system

Abstract

The system according to the embodiment aims to maximize mutual benefits in price negotiations. [Solution] The system according to the embodiment comprises an input unit, an exchange unit, an analysis unit, and a proposal unit. The input unit inputs the minimum price, maximum price, and negotiation priority. The exchange unit exchanges the information input by the input unit with the other party's AI. The analysis unit analyzes the information exchanged by the exchange unit. The proposal unit proposes an appropriate price based on the information analyzed by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has a problem in that it lacks an efficient method for maximizing mutual benefits in price negotiations.

[0005] The system according to the embodiment aims to maximize mutual benefits in price negotiations. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an exchange unit, an analysis unit, and a proposal unit. The input unit inputs the minimum price, maximum price, and negotiation priority. The exchange unit exchanges the information input by the input unit with the other party's AI. The analysis unit analyzes the information exchanged by the exchange unit. The proposal unit proposes an appropriate price based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can maximize mutual benefits in price negotiations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In a price negotiation system according to an embodiment of the present invention, each company prepares an AI for price negotiations. The AI ​​inputs information that can and cannot be shared with the other party, thereby determining a price that maximizes mutual profits. In this price negotiation system, each company inputs information such as its minimum price, maximum price, and negotiation priority into the AI. The AI ​​then exchanges information with the other party's AI, analyzes the other party's information, proposes an optimal price, and proceeds with negotiations, thereby maximizing each company's profits. For example, each company inputs information such as its minimum price, maximum price, and negotiation priority into the AI. One company sets a minimum price for a product, and the other company proceeds with negotiations based on that price. This information is then input into the AI. Next, the AI ​​exchanges information with the other party's AI and analyzes the other party's information. The AI ​​receives the other party's information such as its minimum price, maximum price, and negotiation priority, and calculates the optimal price based on that information. For example, it uses an algorithm to find the optimal price between the other party's minimum price and its own maximum price. Finally, the AI ​​proposes the optimal price and proceeds with negotiations. The AI ​​proposes the optimal price based on the other party's information and proceeds with negotiations. For example, if the price proposed by the AI ​​is higher than the other party's minimum price and lower than the company's maximum price, that price will be the optimal price. In this way, each company's profits will be maximized. This allows the price negotiation system to determine the price that maximizes each company's profits.

[0029] A price negotiation system according to an embodiment includes an input unit, an exchange unit, an analysis unit, and a proposal unit. The input unit inputs a minimum price, a maximum price, and a negotiation priority. For example, the input unit provides an interface for a user to input their company's minimum price, maximum price, and negotiation priority. The user can input, for example, the minimum and maximum acceptable prices for a product or service, and negotiation priority (price, delivery date, quality, etc.). The exchange unit exchanges information with the other party's AI. For example, the exchange unit transmits information input by the user to the other party's AI and obtains information received from the other party's AI. The exchange unit can exchange information using, for example, a secure communication protocol. The analysis unit analyzes information such as the other party's minimum price, maximum price, and negotiation priority. For example, the analysis unit analyzes information received from the other party's AI and generates data for calculating an optimal price. The analysis unit can use, for example, an algorithm to find an optimal price between the other party's minimum price and their company's maximum price. The proposal unit proposes an optimal price based on the other party's information. For example, the proposal unit proposes an optimal price that is higher than the other party's minimum price and lower than the company's maximum price based on the data generated by the analysis unit. The proposal unit, for example, displays the proposed price to the user and provides an interface for proceeding with negotiations. This allows the price negotiation system according to the embodiment to determine a price that maximizes each company's profits.

[0030] The input section allows users to input the minimum price, maximum price, and negotiation priority. The minimum price refers to the lowest acceptable price for a product or service, and the maximum price refers to the highest acceptable price for a product or service. The negotiation priority is a standard for setting priorities such as price, delivery date, and quality. For example, the input section provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The user can input, for example, the minimum acceptable price and maximum acceptable price for a product or service, and the negotiation priority (price, delivery date, quality, etc.). This allows the user to accurately input the information required for negotiations.

[0031] The exchange unit can exchange information with the other AI. The other AI has characteristics such as the algorithms used and learning data. For example, the exchange unit sends information entered by the user to the other AI and retrieves information received from the other AI. The exchange unit can exchange information using, for example, a secure communication protocol. This allows for efficient exchange of information with the other AI.

