Corporate Valuation Processor System

Abstract

To reduce an answer burden of a supplier in company evaluation.SOLUTION: A company evaluation processor system comprises a memory and a processor. The memory: stores master data in which at least one or more questions are associated with answers for each predetermined target company; stores a predetermined point allotment for each question of the master data for each evaluation company; receives an inquiry sheet acquired from an inquiry sheet distribution source by the target company, and an inquiry sheet answer; stores the inquiry sheet answer in the memory as the answer of the master data related to the target company, when one of inquiry sheet questions, and one of each question of the master data related to the target company are similar to one another; and grades the answer of the master data of the target company by using the point allotment according to the evaluation company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a business reputation processor system. [Background technology]

[0002] In recent years, the ESG (Environment, Social, Governance) investment market has been expanding. In the ESG investment market, investors evaluate companies based on ESG factors and decide where to invest. This is because companies that place importance on ESG are expected to have stable management in the future and have high potential for growth.

[0003] In recent years, ESG evaluations, including those on environmental issues and child labor, are being conducted not only within a company but across its entire supply chain. As a result, buyers are increasingly requesting ESG evaluations of business entities (also known as suppliers) that participate in their supply chain systems.

[0004] The mainstream method for buyers to conduct such supplier ESG evaluations is to obtain information by conducting questionnaire surveys of suppliers.

[0005] On the other hand, responding to questionnaire surveys places a heavy burden on suppliers. This is because the number of questions can range from dozens to hundreds, and some questions require suppliers to attach supporting data to support their answers. There are also multiple evaluation agencies, each of which sets similar but not completely identical questions. Furthermore, in addition to the questionnaires from evaluation agencies, buyers may also create their own questions and ask suppliers to respond. As a result, the response rate from suppliers tends to be low.

[0006] Buyers can enter into a contract with a specific rating agency to use the evaluation results of suppliers they do business with. However, it is rare that all of the suppliers they do business with participate in the rating scheme of that rating agency. Therefore, buyers evaluate their suppliers by using multiple rating agencies in combination, or by using questions created by the buyers themselves.

[0007] Furthermore, survey questions tend to share common underlying standards, such as ISO (International Organization for Standardization) 26000, the ISO 14000 series, and the United Nations Global Compact, and differences in questions are often due to differences in resolution. Alternatively, surveys often share common underlying standards, but the format of the questions often differs. For example, surveys may ask about whether or not goals have been set, but may ask about the results of achieving those goals.

[0008] In this context, Patent Document 1, a prior art from the perspective of ESG evaluation, quantitatively collects ESG information on a specific company as data and outputs information based on that data. In other words, Patent Document 1 discloses a technology that quantitatively analyzes ESG data and visualizes the results to support ESG management in the company.

[0009] Furthermore, in the field of natural language processing, feature extraction methods such as Bagof Words, TF-IDF, BM-25, and N-gram are commonly known as techniques for recognizing commonalities in sentences.

[0010] There are many machine learning techniques, but commonly known techniques that are often used as classifiers in natural language processing include support vector machines, decision trees, and k-nearest neighbor methods.

[0011] A prior art technique for natural language processing is a question type learning device shown in Patent Document 2, which configures a highly accurate classifier for identifying question types using N-gram, a natural language processing technique, and Support Vector Machine, a machine learning technique. [Prior art documents] [Patent documents]

[0012] [Patent Document 1] Patent Publication No. 2021-009696

[0013] [Patent Document 2] Japanese Patent Application Laid-Open No. 2004-094521 Summary of the Invention [Problem to be solved by the invention]

[0014] The technology described in Patent Document 1 above makes it possible to collect ESG information from the core system of a target company and perform a quantitative ESG evaluation. However, in an environment where it is common for each evaluation agency to conduct its own survey, as described above, such a system cannot be used as is. Furthermore, responses to survey questions do not necessarily have to be numerical; responses may need to include natural language text based on the results of various data. Furthermore, when responses using quantitative data are required, it may be necessary to process the acquired quantitative data to tailor the responses to the questions asked by each evaluation agency or buyer. Furthermore, the questions may include qualitative questions, and the data required to respond is not limited to quantitative data. Furthermore, to respond to a survey, the vast amount of collected information must be organized into an appropriate format and written as answers to the relevant questions.

[0015] An object of the present invention is to reduce the burden of responses on suppliers in corporate evaluations. [Means for solving the problem]

[0016] The present application includes multiple means for solving at least part of the above-mentioned problems, examples of which are as follows: A system according to one aspect of the present invention for solving the above-mentioned problems is a company evaluation processor system having one or more memories and one or more processors, wherein the memory stores master data for each predetermined target company, in which at least one question is associated with the answer to the question, and stores, for each evaluated company that evaluates the target company, a predetermined score associated with each question in the master data, and the processor receives a questionnaire obtained by the target company from a questionnaire distributor and survey responses that are the target company's answers to one or more survey questions included in the questionnaire, and if any of the survey questions is similar to any of the questions in the master data related to the target company, stores the survey response in the memory as a response to the master data related to the target company, and scores the response in the master data of the target company using the score assigned to the evaluated company, thereby performing and outputting an evaluation of the target company. [Effects of the Invention]

[0017] According to the present invention, it is possible to provide a technology that reduces the burden of responding to a company evaluation on a supplier. Problems, configurations, and effects other than those described above will become apparent from the following description of the preferred embodiment of the invention. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a question and answer / evaluation system. [Figure 2] FIG. 10 is a diagram illustrating an example of answer completion from evidence data. [Figure 3] FIG. 10 is a diagram showing another example of answer completion from evidence data. [Figure 4] FIG. 10 is a diagram illustrating an example of a flowchart of a response support process. [Figure 5] FIG. 10 is a diagram illustrating an example of a data structure of a question material storage area. [Figure 6]FIG. 10 is a diagram illustrating an example of a data structure of an answer history storage area. [Figure 7] FIG. 2 is a diagram illustrating an example of a data configuration of a master data storage area. [Figure 8] FIG. 2 is a diagram illustrating an example of a data configuration of master data. [Figure 9] FIG. 10 is a diagram illustrating an example of a flowchart of a company evaluation process. [Figure 10] FIG. 10 is a diagram illustrating another example of a flowchart of the response support process. [Figure 11] FIG. 10 is a diagram illustrating an example of inter-company evaluation. [Figure 12] FIG. 10 is a diagram illustrating an example of performing factor analysis using master data. [Figure 13] FIG. 2 illustrates an example of a hardware configuration of a processor system. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The examples are illustrative of the present invention, and for clarity of explanation, appropriate omissions and simplifications have been made. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0020] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0021] Examples of various types of information may be described using expressions such as "table," "list," and "queue," but the various types of information may be expressed using data structures other than these. For example, various types of information such as "XX table," "XX list," and "XX queue" may be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable. Furthermore, in the embodiments, identification information described using these expressions is expressed using symbols, numbers, natural language, or a combination thereof, but the identification information may be in a format other than these.

