New business navigation system and new business navigation method

Abstract

To provide a new business navigation system that extracts an existing and new product and service that can be provided to an existing and a new customer by utilizing a private AI within a system and a large-scale AI outside the system.SOLUTION: A new business navigation system (1) of the present invention predicts, on the basis of information on a product and service of an own company and an existing customer accumulated in a database (3) by utilizing a private AI (5) and a large-scale AI (61-64), the new product and service of the own company in a calculation processing unit (2) and extracts the predicted new product and service that match the existing customer information, and predicts, on the basis of the existing customer information, new customer information and extracts existing company product and service that match the predicted new customer information, and further extracts a new product and service that match the new customer information.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system and method for supporting the search for new businesses into which a company can enter by utilizing AI. [Background technology]

[0002] One method for exploring new businesses is "abstract thinking," which involves abstracting concrete objects and inferring the desired concept. However, this method is a manual task and cannot be automated by a computer system.

[0003] On the other hand, as a method for automatically proposing the creation of new businesses, a computer system is known that determines candidates for new business fields by utilizing prior art documents of a company's intellectual property applications, such as Patent Document 1. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent Publication No. 2021-25068 Summary of the Invention [Problem to be solved by the invention]

[0005] The invention described in Patent Document 1 determines potential new business fields based on a range of technologies selected based on patent applications of one's own and other companies, and does not predict the target concept of new products and services of one's own company or new customer information by abstracting specific matters such as existing information on one's own products and services and existing customer information. [Means for solving the problem]

[0006] The new business navigation system according to the present invention includes a database that stores information on existing company products and / or services and existing customer information, a calculation processing unit having an algorithm for searching for new businesses and a prompt generation function, a private AI for processing secret information, and a user interface. The calculation processing unit predicts new company products and / or new company services based on information on the company products and / or services, extracts new company products and / or new company services that match the existing customer information from the predicted new company products and / or new company services, predicts new customer information based on the customer information, and extracts existing company products and / or services that match the predicted new customer information. Furthermore, the new company products and / or new company services or the new customer information are extracted as new products and services that match the new customer information. Note that customer information is comprehensive information about customers, including the customer's company name, customer range, customer needs, etc.

[0007] The new business navigation method according to the present invention is configured to execute the following processes on a computer system: predicting new company products and / or services from existing company products and / or services, extracting company products and / or services that match existing customer information from the predicted new company products and / or services, predicting new customer information from existing customer information, and extracting company products and / or services that match the predicted new customer information from the existing company products and / or services.Furthermore, the new product / service that matches the new customer information is extracted from the new company products and / or new services or the new customer information.

[0008] Furthermore, the new business navigation method according to the present invention includes an abstraction step of abstracting a processing object, and a concretization step of concretizing the abstracted processing object, and further, the above abstraction step and concretization step are each repeated multiple times. Effect of the Invention

[0009] According to the present invention, a computer system can predict new company products and services from existing company product and service information, extract from the predicted new company products and services those that match existing customer information, predict new customer information based on existing customer information, and extract existing company products and / or services that match the predicted new customer information, etc. Also, from the predicted new company products and services, company products and services that match the predicted new customer information, etc. can be extracted. Furthermore, from the predicted new company products and services, new company products and services that match the predicted new customer information, etc. can be extracted from the predicted new customer information. [Brief description of the drawings]

[0010] [Figure 1] 1 is an overall configuration diagram of a new business navigation system according to the present invention; [Diagram 2] FIG. 2 is a schematic diagram showing a search method of the new business navigation system of the present invention. [Diagram 3] FIG. 2 is a flow chart showing the main operations of the new business navigation system of the present invention. [Figure 4] FIG. 1 is a flow diagram showing the operation of predicting new products and services from the company's existing product and service information. [Diagram 5] FIG. 11 is a flow diagram showing an operation of predicting new customer information from existing customer information. [Figure 6] FIG. 11 is a flow diagram showing operations for presenting combinations of new products and new customers. [Figure 7] FIG. 1 is a flow diagram for identifying products or services that a company can provide. [Figure 8] FIG. 1 is a flow chart for listing companies that could become new business partners. [Figure 9] FIG. 1 is a flow chart showing a specific method for implementing the "abstraction idea method." DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS EXAMPLES

[0011] The configuration of the new business navigation system according to the first embodiment of the present invention will be described below. Note that the present embodiment shows one example of an embodiment of the present invention, and it goes without saying that the present invention can be embodied in other forms.

