Information processing systems, information processing methods, and programs

Abstract

This invention provides an information processing system, method, and program that make recruitment activities more efficient. [Solution] In the information processing system 1, the control unit of the server device receives the designation of one or more second users U2 from the first user U1. The first user is an employer or recruitment agency handling job postings, and the second user is a candidate for employment for the job postings. The selection unit, upon receiving the designation, inputs predetermined input information into an artificial intelligence module to select a combination of job postings that the first user can send to the second users, which will be candidates for sending to one or more second users. The display control displays a first screen on the first user's terminal 20 that allows the user to perform the operation of sending job postings to one or more second users, and displays the selected combination of job postings in a manner that the first user can understand.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a technique in which a job-seeking company discloses job information to a personnel agency via the Internet.

Prior Art Document

Patent Document

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] On the other hand, there is still room for improvement in technologies for streamlining the recruitment process.

[0005] In view of the above circumstances, the present invention aims to provide an information processing system and the like that can further streamline the recruitment process.

Means for Solving the Problems

[0006] According to one aspect of the present invention, an information processing system is provided, comprising at least one processor, the processor configured to execute the following steps by reading a program, wherein in the reception step, the system receives the designation of one or more second users from a first user, the first user being an employer or recruitment agency handling job postings, and the second user being a candidate for employment for the job postings; in the selection step, triggered by the execution of the reception step, predetermined input information is input to an artificial intelligence module to select a combination of job postings that the first user can send to the second users from among the job postings that the first user can send to the second users; and in the display control step, a first screen is displayed on the first user's terminal that allows the operation of sending job postings to one or more second users, and the combination of job postings selected in the selection step is displayed on the first screen in a manner that the first user can understand.

[0007] In this configuration, an information processing system, etc., that can make recruitment activities more efficient will be provided. [Brief explanation of the drawing]

[0008] [Figure 1] This is a diagram showing the configuration of Information Processing System 1. [Figure 2] This is a block diagram showing the hardware configuration of server device 10. [Figure 3] This is a block diagram showing the hardware configuration of user terminal 20. [Figure 4] This is a block diagram showing the functions implemented by the server device 10 (control unit 11) and the user terminal 20 (control unit 21). [Figure 5] This is an activity diagram illustrating the information processing of this embodiment. [Figure 6] This is an example of a screen that may be displayed on the first user terminal. [Figure 7] This is an example of a screen that may be displayed on the first user terminal. [Figure 8] This is an example of a screen that may be displayed on the first user terminal. [Figure 9] This is an example of a screen that may be displayed on the first user terminal. [Figure 10] This is an activity diagram illustrating the information processing of this embodiment. [Figure 11] This is an example of a screen that may be displayed on the first user terminal. [Figure 12] This is an example of a screen that may be displayed on the first user terminal. [Figure 13] This is an example of a screen that may be displayed on the first user terminal. [Figure 14] This is an example of a screen that may be displayed on a second user terminal. [Figure 15] This is an example of a screen that may be displayed on the first user terminal. [Modes for carrying out the invention]

[0009] Embodiments of the present invention will be described below. The various features shown in the embodiments below can be combined with each other.

[0010] In other words, the information processing system of this embodiment is as follows. An information processing system, Equipped with at least one processor, The aforementioned processor is configured to perform the following steps by reading a program: In the registration step, the first user can specify one or more second users. The aforementioned first user is an employer or recruitment agency that handles job postings, The second user is a candidate for the job opening, In the selection step, triggered by the execution of the reception step, predetermined input information is entered into the artificial intelligence module to select a combination of job postings that the first user can send to the second user, and that can be used as a candidate to send to the second user (one or more job postings). In the presentation control step, a first screen that enables the terminal of the first user to perform an operation of transmitting job offers to the one or more second users is displayed on the terminal of the first user. An information processing system that causes the combination of job offers selected in the selection step to be displayed on the first screen in a manner that can be grasped by the first user.

[0011] By the way, the program for realizing the software appearing in one embodiment may be provided as a non-temporary computer-readable recording medium that can be read by a computer, may be provided so as to be downloadable 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] Also, in various information processing according to one embodiment, an input and an output corresponding to the input can be realized. Here, if an output is obtained as a result of the input, the form of the information (hereinafter referred to as reference information) referred to in such information processing is not limited. The reference information may be, for example, rule-based information such as a database, a lookup table, a predetermined function (including a judgment formula such as a regression formula constructed by a statistical method), a learned model in which the correlation between the input and the output has been learned in advance, a large language model capable of outputting a desired result by inputting a prompt (these models include parameters for constructing the correlation between the input and the output), or a generative AI such as a vision language model.

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

[0014] Furthermore, a circuit in a broad sense is a circuit realized by combining at least a suitable combination of circuits, circuits, processors, and memory. The processor may be a general-purpose processor or a dedicated circuit. In other words, it includes application-specific integrated circuits (ASICs), programmable logic devices (for example, simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)), etc.

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

[0016] <Information Processing System 1> Figure 1 is a configuration diagram representing an information processing system 1. The information processing system 1 shown as an example in Figure 1 comprises 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 communicate with each other via the communication line 2. Although Figure 1 shows one user terminal 20 used by a first user U1 and one user terminal 20 used by a second user U2, there may be multiple first users U1 and multiple second users U2. Furthermore, the connection between the server device 10 and the plurality of user terminals 20 may be wired or wireless.

[0017] In the example in Figure 1, the first user U1 is an employer or recruitment agency that handles job postings. In this embodiment, "employer" may include organizations such as for-profit corporations (e.g., companies), non-profit organizations (e.g., cooperatives, foundations, etc.), and public corporations (e.g., local governments, etc.). Furthermore, "employer" may be a person in charge of recruitment at the aforementioned types of corporations, and may include a person in charge of the organization's human resources department or a person in charge of the department that hires personnel. In this embodiment, "recruitment agency" is a person who supports the employer's recruitment activities (e.g., recruitment activities) and may be an organization or a person in charge that mediates the communication between the second user U2 (described later) and the employer. Such recruitment agencies are sometimes also called recruitment agencies, headhunters, or agents.

[0018] In the example in Figure 1, the second user U2 is a candidate for a job opening. Here, "candidate" may also be called, for example, "job seeker." A job seeker refers to various people who are looking for work, including, for example, currently employed people (those seeking a career change), unemployed people who wish to find a job, and prospective graduates (job seekers). The second user U2 (candidate) can receive information about job openings from the first user U1, such as through a scout message, and consider applying for the relevant job opening.

[0019] Furthermore, the user terminals 20 used by the first user U1 and the second user U2 may be referred to as "first user terminal," "second user terminal," etc.

[0020] In one embodiment, the information processing system 1 may provide a platform for use by a first user U1 and a second user U2. Such a platform may also be called a platform for recruitment. For example, the information processing system 1 provides and manages a talent matching platform or talent matching services used by the first user U1 and the second user U2. That is, from the perspective of the first user U1, recruitment activities of job seekers, etc., are supported via the server device 10, so it can be said that recruitment support services can be provided. Also, from the perspective of the second user U2, job-seeking activities are supported, so it can be said that job-seeking support services can be provided.

[0021] In one embodiment, the information processing system 1 consists 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.

[0022] <Server device 10> Figure 2 is a block diagram showing the hardware configuration of the server device 10. As shown in Figure 2, the server device 10 comprises a control unit 11, a storage unit 12, a communication unit 13, and a communication bus 14. The control unit 11, the storage unit 12, and the communication unit 13 are electrically connected within the server device 10 via the communication bus 14.

[0023] <Control Unit 11> The control unit 11 performs processing and control of the overall operation 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 predetermined programs stored in the memory unit 12. That is, information processing by software stored in the memory unit 12 is concretely 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. The server device 10 may also be composed of a combination of these.

[0024] <Storage section 12> The storage unit 12 stores various types of information as defined above. This can be done, for example, as a storage device such as a solid-state drive (SSD) that stores various programs related to the server device 10 executed by the control unit 11, or as memory such as random access memory (RAM) that stores temporarily necessary information (arguments, arrays, etc.) related to program calculations. The storage unit 12 stores various programs, variables, etc. related to the server device 10 executed by the control unit 11.

[0025] <Communications Department 13> The communication unit 13 preferably uses wired communication methods such as USB, IEEE1394, Thunderbolt®, and wired LAN network communication, but may also include wireless LAN network communication, mobile communication such as LTE / 5G, and Bluetooth® communication as needed. In other words, it is more preferable to implement it as a collection of these multiple communication methods. That is, the server device 10 may communicate various information from the outside via the communication unit 13 and the network.

[0026] The server device 10 may be on-premises or in a cloud environment. A cloud-based server device 10 may provide the above-mentioned functions and processing in the form of, for example, SaaS (Software as a Service) or cloud computing.

[0027] <User terminal 20> Figure 3 is a block diagram showing the hardware configuration of the user terminal 20. This user terminal 20 is the terminal used by the various users described above. As shown in Figure 3, the user terminal 20 comprises 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. The control unit 21, storage unit 22, communication unit 23, input unit 24, and output unit 25 are electrically connected within the user terminal 20 via the communication bus 26. The explanation of the control unit 21, storage unit 22, and communication unit 23 is the same as the explanation of each part in the server device 10, so it will be omitted.

[0028] <Input section 24> The input unit 24 receives operation inputs made by the user. The operation inputs are transmitted as command signals to the control unit 21 via the communication bus 26. The control unit 21 can perform predetermined controls or calculations based on the transmitted command signals as needed. The input unit 24 may be included in the casing of the user terminal 20 or it may be an external component. 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, the input unit 24 can be a switch button, mouse, trackpad, QWERTY keyboard, etc.

[0029] <Output section 25> The output unit 25 displays a graphical user interface (GUI) screen that can be operated by the user. The output unit 25 may be included in the casing of the user terminal 20 or it may be an external component. Specifically, the output unit 25 can be implemented as a display device such as a CRT display, liquid crystal display, organic EL display, or plasma display. It is preferable that these display devices be used in accordance with the type of user terminal 20.

[0030] Although Figure 1 shows an example where the user terminal 20 is a laptop PC (Personal Computer), the type of terminal used by the user terminal 20 is not particularly limited in this embodiment. That is, each user terminal 20 may be a desktop PC, laptop PC, smartphone, tablet, or any other type of information processing terminal.

[0031] 2. Functional Configuration This section describes the functional configuration of this embodiment. Information processing by software stored in the memory 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 (the processor provided by the information processing system 1).

[0032] Figure 4 is a block diagram showing the functions realized by the server device 10 (control unit 11) and the user terminal 20 (control unit 21).

[0033] As shown in Figure 4A, the server device 10 (control unit 11) may include a registration unit 110, a generation unit 111, a display control unit 112, a reception unit 113, a presentation unit 114, a subscription unit 115, an extraction unit 116, a notification unit 117, a transmission unit 118, a selection unit 119, and an artificial intelligence unit 120. As shown in Figure 4B, the user terminal 20 (control unit 21) may include a display control unit 210 and an operation reception unit 211.

[0034] <Registration Section 110> The registration unit 110 is configured to execute the 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 this embodiment, the registration unit 110 registers one or more of the first user U1 and the second user U2 as users of the platform. In this embodiment, the registration unit 110 may also register a resume or work history document showing the history and skills of the second user U2 in association with that user. Here, the work history document may be called a resume (resume document). Furthermore, in this embodiment, the registration unit 110 may register information about job postings on the platform based on terminal operations of the first user U1, etc. Here, the information about job postings may include, for example, information that can be written on a job posting, and may include information about the job type, annual salary, industry, job level (layer), required skills, employment type, work location, working hours, holidays, and salary. Furthermore, the information regarding job postings may also include information such as the location, number of employees, performance, and corporate culture of the employer providing the job posting. This information registered by the registration unit 110 can be stored in a predetermined memory area. That is, the registration unit 110 can be configured to store information handled by the server device 10, various terminals, etc., in its memory area. Examples of this memory area include the memory area (storage unit 12) of the server device 10 and the memory areas of various devices, but this memory area does not necessarily have to be within the system shown in Figure 1, and the registration unit 110 can also store various types of information in external memory devices, etc.