[0022] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. When there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0023] In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., a CPU or a GPU) and performs processing defined by the program using storage resources (e.g., a memory) and interface devices (e.g., a communication port). Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0024] A program may be installed on a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor and a storage resource for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in an embodiment, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0025] Furthermore, although the present invention is a processor system, it may also be realized as a platform having the functions of the present invention.

[0026] 1 is a diagram showing an example of the configuration of a question answering and evaluation system. For example, the question answering and evaluation system 10 is a company evaluation system including a processor system 100, a network 50, an evaluation agency D computer 300, an evaluation agency E computer 310, a supplier A computer 400, a supplier B computer 410, a supplier C computer 420, a buyer F computer 800, a buyer G computer 810, and an evaluation requester H computer 850.

[0027] The network 50 is, for example, any one of a LAN (Local Area Network), a WAN (Wide Area Network), a VPN (Virtual Private Network), a communication network that uses a general public line such as the Internet in part or in whole, a mobile phone communication network, etc. Note that the network 50 may also be a wireless communication network such as Wi-Fi (registered trademark) or 5G (Generation).

[0028] Evaluation institutions D and E are examples of institutions that evaluate suppliers, which are entities that provide parts and products, in a parts supply network such as a supply chain network. The number of evaluation institutions is not limited to two institutions; there are usually many more. However, in this embodiment, for the sake of simplicity, these two institutions are referred to as evaluation institutions.

[0029] Supplier A, Supplier B, and Supplier C are examples of suppliers that are entities that provide parts and products in a parts supply network such as a supply chain network. The number of suppliers is not limited to only three institutions, and there are usually many more. However, in this embodiment, for the sake of simplicity, these three institutions are considered to be suppliers.

[0030] Buyer F and Buyer G are examples of buyers that purchase parts and products in a parts supply network such as a supply chain network. The number of buyers is not limited to two organizations; there are usually many more. However, in this embodiment, for simplicity, these two organizations are referred to as buyers. When managing and selecting suppliers, buyers may use the evaluation organization, or may evaluate suppliers themselves without using the evaluation organization. Note that in this embodiment, suppliers and buyers are distinguished for simplicity, but when a supplier purchases parts or products, the supplier may also be the buyer, and when another buyer purchases parts or products provided by a buyer, the buyer may also be the supplier.

[0031] Evaluation requester H is an example of an evaluation requester that requests the processor system to evaluate a supplier for the purpose of managing and selecting suppliers. The evaluation requester may be, for example, a buyer, but the evaluation requester may also be someone other than a buyer, such as an investor. In this example of the present embodiment, the evaluation requester and the buyer are described separately to simplify the explanation, but as mentioned above, the buyer may be the evaluation requester.

[0032] Furthermore, by using some or all of the functions of this processor system, a company with multiple affiliated companies can evaluate and manage its own sustainability. In that case, the company in question can be treated as the evaluation requester H and its affiliated companies as suppliers, allowing for an internal ESG evaluation.

[0033] The processor system 100 includes a memory 110, a processing unit 120, an input / output interface 130, and a transmission interface 140. The memory 110 includes a question material storage area 111, an answer history storage area 112, and a master data storage area 113. The processing unit 120 includes a question material receiving unit 121, an answer support unit 122, an answer receiving unit 123, an evidence data processing unit 124, a learning / optimization unit 125, an evaluation unit 126, and a comparison / analysis unit 127. The processor system 100 is a system having one or more processors. The processor system 100 can also be called a company evaluation processor system.

[0034] Figure 2 illustrates an example of the processing system 100's function of completing an answer from evidential data using the evidential data processing unit 124. While terms such as "evidential data," "evidential documents," and "evidence data" are used to refer to the evidential documents or data that form the basis of an answer, these terms are interchangeable. Two patterns of answer support processing are possible. One is a pattern in which the processor system 100 receives question materials from an evaluation institution or buyer, which is called survey agency. The other is a pattern in which the processor system 100 receives question materials from a supplier, which is called respondent assistance. The difference between the two patterns is whether the processor system 100 performs the tasks from receiving the question materials to conducting the survey and submitting the answers on behalf of the supplier, or whether the supplier performs them independently. Despite these differences in methods, the question answering and evaluation system 10 can reduce the supplier's response burden. The example of answer completion from evidential data shown in this figure is an example in which an answer is completed using the answer support processing (respondent assistance). An overview of the respondent assistance processing will be explained using Figure 2. In addition, for the sake of simplicity, the processing unit in FIG. 2 only shows the functions required for the explanation in FIG. 2, but in reality, it is assumed to have the configuration shown in FIG.

[0035] First, supplier A receives a request from evaluation institution D to respond to the questionnaire materials issued by evaluation institution D via network 50. Specifically, supplier A computer 400 receives the questionnaire materials of evaluation institution D from evaluation institution D computer 300.

[0036] Then, supplier A sends the evidence data for questions in the question materials that require evidence data and the question materials to the processor system 100. Specifically, supplier A's computer 400 sends evidence data 90 to the processor system 100. At this time, supplier A does not enter answers to questions in the question materials that require evidence data for their answers. Also, even if attachment of evidence data is not required as an answer to a question, for questions for which answer information can be obtained from the evidence data, the same processing can be performed by attaching evidence data.

[0037] The processor system 100 receives the evidence data 90 and the question materials from the transmission interface 140. Then, the question material receiving unit 121 of the processing unit 120 receives the question materials, and the evidence data 90 is received by the evidence data processing unit 124. Next, the evidence data processing unit 124 performs a predetermined process on the evidence data 90 and creates, from the question materials, a proposed answer to the question to which the evidence data 90 is attached.

