system

Abstract

The system according to this embodiment aims to accurately and efficiently collect and analyze corporate information and to select companies that meet specific criteria. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a pick-up unit. The collection unit collects information such as tags and advertisements embedded in websites, videos and TV commercials, IR information, and employee SNS information. The analysis unit analyzes the information collected by the collection unit. The pick-up unit picks out companies that meet specific conditions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is laborious to collect and analyze corporate information and it is difficult to obtain accurate information.

[0005] The system according to the embodiment aims to accurately and efficiently collect and analyze corporate information and pick up enterprises that meet specific conditions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, and a pick-up unit. The collection unit collects information such as tags and advertisements embedded in websites, videos and TV commercials, IR information, and employee SNS information. The analysis unit analyzes the information collected by the collection unit. The pick-up unit selects companies that meet specific criteria based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately and efficiently collect and analyze corporate information and select companies that meet specific criteria. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The customer approach system according to an embodiment of the present invention is a system that utilizes an AI agent to accurately collect company information and efficiently approach customers. The customer approach system collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee SNS information. Next, the customer approach system analyzes the collected information to understand the tools being used, advertising media, and financial status. Furthermore, based on the analysis results, the customer approach system automatically picks out companies that have just started using web advertising from its assigned customer list, or companies with strong financial performance and rising personnel costs. This allows for quick identification of potential customers or those whose situations match the company's, enabling efficient customer approach. For example, the customer approach system can propose the optimal approach method for a specific company based on the information collected by the AI ​​agent. This improves the efficiency of sales activities and increases customer satisfaction. The customer approach system collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee SNS information. In this process, the AI ​​agent automatically collects the information and stores it in a database. Next, the customer approach system analyzes the collected information. The AI ​​agent analyzes collected information to understand the tools being used, the media outlets used, and the financial situation. For example, it analyzes what kind of advertisements a particular company is running, what tools it is using, and what its financial situation is. Furthermore, based on the analysis results, the customer approach system automatically picks out companies from the assigned customer list that have just started using web advertising, or companies that are performing well financially and have a rising labor cost ratio. The AI ​​agent picks out and lists companies that meet specific criteria based on the collected information. This allows sales representatives to approach customers efficiently. For example, based on the information collected by the AI ​​agent, it can propose the optimal approach method for a particular company. This improves the efficiency of sales activities and increases customer satisfaction.This allows the customer approach system to accurately collect company information and efficiently approach customers.

[0029] The customer approach system according to this embodiment comprises a collection unit, an analysis unit, and a pick-up unit. The collection unit collects information such as tags and advertising placement information embedded in websites, videos and TV commercials, IR information, and employee SNS information. For example, the collection unit analyzes tags embedded in websites to collect advertising placement information. The collection unit can also collect information on videos and TV commercials. For example, the collection unit obtains information from video platforms and TV commercial databases. Furthermore, the collection unit can also collect IR information and employee SNS information. For example, the collection unit collects information from companies' IR sites and employee SNS accounts. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the tools being used, the media used for advertising, and the financial situation. For example, the analysis unit analyzes what kind of advertisements a particular company is placing, what tools it is using, and what its financial situation is. Furthermore, the analysis unit can also analyze the company's performance and market trends based on the collected information. For example, the analysis department analyzes a company's sales data and market share to evaluate its growth potential. The selection department selects companies that meet specific criteria based on the analysis results obtained by the analysis department. For example, the selection department automatically selects companies that have just started using web advertising from its assigned customer list, or companies with strong financial results and rising labor cost ratios. Based on the collected information, the selection department lists companies that meet specific criteria. For example, the selection department automatically extracts companies that meet specific criteria and adds them to the list. As a result, the customer approach system according to this embodiment can accurately collect company information and efficiently approach customers.

[0030] The data collection unit collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee social media information. Specifically, to analyze tags embedded in websites, the data collection unit automatically crawls web pages using crawling technology and extracts tag information. This allows them to understand what kinds of advertisements are being placed and which advertisements are displayed on which pages. Regarding advertising placement information, they obtain data from advertising distribution platforms via APIs and collect detailed information such as the type of advertisement, where it was placed, and the period of placement. For collecting information on videos and TV commercials, they use video platform APIs to obtain video metadata, view counts, and viewer reactions. For TV commercials, they collect information such as broadcast time, broadcasting station, and content from TV broadcast databases. Furthermore, for IR information, they obtain regularly updated information from the company's official IR site and collect important financial information such as financial statements and press releases. For employee social media information, they use social media platform APIs to collect data such as post content, follower count, and engagement rate from employees' public accounts. This allows the data collection unit to gather data from a wide range of sources, enabling a comprehensive understanding of a company's advertising activities, financial situation, employee trends, and more. The collected data is centrally stored in a central database and managed so that it can be used by subsequent analysis and data selection units.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department. Specifically, it processes the collected data using statistical analysis and machine learning algorithms to understand companies' advertising strategies and market trends. For example, it analyzes collected advertising placement information to reveal what kind of advertisements specific companies are placing on which media. This allows for an understanding of companies' advertising strategies and target audiences. It also analyzes collected video and television commercial information to measure the effectiveness of advertisements by evaluating the number of views and viewer reactions. Furthermore, by analyzing IR information, it is possible to understand a company's financial situation and performance, and to assess its growth potential and risks. For example, it analyzes data from financial statements to calculate financial indicators such as sales, profit margins, and debt ratios. It is also possible to understand a company's internal situation and employee morale by analyzing employee social media information. For example, it analyzes employee posts and engagement rates to evaluate a company's internal communication and employee satisfaction. The Analysis Department comprehensively analyzes this data to gain a comprehensive understanding of companies' advertising strategies, market trends, financial situation, and internal conditions. This allows for the identification of a company's strengths and weaknesses, opportunities and threats, and the assessment of its growth potential and risks. The analysis results are provided to the selection department and used as basic data for selecting companies that meet specific criteria.

