System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system according to an embodiment includes a sales information input unit, an action generation unit, and a proposal unit. The sales information input unit receives information from a salesperson. The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. The proposal unit proposes the sales action generated by the action generation unit to the salesperson.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 have had the problem that it takes time and effort for salespeople to decide on their next sales action.

[0005] The system according to the embodiment aims to enable salespeople to efficiently decide on the next sales action. [Means for solving the problem]

[0006] The system according to the embodiment includes a sales information input unit, an action generation unit, and a proposal unit. The sales information input unit receives information from a sales representative. The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. The proposal unit proposes the sales action generated by the action generation unit to the sales representative. [Effects of the Invention]

[0007] The system according to the embodiment can enable a salesperson to efficiently determine the next sales action. [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 support system according to an embodiment of the present invention is a system that uses a generation AI to generate the next sales action from input information in order to conduct sales activities more efficiently. As a result, the sales support system suggests the next action that a sales representative should take, enabling the efficiency of sales activities to be improved and the execution of effective actions.

[0029] A sales support system according to an embodiment includes a sales information input unit, an action generation unit, and a proposal unit. The sales information input unit receives information from a sales representative. For example, the sales representative inputs information about interactions with a customer and the progress of negotiations. The sales information input unit also uses the input information as a prompt for the generation AI. For example, content such as "The first negotiation with customer A has ended, and the customer would like a product demo" is input. The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. For example, the generation AI generates a specific action such as "Propose a date for a product demo to customer A and prepare for the demo" based on the input sales information. The generation AI generates the sales action using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit proposes the sales action generated by the action generation unit to the sales representative. For example, the action proposed is "Propose a date for a product demo to customer A." This enables the sales support system to improve the efficiency of sales activities and execute effective actions.

[0030] The sales information input unit provides real-time feedback on the information entered by sales representatives, improving the accuracy of the input content. For example, when a sales representative enters customer information, the generation AI analyzes the input content in real time and points out typos and inaccurate information. For example, when a customer name or contact information is entered, it compares it with an existing database to provide accurate information. In addition, when a sales representative enters the details of a sales negotiation, the generation AI references past sales negotiation data and provides advice based on similar cases in real time. For example, if there are many questions about a particular product, detailed information about that product is automatically displayed. In addition, the generation AI performs real-time sentiment analysis on the information entered by the sales representative, determines whether the input content is positive or negative, and provides appropriate feedback. For example, if there are many negative expressions, it suggests changing the input content to more positive expressions. This improves the accuracy of the input content.

[0031] The sales information input unit can refer to past success stories and provide advice based on similar cases. For example, when a sales representative inputs interactions with a customer, the generation AI refers to past success stories and provides advice based on similar cases. For example, it can suggest effective approaches based on success stories for customers in a specific industry. Furthermore, when a sales representative inputs the progress of a sales negotiation, the generation AI analyzes past success stories and suggests the next action to take. For example, it can suggest actions with a high success rate in a specific phase. Furthermore, based on the information input by the sales representative, the generation AI refers to past success stories and provides specific advice. For example, it can suggest effective presentation methods based on success stories for a specific product or service. This allows advice to be provided based on past success stories.

[0032] The sales information input unit uses voice recognition technology to input sales information, allowing sales representatives to input information without using their hands. The sales information input unit introduces voice recognition technology so that sales representatives can input customer information by voice. For example, customer names and contact information can be input by voice, and a generation AI automatically converts it into text. The sales information input unit also builds a system in which sales negotiation details and progress status can be input by voice, and a generation AI converts it into text in real time. For example, key points of a negotiation and next actions can be input by voice. The sales information input unit also uses voice recognition technology to enable sales representatives to input information without using their hands. For example, information can be input by voice while driving or on the move, and the generation AI automatically processes it. This allows sales representatives to input information without using their hands.

