System

Abstract

An object of a system according to an embodiment is to enable a corporate sales to efficiently put relative discount details input in natural languages into a fee system.SOLUTION: A system includes an input unit, an analysis unit, and a conversion unit. An inputting part receives relative discount contents inputted by a corporate sales in natural languages. The analysis part analyzes the relative discount contents received by the input part and returns them in a patterned format. The conversion part converts the patterned relative discount contents returned by the analysis part into a data format which can be inputted to a charge system.SELECTED DRAWING: Figure 1

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 requires manual input of the relative discount details set by corporate sales personnel into the pricing system, which is inefficient.

[0005] The system according to the embodiment aims to enable corporate sales personnel to input relative discount details in natural language into the pricing system efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a conversion unit. The input unit accepts relative discount details entered in natural language by a corporate sales representative. The analysis unit analyzes the relative discount details accepted by the input unit and returns them in a patterned format. The conversion unit converts the patterned relative discount details returned by the analysis unit into a data format that can be input into the billing system. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently input relative discount details entered by corporate sales personnel in natural language into the pricing system. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The relative discount management system according to an embodiment of the present invention analyzes the relative discount details entered by a corporate salesperson in natural language and returns them in a patterned format. This automates the complex discount calculations that were previously performed manually, improving business efficiency.

[0029] A relative discount management system according to an embodiment includes an input unit, an analysis unit, and a conversion unit. The input unit accepts relative discount details entered in natural language by a corporate sales representative. For example, the corporate sales representative may enter the details in the form of, "This customer will be given a 10% discount and an additional three-month free period." The input unit can also enter the relative discount details using voice input. The analysis unit analyzes the relative discount details accepted by the input unit and returns them in a patterned format. For example, a generation AI may use a text generation AI (e.g., GPT-3) to analyze the relative discount details, classify them into patterns such as "10% discount" and "three-month free period," and return them to the system. The analysis unit can also learn from past data to generate more accurate discount patterns. The conversion unit converts the patterned relative discount details returned by the analysis unit into a data format suitable for input into the billing system. For example, the conversion unit may convert the patterned relative discount details into CSV or XML format and input them into the billing system. This allows the relative discount management system according to an embodiment to automate complex discount calculations that were previously performed manually, improving business efficiency. For example, the generation AI can analyze relative discount information entered by corporate sales staff in natural language and return it in a patterned format, making it easier for C&O to convert data. It also makes it easier for the fee system to calculate billing amounts, improving work efficiency and ensuring accurate billing amounts.

[0030] In the input section, as corporate sales representatives enter their relative discount details, the generation AI analyzes the input content in real time and proposes an appropriate discount pattern. For example, when corporate sales representatives enter relative discount details, the generation AI analyzes the input content in real time and proposes the optimal discount pattern based on past data. For example, it presents discount patterns that have been successful in the past under similar conditions. In addition, as corporate sales representatives continue to enter their data, the generation AI analyzes the input content in real time and proposes an appropriate discount pattern. For example, it automatically adjusts discount rates and benefits based on the input content. In addition, as corporate sales representatives enter relative discount details, the generation AI analyzes the input content in real time and proposes a discount pattern according to the input content. For example, it presents the optimal discount content based on the customer's past transaction history. This allows for efficient discount settings by proposing appropriate discount patterns in real time as corporate sales representatives enter their data.

[0031] The input unit allows corporate sales representatives to refer to past success stories or similar cases when entering data, thereby deriving the optimal discount details. The input unit, for example, provides a database that allows corporate sales representatives to refer to past success stories or similar cases when entering relative discount details. For example, it displays discount details that have been successful in the past under similar conditions. The input unit also displays past success stories and similar cases in real time as the corporate sales representative continues entering data, thereby deriving the optimal discount details. For example, it uses the discount rates and benefits of successful cases as a reference. The input unit also provides an interface that allows corporate sales representatives to refer to past success stories or similar cases when entering relative discount details. For example, it displays detailed data on success stories and derives the optimal discount details. In this way, the optimal discount details can be derived by referring to past success stories and similar cases.

[0032] The input unit allows corporate sales representatives to input relative discount details using voice input, and the generation AI can analyze the voice and convert it into text. The input unit, for example, builds a system that allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, the input content is automatically converted into text using voice recognition technology. The input unit also allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice in real time and converts it into text. For example, the voice input content is instantly displayed as text. The input unit also develops a system that allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, voice recognition technology is used to achieve high-precision conversion into text. As a result, the use of voice input can make the input work of corporate sales representatives more efficient.

[0033] The input unit can use a visual interface to enable corporate sales representatives to visually set discount details when entering data. The input unit, for example, builds a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, discount rates and benefits are set using drag and drop. The input unit also allows corporate sales representatives to visually set discount details using a visual interface while continuing to enter data. For example, discount details are displayed using graphs and charts. The input unit also develops a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, discount details are set using icons and images. In this way, discount details can be visually set using the visual interface.

[0034] The analysis unit learns from past data and can generate more accurate discount patterns. For example, the generation AI learns from past relative discount data and generates more accurate discount patterns. For example, it proposes optimal discount patterns based on past success stories. The analysis unit also develops algorithms that allow the generation AI to analyze past data and improve the accuracy of discount patterns. For example, it optimizes discount rates and benefits based on past data. The analysis unit also builds a system that allows the generation AI to learn from past relative discount data and generate more accurate discount patterns. For example, it automatically adjusts discount content based on past data. In this way, more accurate discount patterns can be generated by learning from past data.

