System

Abstract

An object of a system according to an embodiment is to efficiently find a new customer candidate.SOLUTION: A system includes an analysis unit and a discovery unit. The Analyzer uses the generated AI to analyze market and business data to discover potential customer candidates. The finding unit lists the customer candidates found by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it takes time and costs money to find potential new customers.

[0005] The system according to the embodiment aims to efficiently discover new customer candidates. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit and a discovery unit. The analysis unit analyzes market data and company data using a generative AI to discover potential customer candidates. The discovery unit lists the customer candidates discovered by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently discover new customer candidates. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The New Customer generation AI system according to an embodiment of the present invention is a system in which the generation AI analyzes market data and company data to discover and list potential customer candidates. As a result, the New Customer generation AI system can improve the efficiency of new customer acquisition and increase the profit margins of companies.

[0029] The New Customer Generation AI system according to the embodiment includes a generation AI, an analysis unit, and a discovery unit. The generation AI analyzes market data and company data. For example, the generation AI analyzes market data using a text generation AI (e.g., LLM). The generation AI can also analyze company data using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from the data and perform analysis based on that information. The analysis unit uses the generation AI to analyze market data and company data and discover potential customer candidates. For example, the analysis unit generates a list of companies likely to be interested in the company's products and services. The analysis unit can also analyze company websites and social media data to understand the company's needs and interests. The analysis unit can also suggest optimal approaches and timing to sales representatives. For example, the analysis unit generates a list of companies that are likely to be interested in a company's products or services. The analysis unit analyzes data from the company's website and social media to understand the company's needs and interests. The analysis unit suggests the optimal approach method and timing to the sales representative. The discovery unit lists the potential customers discovered by the analysis unit. For example, the discovery unit generates the list based on the company's attribute information and interest score. The discovery unit can also set the format of the list and the type of information to be included in the list. The discovery unit can also update and manage the list. For example, the discovery unit generates the list based on the company's attribute information and interest score. The discovery unit sets the format of the list and the type of information to be included in the list. The discovery unit updates and manages the list. As a result, the New Customer generation AI system according to the embodiment can improve the efficiency of new customer acquisition and increase the company's profit margin. For example, the output unit provides the listed potential customers to the sales representative, supporting efficient sales activities.The list of potential customers serves as the basis for sales representatives to approach them efficiently. Sales representatives can use the list to carry out efficient sales activities and achieve results.

[0030] The analysis unit can generate a list of companies that are likely to be interested in a company's products and services. For example, the generation AI analyzes a company's past sales data to identify successful sales techniques. For example, it learns the content and timing of sales emails that have been successful in the past and applies similar techniques to new customers. The analysis unit also analyzes the characteristics of a company's products and services to identify the customer demographic that is most suitable for them. For example, it finds the optimal target market based on product features and price range. The analysis unit also analyzes data from a company's website and social media to understand the company's needs and interests. For example, it analyzes data from a company's official website and blog to identify the company's needs and interests. This improves the accuracy of the company list.

[0031] The analysis unit analyzes data from a company's website and social media to understand the company's needs and interests. For example, the analysis unit uses generative AI to analyze the characteristics of a company's products and services and identify the customer demographic that is most suitable for them. For example, it finds the optimal target market based on the product's features and price range. The analysis unit also analyzes data from a company's official website and blog to identify the company's needs and interests. For example, it analyzes the visit history of a company's official website and blog browsing data to understand the company's needs and interests. The analysis unit also analyzes social media data to identify the company's needs and interests. For example, it analyzes Twitter and Facebook posting data to understand the company's interests. This allows for a detailed understanding of the company's needs and interests.

[0032] The analysis unit can suggest optimal approach methods and timing to sales representatives. For example, the analysis unit uses an emotion estimation function to analyze the emotional state of potential customers in real time and suggest an approach that will elicit the most positive response. For example, it analyzes the customer's facial expressions and tone of voice to approach them at the appropriate time. The analysis unit also analyzes past sales data to identify successful approach methods and timing. For example, it learns the content and sending timing of sales emails that have been successful in the past and applies similar techniques to new customers. The analysis unit also optimizes sales representatives' schedules and supports efficient sales activities. For example, it suggests the optimal order and timing of visits. This improves the efficiency of sales activities.

[0033] The analysis unit can analyze market trends and propose new product development to companies. For example, the generative AI analyzes data from different industries and repurposes customer acquisition methods that have been successful in other industries. For example, it can apply methods that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes market trends and proposes new product development to companies. For example, it can analyze increases and decreases in sales and changes in consumer preferences and propose new product development. The analysis unit can also analyze the characteristics of a company's products and services and propose the development of new products that are most suitable for them. For example, it can propose the development of products that incorporate technological innovations or products aimed at new markets. This makes it possible to develop products that meet market needs.

