Referral program, referral system, and referral method

Abstract

The present invention aims to provide a program that allows each user to easily identify candidates who meet their desired referral criteria (candidate criteria). [Solution] The program of the present invention causes a referral system comprising at least a memory unit and a processing unit to execute a personnel memory function that stores user information, candidate information, and referrer information in the memory unit; a condition memory function that stores candidate conditions desired by one user, which are included in the user information of each user, in the memory unit; and a specification function that, if a candidate who meets the candidate conditions exists and a referrer who can introduce that candidate has a connection with the one user, identifies one or more such candidates as one or more suitable candidates to introduce to the one user.

Description

Technical Field

[0001] The present invention relates to an introduction program, an introduction system, and an introduction method.

Background Art

[0002] In Patent Document 1, as shown in Claim 1, FIGS. 1, 9, 11, etc., a sales support system for realizing optimization of business activities is disclosed. Specifically, this sales support system has the following: (1) a business card management function, (2) a marketing measure function, and (3) a sales support function, and integrally manages the data generated by these functions as a customer medical record. (1) The business card management function refers to a function of digitizing a business card by inputting business card information including the name, organization name, and organization address described on the business card obtained by a system user, and by assigning attribute information of the organization included in the business card information and information about the person with the name included in the business card information. (2) The marketing measure function refers to a function of analyzing and extracting a target that a system user targets for sales from the data created by the business card management function, and by delivering an email to the extracted target, digitizing the behavior of the target and performing customer discovery. (3) The sales support function refers to a function of digitizing information regarding sales activities performed on customers discovered by the marketing measure function.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The sales support system disclosed in Patent Document 1 is intended for use by multiple members (employees) belonging to the same organization, and aims to optimize sales activities (sales activities without waste or errors) by centrally managing data on target organizations. It is not intended to propose and introduce suitable candidates that meet the desired conditions for each of multiple users who are not tied to the same organization.

[0005] One of the objectives of this invention is to provide a program that enables each user to easily identify candidates who meet their desired referral criteria (candidate criteria). [Means for solving the problem]