[0038] Here, for example, it is assumed that the portion of the evidence data 90 to be used in answering a question (the position within the evidence data) has been specified in advance by the learning and optimization unit 125. In this case, when the evidence data processing unit 124 receives the evidence data 90, it extracts the data at that portion and generates an answer to the question.

[0039] Alternatively, if the learning and optimization unit 125 has prepared answer proposals in advance that correspond to the content of the section of the evidence data 90 that will be used to answer the question, the evidence data processing unit 124 selects the answer proposal that best matches the content of the evidence data 90 and creates an answer to the question.

[0040] Even when a question is not a free-form answer but one that requires a choice from options, the evidence data processing unit 124 can similarly create an answer to the question. For example, if the learning and optimization unit 125 has prepared in advance which option to select based on the content of the section of the evidence data 90 to be used as the answer, the evidence data processing unit 124 selects the option that best matches the content of the evidence data 90 to create the answer to the question. For example, if the evidence data 90 contains data with an appropriate title and data type, the evidence data processing unit 124 selects the option "We are implementing efforts" as the answer to the question.

[0041] Alternatively, even if the question requires a numerical answer, the evidence data processing unit 124 can similarly generate an answer to the question. For example, if the evidence data 90 is a CSV (Comma Separated Value) file containing numerical values, the evidence data processing unit 124 reads the CSV file, extracts one or more parts necessary for the answer, and performs calculations using these as input variables for a predetermined arithmetic operation to generate an answer to the question. In this case, the learning and optimization unit 125 pre-stores the row and column positions required for generating the answer, the formulas required for the calculation, etc., and executes these contents to generate an answer to the question. Note that the row and column positions required for generating the answer, the formulas required for the calculation, etc. are basically assumed to remain unchanged from the previous year and are stored by the learning and optimization unit 125 using the contents of past answers. Furthermore, the evidence data processing unit 124 may extract multiple pieces of data from predetermined positions in the read dataset and use them as input variables, and may also use external data collected by web crawling processing as input variables to perform calculations using predetermined formulas and use the results to complement the questionnaire responses.

[0042] The answer support unit 122 records the created answer as an answer to the question material and sends it to the supplier A computer 400 via the transmission interface 140. Furthermore, supplier A, having received the proposed answer from the processor system 100, makes any necessary corrections to the input proposed answer and sends the evaluation institution D's question material with the completed answer back to the processor system 100. The sent material is received by the answer receiving unit 123 of the processing unit 120 via the transmission interface 140, and updates the learning and optimization unit 125 regarding whether the answer has been corrected and the content of the corrections. In other words, the learning and optimization unit 125 performs machine learning using the information on whether the questionnaire answers have been corrected and the corrected answer, and builds a trained model of supplier A (for each target company).

[0043] Then, the supplier A computer 400 sends the evaluation institution D's question materials with completed answers and the evidence data to the evaluation institution D computer 300. If there are any corrections to the proposed answer, there is a high possibility that the proposed answer extracted from the evidence data is incorrect. In this case, the learning and optimization unit 125 needs to change the reference points or calculation formulas in the evidence data 90. Therefore, the learning and optimization unit 125 re-learns the format of the evidence data 90, re-learns the calculation formulas, or re-learns the evidence data collected by web crawling.

[0044] FIG. 3 is a diagram showing another example of the function of the processor system 100, in which the evidence data processing unit 124 complements an answer from evidence data. In this example, the evidence data 90 is not attached by supplier A, but the processor system 100 collects information collected by the information collection unit 401 of the supplier A computer 400. Specifically, the information collection unit 401 collects in advance numerical data of monitoring targets from each facility and equipment owned by the supplier (for example, the supplier A equipment computer 400' in FIG. 3). Alternatively, the information collection unit 401 obtains web information in advance by performing web crawling or the like. Then, the evidence data processing unit 124 collects the evidence data 90 collected by the information collection unit 401 at the timing of answer support.

[0045] Furthermore, in FIG. 3, the evidence data processing unit 124 acquires external data 60 when there is insufficient external data 60 for the arithmetic operations required to calculate the answer. The external data 60 is, for example, an emission intensity used to calculate CO2 emissions. More specifically, the amount of electricity consumption for the target year corresponds to the numerical data to be monitored from facilities, equipment, etc. owned by the supplier. The evidence data processing unit 124 calculates the amount of CO2 emissions by multiplying the amount of electricity consumption by the emission intensity for electricity, which corresponds to the external data 60.

[0046] FIG. 4 shows an example of the processing flow of the response support process (survey agency). The response support process (survey agency) is started when a start instruction is received from an evaluation agency, buyer, etc. Alternatively, the response support process (survey agency) may be started at a predetermined date and time (e.g., 6:00 AM every day) or at predetermined intervals (e.g., every 12 hours). The response support process (survey agency) is performed when the processor system 100 acts on behalf of the processor system 100 to perform tasks from receiving question materials to conducting the survey and sending the responses.

[0047] First, the question material receiving unit 121 receives and stores question materials from the evaluation agency or the buyer (step S101). Specifically, the question material receiving unit 121 receives question materials from the evaluation agency D computer 300, the evaluation agency E computer 310, the buyer F computer 800, and the buyer G computer 810. The question material receiving unit 121 breaks down the received question materials into question units, reconstructs them, and stores them in the question material storage area 111 (FIG. 5).

[0048] Then, the response support unit 122 transmits the question materials to the supplier via the transmission interface 140 (step S102).

[0049] Then, the evidence data processing unit 124 receives the evidence documents or data from the suppliers (step S103). Specifically, the evidence data processing unit 124 receives the evidence documents or data (sometimes collectively referred to as a data set) related to the answers to the questions from the supplier A computer 400, the supplier B computer 410, and the supplier C computer 420.

[0050] If the creation date of the data set received in step S103 is before the evaluation period, the evidence data processing unit 124 determines that the data is insufficient as evidence or data, and displays a message to that effect on the supplier's computer.