[0034] The analysis unit can analyze the results of sales activities and propose the next action. For example, the analysis unit's generative AI monitors market fluctuations in real time and approaches customers at the optimal timing. For example, it sends sales emails the moment market demand increases. The analysis unit also identifies the next customer to approach based on data from past sales activities. For example, it analyzes past closing rates and customer feedback and proposes the next action. The analysis unit also analyzes the results of sales activities and proposes the next step. For example, it proposes follow-up methods and next steps. This maximizes the effectiveness of sales activities.

[0035] The analysis unit analyzes a company's purchasing history and can predict future needs from past purchasing patterns. For example, the analysis unit uses a generative AI to analyze a company's purchasing history and predict future needs from past purchasing patterns. For example, for a company that regularly purchases a specific product, the analysis unit predicts the timing of the next purchase. The analysis unit also predicts future needs based on purchasing history data. For example, it analyzes seasonal purchasing trends and the purchase frequency of specific products to predict future needs. The analysis unit also predicts future needs based on purchasing pattern data. For example, it predicts future needs using trend analysis and predictive models. This makes it possible to predict future needs and take appropriate approaches.

[0036] The analysis unit can analyze data in different languages ​​to discover international customer candidates. For example, the generation AI in the analysis unit analyzes data in different languages ​​to discover international customer candidates. For example, it analyzes data in English, Chinese, Spanish, etc. to identify international customer candidates. The analysis unit also discovers international customer candidates based on data in different languages. For example, it identifies customers in a specific region or customers in an international market. The analysis unit also analyzes data in different languages ​​to discover international customer candidates. For example, it analyzes data in English, Chinese, Spanish, etc. to identify international customer candidates. This enables efficient discovery of international customer candidates.

[0037] The analysis unit can analyze the entire sales process and automatically generate the optimal sales flow. For example, the generation AI in the analysis unit analyzes the entire sales process and automatically generates the optimal sales flow. For example, it evaluates the effectiveness of each step and proposes the most efficient flow. The analysis unit also analyzes each step of the sales process in detail and proposes the optimal flow. For example, it analyzes the details of each step and how to make the process more efficient and proposes the optimal flow. The analysis unit also analyzes the entire sales process and automatically generates the optimal sales flow. For example, it evaluates the effectiveness of each step and proposes the most efficient flow. This improves the efficiency of the sales process.

[0038] The analysis unit can analyze the performance data of sales representatives and propose the optimal training plan for each individual. For example, the analysis unit uses a generation AI to analyze the performance data of sales representatives and propose the optimal training plan for each individual. For example, it customizes the training content based on past performance and skill sets. The analysis unit also proposes the optimal training plan for each individual based on the performance data of sales representatives. For example, it proposes training for specific issues or training to improve skills. The analysis unit also analyzes the performance data of sales representatives and proposes the optimal training plan for each individual. For example, it customizes the training content based on past performance and skill sets. This allows sales representatives to improve their skills.

[0039] The analysis unit can analyze sales processes in different industries and repurpose successful cases. For example, the generation AI analyzes sales processes in different industries and repurposes successful cases. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes sales processes in different industries and repurposes successful cases. For example, analyzing successful cases of a specific strategy or a specific process and applying them to other industries. The analysis unit also analyzes sales processes in different industries and repurposes successful cases. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. This makes it possible to utilize successful cases from different industries.

[0040] The analysis unit monitors each step of the sales process in real time and can issue alerts at the optimal timing. For example, the generation AI monitors each step of the sales process in real time and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. The analysis unit also monitors each step of the sales process in detail and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. The analysis unit also monitors each step of the sales process in real time and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. This allows the user to take appropriate action at each step of the sales process.

[0041] The analysis unit can analyze a company's inventory data and automatically generate an optimal distribution plan. In the analysis unit, for example, the generation AI analyzes a company's inventory data and automatically generates an optimal distribution plan. For example, it proposes an optimal distribution plan based on inventory turnover and demand forecasts. The analysis unit also analyzes the details of the inventory data and proposes an optimal distribution plan. For example, it analyzes inventory quantity and turnover and proposes an optimal distribution plan. The analysis unit also analyzes a company's inventory data and automatically generates an optimal distribution plan. For example, it proposes an optimal distribution plan based on inventory turnover and demand forecasts. This allows for optimization of the distribution plan.

