system

Abstract

The system according to the embodiment aims to efficiently conduct sales training and improve sales performance. [Solution] A system according to an embodiment includes a generation unit, a support unit, an analysis unit, and a feedback unit. The generation unit generates a sales training script. The support unit provides real-time sales support based on the script generated by the generation unit. The analysis unit analyzes customer behavior patterns based on customer behavior data and past transaction history. The feedback unit provides feedback based on a user's progress and performance.

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 had the problem that sales training is not carried out efficiently and there are limitations to improving sales performance.

[0005] The system according to the embodiment aims to efficiently conduct sales training and improve sales performance. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a support unit, an analysis unit, and a feedback unit. The generation unit generates a sales training script. The support unit provides real-time sales support based on the script generated by the generation unit. The analysis unit analyzes customer behavior patterns based on customer behavior data and past transaction history. The feedback unit provides feedback based on a user's progress and performance. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently conduct sales training and improve sales performance. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sales consultant training system according to an embodiment of the present invention is a system for developing sales consultants with advanced sales skills using AI. This system generates sales training scripts, analyzes customer psychology, and provides advice. The system provides real-time sales support and role-plays for particularly difficult cases. Furthermore, the system provides regular feedback based on the user's progress and performance, adaptively improving. This makes sales training more efficient and significantly improves sales performance. For example, the system generates sales training scripts based on data on past successful sales pitches and customer reactions. The system then analyzes large amounts of data to automatically create effective sales pitches. For example, by generating scripts based on past success stories, users can learn effective sales pitches. Next, the system analyzes customer psychology based on customer behavior data and past transaction history. For example, it analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. This allows users to appropriately approach customers. Furthermore, the system provides real-time sales support and role-plays for particularly difficult cases. For example, the system interacts with users based on pre-prepared scenarios to simulate actual sales situations. This allows users to acquire practical skills. Finally, the system provides regular feedback based on the user's progress and performance, allowing for adaptive improvement. For example, the system analyzes the user's training data and provides appropriate feedback based on their progress. This allows users to identify their weaknesses and improve their skills efficiently. This makes the sales consultant training system more efficient and significantly improves sales performance.

[0029] A sales consultant training system according to an embodiment includes a generation unit, a support unit, an analysis unit, and a feedback unit. The generation unit generates a sales training script. The generation unit generates the script based on, for example, past successful sales pitches and customer response data. The generation unit can generate a script based on, for example, past success stories. The generation unit can also generate a script based on, for example, customer response data. The generation unit can analyze large amounts of data using, for example, AI to automatically create effective sales pitches. The support unit provides real-time sales support based on the script generated by the generation unit. The support unit can, for example, interact with a user based on a pre-prepared scenario and provide role-playing. The support unit can, for example, simulate actual sales situations using AI. The support unit can, for example, provide role-playing for particularly difficult cases for a user. The analysis unit analyzes customer psychology based on customer behavior data and past transaction history. The analysis unit analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis unit can, for example, analyze customer psychology using AI. The analysis unit can, for example, analyze customer behavior patterns. The feedback unit provides feedback based on the user's progress and performance. The feedback unit can, for example, analyze the user's training data and provide appropriate feedback according to the user's progress. The feedback unit can, for example, use AI to analyze the user's progress and performance and adaptively improve. As a result, the sales consultant training system according to the embodiment can make sales training more efficient and significantly improve sales results.

[0030] The generation unit can generate a script based on past successful sales pitches or customer reaction data. The generation unit can, for example, generate a script based on past success cases. The generation unit can also generate a script based on customer reaction data. The generation unit can, for example, use AI to analyze large amounts of data and automatically create effective sales pitches. This allows users to learn effective sales pitches by generating scripts based on past success cases. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can generate a script using an AI model that inputs past success cases or customer reaction data and outputs a script.

[0031] The analysis unit can analyze customer behavior patterns based on customer behavior data or past transaction history. The analysis unit can analyze data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis unit can analyze customer psychology using AI, for example. The analysis unit can analyze customer behavior patterns, for example. By analyzing customer psychology based on customer behavior data and past transaction history, users can take an appropriate approach to customers. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can analyze customer psychology using an AI model that inputs customer behavior data and past transaction history and outputs analysis results of customer psychology.