[0032] The analysis unit can analyze the other party's minimum price, maximum price, and negotiation priority information. The minimum price refers to the lowest acceptable price for a product or service, and the maximum price refers to the highest acceptable price for a product or service. Negotiation priority is a criterion for setting priorities such as price, delivery time, and quality. For example, the analysis unit analyzes information received from the other party's AI and generates data for calculating the optimal price. The analysis unit can use an algorithm to find the optimal price between the other party's minimum price and our own maximum price, for example. This allows the other party's information to be analyzed accurately.

[0033] The proposal unit can propose an appropriate price based on the other party's information. The appropriate price is calculated based on criteria such as market price or cost-based price. For example, the proposal unit proposes an optimal price that is above the other party's minimum price and below the company's maximum price based on the data generated by the analysis unit. The proposal unit, for example, displays the proposed price to the user and provides an interface for proceeding with negotiations. This makes it possible to propose the optimal price.

[0034] The input unit can analyze past negotiation history and automatically suggest appropriate minimum and maximum prices. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the input unit automatically suggests an optimal price range based on the minimum and maximum prices previously set by the user. It can also analyze the price range of successful negotiations from the past negotiation history and suggest an optimal price. Furthermore, it can automatically suggest an optimal price under specific conditions by referring to the user's past negotiation history. This improves the success rate of negotiations by suggesting an optimal price based on the past negotiation history.

[0035] The input unit can refer to product market trends and competitor pricing information in real time during input and input an appropriate price. Market trends include information on market supply and demand, competitor trends, etc. For example, the input unit suggests optimal minimum and maximum prices based on real-time market trends. It can also obtain competitor pricing information in real time and input optimal prices based on that information. It can also analyze the balance of market supply and demand in real time and input optimal prices. This allows for setting competitive prices by inputting optimal prices based on market trends and competitor information.

[0036] The input unit can input an appropriate price for each region based on the user's geographical location information at the time of input. The geographical location information includes GPS data, region-specific price information, and the like. For example, the input unit can input the optimal price based on the user's current location by referring to the market price for that region. The input unit can also analyze supply and demand for each region based on the geographical location information and input the optimal price. Furthermore, the input unit can propose region-specific pricing based on the user's geographical location information. This makes it possible to set region-specific prices by inputting the optimal price based on the geographical location information.

[0037] The input unit can analyze the user's social media activity at the time of input and input relevant price information. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the input unit can analyze the user's social media activity and input the optimal price based on the product or service the user is interested in. It can also suggest prices for products that are in high demand based on the user's social media comments and posts. Furthermore, it can analyze trends from the user's social media activity and input the optimal price. This makes it possible to set prices that meet the user's needs by inputting the optimal price based on social media activity.

[0038] When exchanging information, the exchange unit can select an appropriate information exchange method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the exchange unit analyzes the past negotiation history of the other party's AI and selects the optimal timing for information exchange. It can also exchange information by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, it can select the optimal information exchange method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal information exchange method based on the past negotiation history.

[0039] When exchanging information, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation score. For example, if the other party's AI is highly reliable, the exchange unit will provide detailed information. Also, if the other party's AI is low in reliability, the exchange unit can limit the level of detail of the information provided. Furthermore, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. This allows for appropriate information exchange by adjusting the level of detail of the information according to the other party's reliability.

[0040] When exchanging information, the exchange unit can prioritize the exchange of highly relevant information based on the geographical location information of the other AI. Geographical location information includes GPS data and region-specific information. For example, the exchange unit prioritizes the exchange of highly relevant information based on the geographical location information of the other AI. It can also prioritize the exchange of region-specific information taking geographical location information into consideration. Furthermore, it can select the optimal method of information exchange based on the geographical location information of the other AI. This enables efficient information exchange by exchanging highly relevant information based on geographical location information.

[0041] When exchanging information, the exchange unit can analyze the social media activity of the other AI and exchange relevant information. Social media activity includes information such as the content of posts, number of followers, and engagement rate. For example, the exchange unit can analyze the social media activity of the other AI and prioritize the exchange of relevant information. It can also exchange optimal information based on the other AI's social media comments and posts. Furthermore, it can analyze trends based on the other AI's social media activity and exchange relevant information. This enables efficient information exchange by exchanging relevant information based on social media activity.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the analysis unit analyzes the past negotiation history of the other party's AI to provide highly accurate results. The analysis unit can also refer to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the optimal analysis method can be selected based on the other party's AI's past negotiation history. This allows for more accurate analysis by improving the accuracy of the analysis based on past negotiation history.

