system

Abstract

The system according to this embodiment aims to identify key personnel within a company and efficiently build relationships with them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a listing unit, and a provision unit. The collection unit collects publicly available company information, social media data, and internal data. The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. The listing unit lists the information of the key people identified by the analysis unit. The provision unit provides the information listed by the listing unit to sales representatives.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that it is difficult to identify key personnel within a company and efficiently build relationships.

[0005] The system according to the embodiment aims to identify key personnel within a company and efficiently build relationships.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a provision unit. The collection unit collects publicly available company information, social media data, and internal data. The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. The listing unit compiles a list of key personnel identified by the analysis unit. The provision unit provides the information compiled by the listing unit to sales representatives. [Effects of the Invention]

[0007] The system according to this embodiment can identify key personnel within a company and efficiently build relationships with them. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 relationship-building support system according to an embodiment of the present invention is a system that utilizes generative AI to automatically identify key persons within a company and support relationship building. This relationship-building support system collects publicly available company information, social media, and internal data, and analyzes the collected data to list important contacts and influential individuals. For example, the relationship-building support system collects data from various sources such as the company's website, news articles, social media posts, and internal customer databases. Next, the generative AI analyzes the collected data to identify important contacts and influential individuals within the company. For example, it can analyze statements made by executives and the content of social media posts to determine the influence of those individuals. This allows sales representatives to identify key persons with whom they should build relationships. Furthermore, it lists information on the identified key persons. The listed information includes the key person's name, position, contact information, and scope of influence. This allows sales representatives to efficiently build relationships. For example, by contacting the listed key persons at the appropriate time, opportunities for upselling and cross-selling can be maximized. This mechanism significantly reduces the time sales representatives spend identifying key persons. Conventional methods required significant time and effort to identify key decision-makers. However, with this invention, the generating AI automatically identifies and lists key decision-makers, allowing sales representatives to efficiently build relationships. This leads to increased efficiency in overall sales activities and higher sales. For example, when a corporate sales representative is developing new clients, this invention allows them to quickly identify key decision-makers within the company and build relationships. This maximizes opportunities for upselling and cross-selling, improving the results of sales activities. Furthermore, by using key decision-maker information to make appropriate approaches to existing customers, customer satisfaction can be improved. In short, this relationship-building support system enables sales representatives to efficiently build relationships.

[0029] The relationship-building support system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a provision unit. The collection unit collects publicly available company information, social media, and internal data. For example, the collection unit collects data from company websites, news articles, social media posts, and internal customer databases. For example, the collection unit can obtain information on executives from company websites. The collection unit can also identify influential individuals from social media posts. Furthermore, the collection unit can collect customer information from internal customer databases. The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. For example, the analysis unit can analyze the statements of executives and the content of social media posts to determine the influence of those individuals. For example, the analysis unit can use text mining technology to analyze the content of executives' statements. The analysis unit can also use network analysis technology to analyze the content of social media posts. Furthermore, the analysis unit can use machine learning algorithms to analyze information from customer databases. The listing unit lists information on key people identified by the analysis unit. The listing unit lists information such as the names, titles, contact information, and scope of influence of key persons. For example, the listing unit can list the names and titles of key persons. The listing unit can also list the contact information of key persons. Furthermore, the listing unit can also list the scope of influence of key persons. The providing unit provides the information listed by the listing unit to sales representatives. The providing unit can, for example, notify sales representatives of the listed information. Furthermore, the providing unit can adjust the timing of providing the listed information to sales representatives. Furthermore, the providing unit can customize the content of the listed information provided to sales representatives. As a result, the relationship-building support system according to this embodiment enables sales representatives to efficiently build relationships. Some or all of the above-described processes in the collection unit, analysis unit, listing unit, and providing unit may be performed using AI, for example, or without using AI.For example, the data collection unit can use AI to perform web scraping to obtain information about company executives from their websites. The analysis unit can use AI to perform text mining to analyze the collected data. The listing unit can use AI to organize information to create a list of identified key personnel. The delivery unit can use AI to adjust the timing of notifications to provide the listed information to sales representatives.

[0030] The data collection department collects publicly available corporate information, social media data, and internal data. Specifically, it collects data from corporate websites, news articles, social media posts, and internal customer databases. For example, corporate websites can provide information on executives, the latest corporate news, and press releases. This allows for an understanding of the company's latest trends and key personnel. News articles collect information on events and market trends related to the company, providing insights into the company's external environment. Social media posts collect statements and reactions from the company, its executives, employees, and customers, allowing for the identification of the company's reputation and influential figures. For example, using specific hashtags and keywords to collect corporate-related posts can help identify influential users and trends. Furthermore, internal customer databases collect existing customer information and past transaction history, providing data for analyzing customer needs and behavioral patterns. This allows the data collection department to gather a wide range of data from diverse sources, enabling a comprehensive understanding of the company's internal and external situation. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and listing departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the collection unit to identify important contacts and influential individuals. Specifically, it can analyze executive statements and social media posts to determine the influence of those individuals. For example, text mining technology can be used to analyze executive statements and evaluate the potential impact of those statements. Text mining technology uses natural language processing (NLP) to analyze text data, extracting keywords and performing sentiment analysis. This allows for the determination of whether an executive's statement is positive or negative and the evaluation of its influence. Furthermore, network analysis technology can be used to analyze social media posts and identify influential users and their networks. Network analysis technology analyzes the relationships between users on social media and visualizes the networks of influential users and their followers. In addition, machine learning algorithms can be used to analyze information in the customer database and identify important customers and their behavioral patterns. Machine learning algorithms can analyze large amounts of data and discover patterns and trends. As a result, the analysis unit can quickly and accurately analyze the collected data and identify important contacts and influential individuals. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the behavioral patterns of specific executives or customers based on past statement data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The listing unit compiles information on key individuals identified by the analysis unit. Specifically, it lists information such as the key individual's name, title, contact information, and scope of influence. For example, listing the key individual's name and title makes it possible to identify important individuals within the company and facilitate contact with them. Listing the key individual's contact information allows for quick communication. Furthermore, listing the key individual's scope of influence allows for an understanding of their influence and enables effective approaches. The listing unit organizes this information and provides it in a format easily accessible to sales representatives. For example, the listed information can be stored digitally and provided as a database with search functionality. This allows sales representatives to quickly search for necessary information and efficiently build relationships. In addition, the listing unit can regularly update the listed information to provide the latest information. For example, it can quickly reflect newly identified key individuals and changed contact information, ensuring that the information is always up-to-date. The listing unit can also organize the listed information by category and filter it based on specific criteria. This allows sales representatives to quickly identify key individuals who meet specific criteria and approach them effectively. The listing section then provides sales representatives with crucial information to efficiently build relationships, maximizing the overall effectiveness of the system.