[0033] The sales information input unit automatically suggests related materials and data when sales information is entered, making the input work more efficient. In the sales information input unit, for example, when a sales representative enters customer information, the generation AI automatically suggests related materials and data. For example, it automatically displays past sales negotiation history and product catalogs. In addition, when entering sales negotiation details, the generation AI automatically suggests related materials and data, making the input work more efficient. For example, it automatically displays product technical specifications and price lists. In addition, the sales information input unit builds a system in which the generation AI automatically suggests related materials and data based on the information entered by the sales representative. For example, it automatically displays market reports and competitive analyses related to the customer's industry. This makes the input work more efficient.

[0034] The action generation unit can analyze past sales data, learn, and propose the most effective action patterns. In the action generation unit, for example, the generation AI analyzes past sales data and learns the most effective action patterns. For example, it proposes actions with a high success rate for specific industries or customer types. In addition, the action generation unit analyzes past data based on information entered by sales representatives and proposes optimal actions. For example, it presents actions with a high success rate in specific phases. In addition, the action generation unit builds a system in which the generation AI learns the most effective action patterns based on past sales data and proposes the next action to be taken. For example, it proposes actions based on success stories for specific products or services. In this way, optimal action patterns are proposed based on past sales data.

[0035] The action generation unit can take into account competitors' actions and market trends when generating sales actions. For example, the action generation unit uses the generation AI to analyze competitors' actions and market trends and propose the next sales action based on that. For example, if a competitor announces a new product, the action generation unit proposes a counter action. The action generation unit also considers market trends based on information entered by sales representatives and proposes the optimal action. For example, it proposes actions that meet specific market needs. The action generation unit also builds a system in which the generation AI analyzes competitors' actions and market trends in real time and proposes the next sales action based on that. For example, it proposes an action to counter a competitor's pricing strategy. This allows sales actions to be generated that take into account competitors' actions and market trends.

[0036] The action generation unit can generate sales actions by incorporating success stories from different industries and fields. For example, the generation AI in the action generation unit analyzes success stories from different industries and fields and generates the next sales action based on them. For example, applying success stories from the technology field to the consumer market. The action generation unit also proposes actions that incorporate success stories from different industries based on information entered by sales representatives. For example, it proposes actions that apply medical technology to everyday life. The action generation unit also builds a system in which the generation AI generates the next sales action based on success stories from different fields. For example, it proposes actions that combine the needs of different industries. This generates sales actions that incorporate success stories from different industries and fields.

[0037] The action generation unit can simulate sales actions based on multiple scenarios and select the optimal action. For example, the action generation unit builds a system in which a generation AI simulates sales actions based on multiple scenarios and selects the optimal action. For example, it compares the success rates of different scenarios. The action generation unit also simulates multiple scenarios based on information entered by a sales representative and proposes the optimal action. For example, it simulates different approach methods. The action generation unit also develops a system in which a generation AI simulates sales actions based on multiple scenarios and selects the optimal action. For example, it simulates actions under different market conditions. This allows the optimal sales action to be selected based on multiple scenarios.

[0038] The proposal unit can add a function to evaluate the feasibility and risk of sales actions proposed by the generation AI. For example, the proposal unit adds a function to evaluate the feasibility of sales actions proposed by the generation AI. For example, it evaluates whether the proposed actions are realistically feasible and prioritizes actions with high feasibility. The proposal unit also adds a function to evaluate the risk of sales actions proposed by the generation AI. For example, it analyzes the risks inherent in proposed actions and prioritizes actions with low risk. The proposal unit also builds a system in which the generation AI evaluates the feasibility and risk of the proposed action and proposes the optimal action before the sales representative executes it. For example, it adjusts the action based on the results of the risk assessment. This evaluates the feasibility and risk of the proposed sales action.