[0035] The analysis unit can refer to data from different industries or regions and derive optimal discount patterns. For example, the analysis unit builds a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it proposes discount patterns based on success stories from different industries. The analysis unit also develops an algorithm in which the generation AI analyzes data from different regions and derives optimal discount patterns. For example, it proposes discount content that takes into account the market characteristics of each region. The analysis unit also develops a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it integrates data from different industries and regions to optimize discount patterns. This makes it possible to derive optimal discount patterns by referring to data from different industries and regions.

[0036] When analyzing, the analysis unit can visually return discount details using images or graphs. The analysis unit, for example, builds a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, it displays discount rates and benefits in graphs. The analysis unit also develops an algorithm that automatically generates images and graphs to visually display the discount details analyzed by the generation AI. For example, it returns the discount details in a format that is visually easy to understand. The analysis unit also develops a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, visually displaying discount patterns makes them easier for corporate sales representatives to understand. This makes it possible to visually return discount details using images and graphs.

[0037] When analyzing, the analysis unit can return discount details in different languages, making it possible to accommodate international customers. For example, the analysis unit can build a system that returns discount details analyzed by the generation AI in different languages, making it possible to accommodate international customers. For example, the discount details can be displayed in multiple languages, such as English and Chinese. The analysis unit can also develop an algorithm that automatically translates the discount details analyzed by the generation AI into different languages ​​to accommodate international customers. For example, the discount details can be returned in multiple languages. The analysis unit can also develop a system that returns discount details analyzed by the generation AI in different languages, making it possible to accommodate international customers. For example, the discount details can be displayed in different languages ​​in real time. This allows the discount details to be returned in different languages, making it possible to accommodate international customers.

[0038] The conversion unit can develop algorithms that allow C&O to automatically convert data based on the data returned by the generation AI. For example, the conversion unit develops algorithms that allow C&O to automatically convert data based on the data returned by the generation AI. For example, it converts the output of the generation AI into a format that can be directly input into the billing system. The conversion unit also builds a system that allows C&O to automatically convert data based on the data returned by the generation AI. For example, it automates the data conversion process, reducing manual work. The conversion unit also develops algorithms that allow C&O to automatically convert data based on the data returned by the generation AI, improving conversion accuracy. For example, it automatically recognizes and converts differences in data formats. This allows C&O to automatically convert data, reducing manual work.

[0039] The conversion unit can enable C&O to handle data of different formats when converting data. For example, the conversion unit builds a system that can handle data of different formats when C&O performs data conversion. For example, it automatically converts different data formats such as CSV, XML, and JSON. The conversion unit also develops algorithms that automatically recognize data of different formats and convert it into an appropriate format when C&O performs data conversion. For example, it automatically detects and converts differences in data formats. The conversion unit also develops a system that can handle data of different formats when C&O performs data conversion, improving conversion accuracy. For example, it sets conversion rules for handling different data formats in a uniform manner. This allows it to handle data of different formats, thereby improving the flexibility of data conversion.

[0040] The Conversion Department can automate data integration between different systems when C&O performs data conversion. For example, when C&O performs data conversion, the Conversion Department builds a system that automates data integration between different systems. For example, it automates data sending and receiving, reducing manual work. The Conversion Department also develops algorithms that automate data integration between different systems when C&O performs data conversion. For example, it performs data format conversion and integration all at once. The Conversion Department also develops systems that automate data integration between different systems when C&O performs data conversion, improving the accuracy of integration. For example, it performs integration while maintaining data consistency. This automates data integration between different systems, improving the efficiency of data conversion.

[0041] The conversion unit can use a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, the conversion unit builds a system that uses a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, cloud storage is used to centrally manage data. The conversion unit also develops an algorithm that uses a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, data is converted and managed on the cloud. The conversion unit also develops a system that uses a cloud-based system to achieve centralized data management when C&O performs data conversion, improving management accuracy. For example, data version management is performed on the cloud. In this way, centralized data management can be achieved by using a cloud-based system.

[0042] When calculating billing amounts, the billing system can learn from past billing data and perform more accurate calculations. For example, the billing system learns from past billing data and builds a system that calculates more accurate billing amounts. For example, it improves the accuracy of billing amount predictions based on past data. The billing system also analyzes past billing data and develops an algorithm that improves the accuracy of billing amount calculations. For example, it minimizes errors in billing amounts based on past data. The billing system also learns from past billing data and develops a system that calculates more accurate billing amounts. For example, it optimizes a billing amount calculation model based on past data. In this way, by learning from past billing data, more accurate billing amounts can be calculated.

[0043] The billing system can be made to accommodate different currencies or tax rates when calculating billing amounts. For example, the billing system builds a system that can accommodate different currencies and tax rates. For example, it automatically applies multiple currencies and tax rates to calculate billing amounts. The billing system also develops an algorithm that automatically recognizes different currencies and tax rates and calculates the appropriate billing amount. For example, it automates currency conversion and tax rate application. The billing system also develops a system that can accommodate different currencies and tax rates, improving calculation accuracy. For example, it sets calculation rules for handling different currencies and tax rates uniformly. This makes it possible to accommodate different currencies and tax rates, thereby enabling international customers to be accommodated.

[0044] The fee system can visually display the billing amount using a visual interface when calculating the billing amount. For example, the fee system builds a system that visually displays the billing amount using a visual interface when calculating the billing amount. For example, the fee system displays the billing amount using a graph or chart. Also, the fee system develops an algorithm that visually displays the billing amount using a visual interface when calculating the billing amount. For example, the fee system visually displays the breakdown of the billing amount. Also, the fee system develops a system that visually displays the billing amount using a visual interface when calculating the billing amount, improving the display accuracy. For example, the fee system displays fluctuations in the billing amount in a graph. In this way, the billing amount can be visually displayed using the visual interface.