[0043] During analysis, the analysis unit can evaluate the reliability of the other party's AI and adjust the level of detail of the analysis depending on its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation scores. For example, if the other party's AI is highly reliable, the analysis unit will perform a detailed analysis. On the other hand, if the other party's AI is low in reliability, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can evaluate the reliability of the other party's AI and adjust the level of detail of the analysis depending on its reliability. This allows for appropriate analysis by adjusting the level of detail of the analysis depending on the other party's reliability.

[0044] During analysis, the analysis unit can prioritize analysis of highly relevant information based on the geographic location information of the other AI. Geographic location information includes GPS data and region-specific information. For example, the analysis unit prioritizes analysis of highly relevant information based on the geographic location information of the other AI. It can also prioritize analysis of region-specific information taking geographic location information into consideration. Furthermore, it can select the optimal analysis method based on the geographic location information of the other AI. This enables efficient analysis by analyzing highly relevant information based on geographic location information.

[0045] During analysis, the analysis unit can analyze the social media activity of the other AI and analyze related information. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the analysis unit can analyze the other AI's social media activity and prioritize analysis of related information. It can also select the optimal analysis method based on the other AI's social media comments and posts. Furthermore, it can analyze trends and related information based on the other AI's social media activity. This enables efficient analysis by analyzing related information based on social media activity.

[0046] When making a proposal, the proposal unit can select an appropriate proposal method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the proposal unit analyzes the past negotiation history of the other party's AI and selects the optimal proposal method. The proposal unit can also make a proposal by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the proposal unit can select the optimal proposal method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal proposal method based on the past negotiation history.

[0047] When making a proposal, the proposal unit can evaluate the reliability of the other party's AI and adjust the level of detail in the proposal depending on the reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation scores. For example, if the other party's AI is highly reliable, the proposal unit will make a detailed proposal. On the other hand, if the other party's AI is low in reliability, the proposal unit can also make a simplified proposal. Furthermore, the proposal unit can evaluate the reliability of the other party's AI and adjust the level of detail in the proposal depending on the reliability. This makes it possible to make appropriate proposals by adjusting the level of detail in the proposal depending on the other party's reliability.

[0048] When making a proposal, the proposal unit can prioritize highly relevant proposals based on the geographical location information of the other party's AI. Geographical location information includes GPS data, region-specific information, and the like. For example, the proposal unit can prioritize highly relevant proposals based on the geographical location information of the other party's AI. It can also prioritize region-specific proposals by taking the geographical location information into consideration. Furthermore, it can select the optimal proposal method based on the geographical location information of the other party's AI. This enables efficient proposals by making highly relevant proposals based on geographical location information.

[0049] When making a proposal, the proposal unit can analyze the social media activity of the other AI and make relevant proposals. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the proposal unit can analyze the social media activity of the other AI and prioritize relevant proposals. It can also make optimal proposals based on the other AI's social media comments and posts. Furthermore, it can analyze trends and make relevant proposals based on the other AI's social media activity. This enables efficient proposals by making relevant proposals based on social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The input unit can analyze the user's past purchase history and automatically suggest appropriate minimum and maximum prices. The past purchase history includes data such as the prices of purchased products and services, purchase frequency, and purchase timing. For example, the input unit automatically suggests an optimal price range based on the price range of products the user has purchased in the past. It can also analyze price fluctuations during specific seasons or event periods from the past purchase history and suggest optimal prices. Furthermore, it can automatically suggest optimal prices under specific conditions by referring to the user's purchase history. This makes it possible to set prices based on the user's purchasing behavior by suggesting optimal prices based on the user's past purchase history.

[0052] When exchanging information, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation score. For example, if the other party's AI is highly reliable, the exchange unit will provide detailed information. Also, if the other party's AI is low in reliability, the exchange unit can limit the level of detail of the information provided. Furthermore, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. This allows for appropriate information exchange by adjusting the level of detail of the information according to the other party's reliability.

[0053] When making a proposal, the proposal unit can select an appropriate proposal method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the proposal unit analyzes the past negotiation history of the other party's AI and selects the optimal proposal method. The proposal unit can also make a proposal by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the proposal unit can select the optimal proposal method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal proposal method based on the past negotiation history.