[0051] The response support unit 122 then uses the received dataset to perform response complementation processing (step S104). Specifically, as described above, the response support unit 122 complements the questionnaire responses by reading and transcribing one or more pieces of data from a predetermined position in the received dataset. Furthermore, if there is master data for which responses have already been provided to other questionnaires for the same supplier, the response support unit 122 complements the master data responses to the questionnaire questions if the master data does not contain any answers to questions similar to those in the master data. Furthermore, the response support unit 122 uses the trained model of the learning and optimization unit 125 in the response complementation processing for questionnaires for the same supplier that differ in one or more of the survey period and questionnaire distribution source.

[0052] Then, the response support unit 122 sends the proposed response to the supplier (step S105).

[0053] Then, the response receiving unit 123 receives the responses from the suppliers (step S106). Specifically, the response receiving unit 123 receives the responses, supporting documents or data, and corrections to the proposed responses from the supplier A computer 400, the supplier B computer 410, and the supplier C computer 420.

[0054] The learning and optimization unit 125 stores the received answer as an answer history in the answer history storage area 112 (step S107) (FIG. 6). Then, the learning and optimization unit 125 analyzes the content of the correction to the proposed answer, and corrects the program describing the procedure for creating an answer from documentary evidence or data, and the classifier of the learning and optimization unit 125.

[0055] Then, the response support unit 122 transmits the response received from the supplier to the evaluation institution or the buyer that requested the survey (step S108). Specifically, the response support unit 122 transmits the response received from the supplier from the transmission interface 140 to the evaluation institution D computer 300, the evaluation institution E computer 310, the buyer F computer 800, and the buyer G computer 810.

[0056] The above is an example of a flowchart of the response support process (survey proxy). The response support process (survey proxy) can reduce the burden of answering questions. Therefore, the response burden on suppliers in company evaluations can be reduced.

[0057] 5 is a diagram showing an example of the data structure of a question material storage area. The question material storage area 111 stores information about questions posed to suppliers. Specifically, the question material storage area 111 has an issuing institution ID 111a, a material name 111b, a response period 111c, a responding supplier ID 111d, and a question 111e. The issuing institution ID 111a, the material name 111b, the response period 111c, the responding supplier ID 111d, and the question 111e are associated with each other.

[0058] The issuing agency ID 111a stores information specifying the issuing agency ID, which is identification information specifying the issuing agency of the question. In this embodiment, in the case of non-financial information questionnaires and CSR questionnaires, the issuing agencies are evaluation agencies D and E, and in the case of SAQs (self-assessment questionnaires), the issuing agency is buyer F. However, there are cases where the evaluation agency provides the self-assessment questionnaire, and cases where the buyer issues the non-financial information questionnaire or CSR questionnaire.

[0059] The document name 111b stores the document name of the document in which the questions are written. The document name may differ depending on the issuing organization, but in this embodiment, it is a "non-financial information questionnaire," a "CSR (Corporate Social Responsibility) questionnaire," a "self-assessment questionnaire," etc. However, it is not limited to these, and generally, it is sufficient if it requests responses to non-financial information such as ISO26000, ISO14000 series, etc. Furthermore, many of these documents include questions issued by evaluation organizations or buyers with suppliers as respondents.

[0060] The response period 111c includes information that specifies the period to which the document containing the questions is to be answered. Since many evaluation institutions require suppliers to respond with results for the previous year once a year, the response period 111c stores information that specifies the previous year. However, if the questionnaire documents are issued at a different frequency, the response period 111c stores information that specifies the period (first half, second half, first quarter, etc.) corresponding to the response period.

[0061] The reply supplier ID 111d stores information that identifies the entity that will reply to the material identified by the material name 111b.

[0062] The question 111e stores a question sentence (natural language, index, or mathematical formula) included in the material identified by the material name 111b. In Fig. 5, there are only two questions for the sake of simplicity, but in reality there are several to several hundred questions.

[0063] FIG. 6 is a diagram showing an example of the data structure of the response history storage area. The response history storage area 112 stores supplier response data by issuing institution and response period. Specifically, the response history storage area 112 has a responding supplier ID 112a, an issuing institution ID 112b, a response period 112c, and a response data ID 112d. The response data identified by the response data ID 112d includes the response data finally sent to the issuing institution and the attached evidence data. However, if there are multiple responses, such as resubmissions in the same period, the response data 112d may also include a history of the responses.

[0064] 7 is a diagram showing an example of a master data storage area. The master data storage area 113 includes a responding supplier ID 113a, a responding period 113b, an issuing institution ID 113c, and a master data ID 113d.

[0065] The responding supplier ID 113a stores information that identifies the entity that will provide the response. The response period 113b includes information that identifies the period to be responded to. The issuing institution ID 113c stores information that identifies the issuing institution ID, which is identification information that identifies the issuing institution of the question. The master data ID 113d includes information that identifies the master data, described below, created for each supplier in each response period 113b.

[0066] The master data storage area 113 stores, for each supplier, the relationship between the master data ID 113d created in each response period 113b and the issuing institution ID 113c of the evaluation institution or buyer that set the questions used to create the master data ID. For example, the master data ID 113d for a record whose responding supplier ID 113a is "Supplier B" and whose response period 113b is "2019" is created from the answers to questions created by an institution whose issuing institution ID 113c is "Evaluation institution D."

[0067] Note that the master data ID 113d of the record whose responding supplier ID 113a is "Supplier A" and whose response period 113b is "2019" has the issuing institution ID 113c set to "Master Data." This indicates that the master data related to the record is not a response to a question issued by an external institution such as an evaluation institution or buyer, but a direct response to the question in the master data itself.

[0068] FIG. 8 is a diagram showing an example of the data structure of master data. Master data 114 exists for each supplier and for each response period. The master data 114 is data in which a category 114a, a criterion ID 114b, a question 114c, an answer 114d, evidence data 114e, an answer data ID 114f, a score 114g, a scoring criterion 114h, and a score 114i are associated with each other. The category 114a indicates the category to which the question 114c belongs. For example, category "E" is a category related to the environment. The criterion ID 114b is information that is associated one-to-one with the feature vector of the criterion question. The question 114c is a question from an evaluation institution or a buyer that has a feature vector corresponding to the criterion ID 114b. The answer 114d is a response from a supplier that corresponds to the question identified by the question 114c.

[0069] The evidence data 114e is information that identifies the data that serves as evidence associated with the answer 114d. The answer data ID 114f is the same as the answer data ID 112d in the answer history storage area 112, and is linked to the question and answer from the evaluation institution or the buyer.

