JP7382590B1 - Information processing device, information processing method and program - Google Patents

Abstract

[Problem] It is possible to suitably obtain an interesting technology, which is a technology in which a need company is interested, from seed technologies that have actually been matched. [Solution] A management server 20 of an information processing system 1 includes a matching result database 223 that stores information regarding past business matching between a needs company having a needs technology and a seeds company having a seeds technology; The present invention includes an extraction unit 231 that extracts the seed technology possessed by a seed company matched with a company in need, and a generation unit 232 that generates an interesting technology of the company in need from the extracted seed technology. [Selection diagram] Figure 3

Description

The present invention relates to an information processing device, an information processing method, and a program that support business matching for companies.

In recent years, open innovation, which creates new value by combining in-house technology with superior technology from outside companies, has become active in product development at companies. Then, we will efficiently promote open innovation between companies that need outside technology (needs technology) and companies (seed companies) that have technology that can be provided externally (seed technology). In order to do well, business matching, which brings together companies with needs and companies with seeds, is also attracting attention.

Generally, when a company that performs business matching receives a matching request from a company with needs, it searches for seed companies that have seed technology that meets the needs. At this time, the matching accuracy and speed of matching are determined by the correspondence between needs technology and seeds technology.

Conventional technologies that improve the precision of such matching techniques include, for example, a system that performs matching using the match rate of clauses between received needs technologies and seed technologies in a database (Patent Document 1), and explicit needs technologies. There is a matching support device (Patent Document 2) that uses a structured relationship between a candidate company and a candidate company's seed technology for matching evaluation.

Japanese Patent Application Publication No. 2003-186898 JP 2021-193537 Publication

Many of the conventional techniques described above are techniques used by need companies to search for seed companies, and it is difficult for seed companies to use these techniques as they are to search for need companies.

In addition, the technology that a company needs is rarely made available to the world as public information, and because of this background, it is difficult for seed companies that want to actively promote their seed technology to accurately search for companies that need it.

The present invention has been made in consideration of such circumstances, and is an information processing method that can suitably obtain an interesting technology, which is a technology that a company with needs is interested in, from a seed technology that has actually been matched. The purpose is to provide devices, information processing methods, and programs.

In order to solve the above problems, an information processing device according to the present invention associates information regarding past business matching between a first company having a needs technology and a second company having a seeds technology with the degree of success of the matching. a first storage unit for storing; an extraction unit for extracting seed technology possessed by each of the one or more second companies that matched the needs technology of the first company from information regarding the past business matching; The invention is characterized by comprising a generation unit that generates a technology of interest of the first company by performing a weighting operation on one or more of the seeded technologies according to the degree of success of each of them .

Moreover, in order to solve the above-mentioned problems, the present invention provides an information processing method in a search server used for business partner search, in which the past information of a first company having a needs technology and a second company having a seeds technology is provided. storing information related to business matching in a first storage unit of an information processing device in association with the degree of success of the matching; A step of extracting the seed technology possessed by each of the second companies, and performing a weighting operation on the one or more extracted seed technologies according to the degree of success of each one to determine the interest of the first company. The method is characterized by comprising a step of generating a technology.

Furthermore, in order to solve the above-mentioned problems, the program according to the present invention includes information processing information such as past business history between a first company having a need technology and a second company having a seed technology, in an information processing device used for business partner search. a step of storing information regarding matching in a first database in association with the degree of success of the matching ; and one or more of the second companies matching the technology needs of the first company based on the information regarding the past business matching. a step of extracting each seed technology; and a step of generating a technology of interest of the first company by performing a weighting operation on the one or more extracted seed technologies according to the degree of success of each. It is characterized by being executed.

According to the present invention, it is possible to suitably obtain an interesting technology, which is a technology in which a need company is interested, from seed technologies that have actually been matched. As a result, seed companies can suitably find companies with needs that are interested in their technology, and seed companies can search with high accuracy for companies with needs.

1 is a configuration diagram of an information processing system 1 according to an embodiment of the present invention. FIG. 1 is a block diagram showing the configuration of a user terminal 10 in an information processing system 1 according to an embodiment of the present invention. FIG. 2 is a block diagram showing the configuration of a management server 20 in the information processing system 1 according to an embodiment of the present invention. FIG. 2 is a diagram showing an example data structure of a matching result database 223 according to an embodiment of the present invention. It is a diagram showing an example of a data structure of a feature word database 224 according to an embodiment of the present invention. FIG. 2 is a diagram showing an example data structure of a learning model database 225 according to an embodiment of the present invention. It is a flowchart for explaining the overall processing in the management server 20 of the information processing system 1 according to one embodiment of the present invention. It is a flowchart for explaining the search process of a need company having an interesting technology in the management server 20 of the information processing system 1 according to an embodiment of the present invention.