[0032] The support unit can interact with the user based on a scenario prepared in advance and provide role-playing. For example, the support unit can interact with the user based on a scenario prepared in advance and provide role-playing. For example, the support unit can simulate actual sales situations using AI. For example, the support unit can provide role-playing for particularly difficult cases for the user. By providing role-playing based on a scenario prepared in advance, the user can acquire practical skills. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can provide role-playing using an AI model that inputs a scenario prepared in advance and outputs a role-playing.

[0033] The feedback unit can analyze the user's training data and provide feedback based on the user's progress. For example, the feedback unit can analyze the user's training data and provide appropriate feedback according to the user's progress. The feedback unit can, for example, use AI to analyze the user's progress and performance and adaptively improve. By analyzing the user's training data and providing feedback according to the user's progress, the user can identify their weaknesses and efficiently improve their skills. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can provide feedback using an AI model that receives the user's training data as input and outputs feedback.

[0034] The generation unit can analyze past success stories or failure stories to generate a more effective script. For example, the generation unit can identify phrases or approaches to avoid from past failure stories and generate a script that reflects them. For example, the generation unit can compare success stories with failure stories to generate a script that highlights effective elements. For example, the generation unit can generate a script that includes lessons learned from failure stories and alert the user. In this way, by analyzing failure stories, a script that reflects approaches to avoid can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs past success stories or failure stories and outputs a script.

[0035] The generation unit can include content specialized for a specific industry or product when generating a script. For example, the generation unit can generate a script including technical details for the IT industry. For example, the generation unit can generate a script that complies with technical terminology and regulations for the medical industry. For example, the generation unit can generate a script based on customer purchasing behavior for the retail industry. This allows for generating a script specialized for a specific industry or product, thereby providing a more effective sales pitch. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs data related to a specific industry or product and outputs a script.

[0036] When generating a script, the generation unit can customize the script based on the user's past sales performance. For example, the generation unit can generate a script that emphasizes effective approaches based on the user's past success stories. For example, the generation unit can generate a script that reflects approaches to avoid based on the user's past failure stories. For example, the generation unit can generate a script that strengthens specific skills to improve the user's performance. This makes it possible to provide more effective sales pitches by taking the user's past sales performance into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs the user's past sales performance data and outputs a script.

[0037] When generating a script, the generation unit can generate versions that are appropriate for different cultures or regions. For example, the generation unit generates a script that takes cultural background into consideration for the Asian market. For example, the generation unit can generate a script that meets regional needs for the European market. For example, the generation unit can generate a script based on customer purchasing behavior for the North American market. This allows for generating scripts that are appropriate for different cultures and regions, thereby providing more effective sales pitches. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs data related to different cultures and regions and outputs a script.

[0038] When providing support, the support unit can select the optimal support method based on the user's past sales performance. For example, the support unit can suggest an effective approach based on the user's past success stories. For example, the support unit can suggest an approach to avoid based on the user's past failure stories. For example, the support unit can suggest a support method to strengthen a specific skill in order to improve the user's performance. This makes it possible to provide more effective sales support by referring to the user's past sales performance. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can select a support method using an AI model that inputs the user's past sales performance data and outputs the optimal support method.

[0039] During support, the support unit can provide customized role-plays based on specific customer types. For example, for new customers, the support unit can provide role-plays based on a first-meeting scenario. For existing customers, the support unit can provide role-plays based on a follow-up scenario. For difficult customers, the support unit can provide role-plays based on a complaint handling scenario. This allows for more effective sales support by providing role-plays tailored to specific customer types. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide role-plays using an AI model that inputs data related to a specific customer type and outputs a role-play.

[0040] When providing support, the support unit can select the optimal support method based on the user's geographical location information. For example, if the user is in an urban area, the support unit can propose a city-specific sales approach. For example, if the user is in a rural area, the support unit can propose a region-specific sales approach. For example, if the user is overseas, the support unit can propose a sales approach that takes cultural background into consideration. This makes it possible to provide more effective sales support by taking the user's geographical location information into consideration. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can select a support method using an AI model that inputs the user's geographical location information and outputs the optimal support method.

[0041] During support, the support unit can analyze the user's social media activity and provide appropriate advice. For example, if the user is active on social media, the support unit can suggest online sales approaches. For example, if the user posts frequently about a specific industry, the support unit can provide advice specific to that industry. For example, if the user has a large number of followers on social media, the support unit can suggest sales approaches that utilize the user's influence. This makes it possible to provide more effective sales support by analyzing the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide advice using an AI model that inputs the user's social media activity data and outputs appropriate advice.