[0070] The score 114g is a score for quantitatively evaluating the answer 114d. The scoring criteria 114h are scoring criteria for quantitatively evaluating the answer 114d. The score 114i is a score resulting from the quantitative evaluation of the answer 114d. The score 114g and scoring criteria 114h are set for each response period by the person requesting the evaluation. Therefore, the score 114g and scoring criteria 114h can be set in common for all suppliers to be evaluated. It is also possible to divide suppliers into specific groups, for example, groups based on business area or company size, and set the score 114g and scoring criteria 114h for each group.

[0071] The master data 114 associates the questions and answers that a supplier has provided to one or more evaluation institutions or buyers during the response period with a standard ID 114b associated with the standard question. That is, if the questions from the evaluation institution or buyer contain a question that asks the same content as the standard question, the question and its answer are associated as question 114c and answer 114d, respectively, in the row of the standard ID 114b associated with the standard question. On the other hand, if the questions from the evaluation institution or buyer do not contain a question that asks the same content as the standard question, the question 114c and answer 114d in the row of the standard ID 114b are left blank.

[0072] FIG. 9 is a diagram showing an example of a flowchart of the company evaluation process. The company evaluation process classifies questionnaires provided by multiple evaluation agencies and questionnaires created independently by buyers by the content of the questions, and then arranges identical questions side by side to consolidate and display multiple questionnaires. The company evaluation process also uses the points and scoring criteria assigned in advance in the master data to compare evaluations of supplier companies. The company evaluation process begins when a start instruction is received from an evaluation agency, buyer, etc. Alternatively, the company evaluation process may begin at a predetermined date and time (e.g., 6:00 a.m. every day) or at predetermined intervals (e.g., once a month).

[0073] First, the evaluation unit 126 receives the questions and point allocation settings of the master data for the relevant year stored in the master data storage area 113 in the memory 110 from the evaluation requester (evaluation requester H computer 850) (step S201).

[0074] Suppliers may not necessarily answer the questions that the evaluation requester wants, for example, if they respond to a questionnaire from a different evaluation organization than the evaluation requester. Therefore, by allowing the evaluation requester to set score allocations for master data questions, responses to different questionnaires can be evaluated using the same criteria. Specifically, the evaluation unit 126 accepts the setting of score allocations and scoring criteria for the master data 114. At this time, the evaluation requester can freely set the score allocations and scoring criteria for the master data 114. However, considering the effort required to set all score allocations and evaluation criteria, the evaluation unit 126 may accept selections from score allocation proposals pre-stored in the memory 110 of the processor system 100 according to the evaluation requester's needs. Note that questions with a score set to 0 are excluded from the evaluation items as they are not questions for the relevant year.

[0075] Then, the evaluation unit 126 accepts the registration of the supplier to be evaluated by the evaluation requester (step S202). Specifically, the evaluation unit 126 accepts the selection of the responding supplier to be evaluated from the responding supplier IDs 113a in the master data storage area 113.

[0076] Then, the question material receiving unit 121 receives and stores the question materials from a plurality of evaluation agencies or buyers (step S203). Specifically, the question material receiving unit 121 receives question materials from the evaluation agency D computer 300, the evaluation agency E computer 310, the buyer F computer 800, and the buyer G computer 810. The question material receiving unit 121 breaks down the received question materials into question units, reconstructs them, and stores them in the question material storage area 111.

[0077] Then, the question material receiving unit 121 transmits the question materials from the transmission interface 140 to the suppliers registered in step S202 (step S204). Specifically, the question material receiving unit 121 sends the question materials received from the evaluation agency D computer 300, the evaluation agency E computer 310, the buyer F computer 800, and the buyer G computer 810 to the suppliers. Note that the question material receiving unit 121 also sends the question materials received from the evaluation agency or the buyer to suppliers who are not registered as evaluation targets by any evaluation requester. Only the response contents of suppliers registered as evaluation targets by the evaluation requester are used in the evaluation of the evaluation requester.

[0078] Then, the response receiving unit 123 receives the responses from the suppliers (step S205). Specifically, the response receiving unit 123 receives the question materials, the responses, and supporting documents or data from the supplier A computer 400, the supplier B computer 410, and the supplier C computer 420.

[0079] Between step S204 and step S205, in which the supplier answers questions from the evaluation institution or the buyer, or questions 114c in the master data 114, the answer accepting unit 123 may accept answers by manual input, or may use answer supplementation using documentary evidence or data through answer support processing. Alternatively, both may be performed. An example of the process of answer supplementation will be described later with reference to FIG. 10.

[0080] Specifically, the response receiving unit 123 stores in the response history storage area 112, for each supplier, the evaluation institutions or buyers that have been evaluated from the past to the present, the response periods in which the evaluation was received, and the response data at the time of the evaluation, in association with each other. For example, supplier A was evaluated by evaluation institution D in response periods 2020 and 2021. The response receiving unit 123 stores the response data ID for the data in response period 2020 as "AD-2020" and the response data ID for the data in response period 2021 as "AD-2021," respectively. These response data IDs are linked to the actual questions and response data for the questions that are stored.

[0081] Then, the answer receiving unit 123 breaks down the received question materials, answers, documentary evidence or data into question units, reconstructs them and stores them in the master data 114 for the relevant year stored in the master data storage area 113 (step S206).At that time, the answer receiving unit 123 stores the data that will be the originals of the received question materials, answers, documentary evidence or data in a predetermined area of the memory.

[0082] As a method of associating a question with the same content as a question linked to the reference ID 114b of the master data 114, for example, a method of obtaining the questions of the evaluation agency or buyer in advance and manually linking them can be used.

[0083] Alternatively, when questionnaire materials are sent from the evaluation agency, buyer, or supplier being evaluated, the questionnaire materials can be separated by questions, and each question can be automatically assigned to the master data standard ID 114b.

[0084] An example of a method for automatic allocation is the following natural language processing. First, in order to recognize commonalities between questions, the answer support unit 122 generates an appropriate feature vector from the character string of the question using various feature extraction methods, such as Bag of Words, TF-IDF, BM-25, and N-gram, either individually or in combination. Furthermore, the answer support unit 122 classifies the feature vectors generated from the questions into the same class if the feature vectors are considered to have a common meaning with the original question, and assigns a classification ID to them.

