Server device, control method for server device, and program

Abstract

We provide a server device that enables the determination of the success or failure of a project. [Solution] The server device comprises an acquisition means and a judgment control means. The acquisition means acquires project information and candidate information for each of the at least one candidate to be assigned to the project. The judgment control means inputs a prompt generated using the project information and the information for at least one candidate to be assigned to the project into a learning model. By inputting the prompt into the learning model, the judgment control means obtains a judgment result from the learning model regarding the outcome of the project if at least one candidate to be assigned to the project is assigned to the project. The judgment control means outputs the acquired judgment result.

Description

Technical Field

[0001] The present invention relates to a server device, a control method for the server device, and a program.

Background Art

[0002] There is a technology related to the selection of members participating in a project.

[0003] For example, Patent Document 1 describes facilitating the selection of project members while considering the personality characteristics of individual members. The information processing apparatus of Patent Document 1 includes a case information acquisition means, a member information acquisition means, a classification means, a designation means, and a provision means. The case information acquisition means acquires project case information including at least the characteristic information of the members constituting the project team. The member information acquisition means acquires the characteristic information of the members registered in advance. The classification means classifies a set of members including the members included in the project case information and the members registered in advance into a plurality of sets based on the attributes of skills and personality characteristics included in the characteristic information of each member. The designation means designates one or more members as reference members. The provision means provides, based on the classification result classified by the classification means, the information of the members belonging to the same set as each of the designated reference members as the information of candidate members.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When a project is implemented, members are assigned to handle the project's tasks. However, if the assignment of members is made without considering project information and individual member information (such as each member's personality, compatibility with others, and career plans), it is feared that the project's efficiency will suffer. When project efficiency deteriorates, it often leads to failure to meet initial deadlines and budgets, resulting in the project being deemed a failure.

[0006] From this perspective, there is a need to predict the success or failure of a project based on the assumption that members are assigned to the project, but no such technology or service exists.

[0007] Furthermore, Patent Document 1 only discloses technology related to the selection of project members. Therefore, applying the technology disclosed in Patent Document 1 will not satisfy the above requirements.

[0008] The primary objective of this invention is to provide a server device, a control method for the server device, and a program that contribute to determining the success or failure of a project. [Means for solving the problem]

[0009] According to a first aspect of the present invention, a server device is provided, comprising: an acquisition means for acquiring project information and candidate information relating to at least one candidate to be assigned to the project; and a determination control means for inputting a prompt generated using the project information and the candidate to be assigned to the project into a learning model, thereby acquiring a determination result relating to the project's outcome when at least one candidate is assigned to the project from the learning model, and outputting the acquired determination result.

[0010] A second aspect of the present invention provides a control method for a server device, comprising: an acquisition step of acquiring project information and candidate information relating to at least one candidate to be assigned to the project; and a determination control step of inputting a prompt generated using the project information and the candidate to be assigned to the project into a learning model, thereby acquiring a determination result regarding the project's outcome when at least one candidate is assigned to the project from the learning model, and outputting the acquired determination result.

[0011] A third aspect of the present invention is provided for a computer mounted on a server device to perform an acquisition process for acquiring project information and candidate information for each of the at least one candidate to be assigned to the project, and a determination control process for inputting prompts generated using the project information and the at least one candidate to be assigned to the project into a learning model, thereby acquiring a determination result regarding the project's outcome when the at least one candidate to be assigned to the project from the learning model, and outputting the acquired determination result. [Effects of the Invention]

[0012] According to each aspect of the present invention, a server device, a control method for the server device, and a program are provided that contribute to realizing the determination of the success or failure of a project. However, the effects of the present invention are not limited to those described above. The present invention may produce other effects in lieu of or in conjunction with the effects described above. [Brief explanation of the drawing]

[0013] [Figure 1] Figure 1 is a diagram illustrating the outline of one embodiment. [Figure 2] Figure 2 is a flowchart illustrating the overview of the operation of one embodiment. [Figure 3] Figure 3 shows an example of a schematic configuration of an information processing system according to the embodiment of this disclosure. [Figure 4] FIG. 4 is a diagram showing an example of a processing configuration of a server device according to an embodiment of the present disclosure. [Figure 5] FIG. 5 is a diagram showing an example of a display of a terminal according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a diagram showing an example of an employee management database according to an embodiment of the present disclosure. [Figure 7] FIG. 7 is a flowchart showing an example of the operation of an information providing control unit according to an embodiment of the present disclosure. [Figure 8] FIG. 8 is a diagram showing an example of a display of a terminal according to an embodiment of the present disclosure. [Figure 9] FIG. 9 is a diagram showing an example of a display of a terminal according to an embodiment of the present disclosure. [Figure 10] FIG. 10 is a diagram showing an example of a display of a terminal according to an embodiment of the present disclosure. [Figure 11] FIG. 11 is a diagram showing an example of a display of a terminal according to a modified example of an embodiment of the present disclosure. [Figure 12] FIG. 12 is a diagram showing an example of a hardware configuration of a terminal according to the present disclosure.

MODE FOR CARRYING OUT THE INVENTION

[0014] First, an overview of an embodiment will be described. Note that the reference numerals in the drawings appended to this overview are for convenience of each element as an example to assist understanding, and the description of this overview is not intended to be limiting in any way. Also, unless otherwise specified, the blocks described in each drawing represent a configuration of functional units, not a configuration of hardware units. The connection lines between the blocks in each figure include both bidirectional and unidirectional ones. The one-way arrow schematically shows the flow of the main signal (data) and does not exclude bidirectionality. In this specification and the drawings, for elements that can be similarly described, duplicate description may be omitted by attaching the same reference numerals.

