Organization report generation system

Abstract

To provide an organization report generation system capable of easily generating an enterprise report including various standpoints by using an existing learning model and capable of enhancing the reliability of the enterprise report.SOLUTION: An organization report generation system 1 collects news information on a target enterprise and respectively estimates the existence / nonexistence of a relationship between news information and the target enterprise from collected news information by using an existing learning model, whether the news information is affirmative content, and the categories of the industry and the business type of the target enterprise when an enterprise name of the target enterprise for generating the report is inputted. Then, it uses the existing learning model to generate each of a plurality of report items from news information estimated to have a relationship to the target enterprise, an estimation result as to whether or not the news information is affirmative content, and an estimation result of the categories of the industry and the business type of the target enterprise, and collects them to generate a report (an enterprise report) about the target enterprise.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an organizational report generation system that generates a report about an organization using an existing learning model. [Background technology]

[0002] Conventionally, a system has been proposed that delivers company reports containing company information to users via communication lines (see, for example, Patent Document 1). In the conventional system, a company ID set by a user is acquired and stored in the system, the company ID and company information are acquired and stored in a database, and trigger information including the company ID is acquired and stored in the database. The company ID stored in the system for each user ID is then acquired and compared with the trigger information stored in the database, company information is extracted from the database based on the matched company ID, and a company report is created for each user ID. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2002-324162 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the above-mentioned conventional systems, there is no proposal for creating a corporate report using an existing learning model (for example, GPT (Generative Pre-trained Transformer)), particularly for creating a corporate report that includes various perspectives (a corporate report that includes multiple report items). In particular, when creating a report using an existing learning model (for example, GPT), there is a problem that the report generated may include false information, i.e., the reliability of the report may be low.

[0005] The present invention has been made in consideration of the above-mentioned problems, and aims to provide an organizational report generation system that can easily generate reports that include various perspectives (reports about a target organization that include multiple report items) using existing learning models, while also increasing the reliability of the reports. [Means for solving the problem]

[0006] The organizational report generation system of the present invention is an organizational report generation system that generates a report about an organization, and the organizational report generation system includes: an input unit into which the name of a target organization for which the report is to be generated is input; a news information collection unit that collects news information about the target organization based on the organization name input to the input unit; a data preprocessing unit that uses an existing learning model generated by machine learning using predetermined learning data to input the news information collected by the news information collection unit, and estimates and outputs whether the news information has a relationship with the target organization, whether the news information has positive or negative content, and the industry and industry category of the target organization identified from the news information; a report item generation unit that uses the existing learning model to input news information that is estimated to have a relationship with the target organization, the estimation result of whether the news information has positive or negative content, and the estimation result of the industry and industry category of the target organization identified from the news information, and generates and outputs each of a plurality of report items that constitute a report about the target organization; and a report generation unit that compiles the plurality of report items output from the report item generation unit to generate a report about the target organization.

[0007] According to this configuration, when the name of an organization (target organization) for which a report is to be generated is input, a report about the target organization including multiple report items is generated using an existing learning model (e.g., GPT) based on news information (news information about the target organization) collected based on the input organization name. In this case, data preprocessing is first performed using an existing learning model, and from the collected news information, whether or not there is a relationship between the news information and the target organization, whether or not the news information is positive, and the industry and business category of the target organization are estimated. Next, each of the multiple report items is generated using the existing learning model. In this case, since news information estimated to have a relationship with the target organization is used, the generated report items are highly reliable. Furthermore, multiple report items are generated from various perspectives using whether or not the news information is positive and the industry and business category of the target organization. In this way, a report including various perspectives (a report about the target organization including multiple report items) can be easily generated using an existing learning model, and the reliability of the report can be improved.

[0008] In addition, in the organizational report generation system of the present invention, the input unit receives as input the organization name of the target organization as well as homepage information of the target organization, the news information collection unit collects news information about the target organization based on the homepage information of the target organization, the data preprocessing unit does not estimate whether the news information collected from the homepage of the target organization is positive or negative, and the report item generation unit uses the existing learning model to input news information collected from the homepage of the target organization that is estimated to have a relationship with the target organization, and the estimated results of the industry and business category of the target organization identified from the news information collected from the homepage of the target organization, and generates and outputs a news summary that reflects the vision and values ​​of the target organization as one of the multiple report items.

