System

Abstract

To provide a system for identifying potential customers and taking appropriate actions by utilizing data on online users.SOLUTION: The system according to an embodiment includes a collection unit, an identification unit, and a response unit. The collection unit collects data on online users. The identification unit identifies potential customers for a product or service based on the data collected by the collection unit. The response unit takes actions on the potential customers identified by the identification 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had a problem in that they have not been able to effectively utilize online user data to identify potential customers and take appropriate action.

[0005] The system according to the embodiment aims to identify potential customers by utilizing data of online users and to provide appropriate responses. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an identification unit, and a response unit. The collection unit collects data of online users. The identification unit identifies potential customers of a product or service based on the data collected by the collection unit. The response unit responds to the potential customers identified by the identification unit. Effect of the Invention

[0007] The system according to the embodiment can utilize data of online users to identify potential customers and take appropriate action. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The AI ​​assistant system for automating and supporting sales activities according to an embodiment of the present invention is a system that collects data of online users, identifies potential customers, and responds to them. The system includes a collection unit that collects data of online users, an identification unit that identifies potential customers of a product or service based on the collected data, and a response unit that responds to the identified potential customers. For example, the collection unit collects data on the behavioral history and context of online users, and the identification unit identifies the potential of a potential customer based on the collected data. The response unit sends a follow-up message or e-mail to the identified potential customer at a timing according to the potential. The system also includes a reception unit that accepts the designation of a specific product or service, and can identify potential customers of the designated product or service. The system also includes a response unit that provides product information, prices, and promotion information required by a sales representative during a dialogue with a customer in real time. The estimation unit estimates the needs and background of the potential customer based on the collected data, and proposes sales scripts and approaches to the sales representative. The management unit manages the potential of the potential customer in association with the progress of the sales, thereby making sales activities more efficient. The display unit has a function of displaying information of the identified potential customer and adjusting the display order based on the feelings of the sales representative. This will enable sales activities to be more efficient and customers to be served more effectively. As a result, the AI ​​assistant system that automates and supports sales activities will be able to realize sales activities that are more efficient and customers to be served more effectively.

[0029] The AI ​​assistant system for automating and supporting sales activities according to the embodiment includes a collection unit, an identification unit, and a response unit. The collection unit collects data of online users. The data of online users includes, but is not limited to, browsing history, purchase history, click data, and the like. The collection unit, for example, analyzes website access logs to collect user behavior patterns. The collection unit can also collect purchase history of online shopping sites to grasp user purchasing trends. Furthermore, the collection unit can collect social media activity and analyze user interests. The identification unit identifies potential customers of products or services based on the data collected by the collection unit. The identification unit, for example, analyzes the collected data using AI and scores potential customers. The identification unit, for example, uses a machine learning algorithm to identify potential customers from user behavior patterns. The identification unit can also identify potential customers based on past purchase history and products in which the user has shown interest. The response unit responds to the potential customers identified by the identification unit. The response unit, for example, sends follow-up messages and emails. The response unit generates and sends personalized messages to potential customers using, for example, AI. The response unit can also provide product information, prices, and promotion information that is necessary for sales representatives during conversations with customers in real time. As a result, the AI ​​assistant system that automates and supports sales activities according to the embodiment can achieve efficient sales activities and effective customer service.

[0030] The collection unit collects data of online users. The data of online users includes, but is not limited to, browsing history, purchase history, click data, and the like. For example, the collection unit analyzes access logs of websites to collect user behavior patterns. Specifically, detailed data such as pages visited by users, time spent on the site, and links clicked by users are collected, and the user's interests and concerns are analyzed based on these data. The collection unit can also collect purchase history of online shopping sites to grasp the user's purchasing tendency. For example, data such as the type of product purchased by the user in the past, purchase frequency, and purchase time are collected, and the user's purchasing pattern is analyzed based on these data. Furthermore, the collection unit can also collect social media activity to analyze the user's interests and concerns. Specifically, data such as "likes" and posts shared by the user, accounts followed, and commented content are collected, and the user's interests and concerns are grasped in detail based on these data. The collection unit can integrate information obtained from these various data sources to grasp the overall picture of the user. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, data collection can be strengthened during a particular campaign period and the effectiveness of the campaign can be monitored in real time, allowing the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The identification unit identifies potential customers of the product or service based on the data collected by the collection unit. The identification unit, for example, uses AI to analyze the collected data and score potential customers. Specifically, the identification unit uses a machine learning algorithm to identify potential customers from user behavior patterns. For example, data such as the user's browsing history, purchase history, and social media activity is analyzed to score users who show an interest in a specific product. For scoring, a predictive model based on the user's past behavior data is used to quantify each user's purchasing motivation and degree of interest. The identification unit can also identify potential customers based on past purchase history and products in which the user showed interest. For example, if a user who has purchased a specific product in the past is likely to show interest in a similar product again, the identification unit identifies the user as a potential customer. Furthermore, the identification unit can perform more accurate customer identification by taking into account the user's demographic data (age, gender, region, etc.) and psychographic data (interests, values, lifestyle, etc.). This allows the identification unit to quickly and accurately analyze the collected data and efficiently identify potential customers. Furthermore, the identification unit can continuously improve the accuracy of customer identification based on data updated in real time. For example, the predictive model is retrained each time new data is collected to respond to the latest situation. This allows the Identification Department to always provide highly accurate customer identification based on the latest information, maximizing the effectiveness of sales activities.

[0032] The response unit responds to potential customers identified by the identification unit. The response unit, for example, sends follow-up messages and e-mails. Specifically, the response unit uses AI to generate and send personalized messages to potential customers. For example, based on the user's past behavioral data and interests, an individually customized message is created and sent at an appropriate time. This allows the user to receive information that is highly relevant to him or her, which increases the desire to purchase. The response unit can also provide product information, prices, and promotion information required by sales representatives in real time while interacting with customers. For example, when a sales representative is interacting with a customer by phone or chat, AI automatically provides related product information to support quick and accurate response. Furthermore, the response unit can collect customer feedback and continuously improve the accuracy and effectiveness of the response content. For example, the content of the message and the timing of sending are optimized based on the customer's response and purchase history. The response unit can also reliably convey information using multiple communication means. For example, important information is reliably delivered by using not only e-mail but also SMS, push notifications, and messaging functions of social media. This allows the response unit to provide users with quick and reliable responses and maximize the effectiveness of sales activities. In addition, the sales department can plan the next follow-up or promotion based on the customer's purchase history and behavioral data. This will help the sales department build and maintain continuous customer relationships and contribute to the success of long-term sales activities.

[0033] The identification unit identifies the potential of a potential customer based on the data collected by the collection unit, and the response unit can send a follow-up message or e-mail to the potential customer identified by the identification unit based on the potential. The potential includes, for example, a scoring model and behavior analysis, but is not limited to such examples. The identification unit analyzes the collected data using, for example, AI, and scores the potential of the potential customer. For example, the identification unit can evaluate the potential of the potential customer based on the user's behavior pattern and past purchase history. The response unit sends, for example, a follow-up message or e-mail based on the potential. For example, the response unit can generate and send a personalized message to the potential customer using AI. This enables effective sales activities by following up at a timing according to the potential of the potential customer.

[0034] The reception unit receives a designation of a specific product or service, and the identification unit can identify potential customers of the specific product or service whose designation has been accepted by the reception unit. The specific product or service includes, but is not limited to, electronic devices, software services, and the like. The reception unit, for example, receives a designation of the specific product or service from a user. For example, the reception unit can receive the designation from the user through a web form or a chatbot. The identification unit identifies potential customers of the specific product or service whose designation has been accepted by the reception unit. For example, the identification unit can use AI to analyze data related to the designated product or service and identify potential customers. This enables targeted sales activities by identifying potential customers for the specific product or service.

[0035] The collection unit can collect data on the behavior history and context data of online users. The behavior history data includes, for example, page views, click histories, etc., but is not limited to such examples. The collection unit, for example, analyzes website access logs to collect user behavior patterns. For example, the collection unit can collect the history of pages visited by users and links clicked by users. The context data includes, for example, time periods, device information, etc., but is not limited to such examples. The collection unit can collect, for example, information on the time periods when users access and the devices they use. In this way, by collecting the behavior history and context data of online users, it becomes possible to identify customers with higher accuracy.