[0042] The analysis unit analyzes data from different markets and can repurpose distribution methods that have been successful in other markets. For example, the generation AI analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, a distribution method that has been successful in the manufacturing industry can be applied to the service industry. The analysis unit also analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, it analyzes specific delivery methods and inventory management methods and applies them to other markets. The analysis unit also analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, it applies a distribution method that has been successful in the manufacturing industry to the service industry. This makes it possible to utilize successful cases from different markets.

[0043] The analysis unit can predict market demand in real time and distribute products at the optimal timing. For example, the analysis unit uses generative AI to predict market demand in real time and distribute products at the optimal timing. For example, products are shipped the moment demand increases. The analysis unit also predicts demand using predictions and trend analysis based on past data. For example, it analyzes past sales data and consumer behavior data to predict demand. The analysis unit also predicts market demand in real time and distributes products at the optimal timing. For example, products are shipped the moment demand increases. This enables optimal distribution according to market demand.

[0044] The analysis unit can analyze the performance data of sales representatives and propose the optimal incentive plan. For example, the generation AI in the analysis unit analyzes the performance data of sales representatives and proposes the optimal incentive plan. For example, incentives are set based on past performance and target achievement rates. The analysis unit also proposes the optimal incentive plan based on the performance data of sales representatives. For example, it proposes a bonus system or reward system. The analysis unit also analyzes the performance data of sales representatives and proposes the optimal incentive plan. For example, it sets incentives based on past performance and target achievement rates. This helps to improve the motivation of sales representatives.

[0045] The analysis unit can analyze sales data from different industries and repurpose success stories. For example, the generation AI analyzes sales data from different industries and repurposes success stories. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes sales data from different industries and repurposes success stories. For example, analyzing success stories of a specific strategy or a specific process and applying them to other industries. The analysis unit also analyzes sales data from different industries and repurposes success stories. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. This makes it possible to utilize success stories from different industries.

[0046] The analysis unit can optimize the schedules of sales representatives and support efficient sales activities. For example, the generation AI in the analysis unit optimizes the schedules of sales representatives and support efficient sales activities. For example, it proposes the optimal order and timing of visits. The analysis unit also supports efficient sales activities based on the schedules of sales representatives. For example, it proposes the optimal order and timing of visits. The analysis unit also optimizes the schedules of sales representatives and support efficient sales activities. For example, it proposes the optimal order and timing of visits. This allows for the optimization of the schedules of sales representatives.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The New Customer Generation AI system can further include a purchase history analysis unit that analyzes customer purchasing history. The purchase history analysis unit, for example, analyzes data on products and services that a customer has purchased in the past to identify the customer's purchasing patterns. This allows the system to predict the products and services that the customer is likely to purchase next and propose them to sales representatives. The purchase history analysis unit can also evaluate customer value based on the customer's purchasing frequency and purchase amount, and identify customers who should be approached as a priority. Furthermore, the purchase history analysis unit can find cross-selling and up-selling opportunities based on the customer's purchasing history and propose them to sales representatives. This enables effective sales activities based on the customer's purchasing history.

[0049] The New Customer Generation AI system can further be equipped with a competitor analysis unit that analyzes competitor data. For example, the competitor analysis unit analyzes the characteristics of competitors' products and services and compares them with one's own products. This allows one to understand the strengths and weaknesses of one's own products and optimize sales strategies. The competitor analysis unit can also analyze competitors' marketing strategies and pricing, allowing one to review one's own marketing strategies and pricing. Furthermore, the competitor analysis unit can analyze competitors' customer demographics and discover new target markets. This enables effective sales strategies based on competitor data.

[0050] The New Customer Generation AI system can further include a lifestyle analysis unit that analyzes customer lifestyle data. The lifestyle analysis unit analyzes, for example, a customer's hobbies, preferences, and lifestyle habits to propose products and services that are best suited to the customer's lifestyle. This makes it possible to make personalized proposals that match the customer's lifestyle. The lifestyle analysis unit can also find cross-selling and up-selling opportunities based on the customer's lifestyle data and propose them to sales representatives. Furthermore, the lifestyle analysis unit can optimize marketing strategies based on the customer's lifestyle data. This enables effective sales activities based on the customer's lifestyle.

[0051] The New Customer generation AI system can further include a social media analysis unit that analyzes customers' social media data. For example, the social media analysis unit analyzes customers' social media posts and responses to understand their interests and needs. This makes it possible to make personalized proposals based on the customers' social media data. The social media analysis unit can also optimize marketing strategies based on the customers' social media data. Furthermore, the social media analysis unit can find cross-selling and up-selling opportunities based on the customers' social media data and propose them to sales representatives. This enables effective sales activities based on the customers' social media data.