[0012] In Fig. 1, 1 is a new business navigation system of the present invention constructed on, for example, a cloud server. 2 is a calculation processing unit included in 1, which includes a large-scale AI system selection function 21, a prompt generation function 22, and a new business search algorithm 23. Note that the system is not limited to a cloud server, and may be constructed in a fixed server or PC.

[0013] Also, 3 is a database in which customer information, company product and service information, prompts, etc. are accumulated. The database 3 may be located in a separate server outside the new business navigation system 1.

[0014] Reference numeral 4 denotes a user interface for a user to access the new business navigation system 1 from outside, and is connected to a user system 8 via an Internet line 51. Furthermore, reference numeral 5 denotes a private AI that is placed inside the system and processes confidential information as a generation AI.

[0015] 61, 62, 63, and 64 are large-scale AI systems a, b, c, and d, each of which has, for example, 100 billion or more parameters supplied by different external businesses, and which are connected to the new business navigation system 1 via an Internet line 52. Note that the large-scale AI system connected to the new business navigation system 1 is, for example, a generation AI such as ChatGPT, and the number of large-scale AI systems connected is not limited to multiple types, and may be one type. Note that the API in the figure is an abbreviation for Application Programming Interface, and has the function of connecting a large-scale AI system to the new business navigation system, etc.

[0016] In the case of multiple large-scale AI systems, the large-scale AI selection function 21 of the calculation processing unit 2 can select the most appropriate answer from among the answers obtained from the multiple large-scale AI systems. The private AI 5 has a function of executing processing including confidential information that is not to be transmitted to an external large-scale AI system as a generation AI, and the number of parameters is relatively small, for example, about several million, and the contents of the parameters are specialized for learning and inferring corporate information such as products and services. The prompt generation function 22 has a function of generating or storing various commands to be transmitted to the private AI 5 and the large-scale AI systems (61, 62, 63, 64). Furthermore, the new business search algorithm 23 forms the core of the calculation processing unit and has a function of controlling the operation of the entire system in response to instructions from the management system 7 and the user system 8. The new business search algorithm 23 includes, for example, a flowchart as shown in FIG. 9, which will be described later.

[0017] Also, 7 is a management system for managing this new business navigation system 1 from the outside via an Internet line 53. Note that, when the new business navigation system 1 is constructed on a fixed server or a PC, the management system 7 may be constructed integrally with the new business navigation system 1.

[0018] Fig. 2 is a four-quadrant schematic diagram showing an overview of the processing executed by the new business navigation system 1 of the present invention. The vertical direction of Fig. 2 is an axis showing whether the product / service is existing or new, and the horizontal direction is an axis showing whether the customer information is existing or new. That is, the lower left column (column A) of Fig. 2 is an area including existing products / services that match the existing customer information, and the upper left column (column B) is an area including new products / services that match the existing customer information.

[0019] Meanwhile, the lower right column (column C) is an area that includes existing products and services that match the new customer information. Furthermore, the upper right column (column D) is an area that includes new products and services that match the new customer information. The schematic diagram is based on "Ansoff's Growth Matrix", and the present invention is characterized in that predictions are made using private AI within the system and an external large-scale AI system (hereinafter referred to as large-scale AI) when moving from the lower left column (column A) to the upper left column (column B) or the lower right column (column C), and when moving from the upper left column (column B) or the lower right column (column C) to the upper right column (column D), and the process is described below. The "Ansoff's Growth Matrix" is a framework for clearly understanding a company's growth strategy and planning and executing an appropriate business strategy. For example, it is a management method used to further sell existing products and services in new markets, introduce new products and services into existing markets, and as a diversification strategy, sell new products and services into new markets, depending on the market and product perspective and the company's situation and goals. These are then organized into four quadrants as a framework for corporate strategies.

