Business model proposal creation support device, method, computer program

Abstract

We provide technology that can help create multiple different business model proposals easily and without incurring excessive time and cost. The server, acting as a business model proposal creation support device, accepts monetization target feature data, which is natural language data describing the characteristics of the monetization target—the products and services to be monetized—input by the user. Within the server, there is a prerequisite data generation unit 223 that deepens the prerequisites for the existence of the monetization target features in multiple stages, and a rationale data generation unit 224 that estimates the market for the monetization target and deepens the rationale for the existence of the market in multiple stages. Combining the prerequisites and rationale estimated by both units, the business model proposal data generation unit 228 generates multiple or single business model proposals based on a specified number.

Description

Technical Field

[0001] The present invention relates to a technique for assisting in creating a business model plan.

Background Art

[0002] When conducting business, the business model is of utmost importance. For example, when starting a business, it is necessary to construct a business model. The construction of a business model is generally carried out as follows. First, identify the products and services (hereinafter collectively referred to as "revenue generation targets") that will be sold, leased, etc. and generate revenue, and summarize the characteristics of these revenue generation targets (contents, specifications, technologies used, revenue generation conditions such as expected selling price, expected manufacturing cost, expected sales volume, etc., value provided to society, etc.). Then, while performing analyses such as SWOT analysis, PEST analysis, 5 FORCE analysis, Attribute analysis, etc. on the characteristics of the revenue generation targets, or creating charts such as business model maps and business model canvases, create a business model plan, which is a plan for the business model. Once a business model plan is created, using that business model plan as a starting point, it is repeatedly revised, and the business model plan is gradually refined into a business model that is actually used. When the process of refining the business model plan is being carried out, it is not necessary for the business based on that business model plan to be actually implemented. However, if the business based on that business model plan is actually implemented, both the accuracy of revising the business model plan and the speed of refining the business model plan will be significantly improved.

[0003] In any case, one of the important things when creating a business model is to create a business model plan. By using a business model proposal as a starting point and making revisions, the proposal eventually becomes a business model. There may be disagreement about how refined a business model proposal needs to be to be considered a completed business model, but it is certainly true that with each revision made to the initial business model proposal, the proposal becomes more refined and approaches a feasible business model. Therefore, when launching a new business, it is crucial to create a business model proposal as quickly as possible. Furthermore, it is preferable to have multiple initial business model proposals. Selecting one from several different proposals and refining that initial proposal increases the likelihood of obtaining a better business model. [Overview of the Initiative] [Problems that the invention aims to solve]

[0004] However, traditionally, the development of business model proposals was often done manually by individuals with (or with only incomplete) knowledge of the various analyses and chart creation techniques mentioned above. This meant that the quality of the resulting business model proposals could not be guaranteed, and the development process was excessively time-consuming and costly. Furthermore, having multiple different business model proposals created by different people will only increase the time and cost required. If different business model proposals are to be created, the consideration of what to differentiate between them will inevitably become subjective, making it difficult to guarantee the quality of the business model proposals.

[0005] The technology described here for supporting the creation of business model proposals differs from existing methods such as the Business Model Canvas, which are merely static analysis tables that break down business models into their constituent elements. Instead, as detailed below, it incorporates the various elements that constitute a business model and their dynamic relationships, and by manipulating these relationships, it is possible to easily create multiple or single business model proposals. To the best of the inventor's knowledge, no practical technology exists currently to support the creation of such business model proposals.

[0006] The present invention aims to provide a technology for assisting in the creation of business model proposals, and more specifically, a technology that can assist in easily creating multiple different business model proposals without incurring excessive time and cost. [Means for solving the problem]

[0007] To solve the aforementioned problems, the inventor of this application conducted extensive research. As a result, the following findings were obtained. Generally, understanding the characteristics of the product or service to be monetized (for example, the content, specifications, technology used, monetization conditions such as expected selling price, expected manufacturing cost, and expected sales volume, and the value provided to society) is fundamental to creating a business model proposal. Once the monetization target features are determined, it becomes possible to identify the prerequisites for those features to be realized (for example, the existence of patent rights, manufacturing technology, and materials used in manufacturing, if the monetization target is a product). It is also possible to calculate the probability of these prerequisites being met. Based on these prerequisites, it becomes possible to create a business model proposal using predetermined analyses and charts. Incidentally, the "likelihood" (probability of success) of the created business model proposal can generally be calculated not only when the prerequisites for the monetization target features are identified, but also when the prerequisites for those prerequisites are identified, and so on, and the "likelihood" changes each time the prerequisites are explored in depth. On the other hand, once the monetization target features are determined, it becomes possible to estimate the market for selling or otherwise monetizing those features, along with calculating the probability of success. Furthermore, it becomes possible to understand the rationale for the establishment of the estimated market (for example, if the target of revenue generation is a product, the demand for that product, market size, existence of substitute products, price acceptable to customers, etc.). It is also possible to calculate the probability of this rationale being established. Based on this rationale, it becomes possible to create a business model proposal using predetermined analyses and charts. In addition, it becomes possible to calculate the "likelihood" (probability of establishment) of the created business model proposal. On the other hand, this "likelihood" can generally be calculated not only when the rationale for market establishment is understood, but also when the rationale for the rationale is understood, and so on, and it changes each time the rationale is explored in depth. When you delve deeper into the preconditions for the characteristics that will be monetized, those preconditions generally tend to diverge. Similarly, when you delve deeper into the rationale for the market, the rationale generally tends to diverge as well. Business model proposals created based on divergent preconditions and rationale tend to be broader or more vague than business model proposals created based on less divergent preconditions and rationale. The inventors of this application have confirmed the findings described above. The aforementioned preconditions and rationale dynamically interact with each other to construct the proposed business model. Therefore, by varying the number of times the preconditions and rationale are explored in depth, it becomes possible to obtain a variety of different business model proposals. Moreover, the differences in each business model proposal are not limited to differences in scope or narrowness, or vagueness or clarity, but also potentially indicate changes in the definition of the business itself that should be realized and the direction it should aim for, as initially anticipated. For someone creating a business model, having multiple business model proposals with such differences is convenient. The present invention aims to provide a business model creation support device that can create different business model proposals as described above by applying the knowledge described above.

[0008] The present invention is as follows: The present invention is a business model creation support device connected to a network including the Internet, comprising a calculation unit for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input. A device comprising a calculation unit, a recording device, and an input receiving mechanism is typically a computer. The same applies hereafter, but the input receiving mechanism may be an external device. The arithmetic unit then functions as follows by executing the computer program recorded in the recording device. In other words, the computing device receives input from the input receiving mechanism, which is a receiving unit that receives monetization target feature data, which is natural language data describing the characteristics of the monetization target, which are the products and services to be monetized; a prerequisite data generation unit that generates primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid; a prerequisite data generation unit that generates secondary prerequisite data, which is data about secondary prerequisites, by collecting prerequisites from the network for the primary prerequisite to be valid; a prerequisite data generation unit that generates Nth-order prerequisite data, which is data about Nth-order prerequisites, by collecting prerequisites from the network for the N-1th-order prerequisite to be valid; and market data, which is data identifying the market for the monetization target that can be obtained, estimated from the monetization target feature identified by the received monetization target feature data. The system functions as follows: a market data generation unit that generates market data; a basis data generation unit that generates primary basis data, which is data about primary basis data, by collecting from the network the basis for the establishment of a market identified by the market data generated by the market data generation unit; a basis data generation unit that generates nth-order basis data, which is data about nth-order basis data, by collecting from the network the basis for the establishment of the primary basis data; and a business model proposal data generation unit that generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the nth-order preconditions identified by the Nth-order precondition data, the monetization target market identified by the market data, and the nth-order basis identified by the nth-order basis data. Furthermore, the arithmetic unit is configured to receive the value of N used when it functions as the prerequisite data generation unit from the input receiving mechanism, and to receive the value of n used when it functions as the basis data generation unit from the input receiving mechanism.

[0009] This business model creation support device is equipped with a computing unit, which performs its intended functions according to a computer program recorded in a recording device. The computing unit is a device that performs calculations and is typically a CPU (central processing unit). The computing unit may also be a GPU (graphics processing unit), GPGPU (general purpose computing on GPU), etc. The recording device is a device that can record data and is, for example, RAM (random access memory), HDD (hard disk drive), SSD (solid state drive), ROM (read-only memory), etc. The input receiving mechanism is a device that inputs data to the computing unit and is, for example, a numeric keypad, keyboard, trackball, mouse, voice-to-text input device, tap key, image-to-text input device, etc. The input receiving mechanism may also be, for example, a receiving device that receives data via a wired or wireless network, or a transmitting and receiving mechanism that communicates via a network. The computing unit functions as a reception unit that receives input from the input reception mechanism, which is natural language data describing the characteristics of the products and services to be monetized. Monetization characteristics are described in natural language. Natural language is a sentence written in a language that humans can understand, and may include numbers, sequences of numbers, and mathematical formulas, or consist of only these. Monetization characteristic data is generally created by those who seek assistance in creating a business model proposal using this business model proposal creation support device. In many cases, monetization characteristics include multiple characteristics and constitute a set of characteristics. The computing unit functions as a prerequisite data generation unit, generating primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid; generating secondary prerequisite data, which is data about secondary prerequisites, by collecting prerequisites from the network for the primary prerequisite to be valid; and so on, generating Nth-order prerequisite data, which is data about Nth-order prerequisites, by collecting prerequisites from the network for the (N-1)th-order prerequisite to be valid. Here, the computing unit receives the value of N used when functioning as a prerequisite data generation unit from the input receiving mechanism. In other words, the computing unit, which functions as a prerequisite data generation unit, generates primary prerequisite data for primary prerequisites by collecting prerequisites from the network for the monetization target features identified by the monetization target feature data to be valid. The computing unit automatically collects primary prerequisites. To achieve this, the following technologies are necessary: ​​a technology to automatically interpret the meaning of monetization target features from the monetization target feature data; a technology to find prerequisites for the interpreted content from information related to that content based on the interpreted content; and a technology to find and collect the desired information from the network. For example, known artificial intelligence for interpreting the meaning of words is word2vec (released by Google in 2013) and its improved version, CBOW (Continuous Bag-of-Words or Skip-Gram). In addition, other known artificial intelligence for interpreting the meaning of words is RNN (recurrent Neural Network) and its improved version, LSTM (Long Short-Term Memory). Furthermore, as artificial intelligence related to understanding the meaning of text, Word Mover's Distance (WMD, released in 2015), which applies word embeddings to text, and its improved version, Linear-Complexity Relaxed Word Mover's Distance (LC-RWMD), are publicly known. Large-scale language models based on deep neural networks are also publicly known as artificial intelligence related to interpreting the meaning of words and understanding the meaning of text. Monte Carlo tree search, based on deep neural networks, is also publicly known as artificial intelligence related to calculating the probability of a given condition being met. By using such technologies (or technologies with the same purpose), it is possible to automatically interpret the meaning of monetization target features from monetization target feature data, and to find preconditions for the interpreted content from information related to that content based on the interpreted content.Furthermore, regarding technologies for collecting information from networks, crawling, for example, is a well-known technique for finding and collecting desired data recorded on external devices connected to the internet, so if necessary, this technology can be used. As described above, after collecting the primary preconditions, the next step is to collect the preconditions necessary for the primary preconditions to hold from the network to generate secondary precondition data, and so on, until the N-1th precondition is met by collecting the preconditions necessary for the N-1st precondition to hold from the network, thereby generating Nth precondition data. In this way, the business model creation support device according to the present invention deepens the preconditions. Here, the number of preconditions to be deepened is determined in advance by the input from the input receiving mechanism. Therefore, those who intend to create a business model using this business model creation support device can decide how deeply to deepen the preconditions based on their own preferences. Note that each of the primary to Nth preconditions often includes multiple preconditions and constitutes a set of preconditions. Furthermore, the computing unit functions as a market data generation unit, which generates market data that identifies the market for which monetization is possible, estimated from the monetization target features identified by the received monetization target feature data. Market data is data that identifies a market. A market is information such as, for example, which country, for which product or service, in what year, and how much market there is. Market information often includes information about multiple markets. Furthermore, the computing unit functions as a basis data generation unit, generating primary basis data, which is data about primary basis, by collecting from the network the basis for the existence of a market identified by the market data generated by the market data generation unit; generating secondary basis data, which is data about secondary basis, by collecting from the network the basis for the existence of primary basis; and so on, generating nth-order basis data, which is data about nth-order basis, by collecting from the network the basis for the existence of n-1-th-order basis. Here, the computing unit receives the value of n used when functioning as a basis data generation unit from an input receiving mechanism. In this case, the computing unit, which functions as the basis data generation unit, first generates market data. Market data is data that identifies the market for which revenue can be generated, estimated from the characteristics of the target for monetization. In addition, the computing unit generates primary basis data, which is data about the primary basis, by collecting from the network the basis for the existence of the market identified by the market data. The computing unit automatically collects the primary basis. The technology required for this is the same as the technology that can be applied when the computing unit automatically collects the primary preconditions. As described above, after collecting primary evidence, secondary evidence data is generated by collecting evidence from the network that supports the primary evidence, and so on, until n-th order evidence data is generated by collecting evidence from the network that supports the (n-1)th order evidence. In this way, the business model creation support device of the present invention deepens the evidence. Here, the number of orders of evidence to be deepened is determined in advance by input from the input receiving mechanism. Therefore, those who intend to create a business model using this business model creation support device can decide how deeply to deepen the evidence based on their own preferences. Note that primary to nth order evidence each often contains multiple orders of evidence and constitutes a set of orders of evidence. The computing unit then functions as a business model proposal data generation unit that generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the Nth order preconditions identified by the Nth order precondition data, the monetization target market identified by the market data, and the Nth order justifications identified by the Nth order justification data. In short, the business model proposal creation support device generates business model proposal data. The business model proposal is basically automatically generated after the business model proposal creation support device receives the data on the features to be monetized. Therefore, users of this business model proposal creation support device can easily create business model proposals with the device's assistance without spending excessive time and cost. The proposed business model includes, at a minimum, the monetization target features identified by the monetization target feature data, the Nth-order preconditions identified by the Nth-order precondition data, the target market for monetization identified by the market data, and the nth-order justifications identified by the nth-order justification data. Here, both N, the order of the Nth-order preconditions, and n, the order of the nth-order justifications, are determined by the user. Moreover, as already explained, the content of the generated business model changes depending on the values ​​of N and n. By determining the two variables N and n according to their preferences, the user can obtain a draft business model proposal from the business model proposal creation support device that reflects their preferred breadth, vagueness, or direction. Furthermore, in this invention, since there are two variables, N and n, that influence the business model proposal, it is relatively easy to create variations in the business model proposal. It is also naturally easy for the user to obtain multiple business model proposals. Furthermore, when generating the market data mentioned above, or when generating business model proposals from preconditions and evidence, it is possible to perform analyses such as SWOT analysis, PEST analysis, 5 FORCE analysis, and attribute analysis, or to use diagrams such as business model maps and business model canvases. These analyses and the use of diagrams can be performed using artificial intelligence that has been specifically trained for this purpose. It is also preferable to allow the user to select which of these analyses and diagrams to use.

