system

Abstract

The system according to this embodiment aims to enable users to practice effective proposal methods and negotiation strategies in advance, based on the characteristics of their clients' representatives. [Solution] The system according to the embodiment comprises a reception unit, a learning unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs information of the contact person of the business partner. The learning unit learns based on the information input by the reception unit. The generation unit generates advice and suggestions based on the data learned by the learning unit. The simulation unit performs a dialogue simulation based on the advice and suggestions generated by the generation unit. The feedback unit inputs feedback on the dialogue performed by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to practice in advance an effective proposal method or negotiation strategy based on the characteristics of the person in charge of the business partner, and there is a problem that there is a limit to improving the negotiation success rate.

[0005] The system according to the embodiment aims to enable practicing in advance an effective proposal method or negotiation strategy based on the characteristics of the person in charge of the business partner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a learning unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs information about the contact person of the business partner. The learning unit learns based on the information input by the reception unit. The generation unit generates advice and suggestions based on the data learned by the learning unit. The simulation unit performs a dialogue simulation based on the advice and suggestions generated by the generation unit. The feedback unit inputs feedback on the dialogue performed by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows for the practice of effective proposal methods and negotiation strategies based on the characteristics of the client's representative in advance. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business communication support system according to an embodiment of the present invention is a system that precisely analyzes the characteristics of individual business partners using advanced machine learning algorithms based on detailed information such as the personality, past reactions, and behavioral patterns of the business partner's representative. The business communication support system allows users to conduct real-time dialogue simulations with a virtual model of the business partner generated by AI before actual business negotiations or meetings, enabling them to practice effective proposal methods and negotiation strategies in advance. Furthermore, by inputting feedback after actual meetings, the business communication support system allows the AI ​​to continuously learn and provide more refined advice. For example, the business communication support system can be widely used from training new salespeople to developing strategies for veteran salespeople, contributing to improved negotiation success rates, increased customer satisfaction, and the building of long-term business relationships. The business communication support system goes beyond being a mere simulation tool; it functions as a comprehensive communication enhancement platform that continuously improves the user's interpersonal skills and supports the growth of business professionals. As a result, the business communication support system can improve the user's interpersonal skills and increase the success rate of business negotiations.

[0029] The business communication support system according to this embodiment comprises a reception unit, a learning unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs information about the contact person at the business partner. This information includes, but is not limited to, names, job titles, and past transaction history. The reception unit can, for example, allow users to manually input the contact person's information. The reception unit can also automatically collect information about the contact person at the business partner. For example, the reception unit can obtain information about the contact person at the business partner from a database. The learning unit learns based on the information input by the reception unit. The learning unit analyzes the personality, past reactions, and behavioral patterns of the contact person at the business partner using, for example, a machine learning algorithm. The learning unit can, for example, analyze the content of past email replies and conversation records of the contact person at the business partner. The learning unit can also analyze the behavioral patterns of the contact person at the business partner. The generation unit generates advice and suggestions based on the data learned by the learning unit. The generation unit generates, for example, personalized advice and suggestions. The generation unit can, for example, propose effective sales strategies and communication methods to the user. The generation unit can also instruct the user on appropriate sales talk. The simulation unit performs dialogue simulations based on the advice and suggestions generated by the generation unit. The simulation unit performs real-time dialogue simulations with, for example, a virtual model of a client generated by AI. The simulation unit allows, for example, the user to practice before an actual dialogue. The simulation unit can also provide simulations to improve the user's communication skills. The feedback unit inputs feedback on the dialogue conducted by the simulation unit. The feedback unit allows, for example, the user to input feedback after an actual meeting. The feedback unit allows, for example, the user to input feedback using a feedback form. The feedback unit also allows the user to input feedback using voice input.As a result, the business communication support system according to this embodiment can improve the user's interpersonal skills and increase the success rate of business negotiations.

[0030] The reception desk inputs information about client contacts. This information includes, but is not limited to, names, job titles, and past transaction history. The reception desk allows users to manually input client contact information. It can also automatically collect client contact information. For example, it can retrieve client contact information from a database. Specifically, the reception desk provides a form for inputting basic client contact information through a user interface. This form includes fields such as name, job title, contact information, past transaction history, and client company information. Users can enter the necessary information into these fields and register it in the system. Furthermore, the reception desk also has the ability to automatically retrieve client contact information by linking with external databases and CRM systems. For example, it can retrieve the latest job title and contact information of client contacts from the company's official website or publicly available business databases. It can also automatically collect and integrate past transaction history and communication history into the system. This allows the reception desk to quickly collect accurate and up-to-date information without requiring users to manually input information. Furthermore, the reception department centrally manages the collected information and provides interfaces for collaboration with other departments and systems. For example, the collected information on client contacts is stored in a database so that the learning and generation departments can access it. This allows the reception department to collect information efficiently and effectively, improving the overall performance of the system.

[0031] The learning unit learns based on information entered by the reception unit. For example, the learning unit uses machine learning algorithms to analyze the personality, past reactions, and behavioral patterns of client representatives. The learning unit can, for example, analyze the content of past email replies and conversation records of client representatives. It can also analyze the behavioral patterns of client representatives. Specifically, the learning unit uses natural language processing (NLP) technology to analyze text data from emails and conversations of client representatives and extract linguistic characteristics and emotional tendencies. This allows it to understand what kind of language and expressions client representatives prefer and how they react in different situations. Furthermore, the learning unit analyzes the behavioral patterns of client representatives based on past transaction and communication history. For example, it can analyze what kinds of transactions were successful and what kinds of proposals were accepted during specific periods to understand the preferences and tendencies of client representatives. Based on this data, the learning unit creates profiles of client representatives and provides foundational information for proposing individual approaches. In addition, the learning unit can continuously incorporate new data and update its models to always perform analysis based on the latest information. This allows the learning unit to accurately understand the personality and behavioral patterns of client representatives and provide a foundation for proposing the most suitable communication methods to users.

[0032] The generation unit generates advice and suggestions based on data learned by the learning unit. For example, the generation unit generates personalized advice and suggestions. For example, the generation unit can suggest effective sales strategies and communication methods to the user. The generation unit can also instruct the user on appropriate sales talk. Specifically, the generation unit generates advice tailored to individual needs and preferences based on the client contact profile information provided by the learning unit. For example, it can suggest suggestions that the client contact has responded favorably to in the past, or sales talk using specific wording. The generation unit uses natural language generation (NLG) technology to generate specific advice and suggestions for the user in written form. This allows the user to take a more effective approach in communicating with client contacts. The generation unit also makes suggestions to optimize the user's sales strategies and communication methods. For example, it can suggest when to approach a specific client and what materials to prepare. Furthermore, the generation unit can also suggest the optimal approach based on the user's past successes and failures. This allows the generation unit to provide the user with specific and practical advice, increasing the success rate of business negotiations.

