Information processing system, information processing method and program

Abstract

To provide an information processing system etc. that can provide more effective learning opportunities. [Solution] According to one aspect of the present invention, an information processing system is provided that includes at least one processor, the processor being configured to execute the following steps by reading a program: in a first acquisition step, first skill information and second skill information are acquired, wherein the first skill information is information regarding skills required for positions being recruited in the organization, and the second skill information is information regarding skills possessed by employees or job seekers belonging to the organization; in an identification step, one or more learning contents to be presented to employees or job seekers based on the acquired first skill information and second skill information; and in a presentation step, the identified learning contents are presented in a manner that can be understood by the employees or job seekers.

Description

[Technical Field]

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

[0002] Patent Document 1 discloses an educational system that utilizes an intranet or the like. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-177660 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is still room for improvement in the technology that provides learning opportunities.

[0005] In view of the above circumstances, the present invention provides an information processing system and the like that can provide learning opportunities more effectively. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system comprising at least one processor, the processor being configured to execute the following steps by reading a program: a first acquisition step acquiring first skill information and second skill information, wherein the first skill information is information regarding skills required for positions being recruited in the organization, and the second skill information is information regarding skills possessed by employees or job seekers belonging to the organization; an identification step identifying one or more learning contents to be presented to employees or job seekers based on the acquired first skill information and second skill information; and a presentation step presenting the identified learning contents in a manner that can be understood by the employees or job seekers.

[0007] According to this aspect, an information processing system or the like is provided that can provide learning opportunities more effectively. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a configuration diagram illustrating an information processing system 1. FIG. [Figure 2] 2 is a block diagram showing the hardware configuration of the server device 10. FIG. [Figure 3] FIG. 2 is a block diagram showing the hardware configuration of the user terminal 20. [Figure 4] 1 is a block diagram showing functions realized by a server device 10 (controller 11) and a user terminal 20 (controller 21). [Figure 5] FIG. 2 is an activity diagram for explaining information processing according to the present embodiment. [Figure 6] 1 is a diagram for explaining a resume document; [Figure 7] 10 is a conceptual diagram for explaining the process of generating a resume document RD by a generation unit 116. FIG. [Figure 8] 10 is an example of a screen displayed when generating available position information. [Figure 9] 10 is an example of a screen displayed when generating available position information. [Figure 10] 10 is an example of a screen displayed when generating available position information. [Figure 11] 10 is an example of a screen displayed on an employee terminal. [Figure 12] 10 is an example of a screen displayed on an employee terminal. [Figure 13] 10 is an example of a screen displayed on an employee terminal. [Figure 14] 10 is an example of a screen displayed on an employee terminal. [Figure 15] 10 is an example of a screen displayed on a position manager terminal. [Figure 16] 10 is an example of a screen displayed on a position manager terminal. [Figure 17] 10 is an example of a screen displayed on an employee terminal. [Figure 18] 10 is an example of a screen displayed on a position manager terminal. [Figure 19] 10 is an example of a screen displayed on a position manager terminal. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described. Note that various features shown in the following embodiments can be combined with each other.

[0010] That is, the information processing system of this embodiment is as follows. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the presentation step, the information processing system presents the identified learning content in a manner that can be understood by the employee or the job seeker.

[0011] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0012] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously learned the correlation between input and output, or a generative AI such as a large-scale language model (these models include parameters that establish the correlation between input and output) or a visual language model that can output a desired result in response to a prompt.

[0013] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0014] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0015] 1. Hardware Configuration This section explains the hardware configuration.

[0016] <Information Processing System 1> 1 is a configuration diagram showing an information processing system 1. The information processing system 1 includes a communication line 2, a server device 10, and a plurality of user terminals 20. The server device 10 and the plurality of user terminals 20 are configured to be able to communicate with each other via the communication line 2. The connection between the server device 10 and the plurality of user terminals 20 may be wired or wireless.

[0017] In the example of FIG. 1 , a position manager U1 is a person who manages information about a specific position in an organization. Here, the "position manager" may be, for example, a person on the reporting line for the position (e.g., a superior, line manager, department head, division head, etc.). The "position manager" may also be a human resources officer, human resources manager, human resources manager, or person who makes human resources decisions (decision maker) related to the position. Note that decision makers may include management or division managers in the organization. Note that the "organization" in this embodiment may include for-profit corporations (e.g., companies), non-profit corporations (e.g., cooperatives, foundations, etc.), public corporations (e.g., local governments, etc.), etc. The "position" may be a position (such as a department or division, or role) within the organization that is recruiting personnel, and the recruited personnel may be a job seeker U3 outside the organization or an employee U2 within the organization.

[0018] In the example of FIG. 1 , employee U2 is a person who belongs to the aforementioned organization. Job seeker U3 refers to various types of people seeking employment, including, for example, employed people (people seeking a job change), unemployed people seeking employment, and prospective new graduates (job seekers). Job seeker U3 typically does not belong to the aforementioned organization. In one aspect, the "position available in the organization" in this embodiment may be one to which employee U2 within the organization can be appointed. In another aspect, the "position available in the organization" in this embodiment may relate to the content of a job offer for job seeker U3. In other words, employee U2 and job seeker U3 may both be people who can be appointed to the aforementioned positions in the organization. From this perspective, these people may also be referred to as "employment candidates."

[0019] The user terminals 20 used by the position manager U1, employee U2, and job seeker U3 may be referred to as "position manager terminal," "employee terminal," "job seeker terminal," etc.

[0020] In one aspect, the information processing system 1 can provide a first platform that supports human resource management (employee management) and the formulation of a personnel strategy in an organization. In the process of such human resource management and the formulation of a personnel strategy, the position manager U1 and the employee U2 can store (register) various pieces of information in the server device 10 via their own terminals (user terminals 20). In addition, when such a first platform is provided, various pieces of information may be transmitted and received between the position manager U1 and the employee U2.

[0021] The information processing system 1 may also provide a second platform used by an organization (position manager U1) and multiple job seekers U3. Such a second platform may be referred to as a platform for recruiting. In other words, from the perspective of the organization (position manager U1), a recruitment support service may be provided since the organization's (position manager U1's) recruitment activities are supported via the server device 10. From the perspective of the job seekers U3, a job search support service may be provided since the job seekers U3's job search activities are supported.

[0022] In this embodiment, at least one of the first platform and the second platform may be provided. Also, a platform having the functions of both the first platform and the second platform may be realized by the server device 10 or the like. In the example shown in FIG. 1, an example of an information processing system 1 is shown in which both an employee U2 who belongs to an organization and a job seeker U3 who does not belong to an organization are involved. In this specification, the first platform and the second platform may be collectively referred to simply as the "platform."

[0023] In one embodiment, the information processing system 1 is comprised of one or more devices or components. For example, the information processing system 1 may include a server device (e.g., server device 10) having a processor (e.g., control unit 11) and a terminal (e.g., user terminal 20) that can access the server. These components will be described below.

[0024] <Server device 10> Fig. 2 is a block diagram showing the hardware configuration of server device 10. As shown in Fig. 2, server device 10 includes a control unit 11, a storage unit 12, a communication unit 13, and a communication bus 14. Control unit 11, storage unit 12, and communication unit 13 are electrically connected within server device 10 via communication bus 14.

[0025] <Control unit 11> The control unit 11 processes and controls the overall operations related to the server device 10. The control unit 11 is, for example, a central processing unit (CPU). The control unit 11 realizes various functions related to the server device 10 by reading out predetermined programs stored in the storage unit 12. In other words, information processing by software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11. These will be described in more detail in the next section. Note that the control unit 11 is not limited to being a single unit, and the server device 10 may have multiple control units 11 for each function. Furthermore, the server device 10 may be configured with a combination of these.

[0026] <Storage section 12> The memory unit 12 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the server device 10 executed by the control unit 11, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The memory unit 12 stores various programs, variables, etc. related to the server device 10 executed by the control unit 11.

[0027] <Communications Department 13> The communication unit 13 is preferably a wired communication means such as USB, IEEE1394, Thunderbolt (registered trademark), or wired LAN network communication, but may also include wireless LAN network communication, mobile communication such as LTE / 5G, or BLUETOOTH (registered trademark) communication as needed. That is, it is more preferable to implement it as a collection of multiple communication means. That is, the server device 10 may communicate various information from the outside via the communication unit 13 and the network.

[0028] The server device 10 may be an on-premise server or a cloud server. The cloud server device 10 may provide the above-described functions and processes in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0029] <User terminal 20> Fig. 3 is a block diagram showing the hardware configuration of user terminal 20. This user terminal 20 is a terminal used by the various users described above. As shown in Fig. 3, user terminal 20 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, an output unit 25, and a communication bus 26. Control unit 21, storage unit 22, communication unit 23, input unit 24, and output unit 25 are electrically connected via communication bus 26 inside user terminal 20. Description of control unit 21, storage unit 22, and communication unit 23 will be omitted as they are the same as the descriptions of the respective units in server device 10.

[0030] <Input section 24> The input unit 24 accepts operation inputs made by the user. The operation inputs are transferred as command signals to the control unit 21 via the communication bus 26. The control unit 21 can execute predetermined control or calculations based on the transferred command signals as necessary. The input unit 24 may be included in the housing of the user terminal 20 or may be attached externally. For example, the input unit 24 may be implemented as a touch panel integrated with the output unit 25. When the input unit 24 is implemented as a touch panel, the user can input tap operations, swipe operations, etc. to the input unit 24. Instead of a touch panel, a switch button, a mouse, a trackpad, a QWERTY keyboard, etc. can be used as the input unit 24.

[0031] <Output section 25> The output unit 25 displays a screen of a graphical user interface (GUI) that can be operated by the user. The output unit 25 may be included in the housing of the user terminal 20 or may be attached externally. Specifically, the output unit 25 may be implemented as a display device such as a CRT display, a liquid crystal display, an organic EL display, or a plasma display. It is preferable that these display devices are used appropriately depending on the type of user terminal 20.

[0032] 1 shows an example in which the user terminal 20 is a laptop PC (Personal Computer), but in this embodiment, there is no particular limitation on the type of terminal of the user terminal 20. That is, each user terminal 20 may be various information processing terminals such as a desktop PC, a laptop PC, a smartphone, a tablet terminal, etc.

[0033] 2. Functional configuration This section describes the functional configuration of this embodiment. Information processing by software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11 (a processor provided in the information processing system 1).

[0034] FIG. 4 is a block diagram showing functions realized by the server device 10 (controller 11) and the user terminal 20 (controller 21).

[0035] 4A, the server device 10 (control unit 11) may include a registration unit 110, an acquisition unit 111, an identification unit 112, a presentation unit 113, a display control unit 114, a process control unit 115, a generation unit 116, an assignment unit 117, and an artificial intelligence unit 118. As shown in FIG. 4B, the user terminal 20 (control unit 21) may include a display control unit 210 and an operation reception unit 211.

[0036] <Registration Unit 110> The registration unit 110 is configured to execute a registration step. In the registration step, the registration unit 110 performs user registration and various information registration for the platform provided by the information processing system 1. In the example of this embodiment, the registration unit 110 registers one or more of the position manager U1, employee U2, and job seeker U3 as users of the platform. In addition, in the example of this embodiment, the registration unit 110 may register resume documents indicating the career history, skills, etc. of the employee U2 and the job seeker U3 in association with these users. Furthermore, in the example of this embodiment, the registration unit 110 may register information about positions in the organization (vacant position information) on the platform based on terminal operations by the position manager U1, etc. The information registered by the registration unit 110 may be stored in a predetermined memory area. In other words, the registration unit 110 may be configured to store information handled by the server device 10, various terminals, etc. in a memory area. This storage area may be, for example, a storage area (storage unit 12) provided in the server device 10 or a storage area of ​​various devices, but this storage area does not necessarily have to be within the system shown in Figure 1, and the registration unit 110 can also store various information in an external storage device, etc.