[0006] The introductory program of the first embodiment is In a presentation system comprising at least a memory unit and a processing unit, A personnel memory function that stores (A1) user information for each user, (A2) candidate information for multiple candidates to be introduced to each user, consisting of multiple attribute information organized for each candidate, and (A3) introducer information for multiple introducers who have a primary connection with at least one of the multiple candidates and who can introduce at least one of the candidates to each user, in a memory unit managed by the server. A condition storage function that stores in the storage unit the candidate conditions desired by one user, which are included in the user information of each user. (B1) If there is one or more candidates who possess the aforementioned attribute information at a rate equal to or higher than the specified suitability rate for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. Make it run. The introductory program of the second aspect is: In a presentation system comprising at least a memory unit and a processing unit, A human resource memory function that stores in the memory unit (A1) user information for each user, (A2) candidate information for multiple candidates to be introduced to each user, which consists of multiple attribute information organized for each candidate, and (A3) introducer information for multiple introducers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, wherein the multiple attribute information includes acquired information obtained by the introducer who has a primary connection with the candidate from that candidate, A condition storage function that stores in the storage unit the candidate conditions desired by one user, which are included in the user information of each user. (B1) If there is one or more candidates who possess the aforementioned attribute information at a rate equal to or higher than the specified suitability rate for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. Make it run. The introductory program of the third aspect is: A referral program that operates in a referral system including multiple terminals and servers, In the aforementioned referral system, A human resource memory function that stores in a memory unit managed by the server the following: (A1) user information of each user using each of the multiple terminals, (A2) candidate information for multiple candidates to be introduced to each user, consisting of multiple attribute information organized for each candidate, and (A3) introducer information for multiple introducers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, wherein the multiple attribute information includes information obtained by introducers who have a primary connection with the candidate from that candidate, A condition transmission function that allows a user to input desired candidate conditions into a terminal used by that user, and transmits those candidate conditions from the terminal to the server, (B1) If there is one or more candidates who possess the aforementioned attribute information at a rate equal to or higher than the specified suitability rate for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. Make it run. The introductory program of the fourth aspect is: A referral program that operates in a referral system including multiple terminals and servers, In the aforementioned referral system, A human resource memory function that stores in a memory unit managed by the server the following: (A1) User information of each user using each of the multiple terminals; (A2) Candidate information for multiple candidates to be introduced to each user, comprising candidate information consisting of multiple attribute information organized for each candidate; and (A3) Referrer information for multiple referrers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, comprising referrer information consisting of at least one relationship piece of information with at least one of the candidates organized for each referrer, wherein the multiple attribute information includes information obtained from the candidate by the referrer who has a primary connection with the candidate; A condition transmission function that allows a user to input desired candidate conditions into a terminal used by that user, and transmits those candidate conditions from the terminal to the server, (B1) A determination function that determines whether there is one or more candidates who have the specified suitability rate or higher for the candidate conditions, and (B2) whether a referrer who can introduce such one or more candidates has a primary or secondary connection with the one user, If the result of the judgment function is a positive judgment, a content creation function creates at least one referral content to introduce the one user to the one or more suitable candidates, using the user information, candidate information and referrer information. A referral function that refers a user by sending at least one of the aforementioned at least one referral content to a contact included in the candidate information of at least one of the aforementioned at least one suitable candidate, Make it run. The introductory program of the fifth aspect is: In the introduction program of the fourth aspect, moreover, In the aforementioned referral system, An attribute generation function that uses the correlation between multiple attribute pieces of information obtained from multiple candidates, which are included in the candidate information, as training data to perform machine learning and generate an attribute correlation model for predicting unknown attribute pieces when there are unknown attribute pieces for each candidate. Make it run, In the aforementioned decision function, the multiple attribute information to be targeted is the multiple attribute information generated using the attribute correlation model. The introductory program of the sixth aspect is: In the introductory program of the fourth or fifth aspect, moreover, In the aforementioned referral system, Each time the referral function is executed, the system stores in the storage unit the contact result, which indicates whether or not the user was able to contact at least one of the candidates, along with the set information of at least one referral content transmitted by the referral function, the user, and at least one of the candidates. Using the correlation relationship between the set information, the user information, candidate information, and introducer information related to the set information, and the contact result as training data, perform machine learning to generate a content correlation model for at least one piece of introduction content so that the contact result becomes a positive result. Execute Using the content correlation model, the content creation function creates at least one piece of introduction content for introducing the one user to the one or more suitable candidates. The introduction program of the seventh aspect In the introduction program of the sixth aspect Furthermore In the introduction system Every time the result storage function is executed, use the correlation relationship between the user information and candidate information related to the set information and the contact result as training data to perform machine learning and generate a prediction model for the contact results of all candidates for each user. Via the screen of the terminal used by each user, notify part or all of the contact results generated by the result generation function. Execute The introduction program of the eighth aspect In the introduction program of any one of the fourth to seventh aspects The content creation function creates two or more pieces of introduction content. Furthermore In the introduction system Via the terminal used by the one user, provide a selection function for allowing the one user to select which of the two or more pieces of introduction content to send to the one or more suitable candidates. Execute The introduction function sends the introduction content selected by the selection function to the contact. An introduction system of one aspect A plurality of terminals A server that can communicate with the aforementioned multiple terminals via a communication network and has a storage unit that stores an introduction program according to any one of the third to eighth embodiments, It is equipped with. The method for introducing the first aspect is: A method of introduction using the introduction program of the first or second embodiment, The referral system performs the personnel memory function, The aforementioned referral system performs the condition memory function, The process by which the aforementioned referral system performs the aforementioned specific function, Includes. The method for introducing the second aspect is: A method of introduction using the introduction program of the third embodiment, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, Includes. The method for introducing the third aspect is: A method of introduction using the introduction program of the fifth aspect, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, The process by which the aforementioned referral system performs the aforementioned judgment function, The aforementioned introduction system performs the content creation function, The process of the aforementioned referral system performing the referral function, The aforementioned referral system performs the attribute generation function, Includes. The method for introducing the fourth aspect is: A method of introduction using an introduction program of the sixth embodiment, which refers only to the introduction program of the fifth embodiment, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, The process by which the aforementioned referral system performs the aforementioned judgment function, The aforementioned introduction system performs the content creation function, The process of the aforementioned referral system performing the referral function, The aforementioned referral system performs the attribute generation function, The process of the aforementioned referral system executing the result storage function, The process by which the aforementioned introduction system executes the content generation function, Includes. [Effects of the Invention]

[0007] The referral programs in the first to third aspects allow each user to easily identify candidates that meet their preferred referral criteria (candidate criteria). The referral programs in the fourth to eighth aspects allow each user to easily receive referrals for candidates that meet their preferred referral criteria (candidate conditions). One type of referral system can easily provide each user with referrals that match their desired criteria (candidate criteria). The introduction methods described in the first to fourth aspects allow each user to easily find candidates who meet their desired introduction criteria (candidate criteria). [Brief explanation of the drawing]