[0032] The Picking Department selects companies that meet specific criteria based on the analysis results obtained by the Analysis Department. Specifically, it automatically extracts and lists companies that meet specific criteria based on the analysis results. For example, it sets criteria such as "companies that have just started using web advertising from my assigned customer list" or "companies with strong financial results and rising labor cost ratios," and picks out companies that meet these criteria. The Picking Department uses algorithms to efficiently extract companies that meet specific criteria based on the collected information and analysis results. For example, it uses machine learning algorithms to automatically classify companies that meet specific criteria and add them to the list. This allows the Picking Department to quickly and accurately extract and list companies that meet specific criteria. Furthermore, the Picking Department regularly updates the extracted company list to maintain the list based on the latest information. For example, it updates the company list based on newly collected information and analysis results to respond to the latest situation. This allows the Picking Department to always provide a company list based on the latest information and efficiently approach customers. The company list extracted by the Picking Department is provided to the sales and marketing departments and used as basic data for customer outreach. As a result, the customer approach system according to this embodiment can accurately collect company information and efficiently approach customers.

[0033] The data collection unit can collect information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee social media information. For example, the data collection unit can analyze tags embedded in websites to collect advertising placement information. The data collection unit can also collect information on videos and TV commercials. For example, the data collection unit can obtain information from video platforms and TV commercial databases. The data collection unit can also collect IR information and employee social media information. For example, the data collection unit can collect information from a company's IR site and employee social media accounts. This allows the data collection unit to collect information about a company from a variety of sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input tags embedded in websites into an AI and have the AI ​​collect advertising placement information.

[0034] The analysis department can analyze the collected information to understand the tools being used, the media used for advertising, and the financial status of companies. For example, the analysis department can analyze the collected information to understand what kind of advertisements a particular company is running, what tools it is using, and what its financial status is. Based on the collected information, the analysis department can also analyze a company's performance and market trends. For example, the analysis department can analyze a company's sales data and market share to evaluate its growth potential. This allows for a detailed analysis of the collected information and a thorough understanding of the company's situation. Some or all of the above processes performed by the analysis department may be carried out using AI, for example, or not. For example, the analysis department can input the collected information into an AI and have the AI ​​perform the task of understanding the company's situation.

[0035] The selection unit can automatically pick out companies from its assigned customer list that have just started using web advertising, or companies with strong financial results and rising labor costs, based on the analysis results. For example, the selection unit can automatically extract companies that meet specific criteria based on the analysis results and add them to the list. This allows for the automatic selection of companies that meet specific criteria. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the analysis results into AI and have the AI ​​select companies that meet specific criteria.

[0036] The selection unit can propose the optimal approach to a specific company. For example, the selection unit can propose the optimal marketing methods and sales strategies for a specific company. The selection unit can also propose the optimal means of communication for a specific company. For example, the selection unit can propose the optimal email marketing or telemarketing methods for a specific company. This allows the selection unit to propose the optimal approach to a specific company. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can have AI perform the task of proposing the optimal approach to a specific company.

[0037] The data collection unit can adjust the scope of information collected by referring to a company's past advertising history during the collection process. For example, the data collection unit may prioritize collecting information on companies that have placed many advertisements in the past. The data collection unit can also narrow the scope of information collected for companies that have placed few advertisements in the past. The data collection unit can also focus on collecting information on companies that have placed advertisements intensively during a specific period, based on their past advertising history. This allows the data collection unit to adjust the scope of information collected by referring to a company's past advertising history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a company's past advertising history into an AI and have the AI ​​adjust the scope of information to be collected.