[0042] During analysis, the analysis unit can perform analysis based on the customer's past purchase history or current market trends. For example, the analysis unit combines the customer's past purchase history with current market trends for analysis. For example, the analysis unit can predict the customer's future purchasing behavior based on market trends. For example, the analysis unit can compare the customer's purchase history with market trends and propose an optimal approach. This makes it possible to provide a more effective sales approach by combining and analyzing the customer's past purchase history with current market trends. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs customer purchase history data and market trend data and outputs analysis results.

[0043] During analysis, the analysis unit can perform a psychological analysis based on the customer's social media activity. For example, the analysis unit can analyze the customer's social media posts to understand their interests. For example, the analysis unit can evaluate the customer's influence based on the number of followers and engagement rate of the customer. For example, the analysis unit can predict the customer's purchasing intent based on the customer's social media activity. This allows for a more effective sales approach by referring to the customer's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the customer's social media activity data and outputs the results of a psychological analysis.

[0044] During analysis, the analysis unit can perform a psychological analysis based on the customer's geographical location information. For example, if the customer is in an urban area, the analysis unit can perform the analysis taking into account city-specific purchasing behavior. For example, if the customer is in a rural area, the analysis unit can perform the analysis taking into account region-specific purchasing behavior. For example, if the customer is overseas, the analysis unit can perform the analysis taking into account the customer's cultural background. This allows for a more effective sales approach by taking the customer's geographical location information into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the customer's geographical location information and outputs the results of a psychological analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis based on the customer's related literature. For example, the analysis unit identifies the customer's interests and concerns based on related literature that the customer has read in the past. For example, the analysis unit can compare the customer's purchase history with related literature and propose an optimal approach. For example, the analysis unit can predict the customer's purchasing intentions from the customer's related literature. This makes it possible to provide a more effective sales approach by referring to the customer's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the customer's related literature data and outputs analysis results.

[0046] When providing feedback, the feedback unit can provide optimal feedback based on the user's past training data. The feedback unit can provide effective feedback, for example, based on the user's past success stories. The feedback unit can provide feedback that points out areas for improvement, for example, based on the user's past failure stories. The feedback unit can provide feedback that strengthens specific skills, for example, to improve the user's performance. This makes it possible to provide more effective feedback by referencing the user's past training data. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's past training data and outputs optimal feedback.

[0047] The feedback unit can provide customized feedback based on a specific skill set when providing feedback. For example, the feedback unit can provide feedback that points out specific areas for improvement to improve presentation skills. For example, the feedback unit can provide feedback that suggests an effective approach to improve negotiation skills. For example, the feedback unit can provide feedback that includes practical advice to improve customer service skills. This allows for more effective sales training by providing feedback tailored to a specific skill set. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can provide feedback using an AI model that receives data related to a specific skill set as input and outputs customized feedback.

[0048] The feedback unit can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in an urban area, the feedback unit can provide feedback regarding a city-specific sales approach. For example, if the user is in a rural area, the feedback unit can provide feedback regarding a region-specific sales approach. For example, if the user is overseas, the feedback unit can provide feedback that takes cultural background into consideration. This allows for more effective feedback to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's geographical location information and outputs optimal feedback.

[0049] When providing feedback, the feedback unit can analyze the user's social media activity and provide appropriate feedback. For example, if the user is active on social media, the feedback unit can provide feedback regarding online sales approaches. For example, if the user posts frequently about a specific industry, the feedback unit can provide feedback specific to that industry. For example, if the user has a large number of followers on social media, the feedback unit can provide feedback regarding sales approaches that utilize the user's influence. This allows for more effective feedback to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's social media activity data and outputs appropriate feedback.

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

[0051] The generation unit can customize the script based on the user's past sales performance. For example, the generation unit can generate a script that emphasizes effective approaches based on the user's past success stories. The generation unit can also generate a script that reflects approaches that should be avoided based on the user's past failure stories. Furthermore, the generation unit can generate a script that strengthens specific skills to improve the user's performance. This makes it possible to provide more effective sales pitches by taking the user's past sales performance into consideration.

[0052] The analytics department can perform psychology analysis based on customers' social media activity. For example, it can analyze customers' social media posts to understand their interests. It can also evaluate a customer's influence based on the number of followers and engagement rate. Furthermore, it can predict purchasing intent from customers' social media activity. This allows for more effective sales approaches by referring to customers' social media activity.

