. The data includes, as an example, an application AP1. The application AP1 includes a part of the introduction program PG1, a data file DF1, and various libraries LB1. The data file DF1 includes personal information PI of a user U1, which will be described later.

[0017] <Multiple introducer terminals> The multiple introducer terminals 10I are an introducer terminal 10I1 used by the introducer I1, an introducer terminal 10I2 used by the introducer I2, .... Each of the multiple introducer terminals 10I, as an example, runs an introduction program PG1 using an application AP1 (an installed dedicated application, see FIG. 1B).

[0018] <Multiple candidate terminals> The multiple candidate terminals 10C are a candidate terminal 10C1 used by a candidate C1, a candidate terminal 10C2 used by a candidate C2, . . .

[0019] [Management server, multiple storage servers and communication network] As described above, the management server 20 is communicably connected to the multiple terminals 10U, 10I, and 10C via the communication network 30 (FIGS. 1A and 1B). As shown in FIG. 1B, the management server 20 has a processing unit 22 (an example of which is a CPU (Central Processing Unit)) and a memory unit 24 (an example of which is a RAM (Random Access Memory)). The processing unit 22 reads out data stored in the storage unit 24 and performs arithmetic processing. The data includes, as an example, an application AP2. The application AP2 includes a part of the introduction program PG1, a data file DF2, and various libraries LB2. The data file DF2 includes user information UI, candidate information CI, and introducer information II, which will be described later.

[0020] A plurality of storage servers (storage servers 40A, 40B) are communicatively connected to a management server 20 via a communication network 30 (FIG. 1A). The storage servers 40A and 40B have a function of providing information required by the management server 20 in response to a request from the management server 20, for example. Specifically, the management server 20 uses a web-API function to access the storage servers 40A and 40B and obtain complementary information that complements the above-mentioned candidate information CI.

[0021] [Referral Program] The introduction program PG1 has the function of executing multiple basic functions of this embodiment in order to provide information suitable for each user U1, U2, U3, ... who uses each of the multiple user terminals 10U from the management server 20, in other words, to introduce candidates desired by each user U1, U2, U3, .... The introduction program PG1 (see FIG. 1D) causes the introduction system CS to execute a number of basic functions, which will be described later. In the following, a number of basic functions will be explained first, and then the operation of the introduction system CS (the flow of the introduction program PG1) will be explained.

[0022] Multiple basic functions The basic functions include, for example, a talent storage function, a condition sending function, a judgment function, a content creation function, and an introduction function.

[0023] (Human Resources Memory Function) The human resource storage function refers to a function of storing the above-mentioned user information UI, candidate information CI, and introducer information II in a storage medium (eg, the storage unit 24) managed by the management server 20. User Information UI The user information UI is information about each user U1, U2, U3, ... who uses each of the multiple user terminals 10U. Specifically, it is information about each user, such as the name, address, current company (organization), career history to date, educational background, desired candidate information, and other information about each user U1, U2, U3, .... The user information UI may be uploaded in advance from each of the user terminals 10U1, 10U2, 10U3, . . . to the management server 20 via the application AP1, or may be entered via the screen of each user terminal at the time of use and uploaded to the management server 20. Candidate Information CI Candidate information CI is information about multiple candidates C1, C2, ... to be introduced to each user U1, U2, U3, ..., and is information composed of multiple attribute information organized for each candidate. Here, the multiple attribute information is information belonging to each candidate C1, C2, .... Figure 1C is a list of examples of multiple attribute information. If each candidate C1, C2, ... has a direct connection with the introducer I1, I2, ... (meaning that they know each other in person; hereinafter referred to as a first-order connection), that information is also included in the candidate information CI of each candidate C1, C2, .... In addition, the multiple pieces of attribute information of each candidate C1, C2, ... include information learned by introducers I1, I2, ... who have a primary connection with the candidates C1, C2, ... from the candidates C1, C2, .... Here, the information learned is, for example, information that a candidate tells an introducer with a primary connection that the sales department of the company to which he belongs is looking for people with marketing experience related to daily necessities, or that the development department of the company to which he belongs is starting new development of solid-state batteries but is looking for people with several years of experience in material development even if they do not have experience in battery development, and is basically information that is not publicly known. As shown in Figure 1C, the multiple attribute information consists of available information that can be obtained from storage servers 40A, 40B, etc. using communication network 30, etc., and known information that can be obtained by an introducer who has a primary connection with a candidate. In addition, the acquired information is stored in the memory unit 24 as part of the multiple attribute information when an introducer who has obtained the acquired information uploads the acquired information to the management server 20 via his or her own introducer terminal. ·Referrer Information I1 The introducer information I1 is information about each introducer I1, I2, ... who has a primary connection with at least one or more of the multiple candidates C1, C2, ... and can introduce one or more candidates C1, C2, ... to each user U1, U2, U3, ..., and is information composed of at least one or more relationship information with one or more candidates C1, C2, ... organized for each introducer. Specifically, it includes information about each introducer, such as the name, address, current company (organization) to which the introducer belongs, career history to date, educational background, and other information about each introducer, and relationship information (information about the strength of the connection) with each candidate C1, C2, ... with whom the introducer has a primary connection.