[0054] When exchanging information, the exchange unit can prioritize the exchange of highly relevant information based on the geographical location information of the other AI. Geographical location information includes GPS data and region-specific information. For example, the exchange unit prioritizes the exchange of highly relevant information based on the geographical location information of the other AI. It can also prioritize the exchange of region-specific information taking geographical location information into consideration. Furthermore, it can select the optimal method of information exchange based on the geographical location information of the other AI. This enables efficient information exchange by exchanging highly relevant information based on geographical location information.

[0055] When making a proposal, the proposal unit can analyze the social media activity of the other AI and make relevant proposals. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the proposal unit can analyze the social media activity of the other AI and prioritize relevant proposals. It can also make optimal proposals based on the other AI's social media comments and posts. Furthermore, it can analyze trends and make relevant proposals based on the other AI's social media activity. This enables efficient proposals by making relevant proposals based on social media activity.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The input unit inputs the minimum price, maximum price, and negotiation priority. For example, an interface is provided for the user to input their company's minimum price, maximum price, and negotiation priority (price, delivery date, quality, etc.). Step 2: The exchange unit exchanges information with the other AI. For example, it sends information entered by the user to the other AI and receives information received from the other AI. The exchange unit can exchange information using a secure communication protocol. Step 3: The analysis unit analyzes information such as the other party's minimum and maximum prices, negotiation priorities, etc. For example, it analyzes information received from the other party's AI and generates data to calculate the optimal price. The analysis unit can use an algorithm to find the optimal price between the other party's minimum price and our own maximum price. Step 4: The proposal unit proposes the optimal price based on the other party's information. For example, based on the data generated by the analysis unit, it proposes an optimal price that is above the other party's minimum price and below the company's maximum price. The proposal unit displays the proposed price to the user and provides an interface for proceeding with negotiations.

[0058] (Example 2) In a price negotiation system according to an embodiment of the present invention, each company prepares an AI for price negotiations. The AI ​​inputs information that can and cannot be shared with the other party, thereby determining a price that maximizes mutual profits. In this price negotiation system, each company inputs information such as its minimum price, maximum price, and negotiation priority into the AI. The AI ​​then exchanges information with the other party's AI, analyzes the other party's information, proposes an optimal price, and proceeds with negotiations, thereby maximizing each company's profits. For example, each company inputs information such as its minimum price, maximum price, and negotiation priority into the AI. One company sets a minimum price for a product, and the other company proceeds with negotiations based on that price. This information is then input into the AI. Next, the AI ​​exchanges information with the other party's AI and analyzes the other party's information. The AI ​​receives the other party's information such as its minimum price, maximum price, and negotiation priority, and calculates the optimal price based on that information. For example, it uses an algorithm to find the optimal price between the other party's minimum price and its own maximum price. Finally, the AI ​​proposes the optimal price and proceeds with negotiations. The AI ​​proposes the optimal price based on the other party's information and proceeds with negotiations. For example, if the price proposed by the AI ​​is higher than the other party's minimum price and lower than the company's maximum price, that price will be the optimal price. In this way, each company's profits will be maximized. This allows the price negotiation system to determine the price that maximizes each company's profits.

[0059] A price negotiation system according to an embodiment includes an input unit, an exchange unit, an analysis unit, and a proposal unit. The input unit inputs a minimum price, a maximum price, and a negotiation priority. For example, the input unit provides an interface for a user to input their company's minimum price, maximum price, and negotiation priority. The user can input, for example, the minimum and maximum acceptable prices for a product or service, and negotiation priority (price, delivery date, quality, etc.). The exchange unit exchanges information with the other party's AI. For example, the exchange unit transmits information input by the user to the other party's AI and obtains information received from the other party's AI. The exchange unit can exchange information using, for example, a secure communication protocol. The analysis unit analyzes information such as the other party's minimum price, maximum price, and negotiation priority. For example, the analysis unit analyzes information received from the other party's AI and generates data for calculating an optimal price. The analysis unit can use, for example, an algorithm to find an optimal price between the other party's minimum price and their company's maximum price. The proposal unit proposes an optimal price based on the other party's information. For example, the proposal unit proposes an optimal price that is higher than the other party's minimum price and lower than the company's maximum price based on the data generated by the analysis unit. The proposal unit, for example, displays the proposed price to the user and provides an interface for proceeding with negotiations. This allows the price negotiation system according to the embodiment to determine a price that maximizes each company's profits.