[0015] The server device 100 according to an embodiment includes an acquisition unit 101 and a determination control unit 102 (see FIG. 1). The acquisition unit 101 acquires project information and candidate placement information regarding at least one or more candidate placement persons to be placed in the project (step S1 in FIG. 2). The determination control unit 102 inputs a prompt generated using the project information and at least one or more candidate placement person information into the learning model (step S2). By inputting the prompt into the learning model, the determination control unit 102 acquires, from the learning model, a determination result regarding the outcome of the project when at least one or more candidate placement persons are placed in the project (step S3). The determination control unit 102 outputs the acquired determination result (step S4).

[0016] The server device 100 acquires information on a project at a stage prior to the start of development and candidate placement information (for example, the personality and work philosophy of each employee) regarding employees (candidate placement persons) to be placed in the project. The server device 100 generates a prompt using the acquired project information and candidate placement information, and makes a determination regarding the outcome of the above project for a learning model (for example, a large language model). For example, the server device 100 simulates (predicts) the result (success or failure of the project) of the project when candidate placement persons are placed in the project. That is, determination of the success or failure of the project is realized.

[0017] Specific embodiments will be described in more detail below with reference to the drawings.

[0018] [First Embodiment] The first embodiment will be described in more detail with reference to the drawings.

[0019] [Configuration of System] FIG. 3 is a diagram showing an example of the schematic configuration of an information processing system according to an embodiment of the present disclosure. As shown in FIG. 3, the information processing system includes a server device 10.

[0020] Server device 10 is a server that provides various types of information to users. More specifically, server device 10 provides information related to projects such as system development. Server device 10 is installed, for example, on a network (on the cloud).

[0021] Users use terminals 20 such as smartphones and personal computers. Users operate terminals 20 to access server devices 10 and input various information into server devices 10. Alternatively, users operate terminals 20 to retrieve various information from server devices 10.

[0022] The devices shown in Figure 3 (server device 10, terminal 20) are connected by wired or wireless communication means and are configured to communicate with each other.

[0023] The configuration of the information processing system shown in Figure 3 is illustrative and not intended to limit the possible configurations. For example, the system may include multiple server devices 10. Load balancing and redundancy may be achieved by using multiple server devices 10.

[0024] [General operation] Next, we will describe the general operation of the information processing system according to the first embodiment.

[0025] In the first embodiment, the server device 10 will be described as performing a simulation of the results assuming that employees (regular employees, contract employees, etc.) of a company engaged in system development are assigned to a specific project. More specifically, the server device 10 assumes that specific employees have been assigned to a project before it is started, and makes a determination regarding the outcome of the project. For example, the server device 10 determines whether the project will be successful or not.

[0026] <Entering employee information> Users (employees) register their own information with the server device 10. Users access the server device 10 by operating the terminal 20 and register their own information (hereinafter referred to as employee information) with the server device 10. For example, employee information may be registered by the user answering a personality test or questionnaire. For example, the server device 10 may automatically acquire employee information by linking with the human resources system used within the company via an API (Application Programming Interface).

[0027] Specifically, users register employee information such as name, gender, date of birth, address, biometric information (e.g., facial image, fingerprint, iris, retina, voiceprint), telephone number, email address, employee number, department, position, and job title on the server device 10.

[0028] Furthermore, users register their qualifications, work experience, personality, and work ethic as employee information on the server device 10. For example, users register information on their qualifications in accounting / finance, legal, IT (Information Technology), marketing / sales, human resources / labor, management, language, quality control / production management, environment / safety, and technical fields on the server device 10. For example, users register their personality traits such as "short temper," "extroverted," "introverted," "sensing," "intuitive," "thinking," "feeling," "judging," and "perceiving" on the server device 10. For example, users register their work ethic such as "I want to demonstrate leadership in projects," "I want to work with person A," "I want to work in department B," and "I would like to work at an overseas branch in the future" on the server device 10.

[0029] <Generating a Learning Model> System administrators and others collect information about past projects (hereinafter referred to as "past project information"). For example, system administrators and others collect information such as detailed project content, information on the person in charge of the work, and project performance. For example, past project information may be collected automatically. Server device 10 may connect via API with various project management tools used within the company and automatically acquire information such as project progress, task assignments, and completion dates on a regular basis. Server device 10 may also connect via API with an internal document management system used within the company and acquire project-related documents on a regular basis, and analyze the acquired project-related documents using natural language processing technology to extract information about the detailed content and results of the project.

[0030] The project details include, for example, the project name, deadline, budget, estimated man-hours, client information, system specifications, and customer satisfaction. Information on the people involved in the project, such as their names and roles (project leader, team members), is also provided. Project results, such as the project completion date, final cost, and the man-hours worked by each member, are also included.

[0031] Alternatively, if a project is completed within the scheduled deadline and budget, it may be recorded as a "project success." Conversely, if the deadline is not met or the budget is exceeded, it may be recorded as a "project failure." Furthermore, if the project result is a "failure," the factors that led to the failure (e.g., budget overrun) may be collected as part of the past project information.

[0032] System administrators and others collect the above past project information for numerous projects.

[0033] System administrators use acquired past project information as training data to generate a learning model. For example, a system administrator might generate a Large Language Model (LLM). The system administrator generates a learning model that learns the relationship between the detailed content of past projects, information about the people in charge (characteristics; for example, personality, attitude towards work), and project performance. The generated learning model is implemented on the server device 10.