[0033] The information provision department provides the information compiled by the listing department to sales representatives. Specifically, it can notify sales representatives of the compiled information, adjust the timing of provision, and customize the content provided. For example, when notifying sales representatives of the compiled information, information can be quickly transmitted using email or chat tools. The information provision department can also adjust the timing of information provision according to the sales representative's schedule and work status. This allows sales representatives to receive the necessary information at the optimal time and proceed with their work efficiently. Furthermore, the information provision department can customize the compiled information according to the needs of sales representatives. For example, by prioritizing the provision of information on specific customers or specific industries, it can support sales representatives in effectively building relationships. The information provision department can centrally manage this information and link it with other systems and departments as needed. For example, the provided information can be linked with sales support systems and customer relationship management systems, allowing sales representatives to manage information centrally. The information provision department can also collect feedback on the provided information and continuously improve the accuracy and method of provision. This allows the information provision department to provide sales representatives with timely and accurate information and support efficient relationship building. Furthermore, the service provider can use AI to adjust the timing of notifications. For example, by analyzing the past behavior patterns and schedules of sales representatives and providing information at the optimal time, the service provider can maximize the efficiency of the sales representatives' work. In this way, the service provider can provide important support for sales representatives to efficiently build relationships and maximize the effectiveness of the entire system.

[0034] The data collection unit can collect data from sources such as company websites, news articles, social media posts, and internal customer databases. For example, the data collection unit can collect information such as company profiles, product information, and press releases from company websites. It can also collect industry news, corporate news, and economic news from news articles. Furthermore, it can collect information from social media posts. For example, the data collection unit can use web scraping techniques to obtain information on executives from company websites. A news aggregator can be used to collect information from news articles. Social media APIs can be used to collect information from social media posts. This allows for more accurate identification of key players by collecting data from diverse 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 use AI to perform web scraping to obtain information on executives from company websites.

[0035] The analysis unit can analyze the collected data to identify important contacts and influential individuals within the company. For example, the analysis unit can analyze the collected data using text mining techniques. It can also analyze the collected data using network analysis techniques. Furthermore, it can analyze the collected data using machine learning algorithms. For example, the analysis unit can analyze the statements of executives using text mining techniques to determine the influence of those individuals. It can also analyze social media posts using network analysis techniques to determine the influence of those individuals. It can also analyze customer database information using machine learning algorithms to identify important contacts. This allows for the accurate identification of key players within the company through data analysis. 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 use AI to perform text mining in order to analyze the collected data.

[0036] The listing unit can list information such as the names, titles, contact information, and scope of influence of identified key individuals. For example, the listing unit can list the names and titles of key individuals. It can also list the contact information of key individuals. Furthermore, the listing unit can list the scope of influence of key individuals. For example, the listing unit can organize the collected data in order to list the names and titles of key individuals. It can also organize the collected data in order to list contact information. It can also organize the collected data in order to list the scope of influence. This allows sales representatives to efficiently build relationships by listing detailed information about key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to organize information in order to list the information of identified key individuals.

[0037] The service provider can provide listed information to sales representatives and support them in efficiently building relationships. For example, the service provider can notify sales representatives of the listed information. The service provider can also adjust the timing of providing the listed information to sales representatives. Furthermore, the service provider can customize the content of the listed information provided to sales representatives. For example, the service provider can use a notification system to notify sales representatives of the listed information. To adjust the timing of provision, the service provider can consider the sales representatives' schedules. To customize the content of provision, the service provider can provide information tailored to the needs of the sales representatives. This enables sales representatives to efficiently build relationships. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to adjust the timing of notifications to sales representatives to notify them of the listed information.

[0038] The data collection unit can customize the types of data it collects according to the industry and size of the company. For example, in the case of a large company, the data collection unit can collect detailed information such as statements by executives and minutes of shareholder meetings. In the case of a small or medium-sized enterprise, the data collection unit can also focus on collecting publicly available information such as social media posts and news articles. Furthermore, in the case of a startup company, the data collection unit can also collect information such as interviews with founders and comments from investors. For example, the data collection unit can use industry codes or industry classifications to identify the industry of a company. To identify the size of a company, it can use information such as the number of employees, sales, and capital. This enables data collection tailored to 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 use AI to customize the data in order to collect data according to the industry and size of the company.

[0039] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable information. For example, the collection unit prioritizes the collection of information from official company websites and government databases. The collection unit can also exclude unreliable social media posts and rumors from collection. Furthermore, the collection unit can evaluate the reliability of news articles and prioritize the collection of information from reliable media. For example, the collection unit can consider the reliability of the information source, the consistency of the data, and past performance to evaluate the reliability of the data. This improves the accuracy of the analysis results by prioritizing the collection of reliable information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to evaluate the reliability of the information source in order to evaluate the reliability of the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of companies during the collection process. For example, the data collection unit can prioritize the collection of news articles and social media posts related to the location of the company's headquarters. The data collection unit can also collect information on the company's major business locations and assess their geographical influence. Furthermore, the data collection unit can collect data related to the company's international activities and conduct analysis from a global perspective. For example, the data collection unit can use GPS data, address information, and regional codes to collect geographical location information. This allows for the collection of highly relevant data by considering geographical location. 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 use AI to determine data priorities in order to collect highly relevant data by considering the geographical location of companies.

[0041] The data collection unit can collect data to predict future trends by referring to a company's past performance data during the collection process. For example, the data collection unit can collect a company's past sales data and analyze growth trends. It can also collect a company's past investment activities and predict future investment plans. Furthermore, it can collect a company's past market share data and predict changes in the competitive environment. For example, the data collection unit can use financial reports, sales data, performance evaluations, etc., to collect past performance data. This makes it possible to collect data to predict future trends by referring to past performance data. 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 use AI to collect and analyze data in order to predict future trends by referring to a company's past performance data.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can correlate statements made by company executives with fluctuations in stock prices. It can also correlate social media posts with company performance data. Furthermore, it can correlate news article content with fluctuations in a company's market share. For example, the analysis unit can use correlation analysis or network analysis to consider the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform correlation analysis in order to improve the accuracy of the analysis by considering the interrelationships between data.

[0043] The analysis unit can perform analyses while considering the company's industry and market trends. For example, in the case of manufacturing, the analysis unit can analyze data on product production volume and supply chain. In the case of service industries, the analysis unit can also analyze data on customer satisfaction and service quality. Furthermore, in the case of the technology industry, the analysis unit can also analyze technological innovations and patent information. For example, the analysis unit can use industry codes and industry classifications to identify the company's industry. To collect market trends, it can use market reports, trend data, and competitive analysis. This allows for more appropriate analysis by considering the company's industry and market trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can use AI to analyze data in order to perform analyses while considering the company's industry and market trends.