[0060] The input section allows users to input the minimum price, maximum price, and negotiation priority. The minimum price refers to the lowest acceptable price for a product or service, and the maximum price refers to the highest acceptable price for a product or service. The negotiation priority is a standard for setting priorities such as price, delivery date, and quality. For example, the input section provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The user can input, for example, the minimum acceptable price and maximum acceptable price for a product or service, and the negotiation priority (price, delivery date, quality, etc.). This allows the user to accurately input the information required for negotiations.

[0061] The exchange unit can exchange information with the other AI. The other AI has characteristics such as the algorithms used and learning data. For example, the exchange unit sends information entered by the user to the other AI and retrieves information received from the other AI. The exchange unit can exchange information using, for example, a secure communication protocol. This allows for efficient exchange of information with the other AI.

[0062] The analysis unit can analyze the other party's minimum price, maximum price, and negotiation priority information. The minimum price refers to the lowest acceptable price for a product or service, and the maximum price refers to the highest acceptable price for a product or service. Negotiation priority is a criterion for setting priorities such as price, delivery time, and quality. For example, the analysis unit analyzes information received from the other party's AI and generates data for calculating the optimal price. The analysis unit can use an algorithm to find the optimal price between the other party's minimum price and our own maximum price, for example. This allows the other party's information to be analyzed accurately.

[0063] The proposal unit can propose an appropriate price based on the other party's information. The appropriate price is calculated based on criteria such as market price or cost-based price. For example, the proposal unit proposes an optimal price that is above the other party's minimum price and below the company's maximum price based on the data generated by the analysis unit. The proposal unit, for example, displays the proposed price to the user and provides an interface for proceeding with negotiations. This makes it possible to propose the optimal price.

[0064] The input unit can estimate the user's emotions and adjust the timing of inputting the minimum and maximum prices based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the input unit can simplify the input procedure, allowing the user to quickly input the minimum and maximum prices. Alternatively, if the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the input unit can prioritize voice input, allowing the user to quickly input the minimum and maximum prices. This allows the input timing to be adjusted according to the user's emotions, allowing the user to input more appropriate prices.

[0065] The input unit can analyze past negotiation history and automatically suggest appropriate minimum and maximum prices. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the input unit automatically suggests an optimal price range based on the minimum and maximum prices previously set by the user. It can also analyze the price range of successful negotiations from the past negotiation history and suggest an optimal price. Furthermore, it can automatically suggest an optimal price under specific conditions by referring to the user's past negotiation history. This improves the success rate of negotiations by suggesting an optimal price based on the past negotiation history.

[0066] The input unit can refer to product market trends and competitor pricing information in real time during input and input an appropriate price. Market trends include information on market supply and demand, competitor trends, etc. For example, the input unit suggests optimal minimum and maximum prices based on real-time market trends. It can also obtain competitor pricing information in real time and input optimal prices based on that information. It can also analyze the balance of market supply and demand in real time and input optimal prices. This allows for setting competitive prices by inputting optimal prices based on market trends and competitor information.

[0067] The input unit can estimate the user's emotions and automatically set negotiation priorities based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the input unit can prioritize negotiations that are more important. Also, if the user is relaxed, the input unit can flexibly set negotiation priorities. Furthermore, if the user is in a hurry, the input unit can set priorities to quickly advance negotiations. This allows for efficient negotiations by setting negotiation priorities according to the user's emotions.

[0068] The input unit can input an appropriate price for each region based on the user's geographical location information at the time of input. The geographical location information includes GPS data, region-specific price information, and the like. For example, the input unit can input the optimal price based on the user's current location by referring to the market price for that region. The input unit can also analyze supply and demand for each region based on the geographical location information and input the optimal price. Furthermore, the input unit can propose region-specific pricing based on the user's geographical location information. This makes it possible to set region-specific prices by inputting the optimal price based on the geographical location information.

[0069] The input unit can analyze the user's social media activity at the time of input and input relevant price information. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the input unit can analyze the user's social media activity and input the optimal price based on the product or service the user is interested in. It can also suggest prices for products that are in high demand based on the user's social media comments and posts. Furthermore, it can analyze trends from the user's social media activity and input the optimal price. This makes it possible to set prices that meet the user's needs by inputting the optimal price based on social media activity.