[0036] The response unit can instantly provide product information, price, and promotion information while the sales representative is interacting with a potential customer. Product information includes, for example, product specifications, stock status, and the like, but is not limited to such examples. The response unit provides necessary product information in real time while the sales representative is interacting with a customer. For example, the response unit can use AI to instantly provide product specifications and stock status in response to a customer's question. Prices include, for example, discount information, price comparisons, and the like, but are not limited to such examples. The response unit provides the latest price information while the sales representative is interacting with a customer. For example, the response unit can use AI to provide discount information and price comparisons to the customer. Promotion information includes, for example, campaign information, coupon codes, and the like, but is not limited to such examples. The response unit provides the latest promotion information while the sales representative is interacting with a customer. For example, the response unit can use AI to provide campaign information and coupon codes to the customer. This allows the sales representative to provide necessary information in real time, enabling effective sales activities.

[0037] The estimation unit estimates the needs and background of potential customers based on the data collected by the collection unit, and the response unit can propose a sales script or approach to the sales representative according to the needs and background of the potential customers. The needs and background include, but are not limited to, for example, survey results and past purchase history. The estimation unit, for example, uses AI to analyze the collected data and estimates the needs and background of potential customers. For example, the estimation unit can estimate the needs of potential customers based on the survey results and past purchase history of users. The response unit, for example, proposes a sales script or approach to the sales representative according to the estimated needs and background. For example, the response unit can use AI to propose an effective sales script or approach to the sales representative. This enables effective sales activities by proposing a sales script or approach according to the needs and background of customers.

[0038] The management unit manages the potential of the potential customer in association with the progress of sales, and the response unit can conduct sales to the potential customer based on the potential of the potential customer and the progress of sales. The potential includes, for example, a scoring model and behavior analysis, but is not limited to such examples. The management unit analyzes, for example, collected data using AI, and scores the potential of the potential customer. For example, the management unit can evaluate the potential of the potential customer based on the user's behavior pattern and past purchase history. The progress of sales includes, for example, the stage of a business negotiation, the number of follow-ups, etc., but is not limited to such examples. The management unit tracks, for example, the progress of sales, and manages it in association with the potential of the potential customer. For example, the management unit can manage the progress of sales based on the stage of a business negotiation and the number of follow-ups. The response unit performs sales activities based on, for example, the potential of the potential customer and the progress of sales. For example, the response unit can use AI to follow up on potential customers at an appropriate time. This enables effective sales activities by linking and managing the potential of potential customers with the progress of sales.

[0039] The response unit can display information on the potential customer identified by the identification unit on the display unit. The information includes, for example, a customer profile, a past purchase history, and the like, but is not limited to such examples. The response unit, for example, displays information on the potential customer identified by the identification unit on the display unit. For example, the response unit can use AI to display a customer profile and a past purchase history on the display unit. This allows a sales representative to quickly respond by displaying information on the identified potential customer.

[0040] The collection unit can analyze the user's past behavior history and select a data collection method. The past behavior history includes, but is not limited to, for example, page views, click history, and the like. The collection unit can, for example, use AI to analyze the user's past behavior history and select an optimal data collection method. For example, the collection unit can preferentially collect data from websites that the user frequently visited in the past. In addition, the collection unit can preferentially collect data from devices that the user used in the past. In this way, the optimal data collection method can be selected by analyzing the user's past behavior history.

[0041] The collection unit can perform filtering based on the user's current field of interest when collecting data. The current field of interest includes, but is not limited to, recent search history, pages viewed, and the like. The collection unit can, for example, use AI to identify the user's current field of interest and perform filtering when collecting data. For example, the collection unit can preferentially collect data related to topics in which the user is currently interested. In addition, the collection unit can filter data based on keywords recently searched by the user. This makes it possible to collect highly relevant data by filtering data based on the user's current field of interest.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The geographical location information includes, but is not limited to, for example, GPS data, IP address, etc. The collection unit, for example, uses AI to identify the geographical location information of the user, and preferentially collects highly relevant data when collecting data. For example, the collection unit can preferentially collect local news in the area where the user is currently located. In addition, the collection unit can collect nearby event information based on the user's location information. This allows highly relevant data to be preferentially collected by considering the geographical location information of the user.

[0043] The collection unit can analyze the user's social media activity at the time of data collection and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. The collection unit can, for example, use AI to analyze the user's social media activity and collect related data. For example, the collection unit can collect the content of posts from accounts the user follows. In addition, the collection unit can collect information on groups and communities in which the user participates. In this way, the user's social media activity can be analyzed to collect related data.

[0044] At the time of identification, the identification unit can improve the accuracy of identifying a potential customer by referring to past data. Past data includes, for example, past purchase history, past interaction data, and the like, but is not limited to such examples. The identification unit can improve the accuracy of identifying a potential customer by referring to past data using, for example, AI. For example, the identification unit can identify a potential customer based on past purchase history. Furthermore, the identification unit can analyze past interaction data to identify a potential customer. Thereby, by referring to past data, the accuracy of identifying a potential customer can be improved.

[0045] The identification unit can perform the identification by taking into consideration the attribute information of the customer. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to such examples. The identification unit performs the identification by taking into consideration the attribute information of the customer, for example, using AI. For example, the identification unit can identify a potential customer based on the customer's age and gender. The identification unit can also identify a potential customer based on the customer's occupation and income. This makes it possible to identify customers with higher accuracy by taking into consideration the customer's attribute information.

[0046] The identification unit can perform the identification while taking into consideration the geographic distribution of customers. The geographic distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The identification unit performs the identification while taking into consideration the geographic distribution of customers, for example, using AI. For example, the identification unit can identify potential customers based on the customer's place of residence. Also, the identification unit can identify potential customers based on the location of the customer's workplace. In this way, by taking into consideration the geographic distribution of customers, more accurate customer identification is possible.

[0047] The identification unit can improve the accuracy of identification by referring to related literature at the time of identification. Examples of related literature include, but are not limited to, academic papers and industry reports. For example, the identification unit can improve the accuracy of identification by referring to related literature using AI. For example, the identification unit can identify potential customers by referring to the latest marketing research. Also, the identification unit can identify potential customers by referring to industry trend reports. This allows the accuracy of identification to be improved by referring to related literature.

[0048] The response unit can send a message by referring to the customer's past interactions when following up. Past interactions include, but are not limited to, for example, past email history, chat logs, and the like. The response unit can, for example, use AI to refer to the customer's past interactions and send an optimal follow-up message. For example, the response unit can send a follow-up message related to a product that the customer previously purchased. In addition, the response unit can send a follow-up message based on the content of an inquiry made by the customer in the past. In this way, the optimal follow-up message can be sent by referring to the customer's past interactions.

[0049] During follow-up, the correspondence department can adjust the timing of the message based on the customer's current situation. The current situation includes, but is not limited to, for example, current purchasing intention, current interests, and the like. The correspondence department, for example, identifies the customer's current situation using AI and adjusts the timing of the message. For example, the correspondence department can delay sending a follow-up message if the customer is currently busy. Also, the correspondence department can send a follow-up message immediately if the customer is currently online. This allows for effective follow-up by adjusting the timing of the message based on the customer's current situation.

[0050] The response unit can send a message taking into consideration the geographical location information of the customer when following up. The geographical location information includes, but is not limited to, for example, GPS data, IP address, and the like. The response unit can, for example, use AI to identify the geographical location information of the customer and send a follow-up message. For example, the response unit can send a follow-up message including local event information in the area where the customer is currently located. Furthermore, the response unit can send a follow-up message including nearby store information based on the customer's location information. This allows the optimal follow-up message to be sent by taking into consideration the geographical location information of the customer.

[0051] When following up, the correspondence unit can analyze the social media activity of the customer and send a relevant message. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, and the like. The correspondence unit can, for example, use AI to analyze the social media activity of the customer and send a relevant message. For example, the correspondence unit can send a follow-up message related to content shared by the customer on social media. In addition, the correspondence unit can send a follow-up message based on the topic of an online community in which the customer participates. In this way, a relevant follow-up message can be sent by analyzing the social media activity of the customer.

[0052] The reception unit can select the optimal reception method by referring to past reception data at the time of reception. The past reception data includes, for example, past reception history, past interaction data, etc., but is not limited to such examples. The reception unit, for example, uses AI to refer to the past reception data and selects the optimal reception method. For example, the reception unit can preferentially suggest a reception method that the user has frequently used in the past. In addition, the reception unit can predict and suggest the optimal reception method for a specific time period from the user's past reception history. As a result, the optimal reception method can be selected by referring to the past reception data.