Hereinafter, one embodiment of the present invention will be described with reference to the drawings.
FIG. 1 is a configuration diagram of an information processing system 1 according to an embodiment of the present invention. As shown in FIG. 1, the information processing system 1 according to the present embodiment includes a plurality of user terminals 10 and a management server 20, each of which is communicably connected via a wired or wireless network 30 such as the Internet.

In the following, the above information processing system 1 is introduced to a company that provides a company matching service that includes searching for business partner companies (hereinafter referred to as a "management company"), and the information processing system 1 is introduced to a company that provides a company matching service that includes the search for business partner companies. Processes such as searching for companies that have the required technology (need companies) are performed from the company that has the technology (seed companies).

The user terminal 10 is, for example, a personal computer of a manager of a department that searches for business partners in a managed company or a person in charge of the department. When the management company receives a request from a seed company to search for a company in need or a technology in need, a person in charge or the like inputs information such as keywords related to the seed technology via the user terminal 10. Specifically, a person in charge of the user terminal 10 or the like accesses a website managed by the management server 20 and inputs a keyword. The management server 20 searches for a company in need based on the input keyword, and information on the company in need is displayed on a website accessed from the user terminal 10.

Here, the user terminal 10 may be installed not only in a management company but also in a seed company. In this case, a person in charge of a seed company or the like can input information regarding their company's seed technology from their respective user terminals 10 and view information regarding companies with needs searched by the management server 20.

FIG. 2 is a block diagram showing the configuration of the user terminal 10 in the information processing system 1 according to an embodiment of the present invention. As shown in FIG. 2, the user terminal 10 includes an input section 11 such as a keyboard or touch panel through which a user such as a person in charge inputs keywords, commands, etc., input keywords and needs companies and needs generated by the management server 20. It includes a display unit 12 such as a monitor that displays information related to technology, a processing unit 13 that performs various processes, and a communication unit 14 that performs communication processing with the management server 20 and other devices (not shown) through a network 30. There is. The processing unit 13 is composed of a CPU, a ROM storing programs, a RAM, etc., and performs various processes according to the programs stored in the ROM. In this embodiment, the user terminal 10 will be described as a personal computer, but it may also be a communication terminal such as a smartphone or a tablet.

FIG. 3 is a block diagram showing the configuration of the management server 20 in the information processing system 1 according to an embodiment of the present invention. As shown in FIG. 3, the management server 20 includes a communication unit 21 that performs communication processing with the user terminal 10 and other external devices (not shown) via the network 30, and a storage unit that stores information such as programs and various data. 22, a processing section 23 that performs various processes based on the programs and data stored in the storage section 22. The processing unit 23 is composed of a CPU, a ROM storing programs, a RAM, etc., and performs various processes according to the programs stored in the ROM. The management server 20 also includes an input section 24 and a display section 25 that have the same functions and roles as the input section 11 and display section 12 of the user terminal 10.

Here, the storage unit 22 includes a need company database 221 that stores information regarding need companies, a seed company database 222 that stores information regarding seed companies, and a matching database 222 that stores information regarding results regarding past business matching conducted at management companies. A result database 223, a feature word database 224 that stores feature words that indicate the technical characteristics of a company, a learning model storage unit 225 that stores a learning model of feature words based on machine learning, and technologies that companies with needs are interested in. It is equipped with an interesting technology database 226 that stores interesting technologies associated with seed technologies that have actually been matched.

In addition, the processing unit 23 includes an extraction unit 231 that extracts the seed technology possessed by the seed company that matched with the need company from information regarding past business matching, and a generation unit that generates the technology of interest of the need company based on the extracted seed technology. 232, and a similarity calculation unit 233 that calculates the similarity between information regarding the seeds technology and information regarding the technology of interest based on information input from the user terminal 10 or the input unit 24.

In this embodiment, the management server 20 is designed to implement the processing functions of each of the above-mentioned units (communication section 21 to display section 25) with one server, but it is also possible to implement this function with multiple servers. It's okay. Furthermore, each server may be configured with a single computer, or may be configured with a plurality of physically different computers.