[0024] (Conditional sending function) The condition transmission function is a function that allows a user (for example, user U1) to input candidate conditions desired by the user U1 into the user terminal 10U1 used by the user U1 and transmit the candidate conditions from the user terminal 10U1 to the management server 20. Here, the candidate conditions are conditions related to the desired candidate, such as the industry, position, and work location. The candidate conditions may be completed, for example, by displaying an interactive input interface (not shown) on the user terminal 10U1 and having the user U1 input information. Also, for example, a text box (not shown) may be displayed on the user terminal 10U1 and the user U1 may input text that will be the conditions, thereby extracting conditions related to the candidate, such as the industry, position, and work location.

[0025] (judgment function) The judgment function is a function that judges whether (1) there are one or more candidates C1, C2, ... (one or more suitable candidates) who have multiple attribute information with a matching rate equal to or higher than the specified matching rate for the candidate conditions, and (2) whether introducers I1, I2, ... who are capable of introducing one or more suitable candidates have a primary or secondary connection with a user U1. Here, the determined compatibility rate is a percentage value (for example, 60%) that is preset in the introduction program PG1. If the candidate conditions entered by one user U1 consist of, for example, 10 conditions (industry type, job title, work location, size of the candidate's company, etc.), and if multiple attribute information of a certain candidate satisfies 6 or more of the 10 conditions, the certain candidate will meet the determined compatibility rate. In addition, a secondary connection means a person who has a primary connection with an introducer or candidate who is in a primary connection relationship with the introducer I1 of the primary connection from the perspective of one user U1. In other words, it means an acquaintance of a direct acquaintance.

[0026] (Content creation function) The content creation function is a function that, when the result of the judgment function is a positive judgment, creates introduction content to introduce a single user U1 to one or more suitable candidates using user information UI of the single user U1, candidate information CI of one or more suitable candidates, and introducer information II of the introducer. Here, the introduction content may be, for example, a letter of introduction (text), a video (video letter), or audio, or a combination of these. In addition, the introduction content is created by storing a template introduction content in the storage unit 24 of the management server 20, and using information obtained from (1) user information UI, (2) candidate information CI of the matching candidate, and (3) introducer information II of the introducer in the template introduction content. In this case, introduction content that contributes to the relationship between one user U1 and the introducer I1, and the relationship between the introducer I1 and the matching candidate is created. For example, if one or both of these relationships are evaluated as having a high level of intimacy (i.e., a deep relationship), introduction content that reflects the high level of intimacy is created. Specifically, taking the initial greeting as an example, the lower the level of intimacy, the more formal the expression (e.g., "Dear Mr. / Mrs., I am pleased to hear that your company is doing well"), and the higher the level of intimacy, the more casual the expression, like that of a friend (e.g., "It's been a while. How are you?"). These relationships are stored in association with the user information UI, the candidate information CI, and the introducer information II by the human resources storage function.

[0027] (Introduction feature) The introduction function is a function that introduces a user U1 by sending the introduction content created by the content creation function to the contact information (e.g., email address, SNS account, etc.) included in the candidate information CI of a suitable candidate.

[0028] [Referral System Operation (Referral Program Flow)] Next, the operation of the introduction system CS will be described mainly with reference to Fig. 1D. Below, the explanation will be given along with each step (for example, S10 means step 10) of the introduction program PG1 shown in Fig. 1D.