[0045] C&O will develop an algorithm that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists. For example, it will detect abnormal data and issue an alert. C&O will also develop an algorithm that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists. For example, it will detect abnormal patterns and issue an alert. C&O will also develop a system that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists, improving detection accuracy. For example, it will detect abnormal data in real time and issue an alert. This will improve the accuracy of result confirmation by automatically detecting anomalies and issuing an alert if a problem exists.

[0046] When checking results, C&O can refer to past check results and perform the check work efficiently. For example, C&O builds a system that refers to past check results when checking results and performs the check work efficiently. For example, it optimizes the check work based on past data. C&O also develops an algorithm that refers to past check results in real time when checking results and performs the check work efficiently. For example, it optimizes the check work procedure based on past data. C&O also develops a system that refers to past check results when checking results and performs the check work efficiently, improving the accuracy of the check. For example, it improves the accuracy of the check work based on past data. In this way, by referring to past check results, the check work can be performed efficiently.

[0047] C&O can automate data sharing between different systems when checking results. For example, C&O will build a system that automates data sharing between different systems when checking results. For example, it will automate the sending and receiving of data, reducing manual work. C&O will also develop algorithms that automate data sharing between different systems when checking results. For example, it will perform data format conversion and sharing all at once. C&O will also develop a system that automates data sharing between different systems when checking results, improving the accuracy of sharing. For example, it will share data while maintaining data consistency. In this way, automating data sharing between different systems will improve the efficiency of result checking.

[0048] C&O can use a cloud-based system to achieve centralized data management when checking results. For example, C&O builds a system that uses a cloud-based system to achieve centralized data management when checking results. For example, it uses cloud storage to centrally manage data. C&O also develops an algorithm that uses a cloud-based system to achieve centralized data management when checking results. For example, it converts and manages data on the cloud. C&O also develops a system that uses a cloud-based system to achieve centralized data management when checking results, improving management accuracy. For example, it manages data versions on the cloud. In this way, it is possible to achieve centralized data management by using a cloud-based system.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The input unit allows corporate sales representatives to refer to past success stories or similar cases when entering data, thereby deriving the optimal discount content. For example, it displays discount content that has been successful in the past under similar conditions. The input unit also displays past success stories and similar cases in real time as the corporate sales representative continues entering data, thereby deriving the optimal discount content. For example, it uses the discount rates and benefits of successful cases as reference. The input unit also provides an interface that allows corporate sales representatives to refer to past success stories and similar cases when entering relative discount content. For example, it displays detailed data on success stories and derives the optimal discount content. This makes it possible to derive the optimal discount content by referring to past success stories and similar cases.

[0051] The input unit allows corporate sales representatives to input relative discount details using voice input, and the generation AI can analyze the voice and convert it into text. For example, the input content is automatically converted into text using voice recognition technology. The input unit also allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice in real time and converts it into text. For example, the input content is instantly displayed as text. The input unit also develops a system in which corporate sales representatives input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, voice recognition technology is used to achieve high-precision conversion into text. As a result, the use of voice input can make the input work of corporate sales representatives more efficient.

[0052] The input unit can use a visual interface to allow corporate sales representatives to visually set discount details when entering data. For example, discount rates and benefits can be set using drag and drop. The input unit can also use a visual interface to allow corporate sales representatives to visually set discount details while continuing to enter data. For example, the discount details can be displayed using graphs or charts. The input unit can also develop a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, the discount details can be set using icons or images. This allows discount details to be set visually using the visual interface.

[0053] The analysis unit learns from past data and can generate more accurate discount patterns. For example, the generation AI learns from past relative discount data and generates more accurate discount patterns. For example, it proposes optimal discount patterns based on past success stories. The analysis unit also develops algorithms that allow the generation AI to analyze past data and improve the accuracy of discount patterns. For example, it optimizes discount rates and benefits based on past data. The analysis unit also builds a system that allows the generation AI to learn from past relative discount data and generate more accurate discount patterns. For example, it automatically adjusts discount content based on past data. In this way, more accurate discount patterns can be generated by learning from past data.

[0054] The analysis unit can refer to data from different industries or regions and derive optimal discount patterns. For example, a system can be built in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, discount patterns can be proposed based on success stories from different industries. The analysis unit can also develop an algorithm in which the generation AI analyzes data from different regions and derives optimal discount patterns. For example, it can propose discount content that takes into account the market characteristics of each region. The analysis unit can also develop a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it can integrate data from different industries and regions to optimize discount patterns. This makes it possible to derive optimal discount patterns by referring to data from different industries and regions.

[0055] When analyzing, the analysis unit can visually return discount details using images or graphs. For example, a system can be built that visually returns the discount details analyzed by the generation AI using images and graphs. For example, discount rates and benefits can be displayed in graphs. The analysis unit can also develop an algorithm that automatically generates images and graphs to visually display the discount details analyzed by the generation AI. For example, the discount details can be returned in a format that is visually easy to understand. The analysis unit can also develop a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, visually displaying discount patterns makes them easier for corporate sales representatives to understand. This makes it possible to visually return discount details using images and graphs.