[0070] The exchange unit can estimate the user's emotions and adjust the timing of information exchange based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the exchange unit can delay the timing of information exchange and wait until the user is relaxed. Also, if the user is relaxed, the exchange unit can advance the timing of information exchange to quickly advance negotiations. Furthermore, if the user is in a hurry, the exchange unit can optimize the timing of information exchange to quickly exchange information. This allows for efficient information exchange by adjusting the timing of information exchange according to the user's emotions.

[0071] When exchanging information, the exchange unit can select an appropriate information exchange method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the exchange unit analyzes the past negotiation history of the other party's AI and selects the optimal timing for information exchange. It can also exchange information by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, it can select the optimal information exchange method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal information exchange method based on the past negotiation history.

[0072] When exchanging information, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation score. For example, if the other party's AI is highly reliable, the exchange unit will provide detailed information. Also, if the other party's AI is low in reliability, the exchange unit can limit the level of detail of the information provided. Furthermore, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. This allows for appropriate information exchange by adjusting the level of detail of the information according to the other party's reliability.

[0073] The exchange unit can estimate the user's emotions and determine the priority of information to be exchanged based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the exchange unit prioritizes the exchange of information of high importance. Also, if the user is relaxed, the exchange unit can flexibly set the priority of information. Furthermore, if the user is in a hurry, the exchange unit can prioritize the exchange of information that will quickly advance negotiations. This enables efficient information exchange by determining the priority of information according to the user's emotions.

[0074] When exchanging information, the exchange unit can prioritize the exchange of highly relevant information based on the geographical location information of the other AI. Geographical location information includes GPS data and region-specific information. For example, the exchange unit prioritizes the exchange of highly relevant information based on the geographical location information of the other AI. It can also prioritize the exchange of region-specific information taking geographical location information into consideration. Furthermore, it can select the optimal method of information exchange based on the geographical location information of the other AI. This enables efficient information exchange by exchanging highly relevant information based on geographical location information.

[0075] When exchanging information, the exchange unit can analyze the social media activity of the other AI and exchange relevant information. Social media activity includes information such as the content of posts, number of followers, and engagement rate. For example, the exchange unit can analyze the social media activity of the other AI and prioritize the exchange of relevant information. It can also exchange optimal information based on the other AI's social media comments and posts. Furthermore, it can analyze trends based on the other AI's social media activity and exchange relevant information. This enables efficient information exchange by exchanging relevant information based on social media activity.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the analysis unit can use simple analysis criteria to provide quick results. If the user is relaxed, the analysis unit can use detailed analysis criteria to provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can use analysis criteria to provide quick results. This allows for efficient analysis by adjusting the analysis criteria according to the user's emotions.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the analysis unit analyzes the past negotiation history of the other party's AI to provide highly accurate results. The analysis unit can also refer to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the optimal analysis method can be selected based on the other party's AI's past negotiation history. This allows for more accurate analysis by improving the accuracy of the analysis based on past negotiation history.

[0078] During analysis, the analysis unit can evaluate the reliability of the other party's AI and adjust the level of detail of the analysis depending on its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation scores. For example, if the other party's AI is highly reliable, the analysis unit will perform a detailed analysis. On the other hand, if the other party's AI is low in reliability, the analysis unit can also perform a simplified analysis. Furthermore, the analysis unit can evaluate the reliability of the other party's AI and adjust the level of detail of the analysis depending on its reliability. This allows for appropriate analysis by adjusting the level of detail of the analysis depending on the other party's reliability.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotions, highly visible results can be provided.

[0080] During analysis, the analysis unit can prioritize analysis of highly relevant information based on the geographic location information of the other AI. Geographic location information includes GPS data and region-specific information. For example, the analysis unit prioritizes analysis of highly relevant information based on the geographic location information of the other AI. It can also prioritize analysis of region-specific information taking geographic location information into consideration. Furthermore, it can select the optimal analysis method based on the geographic location information of the other AI. This enables efficient analysis by analyzing highly relevant information based on geographic location information.