[0085] The answer support unit 122 then learns multiple patterns of the relationship between this feature vector and category ID, and constructs a classifier that can predict a category ID for a feature vector newly created from an unknown question. The classifier in the answer support unit 122 predicts the category ID using, for example, a support vector machine, a decision tree, or a k-nearest neighbor method. In other words, when the classifier determines that the category ID is the same, the answer support unit 122 determines that the question has a common meaning.

[0086] Alternatively, it is also possible to use a method in which each question is assigned to the reference ID 114b of the master data using artificial intelligence such as a neural network.

[0087] Then, the answer receiving unit 123 determines whether there are any unanswered questions (step S207). Specifically, the answer receiving unit 123 identifies, from among the questions assigned to the master data in step S206, questions for which there are insufficient answers (no answers) as unanswered questions.

[0088] If there are any unanswered questions ("Yes" in step S207), the answer receiving unit 123 requests the suppliers to complete the questions (step S208). Specifically, the answer receiving unit 123 sends messages requesting completion to the supplier A computer 400, the supplier B computer 410, and the supplier C computer 420. Then, the answer receiving unit 123 returns control to step S205.

[0089] If there are no unanswered questions ("No" in step S207), the evaluation unit 126 uses the master data score allocation 114g set in step S201 to evaluate the company (step S209). Specifically, the evaluation unit 126 evaluates the answers using the master data score allocation 114g and the scoring criteria 114h to calculate the score 114i.

[0090] Then, the comparison and analysis unit 127 organizes the evaluation results from step S209 according to the supplier criteria ID 114b, and transmits information showing a comparison of the evaluations of the supplier companies to the evaluation requester (step S210).

[0091] The above is an example of a flowchart for the company evaluation process. The company evaluation process enables the evaluation requester to evaluate suppliers even if the evaluation requester has multiple suppliers, and even if some or all of the suppliers are not under contract with the evaluation agency under contract with the evaluation requester.

[0092] In addition, if a supplier is not evaluated by any evaluation organization or buyer, the supplier can use the answer to question 114c of master data 114 to evaluate the supplier and compare it with other suppliers who have been evaluated by an evaluation organization. In this way, even if a supplier is not evaluated by a buyer, it can use its own assessment sheet instead.

[0093] Furthermore, it is also possible to evaluate suppliers using only the questions 114c in the master data 114, without using the evaluation results of the evaluation agency or buyer.

[0094] When only question 114c of master data 114 is used, it is possible to perform an answer support process different from the answer support process shown in Fig. 4. Fig. 10 shows another example of the process flow of answer support process. This answer support process can be applied to both survey proxy and respondent assistance. Fig. 10 shows an example of application to survey proxy.

[0095] First, the reply support unit 122 transmits the question 114c of the master data 114 to the supplier via the transmission interface 140 (step S301). Then, the reply support unit 122 receives the answer to the question 114c of the master data 114, documentary evidence, or data from the supplier via the transmission interface 140, and stores them in the master data storage area 113 (step S302).

[0096] Then, the question material receiving unit 121 receives and stores the question materials from the evaluation agency or the buyer via the transmission interface 140 (step S303). Specifically, the question material receiving unit 121 receives question materials from the evaluation agency D computer 300, the evaluation agency E computer 310, the buyer F computer 800, and the buyer G computer 810. The question material receiving unit 121 breaks down the received question materials into question units, reconstructs them, and stores them in the question material storage area 111 (FIG. 5).

[0097] Then, for questions that are similar to any of the questions in the master data, the response support unit 122 uses the answers and supporting documents or data assigned to the master data to complement the answers and supporting documents or data for the questions in the evaluation agency's questionnaire (step S304).

[0098] Then, the response support unit 122 transmits the question materials from the evaluation institution and the proposed response completed in step S304 to the supplier via the transmission interface 140 (step S305).

[0099] Then, the response receiving unit 123 receives the response from the supplier (step S306). Specifically, the response receiving unit 123 receives the response confirmed by the supplier, the supporting documents or data, and the correction content of the proposed response that has been corrected as necessary, via the transmission interface 140.

[0100] The learning and optimization unit 125 then stores the received answer as an answer history in the answer history storage area 112 (step S307) (FIG. 6). The learning and optimization unit 125 then analyzes the content of the corrections to the proposed answer, and corrects the program describing the procedure for creating an answer from documentary evidence or data, and the classifier of the learning and optimization unit 125.

[0101] Then, the response support unit 122 transmits the response received from the supplier to the evaluation institution or the buyer that requested the survey (step S308). Specifically, the response support unit 122 transmits the response received from the supplier from the transmission interface 140 to the evaluation institution D computer 300, the evaluation institution E computer 310, the buyer F computer 800, and the buyer G computer 810.

[0102] According to the above-mentioned response support process, suppliers can prepare responses to the master data in advance, instead of answering questions from multiple evaluation institutions and buyers each time. Therefore, when a supplier receives questions from multiple evaluation institutions and buyers, the supplier only needs to confirm the answer plan, and can respond to the questions from multiple evaluation institutions and buyers.

[0103] When the above-described answer support process is applied to respondent assistance, the supplier receives the question materials from the evaluation institution or buyer in step S303. Also, in step S308, the process of sending the answers and supporting documents or data to the evaluation institution or buyer differs in that the supplier sends them. Even with these differences in the flow of sending and receiving the question materials, the question answering and evaluation system 10 can be said to reduce the burden of responding on the supplier.

[0104] FIG. 11 is a diagram illustrating an example of inter-company evaluation. In this embodiment, inter-company evaluation refers to evaluating different suppliers side by side using different questions from different evaluation institutions or buyers. For example, in step S210, the comparison analysis unit 127 outputs a table, such as an inter-company evaluation diagram 30, in which supplier information (questions, answers, and scores) is displayed on the horizontal axis and the criterion ID 114b, i.e., questions, is displayed on the vertical axis. In the example of the inter-company evaluation diagram 30, supplier A only answers questions from evaluation institution D. Meanwhile, supplier C only answers questions from evaluation institution E. The questions from evaluation institution D and evaluation institution E have one or more common questions. By arranging the common questions side by side according to the criterion ID 114b, it becomes possible to perform an inter-company evaluation of supplier A and supplier C using the answers to questions from different evaluation institutions or buyers.