[0020] FIG. 3 is a flow diagram showing the main operations executed by the new business navigation system 1 of the present invention. First, in the flow on the left side, information on existing company products and services is read from the database 3 (S11). Next, new company products and services are predicted from the existing company products and services (S12). Furthermore, new company products and services that match the existing customer information are extracted (S13). Then, new company products and services that match the new customer information are predicted from the new company products and services that match the existing customer information (S14). That is, the flow on the left side reaches the goal of predicting new products and services that match new customer information by a clockwise route from the lower left column (column A) to the upper left column (column B) to the upper right column (column D) in the schematic diagram of FIG. 2.

[0021] Meanwhile, in the flow on the right side of Fig. 3, existing customer information is read from database 3 (S21), then a process of predicting new customer information from the existing customer information (S22) is carried out, existing company products and services that match the new customer information are extracted (S23), and new company products and services that match the new customer information are predicted from the existing company products and services that match the new customer information (S24). In other words, the flow on the right side reaches the goal of predicting new products and services that match the new customer information by taking a counterclockwise route from the lower left column (column A) to the lower right column (column C) to the upper right column (column D) in the schematic diagram of Fig. 2.

[0022] Figure 4 is a flow diagram showing an example of the process (S12) of predicting new in-house products and services from the in-house existing products and services in the left-hand flow of Figure 3 above, in which the process is executed using a private AI within the system and an external large-scale AI.

[0023] In Fig. 4, after extracting major product and service information from the company's existing product and service information (S31), an abstraction command set (prompt) is created for each major product and service for the private AI and large-scale AI (S32). Next, commands are sent to the private AI and large-scale AI (S33), and responses are received from the private AI and large-scale AI (S34). If it is determined that the content of the response is sufficiently abstracted (S35), the response is set as a new product or service (S36).

[0024] On the other hand, if the answer has not been abstracted enough to achieve the original purpose (S35), another command is sent to the private AI and the large-scale AI (S33), and this operation is repeated until the answer is sufficiently abstracted (S34 to S35).

[0025] FIG. 5 is a flow diagram showing an example of the case where the step (S22) of predicting new customer information from the existing customer information in the flow on the right side of FIG. 3 is executed using a private AI and a large-scale AI.

[0026] In FIG. 5, existing customer names or industry fields are extracted from customer information (S41), then an instruction set is created for the private AI and large-scale AI to classify customers by element (S42), the instruction is sent to the private AI and large-scale AI (S43), the answer is received (S44), and the answer is set as a classification of the customers by element (S45).

[0027] Next, a command is sent to the private AI and the large-scale AI to search for new companies that fit the above classifications (S46), and responses are received from the private AI and the large-scale AI (S47), and the responses are set as new customer information for each classification (S48).

[0028] FIG. 6(a) corresponds to step S14 in the flow diagram of FIG. 3, and is a flow diagram showing the operation of presenting combinations of new products and new customers from existing customer information that is compatible with the company's new products and services, using private AI and large-scale AI.

[0029] In Fig. 6(a), the company's new product / service candidates and new customer candidates obtained in the previous steps are listed (S51, S52). Next, the private AI and large-scale AI select companies that may purchase the "new product / service" from the "new customer candidates" and send an instruction to indicate the reason (S53), and receive the answer (S54). Then, from the answer, combinations of new products / services and new customer candidates are set together with the reason included in the answer for each combination (S55, S56), and this is presented to customers as a new business strategy (S57).

[0030] Figure 6(b) is a flow diagram that corresponds to step S24 in the flow diagram of Figure 3 and shows the operation of presenting combinations of new products / services and new customers from the company's existing products / services that match new customer information using private AI and large-scale AI.