[0010] The computing device, which functions as a business model proposal data generation unit, may generate business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the primary to Nth order preconditions identified by the primary to Nth order precondition data, the monetization target market identified by the market data, and the primary to Nth order preconditions identified by the primary to Nth order precondition data. As described above, the proposed business model includes the monetization target features identified by the monetization target feature data, the Nth-order preconditions identified by the Nth-order precondition data, the monetization target market identified by the market data, and the Nth-order rationale identified by the Nth-order rationale data. The proposed business model may also include the primary to Nth-order preconditions identified by the primary to Nth-order precondition data, and the primary to Nth-order rationale identified by the primary to Nth-order rationale data. In this way, users can grasp not only the Nth-order preconditions, but also each precondition from the 1st-order to the (N-1th-order) preconditions that are part of the process of deepening the preconditions leading up to the Nth-order preconditions. As a result, users can grasp the overall flow and evolution of the preconditions, and are more likely to gain ideas for revising the business model proposal from the perspective of preconditions. Similarly, by adopting the above structure, users can grasp not only the nth-order evidence, but also each precondition from the 1st-order to the (n-1st-order) evidence that are part of the process of deepening the evidence leading up to the nth-order evidence. As a result, users can grasp the overall flow and evolution of the evidence, and are more likely to gain ideas for revising the business model proposal from the perspective of evidence. Moreover, in this case, the number of possible business model proposals that can be generated by combining N sets of preconditions (from first-order to nth-order preconditions) and n sets of justifications (from first-order to nth-order justifications) is N × n, which is convenient for generating multiple business model proposals.

[0011] The computing device, which functions as the precondition data generation unit, may be configured to attach data on the probability that each of the first-order to Nth-order preconditions is true. More specifically, probabilities may be attached to each of the first-order to Nth-order preconditions, or to each precondition that is included among them and forms a set. This makes it easier to quantitatively compare the likelihood of multiple business model proposals. To achieve this, it is preferable to implement, for example, a technology (artificial intelligence) in the precondition data generation unit that calculates the probability that the precondition is true by comparing it with other information existing on the network. The computing device, which functions as the business model proposal data generation unit, may generate the business model proposal data by displaying the probabilities identified by the data regarding the probabilities from the first-order preconditions to the Nth-order preconditions. This makes it possible to present the probabilities regarding the above-mentioned preconditions to the user in an easy-to-understand manner. The computing device, which functions as the market data generation unit, may be configured to attach data about the probability of the market being established to the market. More specifically, probabilities may be attached to the market data, or to each market that is included in the data and forms a set. This makes it easier to quantitatively compare the likelihood of multiple business model proposals. To achieve this, it is preferable to implement, for example, a technology (artificial intelligence) in the market data generation unit that calculates the probability of the market being established by comparing it with other information existing on the network. The computing device, which functions as the business model proposal data generation unit, may generate the business model proposal data in such a way that it displays the probability identified by the data regarding the market. This makes it possible to present the probabilities regarding the market described above to the user in an easy-to-understand manner. The computing device, which functions as the basis data generation unit, may be configured to attach data on the probability that each of the first to nth-order basis items is valid. More specifically, probabilities may be attached to each of the first to nth-order basis items, or to each basis item that is included among them and forms a set. This makes it easier to quantitatively compare the likelihood of multiple business model proposals. To achieve this, for example, the basis data generation unit needs to implement a technology (artificial intelligence) that calculates the probability that the basis item is valid by comparing it with other information existing on the network. The computing device, which functions as the business model proposal data generation unit, may generate the business model proposal data by displaying the probabilities identified by the data regarding the probabilities from the first-order to the nth-order evidence. This makes it possible to present the probabilities regarding the above-mentioned evidence to the user in an easy-to-understand manner.

[0012] The calculation device, which functions as the receiving unit, Input reception mechanismTherefore, the system may be configured to accept modification data for modifying at least one of the first to Nth preconditions identified by the first precondition data and the Nth precondition data, and the first to Nth basis identified by the first basis data and the nth basis data. In this case, the computing device that functions as the business model proposal data generation unit may generate business model proposal data using the first to Nth preconditions modified according to the modification data when the computing device that functions as the receiving unit receives the modification data for modifying the first to Nth preconditions identified by the first to Nth precondition data and the Nth precondition data, and using the first to Nth basis modified according to the modification data when the computing device that functions as the receiving unit receives the modification data for modifying the first to Nth basis identified by the first to Nth basis data and the nth basis data. According to this, when a business model proposal is created, the user can manually modify parts of the specified monetization target characteristics, primary to nth preconditions, market, and primary to nth evidence, specifically parts of the primary to nth preconditions and primary to nth evidence, using the business model proposal creation support device, thereby enabling the business model proposal to be modified. It is preferable to give the user an opportunity to revise the assumptions and rationale because, in this invention, these are generated automatically, and therefore, the assumptions and rationale may differ from the user's intentions or may be unacceptable to the user in light of the user's knowledge. Any modification to any of the primary to nth-order preconditions can be a deletion, addition, or change. For example, if any of the primary to nth-order preconditions contain multiple preconditions, then each precondition can be deleted or changed, and new preconditions can be added. The same applies to the rationale. As described above, in the invention of the present application, it is possible to give the user an opportunity to modify the prerequisite conditions and the basis. Similarly, it may be possible to give the user an opportunity to modify the market further. It is preferable to give the user the possibility of modifying the market because, in the present invention, this is automatically generated apart from the user's intention, like the prerequisite conditions and the basis. The business model plan creation device capable of modifying the market can be as follows on the premise of the business model plan creation device capable of modifying the above-mentioned prerequisite conditions and the basis. That is, the arithmetic device functioning as the reception unit is adapted to receive correction data for correcting at least a part of the market specified by the market data, and the arithmetic device functioning as the business model plan data generation unit is configured to, when the arithmetic device functioning as the reception unit receives the correction data for correcting the market specified by the market data, generate business model plan data using the market data corrected according to the correction data. Regarding the market, although it does not have a hierarchical structure, if there are a plurality of things proposed as the market, it is possible to delete and change each market, and it is also possible to add a new market.

[0013] When the business model plan includes not only the Nth-level prerequisite conditions but also the 1st-level to Nth-level prerequisite conditions, the arithmetic device functioning as the business model plan data generation unit may generate the business model plan data by displaying the 1st-level to Nth-level prerequisite conditions in a listable state. For example, if the user can view the 1st-level to Nth-level prerequisite conditions displayed in a listable state on the display, the user can better grasp the overall flow and transition of the prerequisite conditions, and is more likely to obtain some idea for modifying the business model plan from the perspective of the prerequisite conditions. When the business model plan is generated to display the primary prerequisite conditions to the Nth prerequisite conditions in a listable state, the arithmetic unit functioning as the business model plan data generation unit may generate the business model plan data by displaying the primary prerequisite conditions to the Nth prerequisite conditions in a tree diagram. It is possible to display the prerequisite conditions in a tree diagram by associating a certain prerequisite condition with a prerequisite condition of one lower order, and a user viewing the prerequisite conditions arranged in this way can intuitively understand the overall flow and transition of the prerequisite conditions. When the business model plan includes not only the nth basis but also the primary basis to the nth basis, the arithmetic unit functioning as the business model plan data generation unit may generate the business model plan data by displaying the primary basis to the nth basis in a listable state. For example, if a user can view the primary basis to the nth basis displayed in a listable state on a display, the user can better grasp the overall flow and transition of the basis, and is more likely to get some idea for modifying the business model plan from the perspective of the basis. When the business model plan is generated to display the primary basis to the nth basis in a listable state, the arithmetic unit functioning as the business model plan data generation unit may generate the business model plan data by displaying the primary basis to the nth basis in a tree diagram. It is possible to display the basis in a tree diagram by associating a certain basis with a basis of one lower order, and a user viewing the basis arranged in this way can intuitively understand the overall flow and transition of the basis.

[0014] The computing device functions as a risk data generation unit that generates risk data, which is data about the risk that the primary prerequisites to the Nth prerequisites, each identified from the primary prerequisite data to the Nth prerequisite data, and the primary justifications to the Nth justifications, each identified from the primary justification data to the Nth justification data, will no longer be valid, by executing a computer program recorded in the recording device. The computing device, which functions as a business model proposal data generation unit, may also include the risk data in the business model proposal data. In the present invention, the proposed business model is determined by preconditions and rationale. Here, the preconditions are the conditions necessary for the monetization target features to be established, so if the preconditions collapse, the monetization target features, which are fundamental to the proposed business model, also collapse. Similarly, the rationale is the basis for the establishment of a viable market, so if the rationale collapses, the viability of the market, which is the source of profit in the proposed business model, also collapses. In other words, once the preconditions and rationale have been identified, if the risks of the preconditions collapsing and the risks of the rationale collapsing, including probabilities for both, can be grasped, it may be possible to estimate the possibility that the proposed business model and the market that can be acquired may not proceed as expected. The risk data mentioned above represents the possibility that the proposed business model and the market that can be captured may not proceed as expected. The risk data may include probabilities. If the computing device requires risk data, the computing device, which functions as a business model proposal data generation unit, may include risk data in the business model proposal data. This allows users who view the business model proposal to understand the inherent risks within it. The risk data may be data collected from the network by a computing device that functions as a risk data generation unit.

[0015] The arithmetic unit, which functions as a receiving unit, may receive from the input receiving mechanism precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications. At that time, the arithmetic unit may function as an explanation unit that generates explanation data, which is data about a natural language explanation of the business model proposal specified by the business model proposal data that is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined. As mentioned above, the proposed business model data may include primary to nth-order preconditions identified by primary to nth-order precondition data, and primary to nth-order justifications identified by primary to nth-order justification data. In this case, the number of preconditions is N, ranging from the first-order precondition to the nth-order precondition, and the number of justifications is n, ranging from the first-order justification to the nth-order justification. In other words, the number of business model proposals determined by the preconditions and justifications can be generated as many times as the product of N and n. Here, the precondition determination data is data for selecting one of N preconditions, and the rationale determination data is data for selecting one of n rationales. Then, by determining the preconditions with the precondition determination data and determining the rationale with the rationale determination data, the resulting business model proposal is uniquely determined. In the invention described in this paragraph, the computing device generates explanatory data, which is data about a natural language explanation of the proposed business model. The content of the explanation identified by the explanatory data is presented to the user by some means. In this way, the user can obtain a natural language explanation for one business model selected from N × n business model proposals. The explanatory data may be included in the business model proposal data by the computing device, which functions as a business model proposal data generation unit. In this way, the user can view the explanation along with the one business model proposal they have selected. Of course, it is also possible to generate explanatory data for multiple business model proposals selected by the user by having the user select multiple preconditions, or multiple justifications, or by having the user select multiple preconditions and justifications, or to include multiple sets of explanatory data in the business model proposal data. In addition, by having users input the corrected data as described above, when the assumptions, rationale, or a part of the market are manually modified, it is possible to generate corrected assumption determination data and corrected rationale determination data, and then generate a business model proposal based on the assumption data (corrected assumption data if modified) and rationale data (corrected rationale data if modified), thereby making the explanatory data corresponding to the modified business model proposal. Specifically, a business model proposal creation device with a computing unit that functions as an explanatory section can be as follows: That is, the arithmetic unit, which functions as the receiving unit, receives from the input receiving mechanism precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications. In this case, the arithmetic unit functions as an explanation unit that generates explanation data, which is a natural language explanation of the business model proposal specified by the business model proposal data, which is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined. Furthermore, when generating the explanation data, the arithmetic unit, which functions as the explanation unit, may use the corrected precondition data if the precondition data of the order specified by the precondition determination data has been corrected by the correction data, or the corrected justification data if the justification data of the order specified by the justification determination data has been corrected by the correction data. As mentioned above, the revised data allows for the modification of market data in addition to the assumptions and rationale. In this case, if the market data is modified, it is also possible to generate explanatory data using the modified market data. In this case, the business model proposal creation device can be as follows: The arithmetic unit, which functions as the receiving unit of the business model proposal creation device, receives from the input receiving mechanism precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications. The arithmetic unit also functions as an explanation unit that generates explanation data, which is a natural language explanation of the business model proposal specified by the business model proposal data, which is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined, by executing a computer program recorded in the recording device. When generating the explanation data, the arithmetic unit, which functions as the explanation unit, uses the corrected precondition data if the precondition data of the order specified by the precondition determination data has been corrected by the correction data, and uses the corrected justification data if the justification data of the order specified by the justification determination data has been corrected by the correction data.

