System

Abstract

An object of a system according to an embodiment is to provide an efficient and effective training method for improving sales power in a short period of time.SOLUTION: A system includes a sales data input part, an analysis part, and a scenario generation part. The sales data input unit inputs sales data. The analysis part analyzes the business data input by the business data input part. The scenario generation unit generates a training scenario based on a result of the analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have a lack of efficient and effective training methods for improving sales skills in a short period of time.

[0005] The system according to the embodiment aims to provide an efficient and effective training method for improving sales skills in a short period of time. [Means for solving the problem]

[0006] The system according to the embodiment includes a sales data input unit, an analysis unit, and a scenario generation unit. The sales data input unit inputs sales data. The analysis unit analyzes the sales data input by the sales data input unit. The scenario generation unit generates a training scenario based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an efficient and effective training method for improving sales skills in a short period of time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The role-play training system according to an embodiment of the present invention is a system in which sales data is input, analyzed by a generation AI, and training scenarios are generated. This allows the role-play training system to improve sales skills in a short period of time.

[0029] The role-play training system according to the embodiment includes a sales data input unit, an analysis unit, and a scenario generation unit. The sales data input unit inputs sales data. For example, the salesperson's own characteristics and behavioral history, the concept and target of the target product, and various customer information (profiles, communication data (including videos and text), transaction records, etc.) can be input. The analysis unit analyzes the sales data input by the sales data input unit. For example, the generation AI extracts a customer pattern model based on the sales data. The generation AI analyzes customer profiles and past transaction records to identify the customer's interests and concerns. The scenario generation unit generates training scenarios based on the results of the analysis by the analysis unit. For example, the generation AI generates scenario patterns for each to-consumer customer type based on the customer pattern model. The generation AI generates scenarios that indicate what topics should be brought up and what questions should be asked to specific customer types. This allows the role-play training system according to the embodiment to improve sales skills in a short period of time. For example, salespeople can quickly identify customer interests and concerns and take an appropriate approach, thereby increasing the order rate.

[0030] The analysis unit can automatically classify past successful and unsuccessful sales cases based on sales data and identify the factors that led to success and failure. For example, the analysis unit inputs sales data, and the generative AI automatically classifies past successful and unsuccessful cases. For example, it extracts the characteristics and behavioral patterns of successful sales and identifies commonalities between unsuccessful sales. This allows for the identification of factors that led to success and failure in sales, making it possible to provide effective training scenarios.

[0031] When sales data is input, the analysis unit analyzes the salesperson's tone of voice and speaking patterns, and can propose the optimal communication style. For example, the analysis unit inputs sales data, and the generation AI analyzes the salesperson's tone of voice and speaking patterns. For example, it identifies what tone and speaking style is effective with customers. This allows the salesperson's tone of voice and speaking patterns to be analyzed and the optimal communication style to be proposed, thereby increasing sales effectiveness.

[0032] When sales data is input, the analysis unit analyzes the body language and gestures of the salesperson and can suggest areas for improvement in non-verbal communication. For example, the analysis unit inputs sales data, and the generation AI analyzes the body language and gestures of the salesperson. For example, it identifies what gestures are effective with customers. This allows the analysis of the body language and gestures of the salesperson and suggests areas for improvement in non-verbal communication, thereby increasing the effectiveness of sales.

[0033] When sales data is input, the analysis unit can analyze the salesperson's past interactions with customers and identify customer response patterns. For example, the analysis unit inputs sales data, and the generative AI analyzes past interactions with customers. For example, it identifies customer response patterns and clarifies what approaches would be effective. In this way, by analyzing the salesperson's past interactions with customers and identifying customer response patterns, it is possible to provide effective training scenarios.

[0034] The analysis unit can analyze a customer's purchasing history and identify purchasing motivations and purchasing patterns. For example, the analysis unit inputs a customer's purchasing history, and the generation AI analyzes the purchasing motivations and purchasing patterns. For example, it identifies the reasons why a customer purchased a product. This allows for the identification of a customer's purchasing motivations and purchasing patterns, making it possible to provide effective training scenarios.