[0035] <Generation unit 111> The generation unit 111 is configured to execute the generation step. In the generation step, the generation unit 111 generates various information based on information acquired by the server device 10. In this specification, the term "generation" may be replaced with terms such as "creation" as appropriate. For example, the generation unit 111 generates text, documents, graphs, numerical values ​​(scores), figures, etc., based on the acquired information. In this embodiment, the generation unit 111 also generates a job posting group to which one or more job postings can be added. Here, the job postings that can be added to the job posting group are those that the first user U1 can send to the second user U2. The specific contents generated by the generation unit 111 will be explained later.

[0036] <Display Control Unit 112> The display control unit 112 is configured to execute a display control step. In the display control step, the display control unit 112 controls whether or not visual information can be displayed on each user terminal 20. Also in the display control step, the display control unit 112 generates various display information and controls it so that content that can be seen by the user is displayed. The display information may be the information itself that is generated in a manner that can be seen by the user, such as a screen, image, icon, or text, or it may be rendering information for displaying a screen, image, icon, text, etc. on various terminals. In the example of this embodiment, the display control unit 112 displays various screens related to the operation of sending a job posting on the terminal of the first user U1. For example, the display control unit 112 displays a screen (first screen) on the terminal of the first user U1 that allows the operation of sending a job posting to the second user U2. Other content that may be displayed on the user terminal 20 will be explained later.

[0037] <Reception Desk 113> The reception unit 113 is configured to execute the reception step. In the reception step, the reception unit 113 receives various information related to the information processing system 1. For example, the reception unit 113 receives a specification of predetermined information from the first user U1. Details of the information received by the reception unit 113 will be explained later.

[0038] <Presentation part 114> The presentation unit 114 is configured to perform a presentation step. In the presentation step, the presentation unit 114 presents various information to the users of various user terminals 20. This information may be presented based on visual information or auditory information. Details of the processing performed by the presentation unit 114 will be explained later.

[0039] <Joining section 115> The joining unit 115 is configured to execute the joining step. In the joining step, the joining unit 115 adds a predetermined job posting to the job posting group specified by the first user U1. The job posting group here may be prepared in various ways, but as an example, it may be the job posting group presented by the presentation unit 114 shown above.

[0040] <Extraction part 116> The extraction unit 116 is configured to perform the extraction step. In the extraction step, the extraction unit 116 extracts information that satisfies predetermined conditions from the acquired information. Details of this extraction process will be explained later.

[0041] <Notification section 117> The notification unit 117 is configured to execute notification steps. In the notification steps, the notification unit 117 provides various notifications to users related to the information processing system 1. In this embodiment, the notification unit 117 notifies the terminal of the first user U1 of predetermined information (such as information regarding changes). The specific aspects of the notification will be described later.

[0042] <Transmitting section 118> The transmission unit 118 is configured to execute a transmission step. In the transmission step, the transmission unit 118 transmits predetermined information to users related to the information processing system 1. In this embodiment, the transmission unit 118 transmits a job posting specified by the first user U1 to the second user U2. Details of the transmission process performed by the transmission unit 118 will be described later.

[0043] <Selection Department 119> The selection unit 119 is configured to execute the selection step. In the selection step, the selection unit 119 selects one or more pieces of information that satisfy predetermined criteria from among the information that can be handled. In this embodiment, when the reception unit 113 receives the designation of one or more second users U2, the selection unit 119 inputs predetermined input information into the artificial intelligence module, thereby selecting a combination of job postings that can be sent by the first user U1 to the second user U2, and that can be sent to one or more second users U2. Details of the processing performed by the selection unit 119 will be described later.

[0044] <Artificial Intelligence Department 120> The artificial intelligence unit 120 is configured to receive input from each functional unit and return the instructed output, thus constituting an artificial intelligence module. The artificial intelligence used by each functional unit of the server device 10 may be common to all units, or it may be prepared individually for each functional unit.

[0045] The artificial intelligence unit 120 may be an AI (Artificial Intelligence) equipped with learning models such as transformers 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), and language models such as recurrent neural networks (RNNs). The artificial intelligence unit 120 may be, for example, various language models, large-scale language models, general-purpose learning models, generative AI, or AI agents, and may include models provided by services such as OpenAI's GPT, Google's Gemini, and Microsoft's Azure AI Studio. Generative AI includes, for example, text generation AI, image generation AI, and multimodal generation AI. Furthermore, the learning model may be called an artificial intelligence model, machine learning model, or pre-trained model. In addition, the artificial intelligence unit 120 can include any pre-trained model.

[0046] Specific machine learning algorithms used to build trained models include nearest neighbors, naive Bayes, decision trees, support vector machines, and deep learning using neural networks. The artificial intelligence unit 120 can apply these algorithms as appropriate.

[0047] The artificial intelligence unit 120 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 consists of pairs of input data and output data (correct answer data) for training. Furthermore, the trained model may not only be one trained for a specific task, but also a general-purpose learning model that can be used universally for a wide range of tasks.

[0048] The artificial intelligence unit 120 may be a natural language model, and may include a general-purpose learning model such as a Large Language Model (LLM) that has been trained on a vast amount of data. An LLM is a learning model that has been pre-trained on a large amount of data consisting of text data, etc. (for example, (i) web content on the internet, or (ii) data stored in a predetermined database), and can perform various language processing tasks by being given a task. According to the given prompt, it can perform a wide range of natural language processing tasks, such as understanding sentence patterns and context, responding to questions, and generating sentences. Such a general-purpose learning model may include a pre-trained model that can handle various tasks without fine-tuning by using One-shot Learning or Few-shot Learning. Furthermore, a general-purpose learning model can also handle various tasks by Zero-shot Learning. The artificial intelligence used in each functional unit of the control unit 11 may be a separate pre-trained model, or it may be a common general-purpose learning model. A large 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 120 may include small-scale and medium-scale language models as trained models, which are smaller in scale than large-scale language models. Small-scale and medium-scale language models are natural language processing models that have been trained on less data compared to large-scale language models. In addition, the artificial intelligence unit 120 can include any machine learning model, deep learning model, artificial intelligence model, etc. The artificial intelligence unit 120 may be built on a system outside of the information processing system 1. Furthermore, the artificial intelligence unit 120 may be interactive (which may also be read as chat-type or conversation-type) in which it alternately receives input to produce an instructed output and generates and outputs information.

[0049] The trained model included in the artificial intelligence unit 120 can undergo additional training using methods such as transfer learning or fine-tuning. For example, the artificial intelligence unit 120 learns whether or not the output content has been modified by a user or the like. In other words, the artificial intelligence unit 120 may perform additional training and fine-tuning based on the modifications made to the content output by the trained model. Also, for example, whenever new data is registered, the artificial intelligence unit 120 may perform additional training and fine-tuning using this as new training data. This improves the accuracy of the information output from the trained model.

[0050] The learning model included in the artificial intelligence unit 120 may be a learning model (distilled model) obtained by knowledge distillation using the original trained model. In knowledge distillation, a trained model, such as a large-scale language model, is used as the teacher model, and the student model is trained by adjusting its parameters so that the loss of the student model's output relative to the teacher model's output (Soft Target Loss) is small, and that student model becomes the distilled model. Alternatively, the student model may be trained so that the loss of the student model's output relative to the correct labels (Hard Target) of the teacher data (combination of input data and output data of the learning model) is small. Compared to the original trained model (teacher model), the distilled model has similar performance to the trained model but with fewer parameters and a lower processing load. 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 that has been trained using combinations of input and output data from a large-scale language model as training data. Alternatively, when the information processing system 1 is introduced, a large-scale language model may be used as the learning model in each functional unit, and once training data from the large-scale language model has been accumulated, the distilled model obtained by knowledge distillation using that training data may be used as the learning model in each functional unit.

[0052] An AI agent may also be called an autonomous agent. An "AI agent" is a model that, upon input of a goal (objective, purpose, etc.) such as "teach me about XX" or a task such as "output XX," breaks down the processes necessary to reach the goal or accomplish the task into subtasks, actions, etc., and performs necessary data collection and analysis, program generation and execution, etc. The AI ​​agent takes the information and instructions input by the user as its goal, autonomously selects and executes tasks and actions according to the goal, outputs information according to the goal, and does not require user intervention (operation input). Furthermore, the AI ​​agent may autonomously plan and execute, evaluate the execution results itself, and autonomously learn in order to achieve the goal. For example, the AI ​​agent may autonomously update itself based on the execution results of subtasks (e.g., collected information, results of information analysis, etc.).

[0053] <Display Control Unit 210> The display control unit 210 of the user terminal 20 controls the display to show the 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 from users (first user U1, second user U2, etc.) using the user terminal 20.

[0055] 3. Information Processing Methods This section describes the information processing method for the server device 10, with examples. This information processing method may be executed by each part of the server device 10 as individual steps. The various characteristics shown in this section can be combined with each other as long as they do not create technical inconsistencies. As mentioned earlier, the first user U1 may be an employer or a recruitment agency handling job postings, but the following explanation will mainly use an example where the first user U1 is a recruitment agency.

[0056] Figure 5 is an activity diagram illustrating the information processing of this embodiment. As shown in the activity diagram in Figure 5, in the information processing method of this embodiment, operations such as adding job postings to a job posting group can be performed based on terminal operations by the first user U1.

[0057] Such job posting groups may be generated, for example, in response to terminal operations by a first user U1. For example, the first user U1 may request the generation of a job posting group by operating their terminal, and the reception unit 113 of the server device 10 may receive such a generation request, after which the generation unit 111 of the server device 10 may generate the job posting group (activities A101 to A103). Separately, the job posting group may also be generated by someone other than the first user U1 (for example, the platform operator). Such a job posting group may be capable of including one or more job postings. A job posting group may be, for example, a combination of multiple job postings.

[0058] The following describes the screens that may be displayed on the terminal of the first user U1 when generating such a group of job postings. Figure 6 is an example of a screen that may be displayed on the first user's terminal. In the example screen shown in Figure 6, various information related to the job posting group can be entered. Specifically, the "group name" can be entered in form F1, and various conditions related to the job posting group, such as "expected job type," "expected annual salary," and "memo," can be entered in forms (forms F2 to F4). The first user U1 can proceed with the job posting group generation process by entering the required information in each form and then clicking button BT1 or performing other operations. From one perspective, when generating such a group of job postings, the first user U1 can make various settings (condition settings) for the job posting group, and may add job postings that match the set conditions to the job posting group. The conditions set for the job posting group may also be related to the content described in relation to the job posting (content set in relation to the job posting). For example, in addition to the conditions related to the job type (expected job type) and annual salary (expected annual salary) mentioned above, you can also set conditions related to the job posting group or job posting, such as "industry," "job level," "work location," and "required skills."

[0059] The job posting group generated in this way may be displayed on the first user terminal (Activities A104-A105). Typically, the generated job posting group can be used by the first user U1 to aggregate job postings. In other words, the job posting group may be configured to include specific job postings. The process of adding such job postings will be described below.

[0060] In other words, in the example of information processing of this embodiment, a first user U1 specifies a predetermined job posting, which is received by the reception unit 113 of the server device 10, and the received job posting is linked to a job posting group, thereby enabling the job posting to be added.

[0061] The following approach is preferred for the process of adding such job postings. In this embodiment, the receiving unit 113 of the server device 10 may receive job postings registered on the platform. The presentation unit 114 of the server device 10 may present a group of job postings that are correlated with the job posting (first job posting) received by the receiving unit 113. Furthermore, the addition unit 115 of the server device 10 may add the job posting (first job posting) to the job posting group specified by the first user U1 from among the job posting groups presented by the presentation unit 114 (Activities A106~A112). For the purposes of explaining the job posting addition process here, the postings registered on the platform may be referred to as "first job postings" for convenience.

[0062] Figure 7 shows an example of a screen that may be displayed on the first user terminal. The screen shown in Figure 7 is typically used by the first user U1 to add a job posting to a job posting group. Specifically, area Rg1 of the screen shown in Figure 7 may be an operation area for identifying a job posting, and the job posting identified in response to the operation in area Rg1 can be added to the job posting group specified in area Rg2. In the example shown in Figure 7, area Rg1 shows a method for registering a predetermined job posting from a file (such as a PDF file) owned by the first user U1. For example, by operating button BT2 or by dropping a file containing details of the job posting into area Rg1, the file specified by the first user U1 can be registered to the platform. When registering a predetermined job posting to the platform, the registration may be performed in the format of the file prepared by the first user U1, or it may be converted to match a format pre-prepared on the platform before registration. For example, Figure 7 shows a state where a specified file is designated as "Job Posting 1". When registering "Job Posting 1" as a job posting on the platform, the information contained in the "Job Posting 1" file may be transcribed. In performing such a transcription, known technologies for deciphering the information contained in the "Job Posting 1" file (for example, Optical Character Recognition (OCR)) may be used. Furthermore, when registering the job posting after such transcription, a confirmation screen may be displayed on the first user terminal to request confirmation of the contents from the first user U1, as appropriate.

