Information processing system, information processing method and program

Abstract

To solve the problem that a great deal of effort is required to search for sponsors for the implementation of high-quality lessons at educational institutions, and efficient matching between local companies and educational institutions is not being achieved. [Solution] The information processing system of the present invention comprises a sponsor database, a location acquisition unit, and a sponsor candidate selection unit. The sponsor database stores the location S of sponsor candidates, and the location acquisition unit acquires the location E of the educational institution. The sponsor candidate selection unit selects sponsor candidates based on the geographical relevance of location S and location E. This enables efficient matching between locally rooted companies and educational institutions, reducing the burden on educational institutions and promoting educational support in the community.

Description

[Technical Field]

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

[0002] A technology has been disclosed that guides users to sponsor websites when distributing digital content (Patent Document 1).

[0003] A web system has been disclosed that matches schools with students when they are looking for further education or a job, restricting the access to a student's profile information to only schools that meet the conditions specified by the student (Patent Document 2). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-117007 [Patent Document 2] Japanese Patent Publication No. 2020-077084 Summary of the Invention [Problem to be solved by the invention]

[0005] In recent years, there has been a trend toward improving classes and educational programs at educational institutions, but the above-mentioned existing technologies have not yet resolved the issue. Therefore, an object of the present invention is to provide a system and method that solves this problem. [Means for solving the problem]

[0006] The present invention, which aims to solve the above-mentioned problems, provides an information processing system that selects sponsor candidates based on geographical relevance. A first aspect of the present invention includes a configuration that includes a sponsor database that stores location S, which is the location of a sponsor candidate who can cover the cost of classes at an educational institution; a location acquisition unit that acquires location E, which is the location of the educational institution; and a sponsor candidate selection unit that selects the sponsor candidate based on location S and location E. A second aspect of the present invention may also include a configuration that includes the sponsor database, a staff member database that stores location T, which is the location of the organization to which the staff member for the class belongs, and a sponsor candidate selection unit that selects the sponsor candidate based on location S and location T. Furthermore, the system can select the optimal sponsor candidate by conducting a multifaceted evaluation based on attribute information, such as the sponsor candidate's industry, size, corporate philosophy, and preferences, as well as attribute information, such as the type of educational institution, educational content, and preferences.

[0007] Other problems and solutions disclosed in this application will be made clear in the section on preferred embodiments of the invention and the drawings. [Effects of the Invention]

[0008] According to this invention, matching between educational institutions and potential sponsors is automatically performed based on their geographical relevance, allowing educational institutions to significantly reduce the effort required to find sponsors. Community-based sponsors are also able to efficiently support local educational institutions, which contributes to the local community and increases corporate value. Furthermore, matching that takes into account the geographical relevance of three parties, including instructors, reduces travel costs and promotes the effective use of local resources. Additionally, by utilizing actual distance information in addition to administrative divisions, more flexible and effective matching is achieved, resulting in improved quality of the educational environment throughout the region and optimal allocation of resources. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of the overall configuration of an information processing system. [Figure 2] FIG. 2 illustrates an example of a hardware configuration of a management server 2. [Figure 3] FIG. 2 illustrates an example of the software configuration of a management server 2. [Figure 4] FIG. 2 is a diagram illustrating a processing flow in the information processing system. DETAILED DESCRIPTION OF THE INVENTION

[0010] <Background of the invention> In recent years, educational institutions have been working to enhance their classes and educational programs. One challenge that has arisen in this effort is how to utilize resources outside the institution, such as recruiting external talent, for this purpose. However, funding from external sponsors such as companies and organizations plays an important role in this endeavor. However, there is currently no adequate system in place to efficiently match educational institutions with potential sponsors, and educational institutions spend a great deal of time and effort searching for suitable sponsors. In particular, community-based companies and organizations are often interested in contributing to educational institutions in their local areas, but there is no system that automatically matches them based on geographical relevance.

[0011] Additionally, selecting the instructors and experts who will teach classes is also a key issue when implementing programs at educational institutions. Matching educational institutions, sponsors, and instructors, taking into account their geographical relationships, is important from the perspectives of making effective use of local resources, reducing travel costs, and contributing to the local community. However, no system has been established to manage and process these elements in an integrated manner. As a result, educational institutions continue to be forced to allocate resources that should be focused on improving the content and quality of their classes to searching for and selecting sponsors and instructors.

[0012] Furthermore, when matching based on geographic proximity, it is necessary to consider not only simple administrative divisions but also actual physical distance and transportation access, but there are no systems available for selecting sponsors and instructors that utilize this multifaceted geographic information.As a result, optimal matching between educational institutions, sponsors, and instructors is not achieved, and the efficient allocation of educational resources throughout the local community is hindered.Under these circumstances, there is a strong demand for an information processing system that utilizes geographic information to efficiently match educational institutions, sponsors, and instructors.

[0013] The above-described background to the invention is merely an example of one specific problem that the present invention is intended to solve, and the problem that the present invention is intended to solve is not limited to this.

[0014] <Summary of the Invention> An information processing system and method according to one embodiment of the present invention are described below. The present invention provides a technology for efficiently selecting sponsors to cover the costs of classes at educational institutions. The basic concept of the present invention is to select appropriate sponsor candidates based on the geographical relevance between the locations of the sponsor candidates and the locations of the educational institution and the instructor. The present invention includes multiple embodiments. The first embodiment is a matching system based on the geographical relevance between the educational institution and the sponsor candidates. The second embodiment is a matching system based on the geographical relevance between the organization to which the instructor belongs and the sponsor candidates. These embodiments can be implemented independently or in combination. The technical concept of the present invention, i.e., efficient matching utilizing geographical relevance, applies to both embodiments. This concept can be implemented using a computer system or provided as a personal consulting service. Below, we first describe the basic methodology, followed by detailed system implementation.

[0015] <Basic methodology> The core methodology of this invention is matching based on geographical relevance. Specifically, the relevance between the location of the educational institution (Location E) and the location of the potential sponsor (Location S) is analyzed, and sponsors that are geographically close are selected. This method is based on the idea of ​​promoting locally rooted educational support and building relationships that are beneficial to both sponsors and educational institutions.