[0037] <Acquisition part 111> The acquisition unit 111 is configured to be able to execute an acquisition step. In the acquisition step, the acquisition unit 111 receives various information related to the information processing system 1. In the example of this embodiment, the acquisition unit 111 acquires first skill information and second skill information as a first acquisition step. Here, the first skill information is information related to skills required for a position being recruited in an organization. The second skill information is information related to skills possessed by an employee U2 or a job seeker U3 who belongs to the organization. Details of the content acquired by the acquisition unit 111 will be explained later.

[0038] <Specific part 112> The identification unit 112 is configured to be able to execute an identification step. In the identification step, the identification unit 112 performs various identifications based on the information acquired by the server device 10. In this embodiment, as the identification step, the identification unit 112 identifies one or more learning content items to present to the employee U2 or job seeker U3 based on the acquired first skill information and second skill information. Details of the processing performed by the identification unit 112 will be explained later.

[0039] <Presentation part 113> The presentation unit 113 is configured to be able to execute a presentation step. In the presentation step, the presentation unit 113 presents various information to users of various user terminals 20. Note that such information may be presented based on visual information or auditory information. In this embodiment, the presentation step involves the presentation unit 113 presenting the identified learning content in a format that can be understood by employee U2 or job seeker U3. Details of the processing performed by the presentation unit 113 will be described later.

[0040] <Display control unit 114> The display control unit 114 is configured to be able to execute a display control step. In the display control step, the display control unit 114 controls whether or not visual information is displayed on each user terminal 20. Also, in the display control step, the display control unit 114 generates various display information and controls so that content visible to the user is displayed. Note that the display information may be information itself generated in a manner visible to the user, such as a screen, an image, an icon, or text, or may be rendering information for displaying a screen, an image, an icon, or text on various terminals, for example. The content that can be displayed on the user terminal 20 will be explained later.

[0041] <Processing control unit 115> The process control unit 115 is configured to be able to execute process control steps. In the process control steps, the process control unit 115 performs various controls according to the acquired conditions, etc. Details of the processes performed by the process control unit 115 will be explained later.

[0042] <Generation unit 116> The generating unit 116 is configured to be able to execute a generating step. In the generating step, the generating unit 116 generates various pieces of information based on information acquired by the server device 10. In this specification, the term "generate" may be appropriately interpreted as a term such as "create." As an example, the generating unit 116 generates sentences, documents, graphs, numerical values ​​(scores), figures, etc. based on the acquired information. The specific content generated by the generating unit 116 will be explained later.

[0043] <Granting Section 117> The assigning unit 117 is configured to be able to execute an assigning step. In the assigning step, the assigning unit 117 assigns tags representing various objects related to the information processing system 1 to the objects. The assigning unit 117 may assign tags to the objects based on terminal operations by various users, or may assign tags to the objects based on the function of the artificial intelligence unit 118, which will be described later. Details of the tags that the assigning unit 117 can assign will be described later.

[0044] <Artificial Intelligence Department 118> The artificial intelligence unit 118 is configured to receive input from each functional unit and return the instructed output. The artificial intelligence used by the server device 10 for each functional unit may be a common one, or may be prepared individually for each functional unit.

[0045] The artificial intelligence unit 118 is an AI (Artificial Intelligence) equipped with a learning model such as a language model such as a Transformer including GPT (Generative Pretrained Transformer, including GPT-1, GPT-2, GPT-3, and GPT-4), BERT (Bidirectional Encoder Representations from Transformers), BART (Bidirectional and Auto-regressive Transformer), or a Recurrent Neural Network (RNN), and may include a generative AI or an AI agent. The learning model may also be called an artificial intelligence model, a machine learning model, or a trained model.

[0046] The language model is an example of a learning model based on a machine learning algorithm. Specific examples of machine learning algorithms include nearest neighbor methods, naive Bayes methods, decision trees, support vector machines, and deep learning using neural networks. The artificial intelligence unit 118 can apply the above algorithms as appropriate.

[0047] The artificial intelligence unit 118 may have a trained model constructed by a learning method such as supervised learning, unsupervised learning, or self-supervised learning. In supervised learning, machine learning is performed using training data (training data). The training data consists of pairs of input data for learning and output data (correct answer data). Furthermore, the language model may not only be trained for a specific task, but also be a general-purpose model that can be used for a wide range of tasks.

[0048] The artificial intelligence unit 118 may use a natural language model as the artificial intelligence, or may include a general-purpose natural language processing trained model such as a large-scale language model (LLM) trained on a huge amount of data. An LLM is a learning model trained in advance on a large amount of data (e.g., (i) web content on the Internet, or (ii) data stored in a specified database) composed of text data, etc., and can perform various language processing tasks when given a task. Following given prompts, it can perform a wide range of natural language processing tasks, such as understanding sentence patterns and context, answering questions, and generating sentences. Such a general-purpose learning model may include a language model capable of handling various tasks without fine-tuning, using one-shot learning, few-shot learning, etc. Furthermore, a general-purpose learning model can also handle various tasks using zero-shot learning. The artificial intelligence used in each functional unit of the control unit 11 may be a separate learning model, or a common general-purpose learning model. A large-scale language model is a type of generative AI and includes models provided by services such as OpenAI's GPT, Google's Gemini, and Microsoft's Azure AI Studio. Furthermore, the artificial intelligence unit 118 may include, as artificial intelligence, a small-scale language model or a medium-scale language model that is smaller in scale than a large-scale language model. The small-scale language model and the medium-scale language model are natural language processing models that are trained based on less data than the large-scale language model. In addition, the artificial intelligence unit 118 may include any machine learning model, deep learning model, artificial intelligence model, etc. The artificial intelligence unit 118 may be constructed in a system external to the information processing system 1. Furthermore, the artificial intelligence unit 118 may be of an interactive type (which may be interpreted as a chat type or a conversation type) that alternately receives input for performing instructed output and generates and outputs information.

[0049] The learning model included in the artificial intelligence unit 118 can perform additional learning using techniques such as transfer learning or fine tuning. For example, the artificial intelligence unit 118 learns whether the output content has been modified by a user or the like. That is, the artificial intelligence unit 118 may perform additional learning and fine tuning based on modifications to the content output by the learning model. Furthermore, for example, each time new data is registered, the artificial intelligence unit 118 may perform additional learning and fine tuning using the new data as new training data. This improves the accuracy of the information output from the learning model.

[0050] The learning model included in the artificial intelligence unit 118 may be a learning model (distilled model) obtained by knowledge distillation using an original trained model. In knowledge distillation, a trained model such as a large-scale language model is used as a teacher model, and the parameters of the student model (distilled model) are adjusted to reduce the output loss (Soft Target Loss) of the student model (distilled model) relative to the output (Soft Target) of the teacher model, thereby learning the student model, which becomes the distilled model. Alternatively, the student model may be learned to reduce the output loss (Hard Target Loss) of the student model relative to the correct label (Hard Target) of the teacher data (a combination of input data and output data of the learning model). Compared to the original trained model (teacher model), the distilled model has a smaller number of parameters and a smaller processing load while maintaining performance similar to the trained model. Therefore, using a distilled model can reduce the cost of the information processing system 1.

[0051] For example, the learning model used in each functional unit may be a distilled model trained using a combination of input data and output data in a large-scale language model as training data. Furthermore, when the information processing system 1 is introduced, a large-scale language model may be used as the learning model used in each functional unit, and when training data from the large-scale language model is accumulated, a distilled model obtained by knowledge distillation using the training data may be used as the learning model used in each functional unit.

[0052] AI agents may also be called autonomous agents. An "AI agent" is a model that, when given a goal (purpose, objective, etc.) such as "teach me XX" or a task such as "output XX," breaks down the processing required to reach the goal or accomplish the task into subtasks, actions, etc., and performs tasks such as collecting and analyzing necessary data, generating and executing programs, etc. AI agents target information and instructions input by a user, autonomously select and execute tasks and actions according to the goal, and output information according to the goal, without requiring user intervention (operational input). AI agents may also autonomously learn to achieve their goals by autonomously creating and executing plans and evaluating the results. For example, an AI agent may be autonomously updated based on the results of subtask execution (e.g., collected information, information analysis results, etc.).

[0053] <Display control unit 210> The display control unit 210 of the user terminal 20 controls the display of a screen indicated by the screen data transmitted from the server device 10.

[0054] <Operation Reception Unit 211> The operation reception unit 211 of the user terminal 20 receives operations by users who use the user terminal 20 (position manager U1, employee U2, job seeker U3, etc.).

[0055] 3. Information Processing Method In this section, an information processing method for the server device 10 will be described with examples. This information processing method may be executed as each step by each unit of the server device 10. Note that the various features described in this section can be combined with each other as long as no technical contradiction occurs.

[0056] 5 is an activity diagram for explaining the information processing of this embodiment. As shown in the activity diagram of FIG. 5, in the information processing method of this embodiment, server device 10 identifies study content and presents it to user terminal 20.

[0057] When identifying such learning content, it is preferable that a resume document for employee U2 or job seeker U3 be prepared in advance. When identifying learning content, it is also preferable that information about positions in the organization (vacant position information) be prepared in advance. The resume document may be prepared manually (by employee U2 or job seeker U3, etc.), and the vacant position information may be prepared manually (by position manager U1, etc.). However, the following configuration may also be adopted. That is, the generation unit 116 of server device 10 may generate a resume document for employee U2 or job seeker U3 by inputting the registration information of employee U2 or job seeker U3 registered in the database into an artificial intelligence module. The generation unit 116 of server device 10 may also generate vacant position information by inputting predetermined input information into the artificial intelligence module. Such resume documents and vacant position information are described below.

[0058] <Resume document generation> FIG. 6 is a diagram illustrating a resume document. This figure shows a resume document RD prepared by employee U2 displayed on the employee's terminal screen. The resume document RD for employee U2 contains information about employee U2 and is sometimes referred to as a "human resources management document" or "internal human resources management document." This resume document RD may also be referred to as a resume of employee U2. Such a resume document RD typically includes registered information in an employee database and may include a career summary, previous positions, job types, skills and experience, knowledge and learning, and a history within the organization (internal history) of employee U2. The career summary is, for example, a summary of the career of employee U2. The internal history is, for example, a chronological description of the employee's career within the organization. Furthermore, the resume document RD may be an internal resume based on the registered information in the employee database (e.g., a document aggregating the registered information). The resume document RD may also include information about the career history of the employee U2 outside the organization (e.g., previous job and career history before that). The resume document RD may typically be made available for viewing within a predetermined range within the organization.

[0059] 6 shows an example in which the items described in the resume document RD include employee U2's career summary, previous positions, previous job types, skills and experience, knowledge and learning, and company history, but the types and number of items described in the resume document RD may be set arbitrarily depending on the organization's objectives, etc. Note that the items described in such a resume document RD may be related to various registered information registered in the employee database.