[0008] [Figure 1A] This is a schematic diagram of an introduction system for an embodiment of the present invention (hereinafter referred to as this embodiment). [Figure 1B] This is a detailed diagram of the management server and one terminal in this embodiment. [Figure 1C] This is a list of examples of multiple attribute information in this embodiment. [Figure 1D] This is a flowchart of the introductory program based on the basic functions of this embodiment. [Figure 2] This is a flowchart of the introductory program using additional function 1 of this embodiment. [Figure 3]This is a flowchart of the introduction program using the additional function 2 of this embodiment. [Figure 4] This is a schematic diagram of the additional function 3 of this embodiment. [Figure 5] This is a schematic diagram of the additional function 6 of this embodiment. [Figure 6] This is a flowchart of the introductory program using a modified example. [Modes for carrying out the invention]

[0009] First, I will explain the details that the inventor of this application was considering at the time of the commencement of the creation of this invention (hereinafter referred to as the "details of consideration at the commencement of the invention"). Next, this embodiment and several modifications will be described.

[0010] ≪Considerations for the start date≫ For example, in sales activities at many companies, sales representatives exchange numerous business cards with contacts at client companies. It has become common practice to digitize and manage the printed information on these business cards using business card management applications, CRM systems (customer relationship management systems), and other tools. However, much of the information digitized from printed business cards currently remains merely a contact database, and it is difficult to say that it is being used effectively. Problem 1: The information on digitized business cards is not being used effectively. Furthermore, sales activities can be categorized into those conducted through "referrals" (hereinafter referred to as "referral sales") and those conducted through "non-referral methods" (hereinafter referred to as "non-referral sales"), but it is known that the closing rate for deals is significantly higher in referral sales. However, current general referral sales methods involve inefficient processes such as the following: 1. Obtain a letter of introduction from the business partner who will be making the referral. 2. Prior contact with the referral destination and obtaining their consent. 3. Multiple scheduling adjustments for business meetings. 4. Consideration for the relationship between the referrer and the referred party. 5. Follow-up support after referral Currently, these tasks must be handled individually by sales representatives via phone or email, which is time-consuming and labor-intensive. Furthermore, referral sales heavily rely on the personal networks of sales representatives, making it a particularly significant obstacle for new sales representatives, those who have recently transferred to a new department, and those who have just started a business or venture. Problem 2: The current referral sales process is inefficient. Problem 3: Reliance on the personal network of sales representatives. In response to this situation, some customer relationship management (CRM) systems offer features that allow for the management of a company's leads in conjunction with business card management functions. However, these are limited to the simple sharing of contact information and do not achieve the automation or efficiency of the actual referral process. The inventors of this invention have considered improvements and further enhancements to the above-mentioned problems and have completed the present invention.

[0011] This embodiment Next, the introduction system CS of this embodiment (see Figures 1A and 1B) will be described. First, the basic functions, configuration, operation, and effects (hereinafter referred to as the basic form) will be explained, followed by the additional functions, configuration, operation, and effects (hereinafter referred to as the additional form). Please note that for any constituent elements in the additional forms that are equivalent to or nearly equivalent to those used in the explanation of the basic form, the names and symbols of the basic form shall be applied mutatis mutandis.

[0012] <Basic Form of Referral System> [Functions and Configuration of the Referral System] The referral system CS has the function of introducing at least one candidate C1, C2, ... (see Figure 1A) that meets the desired conditions (candidate conditions) of multiple users U1, U2, U3, ... (see Figure 1A). This function is achieved when the referral system CS executes the referral program PG1 (see Figure 1D), which will be described later. Here, the multiple users U1, U2, U3, ... are individuals who wish to be introduced to their desired candidates, and one example is a sales representative. In the following explanation, multiple users U1, U2, U3, ... will be referred to as multiple users U, multiple referrers I1, I2, ... (described later) will be referred to as multiple referrers I, and multiple candidates C1, C2, ... will be referred to as multiple candidates C.

[0013] As shown in Figure 1A, the referral system CS comprises multiple terminals 10U, 10I, and 10C, a management server 20 (an example of a server), a communication network 30 (the internet is an example), and storage servers 40A and 40B. Multiple terminals 10U, 10I, and 10C and the management server 20 are connected to each other via a communication network 30, enabling them to communicate with one another.

[0014] [Multiple devices] As shown in Figure 1A, the multiple terminals 10U, 10I, and 10C are, respectively, multiple user terminals 10U, multiple referrer terminals 10I, and multiple candidate terminals 10C. Any of these devices, for example, are smartphones, smartwatches, mobile phones, tablets, personal computers, or other information and communication terminals that can connect to the communication network 30.

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

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

[0017] <Multiple referrer terminals> The multiple referrer terminals 10I are referrer terminal 10I1 used by referrer I1, referrer terminal 10I2 used by referrer I2, and so on. Each of the multiple referrer terminals 10I runs the referral program PG1 using application AP1 (a dedicated application that is installed, see Figure 1B) as an example.