[0016] There are several main approaches to analyzing geographic relevance. The first is an approach based on administrative divisions, which selects sponsor candidates if the educational institution and sponsor candidate are located in the same local government (the same city, town, village, or prefecture). The second is an approach based on broader regional divisions, which selects sponsor candidates if they belong to the same region (e.g., Tohoku, Kanto, Shikoku, etc.), economic zone, or cultural area. In an international context, broader geographic relevance, such as the same country (e.g., all of Japan) or regional association (e.g., Southeast Asia, ASEAN countries), can also be considered. The third is an approach based on physical distance, which calculates the distance between the educational institution and sponsor candidate and selects sponsor candidates whose distance is within a specified threshold (e.g., 5 km, 10 km, 100 km, etc.). It is also possible to optimize the geographic relevance between the three parties by taking into account the location (location T) of the organization to which the instructor of the course belongs.

[0017] <Terminology> Key terms used in the present invention are defined below. The term "educational institution" refers to an institution that provides primary, secondary, and higher education. Specifically, it includes formal educational institutions such as elementary schools, junior high schools, high schools, technical colleges, universities, and graduate schools. It also includes social education facilities such as vocational schools, miscellaneous schools, vocational training schools, lifelong learning centers, and community centers, as well as education provided within companies. Furthermore, online education platforms, educational NPOs, and international educational institutions are also included in the educational institutions of this invention. Additionally, it also includes educational institutions that meet the needs of diverse learners, such as special needs schools (such as schools for the disabled, schools for the blind, and schools for the deaf), correspondence schools, part-time high schools, night schools (such as night junior high schools, night high schools, and night universities), university-run open courses, and adult education programs.

[0018] "Classes" refer to educational activities conducted at educational institutions to acquire knowledge and skills. This includes not only regular classes included in the regular curriculum, but also advanced and practical educational programs such as special courses, workshops, seminars, extracurricular activities, experiential learning, and internships. "Classes" also include educational activities conducted not only in person, but also online or in a mixed format.

[0019] "Expenses" refers to the financial burden required to conduct classes. Specifically, this includes the labor costs of external personnel in specialized fields such as business education, investment education, intellectual property education, and tax education that are difficult for school faculty to provide, the costs of implementing extracurricular activities required to provide these courses, the cost of workshop materials, facility usage fees, teaching materials, and transportation costs. This generally refers to additional costs borne by educational institutions in addition to the tuition fees paid by students, and by having sponsors cover these costs, it becomes possible to provide high-quality education without any financial burden on students or educational institutions.

[0020] "Sponsor" refers to a company, organization, or individual that covers the cost of tuition at an educational institution. This includes not only for-profit companies, but also non-profit organizations, public institutions, foundations, alumni organizations, individual business owners, and philanthropists. Sponsors do not simply provide funding; they may also provide resources such as expertise, personnel, space, and equipment. Possible motivations for sponsorship include social contribution, contribution to the community, human resource development, corporate branding, and as part of recruitment activities.

[0021] "Location" refers to the physical location of affiliated organizations such as educational institutions, sponsors, and instructors. It includes address information (prefecture, city, ward, town, village, street address, etc.), location information such as latitude and longitude, and information identifying the local government. Broader geographical divisions, such as regions (Tohoku region, Kansai region, etc.), countries (Japan, South Korea, etc.), and regional associations (ASEAN, EU, etc.), are also included in the concept of location. Location is important information used to calculate physical distance and determine regional relevance based on administrative divisions.

[0022] "Instructor" refers to the person who actually teaches and lectures classes at educational institutions. This is primarily intended to be a working adult employed by a company or organization, and their role is to provide specialized knowledge and skills based on practical experience to the educational field. Specific examples include employees of financial institutions providing financial literacy and investment education, engineers at IT companies teaching programming and digital technology, employees at marketing companies providing instruction on business strategy and market analysis, and engineers in the manufacturing industry teaching manufacturing and quality control. Other examples include players and staff at professional sports teams teaching sports business and sponsorship strategies, lawyers at law firms providing legal education, patent attorneys and staff at patent attorney offices providing intellectual property education, and certified public accountants and tax accountants at accounting firms providing accounting and tax education. Furthermore, instructors may include entrepreneurs and managers teaching entrepreneurship and business creation, researchers at research institutes introducing cutting-edge science and technology, NPO and NGO staff teaching about solving social issues and the SDGs, government officials teaching public policy and regional development, medical professionals providing health education, artists and designers implementing creative education, farmers teaching agricultural techniques and food education, and tourism industry workers teaching tourism business and hospitality.Instructors do not necessarily need to hold teaching licenses, but they are expected to have practical experience and expertise in their field and be able to provide students with practical learning.

[0023] <System implementation details> Next, details of the implementation of the present invention as a computer system will be described. Fig. 1 is a diagram showing an example of the overall configuration of an information processing system. The information processing system of this embodiment is configured to include a management server 2. The management server 2 is communicably connected to a user terminal 1 via a communication network. The communication network is, for example, the Internet, and is constructed using a public telephone network, a mobile phone network, a wireless communication path, Ethernet (registered trademark), etc.

[0024] The user terminal 1 is a computer operated by an administrator of an educational institution, a lecturer, a person in charge of a sponsoring company, etc. The user terminal 1 can be, for example, a smartphone, a tablet computer, a personal computer, etc. A user can use this user terminal 1 to register information about an educational institution, search for potential sponsors, apply for sponsorship, etc.

[0025] The management server 2 may be a general-purpose computer such as a workstation or personal computer, or may be logically realized by cloud computing. The management server 2 has functional units such as a location acquisition unit, a sponsor candidate selection unit, and a person in charge candidate selection unit.

[0026] <Administration Server> FIG. 2 is a diagram illustrating an example of the hardware configuration of the management server 2. Note that the illustrated configuration is an example, and other configurations may also be used. The management server 2 includes a CPU 201, a memory 202, a storage device 203, a communication interface 204, an input device 205, and an output device 206. The storage device 203 stores various data and programs, and is, for example, a hard disk drive, a solid state drive, or a flash memory. The communication interface 204 is an interface for connecting to a communication network, and is, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for wireless communication, or a USB (Universal Serial Bus) connector or an RS232C connector for serial communication. The input device 205 is used to input data, and is, for example, a keyboard, a mouse, a touch panel, a button, a microphone, or the like. The output device 206 is used to output data, and is, for example, a display, a printer, a speaker, or the like. Each functional unit of the management server 2 described below is realized by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it, and each storage unit of the management server 2 is realized as part of the storage area provided by the memory 202 and the storage device 203.