[0053] The reception unit can select a reception method in consideration of device information of the user at the time of reception. The device information includes, for example, a smartphone, a tablet, a desktop, and the like, but is not limited to such examples. The reception unit, for example, identifies the device information of the user using AI, and selects the optimal reception method. For example, when the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Also, when the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. In this way, the optimal reception method can be selected by considering the device information of the user.

[0054] The estimation unit can improve the accuracy of the estimation by referring to past data at the time of estimation. The past data includes, for example, past purchase history, past interaction data, and the like, but is not limited to such examples. The estimation unit can improve the accuracy of the estimation by referring to the past data, for example, using AI. For example, the estimation unit can estimate needs based on the past purchase history. In addition, the estimation unit can analyze past interaction data and estimate the background. Thereby, the accuracy of the estimation can be improved by referring to the past data.

[0055] The estimation unit can make an estimation by taking into account the attribute information of the customer. The attribute information includes, for example, age, sex, occupation, etc., but is not limited to such examples. The estimation unit makes an estimation by taking into account the attribute information of the customer, for example, using AI. For example, the estimation unit can estimate the needs based on the age and sex of the customer. In addition, the estimation unit can estimate the background based on the occupation and income of the customer. This allows for more accurate estimation by taking into account the attribute information of the customer.

[0056] The estimation unit can perform estimation by taking into consideration the geographical distribution of customers. The geographical distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The estimation unit performs estimation by taking into consideration the geographical distribution of customers, for example, using AI. For example, the estimation unit can estimate needs based on the customer's place of residence. In addition, the estimation unit can estimate the background based on the location of the customer's workplace. This allows for more accurate estimation by taking into consideration the geographical distribution of customers.

[0057] The estimation unit can improve the accuracy of the estimation by referring to related literature during the estimation. The related literature includes, but is not limited to, for example, academic papers, industry reports, and the like. The estimation unit can improve the accuracy of the estimation by referring to related literature using, for example, AI. For example, the estimation unit can estimate the needs by referring to the latest marketing research. In addition, the estimation unit can estimate the background by referring to industry trend reports. As a result, the accuracy of the estimation can be improved by referring to related literature.

[0058] The management unit can improve the accuracy of management by referring to past data during management. Past data includes, for example, past management history, past interaction data, and the like, but is not limited to such examples. The management unit can improve the accuracy of management by referring to past data, for example, using AI. For example, the management unit can adjust the management method based on the past management history. In addition, the management unit can analyze past interaction data and adjust the management method. In this way, the accuracy of management can be improved by referring to past data.

[0059] During management, the management unit can perform management taking into consideration customer attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to such examples. The management unit performs management taking into consideration customer attribute information, for example, using AI. For example, the management unit can adjust the management method based on the customer's age and gender. The management unit can also adjust the management method based on the customer's occupation and income. This allows for more accurate management by taking into consideration customer attribute information.

[0060] During management, the management department can perform management taking into account the geographical distribution of customers. Geographical distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The management department performs management taking into account the geographical distribution of customers, for example, using AI. For example, the management department can adjust the management method based on the customer's place of residence. In addition, the management department can adjust the management method based on the location of the customer's workplace. This allows for more accurate management by taking into account the geographical distribution of customers.

[0061] The management department can improve the accuracy of management by referring to related literature during management. The related literature includes, but is not limited to, for example, academic papers, industry reports, and the like. The management department can improve the accuracy of management by referring to related literature using, for example, AI. For example, the management department can adjust the management method by referring to the latest marketing research. In addition, the management department can adjust the management method by referring to industry trend reports. In this way, the accuracy of management can be improved by referring to related literature.

[0062] The display unit can select an optimal display method by referring to past display data at the time of display. The past display data includes, for example, past display history, past interaction data, and the like, but is not limited to such examples. The display unit can, for example, use AI to refer to the past display data and select an optimal display method. For example, the display unit can preferentially provide a display method that the user has used preferentially in the past. In addition, the display unit can predict and provide an optimal display method for a specific time period from the user's past display history. This allows the optimal display method to be selected by referring to the past display data.

[0063] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. The device information includes, for example, a smartphone, a tablet, a desktop, and the like, but is not limited to such examples. The display unit, for example, identifies the device information of the user using AI and selects the optimal display method. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. In this way, the optimal display method can be selected by considering the device information of the user.

[0064] The display unit can provide a multilingual display according to the user's language setting when displaying. The language setting includes, but is not limited to, for example, the language setting of the device and a language selected by the user. The display unit can, for example, use AI to identify the user's language setting and provide a multilingual display. For example, the display unit can automatically set the display language based on the language setting of the user's device. In addition, the display unit can provide a language switching function when the user uses multiple languages. This enables an effective display by providing a multilingual display according to the user's language setting.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit collects device information of the user, and the identification unit can identify products and services that are optimal for the device used by the user based on the collected device information. For example, if the user uses a smartphone, the collection unit can identify apps and accessories that are optimal for the device. Also, if the user uses a tablet, the collection unit can identify e-books and tablet stands that are optimal for the device. Furthermore, if the user uses a desktop, the collection unit can identify software and peripherals that are optimal for the device. This makes it possible to suggest more appropriate products and services based on the device used by the user.

[0067] The identification unit analyzes the user's past purchase history, and the collection unit can identify products or services that the user is likely to repurchase based on the past purchase history. For example, the identification unit can identify products in the same category as products that the user has previously purchased. The identification unit can also identify complementary products or related products to products that the user has previously purchased. Furthermore, the identification unit can identify upgraded versions or new models of products that the user has previously purchased. This makes it possible to suggest products or services that are likely to be repurchased based on the user's past purchase history.

[0068] The collection unit collects the geographical location information of the user, and the identification unit can identify products and services specialized to the user's area based on the collected geographical location information. For example, if the user lives in an urban area, the collection unit can identify products and services that are popular in that area. Also, if the user lives in a rural area, the collection unit can identify products and services that are needed in that area. Furthermore, if the user is participating in a specific event or festival, the collection unit can identify products and services related to the event. This makes it possible to suggest more appropriate products and services based on the user's geographical location information.

[0069] The management unit analyzes the user's past interaction data, and the collection unit can identify products and services in which the user is likely to be interested again based on the past interaction data. For example, the management unit can identify websites and pages that the user frequently visited in the past. The management unit can also identify content on which the user spent a lot of time in the past. Furthermore, the management unit can identify products and services with which the user had a lot of interaction in the past. This makes it possible to suggest products and services in which the user is likely to be interested again based on the user's past interaction data.

[0070] The collection unit analyzes the user's social media activity, and the identification unit can identify products and services related to the user's interests based on the collected social media data. For example, the collection unit can identify accounts the user follows and groups the user participates in. The collection unit can also identify content the user shares and posts the user likes. Furthermore, the collection unit can identify topics the user comments on and discussions the user participates in. This makes it possible to suggest more relevant products and services based on the user's social media activity.

[0071] The management department analyzes the geographic distribution of users, and the collection department can formulate a sales strategy specialized for a specific region based on the geographic distribution. For example, the management department can analyze the number of customers and sales data in a specific region. The management department can also identify popular products and services in a specific region. Furthermore, the management department can analyze the activities of competitors in a specific region. This makes it possible to formulate a more effective region-specific sales strategy based on the geographic distribution of users.

[0072] The process flow of the first embodiment will be briefly described below.

[0073] Step 1: The collection unit collects data of online users. The data of online users includes browsing history, purchase history, click data, etc. The collection unit analyzes website access logs to collect user behavior patterns. It can also collect purchase history from online shopping sites to understand users' purchasing trends. It can also collect social media activity to analyze users' interests. Step 2: The identification unit identifies potential customers for the product or service based on the data collected by the collection unit. The identification unit uses AI to analyze the collected data and score potential customers. It uses machine learning algorithms to identify potential customers from user behavior patterns. It can also identify potential customers based on past purchase history and products they have shown interest in. Step 3: The response department responds to the potential customers identified by the identification department. The response department sends follow-up messages and emails. Using AI, the response department can generate and send personalized messages to potential customers. It can also provide real-time product information, prices, and promotion information that sales reps need while interacting with customers.