[0029] [S10] S10 is a step of executing a condition transmission function. In S10, as an example, a user U1 inputs user information and candidate conditions into an input interface (not shown) of his / her user terminal 10U1 and transmits them to the management server 20. In addition, upon receiving this information, the management server 20 stores this information in the storage unit 24.

[0030] [S20 and S30] S20 and S30 are steps that perform decision functions. First, in S20, a determination is made as to whether there are one or more candidates C1, C2, ... (one or more suitable candidates) who have multiple attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions. If the result is a positive determination, the process proceeds to S30. On the other hand, if the result is a negative determination, the process ends. If S20 is a positive judgment, i.e., if there is one or more matching candidates, in S30, A determination is made as to whether introducers I1, I2, ..., who can introduce one or more suitable candidates, have a primary or secondary connection with one user U1. If the result is a positive determination, the process proceeds to S40. On the other hand, if the result is a negative determination, the process ends.

[0031] [S40] S40 is a step of executing a content creation function.

[0032] [S50] S50 is a step following completion of S40, and is a step for executing an introduction function.

[0033] [Perform other functions (personnel storage function)] The human resource storage function is not illustrated in the flow diagram of FIG. 1D. However, the human resource storage function stores the introducer information II transmitted from the introducer terminals 10I1, 10I2, ... of the registered introducers I1, I2, .... In addition, when there is a change in their own introducer information II (for example, when they change jobs and their affiliated companies change, or when they are promoted and their positions change, etc.), the introducers I1, I2, ... can change their own introducer information II via the introducer terminals 10I1, 10I2, .... In addition, candidate information CI may be sent from the candidate's own terminal (candidate terminal 10C1, 10C2, ...) to the management server 20 and registered therein, or may be sent from introducer terminals 10I1, 10I2, ... to the management server 20 and registered therein by introducers I1, I2, .... Furthermore, if there is missing information in the candidate information CI registered through these (for example, information regarding the name of the affiliated company and age is registered but the job title is not registered), the management server 20 may supplement the information by obtaining the missing information from, for example, an external server (storage servers 40A, 40B (see Figure 1A)). In this way, the personnel storage function has the latest personnel-related information that is constantly updated.

[0034] [effect] As described above, according to the basic form of this embodiment, it is possible for each user to easily realize the introduction of candidates who meet the desired conditions for introduction (candidate conditions). In addition, according to the basic form of this embodiment, (1) the judgment function determines whether an introduction is possible based on whether or not there is a connection between the user, the introducer, and the candidate, and further, (2) the content creation function creates content that reflects the level of intimacy, thereby improving the success rate of the introduction. In this embodiment, the multiple pieces of attribute information for each candidate C1, C2, ... include information learned from the candidate C1, C2, ... by introducers I1, I2, ... who have a primary connection with each candidate. When judging the compatibility rate, the learned information is also considered to be compatible, which is effective in that the contact success rate is very high.

[0035] <Multiple additional features of the referral system> Next, a number of additional functions of the introduction system will be described. The additional functions are based on the basic form described above and further include additional functions.

[0036] [Additional feature 1] 2 is a flow diagram of an introduction program PG2 by the additional function 1 of this embodiment. The additional function 1 differs from the basic function in that S110, S120, S130, and S140 are further added to the basic function (see FIG. 1D). In the basic function, if a negative decision is made in the judgment function (S20 or S30 in FIG. 1D), the candidate is not introduced. In contrast, in additional function 1, S110, S120, S130, and S140 are added, so that if a candidate can be found by lowering the matching rate (S120), the result is notified to the user (S130). If the user wishes to search again with that condition (S140), an introducer with the lowered matching rate condition is introduced.

[0037] [Additional feature 2] 3 is a flow diagram of the introduction program PG3 by the additional function 2 of this embodiment. The additional function 2 differs from the basic function (see FIG. 1D) in that S40 of the basic function is changed to S210 and that S220 is inserted between S40 and S50. With the basic functionality, only one introductory content is created. In contrast, in additional function 2, S40 of the basic function is changed to S210, so that multiple pieces of introductory content are created, and then S220 is executed to allow the user to select an introductory content (selection function). This selection may be made of a part or all of the multiple pieces of introduction content that have been created. In addition, the multiple introduction contents created may all be in the same format (text, video, or audio) and may be combined with differences in their expression methods (phrases), or may all be in different formats and may be combined with the same expression method.