[0016] The inventors of the present invention also propose, as one aspect of the present invention, a method executed by the computing device of a business model draft creation support device connected to a network including the Internet, which includes a computing device for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input. The effect of such a method is equivalent to the effect of the business model draft creation support device according to the present invention. One example of such a method is one in which the calculation unit of a business model proposal creation support device, which is connected to a network including the Internet, is used to perform calculations, record data, and accept data input. Furthermore, this method involves an input receiving process, all of which are executed by a computing device, receiving input from the input receiving mechanism, which is natural language data describing the characteristics of the monetization target, which are the products and services to be monetized, which are the products and services to be monetized, which is monetization target feature data; generating primary precondition data, which is data about primary preconditions, by collecting preconditions from the network for the monetization target feature identified by the received monetization target feature data to be valid; generating secondary precondition data, which is data about secondary preconditions, by collecting preconditions from the network for the primary preconditions to be valid; and generating Nth-order precondition data, which is data about Nth-order preconditions, by collecting preconditions from the network for the N-1th-order preconditions to be valid; and generating precondition data generation process, which generates Nth-order precondition data, which is data about Nth-order preconditions, by collecting preconditions from the network for the N-1th-order preconditions to be valid; and estimating the obtainable revenue from the monetization target feature identified by the received monetization target feature data. The system includes: a market data generation process that generates market data which is data that identifies the target market; a basis data generation process that generates primary basis data which is data about primary basis by collecting from the network the basis for the establishment of the market identified by the market data; a basis data generation process that generates nth-order basis data which is data about nth-order basis by collecting from the network the basis for the establishment of the primary basis; and a business model proposal data generation process that generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the nth-order preconditions identified by the Nth-order precondition data, the target market for monetization identified by the market data, and the nth-order basis identified by the nth-order basis data. This method further includes a process in which the arithmetic unit receives a value of N used when executing the prerequisite data generation process from the input receiving mechanism, and a process in which the arithmetic unit receives a value of n used when executing the basis data generation process from the input receiving mechanism.

[0017] The inventors of the present invention also propose, as one aspect of the present invention, a computer program for causing a predetermined, for example, general-purpose computer to function as a business model creation support device. The effects of such a computer program are equivalent to the effects of the business model creation support device according to the present invention, and also include the ability to cause a predetermined computer to function as a business model creation support device according to the present invention. One example of such a computer program is a business model proposal creation support device connected to a network including the Internet, which includes an arithmetic unit for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input. The program includes a receiving process that receives input from the arithmetic unit and the input receiving mechanism of the business model proposal creation support device, which includes an arithmetic unit for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input, which is natural language data describing the characteristics of the monetization target, which are the products and services to be monetized. The program generates primary precondition data by collecting preconditions from the network for the monetization target characteristics identified by the received monetization target feature data to be valid, which is data about primary preconditions. The program generates secondary precondition data by collecting preconditions from the network for the primary preconditions to be valid, which is data about secondary preconditions. The program also includes a precondition data generation process that generates Nth-order precondition data by collecting preconditions from the network for the N-1th-order preconditions to be valid, which is data about Nth-order preconditions. A computer program for causing a computer to execute a market data generation process which generates market data which is data that identifies the market for which one can be acquired for monetization estimated from characteristic features; a basis data generation process which generates primary basis data which is data about primary basis by collecting from the network the basis for which the market identified by the market data can be established; a basis data generation process which generates nth-order basis data which is data about nth-order basis by collecting from the network the basis for which the primary basis can be established; a basis data generation process which generates nth-order basis data which is data about nth-order basis by collecting from the network the basis for which the (n-1)th-order basis can be established; and a business model proposal data generation process which generates business model proposal data by combining data which includes the market for which one can be acquired for monetization identified by the monetization target characteristic data, the nth-order preconditions identified by the Nth-order precondition data, the market for which one can be acquired for monetization identified by the market data, and the nth-order basis identified by the nth-order basis data, wherein the computer program further provides the arithmetic unit toA computer program that executes the following: a process of receiving the value of N used when executing the prerequisite data generation process from the input receiving mechanism, and a process of receiving the value of n used when executing the basis data generation process from the input receiving mechanism. [Brief explanation of the drawing]

[0018] [Figure 1] A diagram showing the overall configuration of a system including a server, which is a business model creation support device according to one embodiment. [Figure 2] A diagram showing the appearance of the client included in the system shown in Figure 1. [Figure 3] This diagram shows the hardware configuration of the client included in the system shown in Figure 1. [Figure 4] A block diagram showing the functional blocks generated within the client included in the system shown in Figure 1. [Figure 5] A block diagram showing the functional blocks generated within the server included in the system shown in Figure 1. [Figure 6] Figure 1 shows an example of an image displayed on the client's screen. [Figure 7] This figure shows another example of an image displayed on the client's screen, as shown in Figure 1. [Figure 8] Figure 1 shows yet another example of the image displayed on the client's screen. [Figure 9] Figure 1 shows yet another example of the image displayed on the client's screen. [Figure 10] Figure 1 shows yet another example of the image displayed on the client's screen. [Figure 11] Figure 1 shows an example of an image displayed on the client's screen when the user is given the opportunity to modify at least one of the market, prerequisites, or rationale. [Figure 12]Figure 1 shows another example of the image displayed on the client's screen when the user is given the opportunity to modify at least one of the market, prerequisites, or rationale. [Figure 13] Figure 1 shows yet another example of the image displayed on the client's screen, when the user is given the opportunity to modify at least one of the market, prerequisites, or rationale. [Modes for carrying out the invention]

[0019] A preferred embodiment of the present invention will be described below with reference to the drawings. Figure 1 schematically shows the overall configuration of a preferred embodiment of a business model draft creation support system (hereinafter sometimes simply referred to as the "support system") that includes the business model draft creation support device (hereinafter sometimes simply referred to as the "support device") of the present invention. The support system according to this embodiment consists of a client 100 and a server 200. Both are capable of connecting to a network 400. Network 400 is, but is not limited to, the Internet in this embodiment. Network 400 may also include other networks, such as an intranet. In this embodiment, the client 100 is operated by the user and may be, for example, the user's personal property or property owned by the company to which the user belongs. In this embodiment, the server 200 corresponds to the business model draft creation support device as referred to in this application.

[0020] Client 100 includes a computer. More specifically, in this embodiment, client 100 is comprised of a general-purpose computer, focusing on the hardware. Client 100 may have a dedicated computer program (such as an application) installed for use in combination with server 200, and in some cases, it can be distinguished from a general computer by the functions produced by the execution of the computer program. On the other hand, if client 100 is configured to access server 200 using, for example, a general web browser, it may not be distinguishable from a general computer.

[0021] Next, we will describe the configuration of client 100. As a piece of hardware, client 100 is a general-purpose computer, such as a smartphone, tablet, notebook PC, or desktop PC. All of these are capable of communication via network 400. Client 100 is also required to be able to generate the functional blocks described later by installing the computer program described later, and then execute the processes described later, but other specifications are not particularly required as long as this is possible. A commercially available computer is sufficient for client 100 as hardware. For example, if client 100 is a smartphone or tablet, then client 100 as a smartphone could be, for example, an iPhone manufactured and sold by Apple Japan LLC, and client 100 as a tablet could be, for example, an iPad manufactured and sold by Apple Japan LLC. If client 100 is composed of a notebook computer, desktop computer, etc., then commercially available models are acceptable. From here on, however, we will proceed assuming that client 100 is a smartphone or notebook computer. As will be described later, the user will view the business model proposal by displaying an image based on the business model proposal data generated by server 200, which acts as a support device, on the display of their client 100. Considering ease of viewing, it might be more convenient if client 100 is a notebook computer or desktop computer with a large display, as will be described later. However, whether client 100 is a smartphone or a notebook computer or desktop computer, the configuration, functions, and processing that client 100 should perform remain the same in relation to the present invention. At best, if client 100 is a smartphone, the only requirement would be a function to selectively zoom in on a portion of an image when displaying an image based on the business model proposal data generated by server 200 (which acts as a support device) on client 100's display.

[0022] An example of the appearance of client 100 is shown in Figure 2. Client 100 is equipped with a display 101. The display 101 is for displaying still images or moving images, and can be a publicly known or readily available type. For example, the display 101 is a liquid crystal display. The display 101 may also be an external device to client 100. If client 100 is a desktop computer, then the display 101 will be an external type. The client 100 also includes an input device 102. The input device 102 is for the user to provide desired input to the client 100. The input device 102 can be a known or commonly used device. In this embodiment, the input device 102 of the client 100 is a button type, but it is not limited to this, and it is also possible to use a numeric keypad, keyboard, trackball, mouse, well-known voice input using a microphone jack, tap keys, or an image-to-text input device using a camera. In particular, if the client 100 is a notebook computer or a desktop computer, the input device 102 will be a keyboard or mouse, etc. Also, if the display 101 is a touch panel, the display 101 will also function as the input device 102, and this is the case in this embodiment.

[0023] The hardware configuration of client 100 is shown in Figure 3. The hardware includes a CPU (central processing unit) 111, ROM (read-only memory) 112, RAM (random access memory) 113, and an interface 114, which are interconnected by a bus 116. The CPU 111 is an example of an arithmetic unit that performs calculations. The CPU 111 executes the processes described later by running computer programs stored in, for example, ROM 112 or RAM 113. Although not shown in the diagram, the hardware may include an HDD (hard disk drive), SSD (solid state drive), or other high-capacity storage device, and the computer program may be stored in a high-capacity storage device. The CPU 111 may also be another arithmetic unit such as a GPU (graphics processing unit) or GPGPU (general purpose computing on GPU). The computer program referred to herein includes at least a computer program that causes the client 100 to perform the processes described later (for example, the process of displaying the image described later on the display 101). This computer program may be pre-installed on the client 100 or it may be installed afterward. The installation of this computer program on the client 100 may be performed via a predetermined recording medium such as a memory card, or via a network such as a LAN or the Internet. ROM 112 stores computer programs and data necessary for the CPU 111 to perform the processes described later. The computer programs stored in ROM 112 are not limited to those mentioned above; if client 100 is a smartphone, then computer programs and data necessary for client 100 to function as a smartphone, such as those for making calls and sending emails, are also stored. Client 100 is also capable of browsing homepages based on data received via network 400, and implements a publicly known web browser to enable this. RAM113 provides the work area necessary for the CPU111 to perform processing. In some cases, the aforementioned computer programs and data (or parts thereof) may be stored there. Interface 114 is used for data exchange between the CPU 111, RAM 113, etc., connected via bus 116, and the outside world. The display 101 and input device 102 are connected to interface 114. Operation information input from input device 102 is input from interface 114 to bus 116. As is well known, image data for displaying an image on display 101 is sent from bus 116 to interface 114 and output from interface 114 to display 101. Interface 114 is also connected to a known transmission / reception mechanism (not shown) for communicating with the outside world via network 400, which is the Internet. This allows client 100 to send and receive data via network 400. Such data transmission and reception via network 400 may be done via wired or wireless. For example, if client 100 is a smartphone, such communication would normally be done wirelessly. To the extent possible, the configuration of the transmission / reception mechanism can be publicly known or well-known. The data received by the transmitting / receiving mechanism from the network 400 is received by the interface 114, and the data passed from the interface 114 to the transmitting / receiving mechanism is sent by the transmitting / receiving mechanism to an external source via the network 400, for example, to the server 200 in this embodiment.

[0024] When the CPU 111 executes the computer program, functional blocks like those shown in Figure 4 are generated inside the client 100. These functional blocks may be generated solely by the functionality of the aforementioned computer program, which causes the client 100 to perform the processes described below, or they may be generated through the cooperation of the aforementioned computer program and the OS or other computer programs installed on the client 100. Within client 100, an input unit 121, a control unit 122, an image generation unit 123, and an output unit 125 are generated in relation to the functions of the present invention. Of these, the functional blocks that perform information processing, namely the control unit 122 and the image generation unit 123, correspond to a computing device (CPU 111 in this embodiment) in terms of hardware, or are realized by the functions of a computing device that executes information processing according to the instructions of the computer program described above. However, if the functional blocks that perform information processing require some data that is at least temporarily recorded in order to perform the information processing, then some kind of recording device, for example, RAM 113 or a large-capacity recording device in this embodiment, may be required for recording that data. In other words, when viewed as hardware, it is possible that a part of the functional block that performs information processing may include some kind of recording device as a component. From a hardware perspective, the input section 121 and output section 125 correspond to the interface 114, or are realized through the functions of the interface 114. More specifically, conceptually, the input section 121 and output section 125 correspond to the connection between the interface 114 and the bus 116.

[0025] The input unit 121 receives input from the interface 114. The input from interface 114 to input unit 121 includes input from input device 102. The data input from input device 102 includes, although details will be explained later, start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, and modification data. When the above inputs are received from input device 102, all of the data from these inputs are sent from input unit 121 to control unit 122. The data input from interface 114 to input unit 121 also includes data sent from server 200 and received by the transmission / reception mechanism. This data includes, for example, images related to the proposed business model described later, and image data related to images that prompt the user for input when the business model creation support process is being executed. When image data is received by input unit 121 via the transmission / reception mechanism and interface 114, input unit 121 sends it to control unit 122.

[0026] The control unit 122 controls the entire functional block generated within the client 100. The control unit 122 may receive start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, and modification data from the input unit 121. When the control unit 122 receives start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, or modification data, it sends them to the output unit 125 and the image generation unit 123. The control unit 122 may also receive image data sent from the server 200 via the input unit 121. When it receives such image data, the control unit 122 sends it to the image generation unit 123.

[0027] The image generation unit 123 has the function of generating image data, which is data for displaying an image on the display 101, or adjusting image data sent from the server 200 so that it can be displayed on the display 101 (for simplicity, this process is sometimes also referred to as "generating image data"). The image generation unit 123 may receive start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, and justification determination data from the control unit 122. If it receives these, the image generation unit 123 generates image data corresponding to the input data, for example, an image that allows the user to understand that each of these data types has been input. The image generation unit 123 may receive image data sent from the server 200 via the control unit 122. When it receives such data, the image generation unit 123 generates image data corresponding to that data. In any case, the image data generated by the image generation unit 123 is sent from the image generation unit 123 to the output unit 125.

[0028] The output unit 125 outputs data generated by the functional blocks within the client 100 to the interface 114. As described above, the output unit 125 may receive start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, and correction data from the control unit 122. Upon receiving this data, the output unit 125 sends it to the transmission / reception mechanism via the interface 114. The transmission / reception mechanism then sends this data to the server 200 via the network 400. As described above, the output unit 125 may receive image data from the image generation unit 123. The image data is sent from the output unit 125 to the display 101 via the interface 114. The display 101 then displays an image based on the image data.