The needs company database 221 stores a company ID assigned to each company in the information processing system 1 and information regarding the needs technology of the needs company in association with each other. Information regarding the needs technology is stored in text format and updated by the managing company or specially authorized needs companies.
The seed company database 222 stores a company ID assigned to each company in the information processing system 1 and information regarding the seed technology of the seed company in association with each other. Information on seed technology is stored in text format and updated by management companies and specially authorized seed companies.

The matching result database 223 stores information regarding matching results in past business partner searches performed in the information processing system 1. FIG. 4 is a diagram showing an example data structure of the matching result database 223 according to an embodiment of the present invention. Information regarding past matching results includes the management number (propos ID) of past business matching projects, the project name, the management number (ordering company ID) of the needs company that is the ordering company, the name of the ordering company, and the seed company that is the ordering company. The information includes the management number of the company (order company ID), the name of the company receiving the order, information regarding the contents of the seed technology, and information regarding the degree of success of matching.

In this embodiment, the degree of success in matching is determined based on, for example, a list of seed companies prepared by the management company based on the needs technology of the needs company. (2) A track record in which more specific technical information was exchanged before business matching was established (interview completed); (3) A track record in which matching was completed after a meeting was established between the ordering company and the contracted company (matching completed) It is divided into three levels of action. These action levels are just examples, and the success of matching may be limited to only the "matching established" action, or may be divided into more detailed action levels.

The characteristic word database 224 stores, for each company, a plurality of characteristic words representing characteristics of the company. These characteristic words are generated by acquiring public company information and non-public company information regarding needs technology and seeds technology for each company from the needs company database 221 and seeds company database 222 to generate combined text data. It corresponds to technology-related terms obtained through morphological analysis and unnecessary word removal processing. Each feature word is then stored in the feature word database 224 in association with the company ID. Further, the feature word database 224 stores scores based on the frequency of appearance of each of a plurality of feature words in association with each other.

FIG. 5 is a diagram showing an example data structure of the feature word database 224 according to an embodiment of the present invention. In this embodiment, as the number of feature words possessed by each company, the feature words with the highest scores for each company are stored together with their scores, but in this embodiment, as an example, 500 feature words are stored for each company along with their scores. shall be remembered.

Here, the score of the feature word will be explained. In this embodiment, the score of the characteristic word is calculated based on the appearance frequency of each characteristic word in all the text data stored in the needs company database 221 and the seeds company database 222. It is expressed as a numerical value obtained by multiplying the frequency ratio by a predetermined coefficient. In this embodiment, in order to effectively perform business matching particularly in the manufacturing industry, many words unique to the manufacturing industry are stored as feature words, and the information processing system includes a dictionary for the manufacturing industry. There is.

The learning model database 225 stores learning models obtained by machine learning distributed representations of feature words. This learning model analyzes feature words included in public literature information on technology (hereinafter referred to as "technical documents") such as patent documents, company R&D reports, and technology-related news for each company. This method performs deep learning to predict feature words from a given set of words, and generates a distributed representation in which each feature word is expressed as a high-dimensional vector.

FIG. 6 is a diagram showing an example data structure of the learning model database 225 according to an embodiment of the present invention. In the present embodiment, in the learning model database 225, distributed representations of each feature word generated based on the feature words stored in the feature word database 224 are numerically shown in the order of vector dimensions for each feature word. remembered. In this embodiment, as described later, each feature word is constructed as a distributed representation of a high-dimensional vector with at least several hundred dimensions (specifically, a 500-dimensional vector), so 500 numbers are stored. However, the number of dimensions of the vector is not limited to this, and the system design can be such that it can be changed as appropriate.

That is, the learning model database 225 stores a learning model in which distributed representations of feature words obtained by performing machine learning to predict one feature word from a plurality of feature words are stored for each feature word. Further, the feature word database 224 stores scores based on the frequency of appearance of each of a plurality of feature words in association with each other.

The interested technology database 226 stores information regarding technologies that companies that have needed technologies are interested in. In other words, the needs technology is a technology that has been successfully matched in past business matching conducted using the information processing system 1, that is, a technology owned by a needs company that has a track record of matching with a specific seed technology, and is a technology that the needs company is interested in. It can be said that it is an interesting technology because it is a technology that has a long history. By having the information processing system 1 hold information on the needs technology for which business matching has been successful, the seed companies can suitably find need companies that are interested in their technology, and the seed companies can It becomes possible to search for companies with high needs.