[0027] 3 is a diagram showing an example of the software configuration of the management server 2. The management server 2 includes a location acquisition unit 211, a sponsor candidate selection unit 212, a person in charge candidate selection unit 213, a matching unit 214, a notification unit 215, and a storage unit 231. The storage unit 231 includes a sponsor database 232, an educational institution database 233, a person in charge database 234, a class database 235, and a matching history database 236.

[0028] The storage unit 231 stores data used for processing by each functional unit of the management server 2. The storage unit 231 is realized as part of the memory 202 or the storage area of ​​the storage device 203.

[0029] The sponsor database 232 stores information about potential sponsors who can cover the costs of classes at educational institutions. The sponsor database 232 includes the identification information, name, industry, location (location S), contact information, budget size, types of classes that can be supported, past support performance, etc. of the potential sponsors. The location includes location information such as address information and latitude and longitude, and information identifying the local government.

[0030] The sponsor database 232 also records preference conditions of sponsor candidates. For example, these conditions include the type of educational institution to be supported (elementary school, junior high school, high school, technical college, university, etc.), the field to be prioritized in support (science, humanities, sports, etc.), the range of the region to be supported, the attributes of the desired person in charge, and competitive relationships to be avoided. These preference conditions are set based on the corporate philosophy, management strategy, and community contribution policy of the sponsor candidate.

[0031] It is also desirable for the sponsor database 232 to record preference conditions regarding the gender composition of educational institutions. For example, preference conditions based on a company's human resource strategy or social responsibility policy may be included, such as when a company with a high ratio of male employees wishes to support an all-boys school, or when it places importance on supporting an all-girls school from the perspective of promoting women's participation in the workforce. Conversely, industries that currently have a large number of male employees but want to increase the number of female employees (such as the aviation industry or construction industry) may wish to approach female talent by supporting an all-girls school. These preference conditions enable matching that takes into account the sponsor's management strategy and the characteristics of the educational institution.

[0032] The educational institution database 233 stores information about educational institutions. The educational institution database 233 includes the identification information, name, type (elementary school, junior high school, high school, university, etc.), location (location E), contact information, number of students, etc. of the educational institution. The location includes location information such as address information and latitude and longitude, information identifying the local government, etc.

[0033] The educational institution database 233 also records the characteristics and preferences of educational institutions. For example, it includes conditions such as the characteristics of the educational curriculum (general, commercial, industrial, etc.), the educational fields that are particularly emphasized, the industry and size of the desired sponsor, and the industry or company that is to be avoided. This information is set based on the educational policy of the educational institution, the characteristics of the school, the regional characteristics, etc.

[0034] Furthermore, it is desirable that the educational institution database 233 also record information about the gender composition of educational institutions (boys' schools, girls' schools, coeducational schools). Boys' schools and girls' schools each have their own unique educational needs and cultures. For example, boys' schools may have a high need for science and engineering or sports-related classes, while girls' schools may have a high need for classes related to women's career development and leadership development. Recording this information on characteristics makes it possible to match appropriate sponsors and personnel according to the characteristics of each educational institution.

[0035] It is desirable that the educational institution database 233 also record information on the type of educational institution (full-time, part-time, correspondence, etc.) and the characteristics of the target learners (presence or absence of disabilities, age group, adult / working learners, etc.). For special needs schools, the types of disabilities they target (visual impairment, hearing impairment, intellectual disability, physical disability, illness, etc.) and necessary considerations may also be recorded. This information will enable matching with sponsors who can respond to the special needs of each educational institution and with personnel with the appropriate expertise.

[0036] The instructor database 234 stores information about instructors of classes. The instructor database 234 includes the instructor's identification information, name, field of expertise, affiliated organization, location of the affiliated organization (location T), contact information, past teaching performance, etc. The location includes location information such as address information and latitude and longitude, and information identifying the local government.

[0037] The staff member database 234 also records detailed information about the staff member's organization. This information includes, for example, the organization's industry, size, corporate philosophy, social reputation, and information about competing companies. This information is used to evaluate the relationship with the sponsor candidate and their compatibility with the educational institution. The staff member's teaching style, range of fields they can teach, and suitability for specific types of educational institutions are also recorded and used for multifaceted matching evaluations.

[0038] The lesson database 235 stores information about lessons held at educational institutions. The lesson database 235 includes lesson identification information, lesson name, content, target grade, required budget, scheduled date and time of implementation, person in charge, sponsor, etc.

[0039] The matching history database 236 stores the matching history of educational institutions, instructors, and sponsors. The matching history database 236 includes matching identification information, educational institution identification information, class identification information, instructor identification information, sponsor identification information, matching date and time, and class implementation status.

[0040] The location acquisition unit 211 acquires location E, which is the location of the educational institution. The location acquisition unit 211 can read out location information of the educational institution from the educational institution database 233. The location acquisition unit 211 can also acquire location information input by the administrator of the educational institution via the user terminal 1.

[0041] The location acquisition unit 211 can also acquire location information such as latitude and longitude from address information. For example, the address information can be geocoded and converted into latitude and longitude information using an API (Application Programming Interface) of an external map service. The location acquisition unit 211 can also extract information identifying a local government (such as the name of a prefecture, city, town, or village) from the address information.

[0042] The location acquisition unit 211 can also acquire the location T, which is the location of the organization to which the instructor of the class belongs. The location acquisition unit 211 can read out the location information of the organization to which the instructor belongs from the instructor database 234.

[0043] The sponsor candidate selection unit 212 selects sponsor candidates based on the location S and the location E. The sponsor candidate selection unit 212 reads out the location (location S) of the sponsor candidate from the sponsor database 232, and compares it with the location (location E) of the educational institution acquired by the location acquisition unit 211 to select an appropriate sponsor candidate.