[0035] The analysis unit can analyze a customer's social media data to identify the customer's interests and lifestyle. For example, the analysis unit inputs the customer's social media data, and the generation AI analyzes the interests and lifestyle. For example, it identifies what topics the customer is interested in. This allows the customer's interests and lifestyle to be identified, making it possible to provide effective training scenarios.

[0036] The analysis unit can analyze customer geographic data and identify purchasing trends by region. For example, the analysis unit inputs customer geographic data, and the generation AI analyzes purchasing trends by region. For example, it identifies products and services that are popular in a specific region. This allows for identifying purchasing trends by region, making it possible to provide effective training scenarios.

[0037] The analysis unit can analyze customer age group and occupation data to identify purchasing patterns for each target demographic. For example, the analysis unit inputs customer age group and occupation data, and the generation AI analyzes the purchasing patterns for each target demographic. For example, it identifies what products customers of a specific age group or occupation purchase. This allows for identifying purchasing patterns for each target demographic, making it possible to provide effective training scenarios.

[0038] The scenario generation unit can analyze past successful sales scenarios and generate new scenarios that incorporate the success factors. For example, the scenario generation unit inputs past successful sales scenarios, and the generation AI analyzes the success factors. For example, it identifies commonalities between successful scenarios and effective approaches. This allows for the generation of new scenarios that incorporate the success factors of the past, thereby providing effective training scenarios.

[0039] The scenario generation unit can generate individually customized scenarios taking into account the individuality and strengths of salespeople. For example, the scenario generation unit inputs the individuality and strengths of salespeople, and the generation AI generates scenarios that take these into account. For example, it creates scenarios that incorporate topics and approaches that salespeople are good at. This allows for the generation of customized scenarios that take into account the individuality and strengths of salespeople, making it possible to provide effective training scenarios.

[0040] The scenario generation unit can analyze sales scenarios from different industries and generate new scenarios that incorporate success stories from those industries. For example, the scenario generation unit inputs a sales scenario from a different industry, and the generation AI analyzes it. For example, it creates a scenario that incorporates success stories from that industry. This allows for the generation of new scenarios that incorporate success stories from that industry, making it possible to provide effective training scenarios.

[0041] The scenario generation unit can analyze sales scenarios from different cultural spheres and generate scenarios that incorporate an international perspective. For example, the scenario generation unit inputs a sales scenario from a different cultural sphere, and the generation AI analyzes it. For example, it creates a scenario that incorporates an international perspective. This allows for the generation of new scenarios that incorporate an international perspective, making it possible to provide effective training scenarios.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The role-play training system further includes a feedback section. The feedback section can evaluate the salesperson's performance after a training session and provide specific areas for improvement. For example, it can provide feedback on the salesperson's speaking style, gestures, and how they respond to customers. This allows the salesperson to understand their weaknesses and use this knowledge in the next training session. The feedback section can also track the salesperson's progress and support their long-term growth. For example, it can analyze data from past training sessions to identify growth patterns of the salesperson. This makes it possible to continuously support the salesperson's growth.

[0044] The role-play training system further includes a simulation section. The simulation section allows salespeople to simulate interactions with real customers in a virtual environment. For example, salespeople can improve their sales skills in a practical manner through dialogue with virtual customers. The simulation section provides different scenarios and customer types, enabling salespeople to handle a variety of situations. This allows salespeople to gain confidence when dealing with real customers. The simulation section also records the results of the training session and allows them to review them later. This allows salespeople to objectively evaluate their performance and identify areas for improvement.

[0045] The role-play training system also includes a competitive analysis section. The competitive analysis section analyzes competitors' sales techniques and market trends and can provide sales with the latest information. For example, it analyzes competitors' success stories and failure stories to provide sales with useful insights. This allows sales to develop strategies to enhance their competitiveness. The competitive analysis section can also monitor changes in market trends and customer needs in real time and provide sales with advice on how to respond quickly. This allows sales to respond flexibly to market changes and maintain a competitive advantage.

[0046] The role-play training system also includes a personalized learning section. The personalized learning section can customize the training content according to the learning style and pace of the salesperson. For example, if a salesperson prefers visual learning, training scenarios that make extensive use of visual aids can be provided. This allows the salesperson to learn in a way that suits them best and effectively improve their skills. The personalized learning section can also monitor the salesperson's progress in real time and adjust the training content as needed. This allows the salesperson to learn at their own pace and receive effective training.