[0009] According to this configuration, when the target organization's website information is input in addition to the organization name, an existing learning model (e.g., GPT) is used to generate a report about the target organization, including multiple report items, based on news information (news information about the target organization) collected based on the input website information. In this case, data preprocessing using the existing learning model does not estimate whether the news information is positive or negative. When multiple report items are generated using the existing learning model, a news summary reflecting the target organization's vision and values ​​is generated as one of the multiple report items based on news information collected from the target organization's website that is estimated to have a relationship with the target organization, and the estimated industry and business category of the target organization identified from the news information collected from the target organization's website. In this way, a report about the target organization that includes the perspective (report item) of "news summary reflecting the target organization's vision and values" can be generated.

[0010] In addition, in the organizational report generation system of the present invention, the report item generation unit may use the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generate and output a news summary about the environment in which the target organization is located as one of the multiple report items.

[0011] According to this configuration, when multiple report items are generated using an existing learning model (e.g., GPT), a news summary about the environment in which the target organization is located is generated as one of the multiple report items based on news information estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive content, and an estimation result of the industry and business category of the target organization identified from the news information. In this way, a report about the target organization can be generated that includes the perspective (report item) of "a news summary about the environment in which the target organization is located."

[0012] In addition, in the organizational report generation system of the present invention, the report item generation unit may use the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generate and output the analysis results of a SWOT analysis of the target organization as one of the multiple report items.

[0013] With this configuration, when multiple report items are generated using an existing learning model (such as GPT), the analysis results of a SWOT analysis of the target organization are generated as one of the multiple report items based on news information estimated to have a relationship with the target organization, the estimation result of whether the news information has positive content, and the estimation result of the industry and business category of the target organization identified from the news information.In this way, a report on the target organization that includes the perspective (report item) "analysis results of a SWOT analysis of the target organization" can be generated.

[0014] In addition, in the organizational report generation system of the present invention, the report item generation unit may use the existing learning model to input the analysis results of a SWOT analysis of the target organization, and generate and output a PR message created based on the analysis results of an SO analysis, ST analysis, or WO analysis of the target organization as one of the multiple report items.

[0015] According to this configuration, when multiple report items are generated using an existing learning model (such as GPT), a PR message that combines the SO analysis, ST analysis, and WO analysis obtained from the SWOT analysis of the target organization is generated as one of the multiple report items based on the analysis results of the SWOT analysis of the target organization. In this way, a report on the target organization can be generated that includes the perspective (report item) of "a PR message created based on the analysis results of the SO analysis, ST analysis, or WO analysis of the target organization."

[0016] The method of the present invention is a method executed by an organizational report generation system that generates a report about an organization, and the method includes: an input step for inputting the name of a target organization for which the report is to be generated; a news information collection step for collecting news information about the target organization based on the organization name input in the input step; a data preprocessing step for using an existing learning model generated by machine learning using predetermined learning data to input the news information collected in the news information collection step, and estimating and outputting the presence or absence of a relationship between the news information and the target organization, whether the news information has positive or negative content, and the industry and industry category of the target organization identified from the news information; a report item generation step for using the existing learning model to input the news information estimated to have a relationship with the target organization, the estimation result of whether the news information has positive or negative content, and the estimation result of the industry and industry category of the target organization identified from the news information, and generating and outputting each of a plurality of report items that constitute a report about the target organization; and a report generation step for aggregating the plurality of report items output in the report item generation step to generate a report about the target organization.

[0017] Similar to the above system, this method also involves inputting the name of the organization (target organization) for which a report is to be generated. Based on news information (news information about the target organization) collected based on the input organization name, a report about the target organization containing multiple report items is generated using an existing learning model (e.g., GPT). In this case, data preprocessing is first performed using an existing learning model. From the collected news information, the presence or absence of a relationship between the news information and the target organization, whether the news information is positive, and the target organization's industry and business category are estimated. Next, multiple report items are generated using an existing learning model. Since news information estimated to have a relationship with the target organization is used, the generated report items are highly reliable. Furthermore, multiple report items are generated from various perspectives using whether the news information is positive and the target organization's industry and business category. In this way, using an existing learning model, a report containing various perspectives (a report about the target organization containing multiple report items) can be easily generated, and the reliability of the report can be improved.