[0039] The suggestion unit can reflect customers' past responses and feedback in the sales actions suggested by the generation AI. For example, the suggestion unit has the generation AI analyze customers' past responses and feedback and suggest the next sales action based on that. For example, it prioritizes actions for which customers have responded favorably in the past. The suggestion unit also has the generation AI suggest actions that reflect customers' past responses and feedback based on information entered by the sales representative. For example, it avoids actions for which customers have expressed dissatisfaction in the past. The suggestion unit also builds a system in which the generation AI suggests the next sales action based on customers' past responses and feedback. For example, it analyzes customer feedback in real time and suggests the optimal action. This allows sales actions to be suggested that reflect customers' past responses and feedback.

[0040] The proposal unit can add a customization function to adapt the sales actions proposed by the generation AI to different cultures and regions. For example, the proposal unit adds a customization function to adapt the sales actions proposed by the generation AI to different cultures and regions. For example, it proposes actions that take cultural differences into consideration. Furthermore, the proposal unit proposes actions that reflect the specific needs and culture of the region based on information input by the sales representative. For example, it presents approaches that are tailored to local customs and practices. Furthermore, the proposal unit builds a system in which the generation AI proposes sales actions that are adapted to different cultures and regions. For example, it proposes actions that take into consideration the market characteristics and consumer behavior of each region. This allows sales actions that are adapted to different cultures and regions to be proposed.

[0041] The proposal department can share the sales actions proposed by the generation AI within the team and collect feedback from other sales representatives. For example, the proposal department builds a system that shares the sales actions proposed by the generation AI within the team and collects feedback from other sales representatives. For example, it collects comments and evaluations on the proposed actions. The proposal department also allows sales representatives to share the proposed actions within the team and adjust the actions based on feedback from other members. For example, it selects the optimal action for the entire team. The proposal department also develops a system that shares the sales actions proposed by the generation AI within the team and collects feedback in real time. For example, it adds a voting function for the proposed actions. This allows feedback to be collected on the sales actions shared within the team.

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

[0043] Sales support systems can also be equipped with the ability to analyze a customer's purchasing history and automatically select the next product or service to propose. For example, they can propose highly relevant products based on data on products and services a customer has purchased in the past. They can also analyze a customer's purchasing patterns and make proposals according to the season or event. For example, they can propose gift sets during the Christmas season. They can also identify products that are likely to be purchased repeatedly based on a customer's purchasing history and make proposals to encourage repeat purchases. This makes it possible to make effective proposals based on a customer's purchasing history.

[0044] Sales support systems can also be equipped with a schedule management function for sales representatives. For example, they can link with the sales representative's calendar and automatically schedule the next sales meeting or meeting. They can also suggest the optimal visiting route based on the sales representative's schedule. For example, they can suggest an efficient route when visiting multiple customers. They can also provide a reminder function based on the sales representative's schedule to ensure that important tasks and actions are not forgotten. This makes schedule management for sales representatives more efficient.

[0045] Sales support systems can also analyze customers' social media activities and reflect them in sales actions. For example, they can collect information posted by customers on social media to understand their interests. They can also make personalized proposals based on customers' social media activities. For example, if a customer attends a particular event, they can propose products and services related to that event. They can also analyze customers' social media feedback and identify areas for improvement in sales actions. This makes it possible to take effective sales actions based on customers' social media activities.

[0046] Sales support systems can also be equipped with a training function for sales representatives. For example, they can provide training modules for sales representatives to learn about new products and services. They can also suggest customized training programs according to the sales representative's skill level. For example, they can provide basic training for beginners to advanced training for more experienced sales representatives. They can also monitor the sales representative's training progress and suggest additional training as needed. This helps to improve the sales representative's skills.

[0047] The sales support system can also monitor the health status of sales representatives and adjust sales actions based on their health status. For example, if a sales representative is tired, it can advise them to take a break. If a sales representative's health status is not good, it can suggest light exercise or stretching. Furthermore, if a sales representative's health status is good, it can suggest actions to maintain that state. This allows the system to suggest optimal sales actions based on the sales representative's health status.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The sales information input unit receives information from a sales representative. For example, the sales representative inputs information about their interactions with customers and the progress of sales negotiations. The sales information input unit also uses the input information as prompts for the generation AI. For example, the sales representative might input information such as, "The first sales negotiation with Customer A has ended, and the customer would like a product demo." Step 2: The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. For example, the generation AI generates a specific action such as "propose a date for a product demo to customer A and prepare for the demo" based on the input sales information. The generation AI generates the sales action using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The proposal unit proposes the sales action generated by the action generation unit to the sales representative. For example, the action proposed is "Propose a date for a product demonstration to Customer A." This enables the sales support system to improve the efficiency of sales activities and execute effective actions.

