system

Abstract

The system according to this embodiment aims to collect useful information from publicly available information on the internet and internal corporate databases, and to derive applications that are useful for improving internal business operations. [Solution] The system according to the embodiment comprises an information gathering unit, an analysis unit, and a proposal unit. The information gathering unit collects information from publicly available information on the internet and internal corporate databases. The analysis unit analyzes the information collected by the information gathering unit and extracts important points. The proposal unit derives applications that are useful for improving internal business operations based on the information extracted by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 prior art, there is a problem that it is difficult to efficiently collect useful information from public information on the Internet and an enterprise's internal database and derive an application useful for improving in-house operations.

[0005] The system according to the embodiment aims to collect useful information from public information on the Internet and an enterprise's internal database and derive an application useful for improving in-house operations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, an analysis unit, and a proposal unit. The information gathering unit collects information from publicly available information on the internet and from the company's internal databases. The analysis unit analyzes the information collected by the information gathering unit and extracts important points. The proposal unit derives applications that are useful for improving internal business operations based on the information extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can collect useful information from publicly available information on the internet and internal corporate databases, and derive applications that are useful for improving internal business operations. [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 manages communication between multiple 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 AI ​​tool system according to an embodiment of the present invention is a system that automatically collects and analyzes information on the latest AI technologies and implementation examples, which are rapidly evolving, and identifies and matches the optimal application that is useful for improving internal business operations. This AI tool system automatically collects information on the latest AI technologies and implementation examples from publicly available information on the internet and internal corporate databases. Next, it analyzes the collected information and extracts important points. Finally, based on the analyzed information, it identifies and matches the optimal application that is useful for improving internal business operations. For example, the information collection unit automatically collects information on the latest AI technologies and implementation examples from publicly available information on the internet and internal corporate databases. Next, the analysis unit analyzes the collected information and extracts important points. For example, it extracts important points based on frequently occurring keywords or specific patterns. Finally, the proposal unit identifies and matches the optimal application that is useful for improving internal business operations based on the analyzed information. For example, it is expected to have applications in various fields, such as automating data analysis and improving the efficiency of customer service. With this system, companies can effectively utilize the latest AI technologies and achieve business improvements.

[0029] The AI ​​tool system according to this embodiment comprises an information gathering unit, an analysis unit, and a proposal unit. The information gathering unit collects information from publicly available information on the internet and from internal company databases. For example, the information gathering unit collects publicly available information such as news articles, blogs, and social media posts. The information gathering unit can also collect information from internal company databases such as customer databases and sales databases. Furthermore, the information gathering unit can collect information using methods such as web scraping and API usage. The analysis unit analyzes the collected information and extracts important points. For example, the analysis unit extracts important points based on frequently occurring keywords and specific patterns. The analysis unit can also analyze information using natural language processing technology. The proposal unit derives the optimal application that is useful for improving internal business operations based on the analyzed information. For example, the proposal unit can derive applications such as task management tools and data analysis tools. Furthermore, the proposal unit can also match the derived application to the relevant personnel within the company. As a result, the AI ​​tool system according to this embodiment enables companies to effectively utilize the latest AI technology and achieve business improvements.

[0030] The proposal department can match the derived applications to internal personnel. For example, the proposal department matches applications based on the personnel's skill set and job duties. For instance, the proposal department analyzes the personnel's skill set and proposes appropriate applications. The proposal department can also select the optimal application based on job duties. This improves the efficiency of implementation by matching the proposed applications to the appropriate personnel. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can match applications using an AI model that takes the personnel's skill set and job duties as input and outputs the optimal application.

[0031] The information gathering unit can analyze past information gathering history and select a collection method. For example, the information gathering unit can analyze the quality and quantity of information collected in the past and select the most effective collection method. The information gathering unit can also select a collection method that is effective for a specific time period based on past information gathering history. Furthermore, the information gathering unit can prioritize collection from specific information sources based on past information gathering history. This enables efficient information gathering by selecting the optimal collection method through analysis of past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history data into a generating AI and have the generating AI select the optimal collection method.

[0032] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the project the user is currently working on. It can also filter highly relevant information based on the user's areas of interest. Furthermore, the information gathering unit can provide necessary information in a timely manner according to the progress of the user's project. This allows for the efficient collection of highly relevant information by filtering it based on the user's current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the information filtering.