[0018] The program of the present invention is a program executed in an organizational report generation system that generates a report related to an organization. The program causes the organizational report generation system to execute the following steps: an input process in which the name of a target organization for which the report is to be generated is input; a news information collection process in which news information related to the target organization is collected based on the organization name input in the input process; a data preprocessing process in which, using an existing learning model generated by machine learning using predetermined learning data, the news information collected in the news information collection process is used as input to estimate and output whether or not there is a relationship between the news information and the target organization, whether the news information has positive or negative content, and the industry and industry category of the target organization identified from the news information; a report item generation process in which, using the existing learning model, the news information estimated to have a relationship with the target organization, the estimation result of whether the news information has positive or negative content, and the estimation result of the industry and industry category of the target organization identified from the news information are used as input to generate and output each of a plurality of report items that constitute a report related to the target organization; and a report generation process in which the plurality of report items output by the report item generation process are compiled to generate a report related to the target organization.

[0019] Similar to the above system, this program also inputs the name of the organization (target organization) for which a report is to be generated. Based on news information (news information about the target organization) collected based on the input organization name, the program generates a report about the target organization, including multiple report items, using an existing learning model (e.g., GPT). In this case, data preprocessing is first performed using an existing learning model. From the collected news information, the presence or absence of a relationship between the news information and the target organization, whether the news information is positive, and the target organization's industry and business category are estimated. Next, each of the multiple report items is generated using the existing learning model. Since news information estimated to have a relationship with the target organization is used, the generated report items are highly reliable. Furthermore, multiple report items are generated from various perspectives, using whether the news information is positive and the target organization's industry and business category. In this way, using an existing learning model, it is possible to easily generate a report including various perspectives (a report about the target organization, including multiple report items) and increase the reliability of the report. [Effects of the Invention]

[0020] According to the present invention, it is possible to easily generate a report that includes various perspectives using an existing learning model, and to increase the reliability of the report. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a block diagram showing a configuration of an organizational report generation system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram showing an example of news information collected in the embodiment of the present invention. [Figure 3] FIG. 10 is a diagram showing an example of a result of data preprocessing according to an embodiment of the present invention. [Figure 4] FIG. 10 is a diagram showing an example of a news summary (company internal) generated in the embodiment of the present invention. [Figure 5] FIG. 2 is a diagram showing an example of a news summary (outside the company) generated in the embodiment of the present invention. [Figure 6] FIG. 2 is a diagram showing an example of the analysis result of the SWOT analysis according to the embodiment of the present invention. [Figure 7] FIG. 10 is a diagram showing an example of a PR message created from the analysis results of an SO analysis in the embodiment of the present invention. [Figure 8] FIG. 10 is a diagram showing an example of a PR message created from the analysis results of an ST analysis in the embodiment of the present invention. [Figure 9] FIG. 10 is a diagram showing an example of a PR message created from the analysis results of a WO analysis in an embodiment of the present invention. [Figure 10] FIG. 1 is a diagram showing an example of noteworthy news (internal company news) generated in an embodiment of the present invention. [Figure 11] FIG. 2 is a diagram showing an example of noteworthy news (outside a company) generated in an embodiment of the present invention. [Figure 12] FIG. 1 is a diagram showing an example of a mind map generated in an embodiment of the present invention. [Figure 13] FIG. 10 is a diagram showing an example of a time series graph generated in the embodiment of the present invention. [Figure 14] FIG. 3 is a sequence diagram illustrating the operation of the organizational report generation system according to the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] An organizational report generation system according to an embodiment of the present invention will be described below with reference to the drawings. In this embodiment, an organizational report generation system used for generating reports on companies (corporate reports) will be described as an example. The organizational report generation system according to this embodiment has a function for generating corporate reports using an existing learning model. This function is realized by a program stored in the memory area of ​​the organizational report generation system.

[0023] The configuration of an organizational report generating system according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of the organizational report generating system according to this embodiment. As shown in FIG. 1, the organizational report generating system 1 is connected to a user device 2 via a network N such as the Internet. In this embodiment, the organizational report generating system 1 is configured, for example, by a cloud server device. The user device 2 is configured, for example, by a business computer device, and includes an input unit 3 such as a keyboard and a mouse, and a display unit 4 such as a display.

[0024] The organizational report generation system 1 includes an input unit 5, a news information collection unit 6, a data preprocessing unit 7, a report item generation unit 8, a report generation unit 9, an output unit 10, and a storage unit 11. The input unit 5 receives the company name (the company name of the target company for which the report is to be generated, such as "Company A") input via the input unit 3 of the user device 2. In addition to the company name of the target company, the input unit 5 may also receive homepage information of the target company (such as "the URL of Company A's homepage"). The input unit 5 may also receive the period for which news information is to be collected (such as "the past month" or "the past year").