[0034] <Project success / failure assessment> Project managers, who are responsible for assigning tasks to carry out a project, input information about future projects (hereinafter referred to as "target project information") into the server device 10. For example, a project manager inputs the project name, deadline, budget, estimated man-hours, client information, and system specifications into the server device 10 as target project information.

[0035] Furthermore, the project manager inputs information (for example, names) that identifies the personnel (employees) to be assigned to the project into the server device 10. The server device 10 identifies the employees selected by the project manager from among the employees previously registered in the system.

[0036] The server device 10 acquires all or part of the employee information of the identified employee as "candidate information for placement." For example, the server device 10 acquires the qualifications of the employee selected by the project manager, the employee's personality, work ethic, etc., as candidate information for placement.

[0037] The server device 10 uses the acquired target project information and placement candidate information to obtain, using a learning model, the expected results when the placement candidate actually becomes the project's person in charge and the project proceeds.

[0038] For example, server device 10 inputs a prompt to the large-scale language model such as, "Based on the target project information and the candidate information, predict the success or failure of the project if the candidate becomes the project's project manager." Server device 10 then presents the results obtained from the large-scale language model (project success or failure prediction) to the project manager. The project manager then decides on the new project members based on the presented results.

[0039] Next, we will describe the details of each device included in the information processing system according to the first embodiment.

[0040] [Server equipment] Figure 4 is a diagram showing an example of the processing configuration (processing module) of the server device 10 according to the embodiment disclosed herein. Referring to Figure 4, the server device 10 comprises a communication control unit 201, an employee management unit 202, a learning model management unit 203, an information provision control unit 204, and a storage unit 205.

[0041] The communication control unit 201 is a means for controlling communication with other devices. For example, the communication control unit 201 receives data (packets) from terminal 20. The communication control unit 201 also transmits data to terminal 20. The communication control unit 201 passes data received from other devices to other processing modules. The communication control unit 201 transmits data acquired from other processing modules to other devices. In this way, other processing modules send and receive data with other devices via the communication control unit 201. The communication control unit 201 has the function of a receiving unit that receives data from other devices and the function of a transmitting unit that transmits data to other devices.

[0042] The Employee Management Department 202 is a means of controlling and managing employees belonging to a designated organization. The Employee Management Department 202 acquires employee information for each employee working for system development companies, etc.

[0043] For example, the employee management unit 202 retrieves employee information by displaying a GUI (Graphical User Interface) on terminal 20. For example, the employee management unit 202 retrieves employee information using a GUI as shown in Figure 5.

[0044] Employee Management Department 202 acquires information such as name, gender, date of birth, address, biometric information (e.g., facial image), telephone number, email address, employee number, department, position, and job title. Furthermore, Employee Management Department 202 acquires information such as the qualifications and experience held by employees, their personality, and their attitude towards work.

[0045] Upon obtaining employee information such as names, the employee management unit 202 generates an employee ID to identify each employee. The employee ID can be any information that uniquely identifies an employee. For example, the employee management unit 202 may assign a unique value each time an account is created and use that as the employee ID.

[0046] The Employee Management Department 202 stores names, genders, employee numbers, etc., in the employee management database (see Figure 6). Note that the employee management database shown in Figure 6 is an example and is not intended to limit the items to be stored.

[0047] The learning model management unit 203 is a means for controlling and managing the learning model.

[0048] The learning model management unit 203 acquires the learning model prepared by the system administrator or other relevant parties. For example, the learning model management unit 203 acquires the learning model using a GUI. Alternatively, the learning model management unit 203 may acquire the learning model via a USB (Universal Serial Bus) memory or the like.

[0049] Next, we will provide an overview of the learning model and how to generate it.

[0050] A learning model is a model that generates responses to requests. For example, when a learning model receives a prompt (query) generated based on a request from a user, it outputs a response corresponding to that prompt.

[0051] For example, a learning model is composed of a language model. This language model may, but is not limited to, what is called an LLM (Large Language Model).

[0052] A language model is a machine learning model (also called a generative model) that takes language as input and outputs language. A language model learns the relationships between words in a text and generates related strings from a given string. By using a language model trained on sentences and texts in various contexts, it is possible to generate related strings with appropriate content related to the given string.

[0053] For example, let's consider the case where a language model is used in question answering. The language model accepts the question "What kind of country is Japan?" as input. The language model generates a string such as "Japan is an island nation in the Northern Hemisphere" as an answer to the question.

[0054] The method for training a language model is not particularly limited, but one example is one that is trained to output at least one sentence containing the input string. Specifically, the language model is a Generative Pre-trained Transformer (GPT) that outputs a sentence containing the input string by predicting the string that is most likely to follow the input string.

[0055] Other examples of language models include T5 (Text-to-Text Transfer Transformer), BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly optimized BERT approach), and ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately).

[0056] The content generated by a language model is not limited to strings. For example, a language model may generate image data, video data, audio data, or other data formats corresponding to the input string.

[0057] The learning model (language model) is generated based on the training data. Specifically, a large-scale language model is generated using past project information from numerous past projects. In other words, the learning model implemented in the server device 10 is generated using the contents of multiple past projects, information about the personnel assigned to each of the multiple past projects, and information about the performance of each of the multiple past projects as training data.