[0038] The data collection unit can apply different data collection algorithms depending on the industry and size of the company during data collection. For example, the data collection unit can apply a detailed information collection algorithm to large companies to collect a wide range of information. For small and medium-sized enterprises, the data collection unit can apply a simplified information collection algorithm to collect only the minimum necessary information. The data collection unit can also apply industry-specific data collection algorithms to focus on collecting information relevant to each industry. This allows for the application of different data collection algorithms depending on the industry and size of the company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the company's industry and size into an AI and have the AI ​​execute the application of the data collection algorithm.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of companies during the collection process. For example, the data collection unit can prioritize the collection of region-specific advertising placement information based on the company's location. The data collection unit can also collect information on nearby competitors based on the company's geographical location information. The data collection unit can also collect information related to the regional economic situation by considering the company's geographical location information. This allows for the priority collection of highly relevant information by considering the company's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location information into AI and have the AI ​​perform the collection of highly relevant information.

[0040] The data collection unit can analyze a company's social media activities and collect relevant information during the collection process. For example, the data collection unit can analyze the content of posts on a company's official social media accounts and collect relevant advertising information. The data collection unit can also analyze the social media activities of a company's employees and collect internal company information. Based on a company's social media activities, the data collection unit can also collect information related to the company's brand image. This allows for the analysis of a company's social media activities and the collection of relevant information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a company's social media activities into an AI and have the AI ​​collect relevant information.

[0041] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of reliable information. For example, the analysis unit can evaluate the source of the collected information and prioritize the analysis of reliable information. The analysis unit can also evaluate the frequency of information updates and prioritize the analysis of the most recent information. The analysis unit can also evaluate the consistency of the information and prioritize the analysis of consistent information. This allows the analysis unit to evaluate the reliability of the collected information and prioritize the analysis of reliable information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the reliability of the collected information.

[0042] The analysis department can apply different analytical methods depending on the industry and size of the company during the analysis. For example, the analysis department can apply detailed analytical methods to large companies and analyze a wide range of information. For small and medium-sized enterprises, the analysis department can also apply simplified analytical methods and analyze only the minimum necessary information. The analysis department can also apply industry-specific analytical methods and focus on analyzing information relevant to that industry. This allows for the application of different analytical methods depending on the industry and size of the company. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input data on the company's industry and size into AI and have the AI ​​perform the application of analytical methods.

[0043] The analysis department can improve the accuracy of its analysis by referring to the company's past performance data. For example, the analysis department can refer to the company's past sales data to analyze current performance. The analysis department can also refer to the company's past profit data to analyze current profitability. The analysis department can also refer to the company's past growth rate data to analyze current growth. This allows the analysis department to improve the accuracy of its analysis by referring to the company's past performance data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the company's past performance data into AI and have AI perform the task of improving the accuracy of the analysis.

[0044] The analysis department can perform analysis by referring to relevant market data of a company. For example, the analysis department can refer to growth rate data of the company's relevant market to analyze the company's growth potential. The analysis department can also refer to competitive data of the company's relevant market to analyze the company's competitiveness. The analysis department can also refer to demand data of the company's relevant market to forecast the company's demand. This allows the analysis to be performed by referring to relevant market data of a company. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data of a company into an AI and have the AI ​​perform the analysis.

[0045] The selection unit can select the most suitable companies by referring to their past transaction history during the selection process. For example, the selection unit can prioritize selecting companies with a high volume of past transactions. The selection unit can also select companies with concentrated transactions during specific periods based on their past transaction history. The selection unit can also select companies with high transaction frequency based on their past transaction history. This allows the selection of the most suitable companies by referring to their past transaction history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the past transaction history of companies into an AI and have the AI ​​perform the selection of the most suitable companies.

[0046] The selection unit can adjust its selection criteria based on the current market conditions of the companies during the selection process. For example, the selection unit may prioritize selecting companies belonging to growth markets, taking into account the current market conditions. The selection unit may also select companies belonging to highly competitive markets based on the current market conditions. The selection unit may also analyze the current market conditions and select companies belonging to markets with high demand. This allows the selection criteria to be adjusted based on the current market conditions of the companies. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit may input the current market conditions of the companies into the AI ​​and have the AI ​​perform the adjustment of the selection criteria.

[0047] The selection unit can select the most suitable company by considering the geographical distribution of companies during the selection process. For example, the selection unit can select a company based on its location and considering the market conditions specific to that region. The selection unit can also select a company based on its geographical distribution and considering nearby competitors. The selection unit can also select a company based on the geographical distribution and the regional economic conditions. This allows the selection of the most suitable company by considering its geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the geographical distribution of companies into an AI and have the AI ​​perform the selection of the most suitable company.

[0048] The selection unit can improve the accuracy of its selections by referring to relevant company literature during the selection process. For example, the selection unit can refer to relevant company literature, evaluate the company's technological capabilities, and select relevant information. The selection unit can also evaluate the company's research and development status based on relevant company literature and select relevant information. The selection unit can analyze relevant company literature, evaluate the company's market position, and select relevant information. This allows for improved selection accuracy by referring to relevant company literature. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data from relevant company literature into AI and have the AI ​​perform the task of improving the accuracy of its selections.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0051] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0052] The customer approach system can also include a social media analytics department that collects and analyzes customers' social media activity. For example, the social media analytics department collects data on what customers post and which accounts they follow, and analyzes their interests. It can also identify trends in the content customers frequently post and the accounts they follow. Furthermore, based on customers' social media activity, the social media analytics department can suggest highly relevant content and advertisements to them. This enables a more effective approach by leveraging customers' social media activity.