[0060] Here, the "employee database" may be a database in which various data relating to employees U2 and the like is accumulated. The employee database may also be called a "personnel database." The employee database may also be a collection of multiple databases. That is, the employee database may include databases such as a basic information database, a goal evaluation database, a position database, a skill database, a recruitment database, a condition survey database, an organizational diagnosis survey database, an original survey database, an interview database, and an attendance database. Each of these databases stores and / or accumulates individual data (registration information) of employees U2 and the like. The recruitment database may store recruitment data for new graduates and recruitment data for mid-career recruitment. The position database and the skill database may also be part of the basic information database or the goal evaluation database.

[0061] The registered information stored in the employee database may be personnel information. The registered information stored in the employee database may also be classified into multiple categories. The "categories" may include, for example, employee U2's name, gender, age, employee code, contact information, affiliation, employment type, years of service, years of affiliation (department, division, etc.) for each previous affiliation (years of service), work location, job duties, career history, set goals within the organization, evaluation of goals, information about employee U2 before joining the organization (hiring information, resume), condition, engagement (e.g., eNPS), aptitude or personality, interview history (interviewee, comments, etc.), attendance information (overtime hours, number of annual paid holidays, etc.). The hiring information may include the position offered at the time of hiring, annual salary, hiring interviewer, hiring evaluation, aptitude test results, reference check information, etc. Note that information other than hiring information may be information after joining the organization (post-hiring information).

[0062] As explained above, the content to be written in the resume document RD may be input manually (manually) by an employee U2 or the like. The content to be written in the resume document RD may also be generated and input by the function of the generation unit 116. Furthermore, the content to be written in the resume document RD may be input by manually modifying a sentence generated by the function of the generation unit 116 by an employee U2 or the like. That is, the generation unit 116 may generate a resume document RD (or a part of the resume document RD) that includes document components derived from registered information stored in the employee database. The document components here may include sentences created based on the function of the artificial intelligence unit 118. That is, the resume document RD (or a part of the resume document RD) of this embodiment may include text output from the artificial intelligence module (artificial intelligence unit 118) based on registration information registered in the employee database input to the artificial intelligence module (artificial intelligence unit 118) (i.e., the generation unit 116 of the server device 10 may generate the resume document for employee U2 by inputting the registration information of employee U2 registered in the employee database into the artificial intelligence module (artificial intelligence unit 118)). Furthermore, the resume document RD (or a part of the resume document RD) of this embodiment may be input by manually modifying the text output from the artificial intelligence module (artificial intelligence unit 118) by employee U2 or the like. In a typical example, the registration information input to the artificial intelligence module (artificial intelligence unit 118) may include the resume of employee U2 before he / she joined the organization (before joining the company), employee U2's career history within the organization (such as transfer history), goals, evaluations, and other personnel information. For example, the creation of a resume document RD (or a part of the resume document RD) from the registered information may be performed by referring to reference information including a correlation between the registered information and the resume document RD (or a part of the resume document RD). As the reference information here, a resume document creation model, which is a learning model trained to use the registered information as input and the resume document RD (or a part of the resume document RD) as output, may be used. For example, the resume document creation model is a learning model trained using the registered information and the corresponding resume document RD (or a part of the resume document RD) as training data.The generation unit 116 may input the registration information to a resume document creation model included in the artificial intelligence module (artificial intelligence unit 118) and cause the resume document creation model to output a resume document RD (or a portion of the resume document RD). The resume document creation model may be, for example, a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the generation unit 116 may input a prompt containing an instruction to create and output a resume document RD (or a portion of the resume document RD) from the registration information and the registration information to the artificial intelligence module (artificial intelligence unit 118), and cause the artificial intelligence module (artificial intelligence unit 118) to output the resume document RD (or a portion of the resume document RD). Furthermore, the generation unit 116 may input a prompt containing, for example, one or more samples of the registration information and one or more corresponding samples of the resume document RD (or a portion of the resume document RD) to the artificial intelligence module (artificial intelligence unit 118), in addition to the instruction to output the resume document RD and the registration information. This allows the career history, such as skills, experience, and transfer history, of employee U2 to be easily documented and visualized. A more specific example of the generation process of the resume document RD by the generation unit 116 will be described below.

[0063] FIG. 7 is a conceptual diagram illustrating the generation process of a resume document RD by the generation unit 116. The resume document RD of this embodiment may be composed of multiple document components that indicate information such as the work history and attributes of an employee U2 belonging to an organization. These document components may include, for example, first document components that are keywords included in the registered information and second document components that include sentences created based on the registered information. As shown in FIG. 7, the generation unit 116 may acquire various registered information RI1 from multiple databases included in the personnel database D10.

[0064] The generation unit 116 may extract keywords corresponding to the registered information RI1 contained in the personnel database D10 as the first document components DC1, and may also generate sentences based on the registered information RI1 as the second document components DC2. In this embodiment, two or more categories of registered information RI1 related to an employee U2 belonging to an organization may be accepted, and a resume document RD may be generated using the received information. The generation unit 116 may select a conversion procedure for the registered information RI1 into document components depending on the form or category of the registered information RI1. This allows multiple pieces of registered information RI1 from different categories to be converted into appropriate forms as elements of the resume document RD, and then integrated into a single document.

[0065] The registered information RI1 may be information described by keywords or numerical values. Examples of such registered information RI1 include basic information acquired from a basic information database, position information acquired from a position database, skill information acquired from a skill database, condition acquired from a condition survey database, eNPS acquired from an organization diagnosis survey database, and aptitude or personality acquired from an original survey database. The generation unit 116 may generate multiple first document components DC1 from registered information RI1 of one category, or may generate one first document component DC1 from registered information RI1 of multiple categories.

[0066] The registered information RI1 may be written in text. Examples of such registered information RI1 include goals or evaluations obtained from a goal evaluation database, transfer history information obtained from a personnel database, employment information obtained from a recruitment database (e.g., a resume at the time of employment), one-on-one history obtained from an interview database, and attendance information obtained from an attendance database.

[0067] As shown in FIG. 7, the generation unit 116 may generate multiple second document components DC2 from registered information RI1 of one category, or may generate a first document component DC1 and a second document component DC2 from registered information RI1 of one category.

[0068] Extraction of the first document component DC1 from the registered information RI1 may be performed by referring to first reference information including the correlation between the registered information RI1 and the first document component DC1. As the first reference information, for example, an extraction item list is used. The extraction item list lists candidates (samples) for each item, such as department, division, job, skill, qualification, etc., and the generation unit 116 extracts keywords corresponding to each item from the registered information RI1 and sets them as the first document component DC1. Therefore, the first document component DC1 may include multiple keywords.

[0069] Alternatively, a first keyword extraction model may be used as the first reference information. The first keyword extraction model is a learning model trained to input the registered information RI1 and output the first document component DC1. The first document component DC1 is, for example, a keyword representing a skill, experience, qualification, or the like. For example, the first keyword extraction model is a learning model trained using the registered information RI1 and the corresponding first document component DC1 as training data. The generation unit 116 may input the registered information RI1 to the first keyword extraction model of the artificial intelligence module (artificial intelligence unit 118) and cause the first keyword extraction model to output the first document component DC1. Note that the first keyword extraction model may be, for example, a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the generation unit 116 may input a prompt into the first keyword extraction model, which includes an instruction to extract keywords representing a skill, experience, qualification, or the like from the registered information RI1 and the registered information RI1, and cause the first keyword extraction model to output the first document component DC1. In addition, the generation unit 116 may input a prompt into the first keyword extraction model that includes, in addition to the keyword extraction instruction and the registered information RI1, for example, one or more samples of the registered information RI1 and one or more corresponding samples of the first document components DC1.

[0070] The second document component DC2 may include a sentence newly created using keywords included in the registered information RI1. The second document component DC2 may be a summary of a sentence included in the registered information RI1, or a sentence reconstructed using keywords included in the registered information RI1. The second document component DC2 may also be a sentence that is a summary of a sentence (created sentence) such as work history (internal career) created by the generation unit 116. The second document component DC2 may be, for example, a sentence related to work history or a summary sentence of work history and registered information created using the registered information RI1. In this specification, a sentence that summarizes work history (internal career) may also be referred to as a "career summary" or "job summary," etc.

[0071] The second document component DC2, which is a summary of the sentence included in the registered information RI1, may be created by referring to second reference information including the correlation between the registered information RI1 and the second document component DC2. For example, the second reference information may be a summarization model, which is a learning model trained to use the registered information RI1 as input and the second document component DC2 as output. For example, the summarization model is a learning model trained using the registered information RI1 and the corresponding second document component DC2 as training data. The generation unit 116 may input the registered information RI1 to a summarization model included in an artificial intelligence module (artificial intelligence unit 118) and cause the summarization model to output the second document component DC2. The summarization model may also be a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the generation unit 116 may input a prompt containing an instruction to summarize the sentence included in the registered information RI1 and the registered information RI1 to the summarization model, and cause the summarization model to output the second document component DC2. Furthermore, the generation unit 116 may input a prompt into the summarization model, which includes, for example, one or more samples of the registered information RI1 and one or more corresponding samples of the second document components DC2, in addition to the summary creation instruction and the registered information RI1. In this embodiment, the second document components DC2 output from the summarization model in this manner can be used as a sentence (a created sentence) such as the aforementioned work history (internal history). The generation unit 116 may also input the output second document components DC2 (a created document) into the summarization model again to generate a separate second document component DC2. The separately generated second document component DC2 may be used as the aforementioned "career summary" or "job summary." In this case, the input content to the summarization model may be appropriately interpreted as a "created document."

[0072] The generation unit 116 may generate document components for each category from the registered information RI1 for multiple categories, or may generate one document component from the registered information RI1 for multiple categories. Furthermore, the generation unit 116 may generate multiple document components from the registered information RI1 for one category. For example, FIG. 7 shows an example in which a first document component DC1 for "Possessed Skills / Job Type," a second document component DC2 for "Internal Experience," and a second document component DC2 for "Job Summary" are generated from the registered information RI1 for "Goals / Evaluation." Also, FIG. 7 shows an example in which a first document component DC1 for "Educational Background / Job Type / Qualifications at Time of Hiring" and a second document component DC2 for "Offer Content / Evaluation" are generated from the registered information RI1 for "Information at Time of Hiring." The "Job Summary" shown in FIG. 7 may be a summary of a text (such as an internal experience) created based on the registered information for "Goals / Evaluation."

[0073] While FIG. 6 shows a resume document RD for employee U2 of the organization, a resume document RD for job seeker U3 may also be prepared. In this case, various matters related to job seeker U3 may be described item by item. Furthermore, matters described in the resume document RD for job seeker U3 may relate to various registered information registered in the job seeker database. The above-mentioned description of the process for generating the resume document for employee U2 may be interpreted as relating to the process for generating the resume document for job seeker U3, as appropriate.

[0074] <Generating job information> Next, we will explain the recruiting position information related to positions within an organization. In this embodiment, positions within an organization may be registered in advance by a position manager U1 or the like in the platform provided by the information processing system 1. In a typical example, positions recruited by a position manager U1 or the like may be registered as registration information in the platform provided by the information processing system 1 along with recruiting information such as the recruitment background, organization overview, desired talent profile, application requirements (essential requirements, preferred requirements), job details, and prepared scouting text. Here, the recruiting position information may be referred to as "internal position requirements" or "internal position requirements," and may include application requirements for the recruiting position. Note that this registration information (or a portion of the registration information) may be configured to be viewable by employees U2 and job seekers U3. In other words, recruiting positions and recruiting position information may be made public as job postings within and / or outside the organization. This increases the opportunities for employees U2 and job seekers U3 to encounter various positions and provides them with career development opportunities. On the other hand, information about positions and available positions may be viewable only by those with predetermined authority or within a predetermined range.