[0105] FIG. 12 is a diagram showing an example of factor analysis using master data. Note that FIG. 12 shows a simplified version of the master data 114. Standard IDs 114b are generally not deleted but are added as needed. Therefore, upon receiving instructions from a user, the comparison and analysis unit 127 chronologically tracks the response content for the same standard ID 114b stored in the master data 114 for each response period. The comparison and analysis unit 127 then analyzes the factors behind changes over time in the response content for a specific standard ID 114b based on the correlation with fluctuations in the response data for another standard ID 114b.

[0106] The comparison and analysis unit 127 may use one or more reference IDs 114b for analysis. For example, if the question being analyzed is about CO2 emissions, the comparison and analysis unit 127 analyzes the factors by examining correlations with changes in questions about the number of employees, sales, recycled material usage rate, the emission intensity used in the calculation, and whether or not reduction measures have been implemented. Graph 40 in FIG. 12 illustrates an example in which the answers to the question being analyzed and related questions are numerical. However, this is not a limitation; either or both of the answers to the question being analyzed and the related questions may be non-numerical, such as whether or not a company has implemented measures or its policies. The progress of the response results shown in the graph in FIG. 12 can be transmitted from the processor system 100 to the screen of a computer, etc., of the user requesting the evaluation, the supplier itself, or other users, and can be viewed.

[0107] In this case, the master data 114 is created by integrating questionnaires provided by multiple rating agencies and questionnaires created independently by the buyer. Therefore, even if the rating requester (such as a buyer or a supplier who wants to evaluate their own ESG status) joins a rating agency midway through the evaluation process, or even if the rating requester changes the rating agency they contract with midway through the evaluation process, it is possible to evaluate the trends for each evaluation period using the same time-series data. For example, the master data ID 113d of each master data 114 shown in FIG. 12 corresponds to the master data ID 113d for Supplier B's response period 113b from 2019 to 2021. Although Supplier B's questionnaire issuing agency ID 113c for the response period 2019 and that for the response period 2020 and beyond are different, the comparison and analysis unit 127 integrates the questionnaires using the questions in the master data 114 to evaluate them as a series of time-series data.

[0108] According to the present invention, a buyer can evaluate suppliers even if some or all of the suppliers are not under contract with the buyer's evaluation agency. Furthermore, this technology allows suppliers to automatically respond to specific questions by simply attaching supporting documents or data or collecting data via a supplier computer, thereby reducing the burden on suppliers. Furthermore, this technology uses supplier-specific master data to analyze factors that cause variations in the responses to specific questions, facilitating the buyer's evaluation and management of suppliers.

[0109] The processor system 100 also performs machine learning on the relationship between answer information to a question and supporting evidence or data. The answer to the question can be automatically entered simply by attaching supporting evidence or data or by collecting the document or data via a supplier computer. The supporting evidence or data can be an electronic document or a handwritten document converted into digital data using image recognition or PDF. The supporting evidence or data can be in the form of a PDF or other document, numerical data written in a CSV file, numerical data directly obtained from the supplier's facilities and equipment via a supplier computer, or information obtained from the web via web crawling.

[0110] Furthermore, in the processor system 100, master data exists for each supplier and each evaluation period, and by using this supplier-specific master data, the factors behind the supplier's response to a specific question can be identified through a correlation analysis with the response to another question related to the specific question. Since the master data 114 is created by integrating questionnaires provided by multiple evaluation agencies and questionnaires created independently by the buyer, it is possible to evaluate the progress of each evaluation period in chronological order, even if the buyer joins an evaluation agency midway or changes the evaluation agency with which the buyer contracts midway.

[0111] 13 is a diagram illustrating an example of the hardware configuration of a processor system. The processor system 100 can be realized as a general computer 900 including a processor (e.g., a central processing unit (CPU) or a graphics processing unit (GPU)) 901, a hardware memory 902 such as a random access memory (RAM), an external storage device 903 such as a hard disk drive (HDD) or a solid state drive (SSD), a reading device 905 that reads information from a portable storage medium 904 such as a compact disk (CD) or a digital versatile disk (DVD), an input device 906 such as a keyboard, mouse, barcode reader, or touch panel, an output device 907 such as a display, and a communication device 908 that communicates with other computers via a communication network such as a LAN or the Internet, or as a network system including a plurality of such computers 900. The reading device 905 may be capable of not only reading but also writing to the portable storage medium 904.

[0112] The processor 901 executes various processes by executing various predetermined programs loaded from the external storage device 903 into the memory 902. The programs are, for example, application programs that can be executed on an OS (Operating System) program. The programs may be installed into the external storage device 903 from a portable storage medium 904 via the reading device 905, or may be downloaded from a network via the communication device 908 and executed by the processor 901.

[0113] For example, the question material receiving unit 121, the answer support unit 122, the answer receiving unit 123, the evidence data processing unit 124, the learning and optimization unit 125, the evaluation unit 126, and the comparison and analysis unit 127 can be realized by loading programs stored in an external storage device 903 into the memory 902 and executing them on the processor 901. The input / output interface 130 can be realized by the processor 901 using the input device 906, the output device 907, and the communication device 908. The memory 110 can be realized by the processor 901 using the memory 902 or the external storage device 903. The transmission interface 140 can be realized by the processor 901 using the communication device 908.

[0114] The above is an example of a question answering and evaluation system according to an embodiment of the present invention. Note that the present invention is not limited to the above-described examples and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to a system having all of the described configurations. It is possible to replace part of the configuration of an embodiment with another configuration, and it is also possible to add the configuration of another embodiment to the configuration of an embodiment. It is also possible to delete part of the configuration of an embodiment.

[0115] Some or all of the above-described units, configurations, functions, processing units, etc. may be implemented in hardware, for example, by designing them as integrated circuits. Furthermore, the above-described units, configurations, functions, etc. may be implemented in software by a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk, or a recording medium such as an IC card, SD card, or DVD.

