System

Abstract

An object of a system according to an embodiment is to efficiently respond to needs in sales activities and convert information into knowledge.SOLUTION: A system according to an embodiment includes a generation AI, a need handling unit, a knowledge conversion unit, a proposal creation unit, and a service opening unit. The generated AI corresponds to the customer's needs. The needs handling unit handles the needs of the customer. The knowledge conversion unit converts information in the sales activity into knowledge. The proposal document creation unit creates a proposal document. The service opening unit supports a service opening operation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately address needs in sales activities or convert information into knowledge, leaving room for improvement in efficiency.

[0005] The system according to the embodiment aims to efficiently respond to needs in sales activities and turn information into knowledge. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a needs response unit, a knowledge conversion unit, a proposal creation unit, and a service launch unit. The generation AI responds to customer needs. The needs response unit responds to customer needs. The knowledge conversion unit converts information from sales activities into knowledge. The proposal creation unit creates proposals. The service launch unit supports service launch operations. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently respond to needs in sales activities and turn information into knowledge. [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) The generative AI system according to the embodiment of the present invention is a system that supports sales staff in meeting their needs and converting information into knowledge, aiming to "transition from personal sales to digital sales." As a result, the generative AI system can improve the efficiency of sales staff and customer service.

[0029] The generation AI system according to the embodiment includes a needs response unit, a knowledge creation unit, a proposal creation unit, and a service launch unit. The needs response unit responds to customer needs. For example, the generation AI analyzes customer inquiries and requests and generates optimal answers and proposals. The generation AI can also predict future needs based on the customer's past purchase history and inquiry history and create proposals in advance. The generation AI can also analyze customer behavior patterns and predict future needs. The knowledge creation unit creates knowledge from information in sales activities. For example, the generation AI analyzes past proposals and customer interaction history and accumulates it as common knowledge. The generation AI can also analyze voice data from sales activities and automatically convert important information into text to accumulate as knowledge. The generation AI can also analyze sales activity data and build a knowledge base. The proposal creation unit creates proposals. For example, the generation AI automatically generates the content of proposals based on customer requests and needs. The generation AI can also analyze successful proposals in the past, learn their patterns, and generate optimal proposals. The generation AI can also automatically optimize the structure and layout of proposals. The service activation unit supports service activation operations. For example, the generation AI automates service activation procedures and configuration tasks. The generation AI can also automate all procedures required for service activation and monitor the progress of the procedures in real time. The generation AI can also report the progress of service activation procedures to customers in real time. As a result, the generation AI system according to the embodiment can improve the efficiency of sales staff and customer service. For example, automating proposal creation allows sales staff to serve more customers, and automating service activation operations enables faster service delivery to customers. Knowledge sharing is also expected to improve the skills of sales staff as a whole.

[0030] The needs response unit can analyze a customer's past purchase history or inquiry history, predict future needs, and generate proposals in advance. For example, the needs response unit uses a generation AI to analyze a customer's past purchase history and predict future needs based on purchase frequency and purchasing patterns. For example, if a customer purchases a certain product regularly, the unit can predict when that product's inventory will run low and make advance replenishment proposals. The needs response unit can also analyze a customer's inquiry history and predict future inquiries based on information about products and services that have received many inquiries in the past. For example, it can provide related information in advance about products that receive many inquiries during a particular season. The needs response unit can also integrate purchase history and inquiry history and analyze customer behavior patterns to more accurately predict future needs. For example, it can suggest products that a customer who has purchased a specific product is likely to purchase next. This allows the unit to predict a customer's future needs and respond to them in advance, thereby improving customer satisfaction.

[0031] The knowledge creation department can analyze voice data from sales activities and automatically convert important information into text to store as knowledge. For example, the knowledge creation department records conversations during sales activities, and the generation AI analyzes the voice data to automatically convert important information into text. For example, customer requests, questions, and proposals are saved as text data. The knowledge creation department also stores important information in a knowledge base based on the results of the voice data analysis. For example, customer feedback and requests can be registered in the knowledge base so that other sales staff can refer to it. The knowledge creation department also uses voice recognition technology in its generation AI to improve the accuracy of voice data analysis. For example, it performs noise removal and speaker identification to generate accurate text data. In this way, important information from sales activities can be accumulated as knowledge and shared, thereby improving the skills of all sales staff.

