Proposal support system, proposal support method, and proposal support program

Abstract

To improve efficiency of idea proposal support.SOLUTION: A proposal support system executes: acquiring a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group including a first field name in at least one information source out of a first information source related to a first field and a second information source related to a second field different from the first field is a node, and a co-occurrence relation between two first words is a link connecting the nodes; extracting company names in the second field relating to the specific first word, from a second sentence group including a second field name and the specific first word from the at least one information source; associating the specific first word, a specific second word, and the company names in the second field, based on appearance information related to the specific second word that co-occurs with the specific first word in a second word group in the second sentence group; and performing output processing of outputting an analysis result in a displayable manner.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present invention relates to a proposal support system, a proposal support method, and a proposal support program for supporting proposals. [Background technology]

[0002] Patent Document 1 discloses a promising customer prediction device that classifies customers into groups that are meaningful from a sales strategy and predicts promising customers. In this promising customer prediction device, a customer information storage means stores information including not only customer company information, which is publicly disclosed company information, but also sales activity information obtained individually from contact points with customers through sales activities by sales representatives, a promising customer group extraction means classifies customers into clusters based on the information stored in the customer information storage means, a cluster having many customers that fit the correct customer model input to the correct customer model definition means is extracted as a promising cluster, a promising customer identification means quantifies the degree of closeness of each customer to the correct customer model and gives each customer a score, and a result output means displays customers in order of highest score for each cluster and indicates promising clusters. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-027486 A Summary of the Invention [Problem to be solved by the invention]

[0004] As the world demands value creation that goes beyond a single company or a single project, such as social issues, corporate needs are becoming more diverse, and in considering the business continuity of companies as contributions to society and the earth through solving social issues, it has become essential to suggest new proposal destinations in a way that enhances the value of sales proposals by combining sales activity information and information from other companies across cross-domains such as industry and finance, linking the interests of each field and company. Therefore, B2B sales also need new proposal ideas that can meet this demand.

[0005] On the other hand, when it comes to solving social or global problems, there are few cases where a solution can be found in a single field, or the scale is small, so it is necessary to expand the scope of current business into other fields, or to combine existing fields, and design and propose value that spans multiple fields, but coming up with organizational ideas is difficult. Therefore, depending on the salesperson, there are challenges in that it is difficult to come up with ideas, and it takes time to search all the information.

[0006] The present invention aims to improve the efficiency of idea proposal support. [Means for solving the problem]

[0007] A proposal support system which is one aspect of the invention disclosed in the present application is a proposal support system having a processor which executes a program and a storage device which stores the program, wherein the processor executes an acquisition process which acquires a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group containing a first field name is a node in at least one of a first information source related to a first field and a second information source related to a second field different from the first field, and the co-occurrence relationship between two first words is a link connecting the nodes; an extraction process which extracts a company name in the second field related to the specific first word from a second sentence group containing the second field name and the specific first word acquired by the acquisition process in the at least one of the information sources; an analysis process which associates the specific first word, the specific second word, and the company name in the second field extracted by the extraction process based on occurrence information regarding a specific second word that co-occurs with the specific first word in the second word group in the second sentence group; and an output process which outputs the analysis results from the analysis process in a displayable manner. Effect of the Invention

[0008] According to the representative embodiment of the present invention, it is possible to improve the efficiency of the support for suggesting ideas. Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is an explanatory diagram illustrating an example of a system configuration of a network system. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of the proposal support system. [Diagram 3] FIG. 3 is an explanatory diagram showing an example of the company DB. [Figure 4] FIG. 4 is an explanatory diagram illustrating an example of a company information DB. [Diagram 5] FIG. 5 is a flowchart illustrating an example of a cross-domain proposal support process. [Figure 6] FIG. 6 is a flowchart showing a detailed example of the process steps of the co-occurrence network generation process (step S501). [Figure 7] FIG. 7 is an explanatory diagram illustrating an example of the crawling information DB. [Figure 8] FIG. 8 is an explanatory diagram illustrating an example of a co-occurrence probability table. [Figure 9] FIG. 9 is an explanatory diagram illustrating an example of a word appearance count table. [Figure 10] FIG. 10 is an explanatory diagram illustrating an example of a co-occurrence network table. [Figure 11] FIG. 11 is an explanatory diagram showing an example of a co-occurrence network. [Figure 12] FIG. 12 is a flowchart showing a detailed example of the processing procedure of extracting company names of interest from trending words in the second field (step S504). [Figure 13] FIG. 13 is an explanatory diagram showing an example of storage of the second crawling result. [Figure 14] FIG. 14 is a flowchart showing a detailed example of the processing procedure of the word of interest analysis process (step S505). [Figure 15] FIG. 15 is an explanatory diagram illustrating an example of the co-occurrence probability analysis table. [Figure 16] FIG. 16 is an explanatory diagram illustrating an example of the occurrence count analysis table. [Figure 17] FIG. 17 is an explanatory diagram illustrating an example of the focused company number analysis table. [Figure 18] FIG. 18 is an explanatory diagram showing an analysis result display screen example 1. [Figure 19] FIG. 19 is an explanatory diagram showing an example 2 of an analysis result display screen. [Figure 20] FIG. 20 is an explanatory diagram showing an analysis result display screen example 3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] <Figure 1 Network system> 1 is an explanatory diagram showing an example of a system configuration of a network system. The network system 100 includes a proposal support system 101 and a terminal 102. The proposal support system 101 and the terminal 102 are communicatively connected via a network 105 such as the Internet, a LAN (Local Area Network), or a WAN (Wide Area Network).