[0044] The sponsor candidate selection unit 212 can select sponsor candidates based on information identifying the local government and regional divisions included in location S and location E. For example, if the sponsor candidate and the educational institution are located in the same local government (the same city, ward, town, village, or prefecture), that sponsor candidate is selected. Furthermore, as a broader regional division, sponsor candidates can also be selected if they belong to the same region (such as the Tohoku region or Kyushu region) or economic zone. In the case of cross-border educational support, matching within the same country or the same regional association (such as Southeast Asia or ASEAN countries) is also possible. This is based on the idea that when locally rooted companies support local educational institutions, the effects of local contributions and regional branding are enhanced. It is also effective from the perspective of revitalizing the local economy over a wider area and promoting international cultural exchange.

[0045] The sponsor candidate selection unit 212 can also select sponsor candidates based on the location information included in the location S and location E. For example, it calculates the distance between the sponsor candidate and the educational institution, and selects sponsor candidates whose distance is within a predetermined threshold (for example, 5 km, 10 km, etc.). The Haversine formula using latitude and longitude information can be used to calculate the distance. It is also possible to prioritize sponsor candidates according to their distance. For example, the closer a sponsor candidate is to the educational institution, the higher the priority is given to that candidate.

[0046] The sponsor candidate selection unit 212 can select sponsor candidates by taking into consideration the location T. For example, if the sponsor candidate, educational institution, and organization to which the instructor belongs are located in the same municipality, the sponsor candidate is selected with priority. Alternatively, the optimal sponsor candidate is selected by comprehensively evaluating the distance between the sponsor candidate and the educational institution, the distance between the sponsor candidate and the organization to which the instructor belongs, and the distance between the educational institution and the organization to which the instructor belongs.

[0047] Furthermore, the sponsor candidate selection unit 212 can perform a multifaceted evaluation based not only on geographical factors but also on attribute information of the sponsor candidate and the educational institution. It obtains information such as the industry, size, corporate philosophy, and past support performance of the sponsor candidate from the sponsor database 232, and obtains information such as the type of educational institution, educational content, and desired conditions from the educational institution database 233, and evaluates the compatibility between the two. For example, it can perform matching taking into account the relevance between educational content and industry, such as between a technology company and a technical high school, or between a financial institution and a commercial high school.

[0048] The sponsor candidate selection unit 212 can also take into consideration the relationship between the sponsor candidate and the organization to which the instructor belongs. Information such as the industry, size, and social standing of both parties is obtained from the sponsor database 232 and the instructor database 234, and selection is made taking into consideration an appropriate relationship, such as avoiding combinations of companies that are in a competitive relationship. It can also reflect conditions such as when a sponsor candidate has a preference for an organization to which a specific instructor belongs, or conversely, when a sponsor candidate wishes to avoid that organization. The consistency between the corporate image of the sponsor candidate and the social reputation of the organization to which the instructor belongs is also taken into consideration, achieving a match that optimizes the relationship between the three parties.

[0049] The candidate instructor selection unit 213 selects an instructor for the class based on the locations E and T. The candidate instructor selection unit 213 reads the location (location T) of the organization to which the instructor belongs from the instructor database 234, compares it with the location (location E) of the educational institution, and selects an appropriate candidate instructor.

[0050] The candidate instructor selection unit 213 can select candidate instructors based on information identifying the local governments included in the locations E and T. For example, if the organization to which the instructor belongs and the educational institution are located in the same local government, the candidate instructor is selected. This allows classes to be taught by instructors who understand the characteristics and issues of the region.

[0051] The candidate person in charge selection unit 213 can also select candidate person in charge based on the location information included in the location E and location T. For example, it calculates the distance between the organization to which the person in charge belongs and the educational institution, and selects candidate person in charge whose distance is within a predetermined threshold. This reduces the travel burden on the person in charge.

[0052] The matching unit 214 matches the selected sponsor candidates with educational institutions and, if necessary, with person in charge candidates. The matching unit 214 combines the sponsor candidates selected by the sponsor candidate selection unit 212 with the educational institution information read from the educational institution database 233 and the person in charge candidates selected by the person in charge candidate selection unit 213 to propose the optimal match.

[0053] The matching unit 214 can optimize the matching by taking into consideration the budget size of the sponsor candidate, the types of classes that can be supported, the specialty field of the instructor candidate, past teaching performance, etc. The matching unit 214 records the matching results in the matching history database 236.

[0054] The notification unit 215 notifies the selected sponsor candidates, educational institutions, and person in charge candidates of the matching result. The notification unit 215 can notify the relevant parties of the matching result by means of email, SMS (Short Message Service), in-application notification, website notification, or the like.

[0055] The notification unit 215 notifies sponsor candidates of information about the educational institution and class to be supported, the required budget, schedule, etc. The educational institution is notified of information about the sponsor candidates, information about the person in charge, and the next steps toward implementing the class. The person in charge candidate is notified of information about the educational institution that will implement the class, information about the sponsor, details of the class, etc. Furthermore, if multiple sponsor candidates are selected, the notification unit 215 can also notify them of suggestions for support forms such as joint support (where multiple sponsors cooperate to support the same class) or distributed support (where multiple sponsors share the responsibility of an educational program).

[0056] FIG. 4 is a diagram illustrating a processing flow in an information processing system. Note that the processing flow shown in FIG. 4 is an example, and in implementing the present invention, it is possible to change the order of processing, omit some processing, or include additional processing. For example, the selection of sponsor candidates and the selection of person in charge candidates may be performed in parallel, or the selection of person in charge candidates may be performed first, followed by the selection of sponsor candidates. Furthermore, the processing flow can be flexibly configured depending on the purpose and situation of use of the system, such as when adding a process for selecting people with teaching licenses.

[0057] First, the location acquisition unit 211 acquires location E, which is the location of the educational institution (S401). This location information includes address information, position information such as latitude and longitude, and information identifying the local government.

[0058] Next, the location acquisition unit 211 acquires the location T, which is the location of the organization to which the instructor of the class belongs, as necessary (S402). This information is read from the instructor database 234.

[0059] The sponsor candidate selection unit 212 reads out the location S of the sponsor candidate from the sponsor database 232 (S403).

[0060] The sponsor candidate selection unit 212 selects sponsor candidates based on the locations S and E (S404). In this selection, consideration is given to whether the candidates are located in the same municipality, the distance between the locations, and so on.

[0061] The candidate instructor selection unit 213 selects candidate instructors for the class based on the locations E and T as necessary (S405). This selection also takes into consideration whether the candidates are located in the same municipality, the distance between the locations, and the like.