Hereinafter, with reference to the drawings, technologies that need companies are interested in (interesting technologies) are generated based on the results of past business matching in the information processing system 1, and seed companies etc. The process of searching for interesting technology will be explained.

FIG. 7 is a flowchart for explaining overall processing in the management server 20 of the information processing system 1 according to an embodiment of the present invention.
In the following embodiment, an information processing system 1 is introduced in a management company that provides a company matching service, and a user of the management company performs business matching in response to an order for a company search service from an ordering company. It is assumed that the results (success cases, etc.) have already been accumulated (stored) in the matching result database 223 of the management server 20.

<Example 1>
Here, based on the results of past business matching, we will use the technology that was the target of needs technology matching to find out about the technologies (interesting technologies) that need companies that possess needs technologies are interested in. The processing of the information processing system 1 that generates information will be explained.

As described above, the matching result database 223 in the storage unit 22 of the management server 20 stores information regarding past business matching between need companies with need technology and seed companies with seed technology. Therefore, in this process, information on the seed companies (order companies) that are matching partners and information on the degree of success of the matching are extracted for cases in which business matching was successful for each need company (order company) (step S12).

Next, when all the information on the ordering company that is the matching partner for the same project has been extracted (“None” in step S13), the same process is performed for the next business matching project for the ordering company (steps S12-S14). ). When the above process is completed for the ordering company ("None" in step S14), the interest of the needing company is determined by considering the weighting based on the information on the degree of success of the characteristic word information extracted so far. Information regarding the technology is generated (step S15). Thereafter, similar processing is performed for the next ordering company for which business matching has been successful. Such processing is performed for all need companies stored in the matching result database 223. Each process will be specifically explained below.

In the management server 20, upon receiving an instruction from the user terminal 10 or the input unit 24, etc., the extraction unit 231 in the processing unit 23 extracts the needs technology of the need company from information regarding past business matching stored in the matching result database 223. The seed technology possessed by the seed company that matches is extracted (step S12). In past business matching, companies with needs searched for seed companies with seed technology as ordering companies. At this time, there may be one or more seed companies, which are the companies receiving the order, whose seed technology has been successfully matched with the technology needed by the company in need. Furthermore, the seed technologies of these multiple companies may be the same or similar, or dissimilar. However, each seed technology is a technology that has been matched with a needs technology, and can be said to be of interest to needs companies that own the needs technology.

More specifically, in the management server 20, when the first needing company (ordering company) that has been successfully matched and stored in the matching result database 223 is selected ("corresponding" in step S11), the extraction unit 231 refers to the case ID of the matching case and extracts information on the seed company (order company) (for example, order company ID) and information regarding the degree of success of matching for each matching case of the selected needs company; Information regarding the seed technology of the seed company associated with the order receiving company ID is extracted from the seed company database 222 (step S12).

The management server 20 determines whether or not the matching case of the company in need is matched with another company that has received orders (step S13), and if matching has been established with two or more companies that have received orders (step S13). (Yes), the process returns to step S12 and repeats the process. On the other hand, if the management server 20 determines that there is no matching with another order-receiving company for the matching company in need ("None" in step S13), it determines that there is another matching project for the company in need. It is determined whether there is one (step S14).

As a result, if there is another matching case for the company in need (“Yes” in step S14), the process returns to step S12 and repeats the process.

On the other hand, if the management server 20 determines that there is no other company with needs (“none” in step S14), the generation unit 232 responds to the information on the seed companies extracted by the previous processing of the extraction unit 231. By weighting information on the assigned characteristic words and synthesizing them based on information on the degree of success, technologies of interest of companies in need are generated from information on the extracted one or more seed technologies (step S15). In other words, the technology of interest is a bundle of seed technologies that have been matched in the past by companies with needs, and when bundling, the degree of success is given as a weight. The weighting is set by the management company, etc. in the order of (1) favorite registration, (2) successful interview, and (3) successful matching, so that actions closer to successful matching are given higher weights. For example, by setting values such as (1) weighting coefficient for favorite registration = 0.3, (2) weighting coefficient for successful interview = 0.5, and (3) weighting coefficient for successful matching = 1.0, Multiply the information about. These weighting coefficients may be changed by the administrator of the information processing system 1 depending on the operational situation.

