Information provision device, information provision method, and information provision program

Abstract

To enable effective utilization of a language model specialized in a particular field.SOLUTION: An information provision device according to an aspect of an embodiment is configured to collect texts entered by a user and answers output by multiple language models configured to generate answers related to investment policies in response to entered prompts and set up to have different answer trends. The information provision device presents the collected answers to the user. The information provision device inputs the collected answers and the texts into the multiple language models as prompts to output further answers.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The disclosed embodiments relate to an information providing device, an information providing method, and an information providing program. [Background technology]

[0002] 2. Description of the Related Art Conventionally, there is known a system that uses a language model to generate an answer to a question input by a user (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2022-503838 Summary of the Invention [Problem to be solved by the invention]

[0004] While conventional technology generates answers based on news articles, it does not take into account the use of language models specialized for specific domains.

[0005] The present invention has been made in view of the above, and has an object to effectively utilize a language model specialized for a specific field. [Means for solving the problem]

[0006] An information providing device according to one aspect of an embodiment includes a collection unit that collects answers output by a plurality of language models that output answers regarding investment policies based on input prompts, the answers being set so that the answer tendencies of the plurality of language models are different from each other, and text input by a user, a presentation unit that presents the collected answers to the user, and an output control unit that inputs the collected answers and the text to the plurality of language models as prompts, and further causes the answers to be output. Effect of the Invention

[0007] According to one aspect of the embodiment, a language model specialized for a specific field can be effectively utilized. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an overview of an information providing device according to an embodiment. [Diagram 2] FIG. 2 is a functional block diagram illustrating an example of the configuration of an information providing device according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating the discussion. [Figure 4] FIG. 4 is a flowchart illustrating a processing procedure executed by the information providing device according to the embodiment. [Diagram 5] FIG. 5 is a flowchart illustrating a processing procedure executed by the information providing device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing the procedure for processing a discussion. [Figure 7] FIG. 7 is a diagram illustrating an example of a computer hardware configuration that functions as an information providing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The present invention will be described below through embodiments, but the following embodiments do not limit the scope of the invention according to the claims. In addition, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0010] A process flow of the information providing device according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining an overview of the information providing device according to the embodiment. Fig. 1 shows a configuration of an information providing system 1 including an information providing device 10 according to the embodiment.

[0011] 1, the information providing system 1 includes an information providing device 10 and an investment information database 20. The investment information database 20 is a database that stores various data related to investments.

[0012] In the information providing system 1, the investment information database 20 stores information announced by various news media (Nikkei average, short-term and long-term interest rates, employment statistics, etc.) and information acquired by the information providing system 1 itself.

[0013] The investment information database 20 independently acquires and stores information such as information on the condition of agricultural crops and the number of cars parked in parking lots of large supermarkets and the like, obtained from photo analysis of short-range satellites, as well as POS data, individual warehouse information, credit card information, and home sales market data.

[0014] The information provision device 10 is a device to which various devices, including the investment information database 20, are plugged in. In the information provision system 1, in which the various devices include various servers, vehicles, smartphones, and other devices, the information provision device 10 realizes the highest level of artificial intelligence by being plugged in to each device.

[0015] For example, in the information provision system 1, the information provision device 10 and each device including the investment information database 20 incorporate semiconductor chips manufactured by the same manufacturer. Each of these semiconductor chips may perform machine learning or deep learning by itself.

[0016] In addition, the semiconductor chips used in the information providing device 10 and the investment information database 20 are chips of a size suitable for each housing. For example, assuming that the chip size of the information providing device 10 is XL size, L size is used for servers and vehicles, and S or M size is used for user terminals. For terminals with housing sizes even smaller than the user terminals, SS size is used and is put into a SoC (System on a chip) to form a single chip. Note that the number of tiles for each size is 200 tiles for the XL size, 50 tiles for the L size, 20 tiles for the M size, 10 tiles for the S size, and 2 tiles for the SS size, but these are examples and are not limited to the above. If it is desired to attach accessories such as a camera or a microphone to the user terminal, a chip dedicated to accessories other than the semiconductor chip may be set in an empty space near the semiconductor chip in the SoC.