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

[0057] Step 1: The input unit accepts the relative discount details entered by the corporate sales representative in natural language. For example, the corporate sales representative might enter the details in the form of "This customer will receive a 10% discount and will also receive a free three-month period." The input unit can also input the relative discount details using voice input. Step 2: The analysis unit analyzes the relative discount content received by the input unit and returns it in a patterned format. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the relative discount content, classifying "10% discount" and "3-month free period" into their respective patterns, and returns them to the system. The analysis unit can also learn from past data to generate more accurate discount patterns. Step 3: The conversion unit converts the patterned relative discount information returned by the analysis unit into a data format that can be input into the fee system. For example, the conversion unit converts the patterned relative discount information into CSV format or XML format and inputs it into the fee system.

[0058] (Example 2) The relative discount management system according to an embodiment of the present invention analyzes the relative discount details entered by a corporate salesperson in natural language and returns them in a patterned format. This automates the complex discount calculations that were previously performed manually, improving business efficiency.

[0059] A relative discount management system according to an embodiment includes an input unit, an analysis unit, and a conversion unit. The input unit accepts relative discount details entered in natural language by a corporate sales representative. For example, the corporate sales representative may enter the details in the form of, "This customer will be given a 10% discount and an additional three-month free period." The input unit can also enter the relative discount details using voice input. The analysis unit analyzes the relative discount details accepted by the input unit and returns them in a patterned format. For example, a generation AI may use a text generation AI (e.g., GPT-3) to analyze the relative discount details, classify them into patterns such as "10% discount" and "three-month free period," and return them to the system. The analysis unit can also learn from past data to generate more accurate discount patterns. The conversion unit converts the patterned relative discount details returned by the analysis unit into a data format suitable for input into the billing system. For example, the conversion unit may convert the patterned relative discount details into CSV or XML format and input them into the billing system. This allows the relative discount management system according to an embodiment to automate complex discount calculations that were previously performed manually, improving business efficiency. For example, the generation AI can analyze relative discount information entered by corporate sales staff in natural language and return it in a patterned format, making it easier for C&O to convert data. It also makes it easier for the fee system to calculate billing amounts, improving work efficiency and ensuring accurate billing amounts.

[0060] In the input section, as corporate sales representatives enter their relative discount details, the generation AI analyzes the input content in real time and proposes an appropriate discount pattern. For example, when corporate sales representatives enter relative discount details, the generation AI analyzes the input content in real time and proposes the optimal discount pattern based on past data. For example, it presents discount patterns that have been successful in the past under similar conditions. In addition, as corporate sales representatives continue to enter their data, the generation AI analyzes the input content in real time and proposes an appropriate discount pattern. For example, it automatically adjusts discount rates and benefits based on the input content. In addition, as corporate sales representatives enter relative discount details, the generation AI analyzes the input content in real time and proposes a discount pattern according to the input content. For example, it presents the optimal discount content based on the customer's past transaction history. This allows for efficient discount settings by proposing appropriate discount patterns in real time as corporate sales representatives enter their data.

[0061] The input unit allows corporate sales representatives to refer to past success stories or similar cases when entering data, thereby deriving the optimal discount details. The input unit, for example, provides a database that allows corporate sales representatives to refer to past success stories or similar cases when entering relative discount details. For example, it displays discount details that have been successful in the past under similar conditions. The input unit also displays past success stories and similar cases in real time as the corporate sales representative continues entering data, thereby deriving the optimal discount details. For example, it uses the discount rates and benefits of successful cases as a reference. The input unit also provides an interface that allows corporate sales representatives to refer to past success stories or similar cases when entering relative discount details. For example, it displays detailed data on success stories and derives the optimal discount details. In this way, the optimal discount details can be derived by referring to past success stories and similar cases.

[0062] The input unit uses the emotion estimation function to analyze the emotions of the corporate salesperson when they enter their relative discount details, and can make discount proposals that elicit positive emotions. For example, when the corporate salesperson enters relative discount details, the input unit uses the emotion estimation function to analyze their emotions at the time of entry, and makes discount proposals that elicit positive emotions. For example, the input unit displays an encouraging message according to the content of the entry. Furthermore, as the corporate salesperson continues to enter their information, the input unit uses the emotion estimation function to analyze their emotions at the time of entry in real time, and makes discount proposals that elicit positive emotions. For example, the input unit presents success stories to increase motivation. Furthermore, when the corporate salesperson enters relative discount details, the input unit uses the emotion estimation function to analyze their emotions at the time of entry, and makes discount proposals that elicit positive emotions. For example, the input unit provides positive feedback according to the content of the entry. In this way, by analyzing the emotions of the corporate salesperson and making discount proposals that elicit positive emotions, work efficiency is improved.

[0063] The input unit allows corporate sales representatives to input relative discount details using voice input, and the generation AI can analyze the voice and convert it into text. The input unit, for example, builds a system that allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, the input content is automatically converted into text using voice recognition technology. The input unit also allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice in real time and converts it into text. For example, the voice input content is instantly displayed as text. The input unit also develops a system that allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, voice recognition technology is used to achieve high-precision conversion into text. As a result, the use of voice input can make the input work of corporate sales representatives more efficient.

[0064] The input unit can use a visual interface to enable corporate sales representatives to visually set discount details when entering data. The input unit, for example, builds a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, discount rates and benefits are set using drag and drop. The input unit also allows corporate sales representatives to visually set discount details using a visual interface while continuing to enter data. For example, discount details are displayed using graphs and charts. The input unit also develops a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, discount details are set using icons and images. In this way, discount details can be visually set using the visual interface.