[0047] The role-play training system further includes a cross-cultural training section. The cross-cultural training section can provide training to improve sales skills when dealing with customers from different cultural backgrounds. For example, it provides scenarios for learning appropriate communication methods and business practices for customers from different cultural backgrounds. This enables salespeople to effectively deal with international customers. The cross-cultural training section can also provide advice on how to avoid cross-cultural misunderstandings and friction. This enables salespeople to smoothly communicate across cultures and make the most of international business opportunities.

[0048] The role-play training system further includes a data visualization section. The data visualization section visually displays sales data and training results, allowing salespeople to understand their own performance at a glance. For example, graphs and charts can be used to display sales progress and areas for improvement. This allows salespeople to objectively evaluate their own performance and use this information in the next training session. The data visualization section can also provide a function for comparing a salesperson's performance with other salespeople. This allows salespeople to understand their position and approach training with a competitive spirit.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The sales data input section inputs sales data. For example, the salesperson's own characteristics and behavioral history, the product concept and target, various customer information (profile, communication data (including video and text), transaction records, etc.) can be input. Step 2: The analysis unit analyzes the sales data entered by the sales data input unit. For example, the generation AI extracts a customer pattern model based on the sales data. The generation AI analyzes customer profiles and past transaction records to identify customer interests and concerns. Step 3: The scenario generation unit generates training scenarios based on the results of the analysis by the analysis unit. For example, the generation AI generates scenario patterns for each to-consumer customer type based on a customer pattern model. The generation AI generates scenarios that indicate what topics should be brought up and what questions should be asked for specific customer types.

[0051] (Example 2) The role-play training system according to an embodiment of the present invention is a system in which sales data is input, analyzed by a generation AI, and training scenarios are generated. This allows the role-play training system to improve sales skills in a short period of time.

[0052] The role-play training system according to the embodiment includes a sales data input unit, an analysis unit, and a scenario generation unit. The sales data input unit inputs sales data. For example, the salesperson's own characteristics and behavioral history, the concept and target of the target product, and various customer information (profiles, communication data (including videos and text), transaction records, etc.) can be input. The analysis unit analyzes the sales data input by the sales data input unit. For example, the generation AI extracts a customer pattern model based on the sales data. The generation AI analyzes customer profiles and past transaction records to identify the customer's interests and concerns. The scenario generation unit generates training scenarios based on the results of the analysis by the analysis unit. For example, the generation AI generates scenario patterns for each to-consumer customer type based on the customer pattern model. The generation AI generates scenarios that indicate what topics should be brought up and what questions should be asked to specific customer types. This allows the role-play training system according to the embodiment to improve sales skills in a short period of time. For example, salespeople can quickly identify customer interests and concerns and take an appropriate approach, thereby increasing the order rate.

[0053] The analysis unit can automatically classify past successful and unsuccessful sales cases based on sales data and identify the factors that led to success and failure. For example, the analysis unit inputs sales data, and the generative AI automatically classifies past successful and unsuccessful cases. For example, it extracts the characteristics and behavioral patterns of successful sales and identifies commonalities between unsuccessful sales. This allows for the identification of factors that led to success and failure in sales, making it possible to provide effective training scenarios.

[0054] When sales data is input, the analysis unit analyzes the salesperson's tone of voice and speaking patterns, and can propose the optimal communication style. For example, the analysis unit inputs sales data, and the generation AI analyzes the salesperson's tone of voice and speaking patterns. For example, it identifies what tone and speaking style is effective with customers. This allows the salesperson's tone of voice and speaking patterns to be analyzed and the optimal communication style to be proposed, thereby increasing sales effectiveness.

[0055] The analysis unit can use the emotion estimation function to estimate the emotional state of the salesperson from the sales data and generate a training scenario corresponding to the emotion. For example, the analysis unit inputs sales data and estimates the emotional state of the salesperson using the emotion estimation function. For example, it analyzes the stress level and motivation of the salesperson. This allows for the generation of a training scenario corresponding to the emotional state of the salesperson, thereby providing effective training.