[0058] Alternatively, a learning model used for determining the success or failure of a project may be generated by utilizing an existing learning model (language model). For example, transfer learning (fine-tuning) may be performed, in which the weights of a previously generated learning model are trained with new training data. Specifically, the learning model may be given unique characteristics by using an existing language model and performing additional training with a unique training data set. For example, a learning model for determining the success or failure of a project may be generated by preparing a large amount of past project information as training data and adding this training data to the basic learning model.

[0059] The information provision control unit 204 is a means for controlling information provision using a learning model (large-scale language model).

[0060] The information provision control unit 204 has functions as both an acquisition means and a judgment control means. As an acquisition means, the information provision control unit 204 acquires project information and candidate information for each of the at least one candidate to be assigned to the project. As a judgment control means, the information provision control unit 204 inputs a prompt generated using the project information and the information of at least one candidate to be assigned to the project into the learning model. By inputting the prompt into the learning model, the information provision control unit 204 obtains a judgment result from the learning model regarding the project's outcome when at least one candidate to be assigned to the project is assigned to the project. The information provision control unit 204 outputs the acquired judgment result.

[0061] Figure 7 is a flowchart showing an example of the operation of the information provision control unit 204 according to the embodiment disclosed herein. The operation of the information provision control unit 204 will be explained with reference to Figure 7.

[0062] For example, when a project manager or other person accesses a designated webpage, the information provision control unit 204 acquires the information necessary to determine the success or failure of the project.

[0063] Specifically, the information provision control unit 204 obtains target project information for future projects by displaying a GUI, etc., on the terminal 20 (step S101). For example, the information provision control unit 204 obtains target project information using a GUI as shown in Figure 8.

[0064] For example, the information provision control unit 204 obtains the project name, deadline, budget, estimated man-hours, client information, system specifications (information regarding system specifications), etc., from the project manager.

[0065] Furthermore, the information provision control unit 204 acquires candidate information regarding the personnel to be assigned to the target project (step S102). For example, the information provision control unit 204 acquires information (e.g., names) that identifies at least one employee to be assigned to the project using a GUI as shown in Figure 9.

[0066] The information provision control unit 204 searches the employee management database using the acquired employee's name and other information as a key, and identifies the corresponding entry. The information provision control unit 204 acquires all or part of the information stored in the identified entry (account) as placement candidate information.

[0067] The information provision control unit 204 obtains candidate information for each employee when the project manager selects multiple employees as candidates for project assignment.

[0068] In this manner, the information provision control unit 204 acquires all or part of the employee information stored in the employee management database of at least one or more placement candidates selected by the user from among multiple employees, as placement candidate information.

[0069] The information provision control unit 204 uses the acquired target project information and placement candidate information to determine whether the project will succeed or fail if the placement candidate is assigned to the project (step S103). More specifically, the information provision control unit 204 uses a pre-prepared learning model (large-scale language model) to determine whether the project will succeed or fail.

[0070] For example, the information provision control unit 204 generates a prompt (query) such as, "Based on the target project information and the candidate assignment information, determine whether the project will succeed or fail if the candidate is assigned to the project." The actual prompt will include specific target project information and candidate assignment information after the above statement.

[0071] The information provision control unit 204 inputs the generated prompt into the large-scale language model, thereby obtaining the success or failure of the project from the large-scale language model.

[0072] The information provision control unit 204 outputs the success or failure judgment result of the project (output judgment result; step S104). For example, the information provision control unit 204 displays the judgment result on the terminal 20. For example, the information provision control unit 204 displays a screen on the terminal 20 as shown in Figure 10.

[0073] In this way, the information provision control unit 204 uses a large-scale language model as a learning model to perform a determination regarding the project's outcome (for example, determining whether the project is successful or not).

[0074] Specifically, the information provision control unit 204 generates a prompt that instructs the large-scale language model to determine the success or failure of the project using the target project information and the placement candidate information. The information provision control unit 204 inputs the generated prompt into the large-scale language model to obtain a determination result (estimated result) regarding the success or failure of the prompt. The placement candidate information includes at least one of the following: information on the qualifications possessed by the placement candidate, information on the personality of the placement candidate, and the placement candidate's views on work.

[0075] The storage unit 205 is a means for storing information necessary for the operation of the server device 10. The storage unit 205 uses an employee management database to store employee information for each of several employees belonging to a given organization.

[0076] [Terminal] Examples of terminal 20 include mobile devices such as smartphones, mobile phones, game consoles, and tablets, as well as computers (personal computers, laptops). Terminal 20 can be any device or equipment as long as it can receive user input and communicate with the server device 10, etc. The configuration and operation of terminal 20 are obvious to those skilled in the art, so a detailed explanation of terminal 20 is omitted.

[0077] Next, a modified example of the first embodiment will be described.

[0078] <Example 1> The server device 10 may allow users (project managers) to specify candidates for placement in the project, enabling them to specify candidates for each role.

[0079] For example, the information provision control unit 204 may provide an interface that allows the selection of placement candidates for each role within the project (e.g., project leader, sub-leader, team member), as shown in Figure 11.

[0080] In this case, the information provision control unit 204 may generate an instruction (prompt) to determine the success or failure of the project using the acquired role and assignment candidate information. For example, when assigning employee A as project leader, a prompt such as "Based on the target project information and employee A's assignment candidate information, determine the success or failure of the project if employee A is assigned to the project as project leader" may be generated.

[0081] Thus, the information provision control unit 204 may acquire candidate information for at least one candidate for each role in the project. The information provision control unit 204 may input a prompt generated using the project information and the candidate information for at least one candidate for each role in the project into the learning model. By inputting the prompt into the learning model, the information provision control unit 204 may obtain a determination result of whether or not the project will succeed.