That is, the generation unit 232 weights the vector information of the feature word according to the degree of success by referring to the learning model database 225 based on the feature word related to the seed company having the seed technology extracted from the feature word database 224. to generate interesting technologies. Thereby, by handling the characteristics of a company's technology as vector information, calculations for generating an interesting technology can be suitably performed within a computer. Then, the generated technology of interest is stored in the technology of interest database 226 in association with the information of the company in need (for example, ID of the ordering company).
Note that after the processing in step S15 in the management server 20, the process returns to step S11, and the process continues until there are no companies in need of the next successful matching stored in the matching result database 223 ("not applicable" in step S11). The above process continues to generate information regarding the technology of interest to the company in need.

As described above, according to the information processing system 1 according to the present embodiment, an interesting technology that is a technology that a company that has a need technology (a need company) is interested in is preferably selected from seed technologies that have actually been matched. Obtainable. As a result, companies with seed technologies (seed companies) will be able to suitably find companies with needs that are interested in their technology, and seed companies will be able to search for companies with highly accurate needs. .

<Example 2>
Next, a person in charge of the managing company receives a request from the seed company to collect information on the seed technology using the in-house user terminal 10, based on the technology of interest of the need company generated in Example 1 and managed by the management server 20. We will explain the process of inputting information and searching for companies with needs that are interested in the seed technology. In this case, the information processing system 1 will function as a search system.

As described in the first embodiment, the information regarding the technology of interest generated by the generation unit 232 is stored in the technology database of interest 226 for each company in need. Further, the learning model database 225 stores distributed representations of feature words obtained by performing machine learning to predict one feature word from a plurality of feature words as a "learning model" for each feature word. Further, the feature word database 224 stores scores based on the frequency of appearance of each of a plurality of feature words in association with each other.

FIG. 8 is a flowchart for explaining search processing for companies in need that have an interesting technology in the management server 20 of the information processing system 1 according to an embodiment of the present invention. When a person in charge of the management company inputs a keyword related to the seed technology from the input unit 11 of the user terminal 10, the management server 20 acquires the information (step S21).

Next, the similarity calculation unit 233 of the management server 20 obtains a distributed representation of the feature word corresponding to the obtained keyword from the feature word database 224 and generates a composite distributed representation (step S22). Here, vector information based on the keyword is assumed to be first vector information. Then, the similarity calculation unit 233 calculates the similarity between the first vector information and vector information (second vector information) based on the technology of interest of each company stored in the technology database of interest 226 (step S23).

Specifically, the similarity calculation unit 233 obtains distributed representations (high-dimensional vector representations) of characteristic words of each company from the learning model database 225 for each company with needs stored in the technology database of interest, and calculates the composite distributed representation. Generate a representation. In this embodiment, the composite distributed expression is obtained by adding the respective high-dimensional vector expressions given to the characteristic words owned by each company. Specifically, for each hit company, a composite distributed expression is created by adding up all distributed expressions of characteristic words owned by that company (in this embodiment, each company is assigned 500 characteristic words). generate. At this time, the distributed expressions of each feature word may be simply added together, or feature words with higher scores are considered to better express the characteristics of the company, and feature words with lower scores are considered to be more representative of the company. A composite distributed representation may be generated by adding the values with high weighting.
Note that in this embodiment, cosine similarity is used as the calculated similarity.

As described above, in the present embodiment, the similarity calculation unit 233 calculates feature words similar to the feature word corresponding to the acquired keyword based on the learning model, and calculates feature words similar to the feature word corresponding to the keyword and the feature word similar to the feature word. A first vector containing a plurality of feature words and their scores, and a second vector based on a plurality of synthesized feature words and their scores, which are the technologies of interest for each needs company stored in the interest technology database 226. Calculate similarity with vector. Then, based on the magnitude of the degree of similarity calculated by the degree of similarity calculation section 233, information on companies with needs associated with the technology of interest is displayed (output) on the display section 12 of the user terminal 10 (step S24). For example, the list of companies in need is output in a format that can be displayed on a web browser displayed on the user terminal 10 that has accessed the management server 20. This makes it possible to suitably find companies with needs that are interested in the seed technology based on the similarity between vector information from past successful business matching cases.

When displaying a list of companies in need, for user convenience, the company name, address, URL, business summary, etc. associated with the company ID can be included in the company list by referring to the company in need database 221. You can also do this.

Note that the same applies when a person in charge of a company that manages the technology of interest of a company in need uses the input unit 24 of the management server 20, or a person in charge of a seed company searches for a company in need using the user terminal 10 of the company. The explanation will be omitted because the following processing will be performed.