[0038] [Additional feature 3] FIG. 4 is a schematic diagram of the additional function 3 of this embodiment. In the additional function 3, when a candidate has unknown attribute information, the unknown attribute information is predicted using machine learning. Then, the unknown attribute information is regarded as predicted attribute information and a judgment function is executed. Specifically, the following applies: First, the attribute generation function performs machine learning to learn correlations between multiple attribute information CI obtained from multiple candidates C1, C2, ... included in the candidate information CI as training data. Here, correlations between multiple attribute information CI are, for example, correlations found by extracting educational history, work history (current and past affiliated companies and departments), qualifications, age, ... from multiple attribute information obtained from multiple candidates, and comparing and examining these to obtain training data. Then, an attribute correlation model is generated to predict unknown attribute information in cases where each candidate has unknown attribute information. In the judgment function, the target pieces of attribute information are the pieces of attribute information generated using an attribute correlation model. Additional function 3 can improve the hit rate of matching candidates.

[0039] [Additional feature 4] An additional function 4 (not shown) executes a result storage function and a content generation function, which will be described later. Here, the result storage function refers to a function that, each time the introduction function is executed, stores in the memory unit 24, based on a report from a single user (by the user terminal), the contact result as to whether or not the single user was able to contact the at least one or more matching candidates, along with a set of information of at least one or more introduction contents, the single user, and the at least one or more matching candidates sent by the introduction function. In addition, the content generation function refers to a function that uses machine learning to learn the correlation between the set information and the user information UI, candidate information CI, and introducer information II related to the set information, and the contact result, as training data, and generates a content correlation model for at least one or more introduced contents so that the contact result is a positive result. The content creation function then uses a content correlation model to create at least one or more referral contents for introducing a user to one or more suitable candidates. Additional function 4 can improve the hit rate of matching candidates.

[0040] [Additional feature 5] The additional function 5 (not shown) executes a result generating function and a notification function, which will be described later. Here, the result generation function refers to a function that, each time the result storage function of additional function 4 is executed, machine learning is performed using the correlation between the user information UI and candidate information CI related to the set information and the contact results as training data, to generate a prediction model of the contact results of all candidates for each user. The notification function refers to a function for notifying a part or all of the contact results generated by the result generation function via the screen of the user terminal used by each user. Additional function 5 can improve the hit rate of matching candidates.

[0041] [Additional feature 6] 5 is a flow diagram of the introduction program PG4 by the additional function 6 of this embodiment. The additional function 6 differs from the basic function in that S310, which corresponds to a judgment step, is inserted between S20 and S30 of the basic function (see FIG. 1D). Also, although not shown in the figure, this function differs from the basic function in that NG candidates are registered in the user information of each user, in other words, the user can register NG candidates. Here, NG candidates refer to candidates that the user does not want to introduce, even if they meet a preset compatibility rate. With the basic functionality, there is a risk that a rejected candidate may become a suitable introducer. In contrast, in the additional function 6, even if one or more candidates satisfying the predetermined matching rate are extracted in S20 of the basic function, it is examined in S310 whether the one or more candidates are NG candidates for the user. As a result, even if there are one or more candidates satisfying the predetermined matching rate in S20, if they or all of them correspond to NG candidates for the user, the process ends without proceeding to S30. According to additional feature 6, users will not be introduced to unacceptable candidates. In the example of Fig. 6, S310 is placed between S20 and S30, but it may be placed, for example, between S10 and S20, or between S30 and S40. Also, as in the example of Fig. 6, it may be set so that information on NG candidates is sent together with information on the candidate conditions in S10 without determining whether the NG candidates can be introduced, so that they are not extracted from multiple candidates in the first place.

[0042] <<Multiple Modifications>> Although the present invention has been described with reference to the present embodiment, the present invention is not limited to the above-described embodiment. For example, the following modifications are also included.

[0043] For example, in this embodiment, the basic function and additional functions 1 to 6 are described separately, but the introduction program may be capable of executing some or all of these functions and allow the user to select and use additional functions 1 to 6.