[0031] In Fig. 6(b), the company's new product / service candidates and new customer candidates obtained in the previous steps are listed (S511, S512). Next, the company's new products / services that the new customer candidates may purchase are selected for the private AI and large-scale AI, and a command is sent to indicate the reasons for the selection (S513), and the response is received (S514). Then, from the responses, combinations of new products / services and new customer candidates are set together with the reasons included in the responses for each combination (S515, S516), and this is presented to customers as a new business strategy (S517).

[0032] The new business strategy is transmitted to the user system 8, which is the customer's terminal, via the user interface 4 and the Internet line 51, and the processing unit 2 receives evaluation results from the customer, and can learn the evaluation results from the customer and modify the prompts.

[0033] Furthermore, the calculation processing unit 2 may accumulate the evaluation results from the above customers, select the new business strategy with the highest evaluation as a best practice, and use it for the next and subsequent new business explorations.

[0034] As described above, according to the embodiment of the present invention, by utilizing a computer system, private AI, and large-scale AI, each process based on Ansoff's growth matrix can be automated, and new products and services that match new customer information can be automatically extracted from existing company product and service information and existing customer information, and the results can be used by the company itself and presented to customers. EXAMPLES

[0035] In this embodiment, a more detailed example of the operation executed by the new business navigation system 1 of the present invention will be described. Fig. 7 shows a flow diagram for identifying products or services that the company can provide based on customer information stored in the database 3. That is, this flow corresponds to the details of the operation leading to the goal of predicting new products and services that match new customer information in a clockwise path from the lower left column (column A) to the upper left column (column B) to the upper right column (column D) in the schematic diagram of Fig. 2.

[0036] In Fig. 7, first (A) analyzes feedback information from customers and defines the specifications (functions and performance) of a new product that satisfies the needs of existing customers. As a specific process, the problems and improvement requests that customers have stored in the database are analyzed (S611).

[0037] Next, unsatisfied needs of customers are inferred (S612), and specifications (functions and performance) of new products that satisfy the needs of existing customers in the vicinity of existing products and services are defined (S613).

[0038] After that, (b) extract new products and services that the company can realize and analyze technology trends. In other words, based on (a) above, a technology audit is conducted to find new things and services that can be provided by improving existing technologies and services (excluding things and services that are completely new to the company, as they are too far removed).

[0039] Specifically, the company's technologies and services stored in the database are comprehensively evaluated (S614). Next, the strengths and weaknesses of the company's technologies are analyzed within the above scope (S615). Furthermore, it is verified whether the company's strengths can be utilized to overcome its weaknesses (S616).

[0040] Then, by storing the latest trends from external large-scale AI, online information, or information collected by the company in a database, the company applies them to its products and incorporates them into its own company, and scores the possibility (S617). After that, the company extracts new things (products) and services (services) that it can provide in response to the above trends (S618).

[0041] Next, (c) the company analyzes its competitors and identifies new products that it can provide. Specifically, the company extracts information related to its product development from the competing products and industry trends stored in the database (S619).

[0042] Furthermore, identify areas that the above industries and competitors are not working on, and customer needs that competitors are not able to meet (S620). Then, extract the overlap between the specifications of new products that meet the needs of existing customers and the new things or services that your company can provide, and the degree of overlap (S621). Next, list the priority of new products and services that you will be working on that do not have competitors (S622). Then, identify new products and services that your company can provide (S623). EXAMPLES

[0043] In this example, another more detailed operation example executed by the new business navigation system 1 of the present invention will be described. Figure 8 shows a flow diagram for listing companies that can become new business partners based on customer information stored in the database 3. In other words, this flow corresponds to the details of the operation leading to the goal of predicting new products and services that match new customer information in a counterclockwise path from the lower left column (column A) to the upper right column (column D) via the lower right column (column C) in the schematic diagram of Figure 2.

