system

Abstract

The system according to this embodiment aims to efficiently collect and analyze investment information, enabling even beginners to make appropriate investment decisions. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives user requests. The collection unit collects information based on the requests received by the reception unit. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that investment information was scattered, making it difficult for beginners to collect appropriate information and make investment decisions.

[0005] The system according to the embodiment aims to efficiently collect and analyze investment information so that even beginners can make appropriate investment decisions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives user requests. The collection unit collects information based on the requests received by the reception unit. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze investment information, enabling even beginners to make appropriate investment decisions. [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, 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 investment information provision system according to an embodiment of the present invention is an application using a generative AI to improve the current situation where investment information is scattered and to lower the barrier to entry for novice investors. In this system, when a user inputs the stocks of the market they wish to invest in into the application, the generative AI collects, analyzes, and provides relevant investment information. For example, if a user inputs a request such as, "I'm considering investing 500,000 yen in NISA. Please tell me about investment information ranging from tourism to airlines," the generative AI collects information such as the latest financial results, business plans, and new initiatives and provides it to the user. Furthermore, if the user requests additional information, the generative AI will provide even more detailed information in response to that request. For example, in response to a request such as, "Please tell me about the airline's initiatives over the past six months," the AI ​​will provide information such as, "Carbon neutrality-related news on [date]," and "Upward revision of business plan announced on [date]." This mechanism makes it easy for even novice investors to obtain investment information, lowering the barrier to entry for investing. In addition, because the generative AI automatically collects and analyzes information, users can obtain the latest investment information without any effort. Thus, the investment information provision system makes it easy for even novice investors to obtain investment information, lowering the barrier to entry for investing.

[0029] The investment information provision system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives user requests. User requests include, but are not limited to, text format, voice format, and specific questions. For example, the user can input a request to the reception unit such as, "I'm considering investing 500,000 yen in NISA. Please tell me about investment information ranging from tourism to airlines." The collection unit collects information based on the requests received by the reception unit. Information collection includes, but is not limited to, web scraping and data acquisition using APIs. The collection unit collects data from, for example, news sites, government databases, and company websites. The analysis unit analyzes the information collected by the collection unit. Analysis includes, but is not limited to, statistical analysis and the use of machine learning algorithms. The analysis unit analyzes the information using, for example, regression analysis, clustering, and natural language processing. The provision unit provides the analysis results obtained by the analysis unit. Provision includes, but is not limited to, reports, dashboard displays, and notifications. The information provision unit provides detailed information, for example, in response to a user's request. This allows the investment information provision system according to the embodiment to efficiently acquire investment information by collecting, analyzing, and providing information based on user requests.

[0030] The reception desk receives user requests. User requests may include, but are not limited to, text, voice, or specific questions. Specifically, users can enter requests through a web interface or mobile app. For text requests, users use a keyboard to input specific questions or requests. For voice requests, speech recognition technology is used to convert the user's voice into text and analyze the request content. For example, a user might enter a request such as, "I'm considering investing 500,000 yen in a NISA account. Please tell me about investment information ranging from tourism to airlines." The reception desk plays a role in appropriately classifying these requests and passing them on to the subsequent processing departments. Furthermore, the reception desk can analyze the content of user requests and request additional information as needed. For example, if a request is vague or lacks detailed information, the reception desk will ask the user specific questions to clarify the request. This allows the reception desk to accurately understand the user's needs and provide a foundation for appropriate information gathering and analysis.