[0053] When providing support, the support department can select the optimal support method based on the user's geographical location information. For example, if the user is in an urban area, a sales approach specific to that city can be proposed. Also, if the user is in a rural area, a sales approach specific to the region can be proposed. Furthermore, if the user is overseas, a sales approach that takes cultural background into consideration can be proposed. In this way, by taking the user's geographical location information into consideration, more effective sales support can be provided.

[0054] When generating scripts, the generator can include content specialized for a specific industry or product. For example, it can generate scripts that include technical details for the IT industry. It can also generate scripts that address technical terms and regulations for the medical industry. It can also generate scripts based on customer purchasing behavior for the retail industry. This allows for more effective sales pitches by generating scripts specialized for specific industries or products.

[0055] The feedback unit can provide customized feedback based on specific skill sets when providing feedback. For example, to improve presentation skills, feedback can be provided that points out specific areas for improvement. To improve negotiation skills, feedback can be provided that suggests effective approaches. Furthermore, to improve customer service skills, feedback including practical advice can be provided. This makes it possible to provide more effective sales training by providing feedback tailored to specific skill sets.

[0056] During analysis, the analysis unit can perform a psychology analysis based on the customer's geographic location information. For example, if the customer is in an urban area, the analysis can take into account purchasing behavior specific to that city. Also, if the customer is in a rural area, the analysis can take into account purchasing behavior specific to that region. Furthermore, if the customer is overseas, the analysis can take into account their cultural background. This allows for a more effective sales approach by taking into account the customer's geographic location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The generator generates a sales training script. The generator generates the script based on, for example, past successful sales pitches and customer response data. The generator can use AI to analyze large amounts of data and automatically create effective sales pitches. Step 2: The support unit provides real-time sales support based on the script generated by the generation unit. For example, the support unit may interact with the user based on a scenario prepared in advance and provide role-playing. The support unit can simulate actual sales situations using AI. Step 3: The analysis department analyzes customer psychology based on customer behavior data and past transaction history. For example, the analysis department analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis department can use AI to analyze customer psychology. Step 4: The feedback unit provides feedback based on the user's progress and performance. The feedback unit may, for example, analyze the user's training data and provide appropriate feedback based on the user's progress. The feedback unit may use AI to analyze the user's progress and performance and adaptively improve.

[0059] (Example 2) A sales consultant training system according to an embodiment of the present invention is a system for developing sales consultants with advanced sales skills using AI. This system generates sales training scripts, analyzes customer psychology, and provides advice. The system provides real-time sales support and role-plays for particularly difficult cases. Furthermore, the system provides regular feedback based on the user's progress and performance, adaptively improving. This makes sales training more efficient and significantly improves sales performance. For example, the system generates sales training scripts based on data on past successful sales pitches and customer reactions. The system then analyzes large amounts of data to automatically create effective sales pitches. For example, by generating scripts based on past success stories, users can learn effective sales pitches. Next, the system analyzes customer psychology based on customer behavior data and past transaction history. For example, it analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. This allows users to appropriately approach customers. Furthermore, the system provides real-time sales support and role-plays for particularly difficult cases. For example, the system interacts with users based on pre-prepared scenarios to simulate actual sales situations. This allows users to acquire practical skills. Finally, the system provides regular feedback based on the user's progress and performance, allowing for adaptive improvement. For example, the system analyzes the user's training data and provides appropriate feedback based on their progress. This allows users to identify their weaknesses and improve their skills efficiently. This makes the sales consultant training system more efficient and significantly improves sales performance.

[0060] A sales consultant training system according to an embodiment includes a generation unit, a support unit, an analysis unit, and a feedback unit. The generation unit generates a sales training script. The generation unit generates the script based on, for example, past successful sales pitches and customer response data. The generation unit can generate a script based on, for example, past success stories. The generation unit can also generate a script based on, for example, customer response data. The generation unit can analyze large amounts of data using, for example, AI to automatically create effective sales pitches. The support unit provides real-time sales support based on the script generated by the generation unit. The support unit can, for example, interact with a user based on a pre-prepared scenario and provide role-playing. The support unit can, for example, simulate actual sales situations using AI. The support unit can, for example, provide role-playing for particularly difficult cases for a user. The analysis unit analyzes customer psychology based on customer behavior data and past transaction history. The analysis unit analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis unit can, for example, analyze customer psychology using AI. The analysis unit can, for example, analyze customer behavior patterns. The feedback unit provides feedback based on the user's progress and performance. The feedback unit can, for example, analyze the user's training data and provide appropriate feedback according to the user's progress. The feedback unit can, for example, use AI to analyze the user's progress and performance and adaptively improve. As a result, the sales consultant training system according to the embodiment can make sales training more efficient and significantly improve sales results.