[0063] With the job postings to be registered specified in this manner, the process of adding the job postings can be advanced by further specifying one or more job posting groups shown in area Rg2 and pressing button BT3. In other words, this operation allows the job postings specified in area Rg1 to be added to the specified job posting groups. Note that the specification of job posting groups in area Rg2 may be done based on operations on various objects, such as the object OBJ1 shown. Figure 7 shows a configuration in which one job posting group is specified by object OBJ1, but it may be possible to specify multiple job posting groups by operating object OBJ1. In this case, pressing button BT3 will allow the job posting process to be added to each of the specified job posting groups. Also, Figure 7 shows a configuration in which one job posting, "Job Posting 1," is added to a job posting group, but multiple job postings may be specified as targets for addition by the first user U1. In this case, pressing button BT3 will add each of the specified job postings to the specified job posting group.

[0064] Here, an example is shown in which a job posting registered by the first user U1 (the first user U1 itself) is added to a predetermined job posting group. However, the operation of registering a job posting and the operation of adding the registered job posting to the job posting group may be performed by different users. That is, the reception unit 113 of the server device 10 can accept a job posting registered by a person other than the first user U1 who is specified during the addition operation, as the first job posting. Here, "a different person" may be a person belonging to the same organization as the first user U1 who performs the operation of adding the job posting to the group, or a person belonging to a different organization. For example, a user A (person in charge) belonging to a predetermined recruitment agency can add a job posting registered by a user B (another person in charge) belonging to the same recruitment agency to a predetermined job posting group. Such an arrangement may be adopted by person A to streamline their own recruitment work, but separately, the content generated in response to the job posting addition operation may be configured to be shared within the same organization as appropriate.

[0065] To explain from another perspective, the first user U1 can specify a particular job posting and add it to the job posting group. The target of this specification may be a job posting that U1 is about to register, or a job posting that has already been registered (the person who made the registration may be the first user U1 who is adding the job posting to the job posting group, or a different person). The reception unit 113 of the server device 10 can then accept one or more of these as the first job posting.

[0066] Furthermore, when a job posting to be registered is specified, all job posting groups that can be joined by the operation of the first user U1 may be displayed on the screen, or the system may narrow down the list to only those job posting groups that have a correlation with the job posting, and then display the job posting groups on the screen. In the case of such narrowing, the display order may be adjusted as appropriate according to the level of correlation. For example, the following shows an embodiment in which the generation unit 111 generates first correlation information, and the display order may be adjusted according to the level of correlation corresponding to this first correlation information.

[0067] Here, in the screen displaying such a group of job postings, the following configuration may be adopted. That is, the generation unit 111 of the server device 10 may generate first correlation information showing the correlation between the first job posting and the job posting group, based on the information regarding the first job posting and the information regarding the job posting group, and the first reference information. The presentation unit 114 of the server device 10 may then present the generated first correlation information in association with the job posting group.

[0068] Specifically, the example shown in Figure 7 illustrates how various job posting groups are associated with a "match score." In other words, a first user U1, viewing the screen shown in Figure 7, can intuitively understand which job posting groups are correlated with the job posting they have specified. While a score like "match score" is typical of the first correlation information described above, it is also possible to present, in addition to or instead of such a score, text or shapes (e.g., ○, △, ×, etc.; the same applies to "shapes" below) that express the correlation between job postings and job posting groups as the first correlation information.

[0069] The generation of such first correlation information may be achieved by various means, but for example, it may be achieved as follows based on the functions of the artificial intelligence unit 120. That is, in this embodiment, the first reference information may include a first correlation information generation model, which is a machine learning model or a generation AI, that is capable of taking information about the first job posting and information about the job posting group as input and outputting the first correlation information. If the first correlation information generation model includes a learning model, the generation unit 111 may input information about the first job posting and information about the job posting group into the first correlation information generation model and execute a process to output the first correlation information using the first correlation information generation model. If the first correlation information generation model includes a generation AI, the generation unit 111 may input an instruction to create first correlation information based on information about the first job posting and information about the job posting group, as well as information about the first job posting and information about the job posting group, into the generation AI and execute a process to output the first correlation information using the generation AI.

[0070] The first reference information may include a set of parameters for generating the first correlation information from information about the first job posting and information about the job posting group. For example, the first reference information may be various pre-trained models. For example, the first reference information may include a first correlation information generation model which is a dedicated learning model or a general-purpose learning model that has been machine-trained to take information about the first job posting and information about the job posting group as input and output the first correlation information.

[0071] The first correlation information generation model may be included in the artificial intelligence unit 120. The first correlation information generation model, which is a dedicated learning model, may be constructed by training using, for example, data on the first job posting and the job posting group, and the corresponding data on the first correlation information, as training data. In such a first correlation information generation model, parameters calculated and tuned through training construct the correlation between the information on the first job posting and the job posting group, and the first correlation information. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0072] If the first correlation information generation model is a general-purpose learning model (for example, a language model such as a large-scale language model), the generation unit 111 inputs a prompt to the first correlation information generation model that includes information about the first job posting and information about the job posting group, and an instruction to take the information about the first job posting and information about the job posting group as input and output the corresponding first correlation information, causing the first correlation information generation model to output the first correlation information. The generation unit 111 may also generate a prompt that gives the first correlation information generation model an instruction to create the first correlation information and input the prompt to the first correlation information generation model. Alternatively, the generation unit 111 may input a prompt to the first correlation information generation model that includes, in addition to information about the first job posting and information about the job posting group and the instruction to create and output the first correlation information, inserts, for example, one or more samples of information about the first job posting and information about the job posting group, and one or more samples of the corresponding first correlation information, as examples, samples, or training data of input and output pairs. Here, parameters for constructing the first correlation information generation model and prompts containing instructions to output first correlation information corresponding to information about the first job posting and information about the job posting group construct the correlation between information about the first job posting and information about the job posting group and the first correlation information. Note that the general-purpose learning model may include a generative AI capable of generating arbitrary output information based on input information. The generative AI of the general-purpose learning model is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be performed.

[0073] Furthermore, when evaluating the correlation with such first job postings, the information about the job posting group referenced may be as follows: In other words, the information about the job posting group in this embodiment may include information set by the first user U1 for the job posting group and / or information about the job postings that belong to the job posting group.

[0074] In Figure 6 shown earlier, the first user U1 sets various conditions when registering a group of job postings. That is, "information regarding job posting groups" may include such information set (pre-registered) by the first user U1, and the correlation between job postings and job posting groups may be evaluated based on this information.

[0075] Furthermore, in the example shown in Figure 7, the number of job postings already included in each job posting group (number of registered job postings) is indicated. The "information regarding job posting groups" mentioned above may include information about the job postings included in such job posting groups, and the correlation between job postings and job posting groups may be evaluated based on such information.

[0076] Furthermore, the "information on job postings (information on the first job posting)" that may be referenced when generating the first correlation information may include various types of information that may be registered as a job posting. Here, the information on job postings may include, for example, information that may be written on a job posting, and may include information on job title, annual salary, industry, job position (layer), required skills, employment type, work location, working hours, holidays, and salary. In addition, the information on job postings may also include information such as the location of the employer providing the job posting, the number of employees, performance, and corporate culture.

[0077] The first correlation information here may be generated by the functions of the artificial intelligence unit 120, etc., as follows. For example, first, the generation unit 111 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to convert the information related to the first job posting into vector data. At the same time, the generation unit 111 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to similarly convert the information related to the job posting group into vector data. Then, the generation unit 111 of the server device 10 may cause the artificial intelligence unit 120 (artificial intelligence module) to generate the first correlation information based on the closeness of the distance between the two sets of vector data. The closeness of the distance between the vector data here may be evaluated by various methods, for example, by the angle between the two vectors (cosine similarity). The level of correlation here may also be scored, and the score representing the level of correlation may be displayed on the screen of the terminal of the first user U1.

[0078] As described above, a specified job posting can be added to a job posting group, but the following methods may be adopted in relation to such an addition operation.

[0079] In other words, the extraction unit 116 of the server device 10 in this embodiment may extract from the platform a second job posting that correlates with the first job posting received by the reception unit 113 or the job posting group specified when performing the subscription operation. The display unit 114 of the server device 10 may then display the extracted second job posting on the terminal of the first user U1.

[0080] Figure 8 shows an example of a screen that may be displayed on the first user terminal. The screen shown in Figure 8 is displayed when "Job Posting 1" is added to a predetermined job posting group ("Finance (Manager and above)"). In other words, the extraction unit 116 can extract job postings that correlate with the aforementioned first job posting ("Job Posting 1") or the job posting group to which it has been added ("Finance (Manager and above)"). In the example shown in Figure 8, it is possible to check each of the extracted job postings using the checkbox CB1, and by checking this box and then pressing the button BT5, the specified job posting can be further added to the job posting group. In the example shown in Figure 8, a button BT4 is provided associated with each of the job postings. When such a button BT4 is pressed, the user may be redirected to a screen where they can view the details of the corresponding job posting.

[0081] The extraction of the second job posting may be performed by referring to reference information that includes the correlation between the first job posting or a specified group of job postings and the second job posting. The reference information here may include a set of parameters for generating the second job posting from the first job posting or a specified group of job postings. For example, the reference information may include various pre-trained models. For example, the reference information may include a dedicated training model or a general-purpose training model, which is a second job posting extraction model, that takes the first job posting or a specified group of job postings as input and outputs the second job posting. In this case, the extraction unit 116 inputs the first job posting or a specified group of job postings into the second job posting extraction model and causes the second job posting extraction model to output the second job posting.

[0082] The second job posting extraction model may be included in the artificial intelligence unit 120. The second job posting extraction model, which is a dedicated learning model, is a learning model that has learned using the first job posting or a specified group of job postings and the corresponding second job postings as training data. In such a second job posting extraction model, parameters calculated and tuned through learning build a correlation between the first job posting or the specified group of job postings and the second job postings. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0083] If the second job posting extraction model is a general-purpose learning model (for example, a language model such as a large-scale language model), the extraction unit 116 may input a prompt to the artificial intelligence unit 120 (artificial intelligence module) that includes an instruction to output correlated job postings (second job postings) from the first job posting or a specified job posting group, and the first job posting or the specified job posting group, causing the extraction unit 116 to output job postings correlated with the first job posting or the specified job posting group (second job postings). Alternatively, the extraction unit 116 of the server device 10 may generate a prompt that gives an instruction to the second job posting extraction model to create a second job posting, and input this prompt to the second job posting extraction model. In addition to the instruction to output a second job posting and the first job posting or the specified job posting group, the artificial intelligence unit 120 (artificial intelligence module) may input a prompt that includes, for example, one or more samples of the first job posting or the specified job posting group and one or more samples of the extracted job postings corresponding to them. Here, parameters for constructing the second job posting extraction model and prompts containing instructions to output the second job posting corresponding to the first job posting or a specified group of job postings establish a correlation between the first job posting or the specified group of job postings and the second job posting. The general-purpose learning model may include a generative AI capable of generating arbitrary output information based on input information. The generative AI in the general-purpose learning model is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be performed.

[0084] The second job posting may be extracted as follows. First, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to convert the first job posting or the designated group of job postings into vector data. At the same time, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to similarly convert information about various job postings into vector data. Then, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to extract job postings (second job postings) that are correlated with the first job posting or the designated group of job postings from among the various job postings, based on the proximity of the two sets of vector data. The extracted job postings can then be displayed as second job postings on the screen of the terminal of the first user U1. The proximity of the vector data here may be evaluated by various methods, for example, by the angle between the two vectors (cosine similarity). Furthermore, the level of correlation here may be scored, and the second job postings may be displayed on the screen of the first user U1's terminal in order of the highest score. Also, the score representing the level of correlation may be displayed on the screen of the first user U1's terminal.