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

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

[0020] Multiple storage servers (storage servers 40A and 40B) are connected to the management server 20 via a communication network 30, enabling communication between them (Figure 1A). Storage servers 40A and 40B have the function of providing information necessary for management server 20 in response to requests from management server 20. Specifically, management server 20 accesses storage servers 40A and 40B using web-API functionality to obtain supplementary information that complements the aforementioned candidate information CI.

[0021] [Introduction Program] The referral program PG1 has the function of performing several 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, ... Then, the referral program PG1 (see Figure 1D) causes the referral system CS to execute several basic functions, which will be described later. Below, we will first explain several basic functions, and then describe the operation of the referral system CS (the flow of the referral program PG1).

[0022] <Multiple basic functions> These multiple basic functions include, for example, a personnel memory function, a condition transmission function, a decision-making function, a content creation function, and a referral function.

[0023] (Human resource memory function) The personnel memory function refers to the function of storing the aforementioned user information UI, candidate information CI, and referrer information II in a storage medium managed by the management server 20 (for example, the storage unit 24). • User information UI The user information UI contains information about each user U1, U2, U3, ... who uses one of the multiple user terminals 10U. Specifically, it includes each user U1, U2, U3, ...'s name, address, current company (organization), career history to date, educational background, desired candidate information, and other information about each user. The user information UI can be uploaded in advance from each user terminal 10U1, 10U2, 10U3, ... to the management server 20 via application AP1, or it can be entered via the screen of each user terminal during use and then uploaded to the management server 20. Candidate Information CI Candidate Information CI (Candidacy Information) is information about multiple candidates C1, C2, ... intended for introduction to each user U1, U2, U3, ... and consists of multiple attribute pieces of information organized for each candidate. Here, multiple attribute pieces of information refer to information belonging to each candidate C1, C2, ... Figure 1C is a list of examples of multiple attribute pieces of information. If each candidate C1, C2, ... has a direct connection (meaning they are acquainted with each other; hereinafter referred to as a primary connection) with the referrer I1, I2, ..., that information will also be included in the candidate information CI for each candidate C1, C2, .... Furthermore, the multiple attribute information of each candidate C1, C2, ... includes information obtained by referrers I1, I2, ... who have a primary connection with that candidate C1, C2, ... from that candidate C1, C2, .... Here, information obtained is, for example, information that a candidate has told a referrer with whom they have a primary connection that their company's sales department is looking for someone with experience in marketing consumer goods, or that their company's development department is starting to develop solid-state batteries and is looking for someone with several years of experience in materials development, even if they do not have experience in battery development. This information is basically not publicly known. As shown in Figure 1C, the multiple attribute information consists of obtainable information that can be acquired from storage servers 40A, 40B, etc., using the communication network 30, etc., and known information that can be acquired because the introducer has a primary connection with a candidate. Furthermore, the acquisition of information is stored in the storage unit 24 as part of multiple attribute information when a referrer who has obtained the information uploads that information to the management server 20 via their referrer terminal. Referrer Information I1 Referrer Information I1 refers to information about each referrer I1, I2, ... who has a primary connection with at least one of multiple candidates C1, C2, ... and is capable of referring one or more candidates C1, C2, ... to each user U1, U2, U3, ... and consists of information about at least one relationship with one or more candidates C1, C2, ... organized for each referrer. Specifically, it includes information about each referrer I1, I2, ... such as their name, address, current company (organization), career history to date, educational background, and other information about each referrer, as well as relationship information (information about the strength of the connection) with each candidate C1, C2, ... with whom they have a primary connection.

[0024] (Conditional sending function) The condition transmission function allows a user (for example, user U1) to input their desired candidate criteria into a user terminal 10U1, and then transmits those criteria from the user terminal 10U1 to the management server 20. Here, candidate criteria are conditions related to the desired candidate, such as industry, position, and work location. Candidate criteria may be completed, for example, by displaying an interactive input interface (not shown) on the user terminal 10U1 and having user U1 input the criteria. Alternatively, for example, a text box (not shown) may be displayed on the user terminal 10U1, allowing user U1 to input the conditional text to extract the conditions related to the candidate, such as industry, position, and work location.