[0025] The news information gathering unit 6 has a function of collecting news information about a target company based on the company name input into the input unit 5. When the target company's website information and a period are input into the input unit 5 in addition to the company name of the target company, the news information gathering unit 6 can collect news information about the target company based on the website information and the period. For example, the news information gathering unit 6 collects news information about the target company by crawling. The news information gathering unit 6 may also collect news information about the target company using multiple crawlers (for example, a proprietary crawler that crawls highly reliable news sites and an external crawler provided by an external business operator).

[0026] FIG. 2 is a diagram showing an example of news information (main text) collected by the news information collection unit 6. FIG. 2 shows an example in which a company name "Company A," "Company A's homepage information (URL)," and a period of "past one year" are input. Such various news information is collected by crawling. Note that known techniques can be used for crawling.

[0027] The data preprocessing unit 7 has a function of performing data preprocessing using an existing learning model. Specifically, the data preprocessing using the existing learning model is performed by inputting news information collected by the news information collection unit 6 and estimating and outputting the presence or absence of a relationship between the news information and the target company, whether the news information has positive or negative content, and the industry and business category of the target company identified from the news information. In this case, the data preprocessing unit 7 does not estimate whether the news information collected from the target company's homepage has positive or negative content. The existing learning model is a learning model (such as "GPT") generated by machine learning using predetermined learning data. Any method, such as deep learning using a neural network, is used for machine learning.

[0028] In this embodiment, the data preprocessing unit 7 generates a prompt for data preprocessing that "infers, based on the input news information and the name of the target company, whether there is a relationship between the news information and the target company, whether the news information has a positive or negative content, and the industry and business category of the target company identified from the news information," and inputs the prompt into an existing learning model. As a result, the data preprocessing unit 7 can obtain the results of data preprocessing as output from the existing learning model.

[0029] FIG. 3 is a diagram showing an example of the results of data preprocessing (estimated results using an existing learning model). In the example of FIG. 3, the estimated result of whether or not there is a relationship between the news information and the target company is entered as "Yes" or "No" in the "Relationship" column. Also, in the example of FIG. 3, the estimated result of whether the news information has a positive or negative content is entered as "Positive" or "Negative" in the "Tone" column. Furthermore, in the example of FIG. 3, the estimated results of the industry and business category of the target company identified from the news information are entered as "Investment" and "Financial Performance" in the "Industry" and "Business Type" columns, respectively. Note that, as shown in FIG. 3, the results of data preprocessing may also include the "title (title of the news information)," "URL (source of the news information)," "classification (whether the source of the news information is internal or external to the company)," and "date (publication date of the news information)" of the news information.

[0030] The report item generation unit 8 has a function of using an existing learning model (e.g., "GPT") to generate and output each of multiple report items that make up a report on the target company (corporate report) from the results of data preprocessing. Specifically, using an existing learning model, the unit generates and outputs each of multiple report items using news information that is estimated to have a relationship with the target company, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target company identified from the news information. Note that the report item generation unit 8 can also generate and output other report items using known technology. Details of the functions of the report item generation unit 8 will be described later.

[0031] The report generation unit 9 has a function of aggregating multiple report items output from the report item generation unit 8 to generate a report (company report) about the target company. When aggregating multiple report items, known techniques can be used. The output unit 10 has a function of outputting the company report generated by the report generation unit 9. The company report generated by the report generation unit 9 is sent from the output unit 10 to the user device 2 and displayed on the display unit 4 of the user device 2. The storage unit 11 is composed of a large-capacity memory, etc., and stores data and programs necessary for generating the company report.

[0032] Here, we will explain in detail the functions of the report item generation unit 8. The report item generation unit 8 has, as functional blocks, a news summary generation unit 80, a SWOT analysis unit 81, a PR message generation unit 82, a featured news generation unit 83, a mind map generation unit 84, and a time series graph generation unit 85.

[0033] The news summary generation unit 80 has the function of using an existing learning model to input news information collected from the target company's homepage that is estimated to have a relationship with the target company, and the estimated results of the target company's industry and business category identified from the news information collected from the target company's homepage, and to generate and output a news summary (internal to the company) that reflects the target company's vision and values ​​as one of multiple report items.

[0034] In this embodiment, the news summary generation unit 80 generates a prompt for generating a news summary (internal company) that "generates a news summary (internal company) that reflects the vision and values ​​of the target company based on news information that is estimated to have a relationship with the target company and the estimated results of the industry and business category of the target company," and inputs the prompt into an existing learning model. As a result, the news summary generation unit 80 can obtain a "news summary (internal company)" as shown in Figure 4 as an output from the existing learning model.