[0017] In this way, in the information providing system 1, the use of semiconductor chips made by the same manufacturer in various devices connected to the information providing device 10 makes it possible to keep the information providing system 1 secure. In other words, in the information providing system 1, hacking, virus infection, and deep fakes can be avoided with a high degree of accuracy, and privacy can be ensured between the information providing device 10 and data source providers including the investment information database 20.

[0018] In this embodiment, the information providing device 10 learns a model based on a data source acquired from the investment information database 20. The model is a language model. As a language model, for example, ChatGPT by OpenAI Inc. is known (Reference: https: / / openai.com / blog / chatgpt). The language model may be a language model such as GAN (Generative Adversarial Networks) or VAE (Variational Autoencoder) that uses a neural network.

[0019] In this embodiment, the language model generates a text response to a text of a query (hereinafter, a prompt) input by a user. The language model is trained in advance using a data source. The language model may be trained by known machine learning techniques.

[0020] This enables the language model to generate answers based on the data source. In this embodiment, the information providing device 10 plugs in to the investment information database 20 (step S1), and learns the language model based on the data stored in the investment information database 20 (step S2).

[0021] In this way, the information providing device 10 trains the language model on various data stored in the investment information database 20. As a result, the language model generates a response to an input prompt based on the trained data on investment. In other words, the information providing device 10 can generate a language model specialized for the investment field.

[0022] In this embodiment, a language model specialized in the investment field is used to provide investment advice. Specifically, in this embodiment, accounts (roles) of "Investors A to C", "Experts A to C", "Judge System", and "Risk Management" are set in advance by system prompts of the language model.

[0023] Here, accounts in this embodiment include bot accounts and human accounts. A bot account is a language model in which a system prompt can be set. A bot account outputs a response using a language model. On the other hand, a human account is a human being. In other words, a human account outputs a response according to a human input.

[0024] In Figure 1, "Investors A-C," "Experts A-C," "Judge System," and "Risk Management" are high-level accounts. High-level accounts include at least either bot accounts or human accounts. The white circles in Figure 1 represent bot accounts. The black circles in Figure 1 represent human accounts.

[0025] Hereinafter, bot accounts or human accounts included in a higher-level account may be referred to as lower-level accounts. In addition, when it is clear from the preceding and following explanations or drawings, or when there is no need to distinguish between higher-level accounts and lower-level accounts, the words "higher" and "lower" will be omitted and the term will simply be used as "accounts."

[0026] In the following explanation, we will assume that all accounts are bot accounts. The configuration in Figure 1 can be realized by replacing bot accounts with human accounts as appropriate, or by adding human accounts as subordinate accounts to higher-level accounts.

[0027] "Investors A to C" are higher-level accounts corresponding to, for example, institutional investors who manage so-called funds and individual investors. In this embodiment, Investor A is made up of Investor A-1, Investor A-2, and Investor A-3, Investor B is made up of Investor B-1, Investor B-2, Investor B-3, etc., and each of Investors A to C is made up of multiple lower-level accounts, and Investor A draws a conclusion regarding the investment policy as Investor A through discussions between Investors A-1, A-2, and A-3. For example, Investor A-1 may place importance on the inflation rate, Investor A-2 may place importance on employment statistics, and Investor A-3 may place importance on the mining and manufacturing production index, and each of them will hold discussions and draw a conclusion as Investor A. In this way, there are many people in the world, 1. Inflation rate: Indicates the rate of price increase. 2. Unemployment rate: The percentage of the labor force that is unemployed. 3. GDP Growth Rate: It indicates the growth of gross domestic product. 4. Consumer Confidence Index: Consumer confidence in the economy. 5. Industry Production Index: This indicates production activity in the manufacturing, mining and utilities industries. 6. Job Gains: The number of new employees. 7. Real Wage: The movement of wages taking into account price changes. 8. Home Sales Data: Sales of new and existing homes. 9. Consumer Price Index (CPI): Indicates price changes for general consumers. 10. Producer Price Index (PPI): Price changes at the production stage. 11. Retail Sales: Retail sales figures. 12. Business Inventory: The amount of stock of goods and materials held by a business. 13. International Trade Balance: The difference between exports and imports. 14. Manufacturing Order Data: Manufacturing order status. 15. Non-manufacturing activity index: Indicates economic activity in the service sector, etc. 16. Financial market trends: stock prices, bond yields, currency prices, etc. 17. Bank Loan Trends: The state of bank lending activity. 18. Core inflation: Inflation excluding food and energy. 19. Wage growth rate: It indicates the growth in average wages. 20. Trends in the global economy: Economic growth rates and policy trends in major countries. There are various indicators such as the above, and the feature of this event is that investors who place importance on each of these indicators are divided into groups and have a discussion. "Expert A to C" are expert accounts in various fields related to investment.