[0061] The generation unit can generate a script based on past successful sales pitches or customer reaction data. The generation unit can, for example, generate a script based on past success cases. The generation unit can also generate a script based on customer reaction data. The generation unit can, for example, use AI to analyze large amounts of data and automatically create effective sales pitches. This allows users to learn effective sales pitches by generating scripts based on past success cases. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can generate a script using an AI model that inputs past success cases or customer reaction data and outputs a script.

[0062] The analysis unit can analyze customer behavior patterns based on customer behavior data or past transaction history. The analysis unit can analyze data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis unit can analyze customer psychology using AI, for example. The analysis unit can analyze customer behavior patterns, for example. By analyzing customer psychology based on customer behavior data and past transaction history, users can take an appropriate approach to customers. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can analyze customer psychology using an AI model that inputs customer behavior data and past transaction history and outputs analysis results of customer psychology.

[0063] The support unit can interact with the user based on a scenario prepared in advance and provide role-playing. For example, the support unit can interact with the user based on a scenario prepared in advance and provide role-playing. For example, the support unit can simulate actual sales situations using AI. For example, the support unit can provide role-playing for particularly difficult cases for the user. By providing role-playing based on a scenario prepared in advance, the user can acquire practical skills. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can provide role-playing using an AI model that inputs a scenario prepared in advance and outputs a role-playing.

[0064] The feedback unit can analyze the user's training data and provide feedback based on the user's progress. For example, the feedback unit can analyze the user's training data and provide appropriate feedback according to the user's progress. The feedback unit can, for example, use AI to analyze the user's progress and performance and adaptively improve. By analyzing the user's training data and providing feedback according to the user's progress, the user can identify their weaknesses and efficiently improve their skills. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can provide feedback using an AI model that receives the user's training data as input and outputs feedback.

[0065] The generation unit can estimate the user's emotions and change the content of the script based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate a script that includes humor to relax the user. For example, if the user is confident, the generation unit can generate a script that includes challenging talk. For example, if the user is tired, the generation unit can generate a concise and to-the-point script. This allows for adjusting the content of the script according to the user's emotions, thereby providing a more effective sales pitch. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs user emotion data and outputs the content of the script.

[0066] The generation unit can analyze past success stories or failure stories to generate a more effective script. For example, the generation unit can identify phrases or approaches to avoid from past failure stories and generate a script that reflects them. For example, the generation unit can compare success stories with failure stories to generate a script that highlights effective elements. For example, the generation unit can generate a script that includes lessons learned from failure stories and alert the user. In this way, by analyzing failure stories, a script that reflects approaches to avoid can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs past success stories or failure stories and outputs a script.

[0067] The generation unit can include content specialized for a specific industry or product when generating a script. For example, the generation unit can generate a script including technical details for the IT industry. For example, the generation unit can generate a script that complies with technical terminology and regulations for the medical industry. For example, the generation unit can generate a script based on customer purchasing behavior for the retail industry. This allows for generating a script specialized for a specific industry or product, thereby providing a more effective sales pitch. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs data related to a specific industry or product and outputs a script.

[0068] The generation unit can estimate the user's emotions and change the length of the script based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point script. For example, if the user is relaxed, the generation unit can generate a longer script with detailed explanations. For example, if the user is excited, the generation unit can generate a script with visually stimulating effects. This allows for adjusting the length of the script according to the user's emotions, making it possible to provide a more effective sales pitch. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs user emotion data and outputs the length of the script.

[0069] When generating a script, the generation unit can customize the script based on the user's past sales performance. For example, the generation unit can generate a script that emphasizes effective approaches based on the user's past success stories. For example, the generation unit can generate a script that reflects approaches to avoid based on the user's past failure stories. For example, the generation unit can generate a script that strengthens specific skills to improve the user's performance. This makes it possible to provide more effective sales pitches by taking the user's past sales performance into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs the user's past sales performance data and outputs a script.

[0070] When generating a script, the generation unit can generate versions that are appropriate for different cultures or regions. For example, the generation unit generates a script that takes cultural background into consideration for the Asian market. For example, the generation unit can generate a script that meets regional needs for the European market. For example, the generation unit can generate a script based on customer purchasing behavior for the North American market. This allows for generating scripts that are appropriate for different cultures and regions, thereby providing more effective sales pitches. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate a script using an AI model that inputs data related to different cultures and regions and outputs a script.