[0074] (Example 2) The AI ​​assistant system for automating and supporting sales activities according to an embodiment of the present invention is a system that collects data of online users, identifies potential customers, and responds to them. The system includes a collection unit that collects data of online users, an identification unit that identifies potential customers of a product or service based on the collected data, and a response unit that responds to the identified potential customers. For example, the collection unit collects data on the behavioral history and context of online users, and the identification unit identifies the potential of a potential customer based on the collected data. The response unit sends a follow-up message or e-mail to the identified potential customer at a timing according to the potential. The system also includes a reception unit that accepts the designation of a specific product or service, and can identify potential customers of the designated product or service. The system also includes a response unit that provides product information, prices, and promotion information required by a sales representative during a dialogue with a customer in real time. The estimation unit estimates the needs and background of the potential customer based on the collected data, and proposes sales scripts and approaches to the sales representative. The management unit manages the potential of the potential customer in association with the progress of the sales, thereby making sales activities more efficient. The display unit has a function of displaying information of the identified potential customer and adjusting the display order based on the feelings of the sales representative. This will enable sales activities to be more efficient and customers to be served more effectively. As a result, the AI ​​assistant system that automates and supports sales activities will be able to realize sales activities that are more efficient and customers to be served more effectively.

[0075] The AI ​​assistant system for automating and supporting sales activities according to the embodiment includes a collection unit, an identification unit, and a response unit. The collection unit collects data of online users. The data of online users includes, but is not limited to, browsing history, purchase history, click data, and the like. The collection unit, for example, analyzes website access logs to collect user behavior patterns. The collection unit can also collect purchase history of online shopping sites to grasp user purchasing trends. Furthermore, the collection unit can collect social media activity and analyze user interests. The identification unit identifies potential customers of products or services based on the data collected by the collection unit. The identification unit, for example, analyzes the collected data using AI and scores potential customers. The identification unit, for example, uses a machine learning algorithm to identify potential customers from user behavior patterns. The identification unit can also identify potential customers based on past purchase history and products in which the user has shown interest. The response unit responds to the potential customers identified by the identification unit. The response unit, for example, sends follow-up messages and emails. The response unit generates and sends personalized messages to potential customers using, for example, AI. The response unit can also provide product information, prices, and promotion information that is necessary for sales representatives during conversations with customers in real time. As a result, the AI ​​assistant system that automates and supports sales activities according to the embodiment can achieve efficient sales activities and effective customer service.

[0076] The collection unit collects data of online users. The data of online users includes, but is not limited to, browsing history, purchase history, click data, and the like. For example, the collection unit analyzes access logs of websites to collect user behavior patterns. Specifically, detailed data such as pages visited by users, time spent on the site, and links clicked by users are collected, and the user's interests and concerns are analyzed based on these data. The collection unit can also collect purchase history of online shopping sites to grasp the user's purchasing tendency. For example, data such as the type of product purchased by the user in the past, purchase frequency, and purchase time are collected, and the user's purchasing pattern is analyzed based on these data. Furthermore, the collection unit can also collect social media activity to analyze the user's interests and concerns. Specifically, data such as "likes" and posts shared by the user, accounts followed, and commented content are collected, and the user's interests and concerns are grasped in detail based on these data. The collection unit can integrate information obtained from these various data sources to grasp the overall picture of the user. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, data collection can be strengthened during a particular campaign period and the effectiveness of the campaign can be monitored in real time, allowing the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0077] The identification unit identifies potential customers of the product or service based on the data collected by the collection unit. The identification unit, for example, uses AI to analyze the collected data and score potential customers. Specifically, the identification unit uses a machine learning algorithm to identify potential customers from user behavior patterns. For example, data such as the user's browsing history, purchase history, and social media activity is analyzed to score users who show an interest in a specific product. For scoring, a predictive model based on the user's past behavior data is used to quantify each user's purchasing motivation and degree of interest. The identification unit can also identify potential customers based on past purchase history and products in which the user showed interest. For example, if a user who has purchased a specific product in the past is likely to show interest in a similar product again, the identification unit identifies the user as a potential customer. Furthermore, the identification unit can perform more accurate customer identification by taking into account the user's demographic data (age, gender, region, etc.) and psychographic data (interests, values, lifestyle, etc.). This allows the identification unit to quickly and accurately analyze the collected data and efficiently identify potential customers. Furthermore, the identification unit can continuously improve the accuracy of customer identification based on data updated in real time. For example, the predictive model is retrained each time new data is collected to respond to the latest situation. This allows the Identification Department to always provide highly accurate customer identification based on the latest information, maximizing the effectiveness of sales activities.

[0078] The response unit responds to potential customers identified by the identification unit. The response unit, for example, sends follow-up messages and e-mails. Specifically, the response unit uses AI to generate and send personalized messages to potential customers. For example, based on the user's past behavioral data and interests, an individually customized message is created and sent at an appropriate time. This allows the user to receive information that is highly relevant to him or her, which increases the desire to purchase. The response unit can also provide product information, prices, and promotion information required by sales representatives in real time while interacting with customers. For example, when a sales representative is interacting with a customer by phone or chat, AI automatically provides related product information to support quick and accurate response. Furthermore, the response unit can collect customer feedback and continuously improve the accuracy and effectiveness of the response content. For example, the content of the message and the timing of sending are optimized based on the customer's response and purchase history. The response unit can also reliably convey information using multiple communication means. For example, important information is reliably delivered by using not only e-mail but also SMS, push notifications, and messaging functions of social media. This allows the response unit to provide users with quick and reliable responses and maximize the effectiveness of sales activities. In addition, the sales department can plan the next follow-up or promotion based on the customer's purchase history and behavioral data. This will help the sales department build and maintain continuous customer relationships and contribute to the success of long-term sales activities.

[0079] The identification unit identifies the potential of a potential customer based on the data collected by the collection unit, and the response unit can send a follow-up message or e-mail to the potential customer identified by the identification unit based on the potential. The potential includes, for example, a scoring model and behavior analysis, but is not limited to such examples. The identification unit analyzes the collected data using, for example, AI, and scores the potential of the potential customer. For example, the identification unit can evaluate the potential of the potential customer based on the user's behavior pattern and past purchase history. The response unit sends, for example, a follow-up message or e-mail based on the potential. For example, the response unit can generate and send a personalized message to the potential customer using AI. This enables effective sales activities by following up at a timing according to the potential of the potential customer.

[0080] The reception unit receives a designation of a specific product or service, and the identification unit can identify potential customers of the specific product or service whose designation has been accepted by the reception unit. The specific product or service includes, but is not limited to, electronic devices, software services, and the like. The reception unit, for example, receives a designation of the specific product or service from a user. For example, the reception unit can receive the designation from the user through a web form or a chatbot. The identification unit identifies potential customers of the specific product or service whose designation has been accepted by the reception unit. For example, the identification unit can use AI to analyze data related to the designated product or service and identify potential customers. This enables targeted sales activities by identifying potential customers for the specific product or service.

[0081] The collection unit can collect data on the behavior history and context data of online users. The behavior history data includes, for example, page views, click histories, etc., but is not limited to such examples. The collection unit, for example, analyzes website access logs to collect user behavior patterns. For example, the collection unit can collect the history of pages visited by users and links clicked by users. The context data includes, for example, time periods, device information, etc., but is not limited to such examples. The collection unit can collect, for example, information on the time periods when users access and the devices they use. In this way, by collecting the behavior history and context data of online users, it becomes possible to identify customers with higher accuracy.

[0082] The response unit can instantly provide product information, price, and promotion information while the sales representative is interacting with a potential customer. Product information includes, for example, product specifications, stock status, and the like, but is not limited to such examples. The response unit provides necessary product information in real time while the sales representative is interacting with a customer. For example, the response unit can use AI to instantly provide product specifications and stock status in response to a customer's question. Prices include, for example, discount information, price comparisons, and the like, but are not limited to such examples. The response unit provides the latest price information while the sales representative is interacting with a customer. For example, the response unit can use AI to provide discount information and price comparisons to the customer. Promotion information includes, for example, campaign information, coupon codes, and the like, but is not limited to such examples. The response unit provides the latest promotion information while the sales representative is interacting with a customer. For example, the response unit can use AI to provide campaign information and coupon codes to the customer. This allows the sales representative to provide necessary information in real time, enabling effective sales activities.