[0033] The simulation unit performs dialogue simulations based on the advice and suggestions generated by the generation unit. For example, the simulation unit performs real-time dialogue simulations with a virtual model of a business partner generated by AI. The simulation unit allows users to practice before actual dialogues. The simulation unit can also provide simulations to improve users' communication skills. Specifically, the simulation unit generates a virtual model of a business partner's representative, and users can practice in an environment close to actual business negotiations by interacting with this model. This virtual model reproduces the personality and reaction patterns of the business partner's representative based on profile information provided by the learning unit. Through the simulation, users can predict the reactions of the business partner's representative and learn appropriate response methods. The simulation unit analyzes the content and progress of the dialogue in real time and provides feedback to the user. For example, it can evaluate the user's statements, tone, and timing, and point out areas for improvement. The simulation unit can also prepare multiple scenarios and train users to handle various situations. In this way, the simulation unit can help users improve their communication skills and respond with confidence in actual business negotiations.

[0034] The feedback unit receives feedback on the dialogue conducted by the simulation unit. For example, users can enter feedback after an actual meeting. The feedback unit allows users to enter feedback using a feedback form, or they can enter feedback using voice input. Specifically, the feedback unit provides a form for entering feedback through a user interface. This form includes fields such as the content of the dialogue, successes, areas for improvement, and next-time approaches. Users can enter the necessary information into these fields and register it in the system. Furthermore, the feedback unit has a voice input function, allowing users to enter feedback by voice. Voice-entered feedback is converted to text using speech recognition technology and stored in the system. The feedback unit centrally manages this feedback information and stores it in a database for access by the learning and generation units. This allows the feedback unit to collect valuable information based on users' actual experiences and improve the overall system performance. Furthermore, the feedback unit can analyze the collected feedback information and suggest specific areas for improvement and next-time approaches to the user. This allows the feedback department to continuously improve users' interpersonal skills and support them in increasing their success rate in business negotiations.

[0035] The learning unit can analyze the personality, past reactions, and behavioral patterns of client representatives. For example, the learning unit can administer a personality test to a client representative and analyze their personality based on the results. The learning unit can also analyze the content of past email replies from client representatives to understand their past reactions. Furthermore, the learning unit can analyze records of conversations with client representatives to identify their behavioral patterns. As a result, the learning unit can generate more precise advice and suggestions by analyzing the personality, past reactions, and behavioral patterns of client representatives. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the results of a client representative's personality test into a generating AI and have the generating AI perform the personality analysis.

[0036] The generation unit can generate personalized advice and suggestions. For example, the generation unit can provide customized advice based on the user's needs. The generation unit can also generate suggestions based on past data. Furthermore, the generation unit can provide suggestions tailored to the individual needs of the user. In this way, the generation unit can provide more effective advice to the user by generating personalized advice and suggestions. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customized advice based on the user's needs into a generation AI and have the generation AI perform the generation of advice.

[0037] The simulation unit can perform real-time dialogue simulations with a virtual model of a trading partner generated by AI. For example, the simulation unit allows users to practice before actual dialogues. The simulation unit can also provide simulations to improve users' communication skills. Furthermore, by performing real-time dialogue simulations with a virtual model of a trading partner generated by AI, users can practice before actual dialogues and improve their communication skills. Some or all of the above-described processes in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input a virtual model of a trading partner generated by AI into a generating AI and have the generating AI execute a dialogue simulation.

[0038] The feedback unit can receive feedback after the actual meeting. For example, the user can enter feedback using a feedback form. Alternatively, the user can enter feedback using voice input. Furthermore, by receiving feedback after the actual meeting, the AI ​​can continuously learn and improve the accuracy and suitability of its suggestions. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user-entered feedback into a generating AI and have the generating AI analyze the feedback.

[0039] The learning unit can continuously learn from feedback and improve the accuracy and suitability of its suggestions. For example, the learning unit can analyze the content of the feedback to improve the accuracy of its suggestions. It can also improve the suitability of its suggestions based on the content of the feedback. Furthermore, by continuously learning from feedback, the learning unit can improve the accuracy and suitability of its suggestions. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the content of the feedback into a generating AI and have the generating AI perform improvements to the accuracy and suitability of its suggestions.

[0040] The reception desk can analyze the user's past input history when they enter information about a business partner and suggest the most suitable input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that will be used at specific times based on the user's past input history. This allows for efficient information entry by suggesting the most suitable input method through analysis of the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the most suitable input method.

[0041] The reception system can filter input data based on the user's current projects and areas of interest when the user enters information about a business partner. For example, the reception system can prioritize displaying information related to the user's current ongoing projects. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the reception system can suggest relevant information by referring to the user's past project history. This allows for the efficient collection of highly relevant information by filtering input data based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering of the input data.

[0042] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when inputting information about a business partner. For example, if the user is in a specific region, the reception desk can prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception desk can suggest highly relevant information based on their current location. Additionally, if the user is in a specific location, the reception desk can prioritize inputting information related to that location. This enables efficient information gathering by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when the user enters information about a client's representative. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant information. The reception desk can also consider the user's social media followers and friendships to prompt the user to input highly relevant information. Furthermore, the reception desk can suggest relevant information by referring to the user's social media activity history. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI suggest relevant information.

[0044] The learning unit can analyze the past reactions and behavioral patterns of client representatives during the learning process and optimize its learning algorithm. For example, the learning unit can analyze past emails and messages from client representatives to learn their reaction patterns. It can also analyze past conversation history from client representatives to learn their behavioral patterns. Furthermore, the learning unit can analyze past behavioral history from client representatives to optimize its learning algorithm. By analyzing the past reactions and behavioral patterns of client representatives, the learning algorithm can be optimized, enabling the generation of more precise advice and suggestions. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past reaction data from client representatives into a generating AI and have the generating AI optimize the learning algorithm.

[0045] The learning unit can perform learning while considering the attribute information of the client's contact person. For example, the learning unit can consider the age and gender of the client's contact person. It can also consider the occupation and position of the client's contact person. Furthermore, the learning unit can consider the interests and concerns of the client's contact person. This allows for more effective learning by considering the attribute information of the client's contact person. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the attribute information of the client's contact person into a generating AI and have the generating AI perform the learning.

[0046] The learning unit can perform learning while considering the geographical distribution of customer contacts. For example, the learning unit can perform learning while considering the characteristics of the region where the customer contacts are located. The learning unit can also learn the characteristics of each region based on the geographical distribution of customer contacts. Furthermore, the learning unit can analyze the geographical distribution of customer contacts and optimize the learning algorithm. This makes it possible to perform more effective learning by considering the geographical distribution of customer contacts. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input geographical distribution data of customer contacts into a generating AI and have the generating AI perform the learning.