[0085] In the example shown in Figure 8, the "match score" for various job postings is displayed. Such a match score represents the degree of correlation and may be output along with the second job posting when extracting it. For example, the second job posting extraction model described above may be trained to output the degree of correlation. Furthermore, if the second job posting extraction model includes a generation AI, the instruction to output the second job posting may also include an instruction to output the degree of correlation. When presenting the extracted second job postings, in addition to such scores, or instead of such scores, it is also possible to present text, figures, etc., that express the degree of correlation between the job posting and the job posting group.

[0086] Furthermore, the following configuration may be adopted in relation to the process of adding job postings to job posting groups. That is, the receiving unit 113 of the server device 10 may receive the first job posting registered on the platform, while the generating unit 111 of the server device 10 may generate a new job posting group if there is no job posting group correlated with the first job posting received by the receiving unit 113.

[0087] The new job posting group here may be any group distinct from existing job posting groups. For example, a job posting group called "Other" may be created, and job postings that are difficult to classify into existing job posting groups (first job postings) may be added to the created job posting group. The addition of the first job posting to the job posting group here may be done automatically based on the function of the add-on unit 115 of the server device 10. On the other hand, the addition of the first job posting to the job posting group may be done based on terminal operation (manual operation) by the first user U1.

[0088] On the other hand, the newly generated job posting group may be correlated with the first job posting. The generation of a new job posting group correlated with the first job posting may be performed by referring to one or more of the following: registration information related to the first job posting, attributes of candidates who have an activity history related to the first job posting, activity history of candidates who have an activity history related to the first job posting, attributes of the generated job posting group, registration information of job postings belonging to the generated job posting group, attributes of candidates who have an activity history related to job postings belonging to the generated job posting group, activity history of candidates who have an activity history related to job postings belonging to the generated job posting group, attributes of the first user U1, and activity history of the first user U1.

[0089] For example, in the example shown in Figure 7, there is a job posting group called "Finance (Manager and above)," but there is no job posting group corresponding to "Member Class" in the financial industry. In such a case, if the first job posting is related to "Member Class in the financial industry," a new job posting group such as "Finance (Member Class)" can be generated. The generation unit 111 may automatically determine and generate a name (label) for the new job posting group based on the referenced information (industry, job title, job type, annual salary, etc.). For example, the server device 10 may input the referenced information into the artificial intelligence unit 120 (artificial intelligence module) and have it output a name (label) for the new job posting group. In this case, the artificial intelligence unit 120 may be, for example, a learning model such as a large-scale language model, or it may be an AI that includes a generation AI. In this case, the server device 10 may input an instruction to output a name (label) for the new job posting group based on the referenced information, and a prompt inserting the referenced information into the artificial intelligence unit 120 (artificial intelligence module), and have it output a name (label) for the new job posting group. The example given here illustrates a typical scenario when referring to the registration information (industry, job title, etc.) related to the first job posting. On the other hand, when the generation unit 111 generates a new job posting group and refers to the registration information related to the first job posting, it may also refer to other job posting information in addition to the industry and job title mentioned above. The "job posting information" here may be the same as that shown earlier. In addition, the following information may also be referred to when generating a new job posting group.

[0090] For example, "attributes of candidates with behavioral history related to the first job posting" may refer to the attributes of users who viewed the page displaying the first job posting (first candidate) or the attributes of users who applied for the first job posting (first candidate). The user attributes here may correspond to the candidate's registration information on the platform, and may be the same as, for example, "information about second user U2" (the same applies to the second candidate described later). Furthermore, "behavioral history of candidates with behavioral history related to the first job posting" may refer to the user's (first candidate's) job posting viewing history, application history, viewing time (time spent) on the job posting, search keyword input history, etc. Additionally, "attributes of a generated job posting group" may refer to the attributes of a job posting group that has already been generated based on the actions of first user U1, etc. The attributes here may be the same as "information about the job posting group" described above. Furthermore, "Registration information of job postings belonging to a generated job posting group" may refer to registration information (information about job postings) for job postings that have already joined a job posting group generated based on the actions of the first user U1, etc. Also, "Attributes of candidates with an action history related to job postings belonging to a generated job posting group" may refer to the attributes of users who viewed the page showing the relevant job posting (second candidate) or the attributes of users who applied for the relevant job posting (second candidate). Also, "Action history of candidates with an action history related to job postings belonging to a generated job posting group" may refer to the page viewing history and application history of the aforementioned users (second candidate). In addition, "Attributes of the first user U1" and "Action history of the first user U1" may refer to the registration information of the first user U1 on the platform, the action history of the first user U1 on the platform, etc.

[0091] When User 1 U1 is a recruiter, possible attributes that may be referenced include organizational size, number of employees, industry, job title, location of business, work location, year of establishment, and financial status. When User 1 U1 is a recruitment agency, possible attributes that may be referenced include organizational size, number of employees, location of business, work location, and areas of expertise. When User 1 U1 is a recruiter, possible behavioral history that may be referenced includes the history of messages sent to job candidates via the platform, the hiring history of candidates, and the history of compensation (annual salary) offered. When User 1 U1 is a recruitment agency, possible behavioral history that may be referenced includes the history of messages sent to job candidates via the platform, the history of introducing job candidates to recruiters, and the hiring history of introduced candidates (history of successful placements as a recruitment agency). Of course, the information that may be referenced when generating a new job posting group may be various other types of information not listed here.

[0092] In this manner, if a group of job postings correlated with the first job posting is generated, the joining unit 115 of the server device 10 may add the first job posting to the job posting group generated by the generation unit 111. The addition of the first job posting to the job posting group may also be done automatically based on the functions of the joining unit 115 of the server device 10. Alternatively, the addition of the first job posting to the job posting group may be done based on terminal operation (manual operation) by the first user U1.

[0093] Furthermore, the following methods may be adopted in relation to the process of adding job postings to a job posting group.

[0094] In other words, the receiving unit 113 of the server device 10 may accept the designation of the first job posting registered on the platform based on the message sent by the first user U1. The message is not limited to text; it may also be input as voice data or image data. The presentation unit 114 of the server device 10 may then present a group of job postings that are correlated with the first job posting received by the receiving unit 113 as a response to the message sent by the first user U1.

[0095] From one perspective, the designation of the first job posting and the presentation of job posting groups may be done via a designated chat screen. Figure 9 is an example of a screen that may be displayed on the first user terminal. Specifically, Figure 9 shows a message display area Rg3, where the message sent by the first user U1 (sent message SM) and the reply to the sent message (reply message RM) are displayed. The reply to the content sent to the first user U1 may typically be made based on the functions of the artificial intelligence unit 120 (particularly the AI ​​agent). In other words, the designation of the first job posting and the presentation of job posting groups may be done in a dialogue format with an AI such as an AI agent.

[0096] Figure 9 shows the process of registering the first job posting, joining a job posting group, and presenting the second job posting. The details of these processes may be the same as those described earlier, except that they are performed as a message sent by the first user U1 and as a response to that message. Figure 9 also shows an area Rg4 that displays a confirmation field for the job posting to be registered. Furthermore, Figure 9 shows an area Rg5 that displays a field showing candidates for the job posting group to be joined.

[0097] In other words, the example shown in Figure 9 illustrates a configuration in which a message SM sent by the first user U1 is input to the artificial intelligence unit 120, and a reply message RM (response) is output and presented.

[0098] The output of the reply message RM may be performed by referring to reference information that includes the correlation between the transmitted message SM and the reply message RM. The reference information here may include a set of parameters for generating the reply message RM from the transmitted message SM. For example, the reference information may include various trained models. For example, the reference information may include a dedicated trained model or a general-purpose trained model, which is a response output model that takes the transmitted message SM as input and outputs the reply message RM. In this case, the server device 10 inputs the transmitted message SM into the response output model and causes the response output model to output the reply message RM.

[0099] The response output model may be included in the artificial intelligence unit 120. The response output model, which is a dedicated learning model, is a learning model that has learned using the transmitted message SM and its corresponding reply message RM as training data. In such a response output model, parameters calculated and tuned through learning build a correlation between the transmitted message SM and the reply message RM. The dedicated learning model may also include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0100] If the response output model is a general-purpose learning model (for example, a language model such as a large-scale language model), the server device 10 may input a prompt to the artificial intelligence unit 120 (artificial intelligence module) that includes an instruction to output a reply message RM based on the sent message SM, and the sent message SM, causing the server device 10 to output a reply message RM correlated with the sent message SM. Alternatively, the server device 10 may generate a prompt that instructs the response output model to create a reply message RM, and input this prompt to the response output model. In addition to the instruction to output the reply message RM and the sent message SM, the server device 10 may input a prompt to the artificial intelligence unit 120 (artificial intelligence module) that includes, for example, one or more samples of the sent message SM and one or more corresponding samples of the reply message RM. Here, the parameters for constructing the response output model and the prompt that instructs the output of a reply message RM based on the sent message SM construct the correlation between the sent message SM and the reply message RM. The general-purpose learning model may include a generative AI capable of generating arbitrary output information based on input information. The general-purpose learning model generation AI is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be performed. The output reply message RM may be controlled to be displayed in region Rg3.

[0101] In this way, after processing related to the job posting group has been performed, the first user U1 can send the job posting to the second user U2. Figure 10 is an activity diagram illustrating the information processing in this embodiment.

[0102] In other words, when the first user U1 sends a job posting to the second user U2, a predetermined operation screen may be displayed. Specifically, the display control unit 112 of the server device 10 may display a predetermined operation screen in response to the first user U1's request to display the sending operation screen (Activities A201 to A204). That is, the display control unit 112 of the server device 10 may display a screen (first screen) on the first user U1's terminal that allows the user to perform the operation of sending a job posting to the second user U2. Here, the screen (first screen) may be configured to allow the user to specify one or more second users U2 to whom the posting will be sent, and one or more job postings to be sent. The screen displayed when sending a job posting to the second user U2 will be described below.

[0103] Figure 11 shows an example of a screen that may be displayed on the first user terminal. In the screen shown in Figure 11, it is possible to specify a job posting group in area Rg6, and the second user U2 that is correlated with the job posting group specified in area Rg6 is displayed in area Rg7. The first user U1, upon accessing such a screen, can specify the second user U2 to send the job posting by selecting at least some of the second users U2 displayed in area Rg7 (Activities A205-A206). The specification of the second user U2 may be made, for example, based on an operation on checkbox CB2. Here, the specification of the second user U2 may be made for a single second user U2 or for multiple second users U2. Among the extracted second users U2, those whose correlation level meets a predetermined criterion (for example, those above or exceeding a predetermined threshold) may be displayed with checkbox CB2 checked in advance, that is, already specified as second users U2 to send the job posting.

[0104] In relation to the screen shown in Figure 11, the second user U2 may be presented as follows, for example. That is, the extraction unit 116 of the server device 10 may extract a second user U2 that shows a correlation with the job posting group based on information about the second user U2, information about the job posting group, and second reference information. Then, the presentation unit 114 of the server device 10 may present the extracted second user U2 in association with the job posting group.

[0105] In other words, in the example screen shown in Figure 11, the system is controlled so that a second user U2, which is correlated with the specified job posting group based on the operation of object OBJ2, is displayed in area Rg7. The second user U2 displayed in area Rg7 here can be a candidate to whom the first user U1 sends job postings. That is, the receiving unit 113 of the server device 10 can receive a designation from the first user U1 regarding the second user U2 extracted by the extraction unit 116. This allows the job posting transmission operation to proceed efficiently.

[0106] The extraction of such a second user U2 may be achieved by various means, but for example, it may be achieved as follows based on the functions of the artificial intelligence unit 120. That is, in this embodiment, the second reference information may include a second user extraction model, which is a machine learning model or a generative AI, that takes information about the second user U2 and information about the job posting group as input and is capable of extracting second users U2 that show a correlation with the job posting group. The extraction unit 116 of the server device 10 may, if the second user extraction model includes a learning model, input information about the second user U2 and information about the job posting group into the second user extraction model and execute a process to have the second user extraction model extract second users U2 that show a correlation with the job posting group. Alternatively, if the second user extraction model includes a generative AI, the generative AI may be instructed to extract second users U2 that show a correlation with the job posting group based on information about the second user U2 and information about the job posting group, and the generative AI may be instructed to extract second users U2 that show a correlation with the job posting group and execute a process to have the generative AI extract second users U2 that show a correlation with the job posting group.