[0044] The analysis unit can perform analyses while considering the geographical location information of companies. For example, the analysis unit can analyze market trends related to the location of a company's headquarters. It can also analyze data related to a company's major business locations and assess their geographical influence. Furthermore, the analysis unit can analyze data related to a company's international activities and perform analyses from a global perspective. For example, the analysis unit can use GPS data, address information, and regional codes to collect geographical location information. This allows for more appropriate analysis by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze data in order to perform analyses while considering the geographical location information of companies.

[0045] The analysis unit can perform analyses to predict future trends by referring to a company's past performance data during the analysis process. For example, the analysis unit can analyze a company's past sales data to predict growth trends. It can also analyze a company's past investment activities to predict future investment plans. Furthermore, it can analyze a company's past market share data to predict changes in the competitive environment. For example, the analysis unit can use financial reports, sales data, performance evaluations, etc., to collect past performance data. This makes it possible to perform analyses to predict future trends by referring to past performance data. 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 use AI to analyze data in order to predict future trends by referring to a company's past performance data.

[0046] The listing unit can evaluate the scope of influence of key individuals in detail during the listing process. For example, the listing unit can evaluate the number of followers and engagement rates of key individuals on social media. It can also evaluate the extent to which key individuals' statements are covered by the media. Furthermore, the listing unit can evaluate key individuals' past achievements and project success rates. For example, to evaluate the scope of influence, the listing unit can consider factors such as the number of followers, the impact of statements, and the breadth of their network. This allows for a more accurate listing by providing a detailed evaluation of the scope of influence of key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to evaluate the scope of influence of key individuals.

[0047] The listing unit can create lists by considering the past statements and actions of key individuals. For example, the listing unit can analyze the past social media posts of key individuals to assess their influence. It can also analyze the past media appearances and speeches of key individuals to assess their influence. Furthermore, it can analyze the past projects and achievements of key individuals to assess their influence. For example, the listing unit can use social media posting history and meeting transcripts to collect past statements and actions. This allows for more accurate listings by considering the past statements and actions of key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to create lists that take into account the past statements and actions of key individuals.

[0048] The listing unit can consider the geographical location of key individuals when creating a list. For example, the listing unit can prioritize listing information related to the location of a key individual's headquarters. It can also list information about a key individual's major business locations and assess their geographical influence. Furthermore, the listing unit can list information related to a key individual's international activities and conduct analysis from a global perspective. For example, the listing unit can use GPS data, address information, or regional codes to collect geographical location information. This allows for more appropriate listing by considering geographical location information. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to list key individuals while considering their geographical location.

[0049] The listing unit can create lists by referencing industry trends related to key players. For example, the listing unit can list the latest news and trends in the industry to which the key players belong. It can also list the performance of projects and companies in which key players are involved. Furthermore, the listing unit can list information on regulations and legal changes that may affect key players. For example, the listing unit can use market reports, trend data, and competitive analysis to collect industry trends. This allows for more appropriate listings by referencing relevant industry trends. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to create lists by referencing industry trends related to key players.

[0050] The information delivery department can select the optimal information delivery method by referring to the sales representative's past activity history at the time of delivery. For example, the information delivery department can provide information by referring to the sales representative's past successful approach methods. The information delivery department can also analyze the sales representative's past failures and provide information to avoid making the same mistakes. Furthermore, the information delivery department can refer to the sales representative's past customer interaction history and provide information at the optimal timing. For example, the information delivery department can use customer management systems and sales records to collect the sales representative's past activity history. This allows the selection of the optimal information delivery method by referring to the sales representative's past activity history. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can use AI to analyze data in order to select the optimal information delivery method by referring to the sales representative's past activity history.

[0051] The information provider can provide information while considering the scope of influence of key individuals. For example, if a key individual has significant influence, the provider can provide detailed information and aim to build a deep relationship. Conversely, if a key individual's influence is limited, the provider can provide concise information and efficiently build a relationship. Furthermore, the provider can evaluate the scope of influence of key individuals and select an appropriate method of information provision. For example, to evaluate the scope of influence of a key individual, the provider can consider factors such as the number of followers, the impact of their statements, and the breadth of their network. This allows for more appropriate information provision by considering the scope of influence of key individuals. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the provider can use AI to analyze data in order to provide information while considering the scope of influence of key individuals.

[0052] The information delivery unit can select the optimal information delivery method by considering the geographical location of the sales representative at the time of delivery. For example, if the sales representative is on-site, the information delivery unit can provide information in real time. Furthermore, if the sales representative is in a remote location, the information delivery unit can provide information online. In addition, the information delivery unit can provide information at the optimal time while the sales representative is traveling. For example, the information delivery unit can use GPS data, address information, or regional codes to collect the geographical location information of the sales representative. This allows the system to select the optimal information delivery method by considering the geographical location. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not. For example, the information delivery unit can use AI to analyze data in order to select the optimal information delivery method by considering the geographical location information of the sales representative.

[0053] The information delivery department can customize the content of the information provided by referring to the sales representative's past performance data at the time of delivery. For example, the information delivery department can provide effective information based on the sales representative's past success stories. The information delivery department can also analyze the sales representative's past failure stories and provide information to help them avoid making the same mistakes. Furthermore, the information delivery department can refer to the sales representative's past performance data and select the optimal method of information delivery. For example, the information delivery department can use customer management systems and sales records to collect the sales representative's past performance data. This allows the content of the information delivery to be customized by referring to the sales representative's past performance data. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not. For example, the information delivery department can use AI to analyze data in order to customize the content of the information delivery by referring to the sales representative's past performance data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The relationship-building support system can further collect data in its data collection unit to predict future trends by referencing a company's past performance data. For example, it can collect a company's past sales data and analyze growth trends. It can also collect a company's past investment activities and predict future investment plans. Furthermore, it can collect a company's past market share data and predict changes in the competitive environment. This makes it possible to collect data to predict future trends by referring to past performance data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0056] The relationship building support system can further improve the accuracy of its analysis by considering the interrelationships between data in its analysis unit. For example, it can analyze the relationship between statements made by company executives and fluctuations in stock prices. It can also analyze the relationship between the content of social media posts and company performance data. Furthermore, it can analyze the relationship between the content of news articles and fluctuations in a company's market share. By considering the interrelationships between data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0057] The relationship-building support system can further evaluate the scope of influence of key individuals in detail during the listing phase. For example, it can evaluate the number of followers and engagement rate of key individuals on social media. It can also evaluate the extent to which key individuals' statements are covered by the media. Furthermore, it can evaluate key individuals' past achievements and project success rates. This allows for a more accurate listing by providing a detailed evaluation of the scope of influence of key individuals. Some or all of the above processing in the listing phase may be performed using AI or not.