[0052] The New Customer Generation AI system can further include a purchase history analysis unit that analyzes customer purchasing history. The purchase history analysis unit, for example, analyzes data on products and services that a customer has purchased in the past to identify the customer's purchasing patterns. This allows the system to predict the products and services that the customer is likely to purchase next and propose them to sales representatives. The purchase history analysis unit can also evaluate customer value based on the customer's purchasing frequency and purchase amount, and identify customers who should be approached as a priority. Furthermore, the purchase history analysis unit can find cross-selling and up-selling opportunities based on the customer's purchasing history and propose them to sales representatives. This enables effective sales activities based on the customer's purchasing history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The analysis department uses generative AI to analyze market and company data to discover potential customer candidates. For example, generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to extract and analyze important parts of the data. The analysis department generates a list of companies likely to be interested in the company's products and services, and analyzes data from the company's website and social media to understand the company's needs and interests. It can also suggest the best approach and timing to sales representatives. Step 2: The discovery unit creates a list of potential customers discovered by the analysis unit. For example, the discovery unit generates the list based on the company's attribute information and interest scores, and sets the list format and the type of information to be included. It also updates and manages the list.

[0055] (Example 2) The New Customer generation AI system according to an embodiment of the present invention is a system in which the generation AI analyzes market data and company data to discover and list potential customer candidates. As a result, the New Customer generation AI system can improve the efficiency of new customer acquisition and increase the profit margins of companies.

[0056] The New Customer Generation AI system according to the embodiment includes a generation AI, an analysis unit, and a discovery unit. The generation AI analyzes market data and company data. For example, the generation AI analyzes market data using a text generation AI (e.g., LLM). The generation AI can also analyze company data using a multimodal generation AI. The generation AI can also extract and analyze important parts of the data. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to extract particularly important information from the data and perform analysis based on that information. The analysis unit uses the generation AI to analyze market data and company data and discover potential customer candidates. For example, the analysis unit generates a list of companies likely to be interested in the company's products and services. The analysis unit can also analyze company websites and social media data to understand the company's needs and interests. The analysis unit can also suggest optimal approaches and timing to sales representatives. For example, the analysis unit generates a list of companies that are likely to be interested in a company's products or services. The analysis unit analyzes data from the company's website and social media to understand the company's needs and interests. The analysis unit suggests the optimal approach method and timing to the sales representative. The discovery unit lists the potential customers discovered by the analysis unit. For example, the discovery unit generates the list based on the company's attribute information and interest score. The discovery unit can also set the format of the list and the type of information to be included in the list. The discovery unit can also update and manage the list. For example, the discovery unit generates the list based on the company's attribute information and interest score. The discovery unit sets the format of the list and the type of information to be included in the list. The discovery unit updates and manages the list. As a result, the New Customer generation AI system according to the embodiment can improve the efficiency of new customer acquisition and increase the company's profit margin. For example, the output unit provides the listed potential customers to the sales representative, supporting efficient sales activities.The list of potential customers serves as the basis for sales representatives to approach them efficiently. Sales representatives can use the list to carry out efficient sales activities and achieve results.

[0057] The analysis unit can generate a list of companies that are likely to be interested in a company's products and services. For example, the generation AI analyzes a company's past sales data to identify successful sales techniques. For example, it learns the content and timing of sales emails that have been successful in the past and applies similar techniques to new customers. The analysis unit also analyzes the characteristics of a company's products and services to identify the customer demographic that is most suitable for them. For example, it finds the optimal target market based on product features and price range. The analysis unit also analyzes data from a company's website and social media to understand the company's needs and interests. For example, it analyzes data from a company's official website and blog to identify the company's needs and interests. This improves the accuracy of the company list.

[0058] The analysis unit analyzes data from a company's website and social media to understand the company's needs and interests. For example, the analysis unit uses generative AI to analyze the characteristics of a company's products and services and identify the customer demographic that is most suitable for them. For example, it finds the optimal target market based on the product's features and price range. The analysis unit also analyzes data from a company's official website and blog to identify the company's needs and interests. For example, it analyzes the visit history of a company's official website and blog browsing data to understand the company's needs and interests. The analysis unit also analyzes social media data to identify the company's needs and interests. For example, it analyzes Twitter and Facebook posting data to understand the company's interests. This allows for a detailed understanding of the company's needs and interests.