[0025] (judgment function) The judgment function determines whether (1) there is one or more candidates C1, C2, ... (one or more suitable candidates) who have multiple attribute information that exceeds the specified suitability rate for the candidate criteria, and (2) whether a referrer I1, I2, ... who is able to introduce one or more suitable candidates has a primary or secondary connection with a user U1. Here, the defined suitability rate is a percentage value (60% as an example) that is pre-set in the referral program PG1. If a candidate's criteria entered by a user U1 consists of, for example, 10 conditions (industry, job title, work location, size of the candidate's company, etc.), and a candidate's attribute information satisfies 6 or more of those 10 conditions, then that candidate will satisfy the defined suitability rate. Furthermore, a secondary connection refers to someone who has a primary connection with a primary connection referrer I1, who is in a primary connection relationship with a primary connection referrer or candidate, from the perspective of a single user U1. In short, it means an acquaintance of a direct acquaintance.

[0026] (Content creation function) The content creation function, when the judgment function results in a positive judgment, is a function that creates referral content to introduce user U1 to one or more suitable candidates, using the user information UI of one user U1, the candidate information CI of one or more suitable candidates, and the referrer information II of the person who referred them. Here, the introductory content can be, for example, a letter of introduction (text), a video (video letter), an audio recording, or a combination of these. Furthermore, the introduction content is created by storing a template introduction content in the memory unit 24 of the management server 20, and using information obtained from (1) user information UI, (2) candidate information CI of the suitable candidate, and (3) introducer information II of the introducer. In this case, the introduction content is created that contributes to the relationship between a user U1 and introducer I1, and the relationship between introducer I1 and the suitable candidate. For example, if one or both of these relationships are evaluated as having a high degree of intimacy (i.e., a deep relationship), the introduction content is created that reflects the high degree of intimacy. Specifically, taking the initial greeting as an example, the lower the degree of intimacy, the more formal the expression (for example, "Dear Sir / Madam, I hope this letter finds your company in good health and prosperity.") will be, and the higher the degree of intimacy, the more casual the expression will be, like that of a friend (for example, "It's been a while, how are you?"). Furthermore, these relationships are stored in relation to user information UI, candidate information CI, and referrer information II by the personnel memory function.

[0027] (Referral function) The referral function is a feature that introduces a user U1 by sending referral content created using 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] [How the referral system works (flow of the referral program)] Next, the operation of the referral system CS will be explained, mainly with reference to Figure 1D. The following explanation follows each step of the referral program PG1 shown in Figure 1D (for example, S10 means step 10).

[0029] [S10] Step S10 is the step in which the condition transmission function is executed. In S10, as an example, a user U1 inputs user information and candidate conditions into the input interface (not shown) of their user terminal 10U1 and sends them to the management server 20. When the management server 20 receives this information, it stores it in the storage unit 24.

[0030] [S20 and S30] Steps S20 and S30 are steps in which the decision-making function is executed. First, in S20, a determination is made as to whether there is one or more candidates C1, C2, ... (one or more suitable candidates) who possess multiple attribute information that exceeds the specified conformance rate for the candidate criteria. If the result is positive, the process proceeds to S30. Conversely, if the result is negative, the process ends. If S20 is a positive result, that is, if there is one or more suitable candidates, then in S30, A determination is made as to whether referrers I1, I2, ..., who are capable of introducing one or more suitable candidates, have a primary or secondary connection with a user U1. If the determination is positive, the process proceeds to S40. If the determination is negative, the process ends.

[0031] [S40] S40 is the step that performs the content creation function.

[0032] [S50] S50 is a step that follows the completion of S40 and is a step in which the referral function is performed.

[0033] [Execution of other functions (personnel memory function)] The personnel memory function is not illustrated in the flowchart of Figure 1D. However, the personnel memory function stores the referrer information II transmitted sequentially from the referrer terminals 10I1, 10I2, ... of the referrers I1, I2, ... that have been registered. Furthermore, if there are any changes to their referrer information II (for example, if they change jobs and their affiliated company changes, or if they are promoted and their job title changes), referrers I1, I2, ... can change their referrer information II via the referrer terminals 10I1, 10I2, ... Furthermore, candidate information CI may be registered by the person wishing to become a candidate by sending it from their own terminal (candidate terminal 10C1, 10C2, ...) to the management server 20, or by the referrers I1, I2, ... sending it from referrer terminal 10I1, 10I2, ... to the management server 20. Furthermore, if there is missing information in the candidate information CI registered in this way (for example, if information regarding the company name and age is registered, but the job title is not), the management server 20 may, for example, obtain the missing information from an external server (storage servers 40A, 40B (see Figure 1A)) to supplement it. As described above, the personnel memory function has up-to-date personnel information that is constantly updated.