[0032] The proposal creation unit can analyze successful proposals in the past, learn their patterns, and generate optimal proposals. For example, the generation AI in the proposal creation unit analyzes successful proposals in the past and extracts common patterns and elements. For example, it identifies keywords and phrases that frequently appear in successful proposals. The proposal creation unit also learns the patterns of successful proposals and applies those patterns when generating new proposals. For example, it uses the structure and layout of successful proposals as a reference. The generation AI in the proposal creation unit also automatically generates optimal proposals based on past success cases. For example, it generates proposals that are effective for specific industries or customers. This improves the efficiency and accuracy of proposal creation by generating optimal proposals based on past success cases.

[0033] The service launch unit automates all procedures required for service launch and can monitor the progress of the procedures in real time. For example, the service launch unit builds a system in which the generation AI automates the procedures required for service launch and monitors the progress in real time. For example, it automates the creation of contracts and the submission of necessary documents. The service launch unit also monitors the progress of the service launch procedure in real time and responds immediately if a problem occurs. For example, it issues an alert if necessary documents are missing. The service launch unit also monitors the progress of the procedure using the generation AI and reports the progress to the customer in real time. For example, it sends a notification each time each step of the procedure is completed. In this way, the efficiency and accuracy of the procedure are improved by automating the service launch procedure and monitoring the progress in real time.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The generative AI system can also be equipped with a market analysis section that analyzes market trends and provides information useful for sales activities. For example, the generative AI can analyze market trends and competitors' activities and provide sales staff with the latest market information. The generative AI can also predict market needs based on customer purchasing behavior and feedback and propose sales strategies. Furthermore, the generative AI can analyze market trends in specific regions or industries and provide sales staff with information on target markets. This allows sales staff to understand market trends and conduct effective sales activities.

[0036] The generation AI system can also be equipped with a schedule optimization section to improve the efficiency of sales activities. For example, the generation AI can analyze the schedules of sales staff and propose the optimal order of visits and time allocation. The generation AI can also propose efficient routes taking into account the location and priority of customers. Furthermore, the generation AI can automatically generate optimal schedules based on the sales staff's past schedule data. This allows sales staff to visit customers efficiently and make effective use of their time.

[0037] The generative AI system can also be equipped with a performance evaluation module to evaluate the results of sales activities. For example, the generative AI can analyze the activity data of sales staff and evaluate the results of each sales staff member. The generative AI can also evaluate the performance of sales staff based on sales activity KPIs (key performance indicators). Furthermore, the generative AI can predict the results of sales staff based on past data and suggest areas for improvement. This allows sales staff to understand their own performance and use it to make improvements.

[0038] The generative AI system can also be equipped with a cross-selling / upselling suggestion unit that makes cross-selling and up-selling suggestions based on the customer's purchasing history. For example, the generative AI can analyze a customer's past purchase history and suggest related products and services. The generative AI can also suggest more advanced products or additional services for products purchased by the customer. Furthermore, the generative AI can predict and suggest products that the customer is likely to purchase next based on the customer's purchasing patterns. This enables effective suggestions that meet customer needs, which is expected to increase sales.

[0039] The generative AI system can also be equipped with a training support section that supports the training of sales staff. For example, the generative AI can analyze the sales staff's past activity data and identify areas that require training. The generative AI can also suggest training programs according to the sales staff's skill level. Furthermore, the generative AI can monitor the progress of training and provide feedback as needed. This allows sales staff to improve their skills and conduct effective sales activities.

[0040] The processing flow of the first embodiment will be briefly explained below.