[0062] The matching unit 214 matches the selected sponsor candidates with educational institutions and, if necessary, with staff candidates (S406). This matching also takes into consideration budget size, type of classes, and fields of expertise.

[0063] The matching unit 214 records the matching result in the matching history database 236 (S407).

[0064] Finally, the notification unit 215 notifies the matching results (S408). When this system is used as an auxiliary tool for consulting, the notification is mainly sent to the consultant. The notification contents include information on the selected sponsor candidates, information on the educational institutions, information on the candidate staff, and the reasons for matching (geographical proximity, industry affinity, etc.) as requested by the system user. Notifications are sent by means of email, in-application notifications, etc. Consultants can use the notification results to make proposals to actual sponsor candidates, educational institutions, and candidate staff.

[0065] <Simple implementation method> In addition to the implementation using the computer system described above, the present invention can also be implemented using relatively simple information processing tools. For example, the information processing method of the present invention can be realized using general-purpose tools such as spreadsheet software or database software.

[0066] Specifically, location information for educational institutions and sponsor candidates within the region is collected and managed as a data table. Distance calculations are applied to this data, and processing is performed to extract sponsor candidates located within a certain radius (for example, within a 10km radius) for each educational institution. Alternatively, information processing is performed to extract sponsor candidates within the same city, ward, town, or village. Furthermore, location information for teacher candidates is managed in the same way, and calculations are performed to evaluate the geographical relevance of the three.

[0067] Even with such a simple information processing method, the basic principle of matching based on geographical relevance can be realized. In particular, when targeting small regions or a limited number of educational institutions and sponsors, effective matching support can be achieved through basic data processing using spreadsheet software, etc., without the need for an advanced computer system.

[0068] <Implementation as a supplementary tool for consulting services> The present invention can be implemented as an auxiliary tool for an education support consulting service, in which case the information processing system of the present invention functions as an auxiliary tool for supporting the work of a consultant.

[0069] Specifically, the information processing system of the present invention collects and manages information on educational institutions. In particular, it acquires and processes information such as the location of the educational institution (location E), course content, required budget, and target grade level. This information is acquired from data entered through interviews or questionnaires with the educational institution by consultants, or as the results of a survey of publicly available information. Next, the system collects and manages information on potential sponsors within the region. Information such as the location (location S) of the potential sponsors, their industry, size, budget, types of courses that can be supported, and past support performance is stored in a database.

[0070] Based on the collected information, the information processing system of the present invention performs a geographical relevance analysis. It checks whether the educational institution and the potential sponsor are located in the same municipality and calculates the actual distance between them. If necessary, it also takes into account the location of the potential instructor (location T). This analysis utilizes map information processing functions and distance calculation algorithms.

[0071] Based on the results of the geographical relevance analysis, the information processing system of the present invention creates a list of potential sponsors. This list prioritizes sponsors with a high degree of geographical relevance to the educational institution. The candidates are also ranked based on a comprehensive evaluation that takes into account factors such as the relevance of the industry and course content, and suitability of the budget. This information processing allows consultants to efficiently identify and approach potential sponsors.

[0072] The information processing system of the present invention also provides functions to manage information about selected sponsor candidates and support information sharing between educational institutions and sponsors. Data processing functions such as recording contract terms, managing schedules, and managing contact information streamline the entire matching process. In addition, the function to record and analyze matching history makes it possible to extract optimal matching patterns from past cases and use them to improve future matching accuracy.

[0073] In such an embodiment as an auxiliary tool, the present invention assists in the analysis of geographical relevance and the proposal of optimal matching, which are normally performed based on the consultant's specialized knowledge and judgment, thereby enabling these to be performed efficiently.The information processing system of the present invention does not necessarily need to be an advanced program or software, and can be implemented as a relatively simple information processing method, such as managing location information and calculating distances using spreadsheet software.What is important is the essential methodology of information processing, matching based on geographical relevance, and its implementation can be flexibly designed at various levels.

[0074] One of the key features of this invention is that it does not require educational institutions to contribute funds. By having the sponsoring company cover all costs, even small educational institutions in economically disadvantaged regional cities can, with the support of local businesses, provide specialized, high-quality educational programs on a par with those offered by urban educational institutions. This significantly contributes to narrowing regional educational disparities and has social significance in providing equal educational opportunities to all students. Furthermore, by having local businesses contribute to local education, it creates a virtuous cycle of human resource development and economic revitalization throughout the region, contributing to the creation of a sustainable local community. In this way, this invention goes beyond being a simple matching system and can become an important tool for achieving educational equity and resolving social issues.

[0075] As described above, the present invention can be implemented in various forms. The optimal embodiment can be selected depending on the situation and scale, such as automated implementation using a computer system, implementation using simple tools, or provision as a supplementary tool for consulting services. In all forms, the essential methodology of the present invention—efficiently selecting appropriate sponsor candidates based on geographic relevance—is applied. This includes selection based on the locations of the educational institution and the sponsor candidates, selection based on the organization to which the instructor belongs and the location of the sponsor candidates, or a combination of these. In particular, selecting sponsors located in the same municipality or nearby can promote locally rooted educational support and enhance the sponsor's local contributions and regional branding. Furthermore, taking the instructor's location into consideration can optimize the geographical relationship between the three parties, reducing travel burdens and contributing to classes that take advantage of local characteristics. This allows for the establishment of a beneficial relationship between educational institutions, sponsors, and instructors, contributing to improved education quality and the development of local communities.

[0076] Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention.

[0077] <Variation 1> In the above-described embodiment, an example was shown in which the sponsor candidate selection unit 212 selects sponsor candidates based on location information, but the present invention is not limited to this. For example, the sponsor candidate selection unit 212 can also make selections based on multifaceted evaluation criteria in addition to location information. Specifically, the sponsor database 232 records information such as each sponsor candidate's industry (manufacturing, IT, finance, etc.), company size (large company, small or medium-sized enterprise, venture company, etc.), corporate philosophy, support track record, and social reputation, and performs comprehensive matching taking these factors into consideration.