[0116] It should be noted that the control lines and information lines in the above-described embodiments are those considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be considered that almost all components are interconnected. The present invention has been described above, focusing on the embodiments. [Explanation of symbols]

[0117] 10: Question answer and evaluation system, 50: network, 100: processor system, 110: memory, 111: question material storage area, 112: answer history storage area, 113: master data storage area, 114: master data, 120: processing unit, 121: question material reception unit, 122: answer support unit, 123: answer reception unit, 124: evidence data processing unit, 125: learning and optimization unit, 126: evaluation unit, 127: comparison analysis unit, 130: input / output interface, 140: transmission interface, 300: evaluation agency D computer, 310: evaluation agency E computer, 400: supplier A computer, 410: supplier B computer, 420: supplier C computer, 800: buyer F computer, 810: buyer G computer, 850: evaluation requester H computer.

Claims

1. 1. A business valuation processor system having one or more memories and one or more processors, The memory stores master data associating at least one question with an answer to the question for each predetermined target company, and stores a predetermined score associated with each question in the master data for each evaluation company that evaluates the target company, The processor: The target company receives a questionnaire obtained from a questionnaire distributor and a questionnaire response, which is the target company's response to one or more questionnaire questions included in the questionnaire; If any of the questionnaire questions and any of the questions in the master data related to the target company are similar to each other, the questionnaire response is stored in the memory as the response to the master data related to the target company; Using the points allocated according to the evaluated company, the responses of the master data of the target company are scored, thereby evaluating the target company and outputting the evaluation. A business valuation processor system comprising:

2. 2. The business reputation processor system of claim 1, The processor: A second target company, different from the target company, receives a second questionnaire obtained from the survey questionnaire distributor or a second survey questionnaire distributor different from the survey questionnaire distributor, and second survey questionnaire responses from the second target company to second survey questionnaire questions, which are one or more questions included in the second questionnaire; If any of the questions in the second questionnaire and any of the questions in the master data related to the second target company are similar to each other, the second questionnaire response is stored in the memory as the response in the master data related to the second target company; Using the points allocated according to the evaluated company, the responses of the master data of the target company and the responses of the master data of the second target company are scored, and evaluations of the target company and the second target company are output so as to be comparable. A business valuation processor system comprising:

3. 2. The business reputation processor system of claim 1, The processor: receiving a dataset related to the questionnaire responses; reading one or more pieces of data from a predetermined location in the dataset and using them to complete the questionnaire responses; A business valuation processor system comprising:

4. 2. The business reputation processor system of claim 1, the processor selectively accepts one of a plurality of predetermined point allocation plans from the evaluated company and stores it in the memory as the point allocation, or accepts an input of the point allocation from the evaluated company and stores it in the memory; A business valuation processor system comprising:

5. 2. The business reputation processor system of claim 1, The processor: When the target company receives a second questionnaire obtained from a second questionnaire distribution source different from the first questionnaire distribution source, If any of the second questionnaire questions, which are one or more questions included in the second questionnaire, is similar to any of the questions in the master data related to the target company, the answer in the master data related to the target company is taken as the answer to the second questionnaire question; A business valuation processor system comprising:

6. 2. The business reputation processor system of claim 1, The memory stores the master data for each of the predetermined target companies for each predetermined period; The processor: a correlation analysis between the plurality of questions regarding the change over time in the responses related to numerical data among the responses of the target company for the period, and a factor analysis is performed to present the result to the target company; A business valuation processor system comprising:

7. 1. A business valuation processor system having one or more memories and one or more processors, The memory stores master data for each predetermined target company, the master data associating at least one question with an answer to the question, The processor: Sending the master data to the target company; receiving answers to each question in the master data from the target company and a data set related to the answers; When the target company receives the questionnaire obtained from the questionnaire distributor, if any of the questionnaire questions, which are one or more questions included in the questionnaire, is similar to any of the questions in the master data related to the target company, the answer to the questionnaire question and the related data set in the master data related to the target company are used as the answer to the questionnaire question. A business valuation processor system comprising:

8. 8. A business reputation processor system according to claim 7, comprising: The processor: reading one or more pieces of data from predetermined locations within the relevant dataset and using them to complete the answers to the questionnaire questions; A business valuation processor system comprising:

9. 1. A business valuation processor system having one or more memories and one or more processors, The memory stores master data for each predetermined target company, the master data associating at least one question with an answer to the question, The processor: Accepting a questionnaire obtained by the target company from a questionnaire distributor, questionnaire responses that are the target company's responses to one or more questionnaire questions included in the questionnaire, and a dataset related to the questionnaire responses; reading one or more pieces of data from a predetermined location in the dataset and using them to complete the questionnaire responses; A business valuation processor system comprising:

10. 10. The business reputation processor system of claim 9, The processor: selecting an answer from one or more predetermined answer proposals according to one or more pieces of data at a predetermined position in the read data set, and performing the completion; A business valuation processor system comprising:

11. 10. The business reputation processor system of claim 9, the memory stores a predetermined calculation formula having a plurality of input variables; the processor extracts a plurality of data items at predetermined positions in the read data set, uses the extracted data items as the input variables, performs calculations using the formula, and uses the results to complete the questionnaire responses; A business valuation processor system comprising:

12. 10. The business reputation processor system of claim 9, the memory stores a predetermined calculation formula having a plurality of input variables; the processor extracts a plurality of data items at predetermined positions within the read dataset and uses them as the input variables, and also performs calculations using the external data collected by crawling processing as the input variables according to the formula, and uses the calculations to complement the questionnaire responses; A business valuation processor system comprising:

13. 10. The business reputation processor system of claim 9, The data set is data sent from the target company or data sent from a predetermined computer of the target company at predetermined intervals. A business valuation processor system comprising:

14. 10. The business reputation processor system of claim 9, The processor performs machine learning using the presence or absence of corrections to the survey responses and the response information after corrections to construct a trained model for each of the target companies; The trained model is used in a process of complementing a second questionnaire response to a second questionnaire of the target company that has a different survey period or a different questionnaire distribution source from the second questionnaire. A business valuation processor system comprising:

15. 10. The business reputation processor system of claim 9, When the data set includes the data whose creation date is before the evaluation period, the processor Output a message indicating that the document is insufficient as evidence. A business valuation processor system comprising:

16. 10. A business reputation processor system according to claim 1, claim 7 or claim 9, comprising: At least one of the master data questions is a question about non-financial information related to management. A business valuation processor system comprising:

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