[0033] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize the collection of information related to that region. Furthermore, if the user is on a business trip, the information gathering unit can also collect information related to the destination. Additionally, if the user is at home, the information gathering unit can prioritize the collection of information around their home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information gathering unit may be performed using AI, or without AI. For example, the information gathering unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0034] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can collect relevant information based on information shared by the user on social media. It can also analyze the content of posts from accounts that the user follows and collect relevant information. Furthermore, the information gathering unit can analyze the activities of groups and communities that the user participates in and collect relevant information. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0035] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. It can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0036] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a market analysis algorithm to market information. Furthermore, it can apply a customer analysis algorithm to customer information. By applying the appropriate analysis algorithm according to the category of information, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0037] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. 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 input information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0038] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0039] The proposal unit can adjust the level of detail of a proposal based on the importance of the application. For example, the proposal unit can provide detailed proposals for high-importance applications, and concise proposals for low-importance applications. Furthermore, the proposal unit can prioritize proposals according to the importance of the application. This allows for efficient proposals by adjusting the level of detail based on the importance of the application. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application importance data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0040] The proposal unit can apply different proposal algorithms depending on the application category when making a proposal. For example, the proposal unit can apply a data analysis proposal algorithm to a data analysis application. It can also apply a customer service proposal algorithm to a customer service application. Furthermore, it can apply a production management proposal algorithm to a production management application. By applying the appropriate proposal algorithm according to the application category, highly accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application category data into a generating AI and have the generating AI execute the application of the appropriate proposal algorithm.

[0041] The proposal department can determine the priority of proposals based on the application's implementation timeline. For example, it will prioritize proposals for applications that require urgent implementation. It can also postpone proposals for applications with an undetermined implementation timeline. Furthermore, the proposal department can adjust the priority of proposals according to the application's implementation timeline. This allows for efficient proposals by determining the priority of proposals based on the application's implementation timeline. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input application implementation timeline data into a generating AI and have the generating AI determine the priority of proposals.

[0042] The proposal unit can adjust the order of proposals based on the relevance of the applications. For example, the proposal unit will prioritize proposing highly relevant applications. It can also postpone proposing less relevant applications. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the applications. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the applications. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0043] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0044] The information gathering unit can analyze a user's past search history and prioritize the collection of highly relevant information. For example, it can collect the latest relevant information based on keywords the user has previously searched for and pages they have viewed. The information gathering unit can also identify areas of interest from the user's past search history and prioritize the collection of information related to those areas. Furthermore, based on the user's past search history, the information gathering unit can predict information that may be needed in the future and collect it in advance. This allows for the efficient collection of highly relevant information by utilizing the user's past search history.

[0045] The information gathering unit can prioritize collecting region-specific trends and news by taking into account the user's geographical location. For example, if the user is in a specific city, it will collect the latest news and event information related to that city. If the user is traveling, it can also collect tourist information and local trends for their destination. Furthermore, if the user is at home, it can prioritize collecting local news and event information for their immediate surroundings. In this way, by considering the user's geographical location, it can efficiently collect highly relevant information specific to their region.

[0046] The analysis unit can adjust the level of detail of the analysis based on the reliability of the information during the analysis. For example, it can perform a detailed analysis on highly reliable information, and a simplified analysis on less reliable information. Furthermore, it can determine the priority of the analysis according to the reliability of the information. This allows for efficient analysis by adjusting the level of detail based on the reliability of the 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 input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The information gathering unit can analyze users' social media activity and prioritize collecting information that is trending within the user's network. For example, it can analyze the content of posts from accounts that users follow and collect relevant information. It can also analyze the activities of groups and communities that users participate in and collect relevant information. Furthermore, it can collect the latest relevant information based on information that users have shared. In this way, by analyzing users' social media activity, highly relevant information can be efficiently collected.

[0048] The proposal department can analyze the user's past selection history when making proposals and prioritize suggesting highly relevant applications. For example, it can analyze the characteristics of applications the user has previously selected and suggest similar applications. It can also analyze the characteristics of applications the user has previously rejected and suggest applications with different characteristics. Furthermore, based on the user's past selection history, it can predict applications that may be needed in the future and suggest them in advance. In this way, highly relevant applications can be efficiently suggested by utilizing the user's past selection history.

[0049] The following briefly describes the processing flow for example form 1.

[0050] Step 1: The information gathering department collects information from publicly available information on the internet and from the company's internal databases. For example, it collects publicly available information such as news articles, blogs, and social media posts, as well as information from the company's internal databases such as customer databases and sales databases. Furthermore, it collects information using methods such as web scraping and API usage. Step 2: The analysis unit analyzes the information collected by the information collection unit and extracts key points. For example, it extracts key points based on frequently occurring keywords or specific patterns and analyzes the information using natural language processing technology. Step 3: The proposal department identifies applications that can help improve internal business operations based on the information extracted by the analysis department. For example, they identify applications such as task management tools and data analysis tools, and then match these identified applications with the relevant personnel within the company.