[0053] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0054] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0055] The customer approach system can also include a social media analytics department that collects and analyzes customers' social media activity. For example, the social media analytics department collects data on what customers post and which accounts they follow, and analyzes their interests. It can also identify trends in the content customers frequently post and the accounts they follow. Furthermore, based on customers' social media activity, the social media analytics department can suggest highly relevant content and advertisements to them. This enables a more effective approach by leveraging customers' social media activity.

[0056] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0057] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, investor relations (IR) information, and employee social media information. For example, the data collection unit analyzes tags embedded in websites to collect advertising placement information. It also obtains information on videos and TV commercials from video platforms and TV commercial databases. Furthermore, it collects IR information and employee social media information from companies' IR sites and employee social media accounts. Step 2: The analysis department analyzes the information collected by the data collection department. For example, they analyze the collected information to understand the tools being used, the media used for advertising, and the financial situation. Furthermore, they analyze the company's sales data and market share to evaluate the company's growth potential. Step 3: The Picking Unit selects companies that meet specific criteria based on the analysis results obtained by the Analysis Unit. For example, it automatically selects companies that have just started using web advertising from its assigned client list, or companies with strong financial results and rising personnel costs. The Picking Unit automatically extracts companies that meet specific criteria and adds them to the list.

[0060] (Example of form 2) The customer approach system according to an embodiment of the present invention is a system that utilizes an AI agent to accurately collect company information and efficiently approach customers. The customer approach system collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee SNS information. Next, the customer approach system analyzes the collected information to understand the tools being used, advertising media, and financial status. Furthermore, based on the analysis results, the customer approach system automatically picks out companies that have just started using web advertising from its assigned customer list, or companies with strong financial performance and rising personnel costs. This allows for quick identification of potential customers or those whose situations match the company's, enabling efficient customer approach. For example, the customer approach system can propose the optimal approach method for a specific company based on the information collected by the AI ​​agent. This improves the efficiency of sales activities and increases customer satisfaction. The customer approach system collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee SNS information. In this process, the AI ​​agent automatically collects the information and stores it in a database. Next, the customer approach system analyzes the collected information. The AI ​​agent analyzes collected information to understand the tools being used, the media outlets used, and the financial situation. For example, it analyzes what kind of advertisements a particular company is running, what tools it is using, and what its financial situation is. Furthermore, based on the analysis results, the customer approach system automatically picks out companies from the assigned customer list that have just started using web advertising, or companies that are performing well financially and have a rising labor cost ratio. The AI ​​agent picks out and lists companies that meet specific criteria based on the collected information. This allows sales representatives to approach customers efficiently. For example, based on the information collected by the AI ​​agent, it can propose the optimal approach method for a particular company. This improves the efficiency of sales activities and increases customer satisfaction.This allows the customer approach system to accurately collect company information and efficiently approach customers.

[0061] The customer approach system according to this embodiment comprises a collection unit, an analysis unit, and a pick-up unit. The collection unit collects information such as tags and advertising placement information embedded in websites, videos and TV commercials, IR information, and employee SNS information. For example, the collection unit analyzes tags embedded in websites to collect advertising placement information. The collection unit can also collect information on videos and TV commercials. For example, the collection unit obtains information from video platforms and TV commercial databases. Furthermore, the collection unit can also collect IR information and employee SNS information. For example, the collection unit collects information from companies' IR sites and employee SNS accounts. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information to understand the tools being used, the media used for advertising, and the financial situation. For example, the analysis unit analyzes what kind of advertisements a particular company is placing, what tools it is using, and what its financial situation is. Furthermore, the analysis unit can also analyze the company's performance and market trends based on the collected information. For example, the analysis department analyzes a company's sales data and market share to evaluate its growth potential. The selection department selects companies that meet specific criteria based on the analysis results obtained by the analysis department. For example, the selection department automatically selects companies that have just started using web advertising from its assigned customer list, or companies with strong financial results and rising labor cost ratios. Based on the collected information, the selection department lists companies that meet specific criteria. For example, the selection department automatically extracts companies that meet specific criteria and adds them to the list. As a result, the customer approach system according to this embodiment can accurately collect company information and efficiently approach customers.