[0029] Next, we will describe the configuration of server 200. From a hardware perspective, server 200 can be an existing, publicly known or well-known server, especially a cloud server. Its hardware configuration can also be common; broadly speaking, it can follow the hardware configuration of client 100, with a CPU 111, ROM 112, RAM 113, and interface 114 connected via bus 116. However, server 200 would typically have a large-capacity storage device such as an HDD, SSD, or other device connected to bus 116. A computer program is stored in the recording device of the server 200, which in this embodiment is a ROM, RAM, or high-capacity recording device. The computer program referred to here includes a computer program described later that enables the server 200, which is configured as a cloud server, to function as a business model creation support device of the present invention. Of course, other computer programs may also be stored in the recording device. The configuration and functionality of the CPU, ROM, RAM, interfaces, bus, and mass storage device of Server 200 are the same as those of Client 100. The CPU can also be replaced with other computing devices such as a GPU or GPGPU, just as in Client 100. Furthermore, the interface of the server 200 is connected to a transmit / receive mechanism similar to that of the client 100, which enables communication with devices outside the server 200 via the network 400. The transmit / receive mechanism in the server 200 corresponds to the input receiving mechanism in the present invention. Information sent from the bus to the interface is sent to the transmit / receive mechanism, and from the transmit / receive mechanism it is sent to, for example, the client 100 via the network 400. Also, data sent from the client 100 via the network 400 and received by the transmit / receive mechanism is sent from the transmit / receive mechanism to the interface, and from the interface it is sent to the bus. The interface of the server 200 may be connected to a display and input receiving mechanism similar to those of the client 100, but since these are not particularly relevant to the present invention, their explanation will be omitted.

[0030] By executing a computer program that causes Server 200, which is stored in ROM, a large-capacity storage device, etc., inside Server 200, to function as a business model creation support device of the present invention, the following functional blocks are generated inside Server 200. These functional blocks may be generated solely by the computer program that causes Server 200 to function as a business model creation support device of the present invention, or they may be generated through the cooperation of such computer program and the OS or other computer programs installed on Server 200. Furthermore, the computer program may be pre-installed on Server 200, or it may be post-installed on Server 200. In this case, the installation of the computer program on Server 200 may be performed via a predetermined recording medium such as a memory card, or via a network such as a LAN or the Internet. These circumstances are the same as those for Client 100. Within the server 200, an input unit 221, a control unit 222, a prerequisite data generation unit 223, a market data generation unit 224A, a basis data generation unit 224, a risk data generation unit 225, an explanatory data generation unit 226, an image data generation unit 227, a business model proposal data generation unit 228, an output unit 229, and a data recording unit 220 are generated in relation to the functions of the present invention (Figure 5). Of these, the functional blocks that perform information processing—namely, the control unit 222, the prerequisite data generation unit 223, the market data generation unit 224A, the basis data generation unit 224, the risk data generation unit 225, the explanatory data generation unit 226, the image data generation unit 227, and the business model proposal data generation unit 228—correspond to a computing device (CPU in this embodiment) as hardware, or are realized by the functions of a computing device that executes information processing according to the instructions of the computer program described above. However, if a functional block that performs information processing requires some data that is at least temporarily recorded in order to perform the information processing, then some kind of recording device, for example, RAM or a large-capacity recording device in this embodiment, may be required for recording that data. In other words, when viewed as hardware, some of the functional blocks that perform information processing may include some kind of recording device as a component. From a hardware perspective, the input section 221 and the output section 229 correspond to the interface 114, or are realized through the functions of the interface 114. More specifically, conceptually, the input section 221 and the output section 229 correspond to the connection between the interface and the bus. The functional block for recording data, that is, the data recording unit 220, is implemented as hardware by some kind of recording device, in this embodiment by RAM or a high-capacity recording device.

[0031] The input unit 221 receives input from the interface. The data input to the input unit 221 from the interface is data sent from the client 100 via the network 400 and received by the server 200's transmission and reception mechanism. The transmission / reception mechanism may receive start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, and correction data from the client 100. Upon receiving these, the transmission / reception mechanism sends each of these data to the input unit 221, which then sends the received data to the control unit 222.

[0032] The control unit 222 controls the entirety of each functional block generated within the server 200. The control unit 222 may receive start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, justification determination data, and correction data from the input unit 221. Of these, when the control unit 222 receives start data, it instructs the image data generation unit 227 to generate appropriate image data as described later. There are other cases in which the control unit 222 sends similar instructions to the image data generation unit 227. When the control unit receives the feature data to be monetized, it sends it to the prerequisite data generation unit 223, the market data generation unit 224A, the rationale data generation unit 224, the risk data generation unit 225, and the business model proposal data generation unit 228. When the control unit 222 receives prerequisite order data, it sends it to the prerequisite data generation unit 223. When the control unit receives justification order data, it sends it to the justification data generation unit 224. When the control unit 222 receives the precondition determination data and the rationale determination data, it sends them to the explanation data generation unit 226. When the control unit 222 receives the corrected data, it sends it to either the prerequisite data generation unit 223, the market data generation unit 224A, or the basis data generation unit 224. However, if the correction made by the corrected data does not affect the market data, the corrected data will not be sent to the market data generation unit 224A.

[0033] The prerequisite data generation unit 223 generates prerequisite data. The prerequisite data is generated when the prerequisite data generation unit 223 receives the monetization target feature data and the prerequisite degree data. The prerequisite degree data is data corresponding to one of the natural numbers. Here, we assume that the prerequisite degree data is N. The prerequisite data generation unit 223 generates primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid. Next, the preconditions for the first precondition to be met are collected from the network to generate second-order precondition data. Repeating this process, the precondition data generation unit 223 generates Nth-order precondition data by collecting the preconditions for the N-1th-order precondition to be met from the network. When generating each data from the first-order prerequisite data to the Nth-order prerequisite data, for example, monetization target feature data, prerequisite order data, data collected from the network, data recorded in the data recording unit 220, etc., will be used. The method for generating each data from the first-order prerequisite data to the Nth-order prerequisite data will be described in detail later. In this embodiment, the precondition data is a dataset of first-order precondition data to Nth-order precondition data, each specifying the first-order preconditions from the first-order to the Nth-order preconditions. However, as will be described later, the precondition data may also include only the Nth-order precondition data for the Nth-order preconditions. It is also possible to attach data on the probability that each order of precondition (or each precondition that forms the set of preconditions included therein) is met to the precondition data. The generated prerequisite data is sent from the prerequisite data generation unit 223 to the business model proposal data generation unit 228. The prerequisite data generation unit 223 may receive corrected data. When corrected data is received, the prerequisite data generation unit 223 corrects the prerequisite data that has already been generated based on the corrected data. Details of the process when correcting prerequisite data using corrected data will be described later. If the prerequisite data is modified, the prerequisite data generation unit 223 sends the modified prerequisite data to the business model proposal data generation unit 228.

[0034] The market data generation unit 224A generates market data. Market data is generated when the market data generation unit 224A receives the feature data to be monetized. The market data generated by the market data generation unit 224A is data that identifies the market for which monetization is possible, estimated from the monetization target features identified by the monetization target feature data. The market data can also be accompanied by data on the probability of a market being established. When the market data generation unit 224A generates market data, it may also collect data from the network 400 to determine the market that can be assumed based on the monetization target feature data. The method of generating market data will be described in detail later. The market data generation unit 224A sends the generated market data to the basis data generation unit 224. The market data generation unit 224A may receive corrected data. When corrected data is received, the market data generation unit 224A corrects the market data that has already been generated based on the corrected data. Details of the process when correcting market data using corrected data will be described later. If market data is corrected, the market data generation unit 224A sends the corrected market data to the basis data generation unit 224.

[0035] The evidence data generation unit 224 generates evidence data. The rationale data is generated when the rationale data generation unit 224 receives the monetization target feature data, market data, and rationale degree data. The rationale degree data is data corresponding to one of the natural numbers. Here, the rationale degree data is assumed to be n. When generating evidence data, the evidence data generation unit 224 generates primary evidence data, which is data about primary evidence, by collecting from the network the evidence for the establishment of the market identified by the generated market data. Next, secondary evidence data is generated by collecting evidence from the network that is necessary for the primary evidence to be valid. Repeating this process, the evidence data generation unit 224 generates nth-order evidence data, which is data about the nth-order evidence, by collecting evidence from the network that is necessary for the (n-1)th-order evidence to be valid. When generating each data from primary to nth-order evidence data, for example, monetization target feature data, evidence order data, data collected from the network, data recorded in the data recording unit 220, etc., will be used. The method for generating each data from primary to nth-order evidence data will be described in detail later. In this embodiment, the evidence data is a dataset of primary to nth-order evidence data, each identifying a primary to nth-order evidence. However, as will be described later, the evidence data may also include only nth-order evidence data for the nth-order evidence. It is also possible to attach data on the probability that each order of evidence (or each evidence forming the set of evidence contained within it) is valid to the evidence data. The generated evidence data is sent from the evidence data generation unit 224 to the business model proposal data generation unit 228. The data generation unit 224 may receive corrected data. When corrected data is received, the evidence data generation unit 224 corrects the evidence data that has already been generated based on the corrected data. Details of the process when correcting evidence data using corrected data will be described later. If the supporting data is modified, the supporting data generation unit 224 sends the modified supporting data to the business model proposal data generation unit 228.

[0036] The risk data generation unit 225 has the function of generating risk data. Risk data represents the risk that the primary to nth-order preconditions, identified by primary to nth-order precondition data, and the primary to nth-order justifications, identified by primary to nth-order justification data, will cease to be valid. Therefore, the risk data generation unit 225 needs to obtain prerequisite data and justification data in order to generate risk data, and uses the obtained prerequisite data and justification data to generate risk data. In this embodiment, the prerequisite data and justification data are sent from the business model proposal data generation unit 228 to the risk data generation unit 225 as described later, but the prerequisite data may be sent directly from the prerequisite data generation unit 223 to the risk data generation unit 225, for example, and the justification data may be sent directly from the justification data generation unit 224 to the risk data generation unit 225, for example. The risk data generation unit 225 can, of course, collect data from the network and use it to generate risk data. The risk data generation unit 225 uses at least precondition data (e.g., from primary precondition data to nth precondition data) and justification data (e.g., from primary justification data to nth justification data) when generating risk data. However, the risk data generation unit 225 may also use data collected from the network, data recorded in the data recording unit 220, etc., in addition to these to generate the risk data. The method of generating risk data will be described in detail later. The risk data generation unit 225 sends the generated risk data to the business model proposal data generation unit 228.

[0037] The explanatory data generation unit 226 has the function of generating explanatory data. The explanatory data consists of natural language explanations of the proposed business model. By reading the explanations based on this data, users can deepen their understanding of the proposed business model. As will be described later, the explanatory data generation unit 226 receives business model proposal data from the business model proposal data generation unit 228. In addition, as already mentioned, the explanatory data generation unit 226 receives precondition determination data and rationale determination data from the control unit 222. As described above, the prerequisite data consists of N sets ranging from 1st-order prerequisite data to Nth-order prerequisite data. The prerequisite determination data consists of natural numbers less than or equal to N, and is data that identifies one set of prerequisite data whose degree is the number specified by the prerequisite determination data from among the N sets. On the other hand, the justification data consists of n sets ranging from 1st-order justification data to nth-order justification data. The justification determination data consists of natural numbers less than or equal to n, and is data that identifies one set of justification data whose degree is the number specified by the justification determination data from among the n sets. Therefore, while there are N×n possible business model proposals determined by the combination of a set of N preconditions (from primary to nth preconditions) and a set of n justifications (from primary to nth justifications), as described later, by limiting the preconditions and justifications to one each using precondition determination data and justification determination data, one of the N×n business model proposals inherent in the business model proposal data will be selected. The explanatory data generation unit 226 is designed to generate explanatory data about the proposed business model. When the explanatory data generation unit 226 generates explanatory data, it uses one of the N precondition data sets relating to the preconditions identified by the precondition determination data, and one of the n basis data sets relating to the basis identified by the basis determination data, in order to generate the proposed business model to be explained. In addition, when generating such explanatory data, it is also possible to use the data recorded in the data recording unit 220. The generated explanatory data is sent from the explanatory data generation unit 226 to the business model proposal data generation unit 228.

[0038] The business model proposal data generation unit 228 has the function of generating business model proposal data for business model proposals. The business model proposal data consists of data on proposed business models for businesses that monetize monetization targets that possess monetization target characteristics identified by the monetization target characteristic data entered by the user. The business model proposal data generation unit 228 generates business model proposal data using the monetization target feature data received from the control unit 222, the prerequisite data received from the prerequisite data generation unit 223, and the market data and evidence data received from the evidence data generation unit 224. In generating business model proposal data, the business model proposal data generation unit 228 can use other data besides the monetization target feature data, prerequisite data, market data, and evidence data mentioned above, such as risk data generated by the risk data generation unit 225, explanatory data generated by the explanatory data generation unit 226, data read from the data recording unit 220, and data collected from the network 400, for example, by crawling. Details of the business model proposal data and the method for generating it will be explained later. The business model proposal data generation unit 228 generates business model proposal data and then sends it to the explanation data generation unit 226 and the image data generation unit 227.

[0039] The image data generation unit 227 generates image data. The image generated by the image data generation unit 227 is image data for displaying a desired image on a predetermined display. In this embodiment, the image data generated by the image data generation unit 227 is an image for displaying a desired image on the display 101 of the client 100. The image data generation unit 227 generates image data, for example, when instructed by the control unit 222. It also generates image data when it receives business model proposal data from the business model proposal data generation unit 228. Details of the image data content, image data generation method, and generation timing in the image data generation unit 227 will be described later. The image data generation unit 227 sends the generated image data to the output unit 229.