[0107] The second reference information may include a set of parameters for extracting second users U2 that show a correlation with a job posting group from information about second users U2 and information about job posting groups. For example, the second reference information may be various pre-trained models. For example, the second reference information may include a second user extraction model which is a dedicated learning model or a general-purpose learning model that is trained to take information about second users U2 and information about job posting groups as input and output second users U2 that show a correlation with a job posting group.

[0108] The second user extraction model may be included in the artificial intelligence unit 120. The second user extraction model, which is a dedicated learning model, may be constructed by training using, for example, data on second user U2 and job posting groups, and data on second user U2 that shows a correlation with the corresponding job posting groups, as training data. In such a second user extraction model, parameters calculated and tuned through training construct a correlation between the information on second user U2 and job posting groups, and second user U2 that shows a correlation with the job posting groups. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0109] If the second user extraction model is a general-purpose learning model (for example, a language model such as a large-scale language model), the extraction unit 116 inputs a prompt to the second user extraction model that includes information about the second user U2 and information about the job posting group, and an instruction to take the information about the second user U2 and the information about the job posting group as input and output a second user U2 that shows a correlation with the corresponding job posting group, causing the second user extraction model to output a second user U2 that shows a correlation with the job posting group. The extraction unit 116 may also generate a prompt that gives the second user extraction model an instruction to create a second user U2 that shows a correlation with the job posting group, and input the prompt to the second user extraction model. Furthermore, the extraction unit 116 may input prompts to the second user extraction model that include, for example, one or more samples of information about the second user U2 and information about job posting groups, and corresponding samples of the second user U2 that correlate with the job posting groups, as examples, samples, or training data of input and output pairs. Here, the parameters for constructing the second user extraction model and the prompts that include instructions to output the second user U2 that correlates with the job posting groups corresponding to the information about the second user U2 and information about job posting groups construct the correlation between the information about the second user U2 and information about job posting groups and the second user U2 that correlates with the job posting groups. Note that the general-purpose learning model may include a generative AI capable of generating arbitrary output information based on the input information. The generative AI of the general-purpose learning model is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be executed.

[0110] The "information regarding job posting groups" used when extracting such a second user U2 may include information set by the first user U1 for the job posting group and / or information about job postings that are members of the job posting group.

[0111] In other words, as previously stated, "information regarding the job posting group" may include information set (pre-registered) by the first user U1 for the job posting group, and the extraction process for the second user U2 may be performed based on such information. Furthermore, "information regarding the job posting group" may include information regarding job postings that are members of the job posting group. The information regarding job postings here may also include various types of information that can be registered as job postings. For example, the information regarding job postings here may include information that can be written on a job posting, and may include information regarding job title, annual salary, industry, position (layer), required skills, employment type, work location, working hours, holidays, and salary. In addition, the information regarding job postings may also include information such as the location, number of employees, performance, and corporate culture of the employer providing the job posting. The extraction process for the second user U2 may then be performed based on such information.

[0112] On the other hand, "information about the second user U2" may be the registration information that the second user U2 provides when registering on the platform. Typically, this may include the second user U2's desired conditions such as preferred industry and job type, their skills and experience, their desired annual salary, and their desired work location. Furthermore, "information about the second user U2" may also include information described in the second user U2's resume document. In addition, "information about the second user U2" may include the second user U2's activity history on the platform (e.g., job posting viewing history, application history, search history, etc.).

[0113] Furthermore, the following configuration may be adopted as a preferred approach. Specifically, when extracting the second user U2, the information regarding the job posting group may include information about the job postings that belong to the job posting group, such as information about the job title or industry. On the other hand, the information regarding the second user U2 may include information about the second user U2's desired conditions. In other words, by utilizing this information in the extraction process for the second user U2, the extraction of the second user U2 can be performed more reliably.

[0114] On the other hand, the following configuration may be adopted in relation to the extraction process of the second user U2. That is, the extraction unit 116 of the server device 10 may extract the second user U2 that shows a correlation with the job posting group based on vector data (first vector data) corresponding to information about the second user U2 and vector data (second vector data) corresponding to information about the job posting group. The presentation unit 114 of the server device 10 may then present the extracted second user U2 in association with the job posting group.

[0115] More specifically, first, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to convert information about the second user U2 into vector data. At the same time, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to similarly convert information about the job posting group into vector data. Then, the extraction unit 116 of the server device 10 instructs the artificial intelligence unit 120 (artificial intelligence module) to extract second users U2 that are correlated with the job posting group from among the various second users U2. The extracted second users U2 can then be displayed on the screen of the terminal of the first user U1. The proximity of the vector data here can be evaluated by various methods, for example, by the angle between the two vectors (cosine similarity). Furthermore, the level of correlation here may be scored, and the display control may be implemented so that the second users U2 are displayed on the screen of the terminal of the first user U1 in descending order of score. Additionally, a score representing the degree of correlation may be displayed on the screen of the first user U1's device.

[0116] From one perspective, the extraction unit 116 of the server device 10 may extract second users U2 that show a correlation with the job posting group, based on the degree of correlation between the second user U2 and the job posting group. The presentation unit 114 of the server device 10 may then present the extracted second users U2, further linked to the degree of correlation. That is, Figure 11 shows a score for each second user U2 indicating the degree of correlation with the designated job posting group as the "match score". By showing the degree of correlation in this way to the first user U1, it is possible to efficiently select second users U2 to whom job postings will be sent. Note that the degree of correlation presented to the first user terminal does not necessarily have to be presented as a score; it may be presented using a figure, text, or other means that allow the user to grasp the degree of correlation. The method of extracting second users U2 based on the degree of correlation described here can be applied to both the method using the second reference information and the method using conversion to vector data described above.

[0117] Furthermore, in relation to the extraction of such second users U2, the following configuration may be adopted. That is, after the execution of the extraction process for second users U2, if the total number of second users U2 that show a correlation with the job posting group changes, or if the configuration of second users U2 that show a correlation with the job posting group changes, the notification unit 117 of the server device 10 in this embodiment may notify the terminal of the first user U1 of the information regarding the change.

[0118] In the example shown in Figure 11, it is indicated that a "new match" has been generated as a notification (label) for the job posting group "Finance (Manager or above)" and the job posting group "Manufacturing Indirect Departments". In one embodiment, the server device 10 may store the history of the extraction process of second user U2 when a predetermined first user U1 specifies a job posting group. If a change (difference) occurs from this history, the notification unit 117 may notify the terminal of the first user U1 of information regarding such a change. The information regarding the change may indicate a change in the number (total number) of second users U2 that show correlation with the job posting group, or it may indicate a change in the composition of second users U2 that show correlation. Here, "change in composition" may refer to a change in each individual (component user) of the second users U2 that show correlation, for example, if one new user shows correlation and one user shows correlation decreases, the total number remains the same, but the composition of users has changed. Furthermore, the information regarding the change does not necessarily have to be displayed on the operation screen (first screen) for sending job postings. For example, information regarding such changes may be notified via a screen displayed when the first user U1 logs into the platform. Alternatively, the notification unit 117 of the server device 10 may display information regarding the changes on the first user's terminal in the form of a pop-up notification or push notification. When providing such pop-up or push notifications, information specific to the second user U2 related to the change (for example, part of the second user U2's registration information) may also be displayed. Furthermore, when various notifications are provided, links may be included in the notification content, and the system may be configured so that the user can navigate to a page displaying the corresponding second user U2's registration information by operating the link.

[0119] The information regarding the change may include a designated mark, symbol, image, etc. For example, in the example shown in Figure 11, a circular mark is placed in the area indicating the relevant candidate in a designated job posting group, and in the area indicating the corresponding candidate. In other words, the information regarding the change notified by the notification unit 117 may correspond to any display means that enables the first user U1 to grasp the change concerning a designated second user U2.

[0120] In the screen shown in Figure 11, after receiving the designation of the second user U2 who will send the job posting, a screen may be displayed for selecting the job posting to send. Figure 12 is an example of a screen that may be displayed on the first user's terminal.

[0121] In the example screen shown in Figure 12, the second user U2, who is the recipient, is displayed in column C1, and the job posting to be sent can be selected using checkbox CB3. Object OBJ3 is used to adjust the display of the job posting group to which it is attached. For example, based on the operation of object OBJ3, it is possible to adjust whether the column showing the job posting group is displayed in an expanded or collapsed state. After checking the specified job posting with checkbox CB3, the selected job posting can be sent to the designated second user U2 by pressing button BT6 or button BT7. The specific content that is sent will be explained later in relation to Figure 14, etc. Although Figures 11 and 12 show different examples of screens for sending job postings, the screen on which the operation to send job postings to the second user U2 can be performed (first screen) may be a single screen or may span multiple screens. Furthermore, the first screen here may be defined to include screens for operations performed by the first user U1 after pressing buttons BT6 or BT7. For example, when sending a job posting to the second user U2, operations such as creating a scout message and linking the job posting to the scout message may be performed, and the first screen can be defined as a screen that includes such operations.

[0122] As shown in Figure 12, on the screen where it is possible to send job postings (first screen), job postings that have joined a job posting group may be displayed in a manner that makes it possible to recognize that they belong to a job posting group. Figure 12 shows that "Job Posting 1," which was registered on the screen shown in Figure 7, has joined the job posting group "Finance (Manager and above)," and the first user U1 can select the job postings to send to the second user U2 while appropriately determining which job posting group each job posting belongs to.

[0123] In this screen (first screen), the first user U1 may be configured to allow them to specify both job postings that are part of a job posting group and job postings that are not part of a job posting group. For example, Figure 12 shows "Job Posting 10," which is not part of a predetermined job posting group, but can be sent to the first user U1 by the second user U2. Such job postings may also be registered on the platform using a predetermined method. The first user U1 can also send such job postings to the second user U2 based on operations such as checking the checkbox CB3.

[0124] Here, when performing the operation to send a job posting, the following configuration may be adopted. That is, the receiving unit 113 of the server device 10 in this embodiment can accept the designation of one or more second users U2 as an operation on the screen (first screen). Here, the generation unit 111 of the server device 10 may generate second correlation information showing the correlation between one or more second users and job postings, based on the information about the one or more second users U2 that have been designated, the information about the job postings, and the third reference information. The display control unit 112 of the server device 10 may then display the generated second correlation information in association with the job postings.

[0125] Specifically, Figure 12 shows a configuration in which the degree of correlation (score) with the second user U2 (in Figure 12, "ZZ AA") is displayed as the "match score" for various job postings. In other words, the first user U1, upon viewing the screen shown in Figure 12, can intuitively understand which job postings are correlated with the specified second user U2. While scores such as the "match score" are typical of the second correlation information described above, it is also possible to present text, symbols, shapes, color changes, etc., that represent the correlation between the second user U2 and the job postings as second correlation information, in addition to or instead of such scores.

[0126] The generation of such second correlation information may be achieved by various means, but for example, it may be achieved as follows based on the functions of the artificial intelligence unit 120. That is, in this embodiment, the third reference information may include a second correlation information generation model, which is a machine learning model or a generation AI, that is capable of taking information about one or more second users U2 and job postings that have been specified as input and outputting second correlation information. If the second correlation information generation model includes a learning model, the generation unit 111 may input information about one or more second users U2 and job postings that have been specified as input to the second correlation information generation model and execute a process to output second correlation information using the second correlation information generation model. If the second correlation information generation model includes a generation AI, the generation unit 111 may input an instruction to create second correlation information based on information about one or more second users U2 and job postings that have been specified, as well as information about one or more second users U2 and job postings that have been specified, to the generation AI and execute a process to output second correlation information using the generation AI.

[0127] The third reference information may include a set of parameters for generating second correlation information from information about one or more second users U2 that have been specified and information about job postings. For example, the third reference information may be various pre-trained models. For example, the third reference information may include a second correlation information generation model which is a dedicated learning model or a general-purpose learning model that has been machine-trained to take information about one or more second users U2 that have been specified and information about job postings as input and output second correlation information.