[0075] The registered available position information may include text. As described above, the text included in the available position information may be manually (manually) input by a position manager U1 or the like. Furthermore, the text included in the available position information may be generated by the generation unit 116 and then input. Furthermore, the text included in the available position information may be input by manually (manually) modifying information generated by the generation unit 116 or the like. That is, the generation unit 116 may generate text related to the available position information. The text related to the available position information may include text created based on the function of the artificial intelligence unit 118. That is, the generation unit 116 may input input information from a position manager U1 or the like to the artificial intelligence module (artificial intelligence unit 118) to obtain text output from the artificial intelligence module (artificial intelligence unit 118) and use the text in the available position information (that is, the generation unit 116 of the server device 10 may generate available position information for a position by inputting predetermined input information to the artificial intelligence module (artificial intelligence unit 118)). Furthermore, in this embodiment, the information about available positions may be input by manually correcting text output from the artificial intelligence module (artificial intelligence unit 118) by the position manager U1 or the like. Here, the input information input to the artificial intelligence module (artificial intelligence unit 118) may include information input by the position manager U1 or the like, such as the position name (the name of the available position), the desired talent profile, information about employees currently (or previously) holding the position, job type, skills, etc. This allows information about available positions in an organization to be easily written and visualized.

[0076] As an example, the generation unit 116 may input input information entered by a position manager U1 or the like into an artificial intelligence module (artificial intelligence unit 118), thereby outputting (generating) sentences related to the available position information. For example, the input information may be input to a vacant position information generation model possessed by the artificial intelligence unit 118, causing the vacant position information generation model to output sentences related to the available position information related to the input information. The vacant position information generation model here may be a learning model trained to use the input information as input and output sentences related to the available position information related to the input information. In other words, the vacant position information generation model may be a learning model trained using the input information and sentences related to the corresponding available position information as training data. Furthermore, the vacant position information generation model may be a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the generation unit 116 may refer to the input information and input a prompt into the vacant position information generation model, which includes an instruction to generate sentences related to the available position information related to the input information and the input information entered by the position manager U1 or the like, and causes the vacant position information generation model to output sentences related to the available position information. Furthermore, the generation unit 116 may input, to the vacant position information generation model, in addition to the instruction to generate a sentence related to the vacant position information and the input information, for example, a prompt in which one or more samples of input information and one or more corresponding sentences related to the vacant position information are inserted. Note that the sentences related to the vacant position information generated by the generation unit 116 may be sentences that constitute the entire vacant position information or sentences that constitute a part of the vacant position information.

[0077] When generating the available position information, the display control unit 114 of the server device 10 may display the following screen.

[0078] FIG. 8 is an example of a screen displayed when generating available position information. FIG. 9 is an example of a screen displayed when generating available position information. FIG. 10 is an example of a screen displayed when generating available position information. As mentioned above, available position information may be generated by operating a terminal of a position manager U1 or the like. In other words, the various screens shown in FIGS. 8 to 10 may be displayed on the terminal screen of a position manager U1 or the like.

[0079] In an exemplary embodiment, a position manager U1 or the like accessing the screen shown in FIG. 8 inputs information (input information) (one or more) about the position being recruited into forms F1 to F4. The screen example in FIG. 8 shows an example in which the input content includes fields for inputting the position name (the name of the position being recruited), job title, job type, and talent image (talent profile). FIG. 8 shows an example in which forms F1 and F4 allow for free text input, while forms F2 and F3 allow for information selection using a pull-down menu. The talent image (talent profile) field may also allow for free text input, or may be input in the form of selecting an employee U2 belonging to the organization. For example, by switching between "Enter talent image / Specify specific employee," the system may be configured to retrieve information about employee U2 registered in the employee database. For example, selecting "Specify specific employee" displays a screen in which an employee U2 belonging to the organization can be specified, allowing the position manager U1 or the like to select an employee U2 who matches the talent image (talent profile) for the position being recruited. When employee U2 is selected, the registered information of employee U2 may be retrieved from an employee database or the like and used as input information to output (generate) keywords and available position information, which will be described next. After entering the necessary information, the position manager U1 or the like can proceed with the creation process of available position information by pressing button BT1.

[0080] The screen shown in FIG. 9 may be displayed after pressing button BT1 on the screen shown in FIG. 8. Keywords used to generate available position information are presented as options in column C1 on the screen shown in FIG. 9. For example, these options may be displayed in tag format. These options may be referenced when generating available position information. However, in the example screen shown in FIG. 9, tag TG1, indicated by a solid line, is a tag for a keyword referenced when generating available position information, and tag TG2, indicated by a dashed line, is a tag not referenced when generating position information. In this way, tags TG1 and TG2 may be displayed in different ways, but a position manager U1 or the like can switch the tag type by operating the displayed tag. Furthermore, as an example, when button BT1 on the screen shown in FIG. 8 is pressed, tags for options correlated with the content entered in forms F1 to F4 are controlled to be placed in column C1. As an example, when button BT1 is pressed, only tags in the form of tag TG2 are placed in column C1, and this tag TG2 is switched to the form of tag TG1 by operation by the position manager U1 or the like. In this case, the position manager U1 or the like can select tags that need to be referenced when generating available position information. On the other hand, when button BT1 is pressed, only tags in the form of tag TG1 are placed in column C1, and this tag in the form of tag TG1 is switched to the form of tag TG2 by operation by the position manager U1 or the like. In this case, the position manager U1 or the like can select tags that do not need to be referenced when generating available position information. On the other hand, pressing button BT1 may place tags with different display modes, such as the relationship between tags TG1 and TG2 shown in FIG. 9. Also, pressing button BT2 can call up a keyword that has been prepared in advance. That is, the server device 10 may store multiple keywords used when generating available position information as a keyword master. Then, the position manager U1 or the like can select keywords stored in this keyword master and place the selected keywords in column C1.

[0081] In the above-described embodiment, tags corresponding to various keywords may be extracted and displayed (presented) by a function of the server device 10. That is, the server device 10 may be configured to compare various keywords stored in the keyword master with the content entered into forms F1 to F4 by the position manager U1 or the like, extract appropriate keywords from the keyword master, and display them as various tags in the column C1. Note that the function of the artificial intelligence unit 118 may be utilized in this extraction. That is, first, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to convert the content entered into forms F1 to F4 by the position manager U1 or the like into vector data (first vector data). Meanwhile, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to similarly convert the keywords existing in the keyword master into vector data (second vector data). The server device 10 then causes the artificial intelligence module (artificial intelligence unit 118) to extract keywords from the keyword master that are correlated with the content entered into forms F1 to F4 by the position manager U1 or the like, based on the proximity of the distance between the first vector data and the second vector data. Then, tags corresponding to the extracted keywords can be placed in column C1. The proximity of the distance between the vector data here may be evaluated using various methods, such as the angle between the two vectors (cosine similarity). The level of correlation here may be converted into a score, and the keywords may be displayed in column C1 in descending order of the score. A threshold value may be set for the score, and the display mode of the tags, such as the relationship between tags TG1 and TG2, may be switched based on the relationship with this threshold.

[0082] Furthermore, for example, extraction of keywords from content (input information) input by a position manager U1 or the like may be performed by referencing reference information containing correlations between the input information and keywords. Here, a second keyword extraction model, which is a learning model trained to use the input information as input and keywords as output, may be used as the reference information. For example, the second keyword extraction model is a learning model trained using the input information and its corresponding keywords as training data. The server device 10 may input the input information to the second keyword extraction model possessed by the artificial intelligence module (artificial intelligence unit 118) and cause the second keyword extraction model to output keywords. Note that the second keyword extraction model may be, for example, a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the server device 10 may input a prompt containing an instruction to generate and output keywords from the input information and the input information to the artificial intelligence module (artificial intelligence unit 118), and cause the second keyword extraction model possessed by the artificial intelligence module (artificial intelligence unit 118) to output the keywords. In addition, the server device 10 may input a prompt containing, in addition to the keyword extraction instruction and input information, for example, one or more samples of input information and one or more corresponding keyword samples, into the second keyword extraction model of the artificial intelligence module (artificial intelligence unit 118).

[0083] After selecting various tags in this way, the user can press button BT3 to proceed with the operation of generating available position information.

[0084] Pressing button BT3 may transition to the screen shown in FIG. 10. FIG. 10 illustrates an example in which column C2 is an input field for vacant position information. Columns C21 and C22 within column C2 may contain text related to the vacant position information generated by the generation unit 116. In other words, in the above embodiment, the input information entered by the position manager U1 or the like is input to the artificial intelligence module (artificial intelligence unit 118), which outputs (generates) text related to the position information. However, the input information here may be based on a keyword corresponding to tag TG1 shown in FIG. 9. Furthermore, the generation unit 116 of the server device 10 may input, for example, one or more of the content entered into forms F1 to F4 by the position manager U1 or the like, the registration information of the selected employee U2, and the selected keywords to the artificial intelligence module (artificial intelligence unit 118) to generate text related to the vacant position information. In this way, text generated based on the function of the artificial intelligence module (artificial intelligence unit 118) may be inserted into column C21 or column C22.

[0085] The field shown as field C23 in Fig. 10 specifies the skills required for a position. In Fig. 10, a tag TG3 indicates the skills required for a position. The skills required for such a position may correspond to the keywords specified in field C1 in Fig. 9, or may be specified by appropriate input by the position manager U1.

[0086] When a position manager U1 or the like comes across the screen of Figure 10, they can check the contents of the various columns (column C21, column C22, column C23, etc.) within column C2, make any necessary modifications, and then press button BT4 to register the available position information in association with a position within the organization.

[0087] <Identifying and presenting learning content> Returning to the explanation of the identification and presentation of learning content in the information processing of this embodiment, the learning content to be presented is identified based on the first skill information and second skill information acquired by the acquisition unit 111. The acquisition of such various skill information may be triggered, for example, by the operation of the terminal of employee U2 or job seeker U3. Specific operations will be explained below using the screen displayed on employee U2's terminal as an example.

[0088] Fig. 11 is an example of a screen displayed on an employee terminal. The screen shown in Fig. 11 is a management screen (login screen) displayed on the terminal of employee U2, and is displayed when employee U2 logs in to the platform provided by the information processing system 1. On this management screen, various notifications (notifications NT1 to NT3) may be displayed to employee U2.

[0089] Employee U2 who has confirmed such notifications NT1 to NT3 can transition to a page that displays details about the corresponding notification by clicking on the area corresponding to each notification, etc. For example, by clicking on the area corresponding to notification NT1, the page can be transitioned to one where the details of the scout (scout message) can be confirmed.

[0090] 11 also displays information about a specific position for employee U2 as a "skill-matching internal job posting." Regarding such display, the present embodiment may employ the following configuration. Specifically, in the present embodiment, the display control unit 114 of the server device 10 may display, on the screen of the terminal of employee U2 or job seeker U3, a link indicating a position correlated with the resume document RD based on the correlation between the resume document RD for employee U2 or job seeker U3 and the information about the available position. Furthermore, as described above, the resume document RD and the information about the available position may each be generated based on the function of the generation unit 116.