[0056] When sales data is input, the analysis unit analyzes the body language and gestures of the salesperson and can suggest areas for improvement in non-verbal communication. For example, the analysis unit inputs sales data, and the generation AI analyzes the body language and gestures of the salesperson. For example, it identifies what gestures are effective with customers. This allows the analysis of the body language and gestures of the salesperson and suggests areas for improvement in non-verbal communication, thereby increasing the effectiveness of sales.

[0057] When sales data is input, the analysis unit can analyze the salesperson's past interactions with customers and identify customer response patterns. For example, the analysis unit inputs sales data, and the generative AI analyzes past interactions with customers. For example, it identifies customer response patterns and clarifies what approaches would be effective. In this way, by analyzing the salesperson's past interactions with customers and identifying customer response patterns, it is possible to provide effective training scenarios.

[0058] The analysis unit can use the emotion estimation function to estimate the emotional reactions of customers from sales data and generate training scenarios based on the emotions. The analysis unit, for example, inputs sales data and estimates the emotional reactions of customers using the emotion estimation function. For example, it analyzes the joy and dissatisfaction of customers. This allows for the generation of training scenarios based on the emotional reactions of customers, thereby providing effective training.

[0059] The analysis unit can analyze a customer's purchasing history and identify purchasing motivations and purchasing patterns. For example, the analysis unit inputs a customer's purchasing history, and the generation AI analyzes the purchasing motivations and purchasing patterns. For example, it identifies the reasons why a customer purchased a product. This allows for the identification of a customer's purchasing motivations and purchasing patterns, making it possible to provide effective training scenarios.

[0060] The analysis unit can analyze a customer's social media data to identify the customer's interests and lifestyle. For example, the analysis unit inputs the customer's social media data, and the generation AI analyzes the interests and lifestyle. For example, it identifies what topics the customer is interested in. This allows the customer's interests and lifestyle to be identified, making it possible to provide effective training scenarios.

[0061] The analysis unit can use the emotion estimation function to extract emotion patterns from past customer communication data and generate a customer pattern model based on the emotions. The analysis unit, for example, inputs past customer communication data and extracts emotion patterns using the emotion estimation function. For example, it analyzes patterns of customer joy and dissatisfaction. This allows for the generation of a customer pattern model based on the customer's emotion patterns, thereby providing an effective training scenario.

[0062] The analysis unit can analyze customer geographic data and identify purchasing trends by region. For example, the analysis unit inputs customer geographic data, and the generation AI analyzes purchasing trends by region. For example, it identifies products and services that are popular in a specific region. This allows for identifying purchasing trends by region, making it possible to provide effective training scenarios.

[0063] The analysis unit can analyze customer age group and occupation data to identify purchasing patterns for each target demographic. For example, the analysis unit inputs customer age group and occupation data, and the generation AI analyzes the purchasing patterns for each target demographic. For example, it identifies what products customers of a specific age group or occupation purchase. This allows for identifying purchasing patterns for each target demographic, making it possible to provide effective training scenarios.

[0064] The analysis unit can use the emotion estimation function to analyze the real-time emotional reactions of customers and generate a customer pattern model based on emotions. The analysis unit, for example, inputs the real-time emotional reactions of customers and analyzes them using the emotion estimation function. For example, it identifies in what situations customers show positive emotions. This allows for the generation of a customer pattern model based on the real-time emotional reactions of customers, thereby providing an effective training scenario.

[0065] The scenario generation unit can analyze past successful sales scenarios and generate new scenarios that incorporate the success factors. For example, the scenario generation unit inputs past successful sales scenarios, and the generation AI analyzes the success factors. For example, it identifies commonalities between successful scenarios and effective approaches. This allows for the generation of new scenarios that incorporate the success factors of the past, thereby providing effective training scenarios.

[0066] The scenario generation unit can generate individually customized scenarios taking into account the individuality and strengths of salespeople. For example, the scenario generation unit inputs the individuality and strengths of salespeople, and the generation AI generates scenarios that take these into account. For example, it creates scenarios that incorporate topics and approaches that salespeople are good at. This allows for the generation of customized scenarios that take into account the individuality and strengths of salespeople, making it possible to provide effective training scenarios.