[0058] The relationship-building support system can further select the optimal information delivery method by referring to the sales representative's past activity history within the delivery department. For example, it can provide information by referring to the sales representative's past successful approach methods. It can also analyze the sales representative's past failures and provide information to avoid the same mistakes. Furthermore, it can refer to the sales representative's past customer interaction history and provide information at the optimal timing. In this way, the optimal information delivery method can be selected by referring to the sales representative's past activity history. Some or all of the above processing in the delivery department may be performed using AI or not.

[0059] The relationship-building support system can further select the optimal information delivery method in the delivery section, taking into account the geographical location of the sales representative at the time of delivery. For example, if the sales representative is on-site, information can be provided in real time. If the sales representative is in a remote location, information can be provided online. Furthermore, information can be provided at the optimal timing while the sales representative is traveling. In this way, the optimal information delivery method can be selected by taking geographical location information into consideration. Some or all of the above processing in the delivery section may be performed using AI, or may not be performed using AI.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects publicly available company information, social media data, and internal data. For example, it collects data from the company's website, news articles, social media posts, and internal customer databases. The data collection unit can obtain executive information from the company's website, identify influential individuals from social media posts, and collect customer information from internal customer databases. Step 2: The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. For example, it analyzes statements made by executives and social media posts to determine the level of influence they wield. The analysis unit uses text mining, network analysis, and machine learning algorithms to analyze the data. Step 3: The listing unit lists information about the key people identified by the analysis unit. For example, it lists information such as the key person's name, job title, contact information, and scope of influence. The listing unit can list the key person's name, job title, contact information, and scope of influence. Step 4: The delivery department provides the information compiled by the listing department to the sales representatives. For example, the delivery department can notify the sales representatives of the compiled information, adjust the timing of the provision, and customize the content provided.

[0062] (Example of form 2) The relationship-building support system according to an embodiment of the present invention is a system that utilizes generative AI to automatically identify key persons within a company and support relationship building. This relationship-building support system collects publicly available company information, social media, and internal data, and analyzes the collected data to list important contacts and influential individuals. For example, the relationship-building support system collects data from various sources such as the company's website, news articles, social media posts, and internal customer databases. Next, the generative AI analyzes the collected data to identify important contacts and influential individuals within the company. For example, it can analyze statements made by executives and the content of social media posts to determine the influence of those individuals. This allows sales representatives to identify key persons with whom they should build relationships. Furthermore, it lists information on the identified key persons. The listed information includes the key person's name, position, contact information, and scope of influence. This allows sales representatives to efficiently build relationships. For example, by contacting the listed key persons at the appropriate time, opportunities for upselling and cross-selling can be maximized. This mechanism significantly reduces the time sales representatives spend identifying key persons. Conventional methods required significant time and effort to identify key decision-makers. However, with this invention, the generating AI automatically identifies and lists key decision-makers, allowing sales representatives to efficiently build relationships. This leads to increased efficiency in overall sales activities and higher sales. For example, when a corporate sales representative is developing new clients, this invention allows them to quickly identify key decision-makers within the company and build relationships. This maximizes opportunities for upselling and cross-selling, improving the results of sales activities. Furthermore, by using key decision-maker information to make appropriate approaches to existing customers, customer satisfaction can be improved. In short, this relationship-building support system enables sales representatives to efficiently build relationships.

[0063] The relationship-building support system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a provision unit. The collection unit collects publicly available company information, social media, and internal data. For example, the collection unit collects data from company websites, news articles, social media posts, and internal customer databases. For example, the collection unit can obtain information on executives from company websites. The collection unit can also identify influential individuals from social media posts. Furthermore, the collection unit can collect customer information from internal customer databases. The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. For example, the analysis unit can analyze the statements of executives and the content of social media posts to determine the influence of those individuals. For example, the analysis unit can use text mining technology to analyze the content of executives' statements. The analysis unit can also use network analysis technology to analyze the content of social media posts. Furthermore, the analysis unit can use machine learning algorithms to analyze information from customer databases. The listing unit lists information on key people identified by the analysis unit. The listing unit lists information such as the names, titles, contact information, and scope of influence of key persons. For example, the listing unit can list the names and titles of key persons. The listing unit can also list the contact information of key persons. Furthermore, the listing unit can also list the scope of influence of key persons. The providing unit provides the information listed by the listing unit to sales representatives. The providing unit can, for example, notify sales representatives of the listed information. Furthermore, the providing unit can adjust the timing of providing the listed information to sales representatives. Furthermore, the providing unit can customize the content of the listed information provided to sales representatives. As a result, the relationship-building support system according to this embodiment enables sales representatives to efficiently build relationships. Some or all of the above-described processes in the collection unit, analysis unit, listing unit, and providing unit may be performed using AI, for example, or without using AI.For example, the data collection unit can use AI to perform web scraping to obtain information about company executives from their websites. The analysis unit can use AI to perform text mining to analyze the collected data. The listing unit can use AI to organize information to create a list of identified key personnel. The delivery unit can use AI to adjust the timing of notifications to provide the listed information to sales representatives.