[0028] The various fields related to investment include commodities, corporate bonds, real estate, FX, and portfolios. The "Judge System" is an account that judges the results of discussions by each account. For example, the judge system judges the merits of each account's arguments and proposes the content.

[0029] In this embodiment, claims made by each of a plurality of accounts are input as prompts into the language model, and a system prompt is set so that other accounts can debate the input prompts.

[0030] Such a setting enables each account to hold a spontaneous discussion using the language model. As shown in Fig. 1, the information providing device 10 uses the language model to hold a discussion on investment policy by each account (step S3).

[0031] For example, investor accounts A, B, and C have different investment philosophies, and each account debates using a language model. The virtual experts debate billions of times per second. This allows verification and problem solving from every angle, and investment policies and other proposals can be made instantly.

[0032] Similarly, each expert account discusses with multiple accounts in each field and draws a conclusion on the investment policy for each field. Then, the investor account discusses the investment policy based on the discussions in each field. Note that the final discussion may include all accounts, including each account.

[0033] As a result of these discussions, the "judgment system" makes a judgment based on the content of the discussion (step S4). For example, the "judgment system" is made up of multiple accounts. The multiple accounts that make up the judgment system discuss the content of the discussions of the investor accounts, etc., and judge the final investment policy. For example, when a predetermined termination condition is met, such as when the discussion reaches a deadlock, the judgment system determines the most appropriate investment policy by comparing the claims of each account with the data stored in the investment information database 20.

[0034] The "Judge System" then proposes the determined investment policy to actual investors. In other words, the information provision system 1 uses a language model to enable a large amount of virtual discussion regarding investment policies.

[0035] Furthermore, "Risk Management" discusses the risks in the current portfolio based on the latest information registered in the investment information database 20. "Risk Management" is made up of multiple accounts, and the multiple accounts that make up the risk management discuss investment risks. As a result, if the investment risk exceeds a threshold, "Risk Management" advises to discontinue the investment (Step S5).

[0036] In other words, the information providing device 10 can prevent or minimize investment losses and the like by using the "risk management" function.

[0037] In this manner, the information providing device 10 according to the embodiment generates a language model based on various pieces of knowledge registered in the investment information database 20, and after holding a discussion using the language model, makes a proposal regarding an investment policy.

[0038] Therefore, the information providing device 10 according to the embodiment can effectively utilize a language model specialized for a specific field.

[0039] The configuration of the information providing device 10 will be described with reference to Fig. 2. Fig. 2 is a functional block diagram showing an example of the configuration of the information providing device according to the embodiment.

[0040] As shown in FIG. 2, the information providing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13.

[0041] The communication unit 11 transmits and receives information to and from the investment information database 20 and the like via a network.

[0042] The storage unit 12 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. The storage unit 12 stores various programs and various data. The storage unit 12 stores language model information 121.

[0043] The language model information 121 is information about a language model. For example, the language model information 121 is information about parameters for constructing a language model. Such parameters are, for example, weights and biases of a neural network.

[0044] The control unit 13 is a controller, and includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input / output port, and various other circuits. The control unit 13 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 13 includes an acquisition unit 31, a generation unit 32, a discussion unit 33, and a proposal unit 34.

[0045] The acquiring unit 31 acquires data from a data source provider. For example, the acquiring unit 31 acquires data on investments from the investment information database 20 (see FIG. 1). For example, the acquiring unit 31 acquires data every time the investment information database 20 is updated.

[0046] The generation unit 32 generates a language model. Specifically, the generation unit 32 learns the language model based on a data source related to investment. The generation unit 32 also updates the parameters of the model through learning. The generation unit 32 may regenerate the model at each learning timing, or may reflect a data source difference from the previous learning in the model.

[0047] The discussion unit 33 uses a language model that has been generated by learning data stored in a database related to investments and that generates answers to input prompts, to hold a discussion between multiple investor accounts, each with different claims.

[0048] First, the discussion unit 33 sets accounts for each field according to the system prompts of the language model. For example, the discussion unit 33 sets an investor account, expert A to C accounts, a judge system account, and a risk management account.