[0035] In addition, the news summary generation unit 80 has the function of using an existing learning model to input news information that is estimated to have a relationship with the target company, the estimated result of whether the news information has positive or negative content, and the estimated result of the industry and business category of the target company identified from the news information, and then generating and outputting a news summary (outside the company) about the environment in which the target company is located as one of multiple report items.

[0036] In this embodiment, the news summary generation unit 80 generates a prompt for generating a news summary (outside the company) that "generates a news summary (outside the company) about the environment in which the target company is located, based on news information that is estimated to have a relationship with the target company, the estimation result of whether the news information has positive content, and the estimation result of the industry and business category of the target company," and inputs the prompt into an existing learning model. As a result, the news summary generation unit 80 can obtain a "news summary (outside the company)" as shown in Figure 5 as an output from the existing learning model.

[0037] The SWOT analysis unit 81 has the function of using an existing learning model to input news information that is estimated to have a relationship with the target company, the estimated result of whether the news information has positive or negative content, and the estimated result of the industry and business category of the target company identified from the news information, and then generating and outputting the analysis results of the SWOT analysis of the target company as one of multiple report items.

[0038] In this embodiment, the SWOT analysis unit 81 generates a prompt for SWOT analysis that "generates the analysis results of the SWOT analysis of the target company based on the news information estimated to have a relationship with the target company, the estimation result of whether the news information has positive content, and the estimation result of the industry and business category of the target company," and inputs the prompt into an existing learning model. As a result, the SWOT analysis unit 81 can obtain the "analysis results of the SWOT analysis" as shown in Figure 6 as an output from the existing learning model.

[0039] The PR message generation unit 82 has the function of using an existing learning model to input the results of a SWOT analysis of the target company, and generating and outputting a PR message based on the results of either an SO analysis, ST analysis, or WO analysis of the target company as one of multiple report items.

[0040] In this embodiment, the PR message generation unit 82 generates a prompt for generating a PR message (SO analysis pattern) that "generates a PR message based on the analysis results of the target company's SO analysis, based on the analysis results of the target company's SWOT analysis," and inputs the prompt into an existing learning model. As a result, the PR message generation unit 82 can obtain a "PR message (SO analysis pattern)" as shown in FIG. 7 as an output from the existing learning model.

[0041] In addition, in this embodiment, the PR message generation unit 82 generates a prompt for generating a PR message (ST analysis pattern) that "generates a PR message based on the analysis results of the target company's ST analysis, based on the analysis results of the target company's SWOT analysis," and inputs the prompt into an existing learning model. As a result, the PR message generation unit 82 can obtain a "PR message (ST analysis pattern)" as shown in FIG. 8 as an output from the existing learning model.

[0042] In addition, in this embodiment, the PR message generation unit 82 generates a prompt for generating a PR message (WO analysis pattern) that "generates a PR message based on the analysis results of a WO analysis of the target company, based on the analysis results of a SWOT analysis of the target company," and inputs the prompt into an existing learning model. As a result, the PR message generation unit 82 can obtain a "PR message (WO analysis pattern)" as shown in FIG. 9 as an output from the existing learning model.

[0043] The featured news generation unit 83 has the function of assigning weight values ​​to news information that has been subjected to data pre-processing by performing a predetermined weight calculation process, and generating and outputting news information with a high weight value as one of multiple report items as "noteworthy news." In the weight calculation process, a "quality score (0 to 100)" and a "quantity score (0 to 100)" are assigned to the news information. Then, news information with a high weight value (for example, the top five news information with the highest "quantity score") is output as "noteworthy news." Note that the weight calculation process can use known technology.

[0044] In this embodiment, the featured news generation unit 83 can generate "noteworthy news (internal company)" as shown in Fig. 10 based on news information to which "internal company" has been assigned as a "classification" in data preprocessing. Also, the featured news generation unit 83 can generate "noteworthy news (external company)" as shown in Fig. 11 based on news information to which "external company" has been assigned as a "classification" in data preprocessing.

[0045] The mind map generation unit 84 has a function of generating a mind map for the target company based on the news information that has been subjected to data preprocessing. The mind map can be generated using known techniques.