[0082] <Modification 2> When the information provision control unit 204 presents the success or failure of a project to the user (project manager, etc.), it may also provide the judgment result and advice regarding the person in charge of the work.

[0083] For example, if the project result is "failure," the information provision control unit 204 may provide advice to change the project success / failure determination result to "success."

[0084] For example, the information provision control unit 204 replaces some of the employees designated by the project manager, and then determines whether the project is successful or not. The information provision control unit 204 repeats the process of replacing the candidates for placement and determining whether the project is successful until the project is determined to be a "success."

[0085] The information provision control unit 204 proposes to the user that the placement candidates (employees) who have been judged to be successful be designated as project work managers (participating members).

[0086] In this case, the information provision control unit 204 may generate advice using a large-scale language model. For example, the information provision control unit 204 may generate advice such as, "In order to ensure the success of the project, it would be appropriate to assign employee E to the task instead of employee D," and propose it to the project manager or the like.

[0087] Thus, the information provision control unit 204 may generate advice regarding the success or failure of the project determination result and output the generated advice together with the determination result. Furthermore, if the acquired determination result indicates a project failure, the information provision control unit 204 may generate advice regarding the determination result (advice regarding changes to the person in charge of operations so that the project is determined to be successful).

[0088] <Variation 3> The server device 10 may present the project manager or other relevant party with the optimal personnel allocation for the project based on the target project information and the candidate information for each employee.

[0089] In this case, the information provision control unit 204 acquires the target project information. The information provision control unit 204 inputs the target project information and the candidate information for each employee into a large-scale language model to obtain the optimal personnel placement plan for the project.

[0090] For example, the information provision control unit 204 generates a prompt such as, "Based on the target project information and the candidate information for each employee, please propose the most suitable employee for the project." The information provision control unit 204 inputs the generated prompt into a large-scale language model and obtains a response (personnel placement proposal). The information provision control unit 204 then presents the obtained personnel placement proposal to the project manager or other relevant parties.

[0091] <Modification 4> The server device 10 may select members from employee information based on project information and output multiple selection results in order of project success rate. In this case, the server device 10 may also provide the reasons for selecting the project members.

[0092] For example, the server device 10 may make the following presentation. • 95% success rate: Leader (Person A), Members (Persons B, C, and D) • [Reasons for selecting project members] Person A is a veteran leader with extensive experience and a proven track record, particularly in the success of Project X. Person A's leadership skills and expertise are essential to the success of this project. Person B has collaborated with Person A on many projects in the past, and a strong relationship of trust and efficient communication patterns have been established between them. Therefore, Person B can function as Person A's right-hand person and act as a coordinator for the entire team.

[0093] Alternatively, the server device 10 may make the following presentation: • Success rate 85%: Leader (Mr. C), Members (Mr. E, Mr. F, Mr. G) • [Reasons for selecting project members] Despite being young, C possesses excellent technical skills and strong communication abilities, and is highly regarded for his potential as a leader. E is a veteran with extensive experience who can support C and provide advice to the entire team. F and G each have different areas of expertise, complementing the skill sets required for the project. This team composition is well-balanced, providing opportunities for the growth of a young leader while also offering support from experienced members.

[0094] Alternatively, the server device 10 may make the following presentation: • Success rate 83%: Leader (Mr. D), Members (Mr. A, Mr. F, Mr. H) • [Reasons for selecting project members] Person D excels at generating new ideas and can take an innovative project approach. Person A is a seasoned veteran with extensive experience who will translate Person D's novel ideas into a feasible form. Person F provides technical expertise, and Person H excels in project management skills. This team composition strikes a good balance between creativity and execution, and has the potential to combine a new approach with reliable execution. However, Person D's relatively limited leadership experience slightly lowers the chances of success.

[0095] <Modification 5> When the server device 10 outputs a project success / failure determination result based on the project information and the candidate placement information, it may also consider the weighting of each piece of information included in the project information and the candidate placement information.

[0096] For example, the server device 10 may assign weights to important elements among the target project information, such as the project name, deadline, budget, estimated man-hours, client information, and system specifications. For example, if the project prioritizes deadlines, the server device 10 may give a greater weight to the deadline. For example, if the project prioritizes budgets, the server device 10 may give a greater weight to the deadline. For example, if the project prioritizes quality, the server device 10 may give a greater weight to the system specifications.

[0097] For example, the server device 10 may weight important elements among the qualifications, personality, and work ethic of the employee in the candidate information. For example, if the technical difficulty of the project is high, the server device 10 may give a greater weight to the employee's qualifications. For example, if smooth communication among members is important, the server device 10 may give a greater weight to the employee's personality. For example, if increasing the employee's experience level is important, the server device 10 may give a greater weight to the work ethic.

[0098] <Variation 6> It is also possible to use information processing system simulations during the project. Server device 10 can obtain current project information (progress information) and member information, and then advise on how to lead the project to success. For example, server device 10 can show how the success rate will change with the addition or replacement of members. Alternatively, server device 10 can show how the success rate will change with the members' own skill improvement and behavioral improvements.

[0099] As described above, the server device 10 according to the first embodiment acquires target project information for projects that are subject to success or failure determination, and candidate information regarding employees (candidates) who are scheduled to be assigned to those projects. The server device 10 generates a prompt using the acquired target project information and candidate information, and inputs the generated prompt into a large-scale language model. The server device 10 uses the large-scale language model to determine the success or failure of the project. That is, the server device 10 determines the success or failure of the project when candidates are assigned to the project, and outputs the determination result. As a result, the user (for example, a project manager) can select the optimal employee to lead the project to success.