[0041] Step 1: The needs response unit responds to customer needs. For example, the generation AI analyzes customer inquiries and requests and generates optimal answers and proposals. The generation AI can also predict future needs based on the customer's past purchase history and inquiry history and generate proposals in advance. Furthermore, the generation AI can analyze customer behavior patterns and predict future needs. Step 2: The knowledge creation department creates knowledge from information in sales activities. For example, the generation AI analyzes past proposals and customer interaction history and stores it as common knowledge. The generation AI can also analyze voice data from sales activities, automatically convert important information into text, and store it as knowledge. Furthermore, the generation AI can analyze data from sales activities and build a knowledge base. Step 3: The proposal creation unit creates a proposal. For example, the generation AI automatically generates the content of the proposal based on the customer's requests and needs. The generation AI can also analyze past successful proposals, learn their patterns, and generate the optimal proposal. Furthermore, the generation AI can automatically optimize the structure and layout of the proposal. Step 4: The service activation unit supports service activation operations. For example, the generation AI automates the service activation procedures and configuration work. The generation AI also automates all procedures required for service activation and can monitor the progress of the procedures in real time. Furthermore, the generation AI can report the progress of the service activation procedures to the customer in real time.

[0042] (Example 2) The generative AI system according to the embodiment of the present invention is a system that supports sales staff in meeting their needs and converting information into knowledge, aiming to "transition from personal sales to digital sales." As a result, the generative AI system can improve the efficiency of sales staff and customer service.

[0043] The generation AI system according to the embodiment includes a needs response unit, a knowledge creation unit, a proposal creation unit, and a service launch unit. The needs response unit responds to customer needs. For example, the generation AI analyzes customer inquiries and requests and generates optimal answers and proposals. The generation AI can also predict future needs based on the customer's past purchase history and inquiry history and create proposals in advance. The generation AI can also analyze customer behavior patterns and predict future needs. The knowledge creation unit creates knowledge from information in sales activities. For example, the generation AI analyzes past proposals and customer interaction history and accumulates it as common knowledge. The generation AI can also analyze voice data from sales activities and automatically convert important information into text to accumulate as knowledge. The generation AI can also analyze sales activity data and build a knowledge base. The proposal creation unit creates proposals. For example, the generation AI automatically generates the content of proposals based on customer requests and needs. The generation AI can also analyze successful proposals in the past, learn their patterns, and generate optimal proposals. The generation AI can also automatically optimize the structure and layout of proposals. The service activation unit supports service activation operations. For example, the generation AI automates service activation procedures and configuration tasks. The generation AI can also automate all procedures required for service activation and monitor the progress of the procedures in real time. The generation AI can also report the progress of service activation procedures to customers in real time. As a result, the generation AI system according to the embodiment can improve the efficiency of sales staff and customer service. For example, automating proposal creation allows sales staff to serve more customers, and automating service activation operations enables faster service delivery to customers. Knowledge sharing is also expected to improve the skills of sales staff as a whole.

[0044] The needs response unit can analyze a customer's past purchase history or inquiry history, predict future needs, and generate proposals in advance. For example, the needs response unit uses a generation AI to analyze a customer's past purchase history and predict future needs based on purchase frequency and purchasing patterns. For example, if a customer purchases a certain product regularly, the unit can predict when that product's inventory will run low and make advance replenishment proposals. The needs response unit can also analyze a customer's inquiry history and predict future inquiries based on information about products and services that have received many inquiries in the past. For example, it can provide related information in advance about products that receive many inquiries during a particular season. The needs response unit can also integrate purchase history and inquiry history and analyze customer behavior patterns to more accurately predict future needs. For example, it can suggest products that a customer who has purchased a specific product is likely to purchase next. This allows the unit to predict a customer's future needs and respond to them in advance, thereby improving customer satisfaction.

[0045] The knowledge creation department can analyze voice data from sales activities and automatically convert important information into text to store as knowledge. For example, the knowledge creation department records conversations during sales activities, and the generation AI analyzes the voice data to automatically convert important information into text. For example, customer requests, questions, and proposals are saved as text data. The knowledge creation department also stores important information in a knowledge base based on the results of the voice data analysis. For example, customer feedback and requests can be registered in the knowledge base so that other sales staff can refer to it. The knowledge creation department also uses voice recognition technology in its generation AI to improve the accuracy of voice data analysis. For example, it performs noise removal and speaker identification to generate accurate text data. In this way, important information from sales activities can be accumulated as knowledge and shared, thereby improving the skills of all sales staff.