[0040] The output unit 229 outputs data generated by the functional blocks within the server 200 to the interface. As described above, the output unit 229 may receive image data from the image data generation unit 227. When it receives image data, the output unit 229 sends the image data to the transmission / reception mechanism via the interface. The image data is then transmitted from the transmission / reception mechanism to the client 100 via the network 400.

[0041] The data recording unit 220 records data. In this embodiment, it records data that can be used by the prerequisite data generation unit 223 to generate prerequisite data, and data that can be used by the basis data generation unit 224 to generate basis data. The data recorded in the data recording unit 220 is a computer program containing data that functions as artificial intelligence when, for example, the CPU executes it. The data recording unit 220 may also record computer programs (including data) for artificial intelligence to perform analyses necessary for generating business model proposals, such as SWOT analysis, PEST analysis, 5 FORCE analysis, and attribute analysis; computer programs (including data) for artificial intelligence to create diagrams such as business model maps and business model canvases; and computer programs (including data) for artificial intelligence to enable reinforcement learning using deep neural networks and Monte Carlo tree search. In this embodiment, this is the case.

[0042] Next, we will explain how to use the support system described above, and in particular how to use the server 200 as a business model creation support device in the present invention. A user who wishes to use the services provided by the server 200 as a business model creation support device first operates the input device 102 of the client 100 to launch the aforementioned computer program that enables the client 100 to function as the support device of the present invention. Launching the computer program can be done in the usual manner, for example, by clicking an icon displayed on the display 101. When the icon is clicked, start data is input from the input device 102. The start data is sent from the interface 114 to the input unit 121, and from the input unit 121 to the control unit 122. The control unit 122 sends this start data to the output unit 125. The start data is sent from the output unit 125 to the transmission / reception mechanism via the interface 114, and from the transmission / reception mechanism to the server 200 via the network 400. Of course, in order for the server 200 to authenticate the client 100, it is also possible to send data other than the start data, such as a unique user ID and password for each user, from the client 100 to the server 200. These are, of course, well-known technologies. Server 200 receives start data via its transmission / reception mechanism. The start data received by Server 200 is sent from the interface through the input unit 221 to the control unit 222. Upon receiving the start data, the control unit 222 sends an instruction to the image data generation unit 227 to generate a first image, which is an image corresponding to the input start data. Upon receiving this instruction, the image data generation unit 227 generates image data, which is the data for the first image. The content of the first image will be described later. This image data is sent from the image data generation unit 227 to the output unit 229, and from the output unit 229 to the transmission / reception mechanism. The image data is sent from the transmission / reception mechanism to the client 100 via the network 400. The client 100 receives the image data via its transmission / reception mechanism.

[0043] Within client 100, image data travels from the transmission / reception mechanism through interface 114 and input unit 121 to control unit 122. Upon receiving such image data, control unit 122 sends it to image generation unit 123. Image generation unit 123 generates image data for the first image and sends it to output unit 125. Output unit 125 sends this image data to display 101. As a result, the first image is displayed on the display 101. The first image is, for example, like the one shown in Figure 6. The first image includes the text 501 "Monetizable Features", a large rectangular frame 502 below it, and a button 503 labeled "Submit" located further below it. The user enters the monetization target features in the blank box 502 in Figure 6 by operating the input device 102. The monetization target features are descriptions in natural language of the characteristics of the goods or services that will be monetized in the business based on the proposed business model, that is, the products or services that will be the target of monetization. The descriptions may include numbers, sequences of numbers, mathematical formulas, etc., which are not considered typical sentences, or may consist solely of such elements. In addition to the monetization target features, which are written in natural language, box 502 may also contain descriptions in non-natural language that correspond to the monetization target features, but in this embodiment, such descriptions will not be included. As a mere example, let's say the feature to be monetized is a new type of "all-solid-state battery." In that case, for example, the feature to be monetized might be something like this: "It is an all-solid-state battery. The electrolyte is a three-dimensional nanoporous solid silicon. The negative electrode material is a three-dimensional lithium metal. The positive electrode material is a three-dimensional nanoporous NCA metal. The energy density is 2000 Wh / kg. The electrical capacity is 5000 A / g. ..." Once the user has finished writing, they click the button 503 labeled "Submit". This confirms the text written in the box 502, and the monetization feature data, which is data about the monetization feature written in the box, is entered. The monetization feature data is sent from the input device 102 to the input unit 121 via the interface 114, and from the input unit 121 to the control unit 122. The control unit 122 sends the monetization feature data to the output unit 125. The monetization feature data is sent from the output unit 125 to the transmission / reception mechanism via the interface 114, and from the transmission / reception mechanism to the server 200 via the network 400. Server 200 receives the monetization target feature data through its transmission and reception mechanism. The monetization target feature data received by Server 200 is sent from the interface through the input unit 221 to the control unit 222. Upon receiving the feature data to be monetized, the control unit 222 sends it to the prerequisite data generation unit 223, the market data generation unit 224, the rationale data generation unit 224, the risk data generation unit 225, and the business model proposal data generation unit 228. Furthermore, upon receiving the feature data to be monetized, the control unit 222 sends an instruction to the image data generation unit 227 to generate a second image corresponding to the input of the feature data to be monetized. Upon receiving this instruction, the image data generation unit 227 generates image data, which is the data for the second image. The content of the second image will be described later. The image data for the second image generated by the image data generation unit 227 is sent to the client 100 via the network 400 in the same manner as the image data for the first image. The client 100 receives the image data using its transmission and reception mechanism.

[0044] Within client 100, image data travels from the transmission / reception mechanism through interface 114 and input unit 121 to control unit 122. Upon receiving such image data, control unit 122 sends it to image generation unit 123. Image generation unit 123 generates image data for the second image and sends it to output unit 125. Output unit 125 sends this image data to display 101. As a result, the second image is displayed on the display 101. The second image is, for example, like the one shown in Figure 7. The second image includes the text "Prerequisite Order" 511, a frame 512 to its right, the text "Basis Order" 513 located below the text "Prerequisite Order" 511, a rectangular frame 514 to its right, and a button 515 labeled "Submit" located further below. The user enters the prerequisite order in the box 512, which is shown as blank in Figure 7. The prerequisite order is a number that determines how far back the prerequisite order is traced by the prerequisite data generation unit 223 in the server 200, which acts as a support device, as will be described later. The prerequisite order is a natural number, but is generally between 2 and 8. It is not limited to this, but in this embodiment, let's assume that the number 3 is written in box 512. The user enters the evidence order in the box 514, which is shown as blank in Figure 7. The evidence order is a number that determines how far back the evidence data generation unit 224 in the server 200, which acts as a support device, will trace the evidence. The evidence is a natural number, but is generally between 2 and 8. It is not limited to this, but in this embodiment, let's assume that the number 2 is written in box 514. Input device 102 is used to write to frames 512 and 514. Once the user has finished writing to frames 512 and 514, they click the button 515 labeled "Send". This confirms the contents written in frames 512 and 514, and the prerequisite order data, which is data about the prerequisite order written in frame 512, and the justification order data, which is data about the justification order written in frame 514, are input. The prerequisite order data and justification order data are sent from the input device 102 to the input unit 121 via interface 114, and from the input unit 121 to the control unit 122. The control unit 122 sends these two data to the output unit 125. The prerequisite order data and justification order data are sent from the output unit 125 to the transmission / reception mechanism via interface 114, and from the transmission / reception mechanism to the server 200 via network 400. Server 200 receives prerequisite order data and justification order data through its transmission and reception mechanism. These two data sets received by Server 200 are sent from the interface through the input unit 221 to the control unit 222. Upon receiving the prerequisite order data and justification order data, the control unit 222 sends the prerequisite order data to the prerequisite data generation unit 223 and the justification order data to the justification data generation unit 224.

[0045] In this state, the prerequisite data generation unit 223 has received the monetization target feature data and the prerequisite order data from the control unit 222. Once these two sets of data are available, the prerequisite data generation unit 223 generates prerequisite data for the prerequisites. As mentioned above, the number specified in the prerequisite order data is not limited to this, but is 3. The prerequisite data generation unit 223 generates primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid. Next, the precondition data generation unit 223 generates secondary precondition data, which is data about the secondary precondition, by collecting the preconditions necessary for the primary precondition to be met from the network. Repeating the same process, the precondition data generation unit 223 generates tertiary precondition data, which is data about the tertiary precondition, by collecting the preconditions necessary for the secondary precondition to be met from the network. Primary prerequisite data is data about primary prerequisites. Primary prerequisites are the conditions for the monetization target features identified by the monetization target feature data to be valid. In generating primary prerequisite data, the prerequisite data generation unit 223 uses the monetization target feature data and data collected from the network. To do this automatically, a technology is needed to automatically interpret the meaning of the monetization target features from the monetization target feature data, a technology to find prerequisites for the interpreted content from information related to that content based on the interpreted content, and a technology to find and collect the desired information from the network. A probability of that premise being true may be assigned to the first-order premise, or to each premise that is included in the first-order premise and forms a set. In this embodiment, however, probabilities are assigned to some of the premise (for example, those that have a significant impact on the proposed business model) (this is the same for second-order to Nth-order premise). When assigning such probabilities, the premise data generation unit 223 needs to be able to perform a technique to calculate the probability of that premise being true by comparing it with other information existing on the network, for example. Crawling is a known technique for finding and collecting desired information from a network. The prerequisite data generation unit 223 is capable of executing this known or well-known technique. Furthermore, the prerequisite data generation unit 223 is capable of automatically interpreting the meaning of monetization target features from monetization target feature data. The following techniques can be used to make this possible. For example, as artificial intelligence for interpreting the meaning of words, word2vec (released by Google in 2013) and its improved version CBOW (Continuous Bag-of-Words or Skip-Gram) are well-known. In addition, RNN (recurrent Neural Network) and its improved version LSTM (Long Short-Term Memory) are also well-known as artificial intelligence for interpreting the meaning of words. Furthermore, as artificial intelligence related to understanding the meaning of text, Word Mover's Distance (WMD, released in 2015), which applies word embeddings to text, and its improved version, Linear-Complexity Relaxed Word Mover's Distance (LC-RWMD), are publicly known. Also, as artificial intelligence related to interpreting the meaning of words and understanding the meaning of text, large-scale language models based on deep neural networks are publicly known. In addition, Monte Carlo tree search, based on deep neural networks, is publicly known as artificial intelligence related to calculating the probability of a given condition being met. Using such technologies (or technologies with the same purpose), it is possible to automatically interpret the meaning of monetization target features from monetization target feature data, and to find preconditions for the interpreted content from information related to that content based on the interpreted content. Using such artificial intelligence, data about the information that forms the basis of the monetization target feature data is collected by crawling from the network 400. In this embodiment, as described above, computer programs (including data) for various artificial intelligences as described above are recorded in the data recording unit 220. The prerequisite data generation unit 223 reads and uses the recorded computer programs for artificial intelligences from the data recording unit 220 as needed. In this way, primary assumption data for the primary assumptions is generated. Similarly, the precondition data generation unit 223 generates secondary precondition data, which is data about secondary preconditions that are preconditions for the primary precondition to be met. Furthermore, similarly, the precondition data generation unit 223 generates tertiary precondition data, which is data about tertiary preconditions that are preconditions for the secondary precondition to be met. As mentioned above, since the number specified by the prerequisite order data is 3, the maximum order of the data generated as prerequisite data is 3. Therefore, when the 3rd order prerequisite data is generated, the prerequisite data generation unit 223 terminates the generation of prerequisite data. At this stage, the data for each prerequisite included in the 1st order prerequisite data to the 3rd order prerequisite data in this embodiment are arranged to have a tree-like relationship, which will be described later. In this example, the prerequisite data generated by the prerequisite data generation unit 223 includes primary prerequisite data, secondary prerequisite data, and tertiary prerequisite data. The prerequisite data generation unit 223 sends the prerequisite data, including these prerequisites, to the business model proposal data generation unit 228.

[0046] In this state, the market data generation unit 224A receives monetization target feature data from the control unit 222. The market data generation unit 224A generates market data, which is data that identifies profitable markets estimated from the monetization target features identified by the monetization target feature data received. When generating market data, the market data generation unit 224A may, but is not limited to, collect data on the network and use it to generate market data, although in this embodiment the market data generation unit 224A does so. The market data generation unit 224A generates market data using artificial intelligence and crawling techniques, similar to when the prerequisite data generation unit 223 generated prerequisite data. Market data can also be accompanied by data on the probability of each market being established, although this is not limited to such data; however, the market data generation unit 224A in this embodiment is designed to do so. If such probabilities are to be included, the market data generation unit 224A must be able to perform a technique to calculate the probability of the market being established, for example, by comparing it with other information existing on the network. The market data generation unit 224A sends the generated market data to the basis data generation unit 224.

[0047] In this state, the evidence data generation unit 224 receives the monetization target feature data and the evidence order data from the control unit 222, and the market data from the market data generation unit 224A. Once these three data sets are available, the evidence data generation unit 224 generates evidence data about the evidence. As mentioned above, the number specified by the evidence order data is not limited to this, but is 2. The evidence data generation unit 224 generates primary evidence data, which is data about primary evidence, by collecting from the network the evidence for the establishment of a market identified by the generated market data. A probability of the primary basis being valid may be assigned to the primary basis, or to each basis that is included in the primary basis and forms a set, and is not limited to this embodiment, but probabilities are assigned to some of the basis (for example, those that have a greater impact on the proposed business model) (Note that this is the same for secondary basis to n-th (however in this embodiment n=2)-th-order basis). Next, secondary evidence data is generated by collecting evidence from the network that supports the primary evidence. Primary evidence data is data about primary evidence. In generating primary evidence data, the evidence data generation unit 224 uses market data, monetization target feature data, and data collected from the network. To do this automatically, just as the prerequisite data generation unit 223 generated the prerequisite data, the primary evidence data is generated using a computer program for artificial intelligence recorded in the data recording unit 220 and crawling technology. Similarly, the evidence data generation unit 224 generates secondary evidence data, which is data about secondary evidence that is the basis for the existence of primary evidence. If probabilities are to be assigned to the evidence data, the evidence data generation unit 224 needs to be able to perform a technique, for example, to calculate the probability that the evidence is valid by comparing it with other information existing on the network. As mentioned above, since the number specified by the basis order data is 2, the maximum order of the data generated as basis data is 2. Therefore, when the secondary basis data is generated, the basis data generation unit 224 terminates the generation of basis data. At this stage, the data for each basis included in the primary basis data and the secondary basis data in this embodiment are arranged to have a tree-like relationship, which will be described later. In this example, the evidence data generated by the evidence data generation unit 224 includes primary evidence data and secondary evidence data. The evidence data generation unit 224 sends the evidence data, including that information, to the business model proposal data generation unit 228. The evidence data generation unit 224 also sends the market data received from the market data generation unit 224A to the business model proposal data generation unit 228. The transmission of market data and evidence data to the business model proposal data generation unit 228 does not need to occur simultaneously.