[0050] (Example 2) A sales support system according to an embodiment of the present invention is a system that uses a generation AI to generate the next sales action from input information in order to conduct sales activities more efficiently. As a result, the sales support system suggests the next action that a sales representative should take, enabling the efficiency of sales activities to be improved and the execution of effective actions.

[0051] A sales support system according to an embodiment includes a sales information input unit, an action generation unit, and a proposal unit. The sales information input unit receives information from a sales representative. For example, the sales representative inputs information about interactions with a customer and the progress of negotiations. The sales information input unit also uses the input information as a prompt for the generation AI. For example, content such as "The first negotiation with customer A has ended, and the customer would like a product demo" is input. The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. For example, the generation AI generates a specific action such as "Propose a date for a product demo to customer A and prepare for the demo" based on the input sales information. The generation AI generates the sales action using a text generation AI (e.g., LLM) or a multimodal generation AI. The proposal unit proposes the sales action generated by the action generation unit to the sales representative. For example, the action proposed is "Propose a date for a product demo to customer A." This enables the sales support system to improve the efficiency of sales activities and execute effective actions.

[0052] The sales information input unit provides real-time feedback on the information entered by sales representatives, improving the accuracy of the input content. For example, when a sales representative enters customer information, the generation AI analyzes the input content in real time and points out typos and inaccurate information. For example, when a customer name or contact information is entered, it compares it with an existing database to provide accurate information. In addition, when a sales representative enters the details of a sales negotiation, the generation AI references past sales negotiation data and provides advice based on similar cases in real time. For example, if there are many questions about a particular product, detailed information about that product is automatically displayed. In addition, the generation AI performs real-time sentiment analysis on the information entered by the sales representative, determines whether the input content is positive or negative, and provides appropriate feedback. For example, if there are many negative expressions, it suggests changing the input content to more positive expressions. This improves the accuracy of the input content.

[0053] The sales information input unit can refer to past success stories and provide advice based on similar cases. For example, when a sales representative inputs interactions with a customer, the generation AI refers to past success stories and provides advice based on similar cases. For example, it can suggest effective approaches based on success stories for customers in a specific industry. Furthermore, when a sales representative inputs the progress of a sales negotiation, the generation AI analyzes past success stories and suggests the next action to take. For example, it can suggest actions with a high success rate in a specific phase. Furthermore, based on the information input by the sales representative, the generation AI refers to past success stories and provides specific advice. For example, it can suggest effective presentation methods based on success stories for a specific product or service. This allows advice to be provided based on past success stories.

[0054] The sales information input unit can use the emotion estimation function to analyze the emotional state of a sales representative and provide advice to reduce stress and fatigue. For example, when a sales representative enters information, the generation AI analyzes their facial expressions and voice tone to estimate their emotional state in real time. For example, if signs of stress or fatigue are observed, the generation AI may advise them to take a break. The sales information input unit also analyzes the emotional state based on the sales representative's input and provides advice to elicit positive emotions. For example, it may display success stories or encouraging messages. The sales information input unit also monitors the sales representative's emotional state in real time and suggests relaxation and stress relief methods if stress or fatigue is accumulating. For example, it may recommend deep breathing or a short walk. This provides advice to reduce the sales representative's stress and fatigue.