[0064] The data collection department collects publicly available corporate information, social media data, and internal data. Specifically, it collects data from corporate websites, news articles, social media posts, and internal customer databases. For example, corporate websites can provide information on executives, the latest corporate news, and press releases. This allows for an understanding of the company's latest trends and key personnel. News articles collect information on events and market trends related to the company, providing insights into the company's external environment. Social media posts collect statements and reactions from the company, its executives, employees, and customers, allowing for the identification of the company's reputation and influential figures. For example, using specific hashtags and keywords to collect corporate-related posts can help identify influential users and trends. Furthermore, internal customer databases collect existing customer information and past transaction history, providing data for analyzing customer needs and behavioral patterns. This allows the data collection department to gather a wide range of data from diverse sources, enabling a comprehensive understanding of the company's internal and external situation. The data collection department centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and listing departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes data collected by the collection unit to identify important contacts and influential individuals. Specifically, it can analyze executive statements and social media posts to determine the influence of those individuals. For example, text mining technology can be used to analyze executive statements and evaluate the potential impact of those statements. Text mining technology uses natural language processing (NLP) to analyze text data, extracting keywords and performing sentiment analysis. This allows for the determination of whether an executive's statement is positive or negative and the evaluation of its influence. Furthermore, network analysis technology can be used to analyze social media posts and identify influential users and their networks. Network analysis technology analyzes the relationships between users on social media and visualizes the networks of influential users and their followers. In addition, machine learning algorithms can be used to analyze information in the customer database and identify important customers and their behavioral patterns. Machine learning algorithms can analyze large amounts of data and discover patterns and trends. As a result, the analysis unit can quickly and accurately analyze the collected data and identify important contacts and influential individuals. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict the behavioral patterns of specific executives or customers based on past statement data and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0066] The listing unit compiles information on key individuals identified by the analysis unit. Specifically, it lists information such as the key individual's name, title, contact information, and scope of influence. For example, listing the key individual's name and title makes it possible to identify important individuals within the company and facilitate contact with them. Listing the key individual's contact information allows for quick communication. Furthermore, listing the key individual's scope of influence allows for an understanding of their influence and enables effective approaches. The listing unit organizes this information and provides it in a format easily accessible to sales representatives. For example, the listed information can be stored digitally and provided as a database with search functionality. This allows sales representatives to quickly search for necessary information and efficiently build relationships. In addition, the listing unit can regularly update the listed information to provide the latest information. For example, it can quickly reflect newly identified key individuals and changed contact information, ensuring that the information is always up-to-date. The listing unit can also organize the listed information by category and filter it based on specific criteria. This allows sales representatives to quickly identify key individuals who meet specific criteria and approach them effectively. The listing section then provides sales representatives with crucial information to efficiently build relationships, maximizing the overall effectiveness of the system.

[0067] The information provision department provides the information compiled by the listing department to sales representatives. Specifically, it can notify sales representatives of the compiled information, adjust the timing of provision, and customize the content provided. For example, when notifying sales representatives of the compiled information, information can be quickly transmitted using email or chat tools. The information provision department can also adjust the timing of information provision according to the sales representative's schedule and work status. This allows sales representatives to receive the necessary information at the optimal time and proceed with their work efficiently. Furthermore, the information provision department can customize the compiled information according to the needs of sales representatives. For example, by prioritizing the provision of information on specific customers or specific industries, it can support sales representatives in effectively building relationships. The information provision department can centrally manage this information and link it with other systems and departments as needed. For example, the provided information can be linked with sales support systems and customer relationship management systems, allowing sales representatives to manage information centrally. The information provision department can also collect feedback on the provided information and continuously improve the accuracy and method of provision. This allows the information provision department to provide sales representatives with timely and accurate information and support efficient relationship building. Furthermore, the service provider can use AI to adjust the timing of notifications. For example, by analyzing the past behavior patterns and schedules of sales representatives and providing information at the optimal time, the service provider can maximize the efficiency of the sales representatives' work. In this way, the service provider can provide important support for sales representatives to efficiently build relationships and maximize the effectiveness of the entire system.

[0068] The data collection unit can collect data from sources such as company websites, news articles, social media posts, and internal customer databases. For example, the data collection unit can collect information such as company profiles, product information, and press releases from company websites. It can also collect industry news, corporate news, and economic news from news articles. Furthermore, it can collect information from social media posts. For example, the data collection unit can use web scraping techniques to obtain information on executives from company websites. A news aggregator can be used to collect information from news articles. Social media APIs can be used to collect information from social media posts. This allows for more accurate identification of key players by collecting data from diverse 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 use AI to perform web scraping to obtain information on executives from company websites.

[0069] The analysis unit can analyze the collected data to identify important contacts and influential individuals within the company. For example, the analysis unit can analyze the collected data using text mining techniques. It can also analyze the collected data using network analysis techniques. Furthermore, it can analyze the collected data using machine learning algorithms. For example, the analysis unit can analyze the statements of executives using text mining techniques to determine the influence of those individuals. It can also analyze social media posts using network analysis techniques to determine the influence of those individuals. It can also analyze customer database information using machine learning algorithms to identify important contacts. This allows for the accurate identification of key players within the company through data analysis. 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 use AI to perform text mining in order to analyze the collected data.

[0070] The listing unit can list information such as the names, titles, contact information, and scope of influence of identified key individuals. For example, the listing unit can list the names and titles of key individuals. It can also list the contact information of key individuals. Furthermore, the listing unit can list the scope of influence of key individuals. For example, the listing unit can organize the collected data in order to list the names and titles of key individuals. It can also organize the collected data in order to list contact information. It can also organize the collected data in order to list the scope of influence. This allows sales representatives to efficiently build relationships by listing detailed information about key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to organize information in order to list the information of identified key individuals.

[0071] The service provider can provide listed information to sales representatives and support them in efficiently building relationships. For example, the service provider can notify sales representatives of the listed information. The service provider can also adjust the timing of providing the listed information to sales representatives. Furthermore, the service provider can customize the content of the listed information provided to sales representatives. For example, the service provider can use a notification system to notify sales representatives of the listed information. To adjust the timing of provision, the service provider can consider the sales representatives' schedules. To customize the content of provision, the service provider can provide information tailored to the needs of the sales representatives. This enables sales representatives to efficiently build relationships. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to adjust the timing of notifications to sales representatives to notify them of the listed information.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. The data collection unit can also collect data quickly when the user is focused, gathering necessary information in a short time. Furthermore, if the user is tired, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, the data collection unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This allows the data collection timing to be adjusted according to the user's emotions, thereby reducing the user's burden. 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to perform sentiment analysis in order to estimate the user's emotions.

[0073] The data collection unit can customize the types of data it collects according to the industry and size of the company. For example, in the case of a large company, the data collection unit can collect detailed information such as statements by executives and minutes of shareholder meetings. In the case of a small or medium-sized enterprise, the data collection unit can also focus on collecting publicly available information such as social media posts and news articles. Furthermore, in the case of a startup company, the data collection unit can also collect information such as interviews with founders and comments from investors. For example, the data collection unit can use industry codes or industry classifications to identify the industry of a company. To identify the size of a company, it can use information such as the number of employees, sales, and capital. This enables data collection tailored to 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 use AI to customize the data in order to collect data according to the industry and size of the company.