[0046] The proposal creation unit can analyze successful proposals in the past, learn their patterns, and generate optimal proposals. For example, the generation AI in the proposal creation unit analyzes successful proposals in the past and extracts common patterns and elements. For example, it identifies keywords and phrases that frequently appear in successful proposals. The proposal creation unit also learns the patterns of successful proposals and applies those patterns when generating new proposals. For example, it uses the structure and layout of successful proposals as a reference. The generation AI in the proposal creation unit also automatically generates optimal proposals based on past success cases. For example, it generates proposals that are effective for specific industries or customers. This improves the efficiency and accuracy of proposal creation by generating optimal proposals based on past success cases.

[0047] The service launch unit automates all procedures required for service launch and can monitor the progress of the procedures in real time. For example, the service launch unit builds a system in which the generation AI automates the procedures required for service launch and monitors the progress in real time. For example, it automates the creation of contracts and the submission of necessary documents. The service launch unit also monitors the progress of the service launch procedure in real time and responds immediately if a problem occurs. For example, it issues an alert if necessary documents are missing. The service launch unit also monitors the progress of the procedure using the generation AI and reports the progress to the customer in real time. For example, it sends a notification each time each step of the procedure is completed. In this way, the efficiency and accuracy of the procedure are improved by automating the service launch procedure and monitoring the progress in real time.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The generative AI system can also be equipped with an emotion response unit that estimates the customer's emotions and adjusts the response based on the estimated emotions. For example, when a customer makes an inquiry, the generative AI analyzes the customer's tone of voice and phrasing, and if it estimates that the customer is dissatisfied, it will respond quickly and courteously. If it estimates that the customer is satisfied, it can suggest additional services or products. Furthermore, if it estimates that the customer is confused, it can provide more detailed explanations and support. This enables flexible responses based on the customer's emotions, which is expected to improve customer satisfaction.

[0050] The generative AI system can further include a proposal emotion adjustment unit that estimates the customer's emotions and adjusts the content of the proposal based on the estimated emotions. For example, if a customer has expressed dissatisfaction with a previous proposal, the generative AI can take that emotion into account to create a more specific and detailed proposal. Also, if the customer was satisfied with a previous proposal, the generative AI can continue the same approach while adding new proposals. Furthermore, if the customer is interested in the proposal content, additional information and options can be provided. This can improve the success rate of proposals by creating proposals that reflect the customer's emotions.

[0051] The generative AI system can further include an emotional knowledge provision unit that estimates customer emotions and provides knowledge-based information based on the estimated emotions. For example, when a customer makes an inquiry, the generative AI analyzes the customer's emotions and, if it estimates that the customer is feeling anxious, it can provide reassuring information and success stories. If it estimates that the customer is interested, it can provide related detailed information and additional resources. Furthermore, if it estimates that the customer has questions, it can provide FAQs and detailed explanations. This makes it possible to provide appropriate information according to the customer's emotions, thereby improving customer understanding and satisfaction.

[0052] The generative AI system can further include an emotion progress adjustment unit that estimates the customer's emotions and adjusts the progress of the service activation procedure based on the estimated emotions. For example, if the generative AI estimates that the customer is feeling anxious about the service activation procedure, it will explain each step of the procedure in detail and frequently report on the progress. If it estimates that the customer is satisfied with the procedure, it can quickly progress the procedure. Furthermore, if it estimates that the customer has doubts about the procedure, it can provide additional support and explanations. This makes it possible to adjust the progress of the procedure according to the customer's emotions, which is expected to improve customer satisfaction.

[0053] The generative AI system can also be equipped with an emotion feedback unit that estimates customer emotions and provides feedback on sales activities based on the estimated emotions. For example, when a salesperson talks with a customer, the generative AI analyzes the customer's emotions and, if it estimates that the customer is satisfied, provides that feedback to the salesperson and shares the factors behind their success. If it estimates that the customer is dissatisfied, it can suggest areas for improvement. Furthermore, if it estimates that the customer is interested, it can suggest the next action to take. This allows salespersons to receive feedback based on the customer's emotions and use it to improve their sales activities.

[0054] The generative AI system can also be equipped with a market analysis section that analyzes market trends and provides information useful for sales activities. For example, the generative AI can analyze market trends and competitors' activities and provide sales staff with the latest market information. The generative AI can also predict market needs based on customer purchasing behavior and feedback and propose sales strategies. Furthermore, the generative AI can analyze market trends in specific regions or industries and provide sales staff with information on target markets. This allows sales staff to understand market trends and conduct effective sales activities.