[0034] [effect] As described above, according to the basic form of this embodiment, it is possible to easily introduce candidates that meet the desired introduction criteria (candidate criteria) for each user. Furthermore, 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 (2) the content creation function creates content that reflects the level of intimacy, thereby improving the success rate of introductions. In this embodiment, the multiple attribute information for each candidate C1, C2, ... includes information obtained by referrers I1, I2, ... who have a primary connection with each candidate C1, C2, ... from those candidates. When determining the suitability rate, the obtained information is also considered as a factor in determining whether or not it is a good match, which is effective in that it significantly increases the success rate of contacts.

[0035] <Multiple additional features of the referral system> Next, we will explain the various additional functions of the referral system. Additional functions refer to forms that add further functionality to the basic form described above.

[0036] [Additional function 1] Figure 2 is a flowchart of the introduction program PG2 with additional function 1 of this embodiment. Additional function 1 differs from the basic function (see Figure 1D) in that S110, S120, S130 and S140 are added to it. In the basic function, if a negative judgment is made in the judgment function (S20 or S30 in Figure 1D), the candidate will not be introduced. In contrast, with Additional Function 1, steps S110, S120, S130, and S140 are added, so that if a candidate can be found by lowering the precision rate (S120), the result is notified to the user (S130). Then, if the user wishes to perform a search again with those conditions (S140), the referrer with the lower precision rate will be introduced.

[0037] [Additional function 2] Figure 3 is a flowchart of the introduction program PG3 with additional function 2 of this embodiment. Additional function 2 differs from the basic function (see Figure 1D) in that S40 is changed to S210 and S220 is inserted between S40 and S50. With the basic functionality, only one introductory content item is created. In contrast, in additional function 2, the basic function's S40 is changed to S210, which creates multiple referral content items. Subsequently, S220 is executed, allowing the user to select a referral content item (selection function). This selection can be for some or all of the multiple introductory content pieces created. Furthermore, the multiple introductory pieces created may all be in the same format (text, video, or audio) but differ in their expression (phrasing), or they may all be in different formats but use the same expression.

[0038] [Additional function 3] Figure 4 is a schematic diagram of the additional function 3 of this embodiment. Additional function 3 uses machine learning to predict unknown attribute information for a candidate. Then, it treats the unknown attribute information as the predicted attribute information and performs a decision-making function. Specifically, it is as follows: First, the attribute generation function is used to train a machine learning model using the correlation between multiple attribute information CIs obtained from multiple candidates C1, C2, ... included in the candidate information CI, as training data. Here, the correlation between multiple attribute information CIs is determined by extracting, for example, educational background, work history (current and past companies and departments), qualifications, age, ... from multiple attribute information obtained from multiple candidates, comparing and examining these to find correlations, and using this as training data. Furthermore, an attribute correlation model is generated to predict unknown attribute information for each candidate. Furthermore, the decision-making function uses multiple attribute pieces of information generated using an attribute correlation model to determine which attribute pieces of information to target. According to additional function 3, the hit rate of suitable candidates can be improved.

[0039] [Additional function 4] Additional function 4 (not shown in the diagram) executes the result storage function and content generation function, which will be described later. Here, the result storage function refers to a function that, each time the referral function is executed, stores in the storage unit 24, based on the user's report (by the user's terminal), the set information of at least one referral content transmitted by the referral function, the user and at least one suitable candidate, and the contact result of whether or not the user was able to contact at least one suitable candidate. Furthermore, the content generation function refers to a function that uses machine learning to analyze the correlation between set information and the user information UI, candidate information CI, and referrer information II related to said set information, and the contact results, in order to generate a content correlation model for at least one referral content so that the contact results are positive. Furthermore, the content creation function uses a content correlation model to create at least one referral content piece to introduce a given user to one or more suitable candidates. According to additional function 4, the hit rate of suitable candidates can be improved.

[0040] [Additional Function 5] Additional function 5 (not shown) executes the result generation function and 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, uses machine learning to analyze the correlation between user information UI and candidate information CI related to the set information and the contact results as training data, and generates a predictive model of the contact results for all candidates for each user. Furthermore, the notification function refers to a function that notifies users of some or all of the contact results generated by the result generation function via the screen of the user terminal used by each user. According to additional function 5, the hit rate of suitable candidates can be improved.