[0065] The input unit uses the emotion estimation function to predict the customer's emotion when the corporate sales representative enters the information, and can propose a discount based on that emotion. For example, when the corporate sales representative enters relative discount details, the input unit uses the emotion estimation function to predict the customer's emotion and propose a discount based on that emotion. For example, the input unit proposes discount details that the customer has positive emotions about. Furthermore, as the corporate sales representative continues entering the information, the input unit uses the emotion estimation function to predict the customer's emotion in real time and proposes a discount based on that emotion. For example, the input unit adjusts the discount rate or benefit according to the customer's emotion. Furthermore, a system is developed in which the input unit uses the emotion estimation function to predict the customer's emotion when the corporate sales representative enters relative discount details, and proposes a discount based on that emotion. For example, the system presents optimal discount details based on the customer's emotion data. In this way, customer satisfaction is improved by predicting the customer's emotion and proposing a discount based on that emotion.

[0066] The analysis unit learns from past data and can generate more accurate discount patterns. For example, the generation AI learns from past relative discount data and generates more accurate discount patterns. For example, it proposes optimal discount patterns based on past success stories. The analysis unit also develops algorithms that allow the generation AI to analyze past data and improve the accuracy of discount patterns. For example, it optimizes discount rates and benefits based on past data. The analysis unit also builds a system that allows the generation AI to learn from past relative discount data and generate more accurate discount patterns. For example, it automatically adjusts discount content based on past data. In this way, more accurate discount patterns can be generated by learning from past data.

[0067] The analysis unit can refer to data from different industries or regions and derive optimal discount patterns. For example, the analysis unit builds a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it proposes discount patterns based on success stories from different industries. The analysis unit also develops an algorithm in which the generation AI analyzes data from different regions and derives optimal discount patterns. For example, it proposes discount content that takes into account the market characteristics of each region. The analysis unit also develops a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it integrates data from different industries and regions to optimize discount patterns. This makes it possible to derive optimal discount patterns by referring to data from different industries and regions.

[0068] When analyzing, the analysis unit can visually return discount details using images or graphs. The analysis unit, for example, builds a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, it displays discount rates and benefits in graphs. The analysis unit also develops an algorithm that automatically generates images and graphs to visually display the discount details analyzed by the generation AI. For example, it returns the discount details in a format that is visually easy to understand. The analysis unit also develops a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, visually displaying discount patterns makes them easier for corporate sales representatives to understand. This makes it possible to visually return discount details using images and graphs.

[0069] When analyzing, the analysis unit can return discount details in different languages, making it possible to accommodate international customers. For example, the analysis unit can build a system that returns discount details analyzed by the generation AI in different languages, making it possible to accommodate international customers. For example, the discount details can be displayed in multiple languages, such as English and Chinese. The analysis unit can also develop an algorithm that automatically translates the discount details analyzed by the generation AI into different languages ​​to accommodate international customers. For example, the discount details can be returned in multiple languages. The analysis unit can also develop a system that returns discount details analyzed by the generation AI in different languages, making it possible to accommodate international customers. For example, the discount details can be displayed in different languages ​​in real time. This allows the discount details to be returned in different languages, making it possible to accommodate international customers.

[0070] The analysis unit uses the emotion estimation function to collect the emotional reactions of corporate salespeople to the analyzed discount details, and can improve the accuracy of the analysis based on that feedback. For example, the analysis unit uses the emotion estimation function on the discount details analyzed by the generation AI to build a system that collects the emotional reactions of corporate salespeople. For example, it improves the analysis accuracy of the discount details based on the emotion data. The analysis unit also uses the emotion estimation function on the discount details analyzed by the generation AI to collect the emotional reactions of corporate salespeople in real time. For example, it adjusts the discount details based on the emotion score. The analysis unit also uses the emotion estimation function on the discount details analyzed by the generation AI to collect the emotional reactions of corporate salespeople, and develops a system that improves the analysis accuracy based on that feedback. For example, it optimizes discount patterns based on the emotion data. In this way, it is possible to collect the emotional reactions of corporate salespeople and improve the analysis accuracy based on that feedback.

[0071] The conversion unit can develop algorithms that allow C&O to automatically convert data based on the data returned by the generation AI. For example, the conversion unit develops algorithms that allow C&O to automatically convert data based on the data returned by the generation AI. For example, it converts the output of the generation AI into a format that can be directly input into the billing system. The conversion unit also builds a system that allows C&O to automatically convert data based on the data returned by the generation AI. For example, it automates the data conversion process, reducing manual work. The conversion unit also develops algorithms that allow C&O to automatically convert data based on the data returned by the generation AI, improving conversion accuracy. For example, it automatically recognizes and converts differences in data formats. This allows C&O to automatically convert data, reducing manual work.

[0072] The conversion unit can enable C&O to handle data of different formats when converting data. For example, the conversion unit builds a system that can handle data of different formats when C&O performs data conversion. For example, it automatically converts different data formats such as CSV, XML, and JSON. The conversion unit also develops algorithms that automatically recognize data of different formats and convert it into an appropriate format when C&O performs data conversion. For example, it automatically detects and converts differences in data formats. The conversion unit also develops a system that can handle data of different formats when C&O performs data conversion, improving conversion accuracy. For example, it sets conversion rules for handling different data formats in a uniform manner. This allows it to handle data of different formats, thereby improving the flexibility of data conversion.

[0073] The conversion unit uses the emotion estimation function to analyze the emotions of workers when C&O performs data conversion and can make suggestions to improve work efficiency. For example, when C&O performs data conversion, the conversion unit uses the emotion estimation function to analyze the emotions of workers and make suggestions to improve work efficiency. For example, it makes suggestions to help workers relax if they are feeling stressed. Furthermore, when C&O performs data conversion, the conversion unit uses the emotion estimation function to analyze the emotions of workers in real time and make suggestions to improve work efficiency. For example, it suggests an optimal work environment based on the worker's emotion data. Furthermore, when C&O performs data conversion, the conversion unit uses the emotion estimation function to analyze the emotions of workers and develop a system to improve work efficiency. For example, it optimizes the work process based on the worker's emotion data. As a result, the efficiency of data conversion is improved by analyzing the emotions of workers and making suggestions to improve work efficiency.