[0044] In Fig. 8, first (f) an analysis of existing customer profiles is performed. Specifically, the process involves identifying which type of company is generating the highest level of satisfaction and the greatest profits from an existing customer database (S711). Next, the characteristics shared by the above customers (industry type, company size, region, purchasing pattern, etc.) are identified (S712). Then, other companies with similar characteristics to those of the above customers are searched for (S713).

[0045] Next, (g) a list of companies similar to the above customers is made. As a specific process, the industry to which the existing business partners belong is identified from the existing customer database (S714).

[0046] Next, the company researches major companies, emerging companies, and growing companies in the above industries (S715), and makes a list of companies that belong to the same industry as its existing customers and may be interested in its products and services (S716).

[0047] Next, (h) analyze the customers of competitors to set targets for attracting the competitors' customers as new business partners for the company. The specific process is to first investigate which companies the competitors are doing business with (S717).

[0048] Next, the company evaluates what kind of demand the company has for its products and services (S718). Furthermore, the company considers whether the company finds sufficient value in the products and services that the company provides (S719). Then, based on the above information, the company targets the competitors' customers as new business partners for the company (S720).

[0049] The results obtained through the above steps (F), (G), and (H) are scored, either individually or in combination, to create a list of companies that could become new business partners. Finally, the combination of new products and services that the company can provide and potential new customers is determined (S721). EXAMPLES

[0050] The "prediction" explained in S12 and S22 of Fig. 3, the "abstraction command" explained in S32 of Fig. 4, and the "inference" explained in S612 of Fig. 7 can all be realized by an "abstraction idea method" using AI. In this embodiment, a method of realizing the "abstraction idea method" by the system 1 shown in Fig. 1 will be explained using the flowchart in Fig. 9, taking as an example the "step of predicting new customer information from existing customer information" in S22 of Fig. 3, that is, the case of acquiring new "customer needs."

[0051] For example, in S91, the target to be processed using the abstract idea generation method is set as "customer needs." Next, in S92, prompts for abstracting the "customer needs" are read from the prompt database 3 for the private AI and the large-scale AI, and after inputting these into the system 1, they are sent to the private AI and the large-scale AI in S93.

[0052] Next, in S94, the abstracted "customer needs" are received from the private AI and the large-scale AI, and in S95, similar concepts are extracted from the abstracted "customer needs" and classified into one or more concepts.

[0053] Then, in S96, one or more of the concepts classified above are set as "customer needs" level 1.

[0054] Furthermore, in S97, the abstracted "customer needs" obtained above are set again as the "customer needs" in S91, and the process of S92 to S96 above is repeated a specified number of times (for example, M times) to increase the level of abstraction. "Increasing the level of abstraction" means reinterpreting specific events and problems at a more general level. In other words, moving away from detailed facts and concrete examples and focusing on broader concepts and principles makes it easier to find new perspectives and solutions.

[0055] For example, if a specific customer need is "I want to deliver packages to customers quickly" and the specified abstraction count is 3, then abstraction level 1 would be "I want to shorten the time it takes to provide service to customers" or "I want to improve customer satisfaction." If this is further abstracted, abstraction level 2 would be "I want to improve business efficiency" or "I want to speed up service." Similarly, if this is further abstracted, abstraction level 3 would be "Optimize business processes" or "Automate and streamline operations through the introduction of technology."

[0056] Next, the "customer needs" level M, which has been abstracted M times, is concretized. In S98, prompts for concretization of all concepts of the "customer needs" level M are input from the prompt database to the private AI and the large-scale AI. Next, in S99, the above prompts are sent to the private AI and the large-scale AI, and in S910, the concretized "customer needs" are received from the private AI and the large-scale AI.

[0057] In S911, similar concepts are extracted from the above-mentioned embodied "customer needs" and classified into one or more concepts, and in S912, the obtained one or more concepts are set as "customer needs" level 1. Then, in S913, the processes of S98 to S912 are repeated a specified number of times (for example, N times) to convert abstract concepts and ideas into a more concrete and actionable form, "customer needs" level N.