[0055] The sales information input unit uses voice recognition technology to input sales information, allowing sales representatives to input information without using their hands. The sales information input unit introduces voice recognition technology so that sales representatives can input customer information by voice. For example, customer names and contact information can be input by voice, and a generation AI automatically converts it into text. The sales information input unit also builds a system in which sales negotiation details and progress status can be input by voice, and a generation AI converts it into text in real time. For example, key points of a negotiation and next actions can be input by voice. The sales information input unit also uses voice recognition technology to enable sales representatives to input information without using their hands. For example, information can be input by voice while driving or on the move, and the generation AI automatically processes it. This allows sales representatives to input information without using their hands.

[0056] The sales information input unit automatically suggests related materials and data when sales information is entered, making the input work more efficient. In the sales information input unit, for example, when a sales representative enters customer information, the generation AI automatically suggests related materials and data. For example, it automatically displays past sales negotiation history and product catalogs. In addition, when entering sales negotiation details, the generation AI automatically suggests related materials and data, making the input work more efficient. For example, it automatically displays product technical specifications and price lists. In addition, the sales information input unit builds a system in which the generation AI automatically suggests related materials and data based on the information entered by the sales representative. For example, it automatically displays market reports and competitive analyses related to the customer's industry. This makes the input work more efficient.

[0057] The sales information input unit can estimate the emotional state of a customer using the emotion estimation function and suggest an appropriate response method to the sales representative. For example, when a sales representative inputs their interactions with a customer, the sales information input unit uses a generation AI to estimate the customer's emotional state and suggest an appropriate response method. For example, if the customer is dissatisfied, the system will suggest an approach to resolving the problem. The sales information input unit also analyzes the customer's emotional state in real time and builds a system that suggests an appropriate response method to the sales representative. For example, if the customer shows interest, the system will suggest providing additional information. The sales information input unit also uses a generation AI to estimate the customer's emotional state based on the information input by the sales representative and suggest an appropriate response method. For example, if the customer is nervous, the system will suggest an approach to help them relax. This allows an appropriate response method to be suggested based on the customer's emotional state.

[0058] The action generation unit can analyze past sales data, learn, and propose the most effective action patterns. In the action generation unit, for example, the generation AI analyzes past sales data and learns the most effective action patterns. For example, it proposes actions with a high success rate for specific industries or customer types. In addition, the action generation unit analyzes past data based on information entered by sales representatives and proposes optimal actions. For example, it presents actions with a high success rate in specific phases. In addition, the action generation unit builds a system in which the generation AI learns the most effective action patterns based on past sales data and proposes the next action to be taken. For example, it proposes actions based on success stories for specific products or services. In this way, optimal action patterns are proposed based on past sales data.

[0059] The action generation unit can take into account competitors' actions and market trends when generating sales actions. For example, the action generation unit uses the generation AI to analyze competitors' actions and market trends and propose the next sales action based on that. For example, if a competitor announces a new product, the action generation unit proposes a counter action. The action generation unit also considers market trends based on information entered by sales representatives and proposes the optimal action. For example, it proposes actions that meet specific market needs. The action generation unit also builds a system in which the generation AI analyzes competitors' actions and market trends in real time and proposes the next sales action based on that. For example, it proposes an action to counter a competitor's pricing strategy. This allows sales actions to be generated that take into account competitors' actions and market trends.

[0060] The action generation unit can use the emotion estimation function to generate optimal sales actions based on the customer's emotional state. In the action generation unit, for example, the generation AI analyzes the customer's emotional state and generates optimal sales actions based on that. For example, if the customer is excited, it suggests providing additional information or a demonstration. The action generation unit also estimates the customer's emotional state based on information entered by the sales representative and suggests optimal actions. For example, if the customer is feeling anxious, it suggests an approach to reassure them. The action generation unit also builds a system in which the generation AI uses the emotion estimation function to analyze the customer's emotional state in real time and generates optimal sales actions based on that. For example, if the customer is feeling positive, it suggests a closing action. In this way, optimal sales actions are generated based on the customer's emotional state.