[0049] After setting these accounts, the discussion unit 33 starts a discussion using the language model. For example, the discussion unit 33 holds a discussion for each field, and then holds a discussion using investor accounts based on the discussion results for each field. Specifically, the discussion unit 33 holds a discussion using the language model by the following steps 1 to 5.

[0050] Step 1: Set up a multi-functional virtual expert There are hundreds of sets of virtual experts in different investment fields. For example, in the real estate field there are specific characters such as "Real Estate Expert A," "Real Estate Expert B," and "Real Estate Expert C," and in FX there are specific characters such as "FX Expert A," "FX Expert B," and "FX Expert C."

[0051] Step 2: Speed ​​up information exchange These experts exchange information based on each data and debate billions of times per second, making it possible to instantly make decisions such as optimal investment policies.

[0052] Step 3: Data handling The virtual experts share various types of information, such as stock prices, and hold discussions, allowing them to quickly reach optimal solutions from multiple perspectives.

[0053] Step 4: Self-study and update If new knowledge or information is discovered during the discussion, it is immediately reflected in the database and can be used in future discussions. This allows the system to provide optimal investment policies based on the latest investment information.

[0054] Step 5: Building consensus The discussion is a process of forming a final "consensus" through the clash of different opinions and viewpoints, so that when investment policy proposals are made, information and knowledge from multiple sources are comprehensively incorporated.

[0055] The above has been explained about the investor account, but for each of the Expert A to C accounts, a character is set, and each account is further divided into various characters, and a consensus is formed as Experts A to C by having each account hold discussions.

[0056] The proposal unit 34 judges the results of the discussion by the discussion unit 33 using a judge account set in the language model, and proposes an investment policy. For example, the proposal unit 34 makes a judgment when a predetermined end condition is met, such as when the discussion by the discussion unit 33 reaches a deadlock.

[0057] For example, the proposal unit 34 refers to various data registered in the investment information database 20, judges the investment policy that is estimated to be the most appropriate among the investment policies discussed by each investor account, and proposes that investment policy. Alternatively, an investment policy may be proposed by adding various consensuses, such as the consensus of expert A, to the consensus result of the investor accounts.

[0058] Furthermore, when the risk management account determines that the risk is high, the suggestion unit 34 advises the investor to withdraw from the investment, etc. This makes it possible to reduce the risk of the investment.

[0059] [Discussion on the inclusion of human accounts] Up to this point, we have explained the discussion assuming that the lower-level accounts are bot accounts. Here, we will explain the discussion when the lower-level accounts include human accounts. In this case, the processing contents of steps 1 and 2 above by the discussion unit 33 will change.

[0060] First, in step 1, the discussion unit 33 changes the configuration of at least some of the higher-level accounts so that they include human accounts as lower-level accounts. For example, the discussion unit 33 replaces bot accounts with human accounts or adds new human accounts. A user, i.e., a real person, is associated with a human account.

[0061] Next, in step 2, the discussion unit 33 presents the text output by each account as an answer to the user associated with the human account. Then, the discussion unit 33 accepts text input from the user. The answer text output by each account and the action of outputting text as an answer are called "statements." "Statements" include text input by users.

[0062] A bot account treats utterances from human accounts in the same way as utterances from bot accounts. For example, a bot account inputs utterances from other bot accounts and human accounts as prompts into its language model and then outputs answers.

[0063] The processing of the discussion unit 33 will be described with reference to Fig. 3. Fig. 3 is a diagram for explaining the discussion. As shown in Fig. 3, the discussion unit 33 conducts the discussion according to the progress of the phase. Here, it is assumed that investor A, which is a higher-level account, is composed of three lower-level accounts, investor A-1, investor A-2, and investor A-3.

[0064] Investor A-1 is a bot account that emphasizes home sales data. For example, the language model of Investor A-1 emphasizes home sales data as the basis and outputs text related to investment.

[0065] Investor A-2 is a bot account that places importance on employment statistics. For example, the language model of Investor A-2 places importance on employment statistics as a basis and outputs text related to investment.

[0066] The substance of the bot account is a language model, and the discussion is realized by the discussion unit 33 repeatedly inputting a prompt to the language model. The prompt is, for example, a statement from the language model in the previous phase. The data to be emphasized in the statement may be set by a system prompt. As a result, each bot account has a different tendency in answering.