[0059] The analysis unit can suggest optimal approach methods and timing to sales representatives. For example, the analysis unit uses an emotion estimation function to analyze the emotional state of potential customers in real time and suggest an approach that will elicit the most positive response. For example, it analyzes the customer's facial expressions and tone of voice to approach them at the appropriate time. The analysis unit also analyzes past sales data to identify successful approach methods and timing. For example, it learns the content and sending timing of sales emails that have been successful in the past and applies similar techniques to new customers. The analysis unit also optimizes sales representatives' schedules and supports efficient sales activities. For example, it suggests the optimal order and timing of visits. This improves the efficiency of sales activities.

[0060] The analysis unit can analyze market trends and propose new product development to companies. For example, the generative AI analyzes data from different industries and repurposes customer acquisition methods that have been successful in other industries. For example, it can apply methods that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes market trends and proposes new product development to companies. For example, it can analyze increases and decreases in sales and changes in consumer preferences and propose new product development. The analysis unit can also analyze the characteristics of a company's products and services and propose the development of new products that are most suitable for them. For example, it can propose the development of products that incorporate technological innovations or products aimed at new markets. This makes it possible to develop products that meet market needs.

[0061] The analysis unit can analyze the results of sales activities and propose the next action. For example, the analysis unit's generative AI monitors market fluctuations in real time and approaches customers at the optimal timing. For example, it sends sales emails the moment market demand increases. The analysis unit also identifies the next customer to approach based on data from past sales activities. For example, it analyzes past closing rates and customer feedback and proposes the next action. The analysis unit also analyzes the results of sales activities and proposes the next step. For example, it proposes follow-up methods and next steps. This maximizes the effectiveness of sales activities.

[0062] The analysis unit can use the emotion estimation function to monitor the emotional state of a potential customer in real time and make the optimal approach. For example, the analysis unit uses the emotion estimation function to analyze the emotional state of a potential customer in real time and propose an approach that will elicit the most positive response. For example, it analyzes the customer's facial expression and tone of voice and makes an approach at the appropriate time. The analysis unit also monitors the emotional state of a sales representative and supports them in making an approach at the optimal time. For example, it provides advice on how to relax if the sales representative is feeling stressed. The analysis unit also monitors the emotional state of a potential customer and makes an approach at the optimal time. For example, it makes an approach when the customer is in a positive emotional state. This makes it possible to make the optimal approach according to the emotional state of the potential customer.

[0063] The analysis unit analyzes a company's purchasing history and can predict future needs from past purchasing patterns. For example, the analysis unit uses a generative AI to analyze a company's purchasing history and predict future needs from past purchasing patterns. For example, for a company that regularly purchases a specific product, the analysis unit predicts the timing of the next purchase. The analysis unit also predicts future needs based on purchasing history data. For example, it analyzes seasonal purchasing trends and the purchase frequency of specific products to predict future needs. The analysis unit also predicts future needs based on purchasing pattern data. For example, it predicts future needs using trend analysis and predictive models. This makes it possible to predict future needs and take appropriate approaches.

[0064] The analysis unit can analyze data in different languages ​​to discover international customer candidates. For example, the generation AI in the analysis unit analyzes data in different languages ​​to discover international customer candidates. For example, it analyzes data in English, Chinese, Spanish, etc. to identify international customer candidates. The analysis unit also discovers international customer candidates based on data in different languages. For example, it identifies customers in a specific region or customers in an international market. The analysis unit also analyzes data in different languages ​​to discover international customer candidates. For example, it analyzes data in English, Chinese, Spanish, etc. to identify international customer candidates. This enables efficient discovery of international customer candidates.

[0065] The analysis unit can analyze the entire sales process and automatically generate the optimal sales flow. For example, the generation AI in the analysis unit analyzes the entire sales process and automatically generates the optimal sales flow. For example, it evaluates the effectiveness of each step and proposes the most efficient flow. The analysis unit also analyzes each step of the sales process in detail and proposes the optimal flow. For example, it analyzes the details of each step and how to make the process more efficient and proposes the optimal flow. The analysis unit also analyzes the entire sales process and automatically generates the optimal sales flow. For example, it evaluates the effectiveness of each step and proposes the most efficient flow. This improves the efficiency of the sales process.

[0066] The analysis unit can analyze the performance data of sales representatives and propose the optimal training plan for each individual. For example, the analysis unit uses a generation AI to analyze the performance data of sales representatives and propose the optimal training plan for each individual. For example, it customizes the training content based on past performance and skill sets. The analysis unit also proposes the optimal training plan for each individual based on the performance data of sales representatives. For example, it proposes training for specific issues or training to improve skills. The analysis unit also analyzes the performance data of sales representatives and proposes the optimal training plan for each individual. For example, it customizes the training content based on past performance and skill sets. This allows sales representatives to improve their skills.