[0100] Furthermore, while the number of companies adopting job-based personnel systems has increased in recent years, projects often fail if personnel are assigned based on a uniform perspective and way of thinking. For example, the personalities of each participating member and the compatibility among them can affect the outcome of a project. For instance, the aforementioned problems can lead to low project productivity, employee turnover, or leave of absence. The information processing system according to the first embodiment can reduce the effort project managers spend on team formation and coordination by presenting optimized personnel assignment plans using a large-scale language model. In addition, employees can expect improved motivation and enhanced self-growth because their skills and experience are fairly evaluated.

[0101] Next, we will describe the hardware of each device that makes up the information processing system. Figure 12 shows an example of the hardware configuration of server device 10.

[0102] The server device 10 can be configured using an information processing device (a so-called computer), and has the configuration illustrated in Figure 12. For example, the server device 10 includes a processor 311, memory 312, input / output interface 313, and communication interface 314, etc. The components of the processor 311, etc., are connected by an internal bus or the like and are configured to communicate with each other.

[0103] However, the configuration shown in Figure 12 is not intended to limit the hardware configuration of the server device 10. The server device 10 may include hardware not shown, and it may not have to have an input / output interface 313 if necessary. Also, the number of processors 311 etc. included in the server device 10 is not limited to the example in Figure 12; for example, multiple processors 311 may be included in the server device 10.

[0104] The processor 311 is a programmable device such as a CPU (Central Processing Unit), MPU (Micro Processing Unit), or DSP (Digital Signal Processor). Alternatively, the processor 311 may be a device such as an FPGA (Field Programmable Gate Array) or ASIC (Application Specific Integrated Circuit). The processor 311 executes various programs, including an operating system (OS).

[0105] Memory 312 includes RAM (Random Access Memory), ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), etc. Memory 312 stores the OS program, application programs, and various data.

[0106] The input / output interface 313 is an interface for a display device or input device (not shown). The display device is, for example, a liquid crystal display. The input device is, for example, a device that accepts user input such as a keyboard, mouse, or touch panel.

[0107] The communication interface 314 is a circuit, module, etc., that communicates with other devices. For example, the communication interface 314 includes a NIC (Network Interface Card), etc.

[0108] The functions of the server device 10 are realized by various processing modules. These processing modules are realized, for example, by the processor 311 executing a program stored in memory 312. The program can also be recorded on a computer-readable storage medium. The storage medium can be a non-transitory material such as semiconductor memory, hard disk, magnetic recording medium, or optical recording medium. In other words, the present invention can also be embodied as a computer program product. Furthermore, the program can be downloaded via a network or updated using the storage medium on which the program is stored. Moreover, the processing module may be realized by a semiconductor chip.

[0109] In addition, terminal 20 can also be configured using an information processing device, similar to server device 10, and its basic hardware configuration is no different from that of server device 10, so its explanation will be omitted.

[0110] The server device 10 is equipped with a computer, and its functions can be realized by having the computer execute a program. Furthermore, the server device 10 executes a control method for the server device 10 using this program.

[0111] [Differentiation] The configuration and operation of the information processing system described in the above embodiment are illustrative examples and are not intended to limit the system configuration.

[0112] Furthermore, the information processing system disclosed in this application can be used for businesses other than system development, and is not limited to system development.

[0113] The server device 10 may estimate (calculate) some of the employee information based on information from the employees. For example, the employee management unit 202 may conduct a predetermined personality test for each employee and store the obtained personality in the employee management database. For example, the employee management unit 202 may ask a user a predetermined question and estimate the user's personality based on the answers obtained. In this case, the employee management unit 202 may perform personality estimation using pre-prepared questions. Alternatively, the employee management unit 202 may perform personality determination (personality estimation) of the user using a large-scale language model.

[0114] When the information provision control unit 204 obtains information (e.g., names) to identify candidates for assignment to a project using a GUI as shown in Figures 9 and 11, it may make it impossible to select employees who are participating in other projects.

[0115] The information provision control unit 204 may fix the employees assigned to projects and then present the project manager, etc., with the simulation results (project success / failure judgment results) of changing the projects assigned to those fixed employees. For example, consider a scenario where there are three employees, A to C, who are not currently involved in any projects, and there are three projects for which development has not yet started and personnel assignments have not yet been decided. In this case, the information provision control unit 204 uses the assignment candidate information for each of the three employees, A to C, to perform a project success / failure judgment for each of the three projects. The information provision control unit 204 then proposes to the project manager, etc., the project that yields the best results for the combination of the three employees, A to C, as the project to be handled by those three employees.

[0116] The server device 10 may provide the project success / failure determination results not only to project managers and others, but also to the candidates for assignment. The information provision control unit 204 may send the determination results to the email address of the employee used for the project success / failure determination. Alternatively, the information provision control unit 204 may change the role of the same employee and send the results of the project success / failure determination to the employee's email address. By presenting the employee with the project success / failure determination results before and after the role change, it is expected that the employee's motivation for the role change will increase.

[0117] The server device 10 may simulate the growth each employee gains from project experience. In this case, the employee's skills and evaluations before and after project experience are measured and collected for past projects. The server device 10 can use the collected skills and evaluations as training data to generate a learning model (large-scale language model) and perform the above simulation. The server device 10 may provide the project manager with simulation results regarding project success / failure and simulation results regarding employee growth for the same employee. Project managers who have access to such information can select personnel for projects while considering the balance between employee growth and project success.