[0091] 11, positions such as "Marketing Manager" and "New Business Development" are displayed on the screen of employee U2's terminal along with an indicator of "match level." Such a display may function as a link, and when such a link is operated, a screen (second screen) showing information about the available positions may be displayed on the terminal of employee U2 or job seeker U3.

[0092] The positions displayed here may be extracted by the function of the artificial intelligence unit 118 as follows. Specifically, first, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to convert the resume document RD of employee U2 or job seeker U3 into vector data (third vector data). Meanwhile, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to similarly convert the available position information for each position into vector data (fourth vector data). The server device 10 then causes the artificial intelligence module (artificial intelligence unit 118) to extract, from the registered available position information, available position information that is correlated with the resume document RD based on the proximity of the distance between the third vector data and the fourth vector data. The position corresponding to the extracted available position information can then be displayed on the screen of the terminal of employee U2 or job seeker U3. The proximity of the distance between the vector data may be evaluated using various methods, such as the angle between the two vectors (cosine similarity). The level of correlation here may be converted into a score, and the position may be displayed on the screen of the terminal of employee U2 or job seeker U3 so that the keywords are arranged in descending order of the score. Also, the score representing the level of correlation may be displayed on the screen of the terminal of employee U2 or job seeker U3.

[0093] Furthermore, for example, extraction of positions from the resume document RD may be performed by referencing reference information including correlations between the resume document RD and information about available positions. Here, the reference information may be a position extraction model, which is a learning model trained to use the resume document RD as input and output organizational positions. For example, the position extraction model is a learning model trained using the resume document RD and the corresponding organizational positions as training data. The server device 10 may input the resume document RD to a position extraction model included in an artificial intelligence module (artificial intelligence unit 118) and cause the position extraction model to output a predetermined position. Note that the position extraction model may be, for example, a learning model such as a language model or a large-scale language model, or may be an AI including a generative AI. In this case, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) a prompt containing an instruction to output correlated positions from the contents of the resume document RD and the resume document RD inserted therein, and cause the artificial intelligence module (artificial intelligence unit 118) to output positions correlated with the resume document RD. Furthermore, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) a prompt that includes, for example, one or more samples of resume documents RD and one or more corresponding samples of positions in the organization in addition to the instruction to output the positions and the resume document RD. Note that the extraction of positions in the organization here may be performed by appropriately referring to information on available positions corresponding to the positions.

[0094] Employee U2 who sees the screen shown in Figure 11 can, for example, click on the area that displays "Marketing Manager" to display information about available positions corresponding to this "Marketing Manager."

[0095] Specifically, employee U2 or the like can request a screen display related to a specific position (activity A101). In one embodiment, server device 10 receives such a display request and acquires various skill information (activities A102 and A103). Then, after identifying learning content, it can present it on employee U2 or the like's terminal (activities A104 to A106).

[0096] More specifically, the acquisition unit 111 of the server device 10 may acquire first skill information and second skill information at a predetermined timing. Here, the first skill information is information about skills required for positions being recruited in an organization. The second skill information is information about skills possessed by an employee U2 or a job seeker U3 who belongs to the organization. Then, the identification unit 112 may identify one or more learning content items to present to the employee U2 or the job seeker U3 based on the acquired first skill information and second skill information. Furthermore, the presentation unit 113 may present the identified learning content items in a manner that can be understood by the employee U2 or the job seeker U3.

[0097] Although the activity diagram shown in FIG. 5 illustrates a case in which various skill information is acquired after a display request for a position is made, the timing of acquiring the various skill information may be set arbitrarily. For example, various skill information may be acquired before a display request for a position is made by employee U2 or the like. In this case, the learning content to be presented to employee U2 may be identified before the display request for a position is made by employee U2 or the like. In such a case, the presentation of the previously identified learning content may be triggered by employee U2 or the like making a display request for a specific position. Of course, the combination of the timing of the identification process by the identification unit 112 and the timing of the presentation process by the presentation unit 113 is not limited to these and may be set appropriately as long as it does not deviate from the spirit of the invention.

[0098] Furthermore, the method for identifying learning content in this embodiment may be set arbitrarily. For example, learning content may be identified as follows. That is, the identification unit 112 of the server device 10 may extract information about skills included in the first skill information but not included in the second skill information as insufficient skill information, and identify learning content corresponding to the extracted insufficient skill information. This allows, for example, employee U2 or job seeker U3 to recognize skills they lack for a desired position or a position of interest, and to receive learning content to acquire those skills. This promotes independent skill acquisition and career development for individuals. Furthermore, organizations (companies) can visualize employees' skill sets and plan and implement strategic human resource development.

[0099] As an example, a case will be described in which the first skill information relates to skills registered according to the screen shown in FIG. 10, and the second skill information relates to skills registered as the resume document RD shown in FIG. 6. In this case, "language proficiency," which is part of the first skill information, is also included as the second skill information, but "statistics" and "spreadsheet," which are also part of the first skill information, are not included as the second skill information. In this case, skills such as "statistics" and "spreadsheet" can be identified as skills lacking in employee U2 (extracted as lacking skill information). The identification unit 112 can then identify learning content corresponding to the skills such as "statistics" and "spreadsheet," and the presentation unit 113 can present the learning content to employee U2. Note that this example illustrates a mode in which lacking skill information is extracted based on whether the skills in the first skill information and the skills in the second skill information completely match. However, even if a specific skill in the first skill information and a specific skill in the second skill information are similar (related) to each other, the aforementioned lacking skill information can be extracted based on various skill information for each of these skills.

[0100] When identifying learning content based on such insufficient skill information, the following method may be adopted. That is, the identification unit 112 may identify (extract) learning content that has a correlation with the insufficient skill information. In this case, for example, prior to the identification process, various learning content may be linked with skill-related information.

[0101] As an example, the identified (extracted) learning content may be extracted by the function of the artificial intelligence unit 118 as follows: First, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to convert the lacking skill information into vector data (fifth vector data). Meanwhile, the server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to similarly convert the contents of various learning content into vector data (sixth vector data). The server device 10 then causes the artificial intelligence module (artificial intelligence unit 118) to extract learning content that is correlated with the lacking skill information from the various learning content based on the proximity of the fifth vector data to the sixth vector data. The extracted learning content can then be presented to the employee U2 or the job seeker U3. The proximity of the vector data may be evaluated using various methods, such as the angle between the vectors (cosine similarity). The correlation level may be expressed as a score, and the learning content may be displayed on the screen of the terminal of employee U2 or job seeker U3 so that the keywords are arranged in descending order of their score. The score representing the correlation level may also be displayed on the screen of the terminal of employee U2 or job seeker U3.

[0102] Alternatively, the learning content to be presented may be identified as follows. That is, the learning content may be identified based on various skill information by referencing reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content. Here, the reference information may be a learning content identification model that is trained to use the first skill information and the second skill information as input and output the learning content. For example, the learning content identification model is a learning model trained using the first skill information, the second skill information, and the corresponding learning content as training data. The identification unit 112 of the server device 10 may input the first skill information and the second skill information into the learning content identification model of the artificial intelligence module (artificial intelligence unit 118) and cause the learning content identification model to output the predetermined learning content. Note that the learning content identification model may be, for example, a learning model such as a language model or a large-scale language model, or an AI including a generative AI. In this case, the identification unit of server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) an instruction to identify correlated learning content by referencing the first skill information and the second skill information, and a prompt that inserts the first skill information and the second skill information, and cause the artificial intelligence module (artificial intelligence unit 118) to output learning content that correlates with the first skill information and the second skill information. Furthermore, the identification unit 112 of server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) a prompt that inserts, for example, one or more samples of the first skill information and the second skill information and one or more samples of learning content corresponding thereto, in addition to the instruction to identify learning content and the first skill information and the second skill information.

[0103] When identifying learning content, the identification unit 112 may refer to the content of the learning content as appropriate. The content of the learning content may include one or more of the title of the learning content, an outline of the learning content, and a transcript of the learning content. The identification unit 112 can identify learning content to present to employee U2, for example, by matching the lacking skill information with the content of the learning content. When the identification unit 112 identifies learning content using a learning content identification model, the content of the learning content may be associated with the model as appropriate.

[0104] Furthermore, the study content may be presented in a variety of ways, including, for example, the ways shown in FIGS. 12 to 14 below.

[0105] FIG. 12 is an example of a screen displayed on an employee terminal. As shown in this figure, the display control unit 114 of the server device 10 may cause the terminal of the employee U2 or the job seeker U3 to display a screen (second screen) showing information about available positions corresponding to the positions. Here, this screen (second screen) may display one or more objects corresponding to the first skill information. The one or more objects may be displayed in a first display mode if the employee U2 or the job seeker U3 has the corresponding first skill information, and in a second display mode if the employee U2 or the job seeker U3 does not have the corresponding first skill information. Furthermore, when an object in the second display mode is operated by the employee U2 or the job seeker U3, learning content related to the first skill information corresponding to the operated object may be presented.

[0106] That is, in the example screen shown in FIG. 12, a tag-shaped object is displayed as an object corresponding to the first skill information. In the example screen shown in FIG. 12, tag TG4, which corresponds to the skill "language ability" possessed by employee U2, and tag TG5, which corresponds to the skills "statistics" and "spreadsheet" possessed by employee U2, are displayed in different ways. In the example shown in FIG. 12, the tags are displayed in different ways using solid and dashed lines, but the display manner of the tags (objects) may be set arbitrarily. For example, the display manner of the objects can be differentiated by an appropriate combination of different object colors, different object sizes, and different fonts of text written in the objects. In the example shown in FIG. 12, learning content may be presented by performing a predetermined operation on tag TG5.

[0107] FIG. 13 is an example of a screen displayed on an employee terminal. That is, in the example screen shown in FIG. 13, a predetermined link LNK is displayed in association with the skill "Statistics." Such a link LNK may be displayed based on various triggers. As an example, such a link LNK may be displayed based on employee U2 placing the cursor on the "Statistics" tag. Then, by operating such a link LNK, the following screen may be displayed.

[0108] FIG. 14 shows an example of a screen displayed on the employee terminal. As an example, the presentation unit 113 can present the identified learning content to employee U2 or job seeker U3 in a list format. That is, the screen shown in FIG. 14 displays learning content related to "statistics" (e.g., learning content CN1, learning content CN2, etc.). Upon viewing this screen, employee U2 can select the learning content he or she wishes to take and take the corresponding learning content. The method for providing learning content may be set arbitrarily. That is, the learning content identified by the identification unit 112 and presented by the presentation unit 113 may be video content, text content, or content that does not correspond to either of these.

[0109] Note that the explanation here shows a mode in which learning content is presented when a specified operation is performed on tag TG5, but the configuration may also be such that specified learning content is presented when a corresponding operation is performed on tag TG4 in the example shown in Figure 12.

[0110] Furthermore, when presenting such learning content, the following aspects may be adopted.

[0111] In one embodiment, the presentation unit 113 may present learning content provided by the organization and learning content provided by entities other than the organization in a manner that allows them to be distinguished from one another. In the example shown in FIG. 14, the presentation of learning content CN1 provided by entities other than the organization and learning content CN2 provided by the organization are controlled to be displayed differently. This presentation mode allows employees U2 and others to more easily understand the learning content they should take. Examples of control over the presentation mode include changing the color of the column or area indicating the learning content, or adding a specific tag or mark. Other examples include, but are not limited to, changing the font of text indicating the learning content. Display control means that highlight specific areas on the screen may be used to distinguish such learning content.