[0062] The data collection unit collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee social media information. Specifically, to analyze tags embedded in websites, the data collection unit automatically crawls web pages using crawling technology and extracts tag information. This allows them to understand what kinds of advertisements are being placed and which advertisements are displayed on which pages. Regarding advertising placement information, they obtain data from advertising distribution platforms via APIs and collect detailed information such as the type of advertisement, where it was placed, and the period of placement. For collecting information on videos and TV commercials, they use video platform APIs to obtain video metadata, view counts, and viewer reactions. For TV commercials, they collect information such as broadcast time, broadcasting station, and content from TV broadcast databases. Furthermore, for IR information, they obtain regularly updated information from the company's official IR site and collect important financial information such as financial statements and press releases. For employee social media information, they use social media platform APIs to collect data such as post content, follower count, and engagement rate from employees' public accounts. This allows the data collection unit to gather data from a wide range of sources, enabling a comprehensive understanding of a company's advertising activities, financial situation, employee trends, and more. The collected data is centrally stored in a central database and managed so that it can be used by subsequent analysis and data selection units.

[0063] The Analysis Department analyzes the information collected by the Data Collection Department. Specifically, it processes the collected data using statistical analysis and machine learning algorithms to understand companies' advertising strategies and market trends. For example, it analyzes collected advertising placement information to reveal what kind of advertisements specific companies are placing on which media. This allows for an understanding of companies' advertising strategies and target audiences. It also analyzes collected video and television commercial information to measure the effectiveness of advertisements by evaluating the number of views and viewer reactions. Furthermore, by analyzing IR information, it is possible to understand a company's financial situation and performance, and to assess its growth potential and risks. For example, it analyzes data from financial statements to calculate financial indicators such as sales, profit margins, and debt ratios. It is also possible to understand a company's internal situation and employee morale by analyzing employee social media information. For example, it analyzes employee posts and engagement rates to evaluate a company's internal communication and employee satisfaction. The Analysis Department comprehensively analyzes this data to gain a comprehensive understanding of companies' advertising strategies, market trends, financial situation, and internal conditions. This allows for the identification of a company's strengths and weaknesses, opportunities and threats, and the assessment of its growth potential and risks. The analysis results are provided to the selection department and used as basic data for selecting companies that meet specific criteria.

[0064] The Picking Department selects companies that meet specific criteria based on the analysis results obtained by the Analysis Department. Specifically, it automatically extracts and lists companies that meet specific criteria based on the analysis results. For example, it sets criteria such as "companies that have just started using web advertising from my assigned customer list" or "companies with strong financial results and rising labor cost ratios," and picks out companies that meet these criteria. The Picking Department uses algorithms to efficiently extract companies that meet specific criteria based on the collected information and analysis results. For example, it uses machine learning algorithms to automatically classify companies that meet specific criteria and add them to the list. This allows the Picking Department to quickly and accurately extract and list companies that meet specific criteria. Furthermore, the Picking Department regularly updates the extracted company list to maintain the list based on the latest information. For example, it updates the company list based on newly collected information and analysis results to respond to the latest situation. This allows the Picking Department to always provide a company list based on the latest information and efficiently approach customers. The company list extracted by the Picking Department is provided to the sales and marketing departments and used as basic data for customer outreach. As a result, the customer approach system according to this embodiment can accurately collect company information and efficiently approach customers.

[0065] The data collection unit can collect information such as tags embedded in websites, advertising placement information, videos, TV commercials, IR information, and employee social media information. For example, the data collection unit can analyze tags embedded in websites to collect advertising placement information. The data collection unit can also collect information on videos and TV commercials. For example, the data collection unit can obtain information from video platforms and TV commercial databases. The data collection unit can also collect IR information and employee social media information. For example, the data collection unit can collect information from a company's IR site and employee social media accounts. This allows the data collection unit to collect information about a company from a variety of sources. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input tags embedded in websites into an AI and have the AI ​​collect advertising placement information.

[0066] The analysis department can analyze the collected information to understand the tools being used, the media used for advertising, and the financial status of companies. For example, the analysis department can analyze the collected information to understand what kind of advertisements a particular company is running, what tools it is using, and what its financial status is. Based on the collected information, the analysis department can also analyze a company's performance and market trends. For example, the analysis department can analyze a company's sales data and market share to evaluate its growth potential. This allows for a detailed analysis of the collected information and a thorough understanding of the company's situation. Some or all of the above processes performed by the analysis department may be carried out using AI, for example, or not. For example, the analysis department can input the collected information into an AI and have the AI ​​perform the task of understanding the company's situation.

[0067] The selection unit can automatically pick out companies from its assigned customer list that have just started using web advertising, or companies with strong financial results and rising labor costs, based on the analysis results. For example, the selection unit can automatically extract companies that meet specific criteria based on the analysis results and add them to the list. This allows for the automatic selection of companies that meet specific criteria. Some or all of the above-described processes in the selection unit may be performed using AI, or not. For example, the selection unit can input the analysis results into AI and have the AI ​​select companies that meet specific criteria.