[0128] The second correlation information generation model may be included in the artificial intelligence unit 120. The second correlation information generation model, which is a dedicated learning model, may be constructed by training using, for example, data on one or more second users U2 that have been specified and information on job postings, and corresponding second correlation information data, as training data. In such a second correlation information generation model, parameters calculated and tuned through training construct a correlation between the information on one or more second users U2 that have been specified and information on job postings, and the second correlation information. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0129] If the second correlation information generation model is a general-purpose learning model (for example, a language model such as a large-scale language model), the generation unit 111 inputs a prompt to the second correlation information generation model that includes information about one or more specified second users U2 and job postings, and an instruction to take the information about one or more specified second users U2 and job postings as input and output the corresponding second correlation information, causing the second correlation information generation model to output the second correlation information. The generation unit 111 may also generate a prompt that gives the second correlation information generation model an instruction to create the second correlation information and input the prompt to the second correlation information generation model. Alternatively, the generation unit 111 may input a prompt to the second correlation information generation model that includes, in addition to the information about one or more specified second users U2 and job postings and the instruction to create and output the second correlation information, an example of input and output pairs, such as a sample of information about one or more specified second users U2 and job postings and a sample of one or more corresponding second correlation information. Here, parameters for constructing a second correlation information generation model and prompts including instructions to output second correlation information corresponding to information about one or more specified second users U2 and job postings build a correlation between the information about the specified second users U2 and job postings and the second correlation information. Note that the general-purpose learning model may include a generative AI capable of generating arbitrary output information based on input information. The generative AI of the general-purpose learning model is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be executed.

[0130] Furthermore, the following configuration may be adopted when performing the operation to send job postings. That is, the receiving unit 113 of the server device 10 in this embodiment can accept the designation of one or more second users U2 as an operation on the screen (first screen). Here, the generation unit 111 of the server device 10 may generate third correlation information showing the correlation between one or more second users U2 and job posting groups based on the information about the one or more second users U2 and job posting groups that have been designated, and fourth reference information. The display control unit 112 of the server device 10 may then display the generated third correlation information in association with job posting groups.

[0131] Specifically, Figure 12 shows a configuration in which the degree of correlation (score) with the second user U2 (in Figure 12, "ZZ AA") is displayed as the "match score" for a job posting group ("Finance (Manager and above)"). In other words, the first user U1, upon viewing the screen shown in Figure 12, can intuitively understand which job posting groups are correlated with the specified second user U2. While scores such as the "match score" are typical of the third-party correlation information described above, it is also possible to present text, symbols, shapes, color changes, etc., that represent the correlation between the second user U2 and the job posting group as third-party correlation information, in addition to or instead of such scores.

[0132] The generation of such third correlation information may be achieved by various means, but for example, it may be achieved as follows based on the functions of the artificial intelligence unit 120. That is, in this embodiment, the fourth reference information may include a third correlation information generation model, which is a machine learning model or a generation AI, that is capable of taking information about one or more second users U2 and information about job posting groups that have been specified as input and outputting third correlation information. If the third correlation information generation model includes a machine learning model, the generation unit 111 may input information about one or more second users U2 and information about job posting groups that have been specified as input to the third correlation information generation model and execute a process to output third correlation information using the third correlation information generation model. If the third correlation information generation model includes a generation AI, the generation unit 111 may input an instruction to create third correlation information based on information about one or more second users U2 and information about job posting groups that have been specified as input to the generation AI and execute a process to output third correlation information using the generation AI.

[0133] The fourth reference information may include a set of parameters for generating third correlation information from information about one or more second users U2 that have been specified and information about job posting groups. For example, the fourth reference information may be various pre-trained models. For example, the fourth reference information may include a third correlation information generation model which is a dedicated learning model or a general-purpose learning model that has been machine-trained to take information about one or more second users U2 that have been specified and information about job posting groups as input and output third correlation information.

[0134] The third correlation information generation model may be included in the artificial intelligence unit 120. The third correlation information generation model, which is a dedicated learning model, may be constructed by training using, for example, data on one or more second users U2 that have been specified and data on job posting groups, and corresponding third correlation information data, as training data. In such a third correlation information generation model, parameters calculated and tuned through training construct a correlation between the information on one or more second users U2 that have been specified and the information on job posting groups, and the third correlation information. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0135] If the third correlation information generation model is a general-purpose learning model (for example, a language model such as a large-scale language model), the generation unit 111 inputs a prompt to the third correlation information generation model that includes information about one or more specified second users U2 and job posting groups, and an instruction to take the information about one or more specified second users U2 and job posting groups as input and output the corresponding third correlation information, causing the third correlation information generation model to output the third correlation information. The generation unit 111 may also generate a prompt that gives the third correlation information generation model an instruction to create the third correlation information and input the prompt to the third correlation information generation model. Alternatively, the generation unit 111 may input a prompt to the third correlation information generation model that includes, in addition to information about one or more specified second users U2 and job posting groups and the instruction to create and output the third correlation information, an example of a sample of information about one or more specified second users U2 and job posting groups and a sample of one or more corresponding third correlation information, as an example, sample, or training data of input and output pairs. Here, parameters for constructing a third correlation information generation model and prompts including instructions to output third correlation information corresponding to information about one or more specified second users U2 and information about job posting groups construct a correlation between the information about one or more specified second users U2 and information about job posting groups and the third correlation information. Note that the general-purpose learning model may include a generative AI capable of generating arbitrary output information based on input information. The generative AI of the general-purpose learning model is a general-purpose generative AI that requires input instructions such as the content of the output information to be generated and the content of the task to be executed.

[0136] Furthermore, the "information regarding the second user U2," "information regarding job postings," and "information regarding job posting groups" used to generate the second and third correlation information can be the same as the information described earlier.

[0137] Furthermore, the following embodiments may be adopted in relation to the screen shown in Figure 12. That is, the screen used to submit a job posting (first screen) may display the registration information of the specified second user U2 (one or more second users U2). Figure 13 is an example of a screen that may be displayed on the first user terminal. In the example screen shown in Figure 13, the registration information for the second user U2 is displayed on the screen as registration information RI. Note that in the example screen shown in Figure 13, the corresponding fields are shown collapsed due to the operation of object OBJ3. The registration information RI displayed in Figure 13 may show all of the information registered on the platform regarding the specified second user U2, or it may show only a part of the registered information. In a typical embodiment, a resume document (resume) for the second user U2 can be displayed as registration information RI for the second user U2 on the screen used to submit a job posting (first screen). Typically, information about such a second user U2 (resume document, resume) may be displayed on the same screen as the job postings that the first user U1 may specify. This makes it easier to efficiently determine which job postings should be sent to the specified second user U2.

[0138] In this embodiment, the reception unit 113 of the server device 10 receives the designation of one or more second users U2 from the first user U1, performs a predetermined selection regarding job postings, and the selection results may be displayed on the first user terminal (Activities A207-A208). Specifically, the selection unit 119 of the server device 10 may, upon receiving the designation of one or more second users U2, input predetermined input information to the artificial intelligence unit 120 (artificial intelligence module) to select a combination of job postings that can be sent by the first user U1 to the second user U2, from among the job postings that the first user U1 can send to the second user U2. The display control unit 112 of the server device 10 may display a screen (first screen) on the first user U1's terminal that allows the operation to send job postings to one or more second users U2, and on this screen (first screen), the combination of job postings selected by the selection unit 119 may be displayed in a manner that the first user U1 can understand.

[0139] The display of the selection results can be configured in various ways, but for example, it may be as follows: On the screen of the first user terminal (first screen), one or more job postings may be set to be available for transmission to the second user U2 based on the switching status of a predetermined object. This setting of availability is not limited to operations on objects such as checkboxes, but may also be performed by swiping or voice input. For job postings that can constitute a combination of job postings selected by the selection unit 119, the switching status of such objects may be set to indicate that they will be transmitted to the second user U2. In a more typical embodiment, among the job postings that constitute a combination of job postings selected by the selection unit 119, job postings whose correlation with one or more second users U2 is above or exceeds a predetermined value may be displayed with the switching status of a predetermined object for that job posting indicating that it will be transmitted.

[0140] In the example shown in Figure 12, "Job Posting 1" and "Job Posting 10" are displayed with checkbox CB3 checked, indicating a high correlation with second user U2. In other words, by first user U1 specifying one or more second users U2, the job postings to be sent may be set without requiring any special operation from first user U1.

[0141] However, the method of presenting the job postings (combinations of job postings) selected by the selection unit 119 is not limited to this. For example, a screen showing the selected job postings may be displayed on the first user terminal. The display method here may be such that the selected job postings are displayed in a separate window or on a side panel of the screen.

[0142] Furthermore, the selection of job posting combinations may be achieved by various methods, but for example, it may be achieved as follows. That is, in this embodiment, the artificial intelligence unit 120 (artificial intelligence module) may include a job posting selection model which is a machine learning model or a generation AI that is capable of taking predetermined input information as input and outputting a combination of job postings. If the job posting selection model includes a machine learning model, the selection unit 119 of the server device 10 may input predetermined input information into the job posting selection model and execute a process to cause the job posting selection model to output a combination of job postings. On the other hand, if the job posting selection model includes a generation AI, the selection unit 119 of the server device 10 may input an instruction to select a combination of job postings based on predetermined input information, along with the predetermined input information, into the generation AI and execute a process to cause the generation AI to output a combination of job postings.

[0143] From one perspective, the selection unit 119 selects a combination of job postings based on predetermined reference information. The reference information here may include a set of parameters for selecting a combination of job postings from predetermined input information. For example, the reference information may be various pre-trained models. For example, the reference information may include a job posting selection model which is a dedicated learning model or a general-purpose learning model that has been machine-trained to be able to select a combination of job postings using predetermined input information as input.

[0144] The job posting selection model may be included in the artificial intelligence unit 120. The job posting selection model, which is a dedicated learning model, may be constructed, for example, by training with data of predetermined input information and corresponding job posting combinations as training data. In such a job posting selection model, parameters calculated and tuned through learning construct a correlation between predetermined input information and job posting combinations. The dedicated learning model may include a generative AI capable of generating answers not included in the training data. The generative AI of the dedicated learning model is a limited-use generative AI that does not require input of instructions such as the content of the output information to be generated or the content of the task to be executed.

[0145] If the job posting selection model is a general-purpose learning model (for example, a language model such as a large-scale language model), the selection unit 119 inputs a prompt to the job posting selection model that includes predetermined input information and an instruction to output a combination of job postings corresponding to the predetermined input information, causing the job posting selection model to output the combination of job postings. The selection unit 119 may also generate a prompt that gives the job posting selection model an instruction to output a combination of job postings and input this prompt to the job posting selection model. In addition to the predetermined input information and the instruction to select a combination of job postings, the selection unit 119 may also input a prompt to the job posting selection model that includes, for example, a sample of the predetermined input information and a sample of one or more corresponding combinations of job postings as examples, samples, or training data of input and output pairs. Here, the parameters that construct the job posting selection model and the prompt that includes an instruction to output a combination of job postings corresponding to the predetermined input information construct a correlation between the predetermined input information and the combination of job postings. The general-purpose learning model may include a generative AI capable of generating arbitrary output information based on the input information. Generative AI for general-purpose learning models is a general-purpose generative AI that requires input such as instructions on the content of the output information to be generated and the content of the task to be performed.

[0146] The predetermined input information used in this selection process may include various types of information that enable efficient selection of job postings. On the other hand, from the perspective of making it easier to select job postings more stably, the predetermined input information may include one or more of the following: registration information for the first user U1, activity history for the first user U1, registration information for one or more second users U2, activity history for one or more second users U2, and registration information for job postings. The registration information here may be the same as the registration content and attributes described earlier. In addition, the activity history for each user may include various operation histories performed by each user through the platform. For example, the login time to the platform, the frequency of sending messages, and the reply rate to received messages for the first user U1 and second user U2 may be included in the predetermined input information. Furthermore, if the first user U1 is a recruiter, the activity history may include the history of messages sent to job candidates, the hiring history of job candidates through the platform, and the history of compensation (annual salary) offers. Furthermore, if the first user U1 is a recruitment agent, the specified information may also include the history of messages sent to job candidates via the platform, the history of introducing job candidates to employers, and the hiring history of the introduced candidates (history of successful placements as a recruitment agent). Of course, the information that can be included in the specified input information may be various other types of information besides those shown here.

[0147] Furthermore, in one embodiment, the predetermined input information may include selection criteria for the combination of job postings set by the first user U1. In other words, the selection content by the selection unit 119 may be adjusted based on the selection criteria set by the first user U1. This makes it easier to ensure that the combination of job postings to be sent to the second user U2 is in line with the intentions of the first user U1.