[0078] For example, selection can be made taking into consideration the relevance of the sponsor candidate's industry and the content of the lessons. The content of the lessons (programming, financial education, manufacturing, etc.) is obtained from the lesson database 235, and the affinity between the industry and the lesson content is scored. Then, sponsor candidates are selected based on an overall score that combines the proximity of location and the affinity of the industry. This allows for more appropriate matching, taking into consideration not only geographical proximity, but also the relevance of the sponsor's business and the content of the lessons.

[0079] Matching can also be performed taking into account the preference conditions of sponsor candidates. Preference information such as the type of educational institution each sponsor wants to support (elementary school, junior high school, high school, technical college, university, etc.), the field they want to prioritize (science, humanities, sports, etc.), and conditions that take into account affinity with the company's image (e.g., technology companies prioritize technical high schools and technical colleges) is recorded in the sponsor database 232. This allows for the exclusion or lowering of priority of educational institutions that do not match the company's preferences, even if they are geographically close, thereby improving the quality of matching.

[0080] Furthermore, the educational institution's preference conditions can also be taken into consideration. Preference information such as the industry, size, corporate philosophy, and past performance of the sponsor desired by the educational institution is recorded in the educational institution database 233. For example, a commercial high school may have a high affinity with financial institutions and retail businesses, while an industrial high school may have a high affinity with the manufacturing and construction industries, making it possible to match candidates taking into consideration compatibility with the educational content. Conditions can also be set if the educational institution wishes to avoid a particular industry or company (e.g., avoiding ties with a particular industry).

[0081] The relationship between the instructor's organization and the educational institution and sponsor is also an important evaluation criterion. Information such as the industry, size, social reputation, and philosophy of the instructor's organization is recorded in the instructor database 234, and an evaluation is made to see whether this information matches the characteristics of the sponsoring company. For example, the sponsor's desire to avoid instructors from competing companies teaching classes, or the educational institution's desire for instructors who fit a specific brand image, can be reflected in the matching process. In addition, the instructor's expertise and past teaching record and the compatibility with the educational institution's class content are also taken into consideration as evaluation criteria.

[0082] This variation can easily be implemented as a consulting service. Consultants can compare and analyze the industry information and course content of potential sponsors, and prioritize the most relevant combinations. For example, they can propose matching course content that is highly compatible with the industry, such as programming education for IT companies, financial literacy education for financial institutions, and science experiments and craft classes for manufacturing companies.

[0083] <Variation 2> In the above-described embodiment, an example was shown in which the sponsor candidate selection unit 212 selects sponsor candidates based on static location information, but the present invention is not limited to this. For example, the sponsor candidate selection unit 212 can also select sponsor candidates using a machine learning model based on past matching history and success cases. Specifically, past matching data (educational institutions, sponsors, course content, success rate, etc.) is extracted from the matching history database 236, and a model that predicts the probability of successful matching is constructed using a machine learning algorithm (random forest, gradient boosting, etc.). Information on a new educational institution, course content, and each sponsor candidate is input into this model, and the sponsor candidate predicted to have the highest probability of success is selected. This method enables sponsor selection that takes into account not only simple geographical conditions but also complex patterns based on past performance, thereby improving the success rate of matching.

[0084] As a consulting service, we can build a database of past success stories and make selections based on the analysis of similar cases. Experienced consultants can identify successful patterns from past cases and apply them to new matching.

[0085] <Variation 3> In the above-described embodiment, an example was shown in which the notification unit 215 notified the relevant parties of the selection results, but the present invention is not limited to this. For example, in addition to notifying the relevant parties of the selection results, the notification unit 215 can also provide functions to support the specific matching process and contract procedures. Specifically, the system provides a template of the contract required between the sponsor and the educational institution, a function that allows the necessary information to be entered into the system to conclude an electronic contract, a calendar integration function that supports schedule adjustments for class implementation, and a function for collecting and analyzing feedback after class implementation. This allows the system to seamlessly manage not only matching proposals but also the entire process leading up to the actual implementation of the class, thereby reducing the burden on the relevant parties and improving the efficiency of class implementation.

[0086] In the consulting service, consultants can directly provide support such as drafting contracts, mediating negotiations on terms, adjusting schedules, etc. In addition, follow-up services such as evaluations after lessons have been conducted, measuring their effectiveness, and proposing improvements can also be provided as added value.

[0087] <Variation 4> In the above-described embodiment, an example was shown in which a one-to-one matching between an educational institution and a sponsor was assumed, but the present invention is not limited to this. For example, the matching unit 214 can perform multiple types of matching in which multiple sponsors support educational programs.

[0088] The first type is "joint sponsorship," in which multiple sponsors cooperate to support the same class. Specifically, we propose matching in which multiple small and medium-sized enterprises share the costs of a class that requires a large budget, or matching in which companies from different industries collaborate to support a comprehensive class. The matching unit 214 analyzes the budget size and complementary industries of each sponsor candidate and calculates the optimal combination.

[0089] The second form is "distributed sponsorship," in which a single educational program (for example, a curriculum of 15 classes per year) is shared among multiple sponsors. For example, three companies may share the cost of five classes per year for 15 classes. Of course, the number of classes per year does not have to be divided equally; it can also be divided unevenly, such as three, five, or seven classes. In this case, each sponsor can provide support independently without being aware of the other sponsors' existence, allowing for flexible lesson plans to be created depending on each sponsor's budget size and the time period in which they can provide support. The matching unit 214 proposes the optimal combination of sponsors and schedule allocation while maintaining consistency throughout the program.

[0090] These multiple sponsorship structures make it possible to realize large-scale classes that would be difficult to support alone, and to provide comprehensive educational programs that incorporate diverse perspectives.In addition, it creates opportunities for sponsors with small budgets to participate in educational support, contributing to the enrichment of educational content and the expansion of social contribution opportunities for diverse sponsors.

[0091] Consulting services can also propose and realize more complex matching schemes, such as coordinating collaboration and division of responsibilities among multiple sponsors. Consultants can provide high-value-added services, such as role design that takes advantage of the characteristics of each sponsor and coordinating between sponsors as needed. Furthermore, in the case of independent, distributed sponsorship, they can provide specialized coordination functions, such as designing the entire educational program and creating appropriate allocation plans for each sponsor.

[0092] <Variation 5> In the above embodiment, the matching of educational institutions, sponsors, and instructors has been mainly described, but the present invention is not limited to this. Taking into account the characteristics of school education, it is also possible to perform matching that includes teaching license holders.