[0112] When presenting such learning content, a display showing correlation with the user or position may be provided as appropriate.

[0113] That is, in one aspect, the generation unit 116 of the server device 10 may generate first correlation information for each learning content based on at least the registration information about the employee U2 or job seeker U3 and information about the learning content. Here, the first correlation information may be information indicating the correlation between the employee U2 or job seeker U3 and the learning content. Then, the presentation unit 113 may present the learning content in association with the first correlation information.

[0114] In the example shown in FIG. 14, the degree of correlation between each learning content and employee U2 is displayed as a "match degree." By adopting this configuration, employee U2 viewing the screen can easily understand which learning content is best for him or her. While this example shows the first correlation information as a score such as a "match degree," a sentence or the like expressing the affinity of the learning content for employee U2 or job seeker U3 may also be used as the first correlation information.

[0115] The first correlation information may be generated by referencing reference information containing a correlation between registration information about employee U2 or job seeker U3 and information about the learning content. Here, the reference information may be a first correlation information generation model, which is a learning model trained to input registration information about employee U2 or job seeker U3 and information about the learning content and output the first correlation information. For example, the first correlation information generation model is a learning model trained using registration information about employee U2 or job seeker U3, information about the learning content, and the corresponding first correlation information as training data. The server device 10 may input the registration information about employee U2 or job seeker U3 and information about the learning content to the first correlation information generation model included in the artificial intelligence module (artificial intelligence unit 118) and cause the first correlation information generation model to output the first correlation information. The first correlation information generation model may be, for example, a learning model such as a language model or a large-scale language model, or an AI including a generation AI. In this case, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) an instruction to generate first correlation information based on registration information about employee U2 or job seeker U3 and information about the learning content, and a prompt containing the registration information about employee U2 or job seeker U3 and information about the learning content, and cause the artificial intelligence module (artificial intelligence unit 118) to output the first correlation information. Furthermore, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) a prompt containing, in addition to the instruction to generate the first correlation information and the registration information about employee U2 or job seeker U3 and information about the learning content, one or more sample combinations of registration information about employee U2 or job seeker U3 and information about the learning content, and one or more corresponding samples of first correlation information. The registration information about employee U2 or job seeker U3 here may be any information registered in the platform described above. For example, information about the resume document RD of employee U2 or job seeker U3 may be used to generate the first correlation information.

[0116] Note that, instead of or in addition to the first correlation information described above, the second correlation information described below may be displayed. Specifically, in one embodiment, the generation unit 116 of the server device 10 may generate second correlation information for each learning content based on at least information about available positions corresponding to the position and information about the learning content. Here, the second correlation information may be information indicating the correlation between the position and the learning content. Then, the presentation unit 113 may present the learning content in association with the second correlation information.

[0117] In other words, the second correlation information here can express the relationship between a position in the organization and the learning content. By adopting this type of configuration, employees U2 and the like who view the screen can easily understand which learning content they should take when considering affinity for the position, etc. Note that the second correlation information may be displayed as a score, as with the first correlation information, or in the form of a sentence or the like that expresses the affinity of the learning content for the position. As an example, a sentence such as "Recommended for applying for a marketing position" may be presented to employees U2 and the like as the second correlation information.

[0118] The second correlation information may be generated by referencing reference information containing a correlation between the available position information and information related to the learning content. Here, a second correlation information generation model, which is a learning model trained to input the available position information and information related to the learning content and output the second correlation information, may be used as the reference information. For example, the second correlation information generation model is a learning model trained using the available position information, information related to the learning content, and the corresponding second correlation information as training data. The server device 10 may input the available position information and information related to the learning content into the second correlation information generation model included in the artificial intelligence module (artificial intelligence unit 118) and cause the second correlation information generation model to output the second correlation information. The second correlation information generation model may be, for example, a learning model such as a language model or a large-scale language model, or may be an AI including a generation AI. In this case, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) an instruction to generate second correlation information based on the information about the available positions and the learning content, and a prompt that includes the information about the available positions and the learning content, and cause the artificial intelligence module (artificial intelligence unit 118) to output the second correlation information. Furthermore, the server device 10 may input to the artificial intelligence module (artificial intelligence unit 118) a prompt that includes, in addition to the instruction to generate the second correlation information and the information about the available positions and the learning content, one or more sample combinations of the information about the available positions and the learning content, and one or more corresponding samples of the second correlation information.

[0119] Additionally, when a learning content is presented, a tag corresponding to the learning content may be displayed. The screen shown in FIG. 14 shows a configuration in which tags related to each learning content are attached as tags TG6. Such tags TG6 may represent, for example, keywords related to each learning content. Keywords here may represent skills related to the learning content, or may represent the target audience for the learning content, the time required to take the learning content, the difficulty level of the learning content, the timing of registration for the learning content, etc. In other words, employees U2 and others who come across such tags TG6 can easily understand the nature of the presented learning content.

[0120] In this manner, the assigning unit 117 of the server device 10 may assign pre-prepared skill-related tags according to the content of the study content, and the presenting unit 113 may present the identified study content with the tags.

[0121] Such tagging of study content may be done manually, or may be done based on the functionality of server device 10 or the like.

[0122] In one embodiment, tags assigned to learning content preferably include tags that represent skills related to the learning content. In this case, the tagging unit 117 of the server device 10 may input the content of the learning content and a predetermined master that stores multiple skill-related tags into an artificial intelligence module, and extract tags to be assigned to the learning content from the predetermined master. The tagging unit 117 may then assign the extracted tags to the learning content.

[0123] More specifically, tags corresponding to the learning content may be extracted and displayed (presented) by a function of server device 10 (assignment unit 117). That is, server device 10 (assignment unit 117) may be configured to compare keywords (skill-related keywords) stored in a predetermined master with the content of the learning content, extract appropriate keywords from the master, and display the keywords in association with the learning content. Note that the function of artificial intelligence unit 118 may be utilized in this extraction. That is, first, the assignment unit 117 of server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to convert the content of the learning content into vector data (seventh vector data). Meanwhile, the assignment unit 117 of server device 10 instructs the artificial intelligence module (artificial intelligence unit 118) to similarly convert keywords present in the master into vector data (eighth vector data). The assigning unit 117 of the server device 10 then causes the artificial intelligence module (artificial intelligence unit 118) to extract keywords that are correlated with the content of the study content from the keywords in the master based on the proximity of the distance between the seventh vector data and the eighth vector data. Then, tags corresponding to the extracted keywords can be displayed in association with the study content. The proximity of the distance between the vector data may be evaluated using various methods, such as the angle between the two vectors (cosine similarity). The level of correlation may be scored, and the keywords may be displayed in descending order of their scores. A threshold value may be set for the score, and the display mode may be controlled based on the relationship with this threshold. The content of the study content may include one or more of the title of the study content, an outline of the study content, and a transcript of the study content.

[0124] The above description has been given in which one learning content is identified and presented to employee U2, etc. However, the identified and presented learning content may be a combination of multiple learning content. For example, multiple learning content systematically combined to acquire a specific theme or skill, or a combination of related learning content may be pre-set and used as the target of identification and presentation. Such a combination of learning content may be called a "course" or "package." The operation of combining such learning content may be performed manually or based on the functions of the server device 10. For example, a combination of highly related learning content may be automatically generated based on the functions of an artificial intelligence module (artificial intelligence unit 118).

[0125] After the learning content is presented to employee U2 or job seeker U3 in the manner described above, employee U2 or job seeker U3 can take the presented learning content as appropriate (activity A107). After that, acquisition unit 111 of server device 10 can acquire the learning content attendance history of employee U2 or job seeker U3 and register such attendance history (activities A108 and A109).

[0126] Based on such a course history, the following display control may be performed. That is, the display control unit 114 of the server device 10 may cause a person belonging to the organization to display a screen (first screen) showing a resume document RD for the employee U2 or the job seeker U3. Here, the screen (first screen) may display information corresponding to the acquired course history.

[0127] The following description will be given by showing a scene in which a position manager U1, who belongs to an organization, views a resume document RD.

[0128] Fig. 15 is an example of a screen displayed on the position manager terminal. Fig. 16 is an example of a screen displayed on the position manager terminal.

[0129] In the example screen shown in Figure 15, the skills corresponding to the learning content taken by employee U2 are listed under the heading "Knowledge and Learning." That is, the display control unit 114 of the server device 10 may display the skills corresponding to the learning content taken by employee U2 or job seeker U3 on the screen (first screen) showing the resume document RD. Note that the learning content taken by employee U2 or job seeker U3 may be displayed on the screen (first screen) showing the resume document RD.

[0130] In one embodiment, the skills displayed on the screen (first screen) showing the resume document RD may be displayed in association with the learning content that has been taken. In the example shown in FIG. 16, the display IND1 is displayed in association with the skill "statistics." In this way, the screen showing the resume document RD may display the learning content that has been taken in association with the skills. This allows an organization, etc. to more appropriately manage information about employees U2 and job seekers U3, and efficiently advance recruitment for positions. Note that the display IND1 may be displayed on the screen in response to various triggers. For example, the display IND1 may be displayed when a position manager U1, etc., places the cursor over the area corresponding to "statistics."

[0131] In one embodiment, the display control unit 114 of the server device 10 may display skills based on attendance at learning content and skills not based on attendance at learning content in a manner that allows them to be distinguished from each other on the screen (first screen) showing the resume document RD. That is, in the examples shown in FIGS. 15 and 16, skills based on attendance at learning content are placed in the "Knowledge & Learning" section, and skills not based on attendance at learning content (typically skills that employee U2 already possesses) are placed in the "Skills & Experience" section. By making each skill identifiable in this way, organizations can more appropriately manage information about employees U2 and job seekers U3 and efficiently recruit for positions. To distinguish each skill, for example, techniques such as differentiating the display style of tags representing each skill can be used. Furthermore, the method of differentiating the display style of tags may be similar to that described above.

[0132] The following screen may be displayed on an employee terminal or the like regarding the learning content that has been taken. FIG. 17 shows an example of a screen displayed on an employee terminal. This FIG. 17 shows the screen displayed when employee U2 displays a screen showing available position information for a specific position. For example, when employee U2 takes learning content related to "statistics," the object (tag) corresponding to the skill "statistics" may be configured to change from before taking the learning content. That is, while the "statistics" tag is displayed as tag TG5 in the screen shown in FIG. 12, the "statistics" tag is displayed as tag TG7 in the screen shown in FIG. 17. In this way, when employee U2 or job seeker U3 takes a specific learning content, the display of the skill corresponding to the learning content may be controlled so that it is clear that the learning content has been taken. FIG. 12 shows an example in which tag TG7 is displayed in a different manner from the aforementioned tags TG4 and TG5. Alternatively, when employee U2 or job seeker U3 takes a specific learning content, the display of the skill corresponding to the learning content may be controlled so that it is clear that the learning content has been taken. In this case, the tag indicated as "statistics" may be displayed in the same manner as the tag indicated as "language ability" (tag TG4).

[0133] In the example shown in FIG. 17, display IND2 is displayed in association with the skill "statistics." In this way, the learning content that has been taken may be displayed in association with the skills on the screen showing the information on available positions. This allows the employee to appropriately manage the learning content that they have taken when considering applying for a position. Display IND2 may be displayed on the screen in response to various triggers. As an example, display IND2 may be displayed when an employee U2 or a job seeker U3 places the cursor over the area corresponding to "statistics."