[0067] The scenario generation unit uses the emotion estimation function to generate a scenario according to the emotional state of the salesperson, and can provide emotion-based training. The scenario generation unit inputs, for example, the emotional state of the salesperson and generates a scenario using the emotion estimation function. For example, it creates a scenario according to the stress level and motivation of the salesperson. In this way, by generating a training scenario according to the emotional state of the salesperson, it is possible to provide effective training.

[0068] The scenario generation unit can analyze sales scenarios from different industries and generate new scenarios that incorporate success stories from those industries. For example, the scenario generation unit inputs a sales scenario from a different industry, and the generation AI analyzes it. For example, it creates a scenario that incorporates success stories from that industry. This allows for the generation of new scenarios that incorporate success stories from that industry, making it possible to provide effective training scenarios.

[0069] The scenario generation unit can analyze sales scenarios from different cultural spheres and generate scenarios that incorporate an international perspective. For example, the scenario generation unit inputs a sales scenario from a different cultural sphere, and the generation AI analyzes it. For example, it creates a scenario that incorporates an international perspective. This allows for the generation of new scenarios that incorporate an international perspective, making it possible to provide effective training scenarios.

[0070] The scenario generation unit uses the emotion estimation function to generate a scenario based on the emotional response of the customer, and can provide emotion-based training. The scenario generation unit, for example, inputs the customer's emotional response and generates a scenario using the emotion estimation function. For example, a scenario is created that includes approaches and topics that correspond to the customer's emotions. In this way, by generating a training scenario based on the customer's emotional response, effective training can be provided.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The role-play training system further includes a feedback section. The feedback section can evaluate the salesperson's performance after a training session and provide specific areas for improvement. For example, it can provide feedback on the salesperson's speaking style, gestures, and how they respond to customers. This allows the salesperson to understand their weaknesses and use this knowledge in the next training session. The feedback section can also track the salesperson's progress and support their long-term growth. For example, it can analyze data from past training sessions to identify growth patterns of the salesperson. This makes it possible to continuously support the salesperson's growth.

[0073] The role-play training system further includes a simulation section. The simulation section allows salespeople to simulate interactions with real customers in a virtual environment. For example, salespeople can improve their sales skills in a practical manner through dialogue with virtual customers. The simulation section provides different scenarios and customer types, enabling salespeople to handle a variety of situations. This allows salespeople to gain confidence when dealing with real customers. The simulation section also records the results of the training session and allows them to review them later. This allows salespeople to objectively evaluate their performance and identify areas for improvement.

[0074] The role-play training system further includes a motivation management unit. The motivation management unit monitors the motivation level of salespeople and can provide feedback and rewards to increase motivation at appropriate times. For example, when a salesperson achieves a target, it can provide praise or rewards. This can maintain the motivation of salespeople and improve their performance. The motivation management unit can also monitor the stress level of salespeople and provide advice on relaxation and stress management as needed. This can support the mental health of salespeople and improve their long-term performance.

[0075] The role-play training system also includes a competitive analysis section. The competitive analysis section analyzes competitors' sales techniques and market trends and can provide sales with the latest information. For example, it analyzes competitors' success stories and failure stories to provide sales with useful insights. This allows sales to develop strategies to enhance their competitiveness. The competitive analysis section can also monitor changes in market trends and customer needs in real time and provide sales with advice on how to respond quickly. This allows sales to respond flexibly to market changes and maintain a competitive advantage.

[0076] The role-play training system further includes an emotional feedback unit. The emotional feedback unit can monitor the emotional state of the salesperson in real time during the training session and provide appropriate feedback. For example, if the salesperson is feeling stressed, the emotional feedback unit can provide advice on how to relax. This allows the salesperson to understand their own emotional state and deal with it appropriately. The emotional feedback unit can also adjust the training scenario based on the emotional state of the salesperson. For example, if the salesperson is highly motivated, the emotional feedback unit can provide a more challenging scenario. This makes it possible to provide effective training that is tailored to the emotional state of the salesperson.