[0048] As described above, the business model proposal data generation unit 228 receives the following data: revenue target feature data from the control unit 222, prerequisite data including primary to tertiary prerequisite data from the prerequisite data generation unit 223, and market data and evidence data including primary and secondary evidence data from the evidence data generation unit 224. Once the monetization target feature data, prerequisite data, market data, and rationale data are available, the business model proposal data generation unit 228 uses them to generate business model proposal data. However, in this embodiment, prior to that, the business model proposal data generation unit 228 sends the prerequisite data and rationale data to the risk data generation unit 225.

[0049] As a result, the risk data generation unit 225 will have all the necessary data: the feature data to be monetized, the prerequisite data, and the supporting data. Once these three data sets are available, the risk data generation unit 225 generates the risk data. The monetization target feature data only needs to be sent before both the monetization target feature data and the prerequisite data reach the risk data generation unit 225. Risk data is data about the risk that the assumptions identified by the assumption data (in this example, the primary to tertiary assumptions identified by the primary to tertiary assumption data) and the rationale identified by the rationale data (in this example, the primary and secondary rationale identified by the primary and secondary rationale data) will no longer hold true. In generating risk data, the risk data generation unit 225 uses prerequisite data, evidence data, and monetization target feature data. To automate this process, the risk data is generated using the computer program for artificial intelligence recorded in the data recording unit 220 and crawling technology, similar to how the prerequisite data generation unit 223 generated the prerequisite data. The risk data generation unit 225 sends the generated risk data to the business model proposal data generation unit 228.

[0050] As described above, the business model proposal data generation unit 228 will have all the necessary data: revenue-generating feature data, prerequisite data, market data, rationale data, and risk data. Once these three data sets are available, the business model proposal data generation unit 228 generates the business model proposal data. In this embodiment, the minimum requirements for generating business model proposal data are revenue-generating feature data, prerequisite data, market data, and rationale data. In other words, risk data is not necessarily required to generate business model proposal data. In that case, the risk data generation unit 225 is unnecessary (or, to put it another way, it is unnecessary to have the CPU function as the risk data generation unit 225). The business model proposal data consists of data on proposed business models for businesses that monetize monetization targets that possess monetization target characteristics identified by the monetization target characteristic data entered by the user. The business model proposal data generation unit 228 generates business model proposal data using the monetization target feature data, the prerequisite data received from the prerequisite data generation unit 223, and the market data and rationale data received from the rationale data generation unit 224. Furthermore, in generating the business model proposal data, the business model proposal data generation unit 228 also uses the risk data created by the risk data generation unit 225. The proposed business model data can be, for example, a collection of revenue-generating feature data, prerequisite data, market data, and rationale data, or a collection of these plus risk data. In that case, the proposed business model data generation unit 228 generates the proposed business model data by combining the above-mentioned data or by generating data that includes the above-mentioned data. As a concrete example of generating data for other business model proposals, the following can be cited. (1) Market data is used to identify markets, including their probability of existence, and to generate data. (2) Prerequisites are then used to identify prerequisites, including their probability of occurrence, and to generate data. (3) Evidence is then used to identify evidence, including its probability of occurrence, and to generate data. (4) Finally, a combination of prerequisite and evidence data is used to generate a "desirable combination" of monetization target feature data and market data. Here, a "desirable combination" refers to, for example, a case where the feasibility of implementing the monetization target feature is 85%, the market size is the largest, and the probability of its occurrence is 95% or higher, or a case where the feasibility of implementing the monetization target feature is 95%, the market size is the second largest, and the probability of its occurrence is the highest. Such conditions are explored using reinforcement learning methods with deep neural network technology, and a business model proposal is generated by exploring combinations of precondition data and evidence data, as well as the overall combination including the reconstructed monetization target feature data and market data. The business model proposal data generation unit 228 can generate business model proposals using other data not mentioned above. For example, data collected from the network through crawling can be used to generate business model proposal data. Furthermore, the explanatory data described later can be used to generate business model proposal data. Furthermore, when generating business model proposal data, the business model proposal data generation unit 228 can utilize various computer programs (including data) recorded in the data recording unit 220. For example, it can utilize artificial intelligence computer programs (including data) for performing analyses such as SWOT analysis, PEST analysis, 5 FORCE analysis, and attribute analysis, as well as artificial intelligence computer programs (including data) for creating diagrams such as business model maps and business model canvases, and artificial intelligence computer programs (including data) for enabling reinforcement learning using deep neural networks and Monte Carlo tree search. This makes it possible to generate business model proposal data by analyzing various information using predetermined analysis methods and diagrams. It is also possible to allow the user to select the analysis methods and charts used by the business model proposal data generation unit 228 to generate the business model proposal data. In that case, for example, the user would be prompted to select at least one of the types of analysis methods or charts used when generating the business model proposal data, and based on the data (analysis method selection data) sent from the client 100 to the server 200 based on the user's input, the business model proposal data generation unit 228 would read and use the computer programs and data for the analysis methods and charts based on the analysis method selection data from the data recording unit 220. For example, a method to prompt input of analysis method selection data would be to display an image prompting input, such as the one shown in Figure 7, on the display 101 of the client 100. It is clear that the server 200 can display any image on the display 101 of the client 100, as the server 200 can display the images shown in Figures 6 and 7 on the display 101. Rather, such technology is not just publicly known, but well-known technology. In images prompting users to input data for selecting an analysis method, it would be helpful to present options for selecting at least one of the following: the analysis method or the type of chart or graph. For example, a pull-down menu is a well-known method for presenting options, and using such a method would allow users to easily select at least one of the analysis method or the type of chart or graph. The business model proposal data generation unit 228 generates business model proposal data and then sends it to the explanation data generation unit 226 and the image data generation unit 227.

[0051] Upon receiving the business model proposal data, the image data generation unit 227 generates image data to present the business model proposal identified by the business model proposal data to the user. Image data is sent from the image data generation unit 227 to the output unit 229, and from the output unit 229 to the transmission / reception mechanism. The image data is sent from the transmission / reception mechanism to the client 100 via the network 400. The client 100 receives the image data at its transmission / reception mechanism. Within client 100, image data travels from the transmission / reception mechanism through interface 114 and input unit 121 to control unit 122. Upon receiving such image data, control unit 122 sends it to image generation unit 123. Image generation unit 123 generates image data for the proposed business model and sends it to output unit 125. Output unit 125 sends this image data to display 101. As a result, an image based on the image data will be displayed on display 101. This image corresponds to the business model proposal identified by the business model proposal data. In this embodiment, image data based on the proposed business model data is sent from the support device, server 200, to the client 100. However, the proposed business model data itself may also be sent from server 200 to client 100.

[0052] Figure 8 shows an example of an image corresponding to the content of the proposed business model data displayed on display 101. In the image shown in Figure 8, the items listed below the text "Monetizable Features" (551) are the monetizable features that have been repeatedly explained up to this point. There are usually multiple monetizable features, and in this example, although the latter half is omitted, six monetizable features of a solid-state battery are listed. As explained up to this point, monetizable features are written in natural language. In the image shown in Figure 8, the items listed below the text "First-order assumptions" (552) are the first-order assumptions that have been repeatedly explained up to this point. There are usually multiple first-order assumptions, and in this example, although the latter half is omitted, there are 10 assumptions. In the image shown in Figure 8, the items listed below the words "Secondary Prerequisite" (553) are the secondary prerequisites that have been explained repeatedly up to this point, and the items listed below the words "Tertiary Prerequisite" (554) are the tertiary prerequisites that have been explained repeatedly up to this point. There are usually multiple secondary and tertiary prerequisites, and this is also the case in this example. The primary, secondary, and tertiary preconditions are arranged in a tree-like structure. That is, at least some of the multiple preconditions included in the secondary preconditions are linked to at least one of the multiple preconditions included in the primary preconditions, and at least some of the multiple preconditions included in the tertiary preconditions are linked to at least one of the multiple preconditions included in the secondary preconditions. Although not done in this embodiment, at least some of the multiple preconditions included in the primary preconditions may be linked to one of the multiple features included in the monetization feature. In general, among the preconditions included in the primary to Nth preconditions that are linked in a tree-like structure, the preconditions included in the primary preconditions may be linked to at least one of the features included in the monetization feature. In other words, each precondition included in the primary to Nth preconditions and the features included in the monetization feature may be linked together in a tree-like structure. A given premise is not necessarily linked to a premise of a lower degree. Furthermore, when a premise is linked to a premise of a lower degree, it may be linked to multiple premises of that lower degree. Also, multiple premises of the same degree may be linked to a single premise of a lower degree. In the image shown in Figure 8, the item listed below the word "market" (555) is the market that has been repeatedly explained up to this point. The market can be singular or plural, but in this embodiment, there are two: electric vehicles and aircraft. In the image shown in Figure 8, the primary evidence described repeatedly up to this point is listed in the column below the words "Primary Evidence" (556), and the secondary evidence described repeatedly up to this point is listed in the column below the words "Secondary Evidence" (557). In most cases, there are multiple primary and secondary evidence, and this is also the case in this example. The primary and secondary evidence are arranged in a tree diagram. The meaning of the term "tree diagram" is the same as in the explanation of the preconditions. Furthermore, among the evidence included in the primary to nth-order evidence linked in the tree diagram, the evidence included in the primary evidence may be linked to at least one of the markets included in the market. In this embodiment, the market identified by the market data includes a percentage in parentheses, such as 98% in "KPI for the EV market in 2035: $250 billion (98%)". This number represents the probability that a $250 billion EV market will exist in 2035. In other words, the probability data included in the market data is reflected in the market. Similarly, in this embodiment, the assumptions identified by the assumption data (for example, the primary assumptions) include a percentage in parentheses, such as 95% in "Optimization of manufacturing costs (95%)". This number represents the probability that the assumption of optimization of manufacturing costs is met. In other words, the probability data attached to the assumption data is reflected in at least part of the assumptions. Similarly, in this embodiment, the evidence identified by the supporting data (e.g., secondary evidence) includes a percentage in parentheses, such as 75% in "20% increase in crude oil prices over 10 years (75%)". This number represents the probability that the evidence that crude oil prices will increase by 20% over 10 years is true. In other words, the probability data attached to the supporting data is reflected in at least part of the evidence. Furthermore, the text 559, enclosed in a box below the "Secondary Evidence" text 557, which states "If the development of shale gas fields in country U is successful, there is a risk that crude oil prices will fall (20%)", represents the risk to the aforementioned evidence. This is an example of a description based on risk data generated by the risk data generation unit 225. It is not necessary for descriptions based on risk data to be enclosed in a box, and they can, of course, be presented to the user in other formats.

[0053] Figure 8 also displays the text "No explanation needed" 561, and to its right, a checkbox 562 where the user can check if they do not need the explanation about the proposed business model described later. By checking checkbox 562, the user can indicate that they do not need the explanation. Figure 8 also shows a rectangular box 564 paired with the text 563 "Degree of preconditions to be selected" and a rectangular box 566 paired with the text 565 "Degree of rationale for selection," which prompt the user to indicate which business model proposal requires explanation when an explanation of the business model proposal is needed. The reason for allowing users to select the order of preconditions and the order of evidence is as follows: As mentioned above, the concept is illustrated in Figure 8. The generated business model proposal includes three types of preconditions, from primary to tertiary preconditions, and two types of evidence, primary and secondary evidence. In other words, the business model proposals shown in Figure 8 contain superimposed business model proposals corresponding to each of the six types of combinations of preconditions and justifications: combinations of primary preconditions and primary justifications, combinations of secondary preconditions and primary justifications (or combinations of up to secondary preconditions and primary justifications), combinations of tertiary preconditions and primary justifications (or combinations of up to tertiary preconditions and primary justifications), combinations of primary preconditions and secondary justifications (or combinations of primary preconditions and up to secondary justifications), combinations of secondary preconditions and secondary justifications (or combinations of up to secondary preconditions and up to secondary justifications), and combinations of tertiary preconditions and secondary justifications (or combinations of up to tertiary preconditions and up to secondary justifications). More generally, when the highest-order precondition is N and the highest-order justification is n, the generated business model proposals contain N × n superimposed business model proposals. In other words, users viewing the chart shown in Figure 8 can see multiple different business model proposals in a single, easily viewable overview. This is extremely valuable for users. Users can select a suitable business model proposal from among various options to use as a starting point. While viewing multiple different business model proposals together may cause confusion in the user's mind, it may also generate ideas that the user hadn't considered before. In this sense, presenting multiple business model proposals in an easily viewable overview is meaningful. By having the user select the degree of the prerequisites and the degree of the rationale, it is possible to identify one of several overlapping business model proposals. Figure 8 also shows a button 567 labeled "Confirm." When button 567 is pressed, the contents of checkbox 562, box 564, and box 566 are confirmed at that time. Furthermore, the numbers that can be written in box 564 are natural numbers less than or equal to the highest-order number among the prerequisites (in this example, any number from 1 to 3), and the numbers that can be written in box 566 are natural numbers less than or equal to the highest-order number among the basis (in this example, either 1 or 2). Error handling is performed if any other number is written. In this embodiment, suppose the user leaves checkbox 562 blank and writes the number 2 in both box 564 and box 566. Here, it is natural that error handling is performed if a number is written in only one of box 564 or box 566, or if a number is written in one or both of box 564 and box 566 but checkbox 562 is checked. In the state described above, when the user presses button 567, the data corresponding to the number in frame 564 is generated as precondition determination data, and the data corresponding to the number in frame 566 is generated as justification determination data. If checkbox 562 is checked and boxes 564 and 566 are blank, and the user presses button 567, unnecessary data will be generated, indicating that the user does not need the explanatory data. In any case, the generated data, just like the data generated by the input device 102 as described above, is sent from the input device 102, through the interface 114 and input unit 112 to the control unit 122, and from there to the output unit 125. The generated data is then sent from the output unit 125 through the interface 114 to the transmission / reception mechanism, and from the transmission / reception mechanism through the network 400 to the server 200.