[0051] (Example of form 2) The AI ​​tool system according to an embodiment of the present invention is a system that automatically collects and analyzes information on the latest AI technologies and implementation examples, which are rapidly evolving, and identifies and matches the optimal application that is useful for improving internal business operations. This AI tool system automatically collects information on the latest AI technologies and implementation examples from publicly available information on the internet and internal corporate databases. Next, it analyzes the collected information and extracts important points. Finally, based on the analyzed information, it identifies and matches the optimal application that is useful for improving internal business operations. For example, the information collection unit automatically collects information on the latest AI technologies and implementation examples from publicly available information on the internet and internal corporate databases. Next, the analysis unit analyzes the collected information and extracts important points. For example, it extracts important points based on frequently occurring keywords or specific patterns. Finally, the proposal unit identifies and matches the optimal application that is useful for improving internal business operations based on the analyzed information. For example, it is expected to have applications in various fields, such as automating data analysis and improving the efficiency of customer service. With this system, companies can effectively utilize the latest AI technologies and achieve business improvements.

[0052] The AI ​​tool system according to this embodiment comprises an information gathering unit, an analysis unit, and a proposal unit. The information gathering unit collects information from publicly available information on the internet and from internal company databases. For example, the information gathering unit collects publicly available information such as news articles, blogs, and social media posts. The information gathering unit can also collect information from internal company databases such as customer databases and sales databases. Furthermore, the information gathering unit can collect information using methods such as web scraping and API usage. The analysis unit analyzes the collected information and extracts important points. For example, the analysis unit extracts important points based on frequently occurring keywords and specific patterns. The analysis unit can also analyze information using natural language processing technology. The proposal unit derives the optimal application that is useful for improving internal business operations based on the analyzed information. For example, the proposal unit can derive applications such as task management tools and data analysis tools. Furthermore, the proposal unit can also match the derived application to the relevant personnel within the company. As a result, the AI ​​tool system according to this embodiment enables companies to effectively utilize the latest AI technology and achieve business improvements.

[0053] The proposal department can match the derived applications to internal personnel. For example, the proposal department matches applications based on the personnel's skill set and job duties. For instance, the proposal department analyzes the personnel's skill set and proposes appropriate applications. The proposal department can also select the optimal application based on job duties. This improves the efficiency of implementation by matching the proposed applications to the appropriate personnel. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can match applications using an AI model that takes the personnel's skill set and job duties as input and outputs the optimal application.

[0054] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit can reduce the frequency of information gathering to alleviate the user's burden. Conversely, if the user is relaxed, the information gathering unit can increase the frequency of information gathering to provide the latest information quickly. Furthermore, if the user is concentrating, the information gathering unit can adjust the timing of information gathering to avoid interrupting their work. By adjusting the timing of information gathering according to the user's emotions, the burden on the user is reduced, and efficient information gathering becomes possible. 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 gathering unit may be performed using AI, or not using AI. For example, the information gathering unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0055] The information gathering unit can analyze past information gathering history and select a collection method. For example, the information gathering unit can analyze the quality and quantity of information collected in the past and select the most effective collection method. The information gathering unit can also select a collection method that is effective for a specific time period based on past information gathering history. Furthermore, the information gathering unit can prioritize collection from specific information sources based on past information gathering history. This enables efficient information gathering by selecting the optimal collection method through analysis of past information gathering history. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history data into a generating AI and have the generating AI select the optimal collection method.

[0056] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the project the user is currently working on. It can also filter highly relevant information based on the user's areas of interest. Furthermore, the information gathering unit can provide necessary information in a timely manner according to the progress of the user's project. This allows for the efficient collection of highly relevant information by filtering it based on the user's current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's project data and area of ​​interest data into a generating AI and have the generating AI perform the information filtering.

[0057] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the information gathering unit will prioritize collecting information of high importance. If the user is relaxed, the information gathering unit can also collect a wider range of information to increase the options available. Furthermore, if the user is focused, the information gathering unit can prioritize collecting information relevant to the current task. This allows for the priority collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0058] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location during information gathering. For example, if the user is in a specific region, the information gathering unit will prioritize the collection of information related to that region. Furthermore, if the user is on a business trip, the information gathering unit can also collect information related to the destination. Additionally, if the user is at home, the information gathering unit can prioritize the collection of information around their home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the information gathering unit may be performed using AI, or without AI. For example, the information gathering unit can input the user's geographical location data into a generating AI and have the generating AI collect highly relevant information.