[0055] The generation AI system can also be equipped with a schedule optimization section to improve the efficiency of sales activities. For example, the generation AI can analyze the schedules of sales staff and propose the optimal order of visits and time allocation. The generation AI can also propose efficient routes taking into account the location and priority of customers. Furthermore, the generation AI can automatically generate optimal schedules based on the sales staff's past schedule data. This allows sales staff to visit customers efficiently and make effective use of their time.

[0056] The generative AI system can also be equipped with a performance evaluation module to evaluate the results of sales activities. For example, the generative AI can analyze the activity data of sales staff and evaluate the results of each sales staff member. The generative AI can also evaluate the performance of sales staff based on sales activity KPIs (key performance indicators). Furthermore, the generative AI can predict the results of sales staff based on past data and suggest areas for improvement. This allows sales staff to understand their own performance and use it to make improvements.

[0057] The generative AI system can also be equipped with a cross-selling / upselling suggestion unit that makes cross-selling and up-selling suggestions based on the customer's purchasing history. For example, the generative AI can analyze a customer's past purchase history and suggest related products and services. The generative AI can also suggest more advanced products or additional services for products purchased by the customer. Furthermore, the generative AI can predict and suggest products that the customer is likely to purchase next based on the customer's purchasing patterns. This enables effective suggestions that meet customer needs, which is expected to increase sales.

[0058] The generative AI system can also be equipped with a training support section that supports the training of sales staff. For example, the generative AI can analyze the sales staff's past activity data and identify areas that require training. The generative AI can also suggest training programs according to the sales staff's skill level. Furthermore, the generative AI can monitor the progress of training and provide feedback as needed. This allows sales staff to improve their skills and conduct effective sales activities.

[0059] The processing flow of the second embodiment will be briefly explained below.

[0060] Step 1: The needs response unit responds to customer needs. For example, the generation AI analyzes customer inquiries and requests and generates optimal answers and proposals. The generation AI can also predict future needs based on the customer's past purchase history and inquiry history and generate proposals in advance. Furthermore, the generation AI can analyze customer behavior patterns and predict future needs. Step 2: The knowledge creation department creates knowledge from information in sales activities. For example, the generation AI analyzes past proposals and customer interaction history and stores it as common knowledge. The generation AI can also analyze voice data from sales activities, automatically convert important information into text, and store it as knowledge. Furthermore, the generation AI can analyze data from sales activities and build a knowledge base. Step 3: The proposal creation unit creates a proposal. For example, the generation AI automatically generates the content of the proposal based on the customer's requests and needs. The generation AI can also analyze past successful proposals, learn their patterns, and generate the optimal proposal. Furthermore, the generation AI can automatically optimize the structure and layout of the proposal. Step 4: The service activation unit supports service activation operations. For example, the generation AI automates the service activation procedures and configuration work. The generation AI also automates all procedures required for service activation and can monitor the progress of the procedures in real time. Furthermore, the generation AI can report the progress of the service activation procedures to the customer in real time.

[0061] 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.

[0062] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0063] 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.

[0064] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0065] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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).

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0078] 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.

[0079] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0090] 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.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0095] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] In the robot 414, 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 robot 414 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.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0115] 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."

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0128] 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. Equipped with generative AI, The generated AI is A needs response department that responds to customer needs; A knowledge creation department that creates knowledge from information in sales activities; a proposal writing department that writes proposals; A service activation unit that supports service activation operations. A system characterized by:

2. The needs response department Analyzing the customer's past purchase history or inquiry history, predicting future needs and generating proposals in advance 2. The system of claim 1.

3. The knowledge generation unit Analyze the voice data of the sales activities, automatically convert important information into text, and store it as knowledge.

2. The system of claim 1.

4. The proposal creation unit Analyze past successful proposals, learn their patterns, and generate the optimal proposal 2. The system of claim 1.

5. The service activation unit Automate all procedures required for service launch and monitor the progress of said procedures in real time 2. The system of claim 1.

Images