[0031] The data collection unit collects information based on requests received by the reception unit. Information collection includes, but is not limited to, web scraping and data acquisition using APIs. Specifically, the data collection unit selects the most suitable information source according to the user's request and collects the necessary data. For example, it uses web scraping techniques to obtain the latest market trends and company performance information from news sites. Web scraping automatically extracts the necessary information from specific web pages and stores it in a database. It also utilizes APIs to obtain reliable data from government databases and company websites. Using APIs allows for the efficient collection of real-time updated data. Furthermore, the data collection unit can also collect data from informal sources such as social media and forums, providing multifaceted information in response to user requests. This enables the data collection unit to quickly collect a wide range of information based on user requests and provide it to the analysis unit. To ensure the quality of the collected data, the data collection unit also includes processes to evaluate the reliability and accuracy of the data and eliminate inaccurate and duplicate data. This allows the data collection unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0032] The analysis unit analyzes the information collected by the data collection unit. Analysis includes, but is not limited to, statistical analysis and the use of machine learning algorithms. Specifically, the analysis unit preprocesses the collected data and converts it into a format suitable for analysis. Preprocessing includes data cleaning, normalization, and feature extraction. Next, the analysis unit analyzes the data using machine learning algorithms. For example, it uses regression analysis to predict the future performance of a particular investment and clustering to group similar investments. It also uses natural language processing techniques to analyze news articles and company reports to extract important information relevant to investments. Furthermore, the analysis unit integrates information from multiple data sources and performs a comprehensive analysis. For example, it combines company performance data and market trend data to evaluate the future growth potential of a particular company. Based on these analysis results, the analysis unit generates specific investment advice tailored to the user's request. This allows the analysis unit to highly analyze the collected data and provide valuable information to the user. Furthermore, the analysis unit continuously improves its algorithms and introduces new analysis methods to enhance the accuracy of its analysis results. This allows the analysis unit to always utilize the latest technology and provide highly accurate analysis results.

[0033] The service provider will provide the analysis results obtained by the analysis provider. This includes, but is not limited to, reports, dashboard displays, and notifications. Specifically, the service provider will provide information in the most suitable format according to the user's request. For example, it may generate reports containing detailed investment advice and provide them to users in PDF format. It may also use dashboard displays to visually show real-time updated investment information. Dashboards will use graphs and charts to allow users to intuitively understand the performance of investment targets and market trends. Furthermore, the service provider will utilize push notifications and email notifications to quickly convey important information and urgent notices to users. For example, if important news regarding a particular investment target occurs, users will be immediately notified to encourage prompt action. It is also important for the service provider to collect user feedback and continuously improve the quality and format of the information provided. For example, by allowing users to evaluate and comment on the information provided, the service provider can review its information delivery methods based on that feedback and provide more useful information to users. This allows the service provider to effectively communicate analysis results to users and support their investment decisions. Furthermore, the service provider can also provide customized information tailored to the individual needs of users. For example, by providing personalized advice based on specific investment strategies and risk tolerance, we can more effectively support users' investment activities.

[0034] The data collection unit can collect data from specific sources. These sources include, but are not limited to, news websites, government databases, and company websites. For example, the data collection unit can collect the latest news articles from news websites. It can also collect official statistical data from government databases. Furthermore, it can collect company press releases and financial reports from company websites. This allows for the provision of reliable information by collecting data from specific sources. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generative AI perform data collection from news websites.

[0035] The analysis unit can analyze information using specific analytical methods. These specific analytical methods include, but are not limited to, regression analysis, clustering, and natural language processing. For example, the analysis unit can use regression analysis to analyze trends in collected data. It can also group data and find patterns using clustering. Furthermore, it can analyze text data using natural language processing to extract important information. This improves the accuracy of the analysis results by using specific analytical methods. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can have a generative AI perform the analysis of the collected data.

[0036] The information provider can provide detailed information in response to user requests. This detailed information may include, but is not limited to, numerical data, graphs, and text reports. For example, the information provider can provide the latest financial results upon user request. It can also provide information on a company's business plans and new initiatives upon user request. Furthermore, it can provide relevant news articles and market trend information upon user request. By providing detailed information in response to user requests, the information provider can deliver information that meets the user's needs. Some or all of the above processing in the information provider may be performed using, for example, AI, or not. For example, the information provider can have a generating AI perform the task of providing information in response to user requests.

[0037] The data collection unit can collect information on recent financial results, business plans, and new initiatives. Recent financial results include, but are not limited to, revenue, profit, and income statements. For example, the data collection unit can collect a company's latest financial reports. It can also collect business plans, such as a company's medium-term plans and strategic objectives. Furthermore, it can collect information on new initiatives, such as new product development and marketing strategies. By collecting information on recent financial results, business plans, and new initiatives, the system can provide up-to-date investment information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI perform the collection of a company's financial results.