[0058] For example, more specific concepts such as "automating the picking process in warehouses" and "introducing delivery methods using drones and self-driving cars" are extracted from "process optimization." In addition, specific customer needs such as "an app that allows customers to check delivery status in real time" and "a demand forecasting system using AI" are extracted from "utilization of technology."

[0059] Then, in S914, if the designated cycle has ended, the process proceeds to S915, and if not, the process returns to S92, and the above steps S92 to S913 are repeated.

[0060] In S915, a prompt for generating a new "customer need" by combining the multiple levels of "customer need" generated up to this point is input from the prompt database. Then, as in the past, in S916, the above prompt is sent to the private AI and the large-scale AI, and in S917, the "customer need" embodied from the private AI and the large-scale AI is received.

[0061] As a result, new "customer needs" can be obtained in S918. For example, by combining an "AI-based demand forecasting system" and an "app that allows customers to check delivery status in real time," a new customer need can be obtained, such as "an integrated system that uses AI to forecast demand and optimize inventory management and delivery plans." In addition, by integrating "automation of the picking process in the warehouse" and "delivery by drones and self-driving cars," a customer need can be achieved in which robots and automated systems pick packages in the warehouse and drones and self-driving cars deliver the picked packages, achieving fast and efficient delivery.

[0062] If the above-mentioned process of abstract ideation is applied to, for example, the step of "predicting new in-house products and services from existing in-house products and services" in S12 explained in Figure 3, then in S91 in Figure 9, "new in-house products and services" would be set instead of "customer needs" as the target to be processed using abstract ideation.

[0063] The subsequent steps are similar to the steps for acquiring new "customer needs", and the steps of abstracting and concretizing "new in-house products and services" are repeated a specified number of times for the private AI and large-scale AI, until the step S918 for acquiring "new in-house products and services" is reached.

[0064] For example, if your company's current product is "glasses under 2,000 yen," then by abstracting this, it becomes a higher-level concept such as low-priced glasses → cost-effective vision correction devices. By making this concrete again, the company can obtain new products and services that fit its existing customer base, such as "a set price for glasses with a different design each month" or "low-cost glasses that can be disposable for one day, which are convenient for travel or emergencies."

[0065] Furthermore, the above abstract ideation method can also be used when "inferring unsatisfied needs of customers" in S612 of Figure 7. In other words, "inferring unsatisfied needs of customers" in Figure 7 is nothing but acquiring "customer needs" in Figure 9.

[0066] The above abstract ideation method can also be used when "listing companies that belong to the same industry as existing customers and may be interested in your company's products and services" in S716 in Figure 8. In other words, these companies can be obtained by setting "companies that belong to the same industry as existing customers and may be interested in your company's products and services" instead of "customer needs" as the target to be processed using the abstract ideation method in S91 in Figure 9.

[0067] For example, the customers of a company whose product is "glasses under 2,000 yen" are individual consumers, but if we abstract this, it becomes "cost-conscious consumers," "fashion-conscious consumers," and "consumers who need vision correction," and if we abstract it further, it becomes the "low-priced eyewear market," the "fast fashion market," and the "health and wellness market."

[0068] By specifying the abstract customer range, it is possible to obtain company names such as "drug store chains," "supermarket chains," and "fast fashion brands." In addition, it is possible to obtain company names in the medical and healthcare industry (related to vision correction) and major e-commerce platforms, allowing companies that have the above individual consumers as customers to consider accessing a variety of industries.

[0069] In addition, the above-mentioned abstract idea generation method can also be used when searching for competitors in S619 of Figure 8, making it possible to perform competitive analysis from a broad perspective that includes not only direct competition, but also indirect competition and potential competition.