[0118] The information processing system disclosed in this application may be used not only for determining the success or failure of a project and proposing personnel placement plans, but also for determining whether or not to hire a job seeker. Specifically, the server device 10 uses information corresponding to the job seeker's placement candidate information and the target project information to determine the success or failure of the project. If the project is determined to be successful, personnel personnel can make a decision to hire the job seeker.

[0119] In the above embodiment, it was explained that the large-scale language model is generated by a device other than the server device 10. However, the large-scale language model may also be generated by the server device 10.

[0120] In the above embodiment, the case in which the employee management database is configured inside the server device 10 was described, but the database may be built on an external database server or the like. In other words, some functions of the server device 10 may be implemented in another device. More specifically, it is sufficient that the "information provision control unit (information provision control means)" etc. described above is implemented in any device included in the system.

[0121] The form of data transmission and reception between each device (server device 10, terminal 20) is not particularly limited, but the data transmitted and received between these devices may be encrypted. Personal information of users is transmitted and received between these devices, and in order to properly protect this information, it is desirable that encrypted data be transmitted and received.

[0122] In the flowcharts (sequence diagrams) used in the above description, multiple processes (processes) are shown in order, but the execution order of the processes performed in the embodiment is not limited to the order in which they are shown. In the embodiment, the order of the illustrated processes can be changed to the extent that it does not impair the content, for example, by executing each process in parallel.

[0123] The embodiments described above are explained in detail to facilitate understanding of the disclosure, and it is not intended that all the configurations described above are necessary. Furthermore, when multiple embodiments are described, each embodiment may be used individually or in combination. For example, it is possible to replace parts of the configuration of one embodiment with those of another embodiment, or to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of one embodiment with those of another.

[0124] As described above, the industrial applicability of the present invention is clear, and it is particularly suitable for application to information processing systems that provide information related to projects.

[0125] Some or all of the above embodiments may also be described as follows, but are not limited to the following:

[0126] [Note 1] A means for acquiring project information and candidate information for each of the at least one candidate candidates to be assigned to the said project. A determination control means inputs a prompt generated using the project information and the information of at least one placement candidate into a learning model, thereby obtaining a determination result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputting the obtained determination result. A server device equipped with the following features.

[0127] [Note 2] The system further includes a storage means for storing employee information for each of several employees belonging to a given organization, The acquisition means is a server device as described in Appendix 1, which acquires all or part of the stored employee information of at least one or more placement candidates selected by the user from among the multiple employees as placement candidate information.

[0128] [Note 3] The acquisition means acquires the candidate information of at least one candidate for each role in the project. The server device described in Appendix 2, wherein the determination control means obtains a determination result regarding the outcome of the project by inputting a prompt generated using the project information and the candidate information of at least one candidate for each role in the project into the learning model.

[0129] [Note 4] The server device described in Appendix 3, wherein the determination control means generates advice regarding the determination result and outputs the generated advice together with the acquired determination result.

[0130] [Note 5] The judgment control means is a server device as described in Appendix 4, which generates advice regarding the judgment result when the acquired judgment result indicates a project failure.

[0131] [Note 6] The learning model is generated using the contents of multiple past projects, information on the personnel assigned to each of the multiple past projects, and information on the performance of each of the multiple past projects as learning data, and is a server device as described in any one of the appendices 1 to 5.

[0132] [Note 7] The server device described in any one of the following appendices 1 to 5, wherein the candidate information for placement includes at least one of the following: information regarding the qualifications possessed by the candidate for placement, information regarding the personality of the candidate for placement, and the candidate's views on work.

[0133] [Note 8] The determination control means is a server device according to any one of the appendices 1 to 5, which uses a large-scale language model as the learning model to make determinations regarding the results of the project.

[0134] [Note 9] A data acquisition step involves acquiring project information and candidate information for each of the at least one candidate candidates who are scheduled to be assigned to the said project. A determination control step involves inputting a prompt generated using the project information and the information of at least one placement candidate into a learning model, thereby obtaining a determination result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputting the obtained determination result. A control method for a server device, comprising the following features.

[0135] [Note 10] The system further includes a memory process for storing employee information for each of several employees belonging to a given organization, The acquisition step is a control method for a server device as described in Appendix 9, which acquires all or part of the stored employee information of at least one placement candidate selected by the user from among the multiple employees as placement candidate information.

[0136] [Note 11] The acquisition step involves acquiring the candidate information of at least one candidate for each role in the project. The control method for a server device as described in Appendix 10, wherein the determination control step obtains a determination result regarding the outcome of the project by inputting a prompt generated using the project information and the candidate information of at least one candidate for each role in the project into the learning model.

[0137] [Note 12] The control method for the server device described in Appendix 11, wherein the judgment control step generates advice regarding the judgment result and outputs the generated advice together with the acquired judgment result.

[0138] [Note 13] The judgment control step is a control method for the server device described in Appendix 12, which generates advice regarding the judgment result when the acquired judgment result indicates a project failure.

[0139] [Note 14] A control method for a server device according to any one of the appendices 9 to 13, wherein the learning model is generated using the contents of multiple past projects, information on the personnel assigned to each of the multiple past projects, and information on the performance of each of the multiple past projects as learning data.

[0140] [Note 15] The server device control method according to any one of the appendices 9 to 13, wherein the candidate information for placement includes at least one of the following: information regarding the qualifications possessed by the candidate for placement, information regarding the personality of the candidate for placement, and the candidate's views on work.