[0093] In many cases, legal and institutional requirements require that classes at schools be conducted under the guidance and supervision of a licensed teacher. Particularly in primary and secondary educational institutions, even when classes are taught by external personnel, it is assumed that a licensed teacher will provide guidance and supervision. Therefore, this invention further enhances the practicality of the system by providing a function that matches appropriate licensed teachers in addition to educational institutions, sponsors, and class instructors.

[0094] Specifically, the management server 2 may additionally be equipped with a teacher license database. This database includes the identification information of teacher license holders, their names, the type of license they hold (elementary school, junior high school, high school, etc.), the subject they teach (Japanese, mathematics, science, etc.), the organization they belong to, their location (Location L), contact information, their field of expertise, and their past teaching record. Detailed information in accordance with the Teacher Certification Act may be recorded regarding the type of teacher license, including classifications such as regular, temporary, and special licenses, as well as first-class, second-class, and specialized licenses. It is also desirable to manage information necessary for legal compliance, such as the expiration date and renewal status of teacher licenses.

[0095] The teacher license database manages information that is particularly important from the perspective of legal compliance. It records in detail the type of teacher license (regular license, temporary license, special license), type of school (kindergarten, elementary school, junior high school, high school, special needs school), subject (Japanese, social studies, mathematics, science, English, information, etc.), license classification (specialized, type 1, type 2), date of acquisition, expiration date, and status of renewal courses. It also includes information on special needs education and qualifications related to club activity coaching. It is desirable for this information to be updated regularly and always maintained in compliance with the latest laws and regulations.

[0096] The teacher license database can record the affiliation relationship between teacher license holders and educational institutions. Since many teacher license holders belong to specific educational institutions, managing this correspondence is effective for efficient matching. Specifically, it is possible to record the association between the identification information of teacher license holders and the identification information of educational institutions. It is also possible to record the correspondence relationship between part-time lecturers and teachers who hold positions at multiple schools and multiple educational institutions. Furthermore, for teacher license holders who belong to boards of education or teacher dispatch organizations, it is possible to record the area in which they can work and the dispatch conditions.

[0097] By taking into consideration the affiliation between the licensed teacher and the educational institution, it is possible to prioritize the selection of teachers from within the same educational institution. This allows teachers who are familiar with the institution's policies and the characteristics of students to provide guidance and supervision, which is expected to result in more effective class implementation. On the other hand, it is also possible to select licensed teachers from other schools as needed, and for particularly highly specialized classes or when special qualifications are required, it is possible to search for and select appropriate teachers within the local area.

[0098] The Teacher License Selection Department can select appropriate licensed teachers based on the course content, target grade level, and location of the educational institution. This selection process takes into account factors such as whether the license type and subject matter fit the course content, geographic proximity to the educational institution, and past teaching experience in similar courses. Particularly important is the compatibility of the course content with the subject. It is desirable to match the appropriate license type in accordance with laws and regulations, such as selecting a licensed teacher for information science for programming education, or a licensed teacher for social studies or business for financial education. Furthermore, because "integrated learning (exploration) time" is a cross-disciplinary and comprehensive learning activity that transcends subject boundaries, it can be taught by anyone with a teaching license in any subject, allowing for selection from a wide range of licensed teachers depending on the course content.

[0099] The Teaching License Selection Department can also take into consideration the affiliation with the educational institution when making its selection. First, it searches for individuals who meet the criteria among those with teaching licenses who belong to the educational institution where the class will be held, and if a suitable candidate is found, it can select them first. This ensures consistency with the educational policy of the educational institution and smooth class management. If there is no suitable candidate, or if special expertise is required, it can also select individuals who meet the criteria from among those with teaching licenses who belong to nearby educational institutions or boards of education, or from retired teachers, etc.

[0100] The Department for Selecting Licensed Teachers can also consider the complementary relationship between the expertise of the instructor and the subject matter expertise of the licensed teacher. For example, by dividing roles such that instructors from industry provide cutting-edge practical knowledge and licensed teachers provide expertise in teaching methods and learning assessment, it is possible to achieve both compliance with laws and regulations and educational effectiveness. In addition, the licensed teacher's years of experience in the educational field, teaching track record in a specific field, and training history can also be used as evaluation factors for selection.

[0101] Furthermore, the Teacher License Selection Department can also consider compatibility with the sponsor and the instructor. It is desirable to evaluate whether the educational philosophy and teaching policy of the instructor are consistent with the sponsor's corporate philosophy and the instructor's expertise, and to match them in a way that ensures smooth collaboration between all four parties. This not only ensures compliance with laws and regulations, but also maximizes educational effectiveness.

[0102] Incorporating a teacher license holder selection unit into the present invention is effective in enhancing practicality in educational settings. In Japan's school education system, the Educational Personnel Certification Act often places certain restrictions on individuals without teaching licenses teaching classes alone. Therefore, when external personnel are involved in classes, it is considered desirable for them to be supervised and guided by someone with an appropriate teaching license. The information processing system of the present invention can incorporate functions to comply with these legal requirements, thereby facilitating smooth implementation in educational settings. Including a teacher license holder selection unit enables matching based on geographic relevance while also taking legal requirements into consideration, thereby enhancing the practical value of the entire system.

[0103] In this way, by matching four parties, including licensed teachers, it is possible to realize high-quality classes that utilize the specialized knowledge of external personnel while meeting legal and institutional requirements. Furthermore, collaboration between licensed teachers and external experts is expected to contribute to improving the professional skills of teachers and developing new teaching methods, further improving the quality of education.

[0104] The above-described embodiment is merely an example for facilitating understanding of the present invention, and is not intended to limit the present invention. The present invention can be modified and improved without departing from the spirit thereof, and it goes without saying that the present invention includes equivalents thereof.