[0054] Server 200 receives either a pair of precondition determination data and justification determination data, or unnecessary data, through its transmission and reception mechanism. The data received by server 200 is sent from the interface through input unit 221 to control unit 222. When the control unit 222 receives unnecessary data, it stops processing. In this case, unless the user operates the client 100 again, there is no need to perform any further information processing within the server 200, and in this embodiment, no further information processing is performed. In this case, the image shown in Figure 8 continues to be displayed on the client 100's display 101. Upon receiving the precondition determination data and the rationale determination data, the control unit 222 sends that data to the explanation data generation unit 226.

[0055] When the explanatory data generation unit 226 receives the precondition determination data and the rationale determination data, it generates explanatory data, which is data about the business model proposal generated by the combination of the preconditions of the order determined by the precondition determination data and the rationale of the order determined by the rationale determination data. When generating explanatory data, the explanatory data generation unit 226 uses the business model draft data that it received earlier. In this embodiment, it was explained that the business model draft data is sent to the explanatory data generation unit 226 immediately after it is generated by the business model draft data generation unit 228, but more precisely, it is sufficient that the explanatory data generation unit 226 is available before it executes the generation of the explanatory data. When the explanatory data generation unit 226 generates explanatory data, it uses the proposed business model data, the precondition determination data, and the rationale determination data. Furthermore, in this embodiment, it is also possible to use the artificial intelligence computer program recorded in the data recording unit 220. Once the explanatory data is generated, the explanatory data generation unit 226 sends the generated explanatory data to the business model proposal data generation unit 228.

[0056] When the business model proposal data generation unit 228 receives the explanatory data, it modifies the business model proposal data so that the content of the explanatory data is embedded into the business model proposal data. The business model proposal data generation unit 228 sends the revised business model proposal data to the image data generation unit 227.

[0057] Upon receiving the revised business model proposal data, the image data generation unit 227 modifies the image data shown in Figure 8, which is used to present the business model proposal identified by the business model proposal data to the user, by embedding explanatory data into it. Image data is sent from the image data generation unit 227 to the output unit 229, and from the output unit 229 to the transmission / reception mechanism. The image data is sent from the transmission / reception mechanism to the client 100 via the network 400. The client 100 receives the image data at its transmission / reception mechanism. Within client 100, image data travels from the transmission / reception mechanism through interface 114 and input unit 121 to control unit 122. Upon receiving such image data, control unit 122 sends it to image generation unit 123. Image generation unit 123 generates image data for the proposed business model and sends it to output unit 125. Output unit 125 sends this image data to display 101.

[0058] Figure 9 shows an example of an image corresponding to the content of the proposed business model data displayed on display 101. The image shown in Figure 9 lacks the series of images at the bottom of the image that were present in the image shown in Figure 8, which allowed the user to select whether or not explanatory data was needed. Instead, at the bottom of the image, there is an explanatory text 571 and a button 572 labeled "Back". By reading explanation 571, users can deepen their understanding of their chosen business model proposal. As mentioned above, when the highest-order precondition is of order N and the highest-order justification is n, the generated business model proposal can be said to contain N × n different business model proposals superimposed on each other. In this case, suppose the user enters 3 as the number that determines the precondition determination data and 2 as the number that determines the justification determination data in boxes 564 and 566, respectively, on the screen shown in Figure 8. In such cases, the generated explanatory data can be for combinations of primary to tertiary preconditions and primary to secondary justifications, that is, it can be an explanation for six different business model proposals. In this way, users can obtain explanations for various business model proposals all at once by inputting precondition determination data and justification determination data once. In this case, the data for the six business model proposals will be displayed in Figure 9. Naturally, the explanatory data generated by the explanatory data generation unit 226 and sent to the business model proposal generation unit 228 will include explanations of the six types mentioned above (that is, the number obtained by X × Y when the degree of the precondition data determined by the precondition determination data is X and the degree of the justification data determined by the justification determination data is Y). In any case, when the user presses button 572 labeled "Back," the image reverts from the one displayed in Figure 9 to the one displayed in Figure 8. This process may be performed under the control of server 200, as before, or it may be executed as a process within client 100. Whatever method is adopted to perform the process of restoring the image to its original state, it is a publicly known or well-known technique. Once the user returns to the screen shown in Figure 8, they can again request an explanation from server 200 about a different business model. This will most likely change the content in the explanation column 571 in Figure 9. Furthermore, the images used to present explanations based on the explanatory data to the user do not necessarily have to be like those shown in Figure 9. For example, the explanations based on the explanatory data can be displayed on the client 100's display 101 on their own. Also, as with the business model proposal data, the server 200 can send the explanatory data itself to the client 100.

[0059] Furthermore, the image based on the proposed business model data initially presented to the user can be different from the one shown in Figure 8, such as the one shown in Figure 11. This screen is designed to give the user the opportunity to modify at least some of the proposed business model shown in Figure 11 (the features to be monetized, the prerequisites, the market, and the rationale (and all of these are the same as those shown in Figure 8)). This screen contains the text 568, which reads, "Do you want to modify one or more of the market, assumptions, or evidence?", along with a button 569Y labeled "Yes" and a button 569N labeled "No". In other words, in this example, the items that the user is given the opportunity to modify are the market (market data), assumptions (assumption data), and evidence (evidence data). It is also possible to prevent the user from being given the opportunity to modify the market (market data).

[0060] Now, suppose the user taps button 569N labeled "No". In this case, data indicating that the user has indicated their intention not to modify the market, assumptions, or rationale is input from the input device 102 to the client 100, as explained above, and then transmitted from the client 100 to the server 200. Upon receiving this data, the control unit 222 of the server 200 sends an instruction to the image data generation unit 227 to generate image data to display the image shown in Figure 8 on the display 101 of the client 100. The image data generated by the image data generation unit 227 is then sent from the image data generation unit 227 to the output unit 229, resulting in the image in Figure 8 being displayed on the display 101 of the client 100. The subsequent steps are as already described. Users can choose to receive or not receive explanations based on explanatory data on display 101, depending on their preference.

[0061] On the other hand, suppose the user taps button 569Y labeled "Yes". In this case, data indicating that the user has expressed an intention to modify at least one of the market, preconditions, or rationale is input from the input device 102 to the client 100, as explained above, and then transmitted from the client 100 to the server 200. Upon receiving this data, the control unit 222 of the server 200 sends an instruction to the image data generation unit 227 to generate image data to display an image like the one shown in Figure 12 on the display 101 of the client 100. The image data generated by the image data generation unit 227 is then sent from the image data generation unit 227 to the output unit 229, resulting in the image in Figure 12 being displayed on the display 101 of the client 100. This image contains the following: This image includes the word "market" 571 and a frame 572 below it that corresponds to the word 571; the word "prerequisites" 573 and a frame 574 below it that corresponds to the word 573; the word "basis" 575 and a frame 576 below it that corresponds to the word 575; and a button 577 with the word "send" written on it. In this embodiment, by presenting the image in Figure 12 to the user, the user is given the opportunity to revise not only the prerequisite data and the supporting data, but also the market data. It is also possible not to give the user the opportunity to revise the market data. If this is done, the word "market" 571 and the frame 572 in Figure 12 are unnecessary, and the word "Do you want to revise one or more of the market, prerequisites, and supporting data?" 568 in Figure 11 would need to be revised to, for example, "Do you want to revise at least one of the prerequisites and supporting data?".

[0062] Once the image in Figure 12 is displayed on the display 101, the user can use the input device 102 to write in frame 572 if they wish to revise the description of the market, in frame 574 if they wish to revise the description of the preconditions, and in frame 576 if they wish to revise the description of the rationale. Revisions to the market description may involve modifying or deleting each market in the Market 555 column of Figure 8, or adding new markets. Revisions to the assumptions may involve modifying or deleting assumptions included in each of the subsequent assumptions that constitute the primary to tertiary conditions through the set of assumptions, or adding new assumptions. The same applies to the rationale. The user taps button 577, labeled "Send," once the entries in frames 572, 574, and 576 are correct. Then, the writing in frames 572, 574, and 576 is finalized and input from the input device 102 to the user terminal 100. Then, the text written in frames 572, 574, and 576 is finalized, and the correction data, which is data about the content written therein, is entered into client 100. If there is text in frame 572, correction data about the market requesting a market correction is generated; if there is text in frame 574, the correction data about the preconditions requesting a precondition correction is corrected; and if there is text in frame 576, the correction data about the basis requesting a basis correction is generated. Conversely, when the button 577 labeled "Send" is tapped, if there is no entry in box 572, no revised data about the market will be generated; if there is no entry in box 574, no revised data about the assumptions will be generated; and if there is no entry in box 576, no revised data about the rationale will be generated. In this embodiment, however, the correction data includes all of the following: correction data requesting market corrections, correction data requesting assumption corrections, and correction data requesting rationale corrections. In any case, the modification data for modifying at least one of the market, assumptions, or rationale is sent from the input device 102 to the input unit 121 via interface 114, and from the input unit 121 to the control unit 122. The control unit 122 sends the modification data to the output unit 125. The monetization feature data is sent from the output unit 125 to the transmission / reception mechanism via interface 114, and from the transmission / reception mechanism to the server 200 via network 400. Server 200 receives the monetization target feature data through its transmission and reception mechanism. The monetization target feature data received by Server 200 is sent from the interface through the input unit 221 to the control unit 222. The control unit 222 determines whether the received correction data pertains to the market, preconditions, or rationale. Then, the control unit 222 sends the correction data pertaining to the market data generation unit 224A, the correction data pertaining to the preconditions data generation unit 223, and the correction data pertaining to the rationale data generation unit 224.

[0063] When the market data generation unit 224A receives the correction data, it corrects the previously generated market data that is specified by the correction data according to the content of the correction data. Then, the market data generation unit 224A sends the corrected market data to the basis data generation unit 224. When the prerequisite data generation unit 223 receives the correction data, the prerequisite data generation unit 223A corrects the prerequisite data that was previously generated and specified by the correction data according to the content of the correction data. Then, the prerequisite data generation unit 223 sends the corrected prerequisite data to the business model proposal data generation unit 228. When the evidence data generation unit 224 receives the revised data, it modifies the previously generated evidence data that is specified by the evidence data according to the content of the revised data. The evidence data generation unit 224 then sends the modified evidence data and the modified market data received from the market data generation unit 224A to the business model proposal data generation unit 228. The transmission of the two sets of data to the business model proposal data generation unit 228 does not need to occur simultaneously.

[0064] The business model proposal data generation unit 228 receives the revised market data, revised assumption data, and revised rationale data and sends them to the risk data generation unit 225. Using this data, the risk data generation unit generates revised risk data and sends the generated revised risk data to the business model proposal data generation unit 228. When the business model proposal data generation unit 228 receives the revised risk data, it generates the revised business model proposal data in the same manner as the original business model proposal data was generated using the monetization target feature data, prerequisite data, market data, rationale data, and risk data. At this time, the business model proposal data generation unit 228 uses the market data that has been modified as market data, the prerequisite data that has been modified as prerequisite data, the basis data that has been modified as evidence data, and the risk data that has been modified as risk data.

[0065] The business model proposal data generation unit 228 generates the revised business model proposal data and then sends it to the explanation data generation unit 226 and the image data generation unit 227. From this point onward, the processing is the same as when the unrevised business model proposal data was sent to the explanation data generation unit 226 and the image data generation unit 227. Upon receiving the business model proposal data, the image data generation unit 227 generates image data to present the business model proposal identified by the business model proposal data to the user. The image data is sent to client 100 via network 400. In client 100, an image based on image data is displayed on display 101. This image corresponds to the proposed business model identified by the revised proposed business model data.

[0066] Figure 13 shows an example of an image corresponding to the content of the revised business model proposal data displayed on display 101. As can be seen by comparing Figure 13 and Figure 8, in the column below the text "Secondary Prerequisites" (553), the prerequisite included in the secondary prerequisite, "Successful mass production in year X (95%)", has been revised to "Successful mass production in year X (96%)". Also, in the column below the text "Tertiary Prerequisites" (554), the prerequisite included in the tertiary prerequisite, "Import from India", has been deleted, and the prerequisite "Successful acquisition of a cobalt mine" has been added. Furthermore, in the section below the word "Market" (555), the market information previously stated as "KPI for the aircraft market in 2035: $250 billion (98%)" has been corrected to "KPI for the aircraft market in 2035: $350 billion (96%)". Furthermore, in the section below the words "Primary Basis" (556), the basis that was included in the primary basis, which was "30% (KPI) (99%) of the aircraft price accounted for by EV batteries," has been corrected to "25% (KPI) (97%) of the aircraft price accounted for by EV batteries." In this way, some of the business model proposal data has been modified. As mentioned above, in this example, the market, assumptions, and rationale were all revised in the revised data. Although the corrected data for market data, corrected data for assumptions, and corrected data for supporting data have not been explained in detail until now, it should be obvious that the corrected data for market data is such that the difference in the market identified by the market data is the difference between those described in Figure 8 and Figure 12; the corrected data for assumptions is such that the difference in assumptions identified by the assumptions data is the difference between those described in Figure 8 and Figure 12; and the corrected data for supporting data is such that the difference in supporting evidence identified by the supporting data is the difference between those described in Figure 8 and Figure 12.