[0074] The Conversion Department can automate data integration between different systems when C&O performs data conversion. For example, when C&O performs data conversion, the Conversion Department builds a system that automates data integration between different systems. For example, it automates data sending and receiving, reducing manual work. The Conversion Department also develops algorithms that automate data integration between different systems when C&O performs data conversion. For example, it performs data format conversion and integration all at once. The Conversion Department also develops systems that automate data integration between different systems when C&O performs data conversion, improving the accuracy of integration. For example, it performs integration while maintaining data consistency. This automates data integration between different systems, improving the efficiency of data conversion.

[0075] The conversion unit can use a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, the conversion unit builds a system that uses a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, cloud storage is used to centrally manage data. The conversion unit also develops an algorithm that uses a cloud-based system to achieve centralized data management when C&O performs data conversion. For example, data is converted and managed on the cloud. The conversion unit also develops a system that uses a cloud-based system to achieve centralized data management when C&O performs data conversion, improving management accuracy. For example, data version management is performed on the cloud. In this way, centralized data management can be achieved by using a cloud-based system.

[0076] The conversion unit uses the emotion estimation function to predict customer emotions when C&O performs data conversion, and can perform data conversion based on those emotions. For example, the conversion unit uses the emotion estimation function to predict customer emotions when C&O performs data conversion, and builds a system that performs data conversion based on those emotions. For example, it selects a data format that satisfies the customer. Furthermore, the conversion unit uses the emotion estimation function to predict customer emotions in real time when C&O performs data conversion, and performs data conversion based on those emotions. For example, it selects the optimal data format based on customer emotion data. Furthermore, the conversion unit uses the emotion estimation function to predict customer emotions when C&O performs data conversion, and develops a system that performs data conversion based on those emotions. For example, it optimizes the data conversion process based on customer emotion data. In this way, customer satisfaction is improved by predicting customer emotions and performing data conversion based on those emotions.

[0077] When calculating billing amounts, the billing system can learn from past billing data and perform more accurate calculations. For example, the billing system learns from past billing data and builds a system that calculates more accurate billing amounts. For example, it improves the accuracy of billing amount predictions based on past data. The billing system also analyzes past billing data and develops an algorithm that improves the accuracy of billing amount calculations. For example, it minimizes errors in billing amounts based on past data. The billing system also learns from past billing data and develops a system that calculates more accurate billing amounts. For example, it optimizes a billing amount calculation model based on past data. In this way, by learning from past billing data, more accurate billing amounts can be calculated.

[0078] The billing system can use the emotion estimation function to evaluate the emotional impact that the calculated billing amount has on the customer and present an optimal billing amount. For example, the billing system uses the emotion estimation function on the calculated billing amount to build a system that evaluates the emotional impact on the customer. For example, it presents a billing amount that satisfies the customer. The billing system also uses the emotion estimation function on the calculated billing amount to develop an algorithm that evaluates the customer's emotional response. For example, it proposes an optimal billing amount based on customer emotion data. The billing system also uses the emotion estimation function on the calculated billing amount to develop a system that evaluates the emotional impact on the customer. For example, it adjusts the billing amount based on the customer emotion data. In this way, it is possible to present an optimal billing amount by evaluating the emotional impact on the customer.

[0079] The billing system can be made to accommodate different currencies or tax rates when calculating billing amounts. For example, the billing system builds a system that can accommodate different currencies and tax rates. For example, it automatically applies multiple currencies and tax rates to calculate billing amounts. The billing system also develops an algorithm that automatically recognizes different currencies and tax rates and calculates the appropriate billing amount. For example, it automates currency conversion and tax rate application. The billing system also develops a system that can accommodate different currencies and tax rates, improving calculation accuracy. For example, it sets calculation rules for handling different currencies and tax rates uniformly. This makes it possible to accommodate different currencies and tax rates, thereby enabling international customers to be accommodated.

[0080] The fee system can visually display the billing amount using a visual interface when calculating the billing amount. For example, the fee system builds a system that visually displays the billing amount using a visual interface when calculating the billing amount. For example, the fee system displays the billing amount using a graph or chart. Also, the fee system develops an algorithm that visually displays the billing amount using a visual interface when calculating the billing amount. For example, the fee system visually displays the breakdown of the billing amount. Also, the fee system develops a system that visually displays the billing amount using a visual interface when calculating the billing amount, improving the display accuracy. For example, the fee system displays fluctuations in the billing amount in a graph. In this way, the billing amount can be visually displayed using the visual interface.

[0081] The billing system can use the emotion estimation function to collect customers' emotional reactions to the calculated billing amount and improve the calculation accuracy based on that feedback. For example, the billing system builds a system that uses the emotion estimation function on the calculated billing amount to collect customers' emotional reactions. For example, the calculation accuracy of the billing amount is improved based on the emotion data. The billing system also uses the emotion estimation function on the calculated billing amount to collect customers' emotional reactions in real time. For example, the billing amount is adjusted based on the emotion score. The billing system also uses the emotion estimation function on the calculated billing amount to collect customers' emotional reactions and develops a system that improves the calculation accuracy based on that feedback. For example, the billing amount calculation model is optimized based on the emotion data. This makes it possible to collect customers' emotional reactions and improve the calculation accuracy based on that feedback.