[0044] Also, for example, in the present embodiment, the specified conformance rate has been described as 60%, but this value does not have to be 60%. Furthermore, the specified conformance rate may be changeable by the user or the program provider.

[0045] Also, for example, in this embodiment, the judgment function judges whether an introducer who can introduce one or more suitable candidates has a primary or secondary connection with a user, but this condition may be a primary connection, or a primary, secondary, or tertiary connection. The range of connections permitted may be changeable by the user or the program provider.

[0046] Also, for example, in this embodiment, the judgment function is executed after the condition transmission function is executed, and further, the content creation function is executed (see FIG. 1D). However, as shown in the flow diagram of the introduction program PG5 of the modified example shown in FIG. 6, after the condition transmission function is executed, the identification function (S20, S30, and S310) may be executed instead of the judgment function, so that the content creation function and the introduction function are not executed. Then, when a matching candidate is identified by the introduction program PG5 of this modified example, multiple attribute information of the matching candidate may be provided to the user, so that the user can utilize the information.

[0047] Also, for example, in this embodiment and the multiple modified examples described above, the introduction system CS is configured on the premise of multiple terminals 10, a management server 20, and a communication network 30 connecting these (see FIG. 1A). However, these functions may be regarded as a system by one computer (not shown) and the introduction program PG1 etc. may be executed. This eliminates the need to transmit each user information UI from a plurality of user terminals 10U (for example, by having the user information input to the computer), so the introduction program may be, for example, S10 in FIG. 1D, FIG. 2, FIG. 3, etc. In other words, the condition transmission function, which is a basic function of the present embodiment, may be eliminated. Instead, the function of storing information transmitted by the condition transmission function in the storage unit 24 may be used as a condition storage function. [Explanation of symbols]

[0048] 10C Candidate Terminal 10I Introducer terminal 10U User Terminal 12 Processing section 14 Storage section 20 Management Server 22 Processing section 24 Memory section 30 Communication Network 40A Storage Server 40B Storage Server AP1 Application AP2 Application C Candidate CS Referral System DF1 Data File DF2 Data File I Referrer II. Introducer Information LB1 Various Libraries LB2 Various Libraries PG1 Referral Program PI personal information U User UI User Information

Claims

1. An introduction system including at least a storage unit and a processing unit, (A1) a human resources storage function that stores in the storage unit the following: (A2) candidate information about a plurality of candidates to be introduced to each user, the candidate information being composed of a plurality of attribute information organized for each candidate; and (A3) introducer information about a plurality of introducers who have a primary connection with at least one or more of the plurality of candidates and are capable of introducing the at least one or more candidates to each user. A condition storage function for storing candidate conditions desired by a user included in the user information of each user in the storage unit; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the Referral program.

2. An introduction system including at least a storage unit and a processing unit, (A1) user information of each user, (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate, and (A3) introducer information about multiple introducers who have a primary connection with at least one of the multiple candidates and are able to introduce the at least one candidate to each user, the human resources storage function storing the multiple attribute information in the storage unit, the human resources storage function storing the multiple attribute information including information obtained from the candidate by the introducer who has a primary connection with the candidate; A condition storage function for storing candidate conditions desired by a user included in the user information of each user in the storage unit; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the Referral program.

3. An introduction program that operates in an introduction system including a plurality of terminals and a server, The introduction system, (A1) user information of each user who uses each of the multiple terminals; (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate; and (A3) introducer information about multiple introducers who have a primary connection with at least one of the multiple candidates and are able to introduce the at least one candidate to each user, the human resources storage function storing the multiple attribute information in a storage unit managed by the server, the multiple attribute information including information obtained from the candidate by an introducer who has a primary connection with the candidate; A condition transmission function that allows a user to input candidate conditions desired by the user into a terminal used by the user and transmits the candidate conditions from the terminal to the server; (B1) when there are one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) when an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user, a function of identifying the one or more candidates as one or more matching candidates to introduce to the one user; Execute the Referral program.