[0071] The support unit can estimate the user's emotions and change real-time advice based on the estimated user emotions. For example, if the user is nervous, the support unit can provide advice to relax. For example, if the user is confident, the support unit can suggest a more challenging approach. For example, if the user is tired, the support unit can provide concise and to-the-point advice. This allows for more effective sales support by adjusting real-time advice according to the user's emotions. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide advice using an AI model that inputs user emotion data and outputs real-time advice.

[0072] When providing support, the support unit can select the optimal support method based on the user's past sales performance. For example, the support unit can suggest an effective approach based on the user's past success stories. For example, the support unit can suggest an approach to avoid based on the user's past failure stories. For example, the support unit can suggest a support method to strengthen a specific skill in order to improve the user's performance. This makes it possible to provide more effective sales support by referring to the user's past sales performance. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can select a support method using an AI model that inputs the user's past sales performance data and outputs the optimal support method.

[0073] During support, the support unit can provide customized role-plays based on specific customer types. For example, for new customers, the support unit can provide role-plays based on a first-meeting scenario. For existing customers, the support unit can provide role-plays based on a follow-up scenario. For difficult customers, the support unit can provide role-plays based on a complaint handling scenario. This allows for more effective sales support by providing role-plays tailored to specific customer types. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide role-plays using an AI model that inputs data related to a specific customer type and outputs a role-play.

[0074] The support unit can estimate the user's emotions and change the timing of support based on the estimated user's emotions. For example, if the user is nervous, the support unit can provide support at a timing that will relax the user. For example, if the user is confident, the support unit can provide support at a timing that will challenge the user. For example, if the user is tired, the support unit can provide support after a break. This allows for more effective sales support to be provided by adjusting the timing of support according to the user's emotions. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide support using an AI model that inputs user emotion data and outputs the timing of support.

[0075] When providing support, the support unit can select the optimal support method based on the user's geographical location information. For example, if the user is in an urban area, the support unit can propose a city-specific sales approach. For example, if the user is in a rural area, the support unit can propose a region-specific sales approach. For example, if the user is overseas, the support unit can propose a sales approach that takes cultural background into consideration. This makes it possible to provide more effective sales support by taking the user's geographical location information into consideration. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can select a support method using an AI model that inputs the user's geographical location information and outputs the optimal support method.

[0076] During support, the support unit can analyze the user's social media activity and provide appropriate advice. For example, if the user is active on social media, the support unit can suggest online sales approaches. For example, if the user posts frequently about a specific industry, the support unit can provide advice specific to that industry. For example, if the user has a large number of followers on social media, the support unit can suggest sales approaches that utilize the user's influence. This makes it possible to provide more effective sales support by analyzing the user's social media activity. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide advice using an AI model that inputs the user's social media activity data and outputs appropriate advice.

[0077] The analysis unit can estimate the user's emotions and change the analysis results of customer psychology based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This allows for adjusting the analysis results of customer psychology according to the user's emotions, thereby providing a more effective sales approach. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the analysis results using an AI model that inputs user emotion data and outputs analysis results of customer psychology.

[0078] During analysis, the analysis unit can perform analysis based on the customer's past purchase history or current market trends. For example, the analysis unit combines the customer's past purchase history with current market trends for analysis. For example, the analysis unit can predict the customer's future purchasing behavior based on market trends. For example, the analysis unit can compare the customer's purchase history with market trends and propose an optimal approach. This makes it possible to provide a more effective sales approach by combining and analyzing the customer's past purchase history with current market trends. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs customer purchase history data and market trend data and outputs analysis results.

[0079] During analysis, the analysis unit can perform a psychological analysis based on the customer's social media activity. For example, the analysis unit can analyze the customer's social media posts to understand their interests. For example, the analysis unit can evaluate the customer's influence based on the number of followers and engagement rate of the customer. For example, the analysis unit can predict the customer's purchasing intent based on the customer's social media activity. This allows for a more effective sales approach by referring to the customer's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the customer's social media activity data and outputs the results of a psychological analysis.

[0080] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for a more effective sales approach by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can adjust the display method using an AI model that inputs user emotion data and outputs a display method.