[0082] C&O will develop an algorithm that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists. For example, it will detect abnormal data and issue an alert. C&O will also develop an algorithm that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists. For example, it will detect abnormal patterns and issue an alert. C&O will also develop a system that enables the generation AI to automatically detect anomalies when results are checked and issue an alert if a problem exists, improving detection accuracy. For example, it will detect abnormal data in real time and issue an alert. This will improve the accuracy of result confirmation by automatically detecting anomalies and issuing an alert if a problem exists.

[0083] When checking results, C&O can refer to past check results and perform the check work efficiently. For example, C&O builds a system that refers to past check results when checking results and performs the check work efficiently. For example, it optimizes the check work based on past data. C&O also develops an algorithm that refers to past check results in real time when checking results and performs the check work efficiently. For example, it optimizes the check work procedure based on past data. C&O also develops a system that refers to past check results when checking results and performs the check work efficiently, improving the accuracy of the check. For example, it improves the accuracy of the check work based on past data. In this way, by referring to past check results, the check work can be performed efficiently.

[0084] C&O can use the emotion estimation function to analyze the emotions of workers when checking results and make suggestions to improve work efficiency. For example, when checking results, C&O uses the emotion estimation function to analyze the emotions of workers and make suggestions to improve work efficiency. For example, if a worker is feeling stressed, C&O makes suggestions to help them relax. C&O also uses the emotion estimation function to analyze the emotions of workers in real time when checking results and make suggestions to improve work efficiency. For example, it suggests an optimal work environment based on the worker's emotion data. C&O also develops a system that uses the emotion estimation function to analyze the emotions of workers when checking results and improve work efficiency. For example, it optimizes work processes based on the worker's emotion data. As a result, the efficiency of result checking is improved by analyzing the emotions of workers and making suggestions to improve work efficiency.

[0085] C&O can automate data sharing between different systems when checking results. For example, C&O will build a system that automates data sharing between different systems when checking results. For example, it will automate the sending and receiving of data, reducing manual work. C&O will also develop algorithms that automate data sharing between different systems when checking results. For example, it will perform data format conversion and sharing all at once. C&O will also develop a system that automates data sharing between different systems when checking results, improving the accuracy of sharing. For example, it will share data while maintaining data consistency. In this way, automating data sharing between different systems will improve the efficiency of result checking.

[0086] C&O can use a cloud-based system to achieve centralized data management when checking results. For example, C&O builds a system that uses a cloud-based system to achieve centralized data management when checking results. For example, it uses cloud storage to centrally manage data. C&O also develops an algorithm that uses a cloud-based system to achieve centralized data management when checking results. For example, it converts and manages data on the cloud. C&O also develops a system that uses a cloud-based system to achieve centralized data management when checking results, improving management accuracy. For example, it manages data versions on the cloud. In this way, it is possible to achieve centralized data management by using a cloud-based system.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] In the input section, as corporate sales representatives enter data, the generation AI analyzes the input content in real time and proposes appropriate discount patterns. For example, it can present discount patterns that have been successful in the past under similar conditions. In addition, as corporate sales representatives continue to enter data, the generation AI analyzes the input content in real time and proposes appropriate discount patterns. For example, it can automatically adjust discount rates and benefits based on the input content. In addition, as corporate sales representatives enter relative discount details, the generation AI analyzes the input content in real time and proposes discount patterns according to the input content. For example, it can present optimal discount details based on the customer's past transaction history. This allows for efficient discount settings by proposing appropriate discount patterns in real time as corporate sales representatives enter data.

[0089] The input unit allows corporate sales representatives to refer to past success stories or similar cases when entering data, thereby deriving the optimal discount content. For example, it displays discount content that has been successful in the past under similar conditions. The input unit also displays past success stories and similar cases in real time as the corporate sales representative continues entering data, thereby deriving the optimal discount content. For example, it uses the discount rates and benefits of successful cases as reference. The input unit also provides an interface that allows corporate sales representatives to refer to past success stories and similar cases when entering relative discount content. For example, it displays detailed data on success stories and derives the optimal discount content. This makes it possible to derive the optimal discount content by referring to past success stories and similar cases.

[0090] The input unit uses the emotion estimation function to analyze the emotions of corporate salespeople when they enter information and can make discount proposals that elicit positive emotions. For example, it displays an encouraging message according to the content of the input. Furthermore, as the corporate salespeople continue to enter information, the input unit uses the emotion estimation function to analyze their emotions at the time of entry in real time and makes discount proposals that elicit positive emotions. For example, it presents success stories to increase motivation. Furthermore, when the corporate salespeople enter relative discount details, the input unit uses the emotion estimation function to analyze their emotions at the time of entry and makes discount proposals that elicit positive emotions. For example, it provides positive feedback according to the content of the input. In this way, business efficiency is improved by analyzing the emotions of corporate salespeople and making discount proposals that elicit positive emotions.

[0091] The input unit allows corporate sales representatives to input relative discount details using voice input, and the generation AI can analyze the voice and convert it into text. For example, the input content is automatically converted into text using voice recognition technology. The input unit also allows corporate sales representatives to input relative discount details using voice input, and the generation AI analyzes the voice in real time and converts it into text. For example, the input content is instantly displayed as text. The input unit also develops a system in which corporate sales representatives input relative discount details using voice input, and the generation AI analyzes the voice and converts it into text. For example, voice recognition technology is used to achieve high-precision conversion into text. As a result, the use of voice input can make the input work of corporate sales representatives more efficient.