[0047] The learning unit can improve the accuracy of its learning by referring to relevant literature on client representatives during the learning process. For example, the learning unit can learn by referring to industry literature related to the client representative. It can also learn by referring to past presentations and papers on client representatives. Furthermore, the learning unit can analyze relevant literature on client representatives and optimize the learning algorithm. This improves the accuracy of learning by referring to relevant literature on client representatives. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature data on client representatives into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0048] The generation unit can adjust the level of detail generated when generating advice and suggestions based on the importance of the client contact. For example, the generation unit can provide detailed advice to important client contacts. It can also provide concise advice to general client contacts. Furthermore, the generation unit can adjust the level of detail of the advice according to the importance of the client contact. This allows for the provision of more appropriate advice and suggestions by adjusting the level of detail based on the importance of the client contact. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input client contact importance data into a generation AI and have the generation AI adjust the level of detail of the advice and suggestions.

[0049] The generation unit can apply different generation algorithms depending on the category of the customer contact when generating advice and suggestions. For example, if the customer contact is a customer, the generation unit can apply an algorithm that generates customer-oriented advice. It can also apply an algorithm that generates partner-oriented advice if the customer contact is a partner. Furthermore, if the customer contact is a supplier, the generation unit can apply an algorithm that generates supplier-oriented advice. This allows for the provision of more appropriate advice and suggestions by applying different generation algorithms depending on the customer contact's category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer contact category data into a generation AI and have the generation AI execute the application of advice and suggestion generation algorithms.

[0050] The generation unit can determine the priority of advice and suggestions based on the response time of the client's representative. For example, if the client's representative responds immediately, the generation unit will prioritize generating advice. Conversely, if the client's representative responds later, the generation unit can also postpone generating advice. Furthermore, the generation unit can adjust the order in which advice is generated based on the response time of the client's representative. This allows for the provision of advice and suggestions at a more appropriate time by prioritizing generation based on the response time of the client's representative. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input client representative response time data into a generation AI and have the generation AI adjust the order in which advice and suggestions are generated.

[0051] The generation unit can adjust the order of generation of advice and suggestions based on the relevance of the customer contact. For example, if a customer contact is an important customer, the generation unit will prioritize generating advice. Conversely, if a customer contact is a general customer, the generation unit may postpone generating advice for that customer. Furthermore, the generation unit can adjust the order of advice generation based on the relevance of the customer contact. This allows for the provision of more appropriate advice and suggestions by adjusting the generation order based on the relevance of the customer contact. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input customer contact relevance data into a generation AI and have the generation AI adjust the order of advice and suggestion generation.

[0052] The simulation unit can improve the accuracy of the simulation by referring to the past responses of the customer representative during the dialogue simulation. For example, the simulation unit can perform the simulation by referring to the customer representative's past emails and messages. The simulation unit can also perform the simulation by referring to the customer representative's past conversation history. Furthermore, the simulation unit can perform the simulation by referring to the customer representative's past behavior history. This improves the accuracy of the simulation by referring to the customer representative's past responses. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the customer representative's past response data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0053] The simulation unit can perform dialogue simulations while considering the attribute information of the client's representative. For example, the simulation unit can perform simulations while considering the age and gender of the client's representative. It can also perform simulations while considering the occupation and position of the client's representative. Furthermore, the simulation unit can perform simulations while considering the interests and concerns of the client's representative. By performing simulations while considering the attribute information of the client's representative, a more effective simulation can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the attribute information of the client's representative into a generating AI and have the generating AI execute the simulation.

[0054] The simulation unit can perform dialogue simulations while considering the geographical distribution of customer representatives. For example, the simulation unit can perform simulations while considering the characteristics of the region where the customer representatives are located. The simulation unit can also reflect regional characteristics in the simulation based on the geographical distribution of customer representatives. Furthermore, the simulation unit can analyze the geographical distribution of customer representatives and optimize the simulation algorithm. This allows for more effective simulations by considering the geographical distribution of customer representatives. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input geographical distribution data of customer representatives into a generating AI and have the generating AI execute the simulation.

[0055] The simulation unit can improve the accuracy of the simulation by referring to relevant literature related to the client's representative during dialogue simulations. For example, the simulation unit can perform simulations by referring to industry literature related to the client's representative. The simulation unit can also perform simulations by referring to the client's representative's past presentations and papers. Furthermore, the simulation unit can analyze the client's representative's relevant literature and optimize the simulation algorithm. This improves the accuracy of the simulation by referring to the client's representative's relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the client's representative's relevant literature data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0056] The feedback unit can suggest the optimal feedback method by referring to the past responses of the client's representative when feedback is entered. For example, the feedback unit can provide feedback by referring to the client's representative's past emails and messages. It can also provide feedback by referring to the client's representative's past conversation history. Furthermore, the feedback unit can provide feedback by referring to the client's representative's past behavior history. In this way, by referring to the client's representative's past responses, it can suggest the optimal feedback method. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the client's representative's past response data into a generating AI and have the generating AI suggest the optimal feedback method.

[0057] The feedback unit can provide feedback while considering the attribute information of the customer contact person when feedback is input. For example, the feedback unit can provide feedback while considering the age and gender of the customer contact person. It can also provide feedback while considering the occupation and position of the customer contact person. Furthermore, the feedback unit can provide feedback while considering the interests and concerns of the customer contact person. In this way, more effective feedback can be provided by considering the attribute information of the customer contact person. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the attribute information of the customer contact person into a generating AI and have the generating AI execute the feedback.

[0058] The feedback unit can provide feedback while considering the geographical distribution of customer contacts when feedback is input. For example, the feedback unit can provide feedback while considering the characteristics of the region where the customer contact is located. The feedback unit can also reflect regional characteristics in the feedback based on the geographical distribution of customer contacts. Furthermore, the feedback unit can analyze the geographical distribution of customer contacts and optimize the feedback algorithm. This allows for the provision of more effective feedback by considering the geographical distribution of customer contacts. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical distribution data of customer contacts into a generating AI and have the generating AI execute the feedback.

[0059] The feedback unit can improve the accuracy of its feedback by referring to relevant literature related to the client's representative when inputting feedback. For example, the feedback unit provides feedback by referring to industry literature related to the client's representative. It can also provide feedback by referring to the client's representative's past presentations and papers. Furthermore, the feedback unit can analyze the client's representative's relevant literature and optimize the feedback algorithm. This improves the accuracy of the feedback by referring to the client's representative's relevant literature. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the client's representative's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the feedback.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past input history when they enter information about a client's representative and suggest the most suitable input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the most suitable input method and enable efficient information entry.

[0062] The reception system can filter input data based on the user's current projects and areas of interest when entering information about client contacts. For example, it can prioritize displaying information related to the user's current projects. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, it can suggest relevant information by referring to the user's past project history. This allows for the efficient collection of highly relevant information by filtering input data based on the user's current projects and areas of interest.

[0063] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when entering information about client contacts. For example, if a user is in a specific region, information related to that region will be prioritized. Furthermore, if a user is on the move, highly relevant information can be suggested based on their current location. Additionally, if a user is in a specific location, information related to that location can be prioritized. This allows for efficient information gathering by prioritizing highly relevant information based on the user's geographical location.