[0038] The analysis unit can analyze the collected information and provide it to the user. The collected information includes, but is not limited to, text data, numerical data, and image data. For example, the analysis unit can analyze the collected text data and extract important information. It can also analyze the collected numerical data and find trends and patterns in the data. Furthermore, it can analyze the collected image data and extract visual information. In this way, by analyzing the collected information, useful information can be provided to the user. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can have a generative AI perform the analysis of the collected data.

[0039] The reception desk can analyze the user's past request history and select an appropriate reception method. For example, the reception desk can automatically display as suggestions the stocks and markets that the user has frequently requested in the past. The reception desk can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest stocks and markets to use at specific times based on the user's past request history. In this way, by analyzing the user's past request history, the reception desk can provide the optimal reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have a generating AI perform the analysis of the user's past request history.

[0040] The reception desk can filter requests based on the user's current investment status and areas of interest. For example, it can refer to the user's current investment portfolio and prioritize displaying relevant stocks and markets. The reception desk can also filter requests based on the user's areas of interest (e.g., environmental, technology, etc.). Furthermore, the reception desk can suggest the optimal request method based on the user's investment goals (short-term, medium-term, long-term). This allows for the provision of highly relevant information by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have a generative AI perform an analysis of the user's investment portfolio.

[0041] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if a user is in a specific region, the reception desk can prioritize providing investment information relevant to that region. Furthermore, if a user is traveling, the reception desk can prioritize providing information about markets and stocks in their travel destination. Additionally, if a user is at home, the reception desk can prioritize providing information about local markets and stocks. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk could have a generating AI perform the analysis of the user's geographical location.

[0042] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can filter requests based on stocks or markets that the user has shown interest in on social media. The reception unit can also analyze investment trends from the user's social media activity and prioritize accepting relevant requests. Furthermore, the reception unit can filter requests by referring to the investment activities of the user's social media followers and friends. This allows the reception unit to accept highly relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can have a generative AI perform the analysis of the user's social media activity.

[0043] The data collection unit can evaluate the reliability of specific information sources and select appropriate sources during information gathering. For example, the data collection unit can evaluate the past performance of each information source and select reliable sources. The data collection unit can also evaluate the expertise and authority of information sources and select the most suitable sources. Furthermore, the data collection unit can evaluate the update frequency and recency of information sources and select the most suitable sources. In this way, reliable information can be collected by evaluating the reliability of specific information sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the reliability evaluation of information sources.

[0044] The data collection unit can prioritize collecting highly relevant information by referring to the user's past investment history during information gathering. For example, the data collection unit can prioritize collecting information related to stocks and markets that the user has invested in in the past. The data collection unit can also prioritize collecting information related to specific industries or sectors from the user's past investment history. Furthermore, the data collection unit can analyze the user's investment patterns and prioritize collecting highly relevant information. This allows for the collection of highly relevant information by referring to the user's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of the user's investment history.

[0045] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting investment information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting information about markets and stocks in the travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting information about local markets and stocks. This allows for the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of the user's geographical location.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on stocks or markets that the user has shown interest in on social media. The data collection unit can also analyze investment trends from the user's social media activity and prioritize the collection of relevant information. Furthermore, the data collection unit can collect information by referring to the investment activities of the user's social media followers and friends. In this way, highly relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of the user's social media activity.

[0047] The analysis unit can evaluate the reliability of the collected information during analysis and improve the accuracy of the analysis results. For example, the analysis unit can evaluate the reliability of each information source and prioritize the analysis of highly reliable data. The analysis unit can also evaluate the consistency of the collected information and analyze data that is consistent. Furthermore, the analysis unit can evaluate the timeliness of the collected information and prioritize the analysis of the most recent data. By evaluating the reliability of the collected information, the accuracy of the analysis results is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the reliability evaluation of the information.

[0048] The analysis unit can prioritize providing highly relevant analysis results by referring to the user's past investment history during analysis. For example, the analysis unit can prioritize providing analysis results related to stocks or markets in which the user has previously invested. Furthermore, the analysis unit can prioritize providing analysis results related to specific industries or sectors based on the user's past investment history. In addition, the analysis unit can analyze the user's investment patterns and prioritize providing highly relevant analysis results. This allows the analysis unit to provide highly relevant results by referring to the user's past investment history. 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 have a generating AI perform the analysis of the user's investment history.