[0067] The analysis unit can use the emotion estimation function to monitor the emotional state of the sales representative and suggest actions to reduce stress. For example, the analysis unit can use the emotion estimation function to monitor the emotional state of the sales representative in real time and suggest actions to reduce stress. For example, suggesting a break to relax. The analysis unit can also monitor the emotional state of the sales representative and suggest actions to reduce stress. For example, suggesting relaxation methods or work adjustments. The analysis unit can also use the emotion estimation function to monitor the emotional state of the sales representative and suggest actions to reduce stress. For example, suggesting a break to relax. This can help reduce stress for the sales representative.

[0068] The analysis unit can analyze sales processes in different industries and repurpose successful cases. For example, the generation AI analyzes sales processes in different industries and repurposes successful cases. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes sales processes in different industries and repurposes successful cases. For example, analyzing successful cases of a specific strategy or a specific process and applying them to other industries. The analysis unit also analyzes sales processes in different industries and repurposes successful cases. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. This makes it possible to utilize successful cases from different industries.

[0069] The analysis unit monitors each step of the sales process in real time and can issue alerts at the optimal timing. For example, the generation AI monitors each step of the sales process in real time and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. The analysis unit also monitors each step of the sales process in detail and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. The analysis unit also monitors each step of the sales process in real time and issues alerts at the optimal timing. For example, it notifies the user when it is time to proceed to the next step. This allows the user to take appropriate action at each step of the sales process.

[0070] The analysis unit can use the emotion estimation function to monitor the emotional state of the customer and perform follow-up at the optimal timing. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of the customer in real time and perform follow-up at the optimal timing. For example, follow-up is performed when the customer is in a positive emotional state. The analysis unit can also monitor the emotional state of the customer and perform follow-up at the optimal timing. For example, follow-up is performed when the customer is in a positive emotional state. The analysis unit can also use the emotion estimation function to monitor the emotional state of the customer in real time and perform follow-up at the optimal timing. For example, follow-up is performed when the customer is in a positive emotional state. This makes it possible to perform optimal follow-up according to the emotional state of the customer.

[0071] The analysis unit can analyze a company's inventory data and automatically generate an optimal distribution plan. In the analysis unit, for example, the generation AI analyzes a company's inventory data and automatically generates an optimal distribution plan. For example, it proposes an optimal distribution plan based on inventory turnover and demand forecasts. The analysis unit also analyzes the details of the inventory data and proposes an optimal distribution plan. For example, it analyzes inventory quantity and turnover and proposes an optimal distribution plan. The analysis unit also analyzes a company's inventory data and automatically generates an optimal distribution plan. For example, it proposes an optimal distribution plan based on inventory turnover and demand forecasts. This allows for optimization of the distribution plan.

[0072] The analysis unit can use the emotion estimation function to monitor the emotional state of the consumer and propose an optimal marketing strategy. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of the consumer in real time and propose an optimal marketing strategy. For example, a promotion is carried out when the consumer is in a positive emotional state. The analysis unit also monitors the emotional state of the consumer and proposes an optimal marketing strategy. For example, a promotion is carried out when the consumer is in a positive emotional state. The analysis unit also uses the emotion estimation function to monitor the emotional state of the consumer in real time and propose an optimal marketing strategy. For example, a promotion is carried out when the consumer is in a positive emotional state. This makes it possible to implement a marketing strategy according to the emotional state of the consumer.

[0073] The analysis unit analyzes data from different markets and can repurpose distribution methods that have been successful in other markets. For example, the generation AI analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, a distribution method that has been successful in the manufacturing industry can be applied to the service industry. The analysis unit also analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, it analyzes specific delivery methods and inventory management methods and applies them to other markets. The analysis unit also analyzes data from different markets and repurposes distribution methods that have been successful in other markets. For example, it applies a distribution method that has been successful in the manufacturing industry to the service industry. This makes it possible to utilize successful cases from different markets.

[0074] The analysis unit can predict market demand in real time and distribute products at the optimal timing. For example, the analysis unit uses generative AI to predict market demand in real time and distribute products at the optimal timing. For example, products are shipped the moment demand increases. The analysis unit also predicts demand using predictions and trend analysis based on past data. For example, it analyzes past sales data and consumer behavior data to predict demand. The analysis unit also predicts market demand in real time and distributes products at the optimal timing. For example, products are shipped the moment demand increases. This enables optimal distribution according to market demand.