[0083] The estimation unit estimates the needs and background of potential customers based on the data collected by the collection unit, and the response unit can propose a sales script or approach to the sales representative according to the needs and background of the potential customers. The needs and background include, but are not limited to, for example, survey results and past purchase history. The estimation unit, for example, uses AI to analyze the collected data and estimates the needs and background of potential customers. For example, the estimation unit can estimate the needs of potential customers based on the survey results and past purchase history of users. The response unit, for example, proposes a sales script or approach to the sales representative according to the estimated needs and background. For example, the response unit can use AI to propose an effective sales script or approach to the sales representative. This enables effective sales activities by proposing a sales script or approach according to the needs and background of customers.

[0084] The management unit manages the potential of the potential customer in association with the progress of sales, and the response unit can conduct sales to the potential customer based on the potential of the potential customer and the progress of sales. The potential includes, for example, a scoring model and behavior analysis, but is not limited to such examples. The management unit analyzes, for example, collected data using AI, and scores the potential of the potential customer. For example, the management unit can evaluate the potential of the potential customer based on the user's behavior pattern and past purchase history. The progress of sales includes, for example, the stage of a business negotiation, the number of follow-ups, etc., but is not limited to such examples. The management unit tracks, for example, the progress of sales, and manages it in association with the potential of the potential customer. For example, the management unit can manage the progress of sales based on the stage of a business negotiation and the number of follow-ups. The response unit performs sales activities based on, for example, the potential of the potential customer and the progress of sales. For example, the response unit can use AI to follow up on potential customers at an appropriate time. This enables effective sales activities by linking and managing the potential of potential customers with the progress of sales.

[0085] The response unit can display information on the potential customer identified by the identification unit on the display unit. The information includes, for example, a customer profile, a past purchase history, and the like, but is not limited to such examples. The response unit, for example, displays information on the potential customer identified by the identification unit on the display unit. For example, the response unit can use AI to display a customer profile and a past purchase history on the display unit. This allows a sales representative to quickly respond by displaying information on the identified potential customer.

[0086] The corresponding unit can adjust the display order of potential customers identified by the identifying unit based on the emotional state of the sales representative. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The corresponding unit, for example, estimates the emotional state of the sales representative using AI, and adjusts the display order of potential customers identified by the identifying unit. For example, when the sales representative is feeling stressed, the corresponding unit can preferentially display customers with high importance. This enables effective sales activities by adjusting the display order according to the emotions of the sales representative. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The collection unit can estimate the emotional state of the user and adjust the timing of data collection based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The collection unit can estimate the emotional state of the user using, for example, AI, and adjust the timing of data collection. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Thereby, by adjusting the timing of data collection according to the user's emotion, the burden on the user can be reduced and detailed data can be collected. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The collection unit can analyze the user's past behavior history and select a data collection method. The past behavior history includes, but is not limited to, for example, page views, click history, and the like. The collection unit can, for example, use AI to analyze the user's past behavior history and select an optimal data collection method. For example, the collection unit can preferentially collect data from websites that the user frequently visited in the past. In addition, the collection unit can preferentially collect data from devices that the user used in the past. In this way, the optimal data collection method can be selected by analyzing the user's past behavior history.

[0089] The collection unit can perform filtering based on the user's current field of interest when collecting data. The current field of interest includes, but is not limited to, recent search history, pages viewed, and the like. The collection unit can, for example, use AI to identify the user's current field of interest and perform filtering when collecting data. For example, the collection unit can preferentially collect data related to topics in which the user is currently interested. In addition, the collection unit can filter data based on keywords recently searched by the user. This makes it possible to collect highly relevant data by filtering data based on the user's current field of interest.

[0090] The collection unit can estimate the emotional state of the user and determine the priority of data to be collected based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The collection unit can estimate the emotional state of the user using, for example, AI, and determine the priority of data to be collected. For example, when the user is excited, the collection unit can preferentially collect the latest trend information. Also, when the user is relaxed, the collection unit can preferentially collect detailed analysis data. This enables effective data collection by determining the priority of data to be collected according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The geographical location information includes, but is not limited to, for example, GPS data, IP address, etc. The collection unit, for example, uses AI to identify the geographical location information of the user, and preferentially collects highly relevant data when collecting data. For example, the collection unit can preferentially collect local news in the area where the user is currently located. In addition, the collection unit can collect nearby event information based on the user's location information. This allows highly relevant data to be preferentially collected by considering the geographical location information of the user.

[0092] The collection unit can analyze the user's social media activity at the time of data collection and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, etc. The collection unit can, for example, use AI to analyze the user's social media activity and collect related data. For example, the collection unit can collect the content of posts from accounts the user follows. In addition, the collection unit can collect information on groups and communities in which the user participates. In this way, the user's social media activity can be analyzed to collect related data.

[0093] The identification unit can estimate the emotional state of the user and adjust the method of identifying potential customers based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The identification unit can estimate the emotional state of the user using, for example, AI, and adjust the method of identifying potential customers. For example, when the user is excited, the identification unit can identify potential customers with a proactive approach. Also, when the user is relaxed, the identification unit can identify potential customers by performing detailed analysis. This enables effective customer identification by adjusting the method of identifying potential customers according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] At the time of identification, the identification unit can improve the accuracy of identifying a potential customer by referring to past data. Past data includes, for example, past purchase history, past interaction data, and the like, but is not limited to such examples. The identification unit can improve the accuracy of identifying a potential customer by referring to past data using, for example, AI. For example, the identification unit can identify a potential customer based on past purchase history. Furthermore, the identification unit can analyze past interaction data to identify a potential customer. Thereby, by referring to past data, the accuracy of identifying a potential customer can be improved.

[0095] The identification unit can perform the identification by taking into consideration the attribute information of the customer. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to such examples. The identification unit performs the identification by taking into consideration the attribute information of the customer, for example, using AI. For example, the identification unit can identify a potential customer based on the customer's age and gender. The identification unit can also identify a potential customer based on the customer's occupation and income. This makes it possible to identify customers with higher accuracy by taking into consideration the customer's attribute information.

[0096] The identification unit can estimate the emotional state of the user and determine the priority of the identified customers based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The identification unit can estimate the emotional state of the user using, for example, AI, and determine the priority of the identified customers. For example, when the user is excited, the identification unit can prioritize customers who approach proactively. Also, when the user is relaxed, the identification unit can perform detailed analysis to determine the priority. This enables effective sales activities by determining the priority of customers according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The identification unit can perform the identification while taking into consideration the geographic distribution of customers. The geographic distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The identification unit performs the identification while taking into consideration the geographic distribution of customers, for example, using AI. For example, the identification unit can identify potential customers based on the customer's place of residence. Also, the identification unit can identify potential customers based on the location of the customer's workplace. In this way, by taking into consideration the geographic distribution of customers, more accurate customer identification is possible.

[0098] The identification unit can improve the accuracy of identification by referring to related literature at the time of identification. Examples of related literature include, but are not limited to, academic papers and industry reports. For example, the identification unit can improve the accuracy of identification by referring to related literature using AI. For example, the identification unit can identify potential customers by referring to the latest marketing research. Also, the identification unit can identify potential customers by referring to industry trend reports. This allows the accuracy of identification to be improved by referring to related literature.

[0099] The corresponding unit can estimate the emotional state of the user and adjust the content of the follow-up message based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The corresponding unit can estimate the emotional state of the user using, for example, AI, and adjust the content of the follow-up message. For example, the corresponding unit can send a proactive follow-up message when the user is excited. Also, the corresponding unit can send a follow-up message including detailed information when the user is relaxed. This enables effective follow-up by adjusting the content of the follow-up message according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] The response unit can send a message by referring to the customer's past interactions when following up. Past interactions include, but are not limited to, for example, past email history, chat logs, and the like. The response unit can, for example, use AI to refer to the customer's past interactions and send an optimal follow-up message. For example, the response unit can send a follow-up message related to a product that the customer previously purchased. In addition, the response unit can send a follow-up message based on the content of an inquiry made by the customer in the past. In this way, the optimal follow-up message can be sent by referring to the customer's past interactions.

[0101] During follow-up, the correspondence department can adjust the timing of the message based on the customer's current situation. The current situation includes, but is not limited to, for example, current purchasing intention, current interests, and the like. The correspondence department, for example, identifies the customer's current situation using AI and adjusts the timing of the message. For example, the correspondence department can delay sending a follow-up message if the customer is currently busy. Also, the correspondence department can send a follow-up message immediately if the customer is currently online. This allows for effective follow-up by adjusting the timing of the message based on the customer's current situation.