[0148] While not necessarily limited to the following, the selection process involving such selection criteria may be as follows. For example, the display control unit 112 of the server device 10 may display a screen for the first user terminal to set the selection criteria. The first user U1, upon accessing the screen, can input predetermined information (selection criteria) to the screen, thereby pre-storing the selection criteria to be used in the selection process. The selection criteria here may be related to the registered content of the job postings, or they may be related to the behavioral characteristics of the first user U1 and the second user U2. For example, the first user U1 may set conditions such as "prioritize job postings in the high salary range," "prioritize job postings with a high probability of receiving a reply from the second user U2," "prioritize job postings with a high success rate (conversion rate) calculated from past performance," or "prioritize job postings that have recently been registered on the platform," or they may set weights (priorities) for multiple of these conditions. Furthermore, by using the selection criteria as part of the input information, the selection unit 119 makes it easier to select combinations of job postings that meet the selection criteria.

[0149] Furthermore, the following configuration may be adopted for the selection process described above. That is, the selection unit 119 of the server device 10 may select a combination of job postings such that the job postings constituting the combination have a predetermined correlation with each other. To explain using the example screen shown in Figure 12, it is preferable that the selection process is performed so that "Job Posting 1" and "Job Posting 10," which constitute the combination of job postings, are correlated with each other. By adopting such a configuration, the first user U1 can efficiently determine what to send to the second user U2. In addition, since the likelihood of sending job postings that are correlated with each other to the second user U2 increases, it becomes easier for the second user U2 to efficiently consider their own job postings. The correlation between job postings constituting the combination of job postings may be evaluated by various methods. For example, the correlation may be evaluated based on the proximity of the vector data corresponding to each job posting. The proximity of the vector data here may be evaluated by various methods, but for example, it may be evaluated by the angle between the two vectors (cosine similarity). In a typical embodiment, the aforementioned job posting selection model may be trained to select combinations of job postings such that the job postings constituting the combination have a predetermined correlation. Furthermore, if the job posting selection model includes a generating AI, the instructions for selecting combinations of job postings may include instructions for selecting combinations of job postings such that the job postings constituting the combination have a predetermined correlation. The degree of correlation between job postings may be displayed on the screen of the first user terminal as a score, text, or the like.

[0150] Furthermore, the following configuration may be adopted for the selection process described above. That is, the selection unit 119 of the server device 10 may select a combination of job postings by aggregating one or more first candidate job postings correlated with one or more second users U2 on a first evaluation axis, and one or more second candidate job postings correlated with one or more second users U2 on a second evaluation axis different from the first evaluation axis. By performing such a selection process, efficient selection of job postings becomes possible. In a typical configuration, the selection unit 119 can select a first candidate job posting on the first evaluation axis using one of the registration information relating to the first user U1 described above, and select a second candidate job posting on the second evaluation axis using one of the registration information relating to the job posting described above. Note that the evaluation axis is not limited to the registration information of job postings, etc. For example, indicators that predict results, such as the response prediction rate based on the past response record of the second user U2, or the decision prediction rate based on the past contract success record when the first user U1 is a recruitment agent, may be used as evaluation axes. Of course, there may be three or more evaluation axes used, in which case a third candidate job posting, a fourth candidate job posting, etc., may be selected. The selection unit 119 can then select a combination of job postings to be displayed on the screen of the first user U1's terminal by aggregating the various candidate job postings that have been selected. In a typical embodiment, the job posting selection model described above may be trained to select a combination of job postings by aggregating one or more first candidate job postings that are correlated with one or more second users U2 on the first evaluation axis, and one or more second candidate job postings that are correlated with one or more second users U2 on the second evaluation axis which is different from the first evaluation axis. Furthermore, if the job posting selection model includes a generation AI, the instruction to select a combination of job postings may include an instruction to select a combination of job postings by aggregating one or more first candidate job postings that are correlated with one or more second users U2 on the first evaluation axis, and one or more second candidate job postings that are correlated with one or more second users U2 on the second evaluation axis which is different from the first evaluation axis.

[0151] In the explanation so far, we have described a configuration in which one combination of job postings, such as "Job Posting 1" and "Job Posting 10," is displayed. However, there may be multiple (multiple types) combinations of job postings that can be displayed. That is, the selection unit 119 of the server device 10 in this embodiment may select multiple combinations of job postings. Here, each of the multiple combinations of job postings may be selected based on different evaluation criteria. The display control unit 112 of the server device 10 may display each of the multiple combinations of job postings selected by the selection unit 119 on the screen (first screen) of the first user terminal in a manner that the first user can understand.

[0152] The evaluation axis used here may be any of the evaluation axes described earlier. For example, the selection unit 119 may select a first combination based on the evaluation axis of "job type" of the second user U2, and a second combination based on the evaluation axis of "annual income" of the second user U2. The display control unit 112 may display each of the selected job offer combinations on the first user terminal in a manner that allows them to be identified from one another. Of course, the evaluation axes used are not limited to these, and multiple types of evaluation axes may be used in combination when selecting a combination of a particular job offer.

[0153] Furthermore, while the above description has shown a configuration in which combinations of job postings that can be sent to a single second user U2, "ZZ AA," are displayed, there may be multiple second users U2 who are designated by the first user U1. That is, the reception unit 113 of the server device 10 in this embodiment may accept designations of multiple second users U2 from the first user U1. Then, the selection unit 119 of the server device 10 in this embodiment may, upon receiving the designation of multiple second users U2, input predetermined input information into the artificial intelligence module to select combinations of job postings that are candidates to be sent to the multiple second users U2. Then, the display control unit 112 of the server device 10 in this embodiment may display a screen (first screen) on the terminal of the first user U1 that allows the operation of sending job postings to multiple second users U2. On this screen (first screen), the combinations of job postings selected by the selection unit 119 may be displayed in a manner that the first user U1 can understand.

[0154] In other words, as explained with respect to the screen shown in Figure 11, in one embodiment, multiple second users U2 can be specified. In this case, the selection unit 119 can select a combination of job postings based on the registration information of each of the specified multiple second users U2. When performing such selection processing, the following registration information may be used. That is, the selection unit 119 of the server device 10 may select a combination of job postings based on one or more of the following: registration information common to each of the multiple second users U2, and information obtained by averaging the registration information of each of the multiple second users U2. By performing such selection processing, it becomes possible to present a combination of job postings to the first user U1 more efficiently. Note that the selection processing when multiple second users U2 are specified may be performed by a different method. For example, the selection unit 119 may input the registration information of second user U2, "A" and "B," into the artificial intelligence unit 120 (artificial intelligence module) to select job postings (or combinations of job postings) corresponding to the registration information of "A" and "B." Then, by aggregating the contents of the selected job postings (or combinations of job postings), a combination of job postings to be displayed on the first user terminal may be determined.

[0155] As described above, once the recipient, the second user U2, and the job posting to be sent are identified, the first user U1 requests the sending of the job posting, and the sending unit 118 of the server device 10 can then process the sending of the job posting to the specified second user U2 (Activities A209~A211).

[0156] As shown in Figure 12 and other figures, the number of job postings sent to the second user U2 is not limited to one. In other words, in this embodiment, the transmission unit 118 of the server device 10 can send multiple job postings to the second user U2. Furthermore, when sending such multiple job postings, it is also possible to send all job postings that have joined a predetermined job posting group at once. That is, the transmission unit 118 of the server device 10 in this embodiment can send all job postings that have joined a job posting group specified by the first user U1 to one or more second users U2 designated as an operation on the screen (first screen) shown on the first user terminal.

[0157] When such a job posting is sent, the following screen may be displayed on the second user's terminal. Figure 14 is an example of a screen that may be displayed on the second user's terminal. In the screen shown in Figure 14, the first user U1 presents multiple job postings to the second user U2, and the first user U1 requests an interview from the second user U2. If button BT8 in Figure 14 is pressed, the user may be redirected to a screen where they can view details about the corresponding job posting.

[0158] As shown in Figure 14, a second user U2 can indicate their intentions to the first user U1 by pressing buttons BT9 and BT10. If button BT9 is pressed, the system may be configured to send a predetermined message back to the first user U1. The message sent back may be a pre-set message or a message entered by the second user U2 each time. In the latter case, a field for entering the message to be sent back may be displayed on the second user's terminal as appropriate. If button BT10 is pressed, in one embodiment, the content sent by the first user U1 may be hidden. Alternatively, if button BT10 is pressed, the system may be configured to send a message to the first user U1 indicating that the second user U2 intends to decline the interview.

[0159] Note that the screen shown in Figure 14 may be displayed when button BT7 in Figure 12 is pressed by the first user U1. For example, when button BT7 in Figure 12 is pressed, a job posting may be sent to the second user U2 without requiring any special message input from the first user U1.

[0160] In response to this, the transmission unit 118 of the server device 10 may send one or more job postings to the designated second user U2 after adding text information about the job postings entered by the first user U1. That is, although Figure 12 shows a configuration in which a button BT6 is used to create a message, when such a button BT6 is pressed, a screen for entering a message to be presented to the second user U2 may be displayed. When sending job postings to the second user U2, the content (text information) entered on such a screen may be shown to the second user U2 as appropriate. The text information here may convey the content of the job postings that the first user U1 sends to the second user U2, or it may convey to the second user U2 an application for an interview, etc.

[0161] On the other hand, after sending a job posting to the second user U2, the first user terminal may display a screen like the following. Figure 15 is an example of a screen that may be displayed on the first user terminal. Specifically, in the example screen shown in Figure 15, a message indicating that an interview has been requested (display IND1) is shown in association with the second user U2 who sent the job posting. By displaying such a message, it is possible to prevent the first user U1 from sending duplicate information to the second user U2. Note that the display indicating that a job posting has been sent is not limited to the form shown in Figure 15. For example, various display means that make it possible to identify the second user U2 who sent the job posting may be applied to the display on the first user terminal, such as graying out the area indicating the second user U2 who sent the job posting.

[0162] As described above, in the information processing method of this embodiment, predetermined information processing is performed, so it can be said that the transmission of job postings from the first user U1 to the second user U2 is made more efficient.

[0163] 4. Others Although embodiments of the present invention have been described above, the present invention is not limited thereto and can be modified as appropriate without departing from the technical spirit of the invention.

[0164] In the above embodiment, the server device 10 performed various storage and control functions, but instead of the server device 10, multiple external devices may be used. That is, various information and programs may be stored in a distributed manner across multiple external devices using blockchain technology or the like. Also, the artificial intelligence unit 120 may be an external component of the server device 10. In that case, the external artificial intelligence unit 120 may be provided by, for example, an artificial intelligence service server, and is configured to receive input from each functional unit of the server device 10, receive requests to execute artificial intelligence services, and return the instructed output as a processing result to the server device 10. The artificial intelligence service server may be a server that provides services using a language model as a learning model, or a server that executes language processing tasks using a language model, and may provide an LLM, generative AI, or AI agent. The artificial intelligence service server may, for example, receive prompt input in the form of text, images, or audio, and generate and respond to the prompt. The server device 10 may also cooperate with the API (Application Programming Interface) of the service server that provides generative AI, etc., and utilize the generative AI, etc.

[0165] In the above embodiment, a configuration was shown in which the first user terminal can specify the job postings to be sent to the second user U2 as the screen displayed on the first user terminal (first screen). In contrast, in one embodiment, the specification of job postings by the first user U1 may be omitted, and the job postings may be sent. For example, the reception unit 113 of the server device 10 may receive the specification of one or more second users U2 through terminal operation by the first user U1, and the selection unit 119 may select job postings (combinations of job postings) to be sent to the second user U2, and the selected job postings may be sent to the second user U2 without requiring any special operation from the first user U1. The specific processing performed by the selection unit 119 when selecting job postings (combinations of job postings) may be the same as described above.

[0166] At least one of the devices included in the information processing system 1 may be located outside the country in which the functions of the information processing system 1 are performed.

[0167] The embodiments of this model are not limited to the information processing system 1, but may also be an information processing method or a program. The information processing method comprises each step executed by the information processing system 1. The program causes a computer to execute each step of the information processing system 1.

[0168] The product may be provided in any of the following embodiments.