[0041] [Additional function 6] Figure 5 is a flowchart of the introduction program PG4 with the additional function 6 of this embodiment. The additional function 6 differs from the basic function (see Figure 1D) in that an S310 corresponding to a decision step is inserted between S20 and S30 of the basic function. Furthermore, although not illustrated, this differs from the basic functionality in that each user's user information includes a list of candidates who are "NG" (Not Recommended), or in other words, users can register candidates they do not want to be introduced to. Here, an "NG" candidate refers to a candidate who, even if they meet the pre-set suitability criteria, the user does not wish to be introduced to. In terms of basic functionality, there is a risk that unsuitable candidates may become suitable referrers. In contrast, in the additional function 6, even if one or more candidates that meet the predetermined suitability rate are extracted in S20 of the basic function, S310 examines whether those one or more candidates are unsuitable candidates for that user. As a result, even if one or more candidates meet the predetermined suitability rate in S20, if one or all of those candidates are unsuitable candidates for that user, the process terminates without proceeding to S30. According to additional feature 6, users will not be shown unsuitable candidates. In the example in Figure 6, S310 is located between S20 and S30, but it could also be located between S10 and S20, or between S30 and S40. Furthermore, as in the example in Figure 6, the system could be configured to send information about NG candidates along with the candidate criteria information in S10, without determining whether an NG candidate could be a suitable candidate, thus preventing the selection from being made from multiple candidates.

[0042] ≪Multiple Variations≫ As described above, the present invention has been explained with reference to this embodiment, but the present invention is not limited to the above-described embodiment. For example, several modifications are also included as follows.

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

[0044] Furthermore, although the specified conformance rate was described as 60% in this embodiment, this value does not have to be 60%. The specified conformance rate may also be changeable by the user or program provider.

[0045] Furthermore, for example, in this embodiment, the judgment function determines whether a referrer capable of introducing one or more suitable candidates has a primary or secondary connection with a user. However, this condition may be defined as having a primary connection, or having a primary, secondary, or tertiary connection. The extent to which connections are permitted may be changed by the user or program provider.

[0046] Furthermore, in this embodiment, for example, the judgment function is executed after the condition transmission function is executed, and then the content creation function is executed (see Figure 1D). However, as shown in the flowchart of the modified introduction program PG5 in Figure 6, after the execution of the condition transmission function, the identification function (S20, S30, and S310) may be executed instead of the judgment function, and the content creation function and introduction function may not be executed. When a suitable candidate is identified in this modified introduction program PG5, the user may be provided with multiple attribute information of the suitable candidate, allowing the user to utilize that information.

[0047] Furthermore, in this embodiment and the aforementioned modifications, the referral system CS is configured to include multiple terminals 10, a management server 20, and a communication network 30 connecting them (see Figure 1A). However, these functions may be considered as a system on a single computer (not shown), and the referral program PG1, etc., may be executed. This eliminates the need to send user information UI from multiple user terminals 10U (for example, by having the user information entered into the computer), so the referral program can, for example, perform S10 in Figures 1D, 2, 3, ... It can be said that it is not necessary. In other words, the condition transmission function, which is a basic function of this embodiment, may be omitted. Instead, the function of storing the information transmitted by the condition transmission function in the storage unit 24 may be called the condition storage function. [Explanation of symbols]

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

Claims

1. In a presentation system comprising at least a memory unit and a processing unit, A personnel memory function that stores in a memory unit managed by the server (A1) user information for each user, (A2) candidate information for multiple candidates to be introduced to each user, consisting of multiple attribute information organized for each candidate, and (A3) introducer information for multiple introducers who have a primary connection with at least one of the multiple candidates and who can introduce at least one of the candidates to each user, A condition storage function that stores in the storage unit the candidate conditions desired by one user, which are included in the user information of each user. (B1) If there is one or more candidates who possess the aforementioned attribute information at a specified conformance rate or higher for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. To execute Referral program.

2. In a presentation system comprising at least a memory unit and a processing unit, A human resource memory function that stores in the memory unit (A1) user information for each user, (A2) candidate information for multiple candidates to be introduced to each user, which consists of multiple attribute information organized for each candidate, and (A3) introducer information for multiple introducers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, wherein the multiple attribute information includes information obtained by the introducer who has a primary connection with the candidate from that candidate, A condition storage function that stores in the storage unit the candidate conditions desired by one user, which are included in the user information of each user. (B1) If there is one or more candidates who possess the aforementioned attribute information at a specified conformance rate or higher for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. To execute Referral program.

3. A referral program that operates in a referral system including multiple terminals and servers, In the aforementioned referral system, A human resource memory function that stores in a memory unit managed by the server the following: (A1) User information of each user using each of the multiple terminals; (A2) Candidate information for multiple candidates to be introduced to each user, consisting of multiple attribute information organized for each candidate; and (A3) Referrer information for multiple referrers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, wherein the multiple attribute information includes information obtained by the referrer who has a primary connection with the candidate from that candidate; A condition transmission function that allows a user to input desired candidate conditions into a terminal used by that user, and transmits those candidate conditions from the terminal to the server, (B1) If there is one or more candidates who possess the aforementioned attribute information at a specified conformance rate or higher for the candidate conditions, and (B2) if a referrer who can introduce such one or more candidates has a primary or secondary connection with the aforementioned user, the identification function identifies such one or more candidates as one or more suitable candidates to introduce to the aforementioned user. To execute Referral program.