[0068] The selection unit can propose the optimal approach to a specific company. For example, the selection unit can propose the optimal marketing methods and sales strategies for a specific company. The selection unit can also propose the optimal means of communication for a specific company. For example, the selection unit can propose the optimal email marketing or telemarketing methods for a specific company. This allows the selection unit to propose the optimal approach to a specific company. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can have AI perform the task of proposing the optimal approach to a specific company.

[0069] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting the latest advertising information and video commercials. If the user is relaxed, the data collection unit may also prioritize collecting investor relations information and employee social media information. If the user is stressed, the data collection unit may collect only high-priority information and reduce the amount of information. This allows the data collection unit to determine the priority of information to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of information to collect.

[0070] The data collection unit can adjust the scope of information collected by referring to a company's past advertising history during the collection process. For example, the data collection unit may prioritize collecting information on companies that have placed many advertisements in the past. The data collection unit can also narrow the scope of information collected for companies that have placed few advertisements in the past. The data collection unit can also focus on collecting information on companies that have placed advertisements intensively during a specific period, based on their past advertising history. This allows the data collection unit to adjust the scope of information collected by referring to a company's past advertising history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input a company's past advertising history into an AI and have the AI ​​adjust the scope of information to be collected.

[0071] The data collection unit can apply different data collection algorithms depending on the industry and size of the company during data collection. For example, the data collection unit can apply a detailed information collection algorithm to large companies to collect a wide range of information. For small and medium-sized enterprises, the data collection unit can apply a simplified information collection algorithm to collect only the minimum necessary information. The data collection unit can also apply industry-specific data collection algorithms to focus on collecting information relevant to each industry. This allows for the application of different data collection algorithms depending on the industry and size of the company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on the company's industry and size into an AI and have the AI ​​execute the application of the data collection algorithm.

[0072] The data collection unit can estimate the user's emotions and filter the information it collects based on those emotions. For example, if the user is excited, the data collection unit can prioritize filtering the latest advertising information and video commercials. If the user is relaxed, the data collection unit can also prioritize filtering IR information and employee social media posts. If the user is stressed, the data collection unit can filter only the most important information, reducing the amount of information collected. This allows for filtering of information collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the filtering of information to be collected.

[0073] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location information of companies during the collection process. For example, the data collection unit can prioritize the collection of region-specific advertising placement information based on the company's location. The data collection unit can also collect information on nearby competitors based on the company's geographical location information. The data collection unit can also collect information related to the regional economic situation by considering the company's geographical location information. This allows for the priority collection of highly relevant information by considering the company's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the company's geographical location information into AI and have the AI ​​perform the collection of highly relevant information.

[0074] The data collection unit can analyze a company's social media activities and collect relevant information during the collection process. For example, the data collection unit can analyze the content of posts on a company's official social media accounts and collect relevant advertising information. The data collection unit can also analyze the social media activities of a company's employees and collect internal company information. Based on a company's social media activities, the data collection unit can also collect information related to the company's brand image. This allows for the analysis of a company's social media activities and the collection of relevant information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on a company's social media activities into an AI and have the AI ​​collect relevant information.

[0075] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually easy-to-understand analysis results. This allows the level of detail of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the level of detail of the analysis.

[0076] The analysis unit can evaluate the reliability of the collected information during analysis and prioritize the analysis of reliable information. For example, the analysis unit can evaluate the source of the collected information and prioritize the analysis of reliable information. The analysis unit can also evaluate the frequency of information updates and prioritize the analysis of the most recent information. The analysis unit can also evaluate the consistency of the information and prioritize the analysis of consistent information. This allows the analysis unit to evaluate the reliability of the collected information and prioritize the analysis of reliable information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI perform the evaluation of the reliability of the collected information.

[0077] The analysis department can apply different analytical methods depending on the industry and size of the company during the analysis. For example, the analysis department can apply detailed analytical methods to large companies and analyze a wide range of information. For small and medium-sized enterprises, the analysis department can also apply simplified analytical methods and analyze only the minimum necessary information. The analysis department can also apply industry-specific analytical methods and focus on analyzing information relevant to that industry. This allows for the application of different analytical methods depending on the industry and size of the company. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input data on the company's industry and size into AI and have the AI ​​perform the application of analytical methods.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0079] The analysis department can improve the accuracy of its analysis by referring to the company's past performance data. For example, the analysis department can refer to the company's past sales data to analyze current performance. The analysis department can also refer to the company's past profit data to analyze current profitability. The analysis department can also refer to the company's past growth rate data to analyze current growth. This allows the analysis department to improve the accuracy of its analysis by referring to the company's past performance data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the company's past performance data into AI and have AI perform the task of improving the accuracy of the analysis.

[0080] The analysis department can perform analysis by referring to relevant market data of a company. For example, the analysis department can refer to growth rate data of the company's relevant market to analyze the company's growth potential. The analysis department can also refer to competitive data of the company's relevant market to analyze the company's competitiveness. The analysis department can also refer to demand data of the company's relevant market to forecast the company's demand. This allows the analysis to be performed by referring to relevant market data of a company. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input relevant market data of a company into an AI and have the AI ​​perform the analysis.