[0011] The proposal support system 101 is composed of one or more computers. The proposal support system 101 is connected to a company's own DB (Data Base) 103. The company's own DB 103 is a database that stores information about the company's own company, and stores document data about, for example, research and development, prototyping, sales, marketing, production, quality, and sales. The document data includes text files and Web pages. The company's own company is a company that is a target of cross-domain proposal support, for example, a company that owns a computer on which the proposal support system 101 is implemented.

[0012] The proposal support system 101 can also access the other company DB 104 via the network 105. The other company DB 104 is a database of companies other than the own company, and similar to the own company DB 103, stores document data relating to, for example, research and development, prototyping, sales, marketing, production, quality and sales. The other companies may be customers of the own company, or companies other than customers. When there is no distinction between the own company and the other companies, they will simply be referred to as companies.

[0013] The terminal 102 has a browser function, and when it receives visualization information from the proposal support system 101, it displays the display information on a display screen. The user may operate the proposal support system 101 directly, or may operate the proposal support system 101 via the terminal 102.

[0014] <Figure 2 Example of hardware configuration of proposal support system 101> FIG. 2 is a block diagram showing an example of a hardware configuration of the proposal support system 101. The proposal support system 101 includes a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF) 205. The processor 201, the storage device 202, the input device 203, the output device 204, and the communication IF 205 are connected by a bus 206. The processor 201 controls the proposal support system 101. The storage device 202 serves as a working area for the processor 201. The storage device 202 is a non-transient or temporary recording medium that stores various programs and data. Examples of the storage device 202 include a ROM (Read Only Memory), a RAM (Random Access Memory), a HDD (Hard Disk Drive), and a flash memory. The input device 203 inputs data. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a microphone, and a sensor. The output device 204 outputs data. The output device 204 may be, for example, a display, a printer, or a speaker. The communication IF 205 is connected to the network 105 and transmits and receives data.

[0015] The proposal support system 101 is configured with one or more computers. Therefore, the proposal support system 101 has one or more processors 201, storage devices 202, input devices 203, output devices 204, and communication IFs 205. The proposal support system 101 may also include a terminal 102.

[0016] <Figure 3 Company DB103> 3 is an explanatory diagram showing an example of the own company DB 103. The own company DB 103 is configured, for example, by the storage device 202, and has a company information DB 301, a crawling information DB 302, a co-occurrence network DB 303, and a cross-domain information DB 304. The crawling information DB 302, the co-occurrence network DB 303, and the cross-domain information DB 304 are databases generated by the proposal support system 101.

[0017] <Figure 4 Corporate Information DB301> FIG. 4 is an explanatory diagram showing an example of company information DB 301. Company information DB 301 is a database that stores information about companies. Company information DB 301 has fields of company ID 401, field 402, and company name 403. Company ID 401 is identification information that uniquely identifies a company. Field 402 is the range of business that the company handles, and is classified, for example, by an industry classification code. Company name 403 is the name of the company.

[0018] <Figure 5 Cross-domain proposal support process> FIG. 5 is a flowchart illustrating an example of a cross-domain proposal support process.

[0019] (Step S500) The proposal support system 101 acquires first company data in a first field, and proceeds to step S501. The first field is the field 402 that the first company handles as its business. The first company is a company that handles the first field as its business. The first company data includes a company ID 401, a field 402, and a company name 403.

[0020] (Step S501) The proposal support system 101 executes a co-occurrence network generation process and proceeds to step S502. The co-occurrence network generation process is a process for generating a co-occurrence network. A co-occurrence network is a network in which words that co-occur in one sentence are treated as nodes and co-occurrence relationships are treated as links. The co-occurrence network is stored in the company DB 103 and displayed on a display, which is an example of the output device 204.

[0021] [Figure 6 Co-occurrence network generation process (step S501)] FIG. 6 is a flowchart showing a detailed example of the process steps of the co-occurrence network generation process (step S501).

[0022] (Step S601) The proposal support system 101 acquires the first crawling condition and proceeds to step S602. The first crawling condition is the crawling word, the crawling period, and the first field acquired in step S500. The proposal support system 101 accepts the input of the crawling word and the crawling period by the user operating the input device 203.

[0023] A crawling word is one or more words used for crawling. The crawling word includes the company name 403 in the first field. When the crawling word is composed of multiple words, it is combined with the company name 403 in the first field under an AND condition. When there are multiple words other than the company name 403 in the first field, the multiple words may be combined under an AND condition, an OR condition, or a mixture of an AND condition and an OR condition.

[0024] The crawling period is the range of publication dates of the document data to be crawled (or the last update date if there was an update). Document data includes document files, web pages, and intra-pages from within and outside the company. Written data also includes sales-related information such as marketing information from each field, weekly sales reports, and sales memos obtained from conversations with customers, as well as manufacturing-related information such as complaints about products.

[0025] (Step S602) The proposal support system 101 crawls at least one of the own company DB 103 and the other company DB 104 under the first crawling condition acquired in step S601 (hereinafter, the first crawling), and acquires sentences corresponding to the crawling words from the first crawled document data (hereinafter, the first crawled sentences). Then, the process proceeds to step S603.

[0026] The first crawled sentence is composed of one or more sentences. Specifically, for example, if a sentence constituting a certain paragraph in the document data of the own company DB 103 and the other company DB 104 contains a crawling word, the sentence is extracted as the first crawled sentence.

[0027] Also, if a sentence in the document data of the own company DB 103 and the other company DB 104 contains a crawling word, the sentence is extracted as a first crawling sentence. In this case, the sentence containing the crawling word and a predetermined number of sentences before and after it may be the first crawling sentence. The predetermined number can be arbitrarily set and changed in the proposal support system 101. The predetermined number of sentences before and after it may not contain the crawling word.

[0028] (Step S603) The proposal support system 101 stores the first crawling result in the crawling information DB 302, and proceeds to step S604.

[0029] [Figure 7 Crawling information DB302] 7 is an explanatory diagram showing an example of the crawling information DB 302. The crawling information DB 302 has the following fields: analysis ID 701, crawling field 702, crawling company name 703, crawling word 704, crawling URL 705, crawling document 706, and crawling document date 707. A combination of values ​​of each field in the same row becomes an entry indicating one first crawling result.

[0030] The analysis ID 701 is identification information that uniquely identifies an analysis. For example, an analysis ID 701 is assigned to one cross-domain proposal support. In this example, "A1" is assigned. When multiple crawled documents 706 are extracted in one crawling, entries with the same analysis ID 701 are registered in the same number as the extracted crawled documents 706. For example, when three crawled documents 706 are extracted in the first crawling A1, three entries with the analysis ID 701 of "A1" are registered.

[0031] The crawling field 702 is the field 402 to be crawled. In the case of the first crawling A1 described above, the crawling field 702 is the field 402 "finance."

[0032] The crawling company name 703 is the company name 403 to be crawled. In the case of the first crawling A1 described above, the crawling company name 703 is the company name 403 "Gold Bank" whose field 402 is "finance".

[0033] The crawling word 704 is a word used for crawling. In the case of the first crawling A1 described above, it is the crawling word "Gold Bank & Trends" included in the first crawling condition acquired in step S601 ("&" is an AND condition).

[0034] The crawling URL 705 is a URL (Uniform Resource Locator) of the crawling destination according to the crawling word 704 .

[0035] The crawled sentence 706 is a sentence including the crawling word 704 extracted by crawling using the crawling word 704. In the case of the first crawling A1 described above, the crawled sentence 706 is the first crawled sentence.

[0036] The crawled document date 707 is the publication date of the crawled document data (or the last update date if there has been an update).

[0037] (Step S604) Returning to FIG. 6, the proposal support system 101 acquires a search keyword and proceeds to step S605. Specifically, for example, the proposal support system 101 accepts input of a search keyword by a user operating the input device 203. The search keyword is one or more words. The search keyword may include a field 402 or a company name 403. When the search keyword is composed of multiple words, the multiple words may be combined with an AND condition, an OR condition, or a mixture of an AND condition and an OR condition.

[0038] (Step S605) The proposal support system 101 extracts the first crawling sentence from the crawling information DB 302 using the search keyword acquired in step S604, and proceeds to step S606. Specifically, for example, the proposal support system 101 extracts the first crawling sentence that matches the search keyword from the crawling sentence 706. The extracted first crawling sentence is referred to as the "extracted first crawling sentence."

[0039] (Step S606) The proposal support system 101 performs morphological analysis on the first crawled sentence extracted in step S605 to break it down into words, retains words that correspond to nouns, and proceeds to step S607.

[0040] (Step S607) The proposal support system 101 calculates the co-occurrence probability between words that co-occur in the extracted first crawled sentence, creates a co-occurrence probability table, and proceeds to step S608. Specifically, for example, when the extracted first crawled sentence is composed of multiple sentences, the proposal support system 101 may calculate the co-occurrence probability between words that co-occur in the entire multiple sentences (hereinafter, the first co-occurrence probability), or may calculate the co-occurrence probability between words that co-occur in each of the multiple sentences (hereinafter, the second co-occurrence probability). It is possible to set and change which of the first co-occurrence probability and the second co-occurrence probability to be adopted in the proposal support system 101. When the first co-occurrence probability and the second co-occurrence probability are not distinguished from each other, they are simply referred to as co-occurrence probability.

[0041] [Figure 8 Co-occurrence probability table] FIG. 8 is an explanatory diagram showing an example of a co-occurrence probability table. The co-occurrence probability table 800 is stored in the co-occurrence network DB 303. The co-occurrence probability table 800 has fields of a co-occurrence ID 801, a first word 802, a second word 803, a co-occurrence probability 804, and an analysis ID 701. The co-occurrence ID 801 is identification information that uniquely identifies a co-occurrence relationship. The first word 802 is one of two words having a co-occurrence relationship. The second word 803 is the other of two words having a co-occurrence relationship. The co-occurrence probability 804 is the probability that the first word 802 and the second word 803 co-occur in the extracted first crawled sentence.

[0042] When the co-occurrence probability 804 is the first co-occurrence probability, it is calculated by the following formula (1).

[0043] Co-occurrence probability=(number of sentences in which the first word 802 and the second word 803 appear) / (number of sentences in which the first word 802 or the second word 803 appears) (1)

[0044] When the co-occurrence probability 804 is the second co-occurrence probability, it is calculated by the following formula (2).

[0045] Co-occurrence probability=(number of sentences in which the first word 802 and the second word 803 appear) / (number of sentences in which the first word 802 or the second word 803 appears) (2)

[0046] (Step S608) Returning to FIG. 6, the proposal support system 101 calculates the number of occurrences of each word in all extracted first crawled sentences, creates a word occurrence number table, and proceeds to step S609.

[0047] [Figure 9 Word occurrence count table] 9 is an explanatory diagram showing an example of a word appearance count table. The word appearance count table 900 is stored in the co-occurrence network DB 303. The word appearance count table 900 has fields of a word 901, an appearance count 902, and an analysis ID 701. The word 901 is a word included in the extracted first crawled sentence. The appearance count 902 is the number of times the word 901 appears in all of the extracted first crawled sentences.

[0048] (Step S609) Returning to FIG. 6, the proposal support system 101 creates a co-occurrence network table, generates a co-occurrence network, displays it on the display, and proceeds to step S502.

[0049] [Figure 10 Co-occurrence network table] 10 is an explanatory diagram showing an example of a co-occurrence network table. The co-occurrence network table 1000 is stored in the co-occurrence network DB 303. The co-occurrence network table 1000 has fields including a co-occurrence ID 801, a first word 802, a first occurrence count 1001, a second word 803, a second occurrence count 1002, a co-occurrence probability 804, and an analysis ID 701. The first occurrence count 1001 is the occurrence count 902 when the word 901 is the first word 802. The second occurrence count 1002 is the occurrence count 902 when the word 901 is the second word 803.

[0050] [Figure 11 Co-occurrence network] FIG. 11 is an explanatory diagram showing an example of a co-occurrence network. The co-occurrence network 1100 is created based on the co-occurrence network table 1000. The co-occurrence network 1100 is composed of a plurality of nodes (circular shapes) and links (line segments) connecting two nodes. A node corresponds to a first word 802 or a second word 803. A link indicates a co-occurrence relationship. The size of a node corresponds to the number of occurrences 902. A larger node indicates a larger number of occurrences 902.

[0051] (Step S502) Returning to Fig. 5, the suggestion support system 101 acquires the attention word X from the co-occurrence network 1100 and proceeds to step S504. Specifically, for example, the suggestion support system 101 acquires, as the attention word X, a word corresponding to a node selected by a user's operation of the input device 203 from the co-occurrence network 1100. Fig. 11 shows that the node indicating the word W3 is selected by the cursor 1101, and thus the word W3 is acquired as the attention word X.

[0052] (Step S503) The proposal support system 101 selects an unselected second field (step S503). The second field is a field different from the first field. In other words, the second field is a field 402 other than the field 402 indicating the name of the first field acquired in step S501. For example, if the first field is "finance", the second field is one or more fields such as "railroad", "service industry", "manufacturing industry", .... The proposal support system 101 selects a field 402 indicating the name of an unselected second field from the one or more second fields.

[0053] (Step S504) The proposal support system 101 executes a process for extracting company names based on trending words in the second field. The process for extracting company names based on trending words in the second field (step S504) is a process for extracting company names 403 of companies in the second field that focus on the trending word X acquired in step S502. Specifically, for example, the process for extracting company names based on trending words in the second field (step S504) is a process for extracting company names 403 of companies in field 402 (specified by company ID 401 corresponding to field 402) indicating the second field related to trending word X.

[0054] [Figure 12 Processing for extracting company names of interest from trending words in the second field (step S504)] FIG. 12 is a flowchart showing a detailed example of the processing procedure of extracting company names of interest from trending words in the second field (step S504).

[0055] (Step S1201) The proposal support system 101 acquires the second crawling condition and proceeds to step S1202. The first crawling condition is the crawling period and the field 402 of the second field selected in step S503. The proposal support system 101 accepts the input of the crawling period, for example, by the user operating the input device 203.

[0056] (Step S1202) The proposal support system 101 crawls at least one of the company's own DB 103 and the other company DB 104 using the attention word X and the second crawling condition (hereinafter, the second crawling) to obtain the second crawled document. Specifically, for example, the proposal support system 101 may perform the second crawling of document data within the crawling period using the attention word X as the crawling word 704, or may perform the second crawling of document data within the crawling period using the attention word X and the second field combined under an AND condition as the crawling word 704.

[0057] The proposal support system 101 may crawl a first field of company DBs among the company DB 103 and the other company DBs 104, may crawl a second field of company DBs, or may crawl the company DBs of the company DB 103 and the other company DBs 104. The proposal support system 101 may crawl a company DB different from the company DB that was the target of the first crawling, as the target of the second crawling.

[0058] (Step S1203) The proposal support system 101 stores the second crawling result in the crawling information DB 302, and proceeds to step S1204.

[0059] [Figure 13 Example of storage of second crawling results] Fig. 13 is an explanatory diagram showing an example of storage of the second crawling result. In Fig. 13, an entry 1300 is added to the crawling information DB 302 as the second crawling result. In the entry 1300, "idle" is stored as the attention word X.

[0060] (Step S1204) Returning to FIG. 12, the proposal support system 101 identifies a company in the second field included in the second crawling text as the attention word of the company name, and proceeds to step S505. The second crawling text is the crawling text 706 in the second crawling result (for example, entry 1300). The company in the second field is the crawling company name 703 in the crawling field 702 in the second crawling result (for example, entry 1300). In the example of FIG. 13, "Iron Railway" is identified as the attention word of the company name.

[0061] (Step S505) Returning to Fig. 5, the proposal support system 101 executes a word of interest analysis process. The word of interest analysis process (step S505) is a process for identifying a word of interest. A word of interest is a word that co-occurs with a word of interest.

[0062] [Figure 14: Target word analysis process (step S505)] FIG. 14 is a flowchart showing a detailed example of the processing procedure of the word of interest analysis process (step S505).

[0063] (Step S1401) The proposal support system 101 extracts words that co-occur with the attention word from the second crawled sentence as the attention word. When extracting the attention word and the attention word from the second crawled sentence on a sentence basis, at least one of the preceding and following sentences may be included in the extraction target. The attention word is stored in the cross-domain information DB 304.

[0064] (Step S1402) The proposal support system 101 calculates occurrence information regarding the word of interest extracted in step S1401. The occurrence information regarding the word of interest is information indicating how often the word of interest or words related to the word of interest appear. Specifically, the occurrence information regarding the word of interest is, for example, the probability of co-occurrence between the word of interest and the word of interest, the number of occurrences of the word of interest, and the company name 403 of the word of interest, the company name of the company of interest. The proposal support system 101 can set at least one of the probability of co-occurrence between the word of interest and the word of interest, the number of occurrences of the word of interest, and the company name 403 of the word of interest, the company name of the company of interest, to the occurrence information regarding the word of interest.

[0065] The co-occurrence probability between the attention word and the target word is calculated by applying the above-mentioned formula (1) or formula (2) to the second crawled text (crawled text 706 in the second crawled result (e.g., entry 1300)). The occurrence count of the target word is the number of times the target word appears in the second crawled text. The company name 403 of the attention word target company name is the crawled company name 703 (e.g., "Iron Railway") in the second crawled result (e.g., entry 1300). Occurrence information regarding the target word is stored in the cross-domain information DB 304.

[0066] (Step S1403) The proposal support system 101 associates the attention word, the target word, and the target company name with each other. Information indicating the association is stored in the cross-domain information DB 304. After that, the process proceeds to step S506.

[0067] [Figures 15 to 17 Cross-domain information DB 304] Here, a specific example of storage in the cross-domain information DB 304 in the target word analysis process (step S505) will be described with reference to FIGS.

[0068] [Figure 15 Co-occurrence probability analysis table] 15 is an explanatory diagram showing an example of a co-occurrence probability analysis table 1500. The co-occurrence probability analysis table 1500 is generated when the occurrence information related to a word of interest is a co-occurrence probability. The co-occurrence probability analysis table 1500 has, as fields, a co-occurrence ID 1501, a first field 1502, a word of interest 1503, a second field 1504, a word of interest 1505, a co-occurrence probability 1506, a word of interest / company name of interest 1507, and a connection 1508.

[0069] The co-occurrence ID 1501 is identification information that uniquely identifies a combination of a word of interest and a word of interest. The first field 1502 is the field 402 that the first company handles as its business. The first company is a company that handles the first field as its business.

[0070] The attention word 1503 is the word acquired in step S502. The second field 1504 is a field different from the first field 1502. The attention word 1505 is the word identified in step S505.

[0071] The co-occurrence probability 1506 is the probability that the attention word 1503 and the target word 1505 co-occur in the second crawled sentence (crawled sentence 706 in the second crawled result (for example, entry 1300)). The attention word target company name 1507 is the company name 403 of the attention word target company name.

[0072] The connection 1508 is information indicating whether or not there is a connection between the attention word 1503, the word of interest 1505, and the attention word company name of interest 1507. "1" indicates a connection, and "0" indicates no connection. In step S1403, the proposal support system 101 sets the value of the connection 1508 to "1" for entries whose co-occurrence probability 1506 is equal to or greater than a threshold, and sets the value of the connection 1508 to "0" for entries whose co-occurrence probability 1506 is not equal to or greater than the threshold.

[0073] In addition, instead of using a threshold value, the proposal support system 101 may set the value of connection 1508 to “1” for entries ranked 1 to n (n is any integer equal to or greater than 1) in descending order of co-occurrence probability 1506, and set the value of connection 1508 to “0” for entries ranked n+1 and beyond.

[0074] [Figure 16 Co-occurrence probability analysis table] 16 is an explanatory diagram showing an example of the occurrence count analysis table 1600. The occurrence count analysis table 1600 is generated when the occurrence information regarding a word of interest is the number of occurrences of the word of interest.

[0075] The occurrence number analysis table 1600 is a table in which the co-occurrence probability 1506 of the co-occurrence probability analysis table 1500 is changed to an occurrence number 1606. The occurrence number 1606 is the number of times that the word of interest 1505 appears in the second crawled sentence.

[0076] In step S1403, the proposal support system 101 sets the value of the connection 1508 to "1" for entries whose occurrence count 1606 is equal to or greater than the threshold, and sets the value of the connection 1508 to "0" for entries whose occurrence count 1606 is not equal to or greater than the threshold.

[0077] In addition, instead of using a threshold value, the proposal support system 101 may set the value of connection 1508 to “1” for entries ranked 1 to n in descending order of occurrence count 1606, and set the value of connection 1508 to “0” for entries ranked n+1 and beyond.

[0078] [Figure 17 Focused Company Number Analysis Table] 17 is an explanatory diagram showing an example of a noted company number analysis table 1700. The noted company number analysis table 1700 is generated when the appearance information related to a noteworthy word is the company name 403 of the attention word noteworthy company name.

[0079] The number of companies of interest analysis table 1700 is a table in which the co-occurrence probability 1506 of the co-occurrence probability analysis table 1500 is changed to the number of companies of interest 1706. The number of companies of interest 1706 is the number of companies that have focused on the word of interest, and specifically, for example, the number of the word of interest company name 1507.

[0080] In step S1403, the proposal support system 101 sets the value of the connection 1508 to "1" for entries in which the number of companies of interest 1706 is equal to or greater than the threshold, and sets the value of the connection 1508 to "0" for entries in which the number of companies of interest 1706 is less than the threshold.

[0081] In addition, instead of using a threshold value, the proposal support system 101 may set the value of connection 1508 to “1” for entries ranked 1st to nth in descending order of the number of companies of interest 1706, and set the value of connection 1508 to “0” for entries ranked n+1 and beyond.

[0082] (Step S506) 5, the proposal support system 101 outputs the analysis result in a displayable manner on a display, which is an example of the output device 204 of the proposal support system 101, or on the display of the terminal 102. The analysis result includes a first field 1502, a word of interest 1503, a second field 1504, a word of interest 1505, and a company name of interest 1507 between the word of interest 1503, the word of interest 1505, and the company name of interest 1507 between the word of interest.

[0083] [Figures 18 to 20 Analysis results display screen] Here, examples of display screens that display the analysis results will be described with reference to FIGS.

[0084] [Figure 18 Analysis result display screen example 1] 18 is an explanatory diagram showing analysis result display screen example 1. Analysis result display screen 1800 is an example of a screen when occurrence information 1801 related to a word of interest is co-occurrence probability 1506. Specifically, for example, analysis result display screen 1800 displays first field 1502, second field 1504, and co-occurrence probability 1506 as analysis results.

[0085] The analysis result display screen 1800 also displays, as the analysis result, a connection relationship graph 1802 indicating the connection 1508 between the attention word 1503, the focus word 1505, and the attention word focus company name 1507. The connection relationship graph 1802 is a graph in which the attention word 1503, the focus word 1505, and the attention word focus company name 1507, each of which has a connection 1508 of "1", are nodes, and the attention word 1503 and the focus word 1505 are connected via the attention word focus company name 1507.

[0086] The bold text in the node indicating the word of interest 1505 indicates the co-occurrence probability 1506 between the word of interest 1505 and the word of interest 1503. For example, the co-occurrence probability 1506 between X indicating the word of interest 1503 and L2 indicating the word of interest 1505 is "0.90."

[0087] By referring to the analysis result display screen 1800, when examining the trending word X in the first field 1502 being “finance”, it can be seen that the cross-domain that is most compatible with the first company in the first field 1502 is the trending word focus companies A and B, among the trending word focus companies A to F dealing with “industry” in the second field 1504, that focus on the trending word L3 that has the highest co-occurrence probability 1506 with the trending word X.

[0088] Therefore, the proposal support system 101 becomes able to suggest to the first company in the first field 1502 a proposal or consideration regarding the focus word L3 in relation to the focus word X to the focus word focusing companies A and B, which focus on the focus word L3 that deals with "industry" in the second field 1504 and has the highest co-occurrence probability 1506 with the focus word X.

[0089] For example, when the attention word X is “idle”, the first company (crawling company name 703) in the first field 1502 is “Gold Bank”, and the focus word L3 is “large-scale energy storage equipment”, the proposal support system 101 becomes able to suggest to the first company “Gold Bank” in the first field 1502 a proposal or consideration regarding the focus word L3 “large-scale energy storage equipment” for the attention word X “idle” to the attention word focus companies A and B that focus on the focus word L3 “large-scale energy storage equipment” which has the highest co-occurrence probability 1506 with the attention word X “idle”.

[0090] The high co-occurrence probability 1506 indicates the high level of interest in the attention word L3 that co-occurs with the attention word X. Therefore, the proposal support system 101 can estimate the effectiveness of a proposal or consideration regarding the attention word L3 "large-scale energy storage equipment" from the first company "Gold Bank" dealing with the first field 1502 corresponding to the co-occurrence network 1100 from which the attention word X "idle" is selected, to the attention word attention companies A and B dealing with the second field 1504 related to the attention word L3 "large-scale energy storage equipment" through cross-domain estimation.

[0091] [Figure 19 Analysis result display screen example 2] 19 is an explanatory diagram showing analysis result display screen example 2. Analysis result display screen 1900 is an example of a screen when occurrence information 1801 related to a word of interest is the number of occurrences 1606. Specifically, for example, analysis result display screen 1900 displays first field 1502, second field 1504, and number of occurrences 1606 as analysis results.

[0092] The analysis result display screen 1900 also displays, as the analysis result, a connection relationship graph 1902 indicating the connection 1508 between the attention word 1503, the focus word 1505, and the attention word focus company name 1507. The connection relationship graph 1902 is a graph in which the attention word 1503, the focus word 1505, and the attention word focus company name 1507, each of which has a connection 1508 of "1", are nodes, and the attention word 1503 and the focus word 1505 are connected via the attention word focus company name 1507.

[0093] The bold text in the node indicating the word of interest 1505 indicates the number of occurrences 1606 of the word of interest 1505. For example, the number of occurrences 1606 of M1 indicating the word of interest 1505 is "42 times."

[0094] By referring to the analysis result display screen 1900, when examining the trending word X in the first field 1502 of “finance”, it can be seen that the cross-domain that is most compatible with the first company in the first field 1502 is trending word focus company F, which focuses on trending word M5 with the highest occurrence count 1606, among trending word focus companies A to C, E, and F that deal with “industry” in the second field 1504.

[0095] Therefore, the proposal support system 101 becomes able to suggest to the first company in the first field 1502 a proposal or consideration regarding the focus word M5 in relation to the focus word X to the focus word focus company F, which focuses on the focus word M5 with the highest occurrence count 1606 dealing with “industry” in the second field 1504.

[0096] For example, if the attention word X is "idle", the first company (crawling company name 703) in the first field 1502 is "Gold Bank", and the focus word M5 is "unmanned store", the proposal support system 101 becomes able to suggest to the first company "Gold Bank" in the first field 1502 a proposal or consideration regarding the focus word M5 "unmanned store" in response to the attention word X "idle" to the focus word focus company F that focuses on the focus word M5 "unmanned store" which has the highest occurrence count 1606.

[0097] The higher the number of occurrences 1606, the higher the interest in the word of interest M5. Therefore, the proposal support system 101 can estimate the effectiveness of a proposal or consideration for the word of interest M5 "unmanned store" from the first company "Gold Bank" dealing in the first field 1502 corresponding to the co-occurrence network 1100 from which the word of interest X "idle" is selected, to the word of interest company F dealing in the second field 1504 related to the word of interest M5 "unmanned store" through cross-domain estimation.

[0098] [Figure 20 Analysis result display screen example 3] 20 is an explanatory diagram showing analysis result display screen example 3. Analysis result display screen 2000 is an example of a screen when occurrence information 1801 related to a word of interest is the number of companies of interest 1706. Specifically, for example, analysis result display screen 1900 displays first field 1502, second field 1504, and number of companies of interest 1706 as analysis results.

[0099] The analysis result display screen 1900 also displays, as the analysis result, a connection relationship graph 2002 indicating the connection 1508 between the attention word 1503, the focus word 1505, and the attention word focus company name 1507. The connection relationship graph 2002 is a graph in which the attention word 1503, the focus word 1505, and the attention word focus company name 1507, each of which has a connection 1508 of "1", are nodes, and the attention word 1503 and the focus word 1505 are connected via the attention word focus company name 1507.

[0100] The bold text in the node indicating the word of interest 1505 indicates the number of companies of interest 1706 for that word of interest 1505. For example, the number of companies of interest 1706 for N2 indicating the word of interest 1505 is "4 companies."

[0101] By referring to the analysis result display screen 2000, when examining the trending word X in the first field 1502 being “finance”, it can be seen that the cross-domain that is most compatible with the first company in the first field 1502 is the trending word focus companies B to F that focus on the trending word N3, which has the largest number of trending companies 1706, among the trending word focus companies A to F that deal with “industry” in the second field 1504.

[0102] Therefore, the proposal support system 101 becomes able to suggest to the first company in the first field 1502 a proposal or consideration regarding the focus word N3 in relation to the focus word X to the focus word focus companies B to F, which focus on the focus word N3 that has the highest number of occurrences 1606 dealing with “industry” in the second field 1504.

[0103] For example, if the attention word X is "idle", the first company (crawling company name 703) in the first field 1502 is "Gold Bank", and the focus word N3 is "direct product sales", the proposal support system 101 becomes able to suggest to the first company "Gold Bank" in the first field 1502 a proposal or consideration regarding the focus word N3 "direct product sales" for the attention word X "idle" to the attention word focus companies B to F that focus on the focus word N3 "direct product sales", which has the largest number of focus companies 1706.

[0104] A large number of the companies of interest 1706 indicates a high level of interest in the word of interest M5. Therefore, the proposal support system 101 can estimate the effectiveness of a proposal or consideration for the word of interest N3 "direct product sales" from the first company "Gold Bank" dealing with the first field 1502 corresponding to the co-occurrence network 1100 from which the word of interest X "idle" is selected, to the companies of interest B to F dealing with the second field 1504 related to the word of interest N3 "direct product sales" by cross-domain estimation.

[0105] (Step S507) Returning to FIG. 5, the proposal support system 101 judges whether or not there is an unselected second field 1504. Specifically, for example, the proposal support system 101 judges whether or not there is an unselected second field 1504 from the field 402 of the company information DB 301. If there is an unselected second field 1504 (step S507: Yes), the proposal support system 101 selects one from the unselected second fields 1504 and returns to step S503. If there is no unselected second field 1504 (step S507: No), the series of processes ends. Note that step S506 may be executed after step S507: No.

[0106] In this way, according to this embodiment, it is possible to improve the efficiency of the support for idea proposal. Specifically, while the world is in need of value creation that is not limited to a single company or a single project, such as social issues, by crossing different fields (cross-domain) such as industry and finance, it is possible to combine sales activity information and external information, link the interests of each field and company, and suggest new proposals in a form that enhances the value of sales proposals. This makes it possible to accelerate organizational ideas in cross-domains, and efficiently concretize proposal stories.

[0107] The present invention is not limited to the above-described embodiments, and includes various modified examples and equivalent configurations within the spirit of the appended claims. 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 those having all of the configurations described. Also, a part of the configuration of one embodiment may be replaced with a configuration of another embodiment. Also, a configuration of another embodiment may be added to a configuration of one embodiment. Also, a part of the configuration of each embodiment may be added, deleted, or replaced with another configuration.

[0108] Furthermore, each of the aforementioned configurations, functions, processing units, processing means, etc. may be realized in hardware, for example by designing some or all of them as an integrated circuit, or may be realized in software by a processor interpreting and executing a program that realizes each function.

[0109] Information such as programs, tables, files, etc. that realize each function can be stored in a storage device such as a memory, a hard disk, or an SSD (Solid State Drive), or in a recording medium such as an IC (Integrated Circuit) card, an SD card, or a DVD (Digital Versatile Disc).

[0110] In addition, the control lines and information lines shown are those considered necessary for the explanation, and do not necessarily show all the control lines and information lines necessary for implementation. In reality, it can be considered that almost all components are connected to each other. [Explanation of symbols]

[0111] 100 Network Systems 101 Proposal Support System 102 Terminals 103 Own company DB 104 Other company DB 201 Processor 202 Storage Devices 301 Company information DB 302 Crawling Information DB 303 Co-occurrence Network DB 304 Cross-domain information DB 402 Fields 403 Company name 800 Co-occurrence Probability Table 900 Word Occurrence Table 1100 Co-occurrence Network 1500 Co-occurrence Probability Analysis Table 1503 Hot Words 1505 Focus Words 1506 Co-occurrence probability 1507 Hot Words and Company Names 1600 Occurrence Analysis Table 1700 Focused Company Number Analysis Table 1800 Analysis result display screen 1802 Connection graph 1900 Analysis result display screen 1902 Connection graph 2000 Analysis result display screen 2002 Connection graph

Claims

1. A proposal support system having a processor that executes a program and a storage device that stores the program, The processor, an acquisition process for acquiring a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group including a name of a first field is defined as a node in at least one of a first information source related to a first field and a second information source related to a second field different from the first field, and a co-occurrence relationship between two first words is defined as a link connecting the nodes; an extraction process for extracting company names in the second field related to the specific first word from a second sentence group including a second field name and the specific first word acquired by the acquisition process from the at least one information source; an analysis process for associating the specific first word, the specific second word, and the company name in the second field extracted by the extraction process based on occurrence information regarding a specific second word that co-occurs with the specific first word in a second word group in the second sentence group; an output process for outputting an analysis result obtained by the analysis process in a displayable manner; A proposal support system comprising:

2. The proposal support system according to claim 1, The processor, executing a generation process for generating the co-occurrence network based on the first set of sentences; In the acquisition process, the processor acquires the specific first word from the co-occurrence network generated by the generation process. A proposal support system comprising:

3. The proposal support system according to claim 1, In the analysis process, the processor calculates, as the occurrence information, a co-occurrence probability between the specific first word and the specific second word in the second sentence group, and associates the specific first word, the specific second word, and a company name in the second field based on the co-occurrence probability. A proposal support system comprising:

4. The proposal support system according to claim 1, In the analysis process, the processor calculates, as the occurrence information, the number of occurrences of the specific second word in the second sentence group, and associates the specific first word, the specific second word, and a company name in the second field based on the number of occurrences. A proposal support system comprising:

5. The proposal support system according to claim 1, In the analysis process, the processor calculates, as the occurrence information, the number of companies related to the specific first word in the second field in the second sentence group, and associates the specific first word, the specific second word, and the company names in the second field based on the number of companies in the second field. A proposal support system comprising:

6. The proposal support system according to claim 1, In the output process, the processor outputs the first field name, the second field name, and the appearance information, and also displays connection information that connects the specific first word, the specific second word, and company names in the second field. A proposal support system comprising:

7. The proposal support system according to claim 3, In the output process, the processor outputs the first field name, the second field name, and the co-occurrence probability, which is the appearance information, and also outputs a graph showing combinations of the specific first words and the specific second words connected based on the co-occurrence probability and company names in the second field in a displayable manner. A proposal support system comprising:

8. The proposal support system according to claim 4, In the output process, the processor outputs the first field name, the second field name, and the occurrence count, which is the occurrence information, and also outputs a graph showing the combination of the specific first word and the specific second word connected based on the occurrence count and the company names in the second field in a displayable manner. A proposal support system comprising:

9. The proposal support system according to claim 5, In the output process, the processor outputs the first field name, the second field name, and the number of companies in the second field, which is the occurrence information, and also outputs a graph in a displayable manner showing the combinations of the specific first words and the specific second words connected based on the number of companies in the second field, and the company names in the second field. A proposal support system comprising:

10. A proposal support method executed by a proposal support system having a processor that executes a program and a storage device that stores the program, The processor, an acquisition process for acquiring a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group including a name of a first field is defined as a node in at least one of a first information source related to a first field and a second information source related to a second field different from the first field, and a co-occurrence relationship between two first words is defined as a link connecting the nodes; an extraction process for extracting company names in the second field from a second sentence group including a second field name and the specific first word acquired by the acquisition process, from the at least one information source; an analysis process for associating the specific first word, the specific second word, and the company name in the second field extracted by the extraction process based on occurrence information regarding a specific second word that co-occurs with the specific first word in a second word group in the second sentence group; an output process for outputting an analysis result obtained by the analysis process in a displayable manner; A proposal support method comprising the steps of:

11. The processor: an acquisition process for acquiring a specific first word from a co-occurrence network in which each first word of a first word group in a first sentence group including a name of a first field is defined as a node in at least one of a first information source related to a first field and a second information source related to a second field different from the first field, and a co-occurrence relationship between two first words is defined as a link connecting the nodes; an extraction process for extracting company names in the second field from a second sentence group including a second field name and the specific first word acquired by the acquisition process, from the at least one information source; an analysis process for associating the specific first word, the specific second word, and the company name in the second field extracted by the extraction process based on occurrence information regarding a specific second word that co-occurs with the specific first word in a second word group in the second sentence group; an output process for outputting an analysis result obtained by the analysis process in a displayable manner; A proposal support program characterized by executing the above.

Images