4. A referral program that operates in a referral system including multiple terminals and servers, In the aforementioned referral system, A human resource memory function that stores in a memory unit managed by the server the following: (A1) User information of each user using each of the multiple terminals; (A2) Candidate information for multiple candidates to be introduced to each user, comprising candidate information consisting of multiple attribute information organized for each candidate; and (A3) Referrer information for multiple referrers who have a primary connection with at least one of the multiple candidates and who are capable of introducing at least one of the multiple candidates to each user, comprising referrer information consisting of at least one relationship piece of information with at least one of the candidates organized for each referrer, wherein the multiple attribute information includes information obtained from the candidate by the referrer who has a primary connection with the candidate; A condition transmission function that allows a user to input desired candidate conditions into a terminal used by that user, and transmits those candidate conditions from the terminal to the server, (B1) A determination function that determines whether there is one or more candidates who have the specified suitability rate or higher for the candidate conditions, and (B2) whether a referrer who can introduce such one or more candidates has a primary or secondary connection with the one user, If the result of the judgment function is a positive judgment, a content creation function creates at least one referral content to introduce the one user to the one or more suitable candidates, using the user information, candidate information and referrer information. A referral function that refers a user by sending at least one of the aforementioned at least one referral content to a contact included in the candidate information of at least one of the aforementioned at least one suitable candidate, To execute Referral program.

5. moreover, In the aforementioned referral system, An attribute generation function that uses the correlation between multiple attribute pieces of information obtained from multiple candidates, which are included in the candidate information, as training data to perform machine learning and generate an attribute correlation model for predicting unknown attribute pieces when there are unknown attribute pieces for each candidate. Make it run, In the aforementioned determination function, the multiple attribute information to be targeted is defined as multiple attribute information generated using the attribute correlation model. The referral program described in claim 4.

6. moreover, In the aforementioned referral system, Each time the referral function is executed, the system stores in the storage unit the contact result, which indicates whether or not the user was able to contact at least one of the candidates, along with the set information of at least one referral content transmitted by the referral function, the user, and at least one of the candidates. A content generation function that uses machine learning to analyze the correlation between the set information and the user information, candidate information, and referrer information related to the set information and the contact results, so as training data, to generate a content correlation model for at least one of the referral contents so that the contact results are positive. Make it run, The content creation function uses the content correlation model to create at least one referral content to introduce the user to one or more suitable candidates. The referral program according to claim 4 or 5.

7. moreover, In the aforementioned referral system, Each time the result storage function is executed, the correlation between user information and candidate information related to the set information and the contact results is used as training data for machine learning to generate a predictive model of the contact results for all candidates for each user. A notification function that notifies each user of some or all of the contact results generated by the result generation function via the screen of the device they are using, To execute The referral program described in claim 6.

8. The aforementioned content creation function creates two or more introductory content pieces. moreover, In the aforementioned referral system, A selection function that allows the user to choose which of the two or more referral contents to send to at least one of the suitable candidates via the terminal used by the user. Make it run, The aforementioned referral function sends the referral content selected by the selection function to the aforementioned contact. The referral program described in claim 4.

9. Multiple devices and A server that can communicate with the aforementioned multiple terminals via a communication network and has a storage unit that stores the referral program described in any one of claims 3 to 8, Equipped with, Referral system.

10. A method of introduction using the introduction program described in claim 1 or 2, The referral system performs the personnel memory function, The aforementioned referral system performs the condition memory function, The process by which the aforementioned referral system performs the aforementioned specific function, including, How to introduce someone.

11. A method of introduction using the introduction program described in claim 3, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, The process by which the aforementioned referral system performs the aforementioned specific function, including, How to introduce someone.

12. A method of introduction using the introduction program described in claim 5, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, The process by which the aforementioned referral system performs the aforementioned judgment function, The aforementioned introduction system performs the content creation function, The process of the aforementioned referral system performing the referral function, The aforementioned referral system performs the attribute generation function, including, How to introduce someone.

13. A method of introduction using the introduction program described in claim 6, which refers only to claim 5, The referral system performs the personnel memory function, The process includes the introduction system executing the condition transmission function, The process by which the aforementioned referral system performs the aforementioned judgment function, The aforementioned introduction system performs the content creation function, The process of the aforementioned referral system performing the referral function, The aforementioned referral system performs the attribute generation function, The process of the aforementioned referral system executing the result storage function, The process by which the aforementioned introduction system executes the content generation function, including, How to introduce someone.

Images