In addition, regarding the needs company database 221 and the matching result database 223, company management and project management are not performed by company, but by more specific management units (for example, management units such as person in charge, department in charge, business division, etc.). Information regarding business matching may be managed separately. In this case, information regarding past business matching is also managed not on a company-by-company basis but on a more broken-down basis. That is, the matching result database 223 stores information regarding past business matching for each subdivided management unit (for example, management unit such as person in charge, department in charge, business division, etc.) within the company in need. Then, the generation unit 232 generates the technology of interest of the company in need for each such management unit, and the technology of interest database 226 is also managed in this subdivided management unit. This allows companies to search for technologies they are interested in by management unit, such as the person in charge or the department in charge, allowing seed companies to search in more detail for companies with needs that are interested in their technology, and making contact with them much more convenient.

Furthermore, in the present embodiment, an example has been described in which all or part of the functions provided by the management server 20 are realized by a SaaS type cloud service. It may also be realized by a service provided via an infrastructure as a service) or an API (Application Programming Interface).

Note that the configuration of the information processing system 1, the configuration of the user terminal 10, and the configuration of the management server 20 described in this embodiment are merely examples, and may be changed within the scope of the present invention. Furthermore, the flow of processing in the management server 20 and the like is just one example, and unnecessary processing steps can be deleted, new processing steps can be added, and processing steps can be replaced within the scope of the present invention.

1 Information processing system 10 User terminal 20 Management server 21 Communication unit 22 Storage unit 23 Processing unit 24 Input unit 25 Display unit 30 Network 221 Need company database 222 Seed company database 223 Matching result database 224 Feature word database 225 Learning model database 226 Interest technology Database 231 Extraction unit 232 Generation unit 233 Similarity calculation unit

Claims (7)

a first storage unit that stores information regarding past business matching between a first company having a needs technology and a second company having a seeds technology in association with the degree of success of the matching ;
an extraction unit that extracts seed technology possessed by each of the one or more second companies that matched the needs technology of the first company from the information regarding the past business matching;
and a generation unit that generates a technology of interest of the first company by performing a weighting operation on one or more of the extracted seed technologies according to the degree of success of each of them. . further comprising a feature word storage unit that stores a plurality of feature words representing characteristics of the seed technology possessed by the second company for each company,
The generation unit generates the technology of interest by weighting vector information based on the characteristic word regarding the extracted second company having the seed technology in accordance with the degree of success and composing the vector information. The information processing device according to claim 1 . a second storage unit that stores the interesting technology generated by the generation unit for each of the first companies;
an input section for entering keywords;
a similarity calculation unit that calculates the similarity between the first vector information based on the keyword and the second vector information based on the interesting technology;
The information processing apparatus according to claim 2 , further comprising: an output unit that outputs the first company associated with the predetermined technology of interest based on the degree of similarity. further comprising a learning model storage unit that stores a distributed representation of the feature words obtained by performing machine learning to predict one feature word from the plurality of feature words for each feature word to serve as a learning model;
The feature word storage unit stores a score based on the frequency of appearance of each of the plurality of feature words in association with each other,
The similarity calculation unit calculates a feature word similar to the feature word corresponding to the keyword based on the learning model, and calculates the feature word corresponding to the keyword, a plurality of feature words similar to the feature word, and the like. 3. A degree of similarity is calculated between the first vector information including the score and the second vector information including a plurality of feature words based on the technology of interest and their scores. The information processing device described in . The first storage unit stores information regarding the past business matching divided into each predetermined management unit of the first company,
The information processing apparatus according to claim 1, wherein the generation unit generates the technology of interest of the first company for each management unit. An information processing method in a search server used for searching for business partners,
storing information regarding past business matching between a first company having a needs technology and a second company having a seeds technology in a first storage unit of the information processing device in association with the degree of success of the matching ;
extracting seed technologies possessed by each of the one or more second companies that matched the needs technology of the first company from the information regarding the past business matching;
An information processing method comprising : performing a weighting operation on one or more of the extracted seed technologies according to the degree of success of each to generate a technology of interest to the first company. Information processing equipment used to search for business partners,
storing information regarding past business matching between a first company having a needs technology and a second company having a seeds technology in a first database in association with the degree of success of the matching ;
a step of extracting seed technologies possessed by each of the one or more second companies that matched the needs technology of the first company from the information regarding the past business matching;
and generating a technology of interest to the first company by performing a weighting operation on one or more of the extracted seed technologies according to the degree of success of each .

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