[0064] The reception desk can analyze a user's social media activity and input relevant information when entering information about a client's representative. For example, it can analyze the content of a user's social media posts and suggest relevant information. It can also consider the user's social media followers and friendships to prompt for highly relevant information. Furthermore, it can suggest relevant information based on the user's social media activity history. This allows for the efficient collection of relevant information by analyzing the user's social media activity.

[0065] The learning unit can optimize its learning algorithm by analyzing the past reactions and behavioral patterns of client representatives during the learning process. For example, it can analyze past emails and messages from client representatives to learn their reaction patterns. It can also analyze past conversation history to learn their behavioral patterns. Furthermore, it can analyze the past behavioral history of client representatives to optimize the learning algorithm. As a result, by analyzing the past reactions and behavioral patterns of client representatives, the learning algorithm can be optimized, enabling the generation of more precise advice and suggestions.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk enters the contact information of the client. This information includes, for example, name, job title, and past transaction history. The reception desk allows users to manually enter the contact information of the client. Alternatively, the reception desk can automatically collect the contact information of the client, for example, by retrieving it from a database. Step 2: The learning unit learns based on the information entered by the reception unit. The learning unit uses machine learning algorithms to analyze the personality, past reactions, and behavioral patterns of the client's representative. For example, it can analyze the content of past email replies and conversation records of the client's representative to analyze their behavioral patterns. Step 3: The generation unit generates advice and suggestions based on the data learned by the learning unit. The generation unit generates personalized advice and suggestions, proposing effective sales strategies and communication methods to the user. It can also provide appropriate sales pitches. Step 4: The simulation unit performs a dialogue simulation based on the advice and suggestions generated by the generation unit. The simulation unit performs a real-time dialogue simulation with a virtual model of a business partner generated by the AI, allowing the user to practice before actual dialogue. It also provides a simulation to improve the user's communication skills. Step 5: The feedback section inputs feedback on the dialogue conducted by the simulation section. The feedback section allows users to input feedback after an actual meeting, and feedback can be entered using a feedback form or voice input.

[0068] (Example of form 2) The business communication support system according to an embodiment of the present invention is a system that precisely analyzes the characteristics of individual business partners using advanced machine learning algorithms based on detailed information such as the personality, past reactions, and behavioral patterns of the business partner's representative. The business communication support system allows users to conduct real-time dialogue simulations with a virtual model of the business partner generated by AI before actual business negotiations or meetings, enabling them to practice effective proposal methods and negotiation strategies in advance. Furthermore, by inputting feedback after actual meetings, the business communication support system allows the AI ​​to continuously learn and provide more refined advice. For example, the business communication support system can be widely used from training new salespeople to developing strategies for veteran salespeople, contributing to improved negotiation success rates, increased customer satisfaction, and the building of long-term business relationships. The business communication support system goes beyond being a mere simulation tool; it functions as a comprehensive communication enhancement platform that continuously improves the user's interpersonal skills and supports the growth of business professionals. As a result, the business communication support system can improve the user's interpersonal skills and increase the success rate of business negotiations.

[0069] The business communication support system according to this embodiment comprises a reception unit, a learning unit, a generation unit, a simulation unit, and a feedback unit. The reception unit inputs information about the contact person at the business partner. This information includes, but is not limited to, names, job titles, and past transaction history. The reception unit can, for example, allow users to manually input the contact person's information. The reception unit can also automatically collect information about the contact person at the business partner. For example, the reception unit can obtain information about the contact person at the business partner from a database. The learning unit learns based on the information input by the reception unit. The learning unit analyzes the personality, past reactions, and behavioral patterns of the contact person at the business partner using, for example, a machine learning algorithm. The learning unit can, for example, analyze the content of past email replies and conversation records of the contact person at the business partner. The learning unit can also analyze the behavioral patterns of the contact person at the business partner. The generation unit generates advice and suggestions based on the data learned by the learning unit. The generation unit generates, for example, personalized advice and suggestions. The generation unit can, for example, propose effective sales strategies and communication methods to the user. The generation unit can also instruct the user on appropriate sales talk. The simulation unit performs dialogue simulations based on the advice and suggestions generated by the generation unit. The simulation unit performs real-time dialogue simulations with, for example, a virtual model of a client generated by AI. The simulation unit allows, for example, the user to practice before an actual dialogue. The simulation unit can also provide simulations to improve the user's communication skills. The feedback unit inputs feedback on the dialogue conducted by the simulation unit. The feedback unit allows, for example, the user to input feedback after an actual meeting. The feedback unit allows, for example, the user to input feedback using a feedback form. The feedback unit also allows the user to input feedback using voice input.As a result, the business communication support system according to this embodiment can improve the user's interpersonal skills and increase the success rate of business negotiations.

[0070] The reception desk inputs information about client contacts. This information includes, but is not limited to, names, job titles, and past transaction history. The reception desk allows users to manually input client contact information. It can also automatically collect client contact information. For example, it can retrieve client contact information from a database. Specifically, the reception desk provides a form for inputting basic client contact information through a user interface. This form includes fields such as name, job title, contact information, past transaction history, and client company information. Users can enter the necessary information into these fields and register it in the system. Furthermore, the reception desk also has the ability to automatically retrieve client contact information by linking with external databases and CRM systems. For example, it can retrieve the latest job title and contact information of client contacts from the company's official website or publicly available business databases. It can also automatically collect and integrate past transaction history and communication history into the system. This allows the reception desk to quickly collect accurate and up-to-date information without requiring users to manually input information. Furthermore, the reception department centrally manages the collected information and provides interfaces for collaboration with other departments and systems. For example, the collected information on client contacts is stored in a database so that the learning and generation departments can access it. This allows the reception department to collect information efficiently and effectively, improving the overall performance of the system.

[0071] The learning unit learns based on information entered by the reception unit. For example, the learning unit uses machine learning algorithms to analyze the personality, past reactions, and behavioral patterns of client representatives. The learning unit can, for example, analyze the content of past email replies and conversation records of client representatives. It can also analyze the behavioral patterns of client representatives. Specifically, the learning unit uses natural language processing (NLP) technology to analyze text data from emails and conversations of client representatives and extract linguistic characteristics and emotional tendencies. This allows it to understand what kind of language and expressions client representatives prefer and how they react in different situations. Furthermore, the learning unit analyzes the behavioral patterns of client representatives based on past transaction and communication history. For example, it can analyze what kinds of transactions were successful and what kinds of proposals were accepted during specific periods to understand the preferences and tendencies of client representatives. Based on this data, the learning unit creates profiles of client representatives and provides foundational information for proposing individual approaches. In addition, the learning unit can continuously incorporate new data and update its models to always perform analysis based on the latest information. This allows the learning unit to accurately understand the personality and behavioral patterns of client representatives and provide a foundation for proposing the most suitable communication methods to users.