[0077] The role-play training system also includes a personalized learning section. The personalized learning section can customize the training content according to the learning style and pace of the salesperson. For example, if a salesperson prefers visual learning, training scenarios that make extensive use of visual aids can be provided. This allows the salesperson to learn in a way that suits them best and effectively improve their skills. The personalized learning section can also monitor the salesperson's progress in real time and adjust the training content as needed. This allows the salesperson to learn at their own pace and receive effective training.

[0078] The role-play training system further includes an emotion analysis unit. The emotion analysis unit can perform a detailed analysis of the emotional state of a salesperson and provide training scenarios based on their emotions. For example, if a salesperson is feeling anxious, it can provide a scenario to help them relax. This allows the salesperson to receive training that suits their emotional state. The emotion analysis unit can also track the emotional state of a salesperson over the long term and adjust the training content according to changes in emotions. This makes it possible to continuously support the emotional state of a salesperson and provide effective training.

[0079] The role-play training system further includes a cross-cultural training section. The cross-cultural training section can provide training to improve sales skills when dealing with customers from different cultural backgrounds. For example, it provides scenarios for learning appropriate communication methods and business practices for customers from different cultural backgrounds. This enables salespeople to effectively deal with international customers. The cross-cultural training section can also provide advice on how to avoid cross-cultural misunderstandings and friction. This enables salespeople to smoothly communicate across cultures and make the most of international business opportunities.

[0080] The role-play training system further includes an emotion monitoring unit. The emotion monitoring unit can monitor the emotional state of the salesperson in real time and provide appropriate feedback during the training session. For example, if the salesperson is nervous, the emotion monitoring unit can provide advice on how to relax. This allows the salesperson to understand their own emotional state and deal with it appropriately. The emotion monitoring unit can also adjust the training scenario based on the emotional state of the salesperson. For example, if the salesperson is highly motivated, the emotion monitoring unit can provide a more challenging scenario. This makes it possible to provide effective training that is tailored to the emotional state of the salesperson.

[0081] The role-play training system further includes a data visualization section. The data visualization section visually displays sales data and training results, allowing salespeople to understand their own performance at a glance. For example, graphs and charts can be used to display sales progress and areas for improvement. This allows salespeople to objectively evaluate their own performance and use this information in the next training session. The data visualization section can also provide a function for comparing a salesperson's performance with other salespeople. This allows salespeople to understand their position and approach training with a competitive spirit.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The sales data input section inputs sales data. For example, the salesperson's own characteristics and behavioral history, the product concept and target, various customer information (profile, communication data (including video and text), transaction records, etc.) can be input. Step 2: The analysis unit analyzes the sales data entered by the sales data input unit. For example, the generation AI extracts a customer pattern model based on the sales data. The generation AI analyzes customer profiles and past transaction records to identify customer interests and concerns. Step 3: The scenario generation unit generates training scenarios based on the results of the analysis by the analysis unit. For example, the generation AI generates scenario patterns for each to-consumer customer type based on a customer pattern model. The generation AI generates scenarios that indicate what topics should be brought up and what questions should be asked for specific customer types.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sales data input section for inputting sales data; an analysis unit that analyzes the sales data input by the sales data input unit; a scenario generation unit that generates a training scenario based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Based on the sales data, the system automatically classifies past sales successes and failures and identifies the factors that led to success and failure.

2. The system of claim 1.

3. The analysis unit When entering the sales data, the system analyzes the salesperson's body language and gestures and suggests ways to improve non-verbal communication.

2. The system of claim 1.

4. The analysis unit Analyze customer purchase history to identify purchasing motivations and patterns 2. The system of claim 1.

5. The scenario generation unit Analyze past successful sales scenarios and generate new scenarios incorporating the success factors.

2. The system of claim 1.

6. The analysis unit The emotional state of the salesperson is estimated from the sales data, and a training scenario is generated according to the emotional state.

2. The system of claim 1.

7. The analysis unit Extract emotional patterns from past customer communication data and generate an emotion-based customer pattern model 2. The system of claim 1.

8. The scenario generation unit The training scenario is generated according to the emotional state of the salesperson, and training based on the emotions is provided.

2. The system of claim 1.

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