[0102] The corresponding unit can estimate the emotional state of the user and determine the priority of the follow-up message based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The corresponding unit can estimate the emotional state of the user using, for example, AI, and determine the priority of the follow-up message. For example, the corresponding unit can preferentially send an important follow-up message when the user is excited. Also, the corresponding unit can preferentially send a detailed follow-up message when the user is relaxed. This enables effective follow-up by determining the priority of the follow-up message according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] The response unit can send a message taking into consideration the geographical location information of the customer when following up. The geographical location information includes, but is not limited to, for example, GPS data, IP address, and the like. The response unit can, for example, use AI to identify the geographical location information of the customer and send a follow-up message. For example, the response unit can send a follow-up message including local event information in the area where the customer is currently located. Furthermore, the response unit can send a follow-up message including nearby store information based on the customer's location information. This allows the optimal follow-up message to be sent by taking into consideration the geographical location information of the customer.

[0104] When following up, the correspondence unit can analyze the social media activity of the customer and send a relevant message. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, the number of followers, and the like. The correspondence unit can, for example, use AI to analyze the social media activity of the customer and send a relevant message. For example, the correspondence unit can send a follow-up message related to content shared by the customer on social media. In addition, the correspondence unit can send a follow-up message based on the topic of an online community in which the customer participates. In this way, a relevant follow-up message can be sent by analyzing the social media activity of the customer.

[0105] The reception unit can estimate the emotional state of the user and adjust the reception method based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The reception unit estimates the emotional state of the user using, for example, AI, and adjusts the reception method. For example, when the user is stressed, the reception unit can provide a simple interface and minimize the input procedure. Also, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. This enables effective reception by adjusting the reception method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] The reception unit can select the optimal reception method by referring to past reception data at the time of reception. The past reception data includes, for example, past reception history, past interaction data, etc., but is not limited to such examples. The reception unit, for example, uses AI to refer to the past reception data and selects the optimal reception method. For example, the reception unit can preferentially suggest a reception method that the user has frequently used in the past. In addition, the reception unit can predict and suggest the optimal reception method for a specific time period from the user's past reception history. As a result, the optimal reception method can be selected by referring to the past reception data.

[0107] The reception unit can estimate the emotional state of the user and determine the priority of reception based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The reception unit can estimate the emotional state of the user using, for example, AI, and determine the priority of reception. For example, when the user is excited, the reception unit can prioritize important receptions. Also, when the user is relaxed, the reception unit can prioritize detailed receptions. This enables effective reception by determining the priority of reception according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0108] The reception unit can select a reception method in consideration of device information of the user at the time of reception. The device information includes, for example, a smartphone, a tablet, a desktop, and the like, but is not limited to such examples. The reception unit, for example, identifies the device information of the user using AI, and selects the optimal reception method. For example, when the user is using a smartphone, the reception unit can provide a reception method that matches the screen size. Also, when the user is using a tablet, the reception unit can provide a reception method that is optimized for a large screen. In this way, the optimal reception method can be selected by considering the device information of the user.

[0109] The estimation unit can estimate the emotional state of the user and adjust the estimation method of the needs and background based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The estimation unit can estimate the emotional state of the user using, for example, AI, and adjust the estimation method of the needs and background. For example, the estimation unit can estimate active needs when the user is excited. Also, the estimation unit can estimate detailed background information when the user is relaxed. This enables effective estimation by adjusting the estimation method of the needs and background according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0110] The estimation unit can improve the accuracy of the estimation by referring to past data at the time of estimation. The past data includes, for example, past purchase history, past interaction data, and the like, but is not limited to such examples. The estimation unit can improve the accuracy of the estimation by referring to the past data, for example, using AI. For example, the estimation unit can estimate needs based on the past purchase history. In addition, the estimation unit can analyze past interaction data and estimate the background. Thereby, the accuracy of the estimation can be improved by referring to the past data.

[0111] The estimation unit can make an estimation by taking into account the attribute information of the customer. The attribute information includes, for example, age, sex, occupation, etc., but is not limited to such examples. The estimation unit makes an estimation by taking into account the attribute information of the customer, for example, using AI. For example, the estimation unit can estimate the needs based on the age and sex of the customer. In addition, the estimation unit can estimate the background based on the occupation and income of the customer. This allows for more accurate estimation by taking into account the attribute information of the customer.

[0112] The estimation unit can estimate the emotional state of the user and determine the priority of the estimation results based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The estimation unit can estimate the emotional state of the user using, for example, AI, and determine the priority of the estimation results. For example, when the user is excited, the estimation unit can provide important estimation results preferentially. Also, when the user is relaxed, the estimation unit can provide detailed estimation results preferentially. This enables effective estimation by determining the priority of the estimation results according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0113] The estimation unit can perform estimation by taking into consideration the geographical distribution of customers. The geographical distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The estimation unit performs estimation by taking into consideration the geographical distribution of customers, for example, using AI. For example, the estimation unit can estimate needs based on the customer's place of residence. In addition, the estimation unit can estimate the background based on the location of the customer's workplace. This allows for more accurate estimation by taking into consideration the geographical distribution of customers.

[0114] The estimation unit can improve the accuracy of the estimation by referring to related literature during the estimation. The related literature includes, but is not limited to, for example, academic papers, industry reports, and the like. The estimation unit can improve the accuracy of the estimation by referring to related literature using, for example, AI. For example, the estimation unit can estimate the needs by referring to the latest marketing research. In addition, the estimation unit can estimate the background by referring to industry trend reports. As a result, the accuracy of the estimation can be improved by referring to related literature.

[0115] The management unit can estimate the emotional state of the user and adjust the management method based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The management unit estimates the emotional state of the user using, for example, AI, and adjusts the management method. For example, the management unit can adopt an active management method when the user is excited. Also, the management unit can adopt a detailed management method when the user is relaxed. This enables effective management by adjusting the management method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0116] The management unit can improve the accuracy of management by referring to past data during management. Past data includes, for example, past management history, past interaction data, and the like, but is not limited to such examples. The management unit can improve the accuracy of management by referring to past data, for example, using AI. For example, the management unit can adjust the management method based on the past management history. In addition, the management unit can analyze past interaction data and adjust the management method. In this way, the accuracy of management can be improved by referring to past data.

[0117] During management, the management unit can perform management taking into consideration customer attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to such examples. The management unit performs management taking into consideration customer attribute information, for example, using AI. For example, the management unit can adjust the management method based on the customer's age and gender. The management unit can also adjust the management method based on the customer's occupation and income. This allows for more accurate management by taking into consideration customer attribute information.

[0118] The management unit can estimate the emotional state of the user and determine the priority of management based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The management unit can estimate the emotional state of the user using, for example, AI, and determine the priority of management. For example, when the user is excited, the management unit can prioritize processing of important management items. Also, when the user is relaxed, the management unit can prioritize processing of detailed management items. This enables effective management by determining the priority of management according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0119] During management, the management department can perform management taking into account the geographical distribution of customers. Geographical distribution includes, but is not limited to, for example, the number of customers by region and sales data by region. The management department performs management taking into account the geographical distribution of customers, for example, using AI. For example, the management department can adjust the management method based on the customer's place of residence. In addition, the management department can adjust the management method based on the location of the customer's workplace. This allows for more accurate management by taking into account the geographical distribution of customers.

[0120] The management department can improve the accuracy of management by referring to related literature during management. The related literature includes, but is not limited to, for example, academic papers, industry reports, and the like. The management department can improve the accuracy of management by referring to related literature using, for example, AI. For example, the management department can adjust the management method by referring to the latest marketing research. In addition, the management department can adjust the management method by referring to industry trend reports. In this way, the accuracy of management can be improved by referring to related literature.

[0121] The display unit can estimate the emotional state of the user and adjust the display method based on the estimated emotional state of the user. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The display unit can estimate the emotional state of the user using, for example, AI, and adjust the display method. For example, when the user is nervous, the display unit can provide an interface with calm colors to reduce visual stress. Also, when the user is enjoying himself, the display unit can provide an interface with bright colors to make input work enjoyable. This allows for effective display by adjusting the display method according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0122] The display unit can select an optimal display method by referring to past display data at the time of display. The past display data includes, for example, past display history, past interaction data, and the like, but is not limited to such examples. The display unit can, for example, use AI to refer to the past display data and select an optimal display method. For example, the display unit can preferentially provide a display method that the user has used preferentially in the past. In addition, the display unit can predict and provide an optimal display method for a specific time period from the user's past display history. This allows the optimal display method to be selected by referring to the past display data.