[0072] The generation unit generates advice and suggestions based on data learned by the learning unit. For example, the generation unit generates personalized advice and suggestions. For example, the generation unit can suggest effective sales strategies and communication methods to the user. The generation unit can also instruct the user on appropriate sales talk. Specifically, the generation unit generates advice tailored to individual needs and preferences based on the client contact profile information provided by the learning unit. For example, it can suggest suggestions that the client contact has responded favorably to in the past, or sales talk using specific wording. The generation unit uses natural language generation (NLG) technology to generate specific advice and suggestions for the user in written form. This allows the user to take a more effective approach in communicating with client contacts. The generation unit also makes suggestions to optimize the user's sales strategies and communication methods. For example, it can suggest when to approach a specific client and what materials to prepare. Furthermore, the generation unit can also suggest the optimal approach based on the user's past successes and failures. This allows the generation unit to provide the user with specific and practical advice, increasing the success rate of business negotiations.

[0073] The simulation unit performs dialogue simulations based on the advice and suggestions generated by the generation unit. For example, the simulation unit performs real-time dialogue simulations with a virtual model of a business partner generated by AI. The simulation unit allows users to practice before actual dialogues. The simulation unit can also provide simulations to improve users' communication skills. Specifically, the simulation unit generates a virtual model of a business partner's representative, and users can practice in an environment close to actual business negotiations by interacting with this model. This virtual model reproduces the personality and reaction patterns of the business partner's representative based on profile information provided by the learning unit. Through the simulation, users can predict the reactions of the business partner's representative and learn appropriate response methods. The simulation unit analyzes the content and progress of the dialogue in real time and provides feedback to the user. For example, it can evaluate the user's statements, tone, and timing, and point out areas for improvement. The simulation unit can also prepare multiple scenarios and train users to handle various situations. In this way, the simulation unit can help users improve their communication skills and respond with confidence in actual business negotiations.

[0074] The feedback unit receives feedback on the dialogue conducted by the simulation unit. For example, users can enter feedback after an actual meeting. The feedback unit allows users to enter feedback using a feedback form, or they can enter feedback using voice input. Specifically, the feedback unit provides a form for entering feedback through a user interface. This form includes fields such as the content of the dialogue, successes, areas for improvement, and next-time approaches. Users can enter the necessary information into these fields and register it in the system. Furthermore, the feedback unit has a voice input function, allowing users to enter feedback by voice. Voice-entered feedback is converted to text using speech recognition technology and stored in the system. The feedback unit centrally manages this feedback information and stores it in a database for access by the learning and generation units. This allows the feedback unit to collect valuable information based on users' actual experiences and improve the overall system performance. Furthermore, the feedback unit can analyze the collected feedback information and suggest specific areas for improvement and next-time approaches to the user. This allows the feedback department to continuously improve users' interpersonal skills and support them in increasing their success rate in business negotiations.

[0075] The learning unit can analyze the personality, past reactions, and behavioral patterns of client representatives. For example, the learning unit can administer a personality test to a client representative and analyze their personality based on the results. The learning unit can also analyze the content of past email replies from client representatives to understand their past reactions. Furthermore, the learning unit can analyze records of conversations with client representatives to identify their behavioral patterns. As a result, the learning unit can generate more precise advice and suggestions by analyzing the personality, past reactions, and behavioral patterns of client representatives. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the results of a client representative's personality test into a generating AI and have the generating AI perform the personality analysis.

[0076] The generation unit can generate personalized advice and suggestions. For example, the generation unit can provide customized advice based on the user's needs. The generation unit can also generate suggestions based on past data. Furthermore, the generation unit can provide suggestions tailored to the individual needs of the user. In this way, the generation unit can provide more effective advice to the user by generating personalized advice and suggestions. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customized advice based on the user's needs into a generation AI and have the generation AI perform the generation of advice.

[0077] The simulation unit can perform real-time dialogue simulations with a virtual model of a trading partner generated by AI. For example, the simulation unit allows users to practice before actual dialogues. The simulation unit can also provide simulations to improve users' communication skills. Furthermore, by performing real-time dialogue simulations with a virtual model of a trading partner generated by AI, users can practice before actual dialogues and improve their communication skills. Some or all of the above-described processes in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input a virtual model of a trading partner generated by AI into a generating AI and have the generating AI execute a dialogue simulation.

[0078] The feedback unit can receive feedback after the actual meeting. For example, the user can enter feedback using a feedback form. Alternatively, the user can enter feedback using voice input. Furthermore, by receiving feedback after the actual meeting, the AI ​​can continuously learn and improve the accuracy and suitability of its suggestions. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user-entered feedback into a generating AI and have the generating AI analyze the feedback.

[0079] The learning unit can continuously learn from feedback and improve the accuracy and suitability of its suggestions. For example, the learning unit can analyze the content of the feedback to improve the accuracy of its suggestions. It can also improve the suitability of its suggestions based on the content of the feedback. Furthermore, by continuously learning from feedback, the learning unit can improve the accuracy and suitability of its suggestions. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the content of the feedback into a generating AI and have the generating AI perform improvements to the accuracy and suitability of its suggestions.

[0080] The reception desk can estimate the user's emotions and adjust the timing of information input by the client representative based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the input timing to help them relax. Conversely, if the user is relaxed, the reception desk can speed up the input timing to efficiently collect information. Furthermore, if the user is in a hurry, the reception desk can optimize the input timing to quickly collect information. This allows for more effective information collection by adjusting the timing of information input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception desk can analyze the user's past input history when they enter information about a business partner and suggest the most suitable input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest information that will be used at specific times based on the user's past input history. This allows for efficient information entry by suggesting the most suitable input method through analysis of the user's past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the most suitable input method.

[0082] The reception system can filter input data based on the user's current projects and areas of interest when the user enters information about a business partner. For example, the reception system can prioritize displaying information related to the user's current ongoing projects. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, the reception system can suggest relevant information by referring to the user's past project history. This allows for the efficient collection of highly relevant information by filtering input data based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering of the input data.

[0083] The reception desk can estimate the user's emotions and prioritize the information to be entered based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize the input of important information. If the user is relaxed, the reception desk may also prioritize the input of detailed information. Furthermore, if the user is in a hurry, the reception desk may also prioritize the input of the most important information. This allows for the priority collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when inputting information about a business partner. For example, if the user is in a specific region, the reception desk can prioritize inputting information related to that region. Furthermore, if the user is on the move, the reception desk can suggest highly relevant information based on their current location. Additionally, if the user is in a specific location, the reception desk can prioritize inputting information related to that location. This enables efficient information gathering by prioritizing the input of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0085] The reception desk can analyze the user's social media activity and input relevant information when the user enters information about a client's representative. For example, the reception desk can analyze the content of the user's social media posts and suggest relevant information. The reception desk can also consider the user's social media followers and friendships to prompt the user to input highly relevant information. Furthermore, the reception desk can suggest relevant information by referring to the user's social media activity history. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input data on the user's social media activity into a generating AI and have the generating AI suggest relevant information.