[0046] In this embodiment, the mind map generation unit 84 can generate a "mind map (inside the company)" as shown in Figure 12(a) based on news information to which "inside the company" has been assigned as a "classification" in data preprocessing. Also, the mind map generation unit 84 can generate a "mind map (outside the company)" as shown in Figure 12(b) based on news information to which "outside the company" has been assigned as a "classification" in data preprocessing.

[0047] The time series graph generation unit 85 has a function of generating a time series graph of news information related to a target company based on the news information that has been subjected to data preprocessing. The time series graph is, for example, a graph in which the horizontal axis represents the date of the news information and the vertical axis represents the number of news information (number of news items). A known technology can be used to generate the time series graph.

[0048] In this embodiment, the time series graph generation unit 85 can generate a "time series graph (a graph shown by white circles and a broken line in FIG. 13)" as shown in Fig. 13 based on news information to which "positive" has been assigned as the "tone" in data pre-processing. Also, the time series graph generation unit 85 can generate a "time series graph (a graph shown by black circles and a broken line in FIG. 13)" as shown in Fig. 13 based on news information to which "negative" has been assigned as the "tone" in data pre-processing.

[0049] The operation of the organizational report generation system 1 configured as above will be described with reference to the sequence diagram of FIG.

[0050] When generating a company report using the organizational report generation system 1 of this embodiment, first, the user inputs, into the user device 2, the company name (e.g., "Company A") for which the company report is to be generated, the company's homepage information (e.g., "the URL of Company A's homepage"), and the period for which news information is to be collected (e.g., "the past year") (S1). The input company name, homepage information, and period information are transmitted from the user device 2 to the organizational report generation system 1 (S2).

[0051] In the organizational report generation system 1, when information such as a company name, website information, and period is input, news information (see FIG. 2) is collected based on that information (S3), and data preprocessing (see FIG. 3) is performed on the collected news information using an existing learning model (e.g., "GPT") (S4). Then, multiple report items are generated based on the results of the data preprocessing using the existing learning model (e.g., "GPT") (S5-S12).

[0052] Specifically, using an existing learning model, a "news summary (internal to the company)" (see Figure 4) that reflects the target company's vision and values ​​is generated as one of multiple report items (S5). Also, using an existing learning model, a "news summary (external to the company)" (see Figure 5) about the environment in which the target company is located is generated as one of multiple report items (S6). Also, using an existing learning model, the "results of a SWOT analysis" (see Figure 6) of the target company is generated as one of multiple report items (S7). Also, using an existing learning model, a "PR message created based on the results of the target company's SO analysis, ST analysis, and WO analysis" (see Figures 7 to 9) is generated as one of multiple report items (S8).

[0053] Furthermore, based on the news information to which "internal company" has been assigned in the data preprocessing, "Noteworthy news (internal company)" (see Figure 10) is generated as one of the multiple report items (S9). Furthermore, based on the news information to which "external company" has been assigned in the data preprocessing, "Noteworthy news (external company)" (see Figure 11) is generated as one of the multiple report items (S10). Furthermore, based on the news information to which data preprocessing has been performed, a "mind map" (see Figure 12) about the target company is generated as one of the multiple report items (S11). Furthermore, based on the news information to which data preprocessing has been performed, a "time series graph" (see Figure 13) of the news information about the target company is generated as one of the multiple report items (S12).

[0054] Then, the multiple report items generated as described above (S5 to S12) are compiled to generate a report (company report) about the target company (S13). The generated company report is transmitted from the organizational report generation system 1 to the user device 2 (S14) and displayed on the display unit 4 of the user device 2 (S15).

[0055] According to the organizational report generation system 1 of this embodiment, when the name of a company (target company) for which a report is to be generated is input, a report about the target company including multiple report items is generated using an existing learning model (e.g., GPT) based on news information (news information about the target company) collected based on the input company name (see FIG. 2). In this case, first, data preprocessing is performed using an existing learning model (see FIG. 3), and from the collected news information, it is estimated whether or not there is a relationship between the news information and the target company, whether or not the news information is positive, and the industry and business category of the target company. Next, each of the multiple report items is generated using an existing learning model (see FIGS. 4 to 9).

[0056] In this case, news information estimated to have a relationship with the target company is used, resulting in a high level of reliability for the generated report items. Furthermore, multiple report items are generated from various perspectives, using whether the news information is positive or not, and the target company's industry and business category. In this way, existing learning models can be used to easily generate reports that include various perspectives (reports about the target company that include multiple report items), while also increasing the reliability of the reports.