[0074] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of reliable information. For example, the collection unit prioritizes the collection of information from official company websites and government databases. The collection unit can also exclude unreliable social media posts and rumors from collection. Furthermore, the collection unit can evaluate the reliability of news articles and prioritize the collection of information from reliable media. For example, the collection unit can consider the reliability of the information source, the consistency of the data, and past performance to evaluate the reliability of the data. This improves the accuracy of the analysis results by prioritizing the collection of reliable information. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can use AI to evaluate the reliability of the information source in order to evaluate the reliability of the data.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated emotions. For example, if the user is in a hurry, the data collection unit may prioritize collecting information on important contacts and influential people. If the user is relaxed, the data collection unit may also collect detailed background information and relevant news articles. Furthermore, if the user is stressed, the data collection unit may prioritize collecting concise and to-the-point information. For example, the data collection unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 without AI. For example, the data collection unit can use AI to perform sentiment analysis in order to estimate the user's emotions.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of companies during the collection process. For example, the data collection unit can prioritize the collection of news articles and social media posts related to the location of the company's headquarters. The data collection unit can also collect information on the company's major business locations and assess their geographical influence. Furthermore, the data collection unit can collect data related to the company's international activities and conduct analysis from a global perspective. For example, the data collection unit can use GPS data, address information, and regional codes to collect geographical location information. This allows for the collection of highly relevant data by considering geographical location. 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 use AI to determine data priorities in order to collect highly relevant data by considering the geographical location of companies.

[0077] The data collection unit can collect data to predict future trends by referring to a company's past performance data during the collection process. For example, the data collection unit can collect a company's past sales data and analyze growth trends. It can also collect a company's past investment activities and predict future investment plans. Furthermore, it can collect a company's past market share data and predict changes in the competitive environment. For example, the data collection unit can use financial reports, sales data, performance evaluations, etc., to collect past performance data. This makes it possible to collect data to predict future trends by referring to past performance data. 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 use AI to collect and analyze data in order to predict future trends by referring to a company's past performance data.

[0078] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise and rapid analysis and provide results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can reduce the complexity of the analysis and provide simpler results. For example, the analysis unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This allows for more appropriate analysis results by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can use AI to perform emotion analysis in order to estimate the user's emotions.

[0079] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can correlate statements made by company executives with fluctuations in stock prices. It can also correlate social media posts with company performance data. Furthermore, it can correlate news article content with fluctuations in a company's market share. For example, the analysis unit can use correlation analysis or network analysis to consider the interrelationships between data. This improves the accuracy of the analysis by considering the interrelationships between data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to perform correlation analysis in order to improve the accuracy of the analysis by considering the interrelationships between data.

[0080] The analysis unit can perform analyses while considering the company's industry and market trends. For example, in the case of manufacturing, the analysis unit can analyze data on product production volume and supply chain. In the case of service industries, the analysis unit can also analyze data on customer satisfaction and service quality. Furthermore, in the case of the technology industry, the analysis unit can also analyze technological innovations and patent information. For example, the analysis unit can use industry codes and industry classifications to identify the company's industry. To collect market trends, it can use market reports, trend data, and competitive analysis. This allows for more appropriate analysis by considering the company's industry and market trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can use AI to analyze data in order to perform analyses while considering the company's industry and market trends.

[0081] 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. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. For example, the analysis unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This allows for the provision of more appropriate information by adjusting the display method of the analysis results according to 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-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform emotion analysis using AI to estimate the user's emotions.

[0082] The analysis unit can perform analyses while considering the geographical location information of companies. For example, the analysis unit can analyze market trends related to the location of a company's headquarters. It can also analyze data related to a company's major business locations and assess their geographical influence. Furthermore, the analysis unit can analyze data related to a company's international activities and perform analyses from a global perspective. For example, the analysis unit can use GPS data, address information, and regional codes to collect geographical location information. This allows for more appropriate analysis by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to analyze data in order to perform analyses while considering the geographical location information of companies.

[0083] The analysis unit can perform analyses to predict future trends by referring to a company's past performance data during the analysis process. For example, the analysis unit can analyze a company's past sales data to predict growth trends. It can also analyze a company's past investment activities to predict future investment plans. Furthermore, it can analyze a company's past market share data to predict changes in the competitive environment. For example, the analysis unit can use financial reports, sales data, performance evaluations, etc., to collect past performance data. This makes it possible to perform analyses to predict future trends by referring to past performance data. 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 use AI to analyze data in order to predict future trends by referring to a company's past performance data.

[0084] The listing unit can estimate the user's emotions and adjust the listing criteria based on the estimated emotions. For example, if the user is relaxed, the listing unit can provide detailed listing criteria. If the user is in a hurry, the listing unit can also provide concise and quick listing criteria. Furthermore, if the user is stressed, the listing unit can provide simple listing criteria. For example, the listing unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This allows for more appropriate listings by adjusting the listing criteria according to 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 listing unit may be performed using AI, for example, or not using AI. For example, the listing unit can use AI to perform emotion analysis to estimate the user's emotions.

[0085] The listing unit can evaluate the scope of influence of key individuals in detail during the listing process. For example, the listing unit can evaluate the number of followers and engagement rates of key individuals on social media. It can also evaluate the extent to which key individuals' statements are covered by the media. Furthermore, the listing unit can evaluate key individuals' past achievements and project success rates. For example, to evaluate the scope of influence, the listing unit can consider factors such as the number of followers, the impact of statements, and the breadth of their network. This allows for a more accurate listing by providing a detailed evaluation of the scope of influence of key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to evaluate the scope of influence of key individuals.

[0086] The listing unit can create lists by considering the past statements and actions of key individuals. For example, the listing unit can analyze the past social media posts of key individuals to assess their influence. It can also analyze the past media appearances and speeches of key individuals to assess their influence. Furthermore, it can analyze the past projects and achievements of key individuals to assess their influence. For example, the listing unit can use social media posting history and meeting transcripts to collect past statements and actions. This allows for more accurate listings by considering the past statements and actions of key individuals. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to create lists that take into account the past statements and actions of key individuals.

[0087] The listing unit can estimate the user's emotions and determine the priority of the listing based on the estimated emotions. For example, if the user is in a hurry, the listing unit will prioritize listing important contacts and influential people. If the user is relaxed, the listing unit can also list detailed background information and relevant news articles. Furthermore, if the user is stressed, the listing unit can prioritize listing concise and to-the-point information. For example, the listing unit can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This enables efficient listing by determining the priority of the listing according to 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 listing unit may be performed using AI, for example, or without AI. For example, the listing unit can use AI to perform sentiment analysis in order to estimate the user's emotions.

[0088] The listing unit can consider the geographical location of key individuals when creating a list. For example, the listing unit can prioritize listing information related to the location of a key individual's headquarters. It can also list information about a key individual's major business locations and assess their geographical influence. Furthermore, the listing unit can list information related to a key individual's international activities and conduct analysis from a global perspective. For example, the listing unit can use GPS data, address information, or regional codes to collect geographical location information. This allows for more appropriate listing by considering geographical location information. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to list key individuals while considering their geographical location.