[0086] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can use detailed data for training. If the user is in a hurry, the learning unit can also prioritize the use of important data for training. Furthermore, if the user is excited, the learning unit can also use visually stimulating data for training. This allows for more effective learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0087] The learning unit can analyze the past reactions and behavioral patterns of client representatives during the learning process and optimize its learning algorithm. For example, the learning unit can analyze past emails and messages from client representatives to learn their reaction patterns. It can also analyze past conversation history from client representatives to learn their behavioral patterns. Furthermore, the learning unit can analyze past behavioral history from client representatives to optimize its learning algorithm. By analyzing the past reactions and behavioral patterns of client representatives, the learning algorithm can be optimized, enabling the generation of more precise advice and suggestions. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past reaction data from client representatives into a generating AI and have the generating AI optimize the learning algorithm.

[0088] The learning unit can perform learning while considering the attribute information of the client's contact person. For example, the learning unit can consider the age and gender of the client's contact person. It can also consider the occupation and position of the client's contact person. Furthermore, the learning unit can consider the interests and concerns of the client's contact person. This allows for more effective learning by considering the attribute information of the client's contact person. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the attribute information of the client's contact person into a generating AI and have the generating AI perform the learning.

[0089] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. It can also decrease the learning frequency when the user is in a hurry. Furthermore, it can adjust the learning frequency when the user is excited. This allows for more effective learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency.

[0090] The learning unit can perform learning while considering the geographical distribution of customer contacts. For example, the learning unit can perform learning while considering the characteristics of the region where the customer contacts are located. The learning unit can also learn the characteristics of each region based on the geographical distribution of customer contacts. Furthermore, the learning unit can analyze the geographical distribution of customer contacts and optimize the learning algorithm. This makes it possible to perform more effective learning by considering the geographical distribution of customer contacts. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input geographical distribution data of customer contacts into a generating AI and have the generating AI perform the learning.

[0091] The learning unit can improve the accuracy of its learning by referring to relevant literature on client representatives during the learning process. For example, the learning unit can learn by referring to industry literature related to the client representative. It can also learn by referring to past presentations and papers on client representatives. Furthermore, the learning unit can analyze relevant literature on client representatives and optimize the learning algorithm. This improves the accuracy of learning by referring to relevant literature on client representatives. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input relevant literature data on client representatives into a generating AI and have the generating AI perform the improvement of learning accuracy.

[0092] The generation unit can estimate the user's emotions and adjust the way advice and suggestions are expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide advice in a gentle tone. If the user is in a hurry, the generation unit can provide advice in a concise and direct tone. Furthermore, if the user is excited, the generation unit can provide advice in a visually stimulating tone. By adjusting the way advice and suggestions are expressed based on the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the way advice and suggestions are expressed.

[0093] The generation unit can adjust the level of detail generated when generating advice and suggestions based on the importance of the client contact. For example, the generation unit can provide detailed advice to important client contacts. It can also provide concise advice to general client contacts. Furthermore, the generation unit can adjust the level of detail of the advice according to the importance of the client contact. This allows for the provision of more appropriate advice and suggestions by adjusting the level of detail based on the importance of the client contact. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input client contact importance data into a generation AI and have the generation AI adjust the level of detail of the advice and suggestions.

[0094] The generation unit can apply different generation algorithms depending on the category of the customer contact when generating advice and suggestions. For example, if the customer contact is a customer, the generation unit can apply an algorithm that generates customer-oriented advice. It can also apply an algorithm that generates partner-oriented advice if the customer contact is a partner. Furthermore, if the customer contact is a supplier, the generation unit can apply an algorithm that generates supplier-oriented advice. This allows for the provision of more appropriate advice and suggestions by applying different generation algorithms depending on the customer contact's category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input customer contact category data into a generation AI and have the generation AI execute the application of advice and suggestion generation algorithms.

[0095] The generation unit can estimate the user's emotions and adjust the length of advice and suggestions based on the estimated emotions. For example, if the user is in a hurry, the generation unit can provide short, concise advice. If the user is relaxed, the generation unit can provide longer advice with more detailed explanations. Furthermore, if the user is excited, the generation unit can provide advice with visually stimulating effects. By adjusting the length of advice and suggestions based on the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of advice and suggestions.

[0096] The generation unit can determine the priority of advice and suggestions based on the response time of the client's representative. For example, if the client's representative responds immediately, the generation unit will prioritize generating advice. Conversely, if the client's representative responds later, the generation unit can also postpone generating advice. Furthermore, the generation unit can adjust the order in which advice is generated based on the response time of the client's representative. This allows for the provision of advice and suggestions at a more appropriate time by prioritizing generation based on the response time of the client's representative. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input client representative response time data into a generation AI and have the generation AI adjust the order in which advice and suggestions are generated.

[0097] The generation unit can adjust the order of generation of advice and suggestions based on the relevance of the customer contact. For example, if a customer contact is an important customer, the generation unit will prioritize generating advice. Conversely, if a customer contact is a general customer, the generation unit may postpone generating advice for that customer. Furthermore, the generation unit can adjust the order of advice generation based on the relevance of the customer contact. This allows for the provision of more appropriate advice and suggestions by adjusting the generation order based on the relevance of the customer contact. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input customer contact relevance data into a generation AI and have the generation AI adjust the order of advice and suggestion generation.

[0098] The simulation unit can estimate the user's emotions and adjust the dialogue simulation method based on the estimated user emotions. For example, if the user is nervous, the simulation unit can provide a simple and easy-to-understand simulation. If the user is relaxed, the simulation unit can also provide a simulation with more detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a concise simulation. This allows for a more effective simulation by adjusting the dialogue simulation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the dialogue simulation method.

[0099] The simulation unit can improve the accuracy of the simulation by referring to the past responses of the customer representative during the dialogue simulation. For example, the simulation unit can perform the simulation by referring to the customer representative's past emails and messages. The simulation unit can also perform the simulation by referring to the customer representative's past conversation history. Furthermore, the simulation unit can perform the simulation by referring to the customer representative's past behavior history. This improves the accuracy of the simulation by referring to the customer representative's past responses. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the customer representative's past response data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0100] The simulation unit can perform dialogue simulations while considering the attribute information of the client's representative. For example, the simulation unit can perform simulations while considering the age and gender of the client's representative. It can also perform simulations while considering the occupation and position of the client's representative. Furthermore, the simulation unit can perform simulations while considering the interests and concerns of the client's representative. By performing simulations while considering the attribute information of the client's representative, a more effective simulation can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the attribute information of the client's representative into a generating AI and have the generating AI execute the simulation.

[0101] The simulation unit can estimate the user's emotions and adjust the order of the dialogue simulation based on the estimated emotions. For example, if the user is nervous, the simulation unit can provide a simulation that starts with a simple dialogue. It can also provide a simulation that starts with a detailed dialogue if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can provide a simulation that starts with a concise dialogue. This allows for a more effective simulation by adjusting the order of the dialogue simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, or not. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the order of the dialogue simulation.