[0057] In addition, in this embodiment, when the homepage information of the target company is input in addition to the company name of the target company, a report on the target company including multiple report items is generated using an existing learning model (e.g., GPT) based on news information (news information about the target company) collected based on the input homepage information.

[0058] In this case, data preprocessing using an existing learning model does not estimate whether the news information is positive or negative. When multiple report items are generated using an existing learning model, a news summary reflecting the target company's vision and values ​​is generated as one of the multiple report items based on the news information collected from the target company's website that is estimated to be related to the target company, and the estimated industry and business category of the target company identified from the news information collected from the target company's website. In this way, a report on the target company can be generated that includes a "news summary (internal company)" perspective (report item) that reflects the target company's vision and values ​​(see Figure 4).

[0059] Furthermore, in this embodiment, when multiple report items are generated using an existing learning model (e.g., GPT, etc.), a news summary about the environment in which the target company operates is generated as one of the multiple report items based on news information estimated to have a relationship with the target company, an estimation result as to whether the news information has positive content, and an estimation result of the industry and business category of the target company identified from the news information. In this way, a report about the target company can be generated that includes a perspective (report item) of "news summary (outside the company)" about the environment in which the target company operates (see FIG. 5).

[0060] Furthermore, in this embodiment, when multiple report items are generated using an existing learning model (e.g., GPT), the analysis results of a SWOT analysis of the target company are generated as one of the multiple report items based on news information estimated to have a relationship with the target company, the estimation result of whether the news information has positive content, and the estimation result of the industry and business category of the target company identified from the news information.In this way, a report on the target company can be generated that includes the perspective (report item) of the "analysis results of the SWOT analysis" of the target company (see FIG. 6).

[0061] Furthermore, in this embodiment, when multiple report items are generated using an existing learning model (e.g., GPT, etc.), a PR message including any of SO analysis, ST analysis, or WO analysis obtained from the SWOT analysis of the target company is generated as one of the multiple report items based on the analysis results of the SWOT analysis of the target company. In this way, a report can be generated as a report on the target company that includes the perspective (report item) of "a PR message created based on any of the analysis results of SO analysis, ST analysis, or WO analysis of the target company" (see FIGS. 7 to 9).

[0062] According to the organizational report generation system 1 of this embodiment, news related to the target company can be obtained in real time from reliable sources, and various analyses related to corporate public relations can be performed. Therefore, detailed and specific original prompts based on the obtained news information can be used to propose SWOT analyses and PR activities suitable for the company or organization.

[0063] In this embodiment, by providing a news information gathering unit 6 (crawl function), it is possible to gather all news related to a company within a specified period. The news information gathering unit 6 is capable of gathering press releases and news from the company's homepage (internal crawl function), and is also capable of gathering global news that mentions the company (external crawl function).

[0064] Furthermore, in this embodiment, by providing a data preprocessing unit 6 (data preprocessing function), it is possible to present each news article to an existing learning model (such as "GPT"), cleanse the data, and obtain various outputs for generating a SWOT analysis. Here, the various outputs include "relationships," "tone," "industry and category classification," "hot news," and "news summary."

[0065] In this case, "Relevance" allows you to determine whether the news is relevant to your organization. "Tone" allows you to analyze the sentiment of each news item. "Industry and Category Classification" allows you to identify whether the retrieved news is in the relevant industry and category of your company. "Trending News" allows you to evaluate all news items with a unique score and select the most noteworthy news for your company or organization. The unique score includes a Quality Score (a trusted news source score) and a Quantity Score (a score that indicates the influence of each news item on the target company, calculated from news distribution and industry and category data). "News Summary (Internal)" allows you to create a summary of your organization's vision and values ​​using trending news and advanced prompts. "News Summary (External)" allows you to generate a summary of external news related to your company's environment using highly reliable information.

[0066] In this embodiment, the mind map generation unit 84 (visualization function) can create a mind map based on the company's related industries and categories. The time series graph generation unit 85 (visualization function) can visualize the relationship between the number of news items related to the company and time. The SWOT analysis unit 81 (SWOT analysis function based on categorized data) can perform a SWOT analysis of the company using categorized data to address the issue of token restrictions. The report generation unit 9 (report output function) can output various analysis results in a specified report layout.

[0067] Although the embodiments of the present invention have been described above by way of example, the scope of the present invention is not limited to these, and can be modified and changed according to the purpose within the scope of the claims.