[0067] Investor A-3 is a human account. A user is associated with investor A-3. In addition, the personality of investor A-3 (what he or she values) may be any type.

[0068] 3, in phase 1, investor A-1 says, "Houses are selling well in Tokyo, so let's invest in real estate in Tokyo." At this point, the discussion unit 33 advances the phase.

[0069] In Phase 2, Investor A-2, based on the statement made by Investor A-1 in Phase 1 as a prompt and inputting it into the language model, says, "But it seems that employment is decreasing in Tokyo, so there may be a risk."

[0070] Next, in phase 3, a human account makes a statement such as, "I heard that more and more people are living in Osaka." The statement of the human account is the content entered by the user. For example, the discussion unit 33 may display the statements of each account on the user's terminal by a chat UI and accept user input from an input field of the chat UI.

[0071] It is not necessary for each account, including bot accounts and human accounts, to make a statement in every phase. The discussion unit 33 only needs to allow at least one account to make a statement in each phase.

[0072] In addition, if the accounts participating in the discussion include human accounts, it is difficult to have users make comments every time in each of the phases, which may number in the billions. For this reason, for example, the discussion unit 33 may accept user comments only in a specific phase, such as once in 10,000 phases.

[0073] The discussion unit 33 may also accept a user's comment when a certain condition is met. The condition may be when a bot account repeats the same comment a certain number of times. It is expected that a stagnant discussion will start to move toward a conclusion by the bot account using the user's comment as a prompt.

[0074] In this way, the discussion unit 33 presents the answer to the user when the answer satisfies the condition. Then, the discussion unit 33 collects the text entered according to the presented answer.

[0075] In addition, users may have knowledge that was not used to train the language model of the bot accounts. In such cases, the inclusion of human accounts in a discussion may lead to more useful conclusions than the inclusion of only bot accounts.

[0076] Next, a process procedure executed by the information providing device 10 according to the embodiment will be described with reference to Fig. 4 and Fig. 5. Fig. 4 and Fig. 5 are flowcharts showing a process procedure executed by the information providing device 10 according to the embodiment.

[0077] First, a procedure for generating a language model will be described with reference to Fig. 4. As shown in Fig. 4, the information providing device 10 first acquires data on investment from the investment information database 20 (step S101). Next, the information providing device 10 generates a language model based on the data acquired from the investment information database 20 (step S102), and ends the process.

[0078] Next, the discussion process using the language model will be described with reference to Fig. 5. As shown in Fig. 5, the information providing device 10 first holds a discussion on an investment policy in each field (step S111). Then, the information providing device 10 holds a discussion on an investment policy between fields (step S112).

[0079] Next, the information providing device 10 judges the results of the discussion (step S113), and then the information providing device 10 proposes an investment policy (step S114), and ends the process.

[0080] The procedure of the discussion process will be described with reference to Fig. 6. Fig. 6 is a flowchart showing the procedure of the discussion process. The process shown in Fig. 6 corresponds to, for example, the process of S111 in Fig. 5.

[0081] 6, when a phase starts, the information providing device 10 determines whether to allow a human account to make a statement (step S201). The conditions for allowing a human account to make a statement include that a prescribed number of phases have passed, that a bot account has repeated the same statement a certain number of times, etc.

[0082] If the information providing device 10 does not allow a human account to make a statement (step S202, No), the information providing device 10 proceeds to step S204. On the other hand, if the information providing device 10 allows a human account to make a statement (step S202, Yes), the information providing device 10 collects the statements of the human account (step S203). For example, the information providing device 10 collects text entered by the user in an input field of the chat UI as the statements of the human account. Also, the information providing device 10 may present the statements of each account to the user immediately before step S203.

[0083] Next, the information providing device 10 collects comments from the bot account (step S204). Here, when the discussion ends (step S205, Yes), the information providing device 10 performs discussion end processing (step S208). For example, the information providing device 10 may determine that the discussion has ended when a prescribed number of phases have passed or when judging is performed by a judge account. The discussion end processing is to transition to a discussion between higher-level accounts (for example, step S3 in FIG. 1).

[0084] If the discussion has not ended (step S205, No), the information providing device 10 advances the phase (step S206). Then, based on the collected comments, the information providing device 10 inputs a prompt into the language model of each bot account (step S207), returns to step S201, and performs processing in the phase after the advance.