[0081] During analysis, the analysis unit can perform a psychological analysis based on the customer's geographical location information. For example, if the customer is in an urban area, the analysis unit can perform the analysis taking into account city-specific purchasing behavior. For example, if the customer is in a rural area, the analysis unit can perform the analysis taking into account region-specific purchasing behavior. For example, if the customer is overseas, the analysis unit can perform the analysis taking into account the customer's cultural background. This allows for a more effective sales approach by taking the customer's geographical location information into account. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can perform the analysis using an AI model that inputs the customer's geographical location information and outputs the results of a psychological analysis.

[0082] During analysis, the analysis unit can improve the accuracy of the analysis based on the customer's related literature. For example, the analysis unit identifies the customer's interests and concerns based on related literature that the customer has read in the past. For example, the analysis unit can compare the customer's purchase history with related literature and propose an optimal approach. For example, the analysis unit can predict the customer's purchasing intentions from the customer's related literature. This makes it possible to provide a more effective sales approach by referring to the customer's related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that inputs the customer's related literature data and outputs analysis results.

[0083] The feedback unit can estimate the user's emotions and change the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback to relax the user. For example, if the user is confident, the feedback unit can provide challenging feedback. For example, if the user is tired, the feedback unit can provide concise and to-the-point feedback. This allows for more effective sales training by adjusting the content of the feedback according to the user's emotions. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that receives user emotion data as input and outputs feedback content.

[0084] When providing feedback, the feedback unit can provide optimal feedback based on the user's past training data. The feedback unit can provide effective feedback, for example, based on the user's past success stories. The feedback unit can provide feedback that points out areas for improvement, for example, based on the user's past failure stories. The feedback unit can provide feedback that strengthens specific skills, for example, to improve the user's performance. This makes it possible to provide more effective feedback by referencing the user's past training data. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's past training data and outputs optimal feedback.

[0085] The feedback unit can provide customized feedback based on a specific skill set when providing feedback. For example, the feedback unit can provide feedback that points out specific areas for improvement to improve presentation skills. For example, the feedback unit can provide feedback that suggests an effective approach to improve negotiation skills. For example, the feedback unit can provide feedback that includes practical advice to improve customer service skills. This allows for more effective sales training by providing feedback tailored to a specific skill set. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can provide feedback using an AI model that receives data related to a specific skill set as input and outputs customized feedback.

[0086] The feedback unit can estimate the user's emotions and change the timing of the feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback at a timing that relaxes the user. For example, if the user is confident, the feedback unit can provide feedback at a challenging timing. For example, if the user is tired, the feedback unit can provide feedback after a break. This allows for more effective sales training by adjusting the timing of feedback according to the user's emotions. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that receives user emotion data as input and outputs the timing of feedback.

[0087] The feedback unit can provide optimal feedback based on the user's geographical location information when providing feedback. For example, if the user is in an urban area, the feedback unit can provide feedback regarding a city-specific sales approach. For example, if the user is in a rural area, the feedback unit can provide feedback regarding a region-specific sales approach. For example, if the user is overseas, the feedback unit can provide feedback that takes cultural background into consideration. This allows for more effective feedback to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's geographical location information and outputs optimal feedback.

[0088] When providing feedback, the feedback unit can analyze the user's social media activity and provide appropriate feedback. For example, if the user is active on social media, the feedback unit can provide feedback regarding online sales approaches. For example, if the user posts frequently about a specific industry, the feedback unit can provide feedback specific to that industry. For example, if the user has a large number of followers on social media, the feedback unit can provide feedback regarding sales approaches that utilize the user's influence. This allows for more effective feedback to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the user's social media activity data and outputs appropriate feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, support unit, analysis unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, support unit, analysis unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, support unit, analysis unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, support unit, analysis unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The support unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The generation unit can customize the script based on the user's past sales performance. For example, the generation unit can generate a script that emphasizes effective approaches based on the user's past success stories. The generation unit can also generate a script that reflects approaches that should be avoided based on the user's past failure stories. Furthermore, the generation unit can generate a script that strengthens specific skills to improve the user's performance. This makes it possible to provide more effective sales pitches by taking the user's past sales performance into consideration.

[0091] The support unit can estimate the user's emotions and change real-time advice based on the estimated user emotions. For example, if the user is nervous, the support unit can provide advice to help them relax. If the user is confident, the support unit can suggest a more challenging approach. Furthermore, if the user is tired, the support unit can provide concise, to-the-point advice. This allows the support unit to provide more effective sales support by adjusting real-time advice according to the user's emotions.