[0068] For example, the above description has been given as an example of generating a report about a company, but the scope of the present invention is not limited to this, and reports about organizations, unions, teams, etc. other than companies can also be generated in a similar manner. [Industrial Applicability]

[0069] As described above, the organizational report generation system of the present invention has the effect of easily generating reports that include various perspectives using existing learning models and increasing the reliability of those reports, and is useful for generating reports about companies (corporate reports), etc. [Explanation of symbols]

[0070] 1. Organizational Report Generation System 2. User Device 3 Input section 4 Display section 5 Input section 6. News Information Collection Department 7 Data preprocessing section 8 Report Item Generation Section 9 Report Generation 10 Output section 11 Storage section 80 News Summary Generation Unit 81 SWOT Analysis Department 82 PR message generation unit 83 Featured News Generation Department 84 Mind Map Generation 85 Time series graph generation part

Claims

1. 1. An organizational report generation system for generating a report about an organization, comprising: The organizational report generation system includes: an input section for inputting the name of the organization for which the report is to be generated; a news information gathering unit that gathers news information about the target organization based on the organization name inputted to the input unit; a data preprocessing unit that uses an existing learning model generated by machine learning using predetermined learning data, and that inputs news information collected by the news information gathering unit to estimate and output whether or not there is a relationship between the news information and the target organization, whether the news information has positive or negative content, and the industry and business category of the target organization identified from the news information; a report item generation unit that uses the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generates and outputs each of a plurality of report items that constitute a report on the target organization; a report generation unit that compiles a plurality of report items output from the report item generation unit to generate a report regarding the target organization; An organizational report generation system comprising:

2. In addition to the organization name of the target organization, homepage information of the target organization is input to the input unit, the news information gathering unit gathers news information about the target organization based on homepage information of the target organization; The data preprocessing unit does not estimate whether the news information collected from the homepage of the target organization is positive or negative, The report item generation unit 2. The organizational report generation system of claim 1, wherein the existing learning model is used to input news information collected from the target organization's homepage that is estimated to have a relationship with the target organization, and the estimated results of the target organization's industry and business category identified from the news information collected from the target organization's homepage, and to generate and output a news summary that reflects the target organization's vision and values ​​as one of the multiple report items.

3. The report item generation unit 2. The organizational report generation system of claim 1, which uses the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result of whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generates and outputs a news summary about the environment in which the target organization is located as one of the multiple report items.

4. The report item generation unit 2. The organizational report generation system of claim 1, which uses the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result of whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generates and outputs the analysis results of a SWOT analysis of the target organization as one of the multiple report items.

5. The report item generation unit 5. The organizational report generation system according to claim 4, wherein the existing learning model is used to input the analysis results of a SWOT analysis of the target organization, and a PR message created based on the analysis results of an SO analysis, ST analysis, or WO analysis of the target organization is generated and output as one of the plurality of report items.

6. 1. A method implemented in an organizational report generation system for generating a report regarding an organization, comprising: The method comprises: an input step in which the name of the organization for which the report is to be generated is input; a news information gathering step of gathering news information about the target organization based on the organization name and the organization name input in the input step; a data preprocessing step in which, using an existing learning model generated by machine learning using predetermined learning data, the news information collected in the news information collection step is input, and the presence or absence of a relationship between the news information and the target organization, whether the news information has positive or negative content, and the industry and business category of the target organization identified from the news information are estimated and output; a report item generation step of using the existing learning model to input news information estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generating and outputting each of a plurality of report items constituting a report on the target organization; a report generation step of generating a report about the target organization by aggregating the plurality of report items output in the report item generation step; A method comprising:

7. A program executed in an organizational report generation system that generates a report about an organization, The program configures the organizational report generation system to: an input process for inputting the name of the organization for which the report is to be generated; a news information gathering process for gathering news information about the target organization based on the organization name input in the input process; a data preprocessing process that uses an existing learning model generated by machine learning using predetermined learning data, and that inputs the news information collected in the news information gathering process to estimate and output the presence or absence of a relationship between the news information and the target organization, whether the news information has positive or negative content, and the industry and business category of the target organization identified from the news information; a report item generation process that uses the existing learning model to input news information that is estimated to have a relationship with the target organization, an estimation result as to whether the news information has positive or negative content, and an estimation result of the industry and business category of the target organization identified from the news information, and generates and outputs each of a plurality of report items that constitute a report on the target organization; a report generation process that compiles a plurality of report items output by the report item generation process and generates a report regarding the target organization; A program that executes.

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