[0081] The selection unit can estimate the user's emotions and determine the priority of companies to select based on the estimated emotions. For example, if the user is excited, the selection unit will prioritize companies with the latest advertising information. If the user is relaxed, the selection unit may also prioritize companies with comprehensive investor relations (IR) information. If the user is stressed, the selection unit may also select only companies of high importance. This allows the selection unit to determine the priority of companies to select based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI perform the determination of company priorities.

[0082] The selection unit can select the most suitable companies by referring to their past transaction history during the selection process. For example, the selection unit can prioritize selecting companies with a high volume of past transactions. The selection unit can also select companies with concentrated transactions during specific periods based on their past transaction history. The selection unit can also select companies with high transaction frequency based on their past transaction history. This allows the selection of the most suitable companies by referring to their past transaction history. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the past transaction history of companies into an AI and have the AI ​​perform the selection of the most suitable companies.

[0083] The selection unit can adjust its selection criteria based on the current market conditions of the companies during the selection process. For example, the selection unit may prioritize selecting companies belonging to growth markets, taking into account the current market conditions. The selection unit may also select companies belonging to highly competitive markets based on the current market conditions. The selection unit may also analyze the current market conditions and select companies belonging to markets with high demand. This allows the selection criteria to be adjusted based on the current market conditions of the companies. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit may input the current market conditions of the companies into the AI ​​and have the AI ​​perform the adjustment of the selection criteria.

[0084] The selection unit can estimate the user's emotions and adjust how the selected companies are displayed based on the estimated emotions. For example, if the user is nervous, the selection unit can provide a simple and highly visible display method. If the user is relaxed, the selection unit can also provide a display method that includes detailed information. If the user is in a hurry, the selection unit can also provide a display method that gets straight to the point. This allows the selection unit to adjust how the selected companies are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust how the companies are displayed.

[0085] The selection unit can select the most suitable company by considering the geographical distribution of companies during the selection process. For example, the selection unit can select a company based on its location and considering the market conditions specific to that region. The selection unit can also select a company based on its geographical distribution and considering nearby competitors. The selection unit can also select a company based on the geographical distribution and the regional economic conditions. This allows the selection of the most suitable company by considering its geographical distribution. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data on the geographical distribution of companies into an AI and have the AI ​​perform the selection of the most suitable company.

[0086] The selection unit can improve the accuracy of its selections by referring to relevant company literature during the selection process. For example, the selection unit can refer to relevant company literature, evaluate the company's technological capabilities, and select relevant information. The selection unit can also evaluate the company's research and development status based on relevant company literature and select relevant information. The selection unit can analyze relevant company literature, evaluate the company's market position, and select relevant information. This allows for improved selection accuracy by referring to relevant company literature. Some or all of the above processes in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input data from relevant company literature into AI and have the AI ​​perform the task of improving the accuracy of its selections.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0089] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0090] The customer approach system can also include a social media analytics department that collects and analyzes customers' social media activity. For example, the social media analytics department collects data on what customers post and which accounts they follow, and analyzes their interests. It can also identify trends in the content customers frequently post and the accounts they follow. Furthermore, based on customers' social media activity, the social media analytics department can suggest highly relevant content and advertisements to them. This enables a more effective approach by leveraging customers' social media activity.

[0091] The customer approach system may further include an emotion analysis unit that estimates the customer's emotions and adjusts the approach based on those emotions. The emotion analysis unit, for example, analyzes the customer's facial expressions and tone of voice to estimate their emotions. If the customer is excited, the emotion analysis unit can adopt an aggressive approach. If the customer is relaxed, it can adopt a gentle approach. Furthermore, if the customer is stressed, it can adopt a more reserved approach. This allows the approach to be adjusted based on the customer's emotions.

[0092] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0093] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0094] The customer approach system can also include a social media analytics department that collects and analyzes customers' social media activity. For example, the social media analytics department collects data on what customers post and which accounts they follow, and analyzes their interests. It can also identify trends in the content customers frequently post and the accounts they follow. Furthermore, based on customers' social media activity, the social media analytics department can suggest highly relevant content and advertisements to them. This enables a more effective approach by leveraging customers' social media activity.

[0095] The customer approach system may further include an emotion analysis unit that estimates the customer's emotions and adjusts the approach based on those emotions. The emotion analysis unit, for example, analyzes the customer's facial expressions and tone of voice to estimate their emotions. If the customer is excited, the emotion analysis unit can adopt an aggressive approach. If the customer is relaxed, it can adopt a gentle approach. Furthermore, if the customer is stressed, it can adopt a more reserved approach. This allows the approach to be adjusted based on the customer's emotions.