[0123] The display unit can estimate the user's emotion and determine the display priority based on the estimated user's emotion. The emotional state includes, for example, facial expression recognition, voice analysis, etc., but is not limited to such examples. The display unit can estimate the user's emotional state using, for example, AI, and determine the display priority. For example, when the user is excited, the display unit can preferentially display important information. Also, when the user is relaxed, the display unit can preferentially display detailed information. This enables effective display by determining the display priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0124] The display unit can select the optimal display method in consideration of the device information of the user at the time of display. The device information includes, for example, a smartphone, a tablet, a desktop, and the like, but is not limited to such examples. The display unit, for example, identifies the device information of the user using AI and selects the optimal display method. For example, when the user is using a smartphone, the display unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the display unit can provide a display method that is optimized for a large screen. In this way, the optimal display method can be selected by considering the device information of the user.

[0125] The display unit can provide a multilingual display according to the user's language setting when displaying. The language setting includes, but is not limited to, for example, the language setting of the device and a language selected by the user. The display unit can, for example, use AI to identify the user's language setting and provide a multilingual display. For example, the display unit can automatically set the display language based on the language setting of the user's device. In addition, the display unit can provide a language switching function when the user uses multiple languages. This enables an effective display by providing a multilingual display according to the user's language setting.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The collection unit collects device information of the user, and the identification unit can identify products and services that are optimal for the device used by the user based on the collected device information. For example, if the user uses a smartphone, the collection unit can identify apps and accessories that are optimal for the device. Also, if the user uses a tablet, the collection unit can identify e-books and tablet stands that are optimal for the device. Furthermore, if the user uses a desktop, the collection unit can identify software and peripherals that are optimal for the device. This makes it possible to suggest more appropriate products and services based on the device used by the user.

[0128] The identification unit analyzes the user's past purchase history, and the collection unit can identify products or services that the user is likely to repurchase based on the past purchase history. For example, the identification unit can identify products in the same category as products that the user has previously purchased. The identification unit can also identify complementary products or related products to products that the user has previously purchased. Furthermore, the identification unit can identify upgraded versions or new models of products that the user has previously purchased. This makes it possible to suggest products or services that are likely to be repurchased based on the user's past purchase history.

[0129] The corresponding unit can estimate the emotional state of the user and adjust the content of the follow-up message based on the estimated emotional state. For example, the corresponding unit can send a proactive follow-up message when the user is excited. Also, the corresponding unit can send a follow-up message including detailed information when the user is relaxed. Furthermore, the corresponding unit can send a simple and short follow-up message when the user is stressed. This allows a more effective follow-up message to be sent according to the emotional state of the user.

[0130] The collection unit collects the geographical location information of the user, and the identification unit can identify products and services specialized to the user's area based on the collected geographical location information. For example, if the user lives in an urban area, the collection unit can identify products and services that are popular in that area. Also, if the user lives in a rural area, the collection unit can identify products and services that are needed in that area. Furthermore, if the user is participating in a specific event or festival, the collection unit can identify products and services related to the event. This makes it possible to suggest more appropriate products and services based on the user's geographical location information.

[0131] The estimation unit can estimate the emotional state of the user and suggest an approach method to the salesperson based on the estimated emotional state. For example, the estimation unit can suggest an aggressive approach when the user is excited. Also, the estimation unit can suggest an approach that provides detailed information when the user is relaxed. Furthermore, the estimation unit can suggest a simple and short approach when the user is stressed. This makes it possible to suggest a more effective sales approach according to the emotional state of the user.

[0132] The management unit analyzes the user's past interaction data, and the collection unit can identify products and services in which the user is likely to be interested again based on the past interaction data. For example, the management unit can identify websites and pages that the user frequently visited in the past. The management unit can also identify content on which the user spent a lot of time in the past. Furthermore, the management unit can identify products and services with which the user had a lot of interaction in the past. This makes it possible to suggest products and services in which the user is likely to be interested again based on the user's past interaction data.

[0133] The display unit can estimate the emotional state of the user and adjust the priority of the information to be displayed based on the estimated emotional state. For example, when the user is excited, the display unit can prioritize displaying important information. When the user is relaxed, the display unit can prioritize displaying detailed information. When the user is stressed, the display unit can prioritize displaying simple and minimum necessary information. This enables more effective information display according to the emotional state of the user.

[0134] The collection unit analyzes the user's social media activity, and the identification unit can identify products and services related to the user's interests based on the collected social media data. For example, the collection unit can identify accounts the user follows and groups the user participates in. The collection unit can also identify content the user shares and posts the user likes. Furthermore, the collection unit can identify topics the user comments on and discussions the user participates in. This makes it possible to suggest more relevant products and services based on the user's social media activity.

[0135] The corresponding unit can estimate the emotional state of the user and adjust the timing of sending the follow-up message based on the estimated emotional state. For example, the corresponding unit can immediately send the follow-up message when the user is excited. Also, the corresponding unit can send the follow-up message including detailed information when the user is relaxed. Furthermore, the corresponding unit can delay the sending of the follow-up message when the user is stressed. This makes it possible to adjust the timing of sending the follow-up message more effectively according to the emotional state of the user.

[0136] The management department analyzes the geographic distribution of users, and the collection department can formulate a sales strategy specialized for a specific region based on the geographic distribution. For example, the management department can analyze the number of customers and sales data in a specific region. The management department can also identify popular products and services in a specific region. Furthermore, the management department can analyze the activities of competitors in a specific region. This makes it possible to formulate a more effective region-specific sales strategy based on the geographic distribution of users.

[0137] The process flow of the second embodiment will be briefly described below.

[0138] Step 1: The collection unit collects data of online users. The data of online users includes browsing history, purchase history, click data, etc. The collection unit analyzes website access logs to collect user behavior patterns. It can also collect purchase history from online shopping sites to understand users' purchasing trends. It can also collect social media activity to analyze users' interests. Step 2: The identification unit identifies potential customers for the product or service based on the data collected by the collection unit. The identification unit uses AI to analyze the collected data and score potential customers. It uses machine learning algorithms to identify potential customers from user behavior patterns. It can also identify potential customers based on past purchase history and products they have shown interest in. Step 3: The response department responds to the potential customers identified by the identification department. The response department sends follow-up messages and emails. Using AI, the response department can generate and send personalized messages to potential customers. It can also provide real-time product information, prices, and promotion information that sales reps need while interacting with customers.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0141] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] For example, the collection unit can collect data on the behavior history and context of online users using the camera 42 and microphone 38B of the smart device 14. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the potential of a potential customer based on the collected data. The response unit is realized, for example, by the control unit 46A of the smart device 14, and sends a follow-up message or e-mail to the identified potential customer. The reception unit accepts the designation of a product or service using, for example, the touch panel 38A or microphone 38B of the smart device 14. The estimation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and estimates the needs and background of a potential customer based on the collected data. The management unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and manages the potential of a potential customer in association with the progress of sales. The display unit is realized, for example, by the display 40A of the smart device 14, and displays information on the identified potential customer. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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, the RAM 30, and the 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] Fig. 4 shows an example of 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.

[0151] 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.

[0152] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] For example, the collection unit can collect data on the behavior history and context of online users using the camera 42 and microphone 238 of the smart glasses 214. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the potential of a potential customer based on the collected data. The response unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends a follow-up message or e-mail to the identified potential customer. The reception unit receives the designation of a product or service using, for example, the microphone 238 of the smart glasses 214. The estimation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and estimates the needs and background of a potential customer based on the collected data. The management unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and manages the potential of a potential customer in association with the progress of sales. The display unit is realized, for example, by the display 40A of the smart glasses 214, and displays information on the identified potential customer. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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, the RAM 30, and the 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0165] 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 performed in a secure state.

[0166] 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.

[0167] 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.

[0168] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0169] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0171] 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 voice indicating a user input for 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.

[0172] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0173] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0174] For example, the collection unit can collect data on the behavior history and context of online users using the camera 42 and microphone 238 of the headset type terminal 314. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the potential of a potential customer based on the collected data. The response unit is realized, for example, by the control unit 46A of the headset type terminal 314, and sends a follow-up message or e-mail to the identified potential customer. The reception unit accepts the designation of a product or service using, for example, the microphone 238 of the headset type terminal 314. The estimation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and estimates the needs and background of the potential customer based on the collected data. The management unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and manages the potential of the potential customer in association with the progress of the business. The display unit is realized, for example, by the display 343 of the headset type terminal 314, and displays information on the identified potential customer. The correspondence between each part and the device or control part is not limited to the above-mentioned example, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0177] 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, the RAM 30, and the 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.