[0049] The analysis unit can prioritize providing highly relevant analysis results by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit will prioritize providing analysis results related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize providing analysis results related to the market or brands of their travel destination. Additionally, if the user is at home, the analysis unit can prioritize providing analysis results related to local markets or brands. This allows for the provision of highly relevant analysis results by considering the user's geographical location. 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 have a generating AI perform the analysis of the user's geographical location.

[0050] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can provide analysis results based on stocks or markets that the user has shown interest in on social media. The analysis unit can also analyze investment trends from the user's social media activity and prioritize providing relevant analysis results. Furthermore, the analysis unit can provide analysis results by referring to the investment activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, highly relevant analysis results can be provided. 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 have a generative AI perform the analysis of the user's social media activity.

[0051] The information provider can select an appropriate method of providing information by referring to the user's past investment history. For example, the provider may prioritize providing information related to stocks or markets in which the user has previously invested. It can also prioritize providing information related to specific industries or sectors based on the user's past investment history. Furthermore, the provider may analyze the user's investment patterns and prioritize providing highly relevant information. This allows the provider to select the optimal method of providing information by referring to the user's past investment history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider may have a generating AI perform the analysis of the user's investment history.

[0052] The information provider can customize the means of providing information based on the user's current investment status. For example, the provider can refer to the user's current investment portfolio and prioritize providing relevant information. The provider can also suggest the optimal method of providing information based on the user's investment goals (short-term, medium-term, long-term). Furthermore, the provider can customize the means of providing information based on the user's investment risk tolerance. This allows for the provision of optimal information to the user by customizing the means of providing information based on the user's current investment status. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can have a generating AI perform an analysis of the user's investment status.

[0053] The information provider can select an appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific region, the provider can prioritize providing investment information related to that region. Furthermore, if the user is traveling, the provider can prioritize providing information about markets and stocks in the travel destination. Additionally, if the user is at home, the provider can prioritize providing information about local markets and stocks. This allows the provider to select the optimal method of information delivery by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can have a generating AI perform the analysis of the user's geographical location.

[0054] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can provide information based on stocks or markets that the user has shown interest in on social media. The provider can also analyze investment trends from the user's social media activity and prioritize providing relevant information. Furthermore, the provider can provide information by referring to the investment activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can have a generative AI perform the analysis of the user's social media activity.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] When receiving a user's request, the reception desk can refer to the user's past investment history and automatically display relevant stocks and markets as suggestions. For example, it can prioritize displaying stocks and markets that the user has frequently requested in the past. The reception desk can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest stocks and markets to use at specific times based on the user's past request history. In this way, by analyzing the user's past request history, the reception desk can provide the most optimal reception method.

[0057] The data collection unit can evaluate the reliability of information sources when collecting data from specific sources and prioritize the selection of highly reliable sources. For example, it can evaluate the past performance of each information source and select the most reliable one. The data collection unit can also evaluate the expertise and authority of information sources and select the most suitable one. Furthermore, the data collection unit can evaluate the update frequency and recency of information sources and select the most suitable one. In this way, by evaluating the reliability of specific information sources, highly reliable information can be collected.

[0058] The analysis unit, when analyzing information using specific analytical methods, can refer to the user's past investment history and prioritize providing highly relevant analysis results. For example, it can prioritize providing analysis results related to stocks or markets the user has invested in in the past. Furthermore, the analysis unit can prioritize providing analysis results related to specific industries or sectors based on the user's past investment history. In addition, the analysis unit can analyze the user's investment patterns and prioritize providing highly relevant analysis results. This allows the system to provide highly relevant analysis results by referencing the user's past investment history.

[0059] The service provider can filter detailed information based on the user's current investment status and areas of interest when providing it in response to user requests. For example, it can refer to the user's current investment portfolio and prioritize displaying relevant stocks and markets. The service provider can also filter requests based on the user's areas of interest (e.g., environmental, technology, etc.). Furthermore, the service provider can suggest the most suitable request method based on the user's investment goals (short-term, medium-term, long-term). This allows for the provision of highly relevant information by filtering based on the user's current investment status and areas of interest.