[0141] [Note 16] The control method for a server device according to any one of the appendices 9 to 13, wherein the determination control step uses a large-scale language model as the learning model to make a determination regarding the results of the project.

[0142] [Note 17] The computer installed in the server device A data acquisition process that acquires project information and candidate information for each of the at least one candidate candidates who are scheduled to be assigned to the said project. A judgment control process that inputs a prompt generated using the project information and the information of at least one placement candidate into a learning model, obtains a judgment result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputs the obtained judgment result. A program to execute.

[0143] [Note 18] Further memory processing is performed to store employee information for each of the multiple employees belonging to a given organization. The acquisition process is the program described in Appendix 17, which acquires all or part of the stored employee information of at least one or more placement candidates selected by the user from among the multiple employees as placement candidate information.

[0144] [Note 19] The acquisition process acquires the candidate information of at least one candidate for each role in the project. The program described in Appendix 18 obtains a determination result regarding the project's outcome by inputting a prompt generated using the project information and the candidate information of at least one candidate for each role in the project into the learning model.

[0145] [Note 20] The judgment control process is the program described in Appendix 19, which generates advice regarding the judgment result and outputs the generated advice together with the acquired judgment result.

[0146] [Note 21] The aforementioned determination control process is the program described in Appendix 20, which generates advice regarding the determination result when the acquired determination result indicates a project failure.

[0147] [Note 22] The learning model is a program described in any one of the appendices 17 to 21, which is generated using the contents of multiple past projects, information on the personnel assigned to each of the multiple past projects, and information on the performance of each of the multiple past projects as learning data.

[0148] [Note 23] The program described in any one of the following appendices 17 to 21 includes, in addition to the candidate information, at least one of the following: information regarding the qualifications possessed by the candidate, information regarding the candidate's personality, and the candidate's views on work.

[0149] [Note 24] The aforementioned decision control process is a program described in any one of the appendices 17 to 21, which uses a large-scale language model as the learning model to make a decision regarding the outcome of the project.

[0150] Furthermore, some or all of the configurations described in Appendices 2 to 8, which are subordinate to Appendice 1 above, may also be subordinate to Appendices 9 and 17 in the same way as those described in Appendices 2 to 8. Moreover, not limited to Appendices 1, 9 and 17, some or all of the configurations described as appendices may also be subordinate to various hardware, software, various recording means for recording software, or systems, without departing from the embodiments described above.

[0151] Furthermore, each disclosure of the above-mentioned prior art documents cited herein is incorporated herein by reference. Although embodiments of the present invention have been described above, the present invention is not limited to these embodiments. It will be understood by those skilled in the art that these embodiments are merely illustrative and that various modifications are possible without departing from the scope and spirit of the present invention. That is, the present invention naturally includes the entire disclosure, including the claims, and various modifications and alterations that can be made by those skilled in the art in accordance with the technical idea. [Explanation of Symbols]

[0152] 10 Server devices 20 devices 100 Server Devices 101 Acquisition method 102 Determination control means 201 Communication Control Unit 202 Employee Management Department 203 Learning Model Management Department 204 Information Provision Control Unit 205 Storage section 311 Processors 312 memory 313 Input / Output Interfaces 314 Communication Interface

Claims

1. A means for acquiring project information and candidate information for each of the at least one candidate candidates to be assigned to the said project. A determination control means inputs a prompt generated using the project information and the information of at least one placement candidate into a learning model, thereby obtaining a determination result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputting the obtained determination result. A server device equipped with the following features.

2. The system further includes a storage means for storing employee information for each of several employees belonging to a given organization, The server device according to claim 1, wherein the acquisition means acquires all or part of the stored employee information of at least one placement candidate selected by the user from among the plurality of employees as placement candidate information.

3. The acquisition means acquires the candidate information of at least one candidate for each role in the project. The server device according to claim 2, wherein the determination control means inputs a prompt generated using the project information and the candidate information of at least one candidate for each role in the project to the learning model to obtain a determination result regarding the outcome of the project.

4. The server device according to claim 3, wherein the determination control means generates advice regarding the determination result and outputs the generated advice together with the acquired determination result.

5. The server device according to claim 4, wherein the determination control means generates advice regarding the determination result when the acquired determination result indicates a project failure.

6. The server device according to any one of claims 1 to 5, wherein the learning model is generated using the contents of multiple past projects, information on the personnel assigned to each of the multiple past projects, and information on the performance of each of the multiple past projects as learning data.

7. The server device according to any one of claims 1 to 5, wherein the candidate information for placement includes at least one of the following: information regarding qualifications possessed by the candidate for placement, information regarding the candidate's personality, and the candidate's views on work.

8. The server device according to any one of claims 1 to 5, wherein the determination control means uses a large-scale language model as the learning model to make determinations regarding the results of the project.

9. A data acquisition step involves acquiring project information and candidate information for each of the at least one candidate candidates who are scheduled to be assigned to the project. A determination control step involves inputting a prompt generated using the project information and the information of at least one placement candidate into a learning model, thereby obtaining a determination result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputting the obtained determination result. A control method for a server device, comprising the following features.

10. The computer installed in the server device A data acquisition process that acquires project information and candidate information for each of the at least one candidate candidates who are scheduled to be assigned to the project. A judgment control process that inputs a prompt generated using the project information and the information of at least one placement candidate into a learning model, obtains a judgment result regarding the project's outcome when at least one placement candidate is assigned to the project from the learning model, and outputs the obtained judgment result. A program to execute.

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