[0081] During analysis, the analysis unit can analyze the social media activity of the other AI and analyze related information. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the analysis unit can analyze the other AI's social media activity and prioritize analysis of related information. It can also select the optimal analysis method based on the other AI's social media comments and posts. Furthermore, it can analyze trends and related information based on the other AI's social media activity. This enables efficient analysis by analyzing related information based on social media activity.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion method. If the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. In this way, by adjusting the way suggestions are presented according to the user's emotions, highly visible suggestions are possible.

[0083] When making a proposal, the proposal unit can select an appropriate proposal method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the proposal unit analyzes the past negotiation history of the other party's AI and selects the optimal proposal method. The proposal unit can also make a proposal by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the proposal unit can select the optimal proposal method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal proposal method based on the past negotiation history.

[0084] When making a proposal, the proposal unit can evaluate the reliability of the other party's AI and adjust the level of detail in the proposal depending on the reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation scores. For example, if the other party's AI is highly reliable, the proposal unit will make a detailed proposal. On the other hand, if the other party's AI is low in reliability, the proposal unit can also make a simplified proposal. Furthermore, the proposal unit can evaluate the reliability of the other party's AI and adjust the level of detail in the proposal depending on the reliability. This makes it possible to make appropriate proposals by adjusting the level of detail in the proposal depending on the other party's reliability.

[0085] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the suggestion unit can prioritize suggestions with high importance. Also, if the user is relaxed, the suggestion unit can flexibly set the priority of suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions to quickly advance negotiations. In this way, efficient suggestions can be made by determining the priority of suggestions according to the user's emotions.

[0086] When making a proposal, the proposal unit can prioritize highly relevant proposals based on the geographical location information of the other party's AI. Geographical location information includes GPS data, region-specific information, and the like. For example, the proposal unit can prioritize highly relevant proposals based on the geographical location information of the other party's AI. It can also prioritize region-specific proposals by taking the geographical location information into consideration. Furthermore, it can select the optimal proposal method based on the geographical location information of the other party's AI. This enables efficient proposals by making highly relevant proposals based on geographical location information.

[0087] When making a proposal, the proposal unit can analyze the social media activity of the other AI and make relevant proposals. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the proposal unit can analyze the social media activity of the other AI and prioritize relevant proposals. It can also make optimal proposals based on the other AI's social media comments and posts. Furthermore, it can analyze trends and make relevant proposals based on the other AI's social media activity. This enables efficient proposals by making relevant proposals based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, exchange unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The exchange unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and exchanges information with the other party's AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information such as the other party's minimum price, maximum price, and negotiation priority. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal price based on the other party's information. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, exchange unit, analysis unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The exchange unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and exchanges information with the other party's AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information such as the other party's minimum price, maximum price, and negotiation priority. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal price based on the other party's information. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, exchange unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The exchange unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and exchanges information with the other party's AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information such as the other party's minimum price, maximum price, and negotiation priority. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal price based on the other party's information. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, exchange unit, analysis unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input their company's minimum price, maximum price, and negotiation priority. The exchange unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and exchanges information with the other party's AI. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information such as the other party's minimum price, maximum price, and negotiation priority. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal price based on the other party's information.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The input unit can analyze the user's past purchase history and automatically suggest appropriate minimum and maximum prices. The past purchase history includes data such as the prices of purchased products and services, purchase frequency, and purchase timing. For example, the input unit automatically suggests an optimal price range based on the price range of products the user has purchased in the past. It can also analyze price fluctuations during specific seasons or event periods from the past purchase history and suggest optimal prices. Furthermore, it can automatically suggest optimal prices under specific conditions by referring to the user's purchase history. This makes it possible to set prices based on the user's purchasing behavior by suggesting optimal prices based on the user's past purchase history.

[0090] When exchanging information, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. The reliability of the other party's AI is evaluated based on criteria such as past performance and evaluation score. For example, if the other party's AI is highly reliable, the exchange unit will provide detailed information. Also, if the other party's AI is low in reliability, the exchange unit can limit the level of detail of the information provided. Furthermore, the exchange unit can evaluate the reliability of the other party's AI and adjust the level of detail of the information according to its reliability. This allows for appropriate information exchange by adjusting the level of detail of the information according to the other party's reliability.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the analysis unit can use simple analysis criteria to provide quick results. If the user is relaxed, the analysis unit can use detailed analysis criteria to provide highly accurate results. Furthermore, if the user is in a hurry, the analysis unit can use analysis criteria to provide quick results. This allows for efficient analysis by adjusting the analysis criteria according to the user's emotions.