[0092] The analytics department can perform psychology analysis based on customers' social media activity. For example, it can analyze customers' social media posts to understand their interests. It can also evaluate a customer's influence based on the number of followers and engagement rate. Furthermore, it can predict purchasing intent from customers' social media activity. This allows for more effective sales approaches by referring to customers' social media activity.

[0093] The feedback unit can estimate the user's emotions and change the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide relaxing feedback. If the user is confident, the feedback unit can provide challenging feedback. Furthermore, if the user is tired, the feedback unit can provide concise and to-the-point feedback. This allows for more effective sales training by adjusting the content of the feedback according to the user's emotions.

[0094] When providing support, the support department can select the optimal support method based on the user's geographical location information. For example, if the user is in an urban area, a sales approach specific to that city can be proposed. Also, if the user is in a rural area, a sales approach specific to the region can be proposed. Furthermore, if the user is overseas, a sales approach that takes cultural background into consideration can be proposed. In this way, by taking the user's geographical location information into consideration, more effective sales support can be provided.

[0095] The analysis unit can estimate the user's emotions and change the analysis results of customer psychology based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. This allows for a more effective sales approach by adjusting the analysis results of customer psychology according to the user's emotions.

[0096] When generating scripts, the generator can include content specialized for a specific industry or product. For example, it can generate scripts that include technical details for the IT industry. It can also generate scripts that address technical terms and regulations for the medical industry. It can also generate scripts based on customer purchasing behavior for the retail industry. This allows for more effective sales pitches by generating scripts specialized for specific industries or products.

[0097] The support unit can estimate the user's emotions and change the timing of support based on the estimated user emotions. For example, if the user is nervous, support can be provided at a time that will relax the user. If the user is confident, support can be provided at a time that will challenge the user. Furthermore, if the user is tired, support can be provided after a break. In this way, more effective sales support can be provided by adjusting the timing of support according to the user's emotions.

[0098] The feedback unit can provide customized feedback based on specific skill sets when providing feedback. For example, to improve presentation skills, feedback can be provided that points out specific areas for improvement. To improve negotiation skills, feedback can be provided that suggests effective approaches. Furthermore, to improve customer service skills, feedback including practical advice can be provided. This makes it possible to provide more effective sales training by providing feedback tailored to specific skill sets.

[0099] During analysis, the analysis unit can perform a psychology analysis based on the customer's geographic location information. For example, if the customer is in an urban area, the analysis can take into account purchasing behavior specific to that city. Also, if the customer is in a rural area, the analysis can take into account purchasing behavior specific to that region. Furthermore, if the customer is overseas, the analysis can take into account their cultural background. This allows for a more effective sales approach by taking into account the customer's geographic location information.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The generator generates a sales training script. The generator generates the script based on, for example, past successful sales pitches and customer response data. The generator can use AI to analyze large amounts of data and automatically create effective sales pitches. Step 2: The support unit provides real-time sales support based on the script generated by the generation unit. For example, the support unit may interact with the user based on a scenario prepared in advance and provide role-playing. The support unit can simulate actual sales situations using AI. Step 3: The analysis department analyzes customer psychology based on customer behavior data and past transaction history. For example, the analysis department analyzes data such as customer purchase history and website browsing history to understand customer needs and interests. The analysis department can use AI to analyze customer psychology. Step 4: The feedback unit provides feedback based on the user's progress and performance. The feedback unit may, for example, analyze the user's training data and provide appropriate feedback based on the user's progress. The feedback unit may use AI to analyze the user's progress and performance and adaptively improve.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 generation unit that generates a sales training script; a support unit that provides sales support in real time based on the script generated by the generation unit; An analysis department that analyzes customer behavior patterns based on customer behavior data and past transaction history, and a feedback unit that provides feedback based on the user's progress and performance. A system characterized by:

2. The generation unit Generate scripts based on past successful sales pitches or customer response data 2. The system of claim 1.

3. The analysis unit Analyze customer behavior patterns based on customer behavior data or past transaction history 2. The system of claim 1.

4. The support unit Interact with users based on pre-prepared scenarios and provide role-playing 2. The system of claim 1.

5. The feedback unit Analyzes user training data and provides feedback based on progress 2. The system of claim 1.

6. The generation unit Infer the user's emotions and change the script content based on the estimated user emotions.

2. The system of claim 1.

7. The generation unit Analyze past successes and failures to generate more effective scripts 2. The system of claim 1.

8. The generation unit Include industry or product specific content when generating scripts 2. The system of claim 1.

Images