[0075] The analysis unit can use the emotion estimation function to monitor the emotional state of the consumer in real time and perform an optimal promotion. The analysis unit, for example, uses the emotion estimation function to monitor the emotional state of the consumer in real time and perform an optimal promotion. For example, a promotion is performed when the consumer is in a positive emotional state. The analysis unit can also monitor the emotional state of the consumer and perform an optimal promotion. For example, a promotion is performed when the consumer is in a positive emotional state. The analysis unit can also use the emotion estimation function to monitor the emotional state of the consumer in real time and perform an optimal promotion. For example, a promotion is performed when the consumer is in a positive emotional state. This makes it possible to perform an optimal promotion according to the emotional state of the consumer.

[0076] The analysis unit can analyze the performance data of sales representatives and propose the optimal incentive plan. For example, the generation AI in the analysis unit analyzes the performance data of sales representatives and proposes the optimal incentive plan. For example, incentives are set based on past performance and target achievement rates. The analysis unit also proposes the optimal incentive plan based on the performance data of sales representatives. For example, it proposes a bonus system or reward system. The analysis unit also analyzes the performance data of sales representatives and proposes the optimal incentive plan. For example, it sets incentives based on past performance and target achievement rates. This helps to improve the motivation of sales representatives.

[0077] The analysis unit can analyze sales data from different industries and repurpose success stories. For example, the generation AI analyzes sales data from different industries and repurposes success stories. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. The analysis unit also analyzes sales data from different industries and repurposes success stories. For example, analyzing success stories of a specific strategy or a specific process and applying them to other industries. The analysis unit also analyzes sales data from different industries and repurposes success stories. For example, applying sales techniques that have been successful in the manufacturing industry to the service industry. This makes it possible to utilize success stories from different industries.

[0078] The analysis unit can optimize the schedules of sales representatives and support efficient sales activities. For example, the generation AI in the analysis unit optimizes the schedules of sales representatives and support efficient sales activities. For example, it proposes the optimal order and timing of visits. The analysis unit also supports efficient sales activities based on the schedules of sales representatives. For example, it proposes the optimal order and timing of visits. The analysis unit also optimizes the schedules of sales representatives and support efficient sales activities. For example, it proposes the optimal order and timing of visits. This allows for the optimization of the schedules of sales representatives.

[0079] The analysis unit can use the emotion estimation function to monitor the emotional state of the sales representative in real time and provide feedback at the optimal timing. For example, the analysis unit can use the emotion estimation function to monitor the emotional state of the sales representative in real time and provide feedback at the optimal timing. For example, feedback is provided when the sales representative is in a positive emotional state. The analysis unit can also monitor the emotional state of the sales representative and provide feedback at the optimal timing. For example, feedback is provided when the sales representative is in a positive emotional state. The analysis unit can also use the emotion estimation function to monitor the emotional state of the sales representative in real time and provide feedback at the optimal timing. For example, feedback is provided when the sales representative is in a positive emotional state. This makes it possible to provide optimal feedback according to the emotional state of the sales representative.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The New Customer Generation AI system can further include a purchase history analysis unit that analyzes customer purchasing history. The purchase history analysis unit, for example, analyzes data on products and services that a customer has purchased in the past to identify the customer's purchasing patterns. This allows the system to predict the products and services that the customer is likely to purchase next and propose them to sales representatives. The purchase history analysis unit can also evaluate customer value based on the customer's purchasing frequency and purchase amount, and identify customers who should be approached as a priority. Furthermore, the purchase history analysis unit can find cross-selling and up-selling opportunities based on the customer's purchasing history and propose them to sales representatives. This enables effective sales activities based on the customer's purchasing history.

[0082] The analysis unit can further use an emotion estimation function to estimate the customer's emotional state to suggest approaches to increase the customer's purchasing motivation. For example, when a customer is in a positive emotional state, it can suggest specific products or services. The analysis unit can also monitor the customer's emotional state and suggest appropriate follow-up if the customer is in a negative emotional state. Furthermore, the analysis unit can carry out promotions at the optimal timing based on the customer's emotional state. This makes it possible to take an effective approach according to the customer's emotional state.

[0083] The New Customer Generation AI system can further be equipped with a competitor analysis unit that analyzes competitor data. For example, the competitor analysis unit analyzes the characteristics of competitors' products and services and compares them with one's own products. This allows one to understand the strengths and weaknesses of one's own products and optimize sales strategies. The competitor analysis unit can also analyze competitors' marketing strategies and pricing, allowing one to review one's own marketing strategies and pricing. Furthermore, the competitor analysis unit can analyze competitors' customer demographics and discover new target markets. This enables effective sales strategies based on competitor data.