[0092] When making a proposal, the proposal unit can select an appropriate proposal method based on the past negotiation history of the other party's AI. The past negotiation history includes data such as the success rate of negotiations and price fluctuations. For example, the proposal unit analyzes the past negotiation history of the other party's AI and selects the optimal proposal method. The proposal unit can also make a proposal by referring to successful negotiation patterns from the other party's AI's past negotiation history. Furthermore, the proposal unit can select the optimal proposal method based on the other party's AI's past negotiation history. In this way, the success rate of negotiations is improved by selecting the optimal proposal method based on the past negotiation history.

[0093] The input unit can estimate the user's emotions and adjust the timing of inputting the minimum and maximum prices based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the input unit can simplify the input procedure, allowing the user to quickly input the minimum and maximum prices. Alternatively, if the user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the input unit can prioritize voice input, allowing the user to quickly input the minimum and maximum prices. This allows the input timing to be adjusted according to the user's emotions, allowing the user to input more appropriate prices.

[0094] When exchanging information, the exchange unit can prioritize the exchange of highly relevant information based on the geographical location information of the other AI. Geographical location information includes GPS data and region-specific information. For example, the exchange unit prioritizes the exchange of highly relevant information based on the geographical location information of the other AI. It can also prioritize the exchange of region-specific information taking geographical location information into consideration. Furthermore, it can select the optimal method of information exchange based on the geographical location information of the other AI. This enables efficient information exchange by exchanging highly relevant information based on geographical location information.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method according to the user's emotions, highly visible results can be provided.

[0096] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the suggestion unit can provide a simple, highly visible suggestion method. If the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. In this way, by adjusting the way suggestions are presented according to the user's emotions, highly visible suggestions are possible.

[0097] When making a proposal, the proposal unit can analyze the social media activity of the other AI and make relevant proposals. Social media activity includes information such as post content, number of followers, and engagement rate. For example, the proposal unit can analyze the social media activity of the other AI and prioritize relevant proposals. It can also make optimal proposals based on the other AI's social media comments and posts. Furthermore, it can analyze trends and make relevant proposals based on the other AI's social media activity. This enables efficient proposals by making relevant proposals based on social media activity.

[0098] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the suggestion unit can prioritize suggestions with high importance. Also, if the user is relaxed, the suggestion unit can flexibly set the priority of suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions to quickly advance negotiations. In this way, efficient suggestions can be made by determining the priority of suggestions according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The input unit inputs the minimum price, maximum price, and negotiation priority. For example, an interface is provided for the user to input their company's minimum price, maximum price, and negotiation priority (price, delivery date, quality, etc.). Step 2: The exchange unit exchanges information with the other AI. For example, it sends information entered by the user to the other AI and receives information received from the other AI. The exchange unit can exchange information using a secure communication protocol. Step 3: The analysis unit analyzes information such as the other party's minimum and maximum prices, negotiation priorities, etc. For example, it analyzes information received from the other party's AI and generates data to calculate the optimal price. The analysis unit can use an algorithm to find the optimal price between the other party's minimum price and our own maximum price. Step 4: The proposal unit proposes the optimal price based on the other party's information. For example, based on the data generated by the analysis unit, it proposes an optimal price that is above the other party's minimum price and below the company's maximum price. The proposal unit displays the proposed price to the user and provides an interface for proceeding with negotiations.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the minimum price, maximum price, and negotiation priority; an exchange unit that exchanges the information input by the input unit with a partner AI; an analysis unit that analyzes the information exchanged by the exchange unit; a proposal unit that proposes an appropriate price based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The input unit Enter minimum and maximum prices and negotiation priority 2. The system of claim 1.

3. The exchange unit is Exchange information with the other party's AI 2. The system of claim 1.

4. The analysis unit Analyze the other party's minimum price, maximum price, and negotiation priority information 2. The system of claim 1.

5. The proposal unit Propose an appropriate price based on the other party's information 2. The system of claim 1.

6. The input unit Inferring user sentiment and adjusting the timing of minimum and maximum price inputs based on the estimated user sentiment 2. The system of claim 1.

7. The input unit Analyze past negotiation history and automatically suggest appropriate minimum and maximum prices 2. The system of claim 1.

8. The input unit When entering prices, refer to product market trends and competitor pricing information in real time and enter the appropriate price.

2. The system of claim 1.

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