[0060] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting investment information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting information about markets and stocks in their travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting information about local markets and stocks. This allows for the collection of highly relevant information by considering the user's geographical location.

[0061] The service provider can analyze a user's social media activity and provide relevant information when offering detailed information in response to a user's request. For example, it can provide information based on stocks or markets the user has shown interest in on social media. Furthermore, the service provider can analyze investment trends from the user's social media activity and prioritize providing relevant information. It can also provide information based on the investment activities of the user's social media followers and friends. This allows the service provider to provide highly relevant information by analyzing the user's social media activity.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user requests. User requests can include, for example, text, audio, or specific questions. For example, a user might enter a request such as, "I'm considering investing 500,000 yen in a NISA account. Please tell me about investment information ranging from tourism to airlines." Step 2: The collection unit collects information based on requests received by the reception unit. Information collection includes, for example, web scraping and data retrieval using APIs. The collection unit collects data from sources such as news sites, government databases, and company websites. Step 3: The analysis unit analyzes the information collected by the data collection unit. Analysis includes, for example, statistical analysis and the use of machine learning algorithms. The analysis unit analyzes the information using techniques such as regression analysis, clustering, and natural language processing. Step 4: The service provider provides the analysis results obtained by the analysis service provider. This includes, for example, report formats, dashboard displays, and notifications. The service provider provides detailed information upon user request.

[0064] (Example of form 2) The investment information provision system according to an embodiment of the present invention is an application using a generative AI to improve the current situation where investment information is scattered and to lower the barrier to entry for novice investors. In this system, when a user inputs the stocks of the market they wish to invest in into the application, the generative AI collects, analyzes, and provides relevant investment information. For example, if a user inputs a request such as, "I'm considering investing 500,000 yen in NISA. Please tell me about investment information ranging from tourism to airlines," the generative AI collects information such as the latest financial results, business plans, and new initiatives and provides it to the user. Furthermore, if the user requests additional information, the generative AI will provide even more detailed information in response to that request. For example, in response to a request such as, "Please tell me about the airline's initiatives over the past six months," the AI ​​will provide information such as, "Carbon neutrality-related news on [date]," and "Upward revision of business plan announced on [date]." This mechanism makes it easy for even novice investors to obtain investment information, lowering the barrier to entry for investing. In addition, because the generative AI automatically collects and analyzes information, users can obtain the latest investment information without any effort. Thus, the investment information provision system makes it easy for even novice investors to obtain investment information, lowering the barrier to entry for investing.

[0065] The investment information provision system according to this embodiment comprises a reception unit, a collection unit, an analysis unit, and a provision unit. The reception unit receives user requests. User requests include, but are not limited to, text format, voice format, and specific questions. For example, the user can input a request to the reception unit such as, "I'm considering investing 500,000 yen in NISA. Please tell me about investment information ranging from tourism to airlines." The collection unit collects information based on the requests received by the reception unit. Information collection includes, but is not limited to, web scraping and data acquisition using APIs. The collection unit collects data from, for example, news sites, government databases, and company websites. The analysis unit analyzes the information collected by the collection unit. Analysis includes, but is not limited to, statistical analysis and the use of machine learning algorithms. The analysis unit analyzes the information using, for example, regression analysis, clustering, and natural language processing. The provision unit provides the analysis results obtained by the analysis unit. Provision includes, but is not limited to, reports, dashboard displays, and notifications. The information provision unit provides detailed information, for example, in response to a user's request. This allows the investment information provision system according to the embodiment to efficiently acquire investment information by collecting, analyzing, and providing information based on user requests.