[0061] The action generation unit can generate sales actions by incorporating success stories from different industries and fields. For example, the generation AI in the action generation unit analyzes success stories from different industries and fields and generates the next sales action based on them. For example, applying success stories from the technology field to the consumer market. The action generation unit also proposes actions that incorporate success stories from different industries based on information entered by sales representatives. For example, it proposes actions that apply medical technology to everyday life. The action generation unit also builds a system in which the generation AI generates the next sales action based on success stories from different fields. For example, it proposes actions that combine the needs of different industries. This generates sales actions that incorporate success stories from different industries and fields.

[0062] The action generation unit can simulate sales actions based on multiple scenarios and select the optimal action. For example, the action generation unit builds a system in which a generation AI simulates sales actions based on multiple scenarios and selects the optimal action. For example, it compares the success rates of different scenarios. The action generation unit also simulates multiple scenarios based on information entered by a sales representative and proposes the optimal action. For example, it simulates different approach methods. The action generation unit also develops a system in which a generation AI simulates sales actions based on multiple scenarios and selects the optimal action. For example, it simulates actions under different market conditions. This allows the optimal sales action to be selected based on multiple scenarios.

[0063] The action generation unit can use the emotion estimation function to consider the emotional state of the sales representative and suggest actions to reduce stress. For example, the action generation unit uses a generation AI to analyze the emotional state of the sales representative and suggest actions to reduce stress. For example, it suggests ways to relax or relieve stress. The action generation unit also uses the generation AI to estimate the emotional state based on information entered by the sales representative and suggest actions to reduce stress. For example, it may advise the sales representative to take a break. The action generation unit also builds a system in which the generation AI uses the emotion estimation function to analyze the emotional state of the sales representative in real time and suggest actions to reduce stress. For example, it suggests actions to elicit positive emotions. This suggests actions to reduce the sales representative's stress.

[0064] The proposal unit can add a function to evaluate the feasibility and risk of sales actions proposed by the generation AI. For example, the proposal unit adds a function to evaluate the feasibility of sales actions proposed by the generation AI. For example, it evaluates whether the proposed actions are realistically feasible and prioritizes actions with high feasibility. The proposal unit also adds a function to evaluate the risk of sales actions proposed by the generation AI. For example, it analyzes the risks inherent in proposed actions and prioritizes actions with low risk. The proposal unit also builds a system in which the generation AI evaluates the feasibility and risk of the proposed action and proposes the optimal action before the sales representative executes it. For example, it adjusts the action based on the results of the risk assessment. This evaluates the feasibility and risk of the proposed sales action.

[0065] The suggestion unit can reflect customers' past responses and feedback in the sales actions suggested by the generation AI. For example, the suggestion unit has the generation AI analyze customers' past responses and feedback and suggest the next sales action based on that. For example, it prioritizes actions for which customers have responded favorably in the past. The suggestion unit also has the generation AI suggest actions that reflect customers' past responses and feedback based on information entered by the sales representative. For example, it avoids actions for which customers have expressed dissatisfaction in the past. The suggestion unit also builds a system in which the generation AI suggests the next sales action based on customers' past responses and feedback. For example, it analyzes customer feedback in real time and suggests the optimal action. This allows sales actions to be suggested that reflect customers' past responses and feedback.

[0066] The proposal unit can use the emotion estimation function to predict the emotional impact that a proposed sales action will have on a customer. For example, the proposal unit uses the emotion estimation function to predict the emotional impact that a sales action proposed by the generation AI will have on a customer. For example, it evaluates whether the proposed action will evoke positive emotions in the customer. The proposal unit also predicts the emotional impact of the action proposed by the generation AI based on information input by the sales representative and proposes the optimal action. For example, it prioritizes actions that evoke positive emotions in the customer. The proposal unit also builds a system in which the generation AI uses the emotion estimation function to predict the emotional impact that a proposed sales action will have on a customer in real time. For example, it adjusts the action based on the customer's emotional state. This allows the emotional impact that the proposed sales action will have on the customer to be predicted.