[0067] At the bottom of Figure 12, the same elements as in Figure 8 are displayed: the text "No explanation needed" (561), a checkbox (562), the text "Degree of prerequisites to select" (563), a box (564), a box (566), and others. Therefore, the user can request an explanation from the server 200 in the manner described above. The operation the user performs on the client 100 to request an explanation from the server 200, and the processes performed by the client 100 and the server 200 in response, are the same as those described earlier. However, since the explanatory data generation unit 226, which generates the explanatory data, receives the revised business model proposal data from the business model proposal data generation unit 228, the explanatory data generated by the explanatory data generation unit 226 will be based on the revised business model proposal data. The explanation of the revised business model will be displayed on the client 100's display 101 in the manner already described, for example, as shown in Figure 9.

[0068] The user may then terminate the computer program, or, if necessary, record image data corresponding to the proposed business model, or business model data received from server 200, on client 100 or other devices. This allows users to consider business model proposals whenever they want.

[0069] In this embodiment, as explained with reference to Figure 8, the system was designed to present the user with a set of business model proposals, each based on multiple preconditions of multiple degrees and multiple justifications of multiple degrees. Conversely, the business model proposal identified by the business model proposal data can also be a single type of business model proposal generated by combining only the set of preconditions of the order specified by the precondition order data entered by the user in client 100 (i.e., the highest-order precondition; in the case of Figure 8, the third-order precondition) and only the set of reasons of the order specified by the reason order data entered by the user in client 100 (i.e., the highest-order reason; in the case of Figure 8, the second-order reason). In that case, the business model proposal data generation unit 228 will generate such business model proposal data. When such business model proposal data is sent from server 200 to client 100, the image displayed on the client 100's display 101 will be, for example, like the one shown in Figure 10.

[0070] <Variation> This document describes a device that assists in creating business model proposals through modification. The business model creation support device in the above-described embodiment was configured as a server 200, which is a so-called cloud server. On the other hand, the business model creation support device in the modified example is a so-called on-premise type. In the modified example, the client 100 that existed in the embodiment does not exist, and a server 200 equipped with a display 101 and an input device 102 having the same functions as the client 100 in the embodiment becomes the business model creation support device. In other words, the server 200 functions as a business model creation support device on its own. Furthermore, regardless of its name, the server 200 does not need to be a server device such as a cloud server when viewed as hardware; it may be composed of a general notebook PC, desktop PC, etc. Roughly speaking, in the modified example, client 100 functioned as an input device 102 that inputs data to server 200 via network 400, and a display 101 that displays image data output by server 200 via network 400. In the modified configuration, client 100 is integrated with server 200, input from input device 102 connected to server 200 is directly input to server 200 without going through the network, and image data output from server 200 is displayed on display 101 connected to server 200 without going through the network 400.

[0071] To explain in more detail, in the above embodiment, the data sent from client 100 to server 200 mainly consisted of start data, prerequisite order data, justification order data, monetization target feature data, prerequisite determination data, and justification determination data. However, in the modified example, all of this data is input to server 200 from input device 102 connected to server 200, and the same information processing that was performed within server 200 in the above embodiment occurs within server 200. On the other hand, in the embodiment described above, the data sent from the server 200 to the client was mainly image data. However, once such image data is generated within the server 200, it can be output directly to the display 101 connected to the server 200 without going through the network 400. In short, by integrating client 100 and server 200, it becomes possible to eliminate the need for data transmission and reception between them over the network. [Explanation of Symbols]

[0072] 100 clients 101 displays 102 Input device 121 Input section 122 Control Unit 123 Image generation unit 125 Output section 220 Data Recording Unit 221 Input section 222 Control Unit 223 Prerequisite Data Generation Unit 224 Data Generation Unit 225 Risk Data Generation Unit 226 Explanation Data Generation Unit 227 Image Data Generation Unit 228 Business Model Proposal Data Generation Department 229 Output section

Claims

1. A business model drafting support device connected to a network including the Internet, comprising a computing unit for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input, The aforementioned computing device executes the computer program recorded in the recording device, A receiving unit receives input from the aforementioned input receiving mechanism, which is natural language data describing the characteristics of the monetization target, which are the characteristics of the monetization target, which are the products and services that are to be monetized. A prerequisite data generation unit generates primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the monetization target feature data received; generates secondary prerequisite data, which is data about secondary prerequisites, by collecting prerequisites from the network for the primary prerequisite to be met; and generates Nth-order prerequisite data, which is data about Nth-order prerequisites, by collecting prerequisites from the network for the N-1th-order prerequisite to be met. A market data generation unit generates market data which is data that identifies the market for the monetization target that can be acquired, estimated from the monetization target features identified by the monetization target feature data received. A basis data generation unit generates primary basis data, which is data about primary basis, by collecting from the network the basis for the existence of a market identified by the market data generated by the market data generation unit; generates secondary basis data, which is data about secondary basis, by collecting from the network the basis for the existence of the primary basis; and generates nth-order basis data, which is data about nth-order basis, by collecting from the network the basis for the existence of n-1th-order basis. A business model proposal data generation unit generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the Nth-order preconditions identified by the Nth-order precondition data, the monetization target market identified by the market data, and the Nth-order justifications identified by the nth-order justification data. It is designed to function as follows: The calculation device is configured to receive the value of N used when it functions as the precondition data generation unit from the input receiving mechanism, and to receive the value of n used when it functions as the basis data generation unit from the input receiving mechanism. A device to support the creation of business model proposals.

2. The computing device, which functions as the business model proposal data generation unit, Business model proposal data is generated by combining data including the monetization target features identified by the monetization target feature data, the primary to Nth order preconditions identified by the primary to Nth order precondition data, the monetization target market identified by the market data, and the primary to Nth order preconditions identified by the primary to Nth order precondition data. A business model drafting support device according to claim 1.

3. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the Nth-order preconditions from the First-order preconditions in an overview format. A business model drafting support device according to claim 2.

4. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the Nth-order preconditions from the First-order preconditions in a tree diagram. A business model drafting support device according to claim 3.

5. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the aforementioned primary evidence to the aforementioned nth-order evidence in an overview format. A business model drafting support device according to claim 2.

6. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the aforementioned primary and nth-order evidence in a tree diagram. A business model drafting support device according to claim 5.

7. The aforementioned computing device executes the computer program recorded in the recording device, Risk data generation unit generates risk data which is data about the risk that the primary preconditions to the Nth preconditions, which are identified from the primary precondition data to the Nth precondition data, and the primary basis to the Nth basis, which are identified from the primary basis data to the Nth basis data, will no longer be valid. It is designed to function as follows: The computing device, which functions as the business model proposal data generation unit, The aforementioned business model proposal data includes the aforementioned risk data. A business model drafting support device according to claim 1.

8. The calculation device, which functions as the receiving unit, The input receiving mechanism receives modification data for modifying at least one of the following: a portion of the primary preconditions identified by the primary precondition data and the nth precondition data, and a portion of the primary basis identified by the primary basis data and the nth basis data, respectively. The computing device, which functions as the business model proposal data generation unit, When the calculation device, which functions as the receiving unit, receives the modified data which modifies the primary preconditions to the Nth preconditions identified from the primary precondition data by the Nth precondition data, the calculation device generates business model proposal data using the modified primary preconditions to the Nth preconditions according to the modified data, and when the calculation device, which functions as the receiving unit, receives the modified data which modifies the primary basis to the Nth basis identified from the primary basis data by the nth basis data, the calculation device generates business model proposal data using the modified primary basis to the Nth basis according to the modified data. A business model drafting support device according to claim 2.

9. The calculation device, which functions as the receiving unit, The system accepts correction data to modify at least a portion of the market identified by the aforementioned market data, The computing device, which functions as the business model proposal data generation unit, When the computing device, which functions as a receiving unit, receives the modified data that modifies the market identified by the market data, it generates business model proposal data using the market data modified according to the modified data. A business model drafting support device according to claim 8.

10. The calculation device, which functions as the receiving unit, The input receiving mechanism receives precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications, The aforementioned computing device executes the computer program recorded in the recording device, It functions as an explanatory unit that generates explanatory data, which is data concerning a natural language explanation of the proposed business model, which is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined. A business model drafting support device according to claim 2.

11. The calculation device, which functions as the receiving unit, The input receiving mechanism receives precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications, The aforementioned computing device executes the computer program recorded in the recording device, It functions as an explanatory unit that generates explanatory data, which is data concerning a natural language explanation of the proposed business model, which is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined. The calculation device, which functions as the explanatory unit, When generating explanatory data, if the precondition data of the order specified by the precondition determination data has been modified by the modification data, the modified precondition data is used; and if the basis data of the order specified by the basis determination data has been modified by the modification data, the modified basis data is used. A business model drafting support device according to claim 8.

12. The calculation device, which functions as the receiving unit, The input receiving mechanism receives precondition determination data, which is a natural number of N or less that selects one of the preconditions, and justification determination data, which is a natural number of n or less that selects one of the justifications, The aforementioned computing device executes the computer program recorded in the recording device, It functions as an explanatory unit that generates explanatory data, which is data concerning a natural language explanation of the proposed business model, which is generated when the precondition data of the order specified by the precondition determination data and the justification data of the order specified by the justification determination data are combined. The calculation device, which functions as the explanatory unit, In generating explanatory data, if the precondition data of the order specified by the precondition determination data has been modified by the modification data, the modified precondition data is used; if the basis data of the order specified by the basis determination data has been modified by the modification data, the modified basis data is used; and if the market specified by the market data has been modified by the modification data, the modified market data is used. A business model drafting support device according to claim 9.

13. The computing device, which functions as the business model proposal data generation unit, The aforementioned business model proposal data includes explanations based on the aforementioned explanatory data. A business model drafting support device according to any one of claims 10 to 12.

14. The calculation device, which functions as the prerequisite data generation unit, From the first-order precondition to the nth-order precondition, The system includes data on the probability that each of the first-order preconditions holds true, up to the Nth-order precondition. A business model drafting support device according to claim 2.

15. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the probabilities identified by the data regarding the probabilities, starting from the first preconditions and including the probabilities identified by the Nth preconditions. A business model drafting support device according to claim 14.

16. The calculation device, which functions as the market data generation unit, The aforementioned market is provided with data regarding the probability of the market being established. A business model drafting support device according to claim 2.

17. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the probability identified by the data regarding the aforementioned probability in the market. A business model drafting support device according to claim 16.

18. The calculation device, which functions as the basis data generation unit, From the primary evidence to the nth evidence, The system includes data on the probability that each of the above primary and nth-order evidence is true. A business model drafting support device according to claim 2.

19. The computing device, which functions as the business model proposal data generation unit, The business model proposal data is generated by displaying the probabilities identified by the data regarding the probabilities, from the primary evidence to the nth-order evidence. A business model drafting support device according to claim 16.

20. A method performed by the computing device of a business model proposal creation support device connected to a network including the Internet, which comprises a computing device for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input, A receiving process that receives input from the aforementioned input receiving mechanism, which is natural language data describing the characteristics of the monetization target, which are the characteristics of the monetization target, which are the products and services that are to be monetized. The prerequisite data generation process involves generating primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid; generating secondary prerequisite data, which is data about secondary prerequisites, by collecting prerequisites from the network for the primary prerequisite to be valid; and generating Nth-order prerequisite data, which is data about Nth-order prerequisites, by collecting prerequisites from the network for the N-1th-order prerequisite to be valid. A market data generation process that generates market data which is data that identifies the market for the monetization target that can be acquired, estimated from the monetization target features identified by the monetization target feature data received, A basis data generation process that generates primary basis data, which is data about primary basis, by collecting from the network the basis for the establishment of a market identified by the market data generated in the market data generation process, and generates secondary basis data, which is data about secondary basis, by collecting from the network the basis for the establishment of the primary basis, and so on, and generates nth-order basis data, which is data about nth-order basis, by collecting from the network the basis for the establishment of n-1th-order basis, A business model proposal data generation process that generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the Nth-order preconditions identified by the Nth-order precondition data, the monetization target market identified by the market data, and the Nth-order justifications identified by the nth-order justification data. It includes, The process by which the arithmetic unit receives the value of N used when executing the prerequisite data generation process from the input receiving mechanism, The process by which the arithmetic unit receives the value of n used when executing the above-mentioned basis data generation process from the input receiving mechanism, A method that includes

21. The calculation unit of a business model proposal creation support device, which is connected to a network including the Internet, comprises a calculation unit for performing calculations, a recording device for recording data, and an input receiving mechanism for receiving data input, A receiving process that receives input from the aforementioned input receiving mechanism, which is natural language data describing the characteristics of the monetization target, which are the characteristics of the monetization target, which are the products and services that are to be monetized. The prerequisite data generation process involves generating primary prerequisite data, which is data about primary prerequisites, by collecting prerequisites from the network for the monetization target feature identified by the received monetization target feature data to be valid; generating secondary prerequisite data, which is data about secondary prerequisites, by collecting prerequisites from the network for the primary prerequisite to be valid; and generating Nth-order prerequisite data, which is data about Nth-order prerequisites, by collecting prerequisites from the network for the N-1th-order prerequisite to be valid. A market data generation process that generates market data which is data that identifies the market for the monetization target that can be acquired, estimated from the monetization target features identified by the monetization target feature data received, A basis data generation process that generates primary basis data, which is data about primary basis, by collecting from the network the basis for the establishment of a market identified by the market data generated in the market data generation process, and generates secondary basis data, which is data about secondary basis, by collecting from the network the basis for the establishment of the primary basis, and so on, and generates nth-order basis data, which is data about nth-order basis, by collecting from the network the basis for the establishment of n-1th-order basis, A business model proposal data generation process that generates business model proposal data by combining data including the monetization target features identified by the monetization target feature data, the Nth-order preconditions identified by the Nth-order precondition data, the monetization target market identified by the market data, and the Nth-order justifications identified by the nth-order justification data. A computer program for executing, The computer program further provides the arithmetic unit to: A process of receiving the value of N used when executing the aforementioned precondition data generation process from the input receiving mechanism, A process of receiving the value of n used when executing the above-mentioned basis data generation process from the input receiving mechanism, A computer program that is designed to execute something.

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