[0102] The simulation unit can perform dialogue simulations while considering the geographical distribution of customer representatives. For example, the simulation unit can perform simulations while considering the characteristics of the region where the customer representatives are located. The simulation unit can also reflect regional characteristics in the simulation based on the geographical distribution of customer representatives. Furthermore, the simulation unit can analyze the geographical distribution of customer representatives and optimize the simulation algorithm. This allows for more effective simulations by considering the geographical distribution of customer representatives. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input geographical distribution data of customer representatives into a generating AI and have the generating AI execute the simulation.

[0103] The simulation unit can improve the accuracy of the simulation by referring to relevant literature related to the client's representative during dialogue simulations. For example, the simulation unit can perform simulations by referring to industry literature related to the client's representative. The simulation unit can also perform simulations by referring to the client's representative's past presentations and papers. Furthermore, the simulation unit can analyze the client's representative's relevant literature and optimize the simulation algorithm. This improves the accuracy of the simulation by referring to the client's representative's relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the client's representative's relevant literature data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0104] The feedback unit can estimate the user's emotions and adjust the feedback input method based on the estimated emotions. For example, if the user is tense, the feedback unit can provide a simple interface and minimize the input steps. If the user is relaxed, the feedback unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the feedback unit can prioritize voice input to allow for quick feedback input. This allows for more effective feedback by adjusting the feedback input method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the feedback input method.

[0105] The feedback unit can suggest the optimal feedback method by referring to the past responses of the client's representative when feedback is entered. For example, the feedback unit can provide feedback by referring to the client's representative's past emails and messages. It can also provide feedback by referring to the client's representative's past conversation history. Furthermore, the feedback unit can provide feedback by referring to the client's representative's past behavior history. In this way, by referring to the client's representative's past responses, it can suggest the optimal feedback method. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the client's representative's past response data into a generating AI and have the generating AI suggest the optimal feedback method.

[0106] The feedback unit can provide feedback while considering the attribute information of the customer contact person when feedback is input. For example, the feedback unit can provide feedback while considering the age and gender of the customer contact person. It can also provide feedback while considering the occupation and position of the customer contact person. Furthermore, the feedback unit can provide feedback while considering the interests and concerns of the customer contact person. In this way, more effective feedback can be provided by considering the attribute information of the customer contact person. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input the attribute information of the customer contact person into a generating AI and have the generating AI execute the feedback.

[0107] The feedback unit can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the feedback unit can prioritize inputting important feedback. If the user is relaxed, the feedback unit can also prioritize inputting detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can prioritize inputting the most important feedback. This allows for more effective feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0108] The feedback unit can provide feedback while considering the geographical distribution of customer contacts when feedback is input. For example, the feedback unit can provide feedback while considering the characteristics of the region where the customer contact is located. The feedback unit can also reflect regional characteristics in the feedback based on the geographical distribution of customer contacts. Furthermore, the feedback unit can analyze the geographical distribution of customer contacts and optimize the feedback algorithm. This allows for the provision of more effective feedback by considering the geographical distribution of customer contacts. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical distribution data of customer contacts into a generating AI and have the generating AI execute the feedback.

[0109] The feedback unit can improve the accuracy of its feedback by referring to relevant literature related to the client's representative when inputting feedback. For example, the feedback unit provides feedback by referring to industry literature related to the client's representative. It can also provide feedback by referring to the client's representative's past presentations and papers. Furthermore, the feedback unit can analyze the client's representative's relevant literature and optimize the feedback algorithm. This improves the accuracy of the feedback by referring to the client's representative's relevant literature. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the client's representative's relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the feedback.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the timing of information entry by the client representative based on those estimates. For example, if the user is stressed, the entry timing can be delayed to help them relax. Conversely, if the user is relaxed, the entry timing can be sped up to efficiently collect information. Furthermore, if the user is in a hurry, the entry timing can be optimized to collect information quickly. In this way, adjusting the timing of information entry based on the user's emotions enables more effective information gathering.

[0112] The learning unit can estimate the user's emotions and select training data based on those emotions. For example, if the user is relaxed, detailed data can be used for training. If the user is in a hurry, important data can be prioritized for training. Furthermore, if the user is excited, visually stimulating data can be used for training. This allows for more effective learning by selecting training data based on the user's emotions.

[0113] The generation unit can estimate the user's emotions and adjust the way advice and suggestions are expressed based on those emotions. For example, if the user is relaxed, it can provide advice in a gentle tone. If the user is in a hurry, it can provide advice in a concise and direct tone. Furthermore, if the user is excited, it can provide advice in a visually stimulating tone. By adjusting the way advice and suggestions are expressed based on the user's emotions, it is possible to provide more effective guidance.

[0114] The simulation unit can estimate the user's emotions and adjust the dialogue simulation method based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand simulation. If the user is relaxed, it can provide a simulation with more detailed information. Furthermore, if the user is in a hurry, it can provide a simulation that gets straight to the point. By adjusting the dialogue simulation method based on the user's emotions, it is possible to provide a more effective simulation.

[0115] The feedback system can estimate the user's emotions and adjust the feedback input method based on those emotions. For example, if the user is nervous, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick feedback input. This allows for more effective feedback by adjusting the feedback input method based on the user's emotions.

[0116] The reception desk can analyze a user's past input history when they enter information about a client's representative and suggest the most suitable input method. For example, it can automatically display information that the user has frequently entered in the past as a suggestion. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest information that the user will use at specific times based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the most suitable input method and enable efficient information entry.

[0117] The reception system can filter input data based on the user's current projects and areas of interest when entering information about client contacts. For example, it can prioritize displaying information related to the user's current projects. It can also filter and display highly relevant information based on the user's areas of interest. Furthermore, it can suggest relevant information by referring to the user's past project history. This allows for the efficient collection of highly relevant information by filtering input data based on the user's current projects and areas of interest.

[0118] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when entering information about client contacts. For example, if a user is in a specific region, information related to that region will be prioritized. Furthermore, if a user is on the move, highly relevant information can be suggested based on their current location. Additionally, if a user is in a specific location, information related to that location can be prioritized. This allows for efficient information gathering by prioritizing highly relevant information based on the user's geographical location.

[0119] The reception desk can analyze a user's social media activity and input relevant information when entering information about a client's representative. For example, it can analyze the content of a user's social media posts and suggest relevant information. It can also consider the user's social media followers and friendships to prompt for highly relevant information. Furthermore, it can suggest relevant information based on the user's social media activity history. This allows for the efficient collection of relevant information by analyzing the user's social media activity.