[0066] The reception desk receives user requests. User requests may include, but are not limited to, text, voice, or specific questions. Specifically, users can enter requests through a web interface or mobile app. For text requests, users use a keyboard to input specific questions or requests. For voice requests, speech recognition technology is used to convert the user's voice into text and analyze the request content. For example, a user might enter a request such as, "I'm considering investing 500,000 yen in a NISA account. Please tell me about investment information ranging from tourism to airlines." The reception desk plays a role in appropriately classifying these requests and passing them on to the subsequent processing departments. Furthermore, the reception desk can analyze the content of user requests and request additional information as needed. For example, if a request is vague or lacks detailed information, the reception desk will ask the user specific questions to clarify the request. This allows the reception desk to accurately understand the user's needs and provide a foundation for appropriate information gathering and analysis.

[0067] The data collection unit collects information based on requests received by the reception unit. Information collection includes, but is not limited to, web scraping and data acquisition using APIs. Specifically, the data collection unit selects the most suitable information source according to the user's request and collects the necessary data. For example, it uses web scraping techniques to obtain the latest market trends and company performance information from news sites. Web scraping automatically extracts the necessary information from specific web pages and stores it in a database. It also utilizes APIs to obtain reliable data from government databases and company websites. Using APIs allows for the efficient collection of real-time updated data. Furthermore, the data collection unit can also collect data from informal sources such as social media and forums, providing multifaceted information in response to user requests. This enables the data collection unit to quickly collect a wide range of information based on user requests and provide it to the analysis unit. To ensure the quality of the collected data, the data collection unit also includes processes to evaluate the reliability and accuracy of the data and eliminate inaccurate and duplicate data. This allows the data collection unit to provide reliable data and improve the overall accuracy and reliability of the system.

[0068] The analysis unit analyzes the information collected by the data collection unit. Analysis includes, but is not limited to, statistical analysis and the use of machine learning algorithms. Specifically, the analysis unit preprocesses the collected data and converts it into a format suitable for analysis. Preprocessing includes data cleaning, normalization, and feature extraction. Next, the analysis unit analyzes the data using machine learning algorithms. For example, it uses regression analysis to predict the future performance of a particular investment and clustering to group similar investments. It also uses natural language processing techniques to analyze news articles and company reports to extract important information relevant to investments. Furthermore, the analysis unit integrates information from multiple data sources and performs a comprehensive analysis. For example, it combines company performance data and market trend data to evaluate the future growth potential of a particular company. Based on these analysis results, the analysis unit generates specific investment advice tailored to the user's request. This allows the analysis unit to highly analyze the collected data and provide valuable information to the user. Furthermore, the analysis unit continuously improves its algorithms and introduces new analysis methods to enhance the accuracy of its analysis results. This allows the analysis unit to always utilize the latest technology and provide highly accurate analysis results.

[0069] The service provider will provide the analysis results obtained by the analysis provider. This includes, but is not limited to, reports, dashboard displays, and notifications. Specifically, the service provider will provide information in the most suitable format according to the user's request. For example, it may generate reports containing detailed investment advice and provide them to users in PDF format. It may also use dashboard displays to visually show real-time updated investment information. Dashboards will use graphs and charts to allow users to intuitively understand the performance of investment targets and market trends. Furthermore, the service provider will utilize push notifications and email notifications to quickly convey important information and urgent notices to users. For example, if important news regarding a particular investment target occurs, users will be immediately notified to encourage prompt action. It is also important for the service provider to collect user feedback and continuously improve the quality and format of the information provided. For example, by allowing users to evaluate and comment on the information provided, the service provider can review its information delivery methods based on that feedback and provide more useful information to users. This allows the service provider to effectively communicate analysis results to users and support their investment decisions. Furthermore, the service provider can also provide customized information tailored to the individual needs of users. For example, by providing personalized advice based on specific investment strategies and risk tolerance, we can more effectively support users' investment activities.

[0070] The data collection unit can collect data from specific sources. These sources include, but are not limited to, news websites, government databases, and company websites. For example, the data collection unit can collect the latest news articles from news websites. It can also collect official statistical data from government databases. Furthermore, it can collect company press releases and financial reports from company websites. This allows for the provision of reliable information by collecting data from specific sources. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generative AI perform data collection from news websites.

[0071] The analysis unit can analyze information using specific analytical methods. These specific analytical methods include, but are not limited to, regression analysis, clustering, and natural language processing. For example, the analysis unit can use regression analysis to analyze trends in collected data. