Management server, proposal generation method, and program

Abstract

To provide a commodity service proposal which contains information of high value reflecting latent needs of a user by repeating questions to an artificial intelligence chat.SOLUTION: In a proposal generating system, a latent need analysis section of a management server extracts present data contained in user information acquired from a user terminal, refers to company information out of the user information and extracts ideal data from a big data server, and compares similar items against each other among items included in the present data and ideal data and calculates a divergence index per item, in order to generate an analysis result according to an item and a type of a divergence index exceeding a reference value. An artificial intelligence chat section acquires question data and analysis results associated with the user information, generates a first question sentence, and a first reply sentence according to a content of the analysis results, generates an N-th question sentence based on an item and an N-th word contained in the question data, generates an N-th reply sentence according to a content of the analysis results, and repeats the generation of question sentences and reply sentences according to an N number.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to a management server and a plan generation system that utilizes an artificial intelligence chat. [Background technology]

[0002] Previously, by using artificial intelligence chatbots such as ChatGPT, users could easily enter questions about the matters they wanted to know and receive answers. However, the answers often contained a broad range of information and did not necessarily contain valuable (critical) information that reflected the user's issues or potential needs. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-194839 A Summary of the Invention [Problem to be solved by the invention]

[0004] For example, Patent Document 1 discloses an artificial intelligence-based service implementation system and an artificial intelligence-based service implementation method.

[0005] However, Patent Document 1 does not intend to provide product and service plan proposals that include valuable information that reflects the user's issues and potential needs by repeatedly asking questions to an AI chat.

[0006] An object of the present invention is to provide a product / service plan including valuable information that reflects a user's potential needs by repeatedly asking questions to an AI chat. [Means for solving the problem]

[0007] Among the inventions disclosed in this application, a brief summary of representative inventions is as follows.

[0008] One embodiment of the present invention includes a user terminal 100. The management server 120 includes a big data server 110 in which big data is stored. The management server 120 is connected to the user terminal 100 and the big data server 110 in which big data is stored via a network, and includes a potential needs analysis unit 140 that extracts current state data 210 included in user information 200 acquired from the user terminal 100, extracts ideal data 310 from the big data server 110 by referring to company information in the user information 200, compares the same items among the items included in the current state data 210 and the ideal data 310 to calculate a deviation index 600 for each item, and generates an analysis result 700 according to the item and type of the deviation index 600 that exceeds a reference value. The management server 120 also has an artificial intelligence chat unit that acquires question data 300 and analysis result 700 linked to the user information 200, generates a first question based on the items and the first word included in the question data 300, generates a first answer according to the content of the analysis result 700 using artificial intelligence, generates an Nth question based on the items and the Nth word included in the question data 300, generates the Nth answer according to the content of the analysis result 700 using artificial intelligence, and repeats the generation of questions and answers according to the number N. Effect of the Invention

[0009] According to the present invention, by repeatedly asking questions to an AI chat system, it is possible to provide a product / service plan that includes valuable information that reflects the user's potential needs. [Brief description of the drawings]

[0010] [Figure 1] 1 is a diagram showing an overview of an example of the configuration of a plan generation system using artificial intelligence chat in one embodiment of the present invention. FIG. [Diagram 2] 1 is a diagram showing an overview of user information and current state data stored in a user information storage unit of a management server according to an embodiment of the present invention. FIG. [Diagram 3]A diagram showing an overview of the question data stored in the question data storage unit of the management server and the ideal data stored in the big data server in one embodiment of the present invention. [Figure 4] FIG. 2 is a diagram showing an overview of the entire process according to an embodiment of the present invention. [Diagram 5] 1A to 1C are diagrams illustrating an overview of a user information acquisition process and a user information storage process according to an embodiment of the present invention. [Figure 6] FIG. 13 is a diagram showing an outline of deviation indexes and analysis results according to an embodiment of the present invention. [Figure 7] FIG. 1 is a diagram showing an overview of a latent needs analysis process according to an embodiment of the present invention. [Figure 8] FIG. 13 is a diagram showing an overview of a chat result according to an embodiment of the present invention. [Figure 9] FIG. 2 is a diagram showing an overview of artificial intelligence chat processing in one embodiment of the present invention. [Figure 10] FIG. 13 is a diagram showing an overview of a project proposal screen according to an embodiment of the present invention. [Figure 11] FIG. 2 is a diagram showing an overview of a project proposal screen generation process according to an embodiment of the present invention. [Figure 12] FIG. 13 is a diagram showing an overview of a project proposal screen providing process according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. Note that this is merely an example, and the technical scope of the present invention is not limited to this example. In addition, in all the drawings for explaining the embodiment, the same parts are generally given the same reference numerals, and repeated explanations will be omitted. <System configuration> FIG. 1 is a diagram showing an outline of a configuration example of a plan generating system using an artificial intelligence chat in one embodiment of the present invention.

[0012] As shown in Figure 1, the plan generation system using artificial intelligence chat includes a user terminal 100, a big data server 110 in which big data is stored, and a management server 120 connected to the user terminal 100 and the big data server 110 in which big data is stored via a network.

[0013] The user terminal 100 is a terminal owned by a user who wishes to propose a project proposal. The big data server 110 is a server in which big data or open data is stored.

[0014] The management server 120 has a user information memory unit 130, a question data memory unit 131, an analysis result memory unit 132, a chat result memory unit 133, a project proposal screen memory unit 134, a latent needs analysis unit 140, an artificial intelligence chat unit 150, a project proposal screen generation unit 160, and a project proposal screen provision unit 170.

[0015] The user information storage unit 130 stores user information 200 (described later, FIG. 2). The question data storage unit 131 stores question data 300 (described later, FIG. 3). The analysis result storage unit 132 stores analysis results 700. The chat result storage unit 133 stores chat results 800. The plan proposal screen storage unit 134 stores a plan proposal screen 1000 (described later, FIG. 10).

[0016] The potential needs analysis unit 140 extracts current state data 210 contained in user information 200 acquired from the user terminal 100, extracts ideal data 310 from the big data server 110 by referring to company information in the user information 200, compares the same items among the items contained in the current state data 210 and the ideal data 310 to calculate a deviation index 600 for each item, and generates an analysis result 700 according to the items and types of deviation indexes 600 that exceed the standard value.

[0017] The artificial intelligence chat unit 150 acquires question data 300 and analysis result 700 linked to user information 200, generates a first question based on the items and the first word included in question data 300, generates a first answer according to the content of analysis result 700 using artificial intelligence, generates an Nth question based on the items and the Nth word included in question data 300, generates an Nth answer according to the content of analysis result 700 using artificial intelligence, and repeats the generation of questions and answers according to the number N.

[0018] The plan screen generating unit 160 generates the plan screen 1000 based on the user information 200, the analysis result 700, and the chat result 800.

[0019] The project proposal screen providing unit 170 provides the generated project proposal screen 1000 to the user terminal 100 . <User information storage unit> FIG. 2(a) is a diagram showing an outline of an example of the configuration of user information 200 stored in the user information storage unit 130 of the management server 120 in one embodiment of the present invention.

[0020] As shown in FIG. 2( a ), the user information 200 is made up of data items such as a user ID, a company name, company information, current situation data 210 , and question data 300 .

[0021] The user ID indicates a code by which the management server 120 identifies the user. The company name indicates the name of the user's company. The company information indicates the user's company information. The current situation data 210 indicates information on current issues in the user's own company, which is acquired by the user terminal 100 accepting input from the user (details will be described later).

[0022] The question data 300 indicates information on a question for which the user wants to receive a response from the AI ​​chat, which the user terminal 100 acquires by accepting an input from the user (details will be described later).

[0023] 2(b) is a diagram showing an outline of a configuration example of current state data 210 included in user information 200 stored in user information storage unit 130 of management server 120 in one embodiment of the present invention. Current state data 210 indicates information such as a current state data ID, a purpose, a rule, an action, and a result.

[0024] The current state data ID indicates a code by which the management server 120 identifies the current state data 210 .

[0025] The objectives, rules, actions, and results represent what the user perceives as the current challenges in his or her company. <Question Data Storage Unit and Big Data Server> FIG. 3(a) is a diagram showing an outline of an example of the structure of question data 300 stored in the question data storage unit 131 of the management server 120 in one embodiment of the present invention.

[0026] 3(a), the question data 300 is composed of data items such as a question data ID, a product / service name, a word 1, a word 2, and a word 3. Note that the number of words is not particularly limited, and the words 2 and after will be generalized as word N using the natural number N for the explanation.

[0027] The question data ID indicates a code by which the management server 120 identifies the question data 300 .

[0028] The product / service name indicates the name of the product / service that the user's company deals in. Examples of product / service names include "consulting training," "furniture," and "marketing training."

[0029] Word 1 indicates the first word that the user asks to get a response from the AI ​​chat. Word 1 should be as short as possible or one word. Examples of Word 1 are "productivity", "finance", "marketing training", etc.

[0030] Word 2 indicates the second word that the user asks to get a response from the AI ​​chat. Word 2 should be as short as possible or one word. Word 2 is, for example, "cost" or "material".

[0031] Word 3 indicates the third word that the user asks to get a response from the AI ​​chat. Word 3 should be as short as possible or one word. Word 3 is, for example, "risk" or "design".

[0032] Word N indicates the Nth word that the user asks to receive a response from the AI ​​chat.

[0033] FIG. 3(b) is a diagram showing an outline of an example of the configuration of ideal data 310 stored in the big data server 110 in one embodiment of the present invention.

[0034] As shown in Fig. 3(b), the ideal data 310 indicates information about problems solved by multiple major companies that can be generally obtained as big data. The ideal data 310 indicates information such as the ideal data ID, purpose, rules, actions, and results.

[0035] The ideal data ID indicates a code by which the management server 120 identifies the ideal data 310 .

[0036] The objectives, rules, actions, and results indicate the issues that have been resolved by multiple major companies. In other words, the "objectives, rules, actions, and results" included in the ideal data 310 indicate issues that have already been resolved by major companies, and are exemplary in content compared to the current situation data 210 held by the user. <Overall processing> FIG. 4 is a diagram showing an overview of the entire process in one embodiment of the present invention.

[0037] First, in S401, the management server 120 performs a user information acquisition process and a user information storage process (described later with reference to FIG. 5).

[0038] Next, in S402, the management server 120 performs a potential needs analysis process (described later with reference to FIGS. 6 and 7).

[0039] Next, in S403, the management server 120 performs an artificial intelligence chat process (described later with reference to FIGS. 8 and 9).

[0040] Next, in S404, the management server 120 performs a plan screen generation process (described later with reference to FIGS. 10 and 11).

[0041] Next, in S405, the management server 120 performs a plan screen providing process (described later with reference to FIG. 12). <User information storage process> 5(a) is a diagram showing an overview of the user information storage process in one embodiment of the present invention. A method in which the management server 120 stores the user information 200 will be described below.

[0042] First, in S501, the management server 120 provides a user information input screen to the user terminal 100. Next, in S502, the user terminal 100 displays the user information input screen on the display.

[0043] Thereafter, when the user uses the system according to the present invention for the first time or when the user changes the question data 300, in S503, the user terminal 100 accepts input of the user information 200 from the user. Note that the user information 200 accepted from the user at this time includes the question data 300.

[0044] Next, in S504, the user terminal 100 transmits to the management server 120 the user information 200 inputted and accepted in S503.

[0045] Next, in S505, the management server 120 receives the user information 200 transmitted in S504, thereby acquiring the user information 200.

[0046] Next, in S506, the management server 120 assigns a user ID to the acquired user information 200 and stores it in the user information storage unit 130. In addition, the management server 120 stores the question data 300 included in the received user information 200 in the question data storage unit 131. <User information acquisition process> 5(b) is a diagram showing an overview of the user information acquisition process in one embodiment of the present invention. A method in which the management server 120 acquires the user information 200 will be described below.

[0047] When the user uses the system according to the present invention for the second or subsequent time, the user terminal 100 accepts input of a user ID from the user in S507.

[0048] Next, in S508, the user terminal 100 transmits to the management server 120 the user ID inputted in S507.

[0049] Thereafter, in S509, the management server 120 acquires from the user information storage unit 130 the user information 200 corresponding to the key of the user ID transmitted in S508. <Deviation index and analysis results> FIG. 6 is a diagram showing an overview of the deviation index 600 and the analysis result 700 in one embodiment of the present invention.

[0050] As shown in FIG. 6, the management server 120 receives user information 200 including company information and current status data 210 about the user's company from the user terminal 100, and first refers to the contents of the company information and extracts ideal data 310 linked to a model company (any major company) having similar contents from the big data server 110.

[0051] For example, if the company information of a user (user ID: U0001, Company A) is "Employees: 90 people" and "Industry: manufacturing", the management server 120 selects an arbitrary model company (major company B) from among multiple companies that have similar company information "Employees: 100 people" and "Industry: manufacturing" and have top-ranking performance, and extracts the ideal data 310 of this model company from the big data server 110. Note that the criteria for this approximation are, for example, the same "industry" and a difference in "number of employees" within a range of 10 people, but are not limited to this.

[0052] As a result, the management server 120 can select an arbitrary major company that is performing well while being placed under similar conditions and environments as the user as an ideal model company, and by comparing with this, calculate the deviation index 600 and analysis result 700 to know the user's potential needs.

[0053] Thereafter, the management server 120 compares the contents of the same items between the acquired current state data 210 and ideal data 310, and calculates the deviation index 600 for each item.

[0054] The deviation index 600 has a larger value as the content of the compared item of the current data 210 and the item of the ideal data 310 deviates more. The deviation index 600 is expressed as a value from 0 to 100, and for example, when the degree of deviation is large, it is shown as "80", and when there is no degree of deviation (the content of the item is completely identical), it is shown as "0", but is not particularly limited to this.

[0055] For example, if the "purpose" of the current data 210 is compared with the "purpose" of the ideal data 310 and found to be identical, the management server 120 calculates the value of the deviation index 600 for this item as "0." Also, if the "rule" of the current data 210 is compared with the "rule" of the ideal data 310 and found to be significantly different, the management server 120 calculates the value of the deviation index 600 for this item as "80."

[0056] Furthermore, when the result of comparing the current state data 210 with the ideal data 310 and calculating the deviation index 600 for each item shows that there are two items, "rules" and "actions", that exceed the predetermined reference value, the management server 120 generates the analysis result 700 as follows: "Potential needs exist in two items, rules and actions, and the potential needs index is 75." The predetermined reference value is not particularly limited, but may be, for example, "60."

[0057] And potential needs exist in "items with a deviation index of 600 or more that exceed the standard value."

[0058] Further, the latent needs index 710 indicates the average value of the deviation index 600 of the items for which there is a latent need. For example, as shown in Fig. 6, if the deviation index 600 of "rule" is "80" and the deviation index 600 of "action" is "70", the latent needs index 710 is their average value, "75".

[0059] Furthermore, the analysis result 700 indicates “how many items have deviation indices 600 that exceed the reference value, and what their types are.” In other words, the analysis result 700 has information on latent needs and latent needs index 710.

[0060] In this way, by comparing the problems solved by major companies that have similar company information to the user's current problems with the problems the user is facing, and analyzing which items there is a discrepancy in and the degree of discrepancy, the management server 120 can grasp the user's potential needs, and then use the processing described below to conduct an artificial intelligence chat and propose optimal plans to the user. <Latent needs analysis processing> FIG. 7 is a diagram showing an outline of the latent needs analysis process in one embodiment of the present invention.

[0061] First, in S701, the potential needs analysis unit 140 of the management server 120 acquires the user information 200 from the user information storage unit 130 and extracts the current situation data 210 included in the user information 200.

[0062] Next, in S702, the latent needs analysis unit 140 refers to the contents of the company information (number of employees, industry, etc.) in the user information 200 acquired in S701, and extracts ideal data 310 linked to a model company with similar contents from the big data server 110.

[0063] Specifically, if the user's company information is "employees: 90" and "industry: manufacturing", the management server 120 selects an arbitrary "model company" from among a number of companies that have similar company information and have top-ranking performance, and extracts ideal data 310 linked to the model company from the big data server 110. Note that the criteria for this "approximation" are, for example, the same "industry" and a difference in "number of employees" within a range of 10 people, but are not limited to this.

[0064] Next, in S703, the potential needs analysis section 140 compares the contents of the same items among the items of the current state data 210 extracted in S701 and the items of the ideal data 310 extracted in S702, and calculates the deviation index 600 for each item.

[0065] The deviation index 600 has a larger value as the content of the compared item of the current data 210 and the content of the compared item of the ideal data 310 deviates more. The deviation index 600 is expressed as, for example, a "numeric value from 0 to 100."

[0066] For example, when comparing the "Objective" item in the current data 210 with the "Objective" item in the ideal data 310, if the contents are identical, the management server 120 calculates the value of the deviation index 600 for this item as "0".

[0067] Similarly, for example, when comparing the "rule" of the current data 210 with the "rule" of the ideal data 310, if there is a large difference, the management server 120 calculates the value of the deviation index 600 for this item as "80".

[0068] There is no particular limitation on how the degree of deviation is quantified as a "deviation index," and this can be done using a program based on well-known technology, for example, by measuring the quantity or quality of similar words, or by measuring the similarity in meanings associated with words.

[0069] Next, in S704, the potential needs analysis section 140 generates an analysis result 700 according to the number and types of items of the deviation index 600 that exceed the reference value.

[0070] For example, if there are two items, "rules" and "actions", that exceed the reference value, the management server 120 generates the analysis result 700 as follows: "Potential needs exist in two items, rules and actions, and the potential needs index is 75." The predetermined reference value is not particularly limited, but may be, for example, "60."

[0071] In addition, potential needs exist in "items with a deviation index of 600 that exceeds the standard value." In addition, the potential needs index indicates the "average value of the deviation index 600 of items for which potential needs exist."

[0072] The analysis result 700 indicates "how many items have a deviation index 600 that exceeds the reference value, and what their types are." In other words, the analysis result 700 has information on latent needs and latent needs index 710.

[0073] Next, in S705, the potential needs analysis section 140 stores the analysis result 700 generated in S704 in the analysis result storage section 132.

[0074] Next, in S706, the potential needs analysis section 140 requests the artificial intelligence chat section 150 to perform artificial intelligence chat processing.

[0075] In this way, by using the analysis results 700 obtained by comparing the user's company information with that of major companies and measuring the extent to which the user's current situation deviates from the ideal, the management server 120 can have the artificial intelligence chat automatically generate answers that clarify the user's potential needs and resolve their issues. <Chat result> FIG. 8 is a diagram showing an overview of a chat result 800 according to an embodiment of the present invention.

[0076] As shown in Figure 8, the artificial intelligence chat unit 150 of the management server 120 acquires question data 300 from the question data storage unit 131 (by processing step S902 described below), and generates question sentence 1 based on the contents of the "product / service name" and "word 1" contained in the acquired question data 300.

[0077] For example, if the question data 300 linked to the user information 200 of a user (ID: U0001) includes "product / service name: consulting training" and "word 1: productivity," the artificial intelligence chat unit 150 uses artificial intelligence to generate question sentence 1 with content such as "Please consider productivity in consulting training."

[0078] Next, the artificial intelligence chat unit 150 generates answer sentence 1 according to the content of question sentence 1 and the content of analysis result 700 acquired (by the process of step S903 described later) using artificial intelligence.

[0079] For example, if the analysis result 700 linked to the user information 200 of a user (ID: U0001) includes "latent needs: rules, behavior" and "latent needs index: 75", the artificial intelligence chat unit 150 uses artificial intelligence to generate answer sentence 1 with content such as "A new sales model is being introduced and measurements and monitoring are required to verify it."

[0080] In addition, in this answer sentence 1, the part "introduce a new sales model" represents a "rule," and the part "measurement and monitoring for verification" represents an "action." In other words, since the analysis result 700 was "latent needs: rules, actions," the AI ​​chat unit 150 generates answer sentence 1 that reflects the contents (rules, actions).

[0081] Next, the AI ​​chat unit 150 generates question sentence 2 based on the contents of “product / service name” and “word 2” contained in the acquired question data 300 .

[0082] For example, when the question data 300 includes "product / service name: consulting training" and "word 2: cost", the AI ​​chat unit 150 generates question sentence 2 with content such as "Please consider the costs involved in consulting training."

[0083] Next, the artificial intelligence chat unit 150 generates an answer sentence 2 according to the contents of the question sentence 2 and the contents of the acquired analysis result 700 using artificial intelligence.

[0084] After that, the AI ​​chat unit 150 repeats generating questions and answers according to the number of words included in the question data 300. Then, the AI ​​chat unit 150 stores the generated questions and answers as chat results 800 in the chat result storage unit 133.

[0085] In this way, by using the analysis results 700 and further repeating the user's question multiple times using the desired words, the management server 120 can have the AI ​​chat automatically generate a critical answer to clarify the user's potential needs and resolve the problem, rather than a meaningless answer that includes a wide range of information. <Artificial Intelligence Chat Processing> FIG. 9 is a diagram showing an outline of the artificial intelligence chat process in one embodiment of the present invention.

[0086] First, in S901, the artificial intelligence chat unit 150 of the management server 120 acquires from the user information storage unit 130 the user information 200 required for processing the artificial intelligence chat requested in S706.

[0087] Next, in S902, the artificial intelligence chat unit 150 acquires, from the question data storage unit 131, the question data 300 linked to the user information 200 acquired in S901.

[0088] Next, in S903, the artificial intelligence chat unit 150 acquires from the analysis result storage unit 132 the analysis result 700 linked to the user information 200 acquired in S901.

[0089] Next, in S904, the AI ​​chat unit 150 generates question sentence 1 by AI based on the items (product / service names, etc.) included in the question data 300 acquired in S902 and the first word (word 1).

[0090] For example, if the question data 300 includes "product / service name: consulting training" and "word 1: productivity", the AI ​​chat unit 150 generates question 1 with AI, such as "Please consider productivity in consulting training." Note that the question may be a preset fixed template, and the method of automatically generating the question may be performed using well-known technology, so the details will be omitted.

[0091] Next, in S905, the AI ​​chat unit 150 generates, by AI, answer sentence 1 in response to the question sentence 1 generated in S904, according to the content of the analysis result 700 acquired in S903.

[0092] For example, if the analysis result 700 includes "latent needs: rules, actions" and "latent needs index: 75", the AI ​​chat unit 150 generates answer sentence 1 by AI with the content of "A new sales model is being introduced, and measurement and monitoring for verification are required." Note that the question sentence may be a fixed template set in advance, and the method of automatically generating the question sentence is also performed using well-known technology, so the details are omitted.

[0093] Next, in S906, the AI ​​chat unit 150 generates a question N by AI based on the Nth word included in the question data 300 acquired in S902.

[0094] Next, in S907, the artificial intelligence chat unit 150 generates an answer sentence N to the generated question sentence N according to the contents of the analysis result 700 by using the artificial intelligence.

[0095] After that, the artificial intelligence chat unit 150 repeats the steps of S906 to S907 according to the number N (=2, 3, . . .).

[0096] Next, in S908, the artificial intelligence chat unit 150 stores the generated question and answer sentences in the chat result storage unit 133 as the chat result 800. That is, the artificial intelligence chat unit 150 stores all of the contents of the questions 1 to N and the answers 1 to N in the chat result storage unit 133 as the chat result 800.

[0097] Next, in S909, the artificial intelligence chat unit 150 requests the plan proposal screen generating unit 160 to generate the plan proposal screen 1000.

[0098] In this way, by using the analysis results 700 to generate a response, the management server 120 can have the artificial intelligence chat automatically generate a response to resolve the problem after clarifying the user's potential needs.

[0099] In addition, by repeating the user's question multiple times using the desired words, the management server 120 can have the AI ​​chat automatically generate sophisticated and critical answers by chaining questions and answers, rather than meaningless answers that include a wide range of information. <Project proposal screen> FIG. 10 is a diagram showing an overview of a project proposal screen 1000 according to an embodiment of the present invention.

[0100] 10, the project proposal screen 1000 includes a user information display area 1010, a potential needs display area 1020, and a project proposal display area 1030. The project proposal screen 1000 is displayed on the user terminal 100.

[0101] The user information display area 1010 displays the contents of the user information 200 acquired (by the processing of step S1101 described later). For example, the user information display area 1010 displays "XX Co., Ltd.", "ID: U0001", "Employees: 90", etc. There is no particular limitation on how the user information display area 1010 is displayed.

[0102] The potential needs display area 1020 displays the contents of the analysis result 700 extracted (by the processing of step S1102 described below). That is, the potential needs display area 1020 displays the potential needs index 710 and items with potential needs included in the analysis result 700. For example, the potential needs display area 1020 displays "Index: 75", "Items: Rules, Actions", etc. There is no particular limitation on the way in which the potential needs display area 1020 is displayed.

[0103] The plan display area 1030 displays the contents of the chat result 800 extracted (by the process of step S1103 described later). That is, the plan display area 1030 displays the contents of the plan obtained based on the contents of the questions 1 to N and the answers 1 to N included in the chat result 800. For example, the plan display area 1030 displays "Productivity: Please introduce a new sales model. Also, measurements and monitoring are required for verification.", "Cost: Before introducing a new sales model, a cost analysis is required on the cost of the product and the material composition.", etc. There is no particular limitation on how the plan display area 1030 is displayed.

[0104] In this way, since the plan proposal screen 1000 has the potential needs display area 1020 and the plan proposal display area 1030, the management server 120 can provide the user with the plan proposal screen 1000 that reflects the analysis results 700 indicating the user's potential needs obtained by the analysis processing, and the chat results 800 indicating the user's questions and the answers to those questions obtained by the artificial intelligence chat processing. <Project proposal screen generation process> FIG. 11 is a diagram showing an outline of the project proposal screen generation process in one embodiment of the present invention.

[0105] First, in S1101, the plan screen generating unit 160 of the management server 120 acquires from the user information storage unit 130 the user information 200 required to generate the plan screen 1000 requested in S909.

[0106] Next, in S1102, the plan screen generating unit 160 extracts from the analysis result storage unit 132 the analysis result corresponding to the user ID included in the user information 200 acquired in S1101.

[0107] Next, in S1103, the project proposal screen generating unit 160 extracts from the chat result storage unit 133 the chat result 800 corresponding to the question data 300 linked to the user information 200 acquired in S1101.

[0108] Next, in S1104, the plan screen generating unit 160 generates the plan screen 1000 based on the acquired user information 200 and the extracted analysis result 700 and chat result 800.

[0109] More specifically, the plan proposal screen generation unit 160 generates the plan proposal screen 1000 by displaying the contents of the user information 200, the latent needs index 710 and items (types) of latent needs contained in the analysis results 700, and the user's questions and answers to the questions obtained by artificial intelligence chat processing contained in the chat results 800.

[0110] Next, in S1105, the plan proposal screen generating unit 160 stores the generated plan proposal screen 1000 in the plan proposal screen storage unit 134.

[0111] Next, in S1106, the plan proposal screen generating unit 160 requests the plan proposal screen providing unit 170 to provide the plan proposal screen 1000. <Proposal screen provision process> FIG. 12 is a diagram showing an outline of a configuration example of the project proposal screen providing process in one embodiment of the present invention.

[0112] First, in S1201, the project proposal screen providing unit 170 of the management server 120 receives the request to provide the project proposal screen 1000 requested in S1106.

[0113] Next, in S1202, the project proposal screen providing unit 170 extracts the project proposal screen 1000 from the project proposal screen storage unit 134.

[0114] Next, in S1203, the project proposal screen providing unit 170 extracts, from the user information storage unit 130, the user information 200 corresponding to the user ID for which the project proposal screen 1000 is to be provided.

[0115] Next, in S1204, the project proposal screen providing unit 170 provides the project proposal screen 1000 extracted in S1202 to the user terminal 100.

[0116] Next, in S1205, the user terminal 100 displays the project proposal screen 1000 provided by the management server 120 in S1203 on the display of the terminal.

[0117] In this way, the management server 120 can provide the user with product and service proposals that include valuable information that reflects the user's potential needs by repeatedly asking questions to the AI ​​chat.

[0118] In other words, the user can easily grasp product and service plan proposals that include valuable information that reflects the potential needs of his or her company. <Effects of this embodiment> According to the embodiment of the present invention described above, the management server 120 can provide the user with product and service proposals that include valuable information that reflects the user's potential needs by repeatedly asking questions to the artificial intelligence chat.

[0119] In other words, by using the analysis result 700 to generate a response, the management server 120 can have the artificial intelligence chat automatically generate a response to resolve the problem after clarifying the user's potential needs.

[0120] In addition, by repeating the user's question multiple times using the desired words, the management server 120 can have the AI ​​chat automatically generate sophisticated and critical answers by chaining questions and answers, rather than meaningless answers that include a wide range of information.

[0121] The user can then easily grasp the product / service plan proposal that includes valuable information that reflects the potential needs of his / her own company.

[0122] Although the invention made by the present inventor has been specifically described based on the embodiment, the present invention is not limited to the above embodiment, and it goes without saying that various modifications can be made without departing from the gist of the invention. For example, the user terminal includes all user terminals of various types, such as notebook PCs and tablet terminals, in addition to smartphones.

[0123] In addition, the above-mentioned embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to those including all of the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add the configuration of another embodiment to the configuration of one embodiment. In addition, other configurations may be added, deleted, or replaced with part of the configuration of each embodiment.

[0124] Furthermore, the above configurations, functions, and processing units may be realized in part or in whole by hardware (for example, an integrated circuit).The above configurations, functions, and processing units may be realized by software installed via a network or a storage medium such as a disk, in which a processor interprets and executes a program that realizes each function, or by a network application such as an ASP.Information such as the programs, tables, and files that realize each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD. [Explanation of symbols]

[0125] 100 user terminals 110 Big Data Server 120 Management Server 130 User information storage unit 131 Question data storage unit 132 Analysis result storage unit 133 Chat result memory unit 134 Project proposal screen memory section 140 Latent Needs Analysis Department 150 AI Chat Department 160 Project proposal screen generation section 170 Project Proposal Screen Provider 1000 Project proposal screen

Claims

1. A latent needs analysis unit acquires user information including current data, company information, and question data, and compares the current data included in the user information with ideal data for exemplary problem-solving items having the same item structure as the current data to generate analysis results. An artificial intelligence chat unit that repeatedly performs the following processes for multiple words: acquiring the question data and analysis results contained in the user information, generating a question sentence based on the items and words contained in the question data, and generating an answer sentence using artificial intelligence according to the content of the analysis results; A management server.

2. A management server according to claim 1, The aforementioned latent needs analysis unit extracts the ideal data from the big data server by referring to the company information included in the user information. Management server.

3. A management server according to claim 1, The latent needs analysis unit compares identical items in the current data and the ideal data to calculate a deviation index for each item, and generates the analysis results according to the items and types of deviation indices that exceed a standard value. Management server.

4. A management server according to claim 1, A project proposal screen generation unit generates a project proposal screen based on the user information, the analysis results, and the chat results including the question and answer texts. A project proposal screen providing unit that provides the generated project proposal screen to the user terminal, A management server.

5. Computers The process involves obtaining user information including current data, company information, and question data, comparing the current data included in the user information with ideal data for exemplary problem-solving items having the same item structure as the current data, and generating analysis results. The process involves obtaining the question data and analysis results included in the user information, generating a question sentence based on the items and words included in the question data, and generating an answer sentence corresponding to the content of the analysis results using artificial intelligence, and repeating this process for multiple words. A method for generating project proposals and executing them.

6. In the method for generating a project proposal according to claim 5, The process includes generating a project proposal screen based on the user information, the analysis results, and the chat results including the question and answer, and providing it to the user's terminal. Methods for generating project proposals.

7. On the computer, A process that acquires user information including current data, company information, and question data, compares the current data included in the user information with ideal data for exemplary problem-solving items having the same item structure as the current data, and generates analysis results. The process involves obtaining the question data and analysis results contained in the user information, generating a question sentence based on the items and words contained in the question data, and generating an answer sentence corresponding to the content of the analysis results using artificial intelligence, and repeating this process for multiple words. A program that executes something.

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