[0089] The listing unit can create lists by referencing industry trends related to key players. For example, the listing unit can list the latest news and trends in the industry to which the key players belong. It can also list the performance of projects and companies in which key players are involved. Furthermore, the listing unit can list information on regulations and legal changes that may affect key players. For example, the listing unit can use market reports, trend data, and competitive analysis to collect industry trends. This allows for more appropriate listings by referencing relevant industry trends. Some or all of the above processing in the listing unit may be performed using AI, for example, or not. For example, the listing unit can use AI to analyze data in order to create lists by referencing industry trends related to key players.

[0090] The information provider can estimate the user's emotions and adjust the timing of information delivery based on the estimated emotions. For example, if the user is relaxed, the provider can select a timing to provide detailed information. If the user is in a hurry, the provider can also quickly provide concise and to-the-point information. Furthermore, if the user is stressed, the provider can select a timing to provide simple information. For example, the provider can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This allows for more appropriate information delivery by adjusting the timing of information delivery according to 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can use AI to perform emotion analysis in order to estimate the user's emotions.

[0091] The information delivery department can select the optimal information delivery method by referring to the sales representative's past activity history at the time of delivery. For example, the information delivery department can provide information by referring to the sales representative's past successful approach methods. The information delivery department can also analyze the sales representative's past failures and provide information to avoid making the same mistakes. Furthermore, the information delivery department can refer to the sales representative's past customer interaction history and provide information at the optimal timing. For example, the information delivery department can use customer management systems and sales records to collect the sales representative's past activity history. This allows the selection of the optimal information delivery method by referring to the sales representative's past activity history. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not using AI. For example, the information delivery department can use AI to analyze data in order to select the optimal information delivery method by referring to the sales representative's past activity history.

[0092] The information provider can provide information while considering the scope of influence of key individuals. For example, if a key individual has significant influence, the provider can provide detailed information and aim to build a deep relationship. Conversely, if a key individual's influence is limited, the provider can provide concise information and efficiently build a relationship. Furthermore, the provider can evaluate the scope of influence of key individuals and select an appropriate method of information provision. For example, to evaluate the scope of influence of a key individual, the provider can consider factors such as the number of followers, the impact of their statements, and the breadth of their network. This allows for more appropriate information provision by considering the scope of influence of key individuals. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the provider can use AI to analyze data in order to provide information while considering the scope of influence of key individuals.

[0093] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is in a hurry, the provider will prioritize providing important information. If the user is relaxed, the provider can also provide detailed information. Furthermore, if the user is stressed, the provider can prioritize providing concise and to-the-point information. For example, the provider can use an emotion analysis algorithm to estimate the user's emotions. The emotion analysis algorithm analyzes the user's facial expressions, voice, and behavioral data to estimate emotions. This enables efficient information delivery by determining the priority of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can use AI to perform emotion analysis to estimate the user's emotions.

[0094] The information delivery unit can select the optimal information delivery method by considering the geographical location of the sales representative at the time of delivery. For example, if the sales representative is on-site, the information delivery unit can provide information in real time. Furthermore, if the sales representative is in a remote location, the information delivery unit can provide information online. In addition, the information delivery unit can provide information at the optimal time while the sales representative is traveling. For example, the information delivery unit can use GPS data, address information, or regional codes to collect the geographical location information of the sales representative. This allows the system to select the optimal information delivery method by considering the geographical location. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or not. For example, the information delivery unit can use AI to analyze data in order to select the optimal information delivery method by considering the geographical location information of the sales representative.

[0095] The information delivery department can customize the content of the information provided by referring to the sales representative's past performance data at the time of delivery. For example, the information delivery department can provide effective information based on the sales representative's past success stories. The information delivery department can also analyze the sales representative's past failure stories and provide information to help them avoid making the same mistakes. Furthermore, the information delivery department can refer to the sales representative's past performance data and select the optimal method of information delivery. For example, the information delivery department can use customer management systems and sales records to collect the sales representative's past performance data. This allows the content of the information delivery to be customized by referring to the sales representative's past performance data. Some or all of the above processes in the information delivery department may be performed using AI, for example, or not. For example, the information delivery department can use AI to analyze data in order to customize the content of the information delivery by referring to the sales representative's past performance data.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The relationship-building support system can further estimate the user's emotions and customize the information on key people listed based on those emotions. For example, if the user is stressed, the listed information can be summarized concisely and the key points highlighted. If the user is relaxed, detailed background information and relevant news articles can be included. Furthermore, if the user is in a hurry, information on the most important contacts and influential people can be prioritized. This enables information delivery tailored to the user's emotions, resulting in efficient relationship building. Emotion estimation is performed using an emotion analysis algorithm, which analyzes the user's facial expressions, voice, and behavioral data.

[0098] The relationship-building support system can further collect data in its data collection unit to predict future trends by referencing a company's past performance data. For example, it can collect a company's past sales data and analyze growth trends. It can also collect a company's past investment activities and predict future investment plans. Furthermore, it can collect a company's past market share data and predict changes in the competitive environment. This makes it possible to collect data to predict future trends by referring to past performance data. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0099] The relationship building support system can further improve the accuracy of its analysis by considering the interrelationships between data in its analysis unit. For example, it can analyze the relationship between statements made by company executives and fluctuations in stock prices. It can also analyze the relationship between the content of social media posts and company performance data. Furthermore, it can analyze the relationship between the content of news articles and fluctuations in a company's market share. By considering the interrelationships between data, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not.

[0100] The relationship-building support system can further evaluate the scope of influence of key individuals in detail during the listing phase. For example, it can evaluate the number of followers and engagement rate of key individuals on social media. It can also evaluate the extent to which key individuals' statements are covered by the media. Furthermore, it can evaluate key individuals' past achievements and project success rates. This allows for a more accurate listing by providing a detailed evaluation of the scope of influence of key individuals. Some or all of the above processing in the listing phase may be performed using AI or not.

[0101] The relationship-building support system can further select the optimal information delivery method by referring to the sales representative's past activity history within the delivery department. For example, it can provide information by referring to the sales representative's past successful approach methods. It can also analyze the sales representative's past failures and provide information to avoid the same mistakes. Furthermore, it can refer to the sales representative's past customer interaction history and provide information at the optimal timing. In this way, the optimal information delivery method can be selected by referring to the sales representative's past activity history. Some or all of the above processing in the delivery department may be performed using AI or not.

[0102] The relationship-building support system can further estimate the user's emotions in its data collection unit and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, data collection can be temporarily stopped and resumed when the user is relaxed. If the user is focused, data collection can be performed quickly to gather necessary information in a short time. Furthermore, if the user is tired, the frequency of data collection can be reduced to alleviate the user's burden. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is performed using an emotion analysis algorithm, which is achieved by analyzing the user's facial expressions, voice, and behavioral data.