[0070] For example, if the competitors of a company whose product is "eyeglasses priced under 2,000 yen" are abstracted, they would be "companies that offer vision correction devices and fashion accessories at low prices" and "companies that develop products for the mass market." If this is made more specific, in addition to direct competitors in the low-priced eyewear market, it is possible to obtain the names of indirect competitors such as companies in the "fashion item market that emphasizes cost performance" and "discount stores that adopt a low-price strategy."

[0071] The "step of predicting new company products and services (S12)" in Fig. 3 described in the first embodiment can be implemented by the algorithm shown in Fig. 9. That is, the steps S31 to S36 in Fig. 4 can be realized by setting "information on existing company products and services" as the target to be abstracted in place of "customer needs" in S91 in Fig. 9.

[0072] Also, the "step of predicting new customer information (S22)" in Fig. 3 described in the first embodiment can be implemented by the algorithm shown in Fig. 9. That is, the steps S41 to S48 in Fig. 5 can be realized by setting "existing customer name or industry field" as the target to be abstracted in place of "customer needs" in S91 in Fig. 9.

[0073] Furthermore, the process (S14) of "predicting new in-house products and services that match new customer information from new in-house products and services that match existing customer information" in FIG. 3 described in the first embodiment can also be implemented using the algorithm shown in FIG. 9. In other words, S51 to S57 in FIG. 6(a) can be realized by setting "new in-house products and services that match new customer information" instead of "customer needs" in S91 in FIG. 9.

[0074] Also, the "step (S24) of predicting new company products and services that match new customer information from existing company products and services that match new customer information" in Fig. 3 described in the first embodiment can be similarly implemented by the algorithm shown in Fig. 9. That is, S511 to S517 in Fig. 6(b) can be realized by setting "new company products and services that match new customer information" instead of "customer needs" in S91 in Fig. 9. [Explanation of symbols]

[0075] 1. New Business Navigation System 2. Processing unit 21 Large-scale AI sorting function 22 Prompt Generation Function 23 New business search algorithm 3. Database 4. User Interface 5. Private AI 51~53 Internet line 61 Large-scale AIa 62 Large-scale AIb 63 Large scale AIc 64 Large-scale AId 7 Management System 8 User System

Claims

1. A database that stores information about the company's existing products and / or services and existing customer information; A processing unit having an algorithm for searching for new businesses and a prompt generation function; A private AI that processes confidential information; A user interface; A new business navigation system comprising: The computing unit predicts new company products and / or new company services based on the information on the company products and / or the company services, Extracting from the predicted new company products and / or new company services those that match the existing customer information; predicting new customer information based on the existing customer information; A new business navigation system characterized by extracting the company's existing products and / or services that match the predicted new customer information.

2. The new business navigation system of claim 1, characterized in that the calculation processing unit predicts new in-house products and / or new in-house services that match the new customer information from new in-house products and / or new in-house services that match the existing customer information.

3. The processing unit selects from the existing company products and / or services that match the new customer information, 2. The new business navigation system according to claim 1, further comprising: a prediction unit for predicting new company products and / or new company services that match the new customer information.

4. A new business navigation system as described in any one of claims 1 to 3, characterized in that predictions in the calculation processing unit are formed by commands and responses executed between the private AI and one or more external large-scale AI systems.

5. A new business navigation system according to any one of claims 1 to 3, characterized in that the prediction infers objectives from an abstraction of components of information about the company's products and / or services and the customer information.

6. The new business navigation system described in claim 4, characterized in that when there are multiple external large-scale AI systems, the calculation processing unit has a function of selecting an optimal response when receiving responses from the multiple large-scale AI systems.

7. The new business navigation system according to claim 4, characterized in that the prediction is performed such that processing including confidential information is performed by the private AI and processing not including the confidential information is performed by the external large-scale AI system.

8. The new business navigation system according to any one of claims 1 to 3, characterized in that the calculation processing unit has a function of excluding pre-set customer information that should be kept confidential from the prompt when generating a prompt for the external large-scale AI system.

9. A new business navigation system as described in any one of claims 1 to 3, characterized in that the user interface transmits the prediction result to a customer's terminal and receives an evaluation result from the customer, and the calculation processing unit modifies the prompt to reflect the evaluation result from the customer.