[0085] The information providing device 10 functions as a collection unit, a presentation unit, and an output control unit. The collection unit collects answers output by a plurality of language models (corresponding to bot accounts) that output answers related to investment policies based on input prompts and are set so that the answer tendencies are different from each other, and text input by a user (corresponding to a human account) (steps S203, S204). The presentation unit presents the collected answers to the user. The output control unit inputs the collected answers and text to the plurality of language models as prompts, and further causes the answers to be output (step S207).

[0086] According to this embodiment, an investment policy can be discussed using a language model trained based on data stored in the investment information database 20 and then an investment policy can be proposed, thereby making it possible to effectively utilize a language model specialized for a specific field.

[0087] 7 is a diagram showing an example of a computer hardware configuration functioning as an information providing device. A program installed in the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or cause the computer 1200 to execute operations or one or more "parts" associated with the device according to the present embodiment, and / or cause the computer 1200 to execute a process or steps of the process according to the present embodiment. Such a program can be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0088] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphic controller 1216, which are connected to each other by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive may be a DVD-ROM drive, a DVD-RAM drive, etc. The storage device 1224 may be a hard disk drive, a solid state drive, etc. The computer 1200 also includes input / output units such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0089] The CPU 1212 operates according to a program stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphic controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into itself, and causes the image data to be displayed on the display device 1218.

[0090] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive reads programs or data from a DVD-ROM or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0091] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or a program that depends on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0092] The programs are provided by a computer-readable storage medium such as a DVD-ROM or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, the RAM 1214, or the ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by implementing operations or processing of information according to the use of the computer 1200.

[0093] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded in the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in the RAM 1214, the storage device 1224, a DVD-ROM, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer area or the like provided on the recording medium.

[0094] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, a DVD drive (DVD-ROM), an IC card, etc. to be read into the RAM 1214, and perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0095] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0096] The above-mentioned programs or software modules may be stored in a computer-readable storage medium on the computer 1200 or in the vicinity of the computer 1200. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0097] The blocks in the flowcharts and block diagrams in the present embodiment may represent stages of a process in which an operation is performed or "parts" of an apparatus responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer readable instructions stored on a computer readable storage medium, and / or a processor provided with computer readable instructions stored on a computer readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as, for example, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like, including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0098] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

[0099] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0100] Computer readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, etc., to cause the processor of the general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, to execute the computer readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0101] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications or improvements can be made to the above embodiments. It is clear from the claims that such modifications or improvements can also be included in the technical scope of the present invention.

[0102] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and may be realized in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is explained using "first," "next," etc. for convenience, it does not mean that it is essential to perform the process in this order. [Explanation of symbols]

[0103] 1 Information provision system 10 Information provision device 11 Communications Department 12 Storage section 13 Control section 20 Investment Information Database 31 Acquisition Department 32 Generation part 33 Discussion Department 34 Proposal Department 121 Language Model Information

Claims

1. a collection unit that collects answers output by a plurality of language models that output answers regarding an investment policy based on an input prompt, the plurality of language models being set so that the answer tendencies are different from each other, and a text input by a user; a presentation unit that presents the collected answers to the user; an output control unit that inputs the collected answers and the text as prompts to the plurality of language models and outputs the answers; An information providing device comprising:

2. The presentation unit presents the answer to the user when the answer satisfies a condition; The collection unit collects the text input in response to the answer presented by the presentation unit.

2. The information providing device according to claim 1.

3. An information providing method executed by an information providing device, comprising: a collection step of collecting answers output by a plurality of language models that output answers regarding an investment policy based on an input prompt, the plurality of language models being set so that the answer tendencies are different from each other, and a text input by a user; a presentation step of presenting the collected answers to the user; an output control step of inputting the collected answers and the text as prompts to the plurality of language models and outputting the answers; 13. An information providing method comprising:

4. a collection step of collecting answers output by a plurality of language models that output answers regarding an investment policy based on an input prompt, the plurality of language models being set so that the answer tendencies are different from each other, and a text input by a user; a presentation step of presenting the collected answers to the user; an output control step of inputting the collected answers and the text as prompts to the plurality of language models and outputting the answers; An information providing program that causes a computer to execute the above steps.

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