[0120] The learning unit can optimize its learning algorithm by analyzing the past reactions and behavioral patterns of client representatives during the learning process. For example, it can analyze past emails and messages from client representatives to learn their reaction patterns. It can also analyze past conversation history to learn their behavioral patterns. Furthermore, it can analyze the past behavioral history of client representatives to optimize the learning algorithm. As a result, by analyzing the past reactions and behavioral patterns of client representatives, the learning algorithm can be optimized, enabling the generation of more precise advice and suggestions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk enters the contact information of the client. This information includes, for example, name, job title, and past transaction history. The reception desk allows users to manually enter the contact information of the client. Alternatively, the reception desk can automatically collect the contact information of the client, for example, by retrieving it from a database. Step 2: The learning unit learns based on the information entered by the reception unit. The learning unit uses machine learning algorithms to analyze the personality, past reactions, and behavioral patterns of the client's representative. For example, it can analyze the content of past email replies and conversation records of the client's representative to analyze their behavioral patterns. Step 3: The generation unit generates advice and suggestions based on the data learned by the learning unit. The generation unit generates personalized advice and suggestions, proposing effective sales strategies and communication methods to the user. It can also provide appropriate sales pitches. Step 4: The simulation unit performs a dialogue simulation based on the advice and suggestions generated by the generation unit. The simulation unit performs a real-time dialogue simulation with a virtual model of a business partner generated by the AI, allowing the user to practice before actual dialogue. It also provides a simulation to improve the user's communication skills. Step 5: The feedback section inputs feedback on the dialogue conducted by the simulation section. The feedback section allows users to input feedback after an actual meeting, and feedback can be entered using a feedback form or voice input.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, simulation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to manually input information about the customer's representative. Alternatively, the reception unit can be implemented by the identification processing unit 290 of the data processing unit 12, which can automatically collect information about the customer's representative from the database 24. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a machine learning algorithm to analyze the personality, past reactions, and behavioral patterns of the customer's representative. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates personalized advice and suggestions. The simulation unit is implemented by the control unit 46A of the smart device 14, which performs real-time dialogue simulations with a virtual model of the customer generated by the AI. The feedback unit is implemented by the control unit 46A of the smart device 14, which allows the user to input feedback after an actual meeting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, simulation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to manually input information about the client's representative. Alternatively, the reception unit can be implemented by the identification processing unit 290 of the data processing unit 12, which can automatically collect information about the client's representative from the database 24. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a machine learning algorithm to analyze the personality, past reactions, and behavioral patterns of the client's representative. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates personalized advice and suggestions. The simulation unit is implemented by the control unit 46A of the smart glasses 214, which performs real-time dialogue simulations with a virtual model of the client generated by the AI. The feedback unit is implemented by the control unit 46A of the smart glasses 214, which allows the user to input feedback after an actual meeting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, simulation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to manually input information about the client's representative. Alternatively, the reception unit can be implemented by the identification processing unit 290 of the data processing unit 12, which can automatically collect information about the client's representative from the database 24. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a machine learning algorithm to analyze the personality, past reactions, and behavioral patterns of the client's representative. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates personalized advice and suggestions. The simulation unit is implemented by the control unit 46A of the headset terminal 314, which performs real-time dialogue simulations with a virtual model of the client generated by the AI. The feedback unit is implemented by the control unit 46A of the headset terminal 314, which allows the user to input feedback after an actual meeting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, learning unit, generation unit, simulation unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to manually input information about the customer's representative. Alternatively, the reception unit can be implemented by the identification processing unit 290 of the data processing unit 12, which can automatically collect information about the customer's representative from the database 24. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses a machine learning algorithm to analyze the personality, past reactions, and behavioral patterns of the customer's representative. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates personalized advice and suggestions. The simulation unit is implemented by the control unit 46A of the robot 414, which performs real-time dialogue simulations with a virtual model of the customer generated by the AI. The feedback unit is implemented by the control unit 46A of the robot 414, which allows the user to input feedback after an actual meeting. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception area where you enter the contact information of the client, A learning unit that learns based on the information entered by the reception unit, A generation unit that generates advice and suggestions based on the data learned by the learning unit, A simulation unit that performs a dialogue simulation based on the advice and suggestions generated by the generation unit, The system includes a feedback unit that receives feedback from the dialogue performed by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned learning unit, Analyze the personality, past reactions, and behavioral patterns of the client's representative. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate personalized advice and suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, Perform real-time dialogue simulations with a virtual model of a trading partner generated by AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Enter your feedback after the actual meeting. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, We continuously learn from feedback and improve the accuracy and suitability of our suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of customer contact information entry based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering information about a business partner, the system analyzes the user's past input history and suggests the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering information about a business partner, the input content is filtered based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information about a business partner, the system prioritizes inputting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering information about a client's contact person, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During the learning process, the algorithm is optimized by analyzing the past reactions and behavioral patterns of client representatives. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During the learning process, the attribute information of the client's contact person will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During the learning process, the geographical distribution of client contacts will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During the learning process, refer to relevant literature from client representatives to improve the accuracy of the learning. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way advice and suggestions are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating advice and proposals, adjust the level of detail based on the importance of the client contact. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating advice and suggestions, different generation algorithms are applied depending on the category of the client contact. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of advice and suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating advice and proposals, prioritize their generation based on the response time of the client's representative. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating advice and suggestions, the order of generation is adjusted based on the relevance of the client contact. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, It estimates the user's emotions and adjusts the dialogue simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During dialogue simulations, we improve the accuracy of the simulations by referring to the past responses of the client's representative. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During the dialogue simulation, the simulation will take into account the attribute information of the client's representative. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, It estimates the user's emotions and adjusts the sequence of dialogue simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During dialogue simulations, the simulation is performed taking into account the geographical distribution of the client's representatives. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During dialogue simulations, we improve the accuracy of the simulations by referring to relevant literature related to the client's representative. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When you submit feedback, we will refer to the client's past responses to suggest the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, the attribute information of the customer's contact person should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, consider the geographical distribution of the client's contact person. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When entering feedback, we improve the accuracy of the feedback by referring to relevant literature from the customer's representative. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where you enter the contact information of the client, A learning unit that learns based on the information entered by the reception unit, A generation unit that generates advice and suggestions based on the data learned by the learning unit, A simulation unit that performs a dialogue simulation based on the advice and suggestions generated by the generation unit, The system includes a feedback unit that receives feedback from the dialogue performed by the simulation unit. A system characterized by the following features.

2. The aforementioned learning unit, Analyze the personality, past reactions, and behavioral patterns of the client's representative. The system according to feature 1.

3. The generating unit is Generate personalized advice and suggestions. The system according to feature 1.

4. The aforementioned simulation unit, Perform real-time dialogue simulations with a virtual model of a trading partner generated by AI. The system according to feature 1.

5. The aforementioned feedback unit is Enter your feedback after the actual meeting. The system according to feature 1.

6. The aforementioned learning unit, We continuously learn from feedback and improve the accuracy and suitability of our suggestions. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of customer contact information entry based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering information about a business partner, the system analyzes the user's past input history and suggests the most suitable input method. The system according to feature 1.

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