[0067] The proposal unit can add a customization function to adapt the sales actions proposed by the generation AI to different cultures and regions. For example, the proposal unit adds a customization function to adapt the sales actions proposed by the generation AI to different cultures and regions. For example, it proposes actions that take cultural differences into consideration. Furthermore, the proposal unit proposes actions that reflect the specific needs and culture of the region based on information input by the sales representative. For example, it presents approaches that are tailored to local customs and practices. Furthermore, the proposal unit builds a system in which the generation AI proposes sales actions that are adapted to different cultures and regions. For example, it proposes actions that take into consideration the market characteristics and consumer behavior of each region. This allows sales actions that are adapted to different cultures and regions to be proposed.

[0068] The proposal department can share the sales actions proposed by the generation AI within the team and collect feedback from other sales representatives. For example, the proposal department builds a system that shares the sales actions proposed by the generation AI within the team and collects feedback from other sales representatives. For example, it collects comments and evaluations on the proposed actions. The proposal department also allows sales representatives to share the proposed actions within the team and adjust the actions based on feedback from other members. For example, it selects the optimal action for the entire team. The proposal department also develops a system that shares the sales actions proposed by the generation AI within the team and collects feedback in real time. For example, it adds a voting function for the proposed actions. This allows feedback to be collected on the sales actions shared within the team.

[0069] The proposal unit uses the emotion estimation function to predict the emotional impact that proposed sales actions will have on sales representatives, and can make proposals that will increase their motivation. For example, the proposal unit uses the emotion estimation function to predict the emotional impact that sales actions proposed by the generation AI will have on sales representatives. For example, it evaluates whether the proposed actions will increase the sales representative's motivation. The proposal unit also predicts the emotional impact of actions proposed by the generation AI based on information input by the sales representative, and makes proposals that will increase motivation. For example, it prioritizes actions that elicit positive emotions. The proposal unit also builds a system in which the generation AI uses the emotion estimation function to predict the emotional impact that proposed sales actions will have on sales representatives in real time. For example, it adjusts the actions based on the emotional state of the sales representative. As a result, the emotional impact that proposed sales actions will have on sales representatives is predicted, and proposals that will increase motivation are made.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] Sales support systems can also be equipped with the ability to analyze a customer's purchasing history and automatically select the next product or service to propose. For example, they can propose highly relevant products based on data on products and services a customer has purchased in the past. They can also analyze a customer's purchasing patterns and make proposals according to the season or event. For example, they can propose gift sets during the Christmas season. They can also identify products that are likely to be purchased repeatedly based on a customer's purchasing history and make proposals to encourage repeat purchases. This makes it possible to make effective proposals based on a customer's purchasing history.

[0072] Sales support systems can also be equipped with a schedule management function for sales representatives. For example, they can link with the sales representative's calendar and automatically schedule the next sales meeting or meeting. They can also suggest the optimal visiting route based on the sales representative's schedule. For example, they can suggest an efficient route when visiting multiple customers. They can also provide a reminder function based on the sales representative's schedule to ensure that important tasks and actions are not forgotten. This makes schedule management for sales representatives more efficient.

[0073] Sales support systems can also analyze customers' social media activities and reflect them in sales actions. For example, they can collect information posted by customers on social media to understand their interests. They can also make personalized proposals based on customers' social media activities. For example, if a customer attends a particular event, they can propose products and services related to that event. They can also analyze customers' social media feedback and identify areas for improvement in sales actions. This makes it possible to take effective sales actions based on customers' social media activities.

[0074] Sales support systems can also be equipped with a training function for sales representatives. For example, they can provide training modules for sales representatives to learn about new products and services. They can also suggest customized training programs according to the sales representative's skill level. For example, they can provide basic training for beginners to advanced training for more experienced sales representatives. They can also monitor the sales representative's training progress and suggest additional training as needed. This helps to improve the sales representative's skills.