[0134] In addition, the following aspects may be adopted for the information processing of this embodiment.

[0135] That is, in this embodiment, the acquisition unit 111 of the server device 10 may acquire information about the interests of the employee U2 or the job seeker U3. Then, the identification unit 112 of the server device 10 may further identify one or more learning content items based on the acquired information about the interests.

[0136] In other words, while the above description illustrates a mode in which the identification unit 112 identifies learning content to present based on the acquired first skill information and second skill information, learning content may also be identified based on the interests of employee U2 and job seeker U3. In one mode, the learning content identified by the first skill information and second skill information may be narrowed down or supplemented based on information related to such interests. This makes it easier to present learning content that is relevant to the interests of employee U2 and job seeker U3, thereby increasing the motivation of employee U2 and job seeker U3 to learn.

[0137] The interest-related information may be any information that may be of interest to employee U2 or job seeker U3, and the acquisition route may be set arbitrarily. Meanwhile, in one aspect, the interest-related information may include information about skills that employee U2 or job seeker U3 is interested in and / or positions that employee U2 or job seeker U3 is interested in. In another aspect, the interest-related information may be acquired based on the employee U2 or job seeker U3 operating their own terminal. For example, the employee terminal screen shown in FIG. 11 displays objects such as object OBJ1 and object OBJ2, which indicate whether or not the employee is interested in a particular position. The interest-related information of employee U2 or job seeker U3 may be acquired based on the operation of such objects displayed on the terminal screen. Alternatively, the interest-related information of employee U2 or job seeker U3 may be acquired by conducting a survey or the like on employee U2 or job seeker U3. When identifying learning content using the learning content identification model described above, such interest-related information may be input into the learning content identification model. The instruction to identify the learning content may also include an instruction to refer to information related to the interest.

[0138] Furthermore, in connection with this embodiment, the following process control may be performed. That is, the process control unit 115 of the server device 10 may control the server device 10 to enable employee U2 or job seeker U3 to apply for a position and / or to send a scouting message regarding the position to employee U2 or job seeker U3, provided that employee U2 or job seeker U3 has a predetermined skill corresponding to the first skill information. As described above, the acquisition unit 111 of the server device 10 may acquire the learning content attendance history of employee U2 or job seeker U3. In this case, the process control unit 115 of the server device 10 may control the server device 10 to enable employee U2 or job seeker U3 to apply for a position and / or to send a scouting message regarding the position to employee U2 or job seeker U3, provided that the acquired attendance history includes a history of employee U2 or job seeker U3 attending predetermined learning content set for the position. This allows for personnel allocation and recruitment activities to be carried out taking into account the learning content attendance history.

[0139] In the example of FIG. 12, button BT5 is shown as a button used by employee U2 when applying for a specific position. Also, in the example of FIG. 15, button BT6 is shown as a button used by position manager U1 when scouting (sending a scout message to) specific employee U2. In this manner, in this embodiment, employee U2 or job seeker U3 may be able to apply for a position, or a scout message may be sent to employee U2 or job seeker U3. Here, in one aspect, the possibility of applying for a position or sending a scout message may be controlled according to predetermined conditions.

[0140] For example, control may be exercised to allow employee U2 or job seeker U3 to apply for a position or send a scout message on the condition that they possess a specific skill corresponding to the first skill information. For example, control may be exercised to allow employee U2 or job seeker U3 to apply for a position or send a scout message on the condition that they possess the "statistics" skill from the "desired skills" list in FIG. 10. Furthermore, control may be exercised to allow employee U2 or job seeker U3 to apply for a position or send a scout message on the condition that they have a history of attending any of the learning content related to the skill "statistics." ... certain learning content designated by position manager U1 or the like (e.g., one or more of the learning content shown in FIG. 14, etc.). Controlling whether or not an application or scout message can be sent may be achieved by various means. For example, whether or not an application or scout message can be sent may be controlled by switching the state of button BT5 or button BT6 between active and inactive depending on whether or not certain conditions are met.

[0141] The manner in which a scout message is sent may be set as appropriate. As an example, a scout message may be sent as follows: A position manager U1, upon viewing a screen displaying a resume document RD as shown in FIG. 15, can proceed with the operation to send a scout message by pressing a button BT6 provided on the screen. FIG. 18 shows an example of a screen displayed on a position manager terminal. In an exemplary embodiment, pressing button BT6 displays an area on the side panel of the screen where a scout message can be created and sent. This area may include, for example, a form F5 for specifying available position information for the position being filled, and a form F6 for setting (inputting) a scout message. After entering the required information, a position manager U1 or the like can press button BT7 to send a scout message to a designated employee U2 or the like. In an exemplary embodiment, by sending a scout message, the job details (job posting) for the available position information identified in form F5 may be configured to be viewable by the employee U2 or job seeker U3 to whom the scout message was sent.

[0142] The acquisition unit 111 may also acquire the learning content attendance history of each of the multiple employees U2 or each of the multiple job seekers U3. The display control unit 114 may then display information corresponding to the learning content attendance history of each of the multiple employees U2 or each of the multiple job seekers U3 on the screen of a terminal belonging to the organization.

[0143] This aspect will be described using an example in which predetermined information is displayed on the terminal of the position manager U1. FIG. 19 shows an example of a screen displayed on the terminal of the position manager. In the example screen shown in FIG. 19, narrowing-down conditions (search conditions) for employee U2 can be set in area Rg1, and the narrowing-down (search) results are displayed in area Rg2. That is, upon viewing the screen shown in FIG. 19, the position manager U1 can input predetermined search conditions into form F7 or form F8 and then press button BT8 to extract employees U2 who satisfy the predetermined conditions from among the employees U2 belonging to the organization. Information about the extracted employee U2 may be displayed in area Rg2. As an example, FIG. 19 shows an example in which the extracted employee U2 is displayed in card format. Note that while FIG. 19 shows an aspect in which a search is performed for employee U2, the search target may also be job seeker U3.

[0144] Information corresponding to the attendance history of acquired learning content may also be displayed on a screen displaying multiple employees U2 and job seekers U3. In other words, in the example shown in FIG. 19, information corresponding to the attendance history of each employee U2 (information shown under the heading "Knowledge / Learning") is displayed. By accessing this information corresponding to attendance history, the position manager U1 can properly identify employees U2 and job seekers U3 who are suitable for the position. As in the previous example, "information corresponding to attendance history" may refer to the attended learning content itself. Furthermore, if skills are represented as "information corresponding to attendance history," these skills may be displayed in association with the attended learning content. Other examples of "information corresponding to attendance history" may include the total number of attendances of learning content, the total time spent attending learning content, and the number of attendances of learning content within a fixed period for a given employee U2 or job seeker U3. Displaying such information may also help understand the learning motivation of the corresponding employee U2, etc. Furthermore, in this embodiment, on a screen displaying multiple employees U2 or job seekers U3, it may be possible to limit the display target or rearrange the display order according to this "information corresponding to the course history" (from one perspective, the display control unit 114 may perform filtering based on the information corresponding to the course history and then display a predetermined result on the terminal of an individual belonging to the organization). For example, employees U2 or job seekers U3 may be sorted in descending order of the total number of times they have attended the learning content or the total duration of attendance, or the results may be narrowed down to employees U2 or job seekers U3 whose total number of times they have attended the learning content or total duration of attendance is equal to or exceeds a predetermined value, and displayed to the position manager U1.

[0145] As described above, according to this embodiment, learning content is identified based on the first skill information and the second skill information, and the identified learning content is presented to employee U2 and job seeker U3. In this respect, this embodiment can be said to provide efficient learning opportunities that are tailored to the skill status of employee U2 and job seeker U3.

[0146] 4.Other Although the embodiment of the present invention has been described above, the present invention is not limited to this and can be modified as appropriate within the scope of the technical idea of ​​the invention.

[0147] In the above embodiment, the server device 10 performs various storage and control functions. However, multiple external devices may be used instead of the server device 10. That is, various information and programs may be distributed and stored in multiple external devices using blockchain technology or the like. The artificial intelligence unit 118 may be external to the server device 10. In this case, the external artificial intelligence unit 118 may be provided, for example, by an artificial intelligence service server and configured to receive input from each functional unit of the server device 10, receive requests to execute an artificial intelligence service, and return the instructed output as a processing result to the server device 10. The artificial intelligence service server may provide a service using a language model as a learning model, or may perform language processing tasks using a language model, and may provide an LLM, a generative AI, or an AI agent. The artificial intelligence service server may receive prompt input, for example, in the form of text, image, or voice, and generate and respond to the prompt. The server device 10 may also cooperate with an API (Application Programming Interface) of a service server that provides the generative AI, etc., to use the generative AI, etc.

[0148] At least one of the devices included in the information processing system 1 may be installed outside the country in which the functions of the information processing system 1 are performed.

[0149] In the above embodiment, learning content is presented in association with the display of available position information. However, server device 10 may present identified learning content to employee terminals or job seeker terminals at any time. For example, presentation unit 113 of server device 10 may present identified learning content in conjunction with the display of the aforementioned management screen (login screen). Note that in this case, the first skill information may correspond to skills required for positions of interest to employee U2 or job seeker U3, which have been previously acquired. As an example, on the platform provided by information processing system 1, employee U2 or job seeker U3 may register specific positions (so-called favorite registration). For example, learning content may be identified and presented based on the first skill information for such registered positions.

[0150] The aspect of this embodiment is not limited to the information processing system 1, and may be an information processing method or a program. The information processing method includes steps executed by the information processing system 1. The program causes a computer to execute the steps of the information processing system 1.

[0151] It may be provided in the following manner.

[0152] (1) An information processing system comprising at least one processor, the processor configured to execute the following steps by reading a program: a first acquisition step of acquiring first skill information and second skill information, wherein the first skill information is information regarding skills required for positions being recruited in an organization, and the second skill information is information regarding skills possessed by employees or job seekers belonging to the organization; a specification step of identifying one or more learning contents to be presented to the employee or job seeker based on the acquired first skill information and second skill information; and a presentation step of presenting the identified learning contents in a manner that can be understood by the employee or job seeker.

[0153] (2) In the information processing system described in (1) above, in the identification step, information regarding skills that are included in the first skill information but not included in the second skill information is extracted as deficient skill information, and learning content corresponding to the extracted deficient skill information is identified.

[0154] (3) In the information processing system described in (1) or (2) above, in a second acquisition step, a history of attendance of the learning content by the employee or the job seeker is acquired, and in a first display control step, a first screen showing a resume document for the employee or the job seeker is displayed to a person belonging to the organization, and information corresponding to the acquired attendance history is displayed on the first screen.

[0155] (4) In the information processing system described in (3) above, in the first display control step, skills corresponding to the learning content taken by the employee or job seeker are displayed on the first screen.

[0156] (5) In the information processing system described in (4) above, the skills displayed on the first screen are displayed in association with the learning content that has been taken.

[0157] (6) In the information processing system described in (4) or (5) above, in the first display control step, skills displayed based on taking the learning content and skills displayed without taking the learning content are displayed on the first screen in a manner that allows them to be distinguished from each other.

[0158] (7) In the information processing system described in any one of (1) to (6) above, in the third acquisition step, the attendance history of the learning content by each of a plurality of employees or each of a plurality of job seekers is acquired, and in the second display control step, information corresponding to the attendance history of the learning content by each of the plurality of employees or each of the plurality of job seekers is displayed on the screen of a terminal of a person belonging to the organization.