[0059] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can collect relevant information based on information shared by the user on social media. It can also analyze the content of posts from accounts that the user follows and collect relevant information. Furthermore, the information gathering unit can analyze the activities of groups and communities that the user participates in and collect relevant information. This allows for the efficient collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0060] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. It can also perform a concise analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0062] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical information. It can also apply a market analysis algorithm to market information. Furthermore, it can apply a customer analysis algorithm to customer information. By applying the appropriate analysis algorithm according to the category of information, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0063] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide an analysis of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0064] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis process. For example, the analysis unit prioritizes the analysis of the most recent information. The analysis unit can also analyze older information as needed. Furthermore, the analysis unit can adjust the priority of analysis according to the timing of information collection. This allows for the prioritization of analysis of the most recent information by determining the priority of analysis based on the timing of information collection. 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 input information collection timing data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0065] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. It can also postpone the analysis of less relevant information. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0066] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions that get straight to the point. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0067] The proposal unit can adjust the level of detail of a proposal based on the importance of the application. For example, the proposal unit can provide detailed proposals for high-importance applications, and concise proposals for low-importance applications. Furthermore, the proposal unit can prioritize proposals according to the importance of the application. This allows for efficient proposals by adjusting the level of detail based on the importance of the application. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application importance data into a generating AI and have the generating AI adjust the level of detail of the proposal.

[0068] The proposal unit can apply different proposal algorithms depending on the application category when making a proposal. For example, the proposal unit can apply a data analysis proposal algorithm to a data analysis application. It can also apply a customer service proposal algorithm to a customer service application. Furthermore, it can apply a production management proposal algorithm to a production management application. By applying the appropriate proposal algorithm according to the application category, highly accurate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application category data into a generating AI and have the generating AI execute the application of the appropriate proposal algorithm.

[0069] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is excited, it can provide suggestions with visually stimulating effects. By adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions.

[0070] The proposal department can determine the priority of proposals based on the application's implementation timeline. For example, it will prioritize proposals for applications that require urgent implementation. It can also postpone proposals for applications with an undetermined implementation timeline. Furthermore, the proposal department can adjust the priority of proposals according to the application's implementation timeline. This allows for efficient proposals by determining the priority of proposals based on the application's implementation timeline. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input application implementation timeline data into a generating AI and have the generating AI determine the priority of proposals.

[0071] The proposal unit can adjust the order of proposals based on the relevance of the applications. For example, the proposal unit will prioritize proposing highly relevant applications. It can also postpone proposing less relevant applications. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the applications. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the applications. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input application relevance data into a generating AI and have the generating AI perform the adjustment of the order of proposals. === Hard Collateral 1-1 === Each of the multiple elements described above, including the information gathering unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart device 14 and collects information from publicly available information on the internet and internal corporate databases. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and derives and matches the optimal application based on the analyzed information. === Hard Collateral 1-2 === Each of the multiple elements described above, including the information gathering unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the smart glasses 214 and collects information from publicly available information on the internet and internal corporate databases. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and derives and matches the optimal application based on the analyzed information. === Hard Collateral 1-3 === Each of the multiple elements described above, including the information gathering unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the headset terminal 314 and collects information from publicly available information on the internet and internal corporate databases. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and derives and matches the optimal application based on the analyzed information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the information gathering unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the information gathering unit is implemented by the control unit 46A of the robot 414 and collects information from publicly available information on the internet and internal corporate databases. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information and extracts important points. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and derives and matches the optimal application based on the analyzed information.

[0072] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0073] The information gathering unit can analyze a user's past search history and prioritize the collection of highly relevant information. For example, it can collect the latest relevant information based on keywords the user has previously searched for and pages they have viewed. The information gathering unit can also identify areas of interest from the user's past search history and prioritize the collection of information related to those areas. Furthermore, based on the user's past search history, the information gathering unit can predict information that may be needed in the future and collect it in advance. This allows for the efficient collection of highly relevant information by utilizing the user's past search history.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis results can be presented in a concise summary. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is focused, the analysis results can be presented using visually easy-to-understand graphs and charts. In this way, by adjusting the presentation method of the analysis results according to the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0075] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of suggestions can be increased to provide more options. Furthermore, if the user is concentrating, the timing of suggestions can be adjusted to avoid interrupting their work. By adjusting the timing of suggestions according to the user's emotions, the system can reduce the user's burden and enable more efficient suggestions.