[0178] 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.

[0179] 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 the voice according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0181] 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 performed in a secure state.

[0182] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0183] 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.

[0184] 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.

[0185] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0186] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0187] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0188] 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 a voice indicating a user input for 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.

[0189] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0190] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0191] For example, the collection unit can collect data on the behavior history and context of online users using the camera 42 and microphone 238 of the robot 414. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and identifies the potential of a potential customer based on the collected data. The response unit is realized, for example, by the control unit 46A of the robot 414, and sends a follow-up message or e-mail to the identified potential customer. The reception unit receives the designation of a product or service using, for example, the microphone 238 of the robot 414. The estimation unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and estimates the needs and background of a potential customer based on the collected data. The management unit is realized, for example, by the identification processing unit 290 of the data processing device 12, and manages the potential of a potential customer in association with the progress of sales. The display unit is realized, for example, by the display 40A of the robot 414, and displays information on the identified potential customer. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0192] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0193] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0194] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0195] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0196] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0197] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0199] 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.

[0200] 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 Universal Serial Bus (USB) 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.

[0201] In addition, 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 upon request from the data processing device 12.

[0202] 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.

[0203] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0204] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0205] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0206] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0207] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0208] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0209] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0210] (Appendix 1) a collection unit for collecting data of online users; an identification unit that identifies potential customers for a product or service based on the data collected by the collection unit; a response unit that responds to the potential customer identified by the identification unit, A system characterized by: (Appendix 2) The identification unit is Identifying the potential of the potential customer based on the data collected by the collection unit; The corresponding portion is Sending a follow-up message or e-mail to the potential customers identified by the identification unit based on the potential level. 2. The system of claim 1, (Appendix 3) A reception unit is provided for receiving a designation of a particular product or service; The identification unit is Identifying potential customers of the specific product or service whose designation has been accepted by the accepting unit 2. The system of claim 1, (Appendix 4) The collecting unit includes: Collect online user behavioral and contextual data 2. The system of claim 1, (Appendix 5) The corresponding portion is Provide instant product information, pricing, and promotional information to salespeople during interactions with said potential customers 2. The system of claim 1, (Appendix 6) an estimation unit that estimates the needs and background of the potential customers based on the data collected by the collection unit; The corresponding portion is Suggest sales scripts or approaches to salespeople based on the needs and background of said potential customers 2. The system of claim 1, (Appendix 7) a management unit that manages the potential of the potential customers in association with a progress of sales to the potential customers, The corresponding portion is Conducting sales to the potential customers based on the potential of the potential customers and the progress of sales to the potential customers. 2. The system of claim 1, (Appendix 8) The corresponding portion is The information of the potential customer identified by the identification unit is displayed on a display unit. 2. The system of claim 1, (Appendix 9) The corresponding portion is Adjusting a display order of the potential customers identified by the identification unit based on the emotional state of the sales representative. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimating a user's emotional state and adjusting the timing of data collection based on the estimated user's emotional state 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze users' past behavioral history and select data collection methods 2. The system of claim 1, (Appendix 12) The collecting unit includes: Filter data as it is collected based on the user's current interests 2. The system of claim 1, (Appendix 13) The collecting unit includes: To estimate a user's emotional state and prioritize data to be collected based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 16) The identification unit is Estimating a user's emotional state and adjusting a method for identifying potential customers based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 17) The identification unit is During identification, reference historical data to improve the accuracy of identifying potential customers 2. The system of claim 1, (Appendix 18) The identification unit is When identifying a customer, consider customer attribute information. 2. The system of claim 1, (Appendix 19) The identification unit is Estimating an emotional state of a user and prioritizing the identified customers based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 20) The identification unit is During identification, the geographical distribution of customers is taken into consideration. 2. The system of claim 1, (Appendix 21) The identification unit is During identification, refer to related literature to improve the accuracy of identification. 2. The system of claim 1, (Appendix 22) The corresponding portion is To estimate a user's emotional state and adjust the content of a follow-up message based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 23) The corresponding portion is Referencing a customer's past interactions when messaging them during follow-up 2. The system of claim 1, (Appendix 24) The corresponding portion is When following up, time your messages based on the customer's current situation 2. The system of claim 1, (Appendix 25) The corresponding portion is Estimating a user's emotional state and prioritizing follow-up messages based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 26) The corresponding portion is When following up, send messages that take into account the customer's geographic location 2. The system of claim 1, (Appendix 27) The corresponding portion is Analyze your customers' social media activity to send relevant messages when following up 2. The system of claim 1, (Appendix 28) The reception department is To estimate a user's emotional state and adjust a reception method based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 29) The reception department is When accepting a call, the most suitable reception method is selected by referring to past reception data. 2. The system of claim 1, (Appendix 30) The reception department is The emotional state of a user is estimated, and reception priority is determined based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 31) The reception department is At the time of reception, the reception method is selected taking into consideration the user's device information. 2. The system of claim 1, (Appendix 32) The estimation unit is A method for estimating a user's emotional state and adjusting a method for estimating needs and background based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 33) The estimation unit is When making estimates, refer to past data to improve the accuracy of the estimates. 2. The system of claim 1, (Appendix 34) The estimation unit is When making an estimation, customer attribute information is taken into consideration. 2. The system of claim 1, (Appendix 35) The estimation unit is A user's emotional state is estimated, and a priority order is determined based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 36) The estimation unit is When making estimates, the geographical distribution of customers is taken into account. 2. The system of claim 1, (Appendix 37) The estimation unit is During estimation, we refer to related literature to improve the accuracy of the estimation. 2. The system of claim 1, (Appendix 38) The management department: To estimate a user's emotional state and adjust a management method based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 39) The management department: Improve the accuracy of management by referring to past data during management 2. The system of claim 1, (Appendix 40) The management department: When managing data, consider customer attribute information. 2. The system of claim 1, (Appendix 41) The management department: To estimate a user's emotional state and determine a management priority based on the estimated emotional state of the user. 2. The system of claim 1, (Appendix 42) The management department: When managing, take into account the geographical distribution of customers. 2. The system of claim 1, (Appendix 43) The management department: When administering, refer to related literature to improve the accuracy of administration. 2. The system of claim 1, (Appendix 44) The display section is To estimate a user's emotional state and adjust a display method based on the estimated emotional state of the user 2. The system of claim 1, (Appendix 45) The display section is When displaying, the optimal display method is selected by referring to past display data. 2. The system of claim 1, (Appendix 46) The display section is Estimate the user's emotions and determine the display priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 47) The display section is When displaying, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 48) The display section is When displayed, it provides multilingual display according to the user's language settings. 2. The system of claim 1, [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit for collecting data of online users; an identification unit that identifies potential customers for a product or service based on the data collected by the collection unit; a response unit that responds to the potential customer identified by the identification unit, A system characterized by:

2. The identification unit is Identifying the potential of the potential customer based on the data collected by the collection unit; The corresponding portion is Sending a follow-up message or e-mail to the potential customers identified by the identification unit based on the potential level.

2. The system of claim 1 .

3. A reception unit is provided for receiving a designation of a particular product or service; The identification unit is Identifying potential customers of the specific product or service whose designation has been accepted by the accepting unit 2. The system of claim 1 .

4. The collecting unit includes: Collect online user behavioral and contextual data 2. The system of claim 1 .

5. The corresponding portion is Provide instant product information, pricing, and promotional information to salespeople during interactions with said potential customers 2. The system of claim 1 .

6. an estimation unit that estimates the needs and background of the potential customers based on the data collected by the collection unit; The corresponding portion is Suggest sales scripts or approaches to salespeople based on the needs and background of said potential customers 2. The system of claim 1 .

7. a management unit that manages the potential of the potential customers in association with a progress of sales to the potential customers, The corresponding portion is Conducting sales to the potential customers based on the potential of the potential customers and the progress of sales to the potential customers.

2. The system of claim 1 .

8. The corresponding portion is The information of the potential customer identified by the identification unit is displayed on a display unit.

2. The system of claim 1 .

9. The corresponding portion is Adjusting a display order of the potential customers identified by the identification unit based on the emotional state of the sales representative.

2. The system of claim 1 .

10. The collecting unit includes: Estimating a user's emotional state and adjusting the timing of data collection based on the estimated user's emotional state 2. The system of claim 1 .

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