[0096] The customer approach system can also include a purchase history analysis unit that collects and analyzes customer purchase history. For example, the purchase history analysis unit collects data on products and services that customers have purchased in the past and analyzes their purchasing trends. The purchase history analysis unit can also identify the product categories and frequency of purchases that customers frequently make. Furthermore, based on the customer's purchase history, the purchase history analysis unit can propose the most suitable products and services to the customer. This enables a more effective approach by leveraging customer purchase history.

[0097] The customer approach system can also include a browsing history analysis unit that collects and analyzes customers' website browsing history. For example, the browsing history analysis unit collects data on the websites visited and the pages viewed by customers, and analyzes their interests and preferences. It can also identify trends in the websites customers frequently visit and the content they view. Furthermore, based on the customer's browsing history, the browsing history analysis unit can suggest highly relevant content and advertisements to the customer. This enables a more effective approach by leveraging the customer's website browsing history.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit collects information such as tags embedded in websites, advertising placement information, videos, TV commercials, investor relations (IR) information, and employee social media information. For example, the data collection unit analyzes tags embedded in websites to collect advertising placement information. It also obtains information on videos and TV commercials from video platforms and TV commercial databases. Furthermore, it collects IR information and employee social media information from companies' IR sites and employee social media accounts. Step 2: The analysis department analyzes the information collected by the data collection department. For example, they analyze the collected information to understand the tools being used, the media used for advertising, and the financial situation. Furthermore, they analyze the company's sales data and market share to evaluate the company's growth potential. Step 3: The Picking Unit selects companies that meet specific criteria based on the analysis results obtained by the Analysis Unit. For example, it automatically selects companies that have just started using web advertising from its assigned client list, or companies with strong financial results and rising personnel costs. The Picking Unit automatically extracts companies that meet specific criteria and adds them to the list.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, and pickup unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects tags and advertising information embedded in websites, videos and TV commercials, IR information, employee SNS information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to understand the tools used, advertising media, financial status, etc. The pickup unit is implemented by the specific processing unit 290 of the data processing device 12 and picks out companies that meet specific conditions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, and pickup unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects tags and advertising information embedded in websites, videos and TV commercials, IR information, employee SNS information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to understand the tools used, advertising media, financial status, etc. The pickup unit is implemented by the specific processing unit 290 of the data processing unit 12 and picks out companies that meet specific conditions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, and pickup unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects tags and advertising information embedded in websites, videos and TV commercials, IR information, employee SNS information, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to understand the tools used, advertising media, financial status, etc. The pickup unit is implemented by the specific processing unit 290 of the data processing unit 12 and picks out companies that meet specific conditions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and pickup unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects tags and advertising information embedded in websites, videos and TV commercials, IR information, employee SNS information, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to understand the tools used, advertising media, financial status, etc. The pickup unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and picks out companies that meet specific conditions based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The collection department collects tags and advertising information embedded on websites, videos, TV commercials, IR information, employee social media information, etc. An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a selection unit that picks out companies that meet specific conditions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is This system collects tags and advertising information embedded on websites, as well as videos, TV commercials, investor relations information, and employee social media posts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected information is analyzed to understand the tools being used, the media outlets used for advertising, and the financial status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned pickup unit is Based on the analysis results, the system automatically picks out companies from your assigned client list that have just started using web advertising, or companies with strong financial results and rising labor costs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned pickup unit is We propose the optimal approach for a specific company. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, we adjust the scope of information collected by referring to the company's past advertising history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the company's industry and size. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and filters the information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During collection, we analyze the company's social media activities and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the level of detail of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, the reliability of the collected information is evaluated, and the most reliable information is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When conducting analysis, different analytical methods are applied depending on the industry and size of the company. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to the company's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we refer to relevant market data of the company. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned pickup unit is We estimate user sentiment and determine the priority of companies to pick based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned pickup unit is During the selection process, the most suitable company is chosen by referring to the company's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned pickup unit is When selecting candidates, adjust the selection criteria based on the company's current market conditions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned pickup unit is We estimate user sentiment and adjust how companies are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned pickup unit is When selecting candidates, the most suitable companies are chosen considering their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned pickup unit is When selecting information, we improve the accuracy of the selection process by referring to relevant company literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects tags and advertising information embedded on websites, videos, TV commercials, IR information, employee social media information, etc. An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a selection unit that picks out companies that meet specific conditions based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is This system collects tags and advertising information embedded on websites, as well as videos, TV commercials, investor relations information, and employee social media posts. The system according to feature 1.

3. The aforementioned analysis unit is The collected information is analyzed to understand the tools being used, the media outlets used for advertising, and the financial status. The system according to feature 1.

4. The aforementioned pickup unit is We propose the optimal approach for a specific company. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is During data collection, we adjust the scope of information collected by referring to the company's past advertising history. The system according to feature 1.

7. The aforementioned collection unit is During data collection, different collection algorithms are applied depending on the company's industry and size. The system according to feature 1.

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