[0169] (1) An information processing system comprising at least one processor, wherein the processor is configured to perform the following steps by reading a program, wherein in the reception step, the system receives the designation of one or more second users from a first user, the first user is an employer or recruitment agency handling job postings, the second user is a candidate for employment for the job postings, in the selection step, triggered by the execution of the reception step, the system inputs predetermined input information into an artificial intelligence module to select a combination of job postings that the first user can send to the second user from among the job postings that the first user can send to the second user, and in the display control step, the system displays a first screen on the first user's terminal that enables the operation of sending job postings to the one or more second users, and on the first screen, the combination of job postings selected in the selection step is displayed in a manner that the first user can understand.

[0170] (2) An information processing system as described in (1) above, wherein the artificial intelligence module includes a job posting selection model which is a machine learning model or a generative AI that is trained to take the predetermined input information as input and output a combination of job postings, wherein in the selection step, if the job posting selection model includes the learning model, the predetermined input information is input to the job posting selection model and the process is executed to cause the job posting selection model to output a combination of job postings, and if the job posting selection model includes the generative AI, the process is executed to input an instruction to select a combination of job postings based on the predetermined input information and the predetermined input information to the generative AI and cause the generative AI to output a combination of job postings.

[0171] (3) An information processing system as described in (1) or (2) above, wherein the predetermined input information includes one or more of the following: registration information relating to the first user, activity history relating to the first user, registration information relating to one or more second users, activity history relating to one or more second users, and registration information relating to the job posting.

[0172] (4) An information processing system according to any one of (1) to (3) above, wherein the predetermined input information includes selection criteria for the combination of job postings set by the first user.

[0173] (5) An information processing system according to any one of (1) to (4) above, wherein in the selection step, the information processing system selects a combination of job postings such that the job postings constituting the combination of job postings have a predetermined correlation with each other.

[0174] (6) An information processing system according to any one of (1) to (5) above, wherein in the selection step, the system selects a combination of job postings by aggregating one or more first candidate job postings that are correlated with one or more second users on a first evaluation axis and one or more second candidate job postings that are correlated with one or more second users on a second evaluation axis different from the first evaluation axis.

[0175] (7) An information processing system according to any one of (1) to (6) above, wherein in the selection step, multiple combinations of job postings are selected, where each of the multiple combinations of job postings is selected based on different evaluation axes, and in the display control step, each of the multiple combinations of job postings selected in the selection step is displayed on the first screen in a manner that can be understood by the first user.

[0176] (8) An information processing system according to any one of (1) to (7) above, wherein in the reception step, the system receives the designation of multiple second users from the first user; in the selection step, triggered by the execution of the reception step, the system selects a combination of job postings to be sent to the multiple second users by inputting predetermined input information into an artificial intelligence module; and in the display control step, the system displays a screen on the first user's terminal as the first screen, which allows the system to perform the operation of sending job postings to multiple second users; and on the first screen, the system displays the combination of job postings selected in the selection step in a manner that can be understood by the first user.

[0177] (9) An information processing system as described in (8) above, wherein in the selection step, the system selects a combination of job postings based on one or more of the following: registration information common to each of the multiple second users, and information obtained by averaging the registration information of each of the multiple second users.

[0178] (10) An information processing system according to any one of (1) to (9) above, wherein the generation step generates a job posting group into which one or more job postings can be added, and the job postings that can be added to the job posting group are those that the first user can send to the second user, and the first screen displayed in the display control step displays the job postings that have been added to the job posting group in a manner that makes it possible to recognize that they have been added to the job posting group.

[0179] (11) An information processing system as described in (10) above, wherein the first screen displayed in the display control step is configured to allow the first user to specify both job postings that have joined the job posting group and job postings that have not joined the job posting group.

[0180] (12) An information processing system according to (10) or (11) above, wherein in the extraction step, a second user that shows a correlation with the job posting group is extracted based on information about the second user and information about the job posting group and predetermined reference information, and in the display control step, the extracted second user is displayed in association with the job posting group.

[0181] (13) An information processing system as described in (12) above, wherein the predetermined reference information includes a second user extraction model which is a machine learning model or a generative AI that takes information about the second user and information about the job posting group as inputs and is capable of extracting a second user that shows a correlation with the job posting group, and in the extraction step, if the second user extraction model includes the machine learning model, the information about the second user and information about the job posting group are input to the second user extraction model and a process is executed in which the second user extraction model extracts a second user that shows a correlation with the job posting group, and if the second user extraction model includes the generative AI, an instruction to extract a second user that shows a correlation with the job posting group based on the information about the second user and information about the job posting group, the information about the second user and information about the job posting group are input to the generative AI and a process is executed in which the generative AI extracts a second user that shows a correlation with the job posting group.

[0182] (14) An information processing system described in (12) or (13) above, wherein the information relating to the job posting group includes information set by the first user for the job posting group; and / or information relating to job postings that are members of the job posting group.

[0183] (15) An information processing system according to any one of (12) to (14) above, wherein the reception step receives a designation from the first user regarding the second user extracted in the extraction step.

[0184] (16) An information processing system according to any one of (1) to (15) above, wherein in the display control step, the first screen displayed has the ability to send one or more job postings to the second user set according to the switching status of a predetermined object, and among the job postings that constitute the combination of job postings selected in the selection step, job postings whose correlation with one or more second users is equal to or exceeds a predetermined value are displayed as being sent according to the switching status of the predetermined object for the job posting.

[0185] (17) An information processing system according to any one of (1) to (16) above, comprising a server device having the processor and a terminal that can access the server device.

[0186] (18) An information processing method comprising each step performed by the information processing system described in any one of (1) to (17) above.

[0187] (19) A program that causes a computer to perform each step of the information processing system described in any one of (1) through (17) above. Of course, this is not always the case.

[0188] Finally, while various embodiments relating to this disclosure have been described, these are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0189] 1: Information Processing System 2: Communication lines 10: Server device 11: Control Unit 12: Storage section 13: Communications Department 14: Communications bus 20: User terminal 21: Control Unit 22: Storage section 23: Communications Department 24: Input section 25: Output section 26: Communications bus 110: Registration Department 111: Generation part 112: Display Control Unit 113: Reception Department 114: Presentation part 115: Participation 116:Extraction part 117: Notification Department 118: Transmitter 119: Selection Department 120: Artificial Intelligence Department 210: Display Control Unit 211: Operation Reception Section BT1~BT10: Buttons C1: Column CB1~CB3: Checkboxes F1~F4: Form IND1 :Display OBJ1~OBJ3: Objects RI: Registration Information RM: Reply message Rg1~Rg7: area SM: Sent message U1: First User U2: Second User

Claims

1. An information processing system, Equipped with at least one processor, The aforementioned processor is configured to perform the following steps by reading a program: In the registration step, the first user can specify one or more second users. The aforementioned first user is an employer or recruitment agency that handles job postings, The second user is a candidate for the job opening, In the selection step, triggered by the execution of the reception step, predetermined input information is input to the artificial intelligence module, thereby selecting a combination of one or more job postings that can be sent to the second user from among the job postings that the first user can send to the second user. Here, the predetermined input information includes at least registration information relating to one or more second users and / or behavioral history relating to one or more second users, and registration information relating to the job posting. In the display control step, a first screen is displayed on the first user's terminal that allows the user to perform an operation to send job postings to one or more second users. An information processing system that displays the combination of job postings selected in the selection step on the first screen in a manner that can be understood by the first user.

2. In the information processing system described in claim 1, The artificial intelligence module includes a job selection model which is a machine learning model or a generative AI that is capable of taking the predetermined input information as input and outputting the combination of job postings. In the aforementioned selection step, If the job posting selection model includes the learning model, the predetermined input information is input to the job posting selection model, and the process is executed to output the combination of job postings using the job posting selection model. If the job posting selection model includes the generation AI, the information processing system inputs an instruction to select a combination of job postings based on predetermined input information, along with the predetermined input information, to the generation AI, and performs a process to output the combination of job postings using the generation AI.

3. In the information processing system described in claim 1, The information processing system further includes one or more of the following as predetermined input information: registration information relating to the first user and behavioral history relating to the first user.

4. In the information processing system described in claim 1, The predetermined input information further includes selection criteria for the combination of job postings set by the first user, in an information processing system.

5. In the information processing system described in claim 1, The selection step involves an information processing system that selects a combination of job postings such that the job postings constituting the combination have a predetermined correlation with each other.

6. In the information processing system described in claim 1, In the selection step, the predetermined input information is input to the artificial intelligence module, The first evaluation axis involves a process to select one or more first candidate job postings that are correlated with one or more second users, A process for selecting one or more second candidate job postings that correlate with one or more second users, using a second evaluation axis different from the first evaluation axis, The process involves aggregating the aforementioned first candidate job posting and the aforementioned second candidate job posting. An information processing system that selects the aforementioned combination of job postings by executing the following.

7. In the information processing system described in claim 1, In the selection step, the predetermined input information is input to the artificial intelligence module, The first evaluation axis involves a process to select a first combination of job postings that correlate with one or more second users, A process for selecting a second combination of job postings that correlate with one or more second users in a second evaluation axis different from the first evaluation axis, By performing this, multiple combinations of the aforementioned job postings are selected, The information processing system, in the display control step, displays each of the multiple job posting combinations selected in the selection step on the first screen in a manner that can be understood by the first user.

8. In the information processing system described in claim 1, In the aforementioned reception step, the first user designates multiple second users, In the selection step, triggered by the execution of the reception step, the predetermined input information is input to the artificial intelligence module to select a combination of job postings that will be sent to multiple second users. In the display control step, the first screen is displayed on the terminal of the first user, which is capable of performing the operation of sending job postings to multiple second users. An information processing system that displays the combination of job postings selected in the selection step on the first screen in a manner that can be understood by the first user.

9. In the information processing system described in claim 8, In the selection step, the information processing system selects the combination of job postings by inputting one or more of the registration information common to each of the multiple second users, and the average of the registration information of each of the multiple second users, into the artificial intelligence module as registration information relating to the one or more second users.

10. In the information processing system described in claim 1, In the generation step, a job posting group is created that can include one or more job postings. Here, the job postings that can be added to the aforementioned job posting group are those that the first user can send to the second user. An information processing system in which, on the first screen displayed in the display control step, job postings that have joined the job posting group are displayed in a manner that makes it possible to recognize that they have joined the job posting group.

11. In the information processing system according to claim 10, An information processing system in which the first screen displayed in the display control step allows the first user to specify both job postings that have joined the job posting group and job postings that have not joined the job posting group.

12. In the information processing system according to claim 10, In the extraction step, based on the information about the second user and the information about the job posting group, and predetermined reference information, a second user that shows a correlation with the job posting group is extracted. The information processing system, in the display control step, displays the extracted second user in association with the job posting group.

13. In the information processing system according to claim 12, The predetermined reference information includes a second user extraction model, which is a machine learning model or generative AI, that takes information about the second user and information about the job posting group as inputs and is capable of extracting a second user that shows a correlation with the job posting group. In the extraction step, If the second user extraction model includes the learning model, information about the second user and information about the job posting group are input into the second user extraction model, and the second user extraction model is used to extract a second user that shows a correlation with the job posting group. An information processing system that, if the second user extraction model includes the generating AI, inputs to the generating AI an instruction to extract a second user that shows a correlation with the job posting group based on information about the second user and information about the job posting group, and performs a process to have the generating AI extract a second user that shows a correlation with the job posting group.

14. In the information processing system according to claim 12, Information regarding the aforementioned group of job postings is, Information set by the first user for the job posting group; and / or Information about job postings that are part of the aforementioned job posting group. Information processing systems, including those mentioned above.

15. In the information processing system according to claim 12, An information processing system that, in the reception step, receives a designation from the first user regarding the second user extracted in the extraction step.

16. In the information processing system described in claim 1, In the display control step, On the first screen that is displayed, the ability to send the one or more job postings to the second user is determined by the switching status of a predetermined object, and An information processing system in which, among the job postings that constitute the combination of job postings selected in the selection step, job postings whose correlation with one or more second users is equal to or exceeds a predetermined value are displayed as having their switching status for the predetermined object of the job posting as being transmitted.

17. In the information processing system described in claim 1, A server device having the aforementioned processor, An information processing system comprising a terminal capable of accessing the aforementioned server device.

18. Information processing method, An information processing method comprising each step performed by the information processing system according to any one of claims 1 to 17.

19. It is a program, A program for causing a computer to perform each step of the information processing system described in any one of claims 1 to 17.

Images