[0092] The input unit can use a visual interface to allow corporate sales representatives to visually set discount details when entering data. For example, discount rates and benefits can be set using drag and drop. The input unit can also use a visual interface to allow corporate sales representatives to visually set discount details while continuing to enter data. For example, the discount details can be displayed using graphs or charts. The input unit can also develop a system that allows corporate sales representatives to visually set discount details using a visual interface when entering relative discount details. For example, the discount details can be set using icons or images. This allows discount details to be set visually using the visual interface.

[0093] The input unit uses the emotion estimation function to predict the customer's emotions when the corporate sales representative enters information, and can propose discounts based on those emotions. For example, it proposes discounts that the customer has positive emotions about. Furthermore, as the corporate sales representative continues entering information, the input unit uses the emotion estimation function to predict the customer's emotions in real time, and proposes discounts based on those emotions. For example, it adjusts discount rates and benefits according to the customer's emotions. Furthermore, a system is developed in which the input unit uses the emotion estimation function to predict the customer's emotions when the corporate sales representative enters relative discount details, and proposes discounts based on those emotions. For example, it presents optimal discount details based on the customer's emotional data. In this way, customer satisfaction is improved by predicting the customer's emotions and proposing discounts based on those emotions.

[0094] The analysis unit learns from past data and can generate more accurate discount patterns. For example, the generation AI learns from past relative discount data and generates more accurate discount patterns. For example, it proposes optimal discount patterns based on past success stories. The analysis unit also develops algorithms that allow the generation AI to analyze past data and improve the accuracy of discount patterns. For example, it optimizes discount rates and benefits based on past data. The analysis unit also builds a system that allows the generation AI to learn from past relative discount data and generate more accurate discount patterns. For example, it automatically adjusts discount content based on past data. In this way, more accurate discount patterns can be generated by learning from past data.

[0095] The analysis unit can refer to data from different industries or regions and derive optimal discount patterns. For example, a system can be built in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, discount patterns can be proposed based on success stories from different industries. The analysis unit can also develop an algorithm in which the generation AI analyzes data from different regions and derives optimal discount patterns. For example, it can propose discount content that takes into account the market characteristics of each region. The analysis unit can also develop a system in which the generation AI refers to data from different industries and regions and derives optimal discount patterns. For example, it can integrate data from different industries and regions to optimize discount patterns. This makes it possible to derive optimal discount patterns by referring to data from different industries and regions.

[0096] When analyzing, the analysis unit can visually return discount details using images or graphs. For example, a system can be built that visually returns the discount details analyzed by the generation AI using images and graphs. For example, discount rates and benefits can be displayed in graphs. The analysis unit can also develop an algorithm that automatically generates images and graphs to visually display the discount details analyzed by the generation AI. For example, the discount details can be returned in a format that is visually easy to understand. The analysis unit can also develop a system that visually returns the discount details analyzed by the generation AI using images and graphs. For example, visually displaying discount patterns makes them easier for corporate sales representatives to understand. This makes it possible to visually return discount details using images and graphs.

[0097] The analysis unit uses the emotion estimation function to collect the emotional reactions of corporate salespeople to the analyzed discount details, and can improve the accuracy of the analysis based on that feedback. For example, a system is constructed that uses the emotion estimation function on the discount details analyzed by the generation AI to collect the emotional reactions of corporate salespeople. For example, the analysis unit improves the analysis accuracy of the discount details based on the emotion data. The analysis unit also uses the emotion estimation function on the discount details analyzed by the generation AI to collect the emotional reactions of corporate salespeople in real time. For example, the discount details are adjusted based on the emotion score. The analysis unit also uses the emotion estimation function on the discount details analyzed by the generation AI to collect the emotional reactions of corporate salespeople, and develops a system that improves the analysis accuracy based on that feedback. For example, the discount pattern is optimized based on the emotion data. In this way, the emotional reactions of corporate salespeople can be collected, and the analysis accuracy can be improved based on that feedback.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The input unit accepts the relative discount details entered by the corporate sales representative in natural language. For example, the corporate sales representative might enter the details in the form of "This customer will receive a 10% discount and will also receive a free three-month period." The input unit can also input the relative discount details using voice input. Step 2: The analysis unit analyzes the relative discount content received by the input unit and returns it in a patterned format. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the relative discount content, classifying "10% discount" and "3-month free period" into their respective patterns, and returns them to the system. The analysis unit can also learn from past data to generate more accurate discount patterns. Step 3: The conversion unit converts the patterned relative discount information returned by the analysis unit into a data format that can be input into the fee system. For example, the conversion unit converts the patterned relative discount information into CSV format or XML format and inputs it into the fee system.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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. [Explanation of symbols]

[0167] 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 unit that accepts relative discount details input in natural language by a corporate sales representative; an analysis unit that analyzes the relative discount content received by the input unit and returns it in a patterned format; a conversion unit that converts the patterned relative discount content returned by the analysis unit into a data format that can be input into a fee system. A system characterized by:

2. The input unit When a corporate salesperson enters information, the generation AI analyzes the input in real time and suggests appropriate discount patterns.

2. The system of claim 1.

3. The analysis unit Learn from past data to generate more accurate discount patterns 2. The system of claim 1.

4. The conversion unit Based on the data returned by the generation AI, C&O will develop an algorithm that automatically converts the data.

2. The system of claim 1.

5. The fee system is Evaluate the emotional impact of calculated billing amounts on customers and propose optimal billing amounts 2. The system of claim 1.

6. The C&O When checking the results, analyze the worker's emotions and make suggestions to improve work efficiency.

2. The system of claim 1.

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