[0103] The relationship-building support system can further estimate the user's emotions in its analysis unit and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, it can perform a detailed analysis to provide deep insights. If the user is in a hurry, it can perform a concise and rapid analysis to provide results that get straight to the point. Furthermore, if the user is stressed, it can reduce the complexity of the analysis and provide simpler results. In this way, by adjusting the analysis algorithm according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is performed using an emotion analysis algorithm, which is achieved by analyzing the user's facial expressions, voice, and behavioral data.

[0104] The relationship-building support system can further estimate the user's emotions in its listing section and adjust the listing criteria based on the estimated emotions. For example, if the user is relaxed, it can provide detailed listing criteria. If the user is in a hurry, it can provide concise and quick listing criteria. Furthermore, if the user is stressed, it can provide simple listing criteria. By adjusting the listing criteria according to the user's emotions, more appropriate listings can be created. Emotion estimation is performed using an emotion analysis algorithm, which is achieved by analyzing the user's facial expressions, voice, and behavioral data.

[0105] The relationship-building support system can further estimate the user's emotions and adjust the timing of information delivery based on those emotions. For example, if the user is relaxed, it can select a time to provide detailed information. If the user is in a hurry, it can quickly provide concise and to-the-point information. Furthermore, if the user is stressed, it can select a time to provide simple information. By adjusting the timing of information delivery according to the user's emotions, more appropriate information can be provided. Emotion estimation is performed using an emotion analysis algorithm, which is achieved by analyzing the user's facial expressions, voice, and behavioral data.

[0106] The relationship-building support system can further select the optimal information delivery method in the delivery section, taking into account the geographical location of the sales representative at the time of delivery. For example, if the sales representative is on-site, information can be provided in real time. If the sales representative is in a remote location, information can be provided online. Furthermore, information can be provided at the optimal timing while the sales representative is traveling. In this way, the optimal information delivery method can be selected by taking geographical location information into consideration. Some or all of the above processing in the delivery section may be performed using AI, or may not be performed using AI.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects publicly available company information, social media data, and internal data. For example, it collects data from the company's website, news articles, social media posts, and internal customer databases. The data collection unit can obtain executive information from the company's website, identify influential individuals from social media posts, and collect customer information from internal customer databases. Step 2: The analysis unit analyzes the data collected by the collection unit to identify important contacts and influential individuals. For example, it analyzes statements made by executives and social media posts to determine the level of influence they wield. The analysis unit uses text mining, network analysis, and machine learning algorithms to analyze the data. Step 3: The listing unit lists information about the key people identified by the analysis unit. For example, it lists information such as the key person's name, job title, contact information, and scope of influence. The listing unit can list the key person's name, job title, contact information, and scope of influence. Step 4: The delivery department provides the information compiled by the listing department to the sales representatives. For example, the delivery department can notify the sales representatives of the compiled information, adjust the timing of the provision, and customize the content provided.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects publicly available company information and social media posts using the camera 42 and communication I / F 44 of the smart device 14, and collects internal data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to identify important contacts and influential individuals. The listing unit lists information on key persons identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides the information listed by the control unit 46A of the smart device 14 to sales representatives. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects publicly available company information and social media posts using the camera 42 and communication I / F 44 of the smart glasses 214, and collects internal data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to identify important contacts and influential individuals. The listing unit lists information on key people identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides the information listed by the control unit 46A of the smart glasses 214 to sales representatives. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects publicly available company information and SNS posts using the camera 42 and communication I / F 44 of the headset terminal 314, and collects internal data using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to identify important contacts and influential individuals. The listing unit lists information on key persons identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides the information listed by the control unit 46A of the headset terminal 314 to sales representatives. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and communication I / F 44 of the robot 414 to collect publicly available company information and social media posts, and the identification processing unit 290 of the data processing unit 12 collects internal data. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to identify important contacts and influential individuals. The listing unit lists information on key persons identified by the identification processing unit 290 of the data processing unit 12. The provision unit provides the information listed by the control unit 46A of the robot 414 to sales representatives. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The data collection department collects publicly available company information, social media data, and internal data. An analysis unit analyzes the data collected by the aforementioned collection unit to identify important contacts and influential individuals, A listing unit that lists information on key persons identified by the aforementioned analysis unit, The system includes a provisioning unit that provides the information listed by the listing unit to sales representatives. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from sources such as company websites, news articles, social media posts, and internal customer databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify key contacts and influential individuals within the company. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned listing unit is, List the names, titles, contact information, and scope of influence of the identified key players. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the listed information to sales representatives and support them in efficiently building relationships. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Customize the types of data collected according to the company's industry and size. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable information is prioritized for collection. 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 prioritizes the data to collect 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 the collection of highly relevant data, taking into account the geographical location of the company. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, we refer to the company's past performance data to gather data for predicting future trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, the company's industry and market trends will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical location information of the companies will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, past performance data of the company is referenced to perform analyses aimed at predicting future trends. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned listing unit is, It estimates the user's sentiment and adjusts the listing criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned listing unit is, When creating the list, thoroughly assess the scope of influence of each key person. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned listing unit is, When creating the list, take into account the past statements and actions of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned listing unit is, It estimates the user's emotions and determines the priority of the list based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned listing unit is, When creating the list, take into account the geographical location of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned listing unit is, When creating the list, refer to the trends in the industries related to the key players. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of information delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, the sales representative's past activity history is referenced to select the most suitable method of information delivery. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, we will consider the scope of influence of key individuals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, the most suitable method of information delivery will be selected, taking into account the geographical location of the sales representative. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the content of the information provided will be customized by referring to the sales representative's past performance data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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 data collection department collects publicly available company information, social media data, and internal data. An analysis unit analyzes the data collected by the aforementioned collection unit to identify important contacts and influential individuals, A listing unit that lists information on key persons identified by the aforementioned analysis unit, The system includes a provisioning unit that provides the information listed by the listing unit to sales representatives. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from sources such as company websites, news articles, social media posts, and internal customer databases. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify key contacts and influential individuals within the company. The system according to feature 1.

4. The aforementioned listing unit is, List the names, titles, contact information, and scope of influence of the identified key players. The system according to feature 1.

5. The aforementioned supply unit is, Provide the listed information to sales representatives and support them in efficiently building relationships. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Customize the types of data collected according to the company's industry and size. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable information is prioritized for collection. The system according to feature 1.

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

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the geographical location of the company. The system according to feature 1.

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