[0105] <Disclosures> The present disclosure also includes the following configurations. [Item 1] An information processing system comprising: a sponsor database that stores locations S, which are the locations of sponsor candidates who can cover the costs of tuition at educational institutions; a location acquisition unit that acquires locations E, which are the locations of the educational institutions; and a sponsor candidate selection unit that selects the sponsor candidates based on the locations S and E. [Item 2] An information processing system as described in item 1, characterized in that the location S and location E include information identifying a local government, and the sponsor candidate selection unit selects the sponsor candidates whose local government is the same. [Item 3] An information processing system according to item 1, wherein the location S and location E include location information, and the sponsor candidate selection unit selects the sponsor candidates according to the distance between the location S and location E. [Item 4] An information processing system according to item 1, comprising: a person in charge database that stores location T, which is the location of the organization to which the person in charge of the class belongs; and a person in charge candidate selection unit that selects the person in charge based on location E and location T, wherein the sponsor candidate selection unit selects the sponsor candidates based on location S, location E, and location T. [Item 5] An information processing method characterized in that a computer executes the steps of: storing a location S, which is the location of a sponsor candidate who can cover the cost of tuition at an educational institution; acquiring a location E, which is the location of the educational institution; and selecting the sponsor candidate based on the location S and location E. [Item 6] A program for causing a computer to execute the steps of: storing location S, which is the location of a sponsor candidate who can cover the cost of tuition at an educational institution; acquiring location E, which is the location of the educational institution; and selecting the sponsor candidate based on location S and location E. [Item 7] An information processing system comprising: a sponsor database that stores locations S, which are the locations of sponsor candidates who can cover the costs of classes at educational institutions; a location acquisition unit that acquires location T, which is the location of the organization to which the person in charge of the class belongs; and a sponsor candidate selection unit that selects the sponsor candidates based on location S and location T. [Item 8] An information processing system comprising: a sponsor database that stores location S, which is the location of a sponsor candidate who can cover the cost of classes at an educational institution; a location acquisition unit that acquires location E, which is the location of the educational institution; a teaching license database that stores location L, which is the location of a teaching license holder who can teach and supervise the classes; a sponsor candidate selection unit that selects the sponsor candidate based on location S and location E; and a teaching license holder selection unit that selects the teaching license holder based on location E and location L. [Item 9] Item 8 is an information processing system comprising: a person in charge database that stores location T, which is the location of the organization to which the person in charge of the class belongs; and a person in charge candidate selection unit that selects the person in charge based on location E and location T, wherein the teaching license holder selection unit selects the teaching license holder based on location E, location T, and location L. [Item 10] An information processing method characterized in that a computer executes the steps of: storing location S, which is the location of a sponsor candidate who can cover the cost of classes at an educational institution; acquiring location E, which is the location of the educational institution; storing location L, which is the location of a teaching license holder who can teach and supervise the classes; selecting the sponsor candidate based on location S and location E; and selecting the teaching license holder based on location E and location L. [Item 11] An information processing system according to item 1, item 7, or item 8, wherein the sponsor candidate selection unit selects multiple sponsor candidates for one class and further comprises a notification unit that proposes forms of joint support or distributed support by the selected multiple sponsor candidates. [Explanation of symbols]

[0106] 1. User terminal 2 Management Server 201 CPU 202 memory 203 Storage device 204 Communication Interface 205 Input Device 206 Output Device 211 Location Acquisition Department 212 Sponsor Candidate Selection Department 213 Personnel Candidate Selection Department 214 Matching Department 215 Notification Department 231 Storage section 232 Sponsor Database 233 Educational Institution Database 234 Personnel Database 235 Class Database 236 Matching History Database

Claims

1. Enter the location S, which is the location of the potential sponsor who can cover the cost of tuition at the educational institution. a sponsor database that stores a person in charge database that stores a location T that is the location of the organization to which the person in charge of the class belongs; an educational institution database that stores a location E that is the location of the educational institution; a location acquisition unit that acquires either the location E or the location T; a sponsor candidate selection unit that selects the sponsor candidates based on the location S, the location E, and the location T; An information processing system comprising:

2. An information processing system as described in Claim 1, wherein location E is different from location T.

3. 3. The information processing system according to claim 2, The location S, the location E, and the location T include information that identifies a local government, the sponsor candidate selection unit selects the sponsor candidates whose local governments are the same between the location S and the location E, or between the location S and the location T; An information processing system characterized by:

4. 3. The information processing system according to claim 2, The location S, the location E, and the location T include location information, the sponsor candidate selection unit selects the sponsor candidates according to the distance between the location S and the location E, or the distance between the location S and the location T; An information processing system characterized by:

5. Enter the location S, which is the location of the potential sponsor who can cover the cost of tuition at the educational institution. and a step of storing a location T that is the location of the organization to which the instructor of the class belongs; storing a location E of the educational institution; obtaining either the location E or the location T; selecting the sponsor candidates based on the location S, the location E, and the location T; An information processing method characterized by being executed by a computer.

6. Enter the location S, which is the location of the potential sponsor who can cover the cost of tuition at the educational institution. and a step of storing a location T that is the location of the organization to which the instructor of the class belongs; storing a location E of the educational institution; obtaining either the location E or the location T; selecting the sponsor candidates based on the location S, the location E, and the location T; A program that causes a computer to execute the following.

7. a sponsor database storing locations S of potential sponsors who can cover the cost of tuition at educational institutions; a location acquisition unit that acquires a location E that is the location of the educational institution; A teacher license holder who can teach and supervise the class is stored in the location L. A permission database and a sponsor candidate selection unit that selects the sponsor candidates based on the location S and the location E; a teacher license holder selection unit that selects the teacher license holder based on the location E and the location L; An information processing system comprising:

8. 8. The information processing system according to claim 7, a person in charge database that stores a location T that is the location of the organization to which the person in charge of the class belongs; a candidate person selection unit that selects the person in charge based on the location E and the location T; Equipped with the teaching license holder selection unit selects the teaching license holder based on the location E, the location T, and the location L; An information processing system characterized by:

9. Enter the location S, which is the location of the potential sponsor who can cover the cost of tuition at the educational institution. and obtaining a location E, which is the location of the educational institution; A step of storing the location L, which is the location of a licensed teacher who can teach and supervise the class. Pu and, selecting the sponsor candidates based on the location S and the location E; selecting the teaching license holder based on the location E and the location L; An information processing method characterized by being executed by a computer.

10. 10. The information processing system according to claim 1 or claim 7, the sponsor candidate selection unit selects a plurality of sponsor candidates for one class, Further comprising a notification unit that proposes a form of joint support or distributed support by the selected plurality of sponsor candidates; An information processing system characterized by:

Images