[0076] The information gathering unit can prioritize collecting region-specific trends and news by taking into account the user's geographical location. For example, if the user is in a specific city, it will collect the latest news and event information related to that city. If the user is traveling, it can also collect tourist information and local trends for their destination. Furthermore, if the user is at home, it can prioritize collecting local news and event information for their immediate surroundings. In this way, by considering the user's geographical location, it can efficiently collect highly relevant information specific to their region.

[0077] The analysis unit can adjust the level of detail of the analysis based on the reliability of the information during the analysis. For example, it can perform a detailed analysis on highly reliable information, and a simplified analysis on less reliable information. Furthermore, it can determine the priority of the analysis according to the reliability of the information. This allows for efficient analysis by adjusting the level of detail based on the reliability of the 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 input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0078] The suggestion function can estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest relaxing applications. If the user is relaxed, it can suggest challenging applications. Furthermore, if the user is focused, it can suggest applications related to their current task. By customizing suggestions according to the user's emotions, it can suggest the most suitable applications for the user.

[0079] The information gathering unit can analyze users' social media activity and prioritize collecting information that is trending within the user's network. For example, it can analyze the content of posts from accounts that users follow and collect relevant information. It can also analyze the activities of groups and communities that users participate in and collect relevant information. Furthermore, it can collect the latest relevant information based on information that users have shared. In this way, by analyzing users' social media activity, highly relevant information can be efficiently collected.

[0080] The analysis unit can estimate the user's emotions and adjust the visual representation of the analysis based on those emotions. For example, if the user is tense, it can provide simple, easy-to-read graphs and charts. If the user is relaxed, it can provide more complex graphs and charts containing detailed data. Furthermore, if the user is excited, it can provide analysis results with visually stimulating effects. By adjusting the visual representation of the analysis based on the user's emotions, it can provide analysis results that are easy for the user to understand.

[0081] The proposal department can analyze the user's past selection history when making proposals and prioritize suggesting highly relevant applications. For example, it can analyze the characteristics of applications the user has previously selected and suggest similar applications. It can also analyze the characteristics of applications the user has previously rejected and suggest applications with different characteristics. Furthermore, based on the user's past selection history, it can predict applications that may be needed in the future and suggest them in advance. In this way, highly relevant applications can be efficiently suggested by utilizing the user's past selection history.

[0082] The suggestion function can estimate the user's emotions and adjust the order of suggestions based on those emotions. For example, if the user is stressed, it will prioritize suggestions of high importance. If the user is relaxed, it can also present a wide range of suggestions. Furthermore, if the user is focused, it can prioritize suggestions related to the current task. By adjusting the order of suggestions according to the user's emotions, it can provide the most suitable suggestions for the user.

[0083] The following briefly describes the processing flow for example form 2.

[0084] Step 1: The information gathering department collects information from publicly available information on the internet and from the company's internal databases. For example, it collects publicly available information such as news articles, blogs, and social media posts, as well as information from the company's internal databases such as customer databases and sales databases. Furthermore, it collects information using methods such as web scraping and API usage. Step 2: The analysis unit analyzes the information collected by the information collection unit and extracts key points. For example, it extracts key points based on frequently occurring keywords or specific patterns and analyzes the information using natural language processing technology. Step 3: The proposal department identifies applications that can help improve internal business operations based on the information extracted by the analysis department. For example, they identify applications such as task management tools and data analysis tools, and then match these identified applications with the relevant personnel within the company.

[0085] 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.

[0086] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0087] 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.

[0088] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0089] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.).

[0101] 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.

[0102] 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. 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.

[0103] 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.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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. 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.

[0119] 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.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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. 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.

[0136] 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.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] [Explanation of symbols]

[0157] 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 Information Gathering Department collects information from publicly available information on the internet and internal corporate databases, An analysis unit analyzes the information collected by the aforementioned information collection unit and extracts important points, Based on the information extracted by the aforementioned analysis unit, a proposal unit derives applications that are useful for improving internal business operations, Equipped with A system characterized by the following features.

2. The aforementioned proposal section is, The resulting applications are then matched with the appropriate personnel within the company. The system according to feature 1.

3. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

4. The aforementioned information gathering unit, Analyze past information gathering history and select the appropriate collection method. The system according to feature 1.

5. The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

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

7. The aforementioned information gathering unit, When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

8. The aforementioned information gathering unit, When gathering information, we analyze users' social media activity and collect relevant information. The system according to feature 1.

Images