10. A process for predicting new company products and / or services from existing company products and / or services; A process of extracting company products and / or company services that match existing customer information from the predicted new company products and / or company services; A process of predicting new customer information from the existing customer information; A process of extracting existing company products and / or services that match the predicted new customer information from the existing company products and / or services; A new business navigation method, comprising: causing a computer system to execute the above steps.

11. The new business navigation method according to claim 10, further comprising a process of extracting from the predicted new company products and / or services that match the predicted new customer information, the computer system being configured to execute the process.

12. The new business navigation method according to claim 10, further comprising a process of extracting new in-house products and / or in-house services that match the predicted new customer information from the predicted new customer information in the computer system.

13. A new business navigation method according to any one of claims 10 to 12, characterized in that the prediction process is carried out via an Internet line between the computer system including a private AI that processes confidential information and one or more external large-scale AI systems.

14. The new business navigation method according to claim 13, wherein the predicting process is performed by a prompt, which is an instruction set for the private AI and the large-scale AI system.

15. The new business navigation method of claim 14, wherein the prompt is configured to cause processes including confidential information to be executed by the private AI and processes not including the confidential information to be executed by the large-scale AI system.

16. 15. The new business navigation method according to claim 14, wherein the prompts are automatically revised in response to evaluation results from customers.

17. The new business navigation method according to any one of claims 10 to 12, characterized in that the prediction process infers a purpose from an abstraction of components of the existing company product and / or service information and the existing customer information.

18. At least one of the process of predicting new company products and / or company services or the process of predicting new customer information from existing customer information, an abstraction step of abstracting a processing target; a concrete implementation step of concretely implementing the abstracted processing object; 11. The new business navigation method according to claim 10, further comprising:

19. The process of extracting a company product and / or a company service that matches the predicted new customer information from the predicted new company product and / or company service includes: an abstraction step of abstracting a processing target; a concrete implementation step of concretely implementing the abstracted processing object; 12. The new business navigation method according to claim 11, further comprising:

20. The process of extracting new company products and / or company services that match the predicted new customer information from the predicted new customer information includes: an abstraction step of abstracting a processing target; a concrete implementation step of concretely implementing the abstracted processing object; 13. The new business navigation method according to claim 12, further comprising:

21. The abstraction step of abstracting the processing object includes: A first step of setting a processing object to be abstracted; a second step of reading a first prompt for abstracting the object; a third step of sending the first prompt to the private AI and the large-scale AI; A fourth step of receiving the processing object abstracted from the private AI and the large-scale AI; a fifth step of extracting similar concepts from the abstracted processing target and classifying them into one or more concepts; a sixth step of re-setting the one or more classified concepts as targets for further abstraction processing; and a seventh step of repeating the first step to the fifth step for the re-set processing target until a designated number of times is reached. The concrete process of concretely realizing the abstracted processing object is performed on the abstracted processing object obtained in the seventh step, an eighth step of reading a second prompt for instantiating the abstracted object; a ninth step of sending the second prompt to the private AI and the large scale AI; A tenth step of receiving the object materialized from the private AI and the large-scale AI; an eleventh step of extracting similar concepts from the embodied processing object and classifying them into one or more concepts; a twelfth step of re-setting the one or more classified concepts as processing targets for further substantiating the one or more classified concepts; a thirteenth step of repeating the eighth step to the twelfth step for the reset processing target until a designated number of times is reached; Step 14 of repeating the second step to the thirteenth step until a designated cycle is reached; a fifteenth step of reading a third prompt for combining the plurality of processing objects instantiated in the steps up to the fourteenth step to generate a new processing object; a sixteenth step of sending the third prompt to the private AI and the large scale AI; A new business navigation method as described in any one of claims 18 to 19, characterized in that the new processing target is obtained by comprising a 17th step of receiving the new processing target from the private AI and the large-scale AI.

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