[0084] The analysis unit can also use the emotion estimation function to monitor the emotional state of sales representatives and provide feedback at the optimal time. For example, when a sales representative is in a positive emotional state, it can provide feedback on success stories and areas for improvement. The analysis unit can also provide appropriate support and advice when a sales representative is in a negative emotional state. Furthermore, the analysis unit can customize training plans based on the sales representative's emotional state. This makes it possible to provide effective feedback and support according to the sales representative's emotional state.

[0085] The New Customer Generation AI system can further include a lifestyle analysis unit that analyzes customer lifestyle data. The lifestyle analysis unit analyzes, for example, a customer's hobbies, preferences, and lifestyle habits to propose products and services that are best suited to the customer's lifestyle. This makes it possible to make personalized proposals that match the customer's lifestyle. The lifestyle analysis unit can also find cross-selling and up-selling opportunities based on the customer's lifestyle data and propose them to sales representatives. Furthermore, the lifestyle analysis unit can optimize marketing strategies based on the customer's lifestyle data. This enables effective sales activities based on the customer's lifestyle.

[0086] The analysis unit can further use the emotion estimation function to monitor the emotional state of the customer and perform follow-up at the optimal timing. For example, a follow-up contact is made when the customer is in a positive emotional state. The analysis unit can also suggest appropriate follow-up when the customer is in a negative emotional state. Furthermore, the analysis unit can customize the content of the follow-up based on the customer's emotional state. This enables effective follow-up according to the customer's emotional state.

[0087] The New Customer generation AI system can further include a social media analysis unit that analyzes customers' social media data. For example, the social media analysis unit analyzes customers' social media posts and responses to understand their interests and needs. This makes it possible to make personalized proposals based on the customers' social media data. The social media analysis unit can also optimize marketing strategies based on the customers' social media data. Furthermore, the social media analysis unit can find cross-selling and up-selling opportunities based on the customers' social media data and propose them to sales representatives. This enables effective sales activities based on the customers' social media data.

[0088] The analysis unit can further use the emotion estimation function to monitor the emotional state of the consumer and propose an optimal marketing strategy. For example, a promotion can be carried out when the consumer is in a positive emotional state. The analysis unit can also monitor the consumer's emotional state and review the appropriate marketing strategy if the consumer is in a negative emotional state. Furthermore, the analysis unit can customize the marketing strategy based on the consumer's emotional state. This enables an effective marketing strategy that is tailored to the consumer's emotional state.

[0089] The New Customer Generation AI system can further include a purchase history analysis unit that analyzes customer purchasing history. The purchase history analysis unit, for example, analyzes data on products and services that a customer has purchased in the past to identify the customer's purchasing patterns. This allows the system to predict the products and services that the customer is likely to purchase next and propose them to sales representatives. The purchase history analysis unit can also evaluate customer value based on the customer's purchasing frequency and purchase amount, and identify customers who should be approached as a priority. Furthermore, the purchase history analysis unit can find cross-selling and up-selling opportunities based on the customer's purchasing history and propose them to sales representatives. This enables effective sales activities based on the customer's purchasing history.

[0090] The analysis unit can also use the emotion estimation function to monitor the emotional state of sales representatives and provide feedback at the optimal time. For example, when a sales representative is in a positive emotional state, it can provide feedback on success stories and areas for improvement. The analysis unit can also provide appropriate support and advice when a sales representative is in a negative emotional state. Furthermore, the analysis unit can customize training plans based on the sales representative's emotional state. This makes it possible to provide effective feedback and support according to the sales representative's emotional state.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The analysis department uses generative AI to analyze market and company data to discover potential customer candidates. For example, generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to extract and analyze important parts of the data. The analysis department generates a list of companies likely to be interested in the company's products and services, and analyzes data from the company's website and social media to understand the company's needs and interests. It can also suggest the best approach and timing to sales representatives. Step 2: The discovery unit creates a list of potential customers discovered by the analysis unit. For example, the discovery unit generates the list based on the company's attribute information and interest scores, and sets the list format and the type of information to be included. It also updates and manages the list.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. An analysis department that uses generative AI to analyze market data and company data to discover potential customer candidates; a discovery unit that lists the customer candidates discovered by the analysis unit. A system characterized by:

2. The analysis unit Analyzing company website and social media data to understand the needs and interests of said companies 2. The system of claim 1.

3. The analysis unit Analyze market trends and propose new product developments to companies 2. The system of claim 1.

4. The analysis unit Analyze the entire sales process and automatically generate the optimal sales flow 2. The system of claim 1.

5. The analysis unit Using emotion estimation capabilities, the company can detect changes in emotions from online activities and help identify potential customers.

2. The system of claim 1.

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