[0159] (8) In the information processing system described in any one of (1) to (7) above, in the first processing control step, the information processing system controls to enable the employee or the job seeker to apply for the position; and / or to enable the employee or the job seeker to receive a scout message regarding the position, on the condition that the employee or the job seeker has a predetermined skill corresponding to the first skill information.

[0160] (9) In the information processing system described in any one of (1) to (8) above, in the second acquisition step, the employee or job seeker's attendance history for the learning content is acquired, and in the second processing control step, the information processing system controls so that the employee or job seeker can apply for the position; and / or a scout message regarding the position can be sent to the employee or job seeker, provided that the acquired attendance history shows that the employee or job seeker has attended a specified learning content set for the position.

[0161] (10) In the information processing system described in any one of (1) to (9) above, in the third display control step, a second screen showing the recruiting position information corresponding to the position is displayed on the terminal of the employee or the job seeker, wherein one or more objects corresponding to the first skill information are displayed on the second screen, and the one or more objects are displayed in a first display mode if the employee or the job seeker has the corresponding first skill information, and in a second display mode if the employee or the job seeker does not have the corresponding first skill information, and when an object in the second display mode is operated by the employee or the job seeker, learning content related to the first skill information corresponding to the operated object is presented, the information processing system is configured.

[0162] (11) In the information processing system described in (10) above, in the fourth display control step, a link indicating a position correlated with the resume document for the employee or job seeker is displayed on the screen of the employee's or job seeker's terminal based on the correlation between the resume document for the employee or job seeker and the information on the available position, and in the third display control step, when the link is operated, the second screen is displayed on the employee's or job seeker's terminal.

[0163] (12) In the information processing system described in (11) above, in the first generation step, the registration information of the employee or the job seeker registered in a database is input into an artificial intelligence module to generate a resume document for the employee or the job seeker, in the second generation step, predetermined input information is input into the artificial intelligence module to generate the information on the available positions, and in the fourth display control step, the link is displayed on the screen of the terminal of the employee or the job seeker based on the correlation between the resume document generated in the first generation step and the information on the available positions generated in the second generation step.

[0164] (13) In the information processing system described in any one of (1) to (12) above, in the fourth acquisition step, information regarding the interests of the employee or job seeker is acquired, and in the identification step, one or more of the learning contents are further identified based on the acquired information regarding the interests.

[0165] (14) In the information processing system described in (13) above, the information regarding the interests includes information regarding skills that the employee or job seeker is interested in; and / or information regarding positions that the employee or job seeker is interested in.

[0166] (15) An information processing system according to any one of (1) to (14) above, wherein in the presentation step, learning content provided by the organization and learning content provided by entities other than the organization are presented in a manner that allows them to be distinguished from each other.

[0167] (16) In the information processing system described in any one of (1) to (15) above, in the third generation step, first correlation information is generated for each learning content based on at least registration information about the employee or job seeker and information about the learning content, wherein the first correlation information is information indicating the correlation between the employee or job seeker and the learning content, and in the presentation step, the learning content is presented in association with the first correlation information.

[0168] (17) In the information processing system described in any one of (1) to (16) above, in the fourth generation step, second correlation information is generated for each learning content based at least on available position information corresponding to the position and information regarding the learning content, wherein the second correlation information is information indicating the correlation between the position and the learning content, and in the presentation step, the learning content is presented in association with the second correlation information.

[0169] (18) In the information processing system described in any one of (1) to (17) above, in the assignment step, a pre-prepared skill-related tag is assigned according to the content of the learning content, and in the presentation step, the identified learning content is presented in a manner in which the tag is assigned.

[0170] (19) In the information processing system described in (18) above, the assignment step is performed by inputting the content of the learning content and a predetermined master storing multiple skill-related tags into an artificial intelligence module, extracting tags to be assigned to the learning content from the predetermined master, and assigning the extracted tags to the learning content.

[0171] (20) The information processing system according to any one of (1) to (19) above, further comprising: a server device having the processor; and a terminal that can access the server device.

[0172] (21) An information processing method, comprising steps executed by the information processing system according to any one of (1) to (20) above.

[0173] (22) A program for causing a computer to execute each step of the information processing system described in any one of (1) to (20) above. Of course, this is not the case.

[0174] Finally, while various embodiments of the present disclosure have been described, they are presented as examples and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. Such embodiments and modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the claims. [Explanation of symbols]

[0175] 1: Information processing system 2: Communication line 10: Server device 11: Control section 12: Storage section 13: Communications Department 14: Communication bus 20: User terminal 21: Control unit 22: Storage section 23: Communications Department 24: Input section 25: Output section 26: Communication bus 110: Registration Department 111: Acquisition Department 112: Specific part 113: Presentation part 114: Display control unit 115: Processing control unit 116 :Generation part 117: Granting section 118: Artificial Intelligence Department 210: Display control unit 211: Operation reception unit BT1~BT8: Buttons C1, C2, C21~C23: Column CN1, CN2: Learning content D10: Personnel Database DC1: First document component DC2: Second Document Component F1~F8: Form IND1, IND2:Display LNK: Link NT1~NT3: Notification OBJ1, OBJ2: Objects RD: Resume document RI1: Registration Information Rg1, Rg2: area TG1~TG7: Tags U1: Position Manager U2: Employee U3: Job seeker

Claims

1. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In a second acquisition step, a history of attendance of the learning content by the employee or the job seeker is acquired; In the first display control step, a first screen showing a resume document for the employee or the job seeker is displayed to a person belonging to the organization; Here, the first screen displays information corresponding to the acquired course history.

2. 2. The information processing system according to claim 1, In the first display control step, skills corresponding to learning content taken by the employee or job seeker are displayed on the first screen.

3. 3. The information processing system according to claim 2, An information processing system in which the skills displayed on the first screen are displayed in association with the learning content that has been taken.

4. 3. The information processing system according to claim 2, In the first display control step, skills displayed based on taking the learning content and skills displayed without taking the learning content are displayed on the first screen in a manner that allows them to be distinguished from each other, in an information processing system.

5. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In a third acquisition step, a history of attendance of the learning content by each of the plurality of employees or each of the plurality of job seekers is acquired; In the second display control step, the information processing system displays information corresponding to the attendance history of the learning content for each of the plurality of employees or each of the plurality of job seekers on the screen of a terminal of a person belonging to the organization.

6. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In the first processing control step, on the condition that the employee or the job seeker has a predetermined skill corresponding to the first skill information, An information processing system that controls whether or not to send a scout message so that a scout message regarding the position can be sent to the employee or the job seeker.

7. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In a second acquisition step, a history of attendance of the learning content by the employee or the job seeker is acquired; In the second process control step, on the condition that the acquired course history indicates that the employee or the job seeker has taken a course of a predetermined learning content set for the position, An information processing system that controls whether or not to send a scout message so that a scout message regarding the position can be sent to the employee or the job seeker.

8. 2. The information processing system according to claim 1, In the third display control step, a second screen showing information about the available position corresponding to the position is displayed on the terminal of the employee or the job seeker, Here, one or more objects corresponding to the first skill information are displayed on the second screen, the one or more objects are displayed in a first display mode when the employee or the job seeker has corresponding first skill information, and in a second display mode when the employee or the job seeker does not have corresponding first skill information; An information processing system configured such that when an object in the second display mode is operated by the employee or the job seeker, learning content related to first skill information corresponding to the operated object is presented.

9. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In the third display control step, a second screen showing information about the available position corresponding to the position is displayed on the terminal of the employee or the job seeker, Here, one or more objects corresponding to the first skill information are displayed on the second screen, the one or more objects are displayed in a first display mode when the employee or the job seeker has corresponding first skill information, and in a second display mode when the employee or the job seeker does not have corresponding first skill information; when the object in the second display mode is operated by the employee or the job seeker, learning content related to first skill information corresponding to the operated object is presented; In a fourth display control step, a link indicating a position correlated with the resume document of the employee or the job seeker is displayed on a screen of the terminal of the employee or the job seeker based on a correlation between the resume document of the employee or the job seeker and the information on the available position; In the third display control step, when the link is operated, the second screen is displayed on the terminal of the employee or job seeker.

10. 10. The information processing system according to claim 9, In a first generation step, registration information of the employee or the job seeker registered in a database is input into an artificial intelligence module to generate a resume document of the employee or the job seeker; In the second generation step, predetermined input information is input to an artificial intelligence module to generate the information on the available positions; In the fourth display control step, the information processing system displays the link on the screen of the employee's or job seeker's terminal based on the correlation between the resume document generated in the first generation step and the available position information generated in the second generation step.

11. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In a fourth obtaining step, information regarding the interests of the employee or the job seeker is obtained; The information processing system further includes, in the identifying step, identifying one or more of the learning contents based on the acquired information about interests.

12. 12. The information processing system according to claim 11, The information about the interests includes: The employee's or job seeker's skills of interest; and / or Information about positions of interest to said employee or said job seeker An information processing system comprising:

13. 2. The information processing system according to claim 1, In the presenting step, the information processing system presents the learning content provided by the organization and the learning content provided by entities other than the organization in a manner that allows them to be distinguished from each other.

14. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In the third generation step, first correlation information is generated for each of the learning content items based on at least the registration information about the employee or the job seeker and information about the learning content items; wherein the first correlation information is information indicating a correlation between the employee or job seeker and the learning content, In the presenting step, the learning content is presented in association with the first correlation information.

15. An information processing system, at least one processor; The processor is configured to execute the following steps by reading the program: In the first acquisition step, first skill information and second skill information are acquired; wherein the first skill information is information about skills required for a position being recruited in an organization, the second skill information is information about skills possessed by employees or job seekers belonging to the organization, In the identification step, one or more learning contents to be presented to the employee or the job seeker are identified based on the acquired first skill information and the acquired second skill information; In the identifying step, one or more learning content items are identified by the following (Process 1) or (Process 2): (Process 1) extracts information about skills that are included in the first skill information but not included in the second skill information as insufficient skill information, and identifies learning content that corresponds to the extracted insufficient skill information; (Process 2) refers to reference information that includes correlations between the first skill information, the second skill information, and the content of the learning content; In the presentation step, the identified learning content is presented in a manner that can be understood by the employee or the job seeker; In the fourth generation step, second correlation information is generated for each of the learning content items based on at least information about available positions corresponding to the positions and information about the learning content items; wherein the second correlation information is information indicating a correlation between the position and the study content, In the presenting step, the learning content is presented in association with the second correlation information.

16. 2. The information processing system according to claim 1, In the assigning step, a prepared skill-related tag is assigned in accordance with the content of the learning content; In the presenting step, the information processing system presents the identified learning content in a format in which the tag is attached.

17. 17. The information processing system according to claim 16, In the imparting step, The content of the learning content and a predetermined master that stores multiple tags related to skills are input into an artificial intelligence module, and tags to be assigned to the learning content are extracted from the predetermined master; An information processing system that assigns the extracted tags to the learning content.

18. 2. The information processing system according to claim 1, a server device having the processor; and a terminal that can access the server device.

19. An information processing method, comprising: An information processing method comprising the steps executed by the information processing system according to any one of claims 1 to 18.

20. A program, A program for causing a computer to execute each step of the information processing system according to any one of claims 1 to 18.

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