4. An introduction program that operates in an introduction system including a plurality of terminals and a server, The introduction system, A human resources storage function that stores, in a storage unit managed by the server, (A1) user information of each user who uses each of the multiple terminals, (A2) candidate information about multiple candidates to be introduced to each user, the candidate information being composed of multiple attribute information organized for each candidate, and (A3) introducer information about multiple introducers who have a primary connection with at least one or more of the multiple candidates and are able to introduce the at least one or more candidates to each user, the introducer information being composed of at least one or more relationship information with the at least one or more candidates organized for each introducer, wherein the multiple attribute information includes information learned from the candidate by an introducer who has a primary connection with the candidate; A condition transmission function that allows a user to input candidate conditions desired by the user into a terminal used by the user and transmits the candidate conditions from the terminal to the server; (B1) a judgment function for judging whether there is one or more candidates having the plurality of attribute information with a matching rate equal to or higher than a specified matching rate for the candidate conditions, and (B2) whether an introducer capable of introducing the one or more candidates has a primary or secondary connection with the one user; a content creation function that creates at least one or more introduction contents for introducing the one user to the one or more suitable candidates as the one or more candidates by using the user information, the candidate information, and the introducer information when a result of the judgment function is a positive judgment; an introduction function for introducing the one user by sending at least one of the at least one or more introduction contents to a contact included in candidate information of at least one of the at least one or more suitable candidates; Execute the Referral program.

5. moreover, The introduction system, an attribute generation function that performs machine learning on correlations between a plurality of pieces of attribute information obtained from the plurality of candidates contained in the candidate information as training data, and generates an attribute correlation model for predicting unknown attribute information when each candidate has unknown attribute information; Run the command, In the determination function, the plurality of pieces of target attribute information are a plurality of pieces of attribute information generated using the attribute correlation model. The referral program of claim 4.

6. moreover, The introduction system, a result storage function that stores in the storage unit, each time the introduction function is executed, a set of information on at least one or more introduction contents, the one user, and the at least one or more matching candidates transmitted by the introduction function, as well as a contact result indicating whether or not the one user has been able to contact at least one or more matching candidates; a content generation function that performs machine learning on the correlation between the set information and the user information, candidate information, and introducer information related to the set information, and the contact result, as training data, to generate a content correlation model of the at least one or more introduced contents so that the contact result is a positive result; Run the command, The content creation function uses the content correlation model to create at least one or more introduction contents for introducing the one user to the one or more match candidates. The referral program according to claim 4 or 5.

7. moreover, The introduction system, a result generation function that performs machine learning on correlations between user information and candidate information related to the set information and the contact results as training data for each execution of the result storage function, and generates a prediction model of contact results of all candidates for each user; a notification function that notifies a part or all of the contact results generated by the result generation function via a screen of a terminal used by each user; Execute the The referral program of claim 6.

8. The content creation function creates two or more pieces of introduction content; moreover, The introduction system, A selection function that allows the one user to select which of the two or more introduction contents to send to the at least one suitable candidate via a terminal used by the one user; Run the command, the introduction function sends the introduction content selected by the selection function to the contact; The referral program of claim 4.

9. Multiple devices and A server capable of communicating with the plurality of terminals via a communication network and having a storage unit that stores the introduction program according to any one of claims 3 to 5 and 8; Equipped with Referral system.

10. A plurality of terminals; A server capable of communicating with the plurality of terminals via a communication network and having a storage unit that stores the introduction program according to claim 7; Equipped with Referral system.

11. A referral method using the referral program according to claim 1 or 2, said referral system executing said talent storage function; said referral system executing said condition storage function; said introduction system executing said specific function; Including, How to introduce.

12. A referral method using the referral program according to claim 3, said referral system executing said talent storage function; said introduction system executing said condition sending function; said introduction system executing said specific function; Including, How to introduce.

13. A referral method using the referral program according to claim 5, said referral system executing said talent storage function; said introduction system executing said condition sending function; said referral system performing said decision function; said introduction system executing said content creation function; said referral system executing said referral function; said referral system executing said attribute generation function; Including, How to introduce.

14. A referral method using the referral program according to claim 6, which refers to claim 5 only, said referral system executing said talent storage function; said introduction system executing said condition sending function; said referral system performing said decision function; said introduction system executing said content creation function; said referral system executing said referral function; said referral system executing said attribute generation function; said referral system executing said result storage function; said introduction system executing said content generation function; Including, How to introduce.

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