[0075] The sales support system can also estimate the customer's emotional state and adjust sales actions based on the estimated emotions. For example, if a customer is dissatisfied, it can suggest an approach to resolve the problem. If a customer is excited, it can suggest providing additional information or a demonstration. Furthermore, if a customer is nervous, it can suggest an approach to relax the customer. In this way, optimal sales actions are suggested based on the customer's emotional state.

[0076] The sales support system can also estimate the emotional state of the sales representative and suggest actions to reduce stress based on the estimated emotions. For example, if a sales representative is feeling stressed, it can suggest ways to relax or relieve stress. If a sales representative is tired, it can also advise them to take a break. Furthermore, if a sales representative is feeling positive, it can suggest actions to maintain that emotion. This allows it to suggest actions to reduce stress and increase motivation for sales representatives.

[0077] The sales support system can also estimate the customer's willingness to purchase and adjust sales actions based on the estimated willingness to purchase. For example, if the customer shows a high willingness to purchase, it can suggest closing actions. If the customer shows a low willingness to purchase, it can also suggest providing additional information or a demonstration. Furthermore, if the customer does not show willingness to purchase, it can suggest an approach to attract their interest. In this way, the optimal sales action is suggested based on the customer's willingness to purchase.

[0078] The sales support system can also estimate the emotional state of the sales representative and adjust the training program based on the estimated emotions. For example, if a sales representative is feeling stressed, it can suggest training that incorporates relaxation techniques. Also, if a sales representative is feeling positive, it can suggest training to maintain that emotion. Furthermore, if a sales representative is tired, it can suggest training that incorporates breaks. In this way, the optimal training program is suggested based on the emotional state of the sales representative.

[0079] The sales support system can further estimate the emotional state of the customer and adjust the marketing campaign based on the estimated emotion. For example, if the customer has positive emotion, it can suggest a campaign to bring out that emotion. Also, if the customer has negative emotion, it can suggest a campaign to alleviate that emotion. Furthermore, if the customer is excited, it can suggest a campaign to maintain that excitement. In this way, the optimal marketing campaign is suggested based on the customer's emotional state.

[0080] The sales support system can also monitor the health status of sales representatives and adjust sales actions based on their health status. For example, if a sales representative is tired, it can advise them to take a break. If a sales representative's health status is not good, it can suggest light exercise or stretching. Furthermore, if a sales representative's health status is good, it can suggest actions to maintain that state. This allows the system to suggest optimal sales actions based on the sales representative's health status.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The sales information input unit receives information from a sales representative. For example, the sales representative inputs information about their interactions with customers and the progress of sales negotiations. The sales information input unit also uses the input information as prompts for the generation AI. For example, the sales representative might input information such as, "The first sales negotiation with Customer A has ended, and the customer would like a product demo." Step 2: The action generation unit analyzes the information received by the sales information input unit and generates the next sales action. For example, the generation AI generates a specific action such as "propose a date for a product demo to customer A and prepare for the demo" based on the input sales information. The generation AI generates the sales action using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The proposal unit proposes the sales action generated by the action generation unit to the sales representative. For example, the action proposed is "Propose a date for a product demonstration to Customer A." This enables the sales support system to improve the efficiency of sales activities and execute effective actions.

[0083] 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.

[0084] 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 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sales information input unit that receives information from sales personnel; an action generation unit that analyzes the information received by the sales information input unit and generates a next sales action; a proposal unit that proposes the sales action generated by the action generation unit to a sales representative. A system characterized by:

2. The business information input unit Provide real-time feedback to the sales representative on the information they enter, improving the accuracy of their input 2. The system of claim 1.

3. The business information input unit Referencing past success stories and providing advice based on similar cases 2. The system of claim 1.

4. The business information input unit Analyze the salesperson's emotional state and provide advice to reduce stress and fatigue 2. The system of claim 1.

5. The business information input unit Using voice recognition technology to input sales information, allowing the salesperson to input the information hands-free 2. The system of claim 1.

6. The business information input unit When entering sales information, related materials and data are automatically suggested, streamlining the input process.

2. The system of claim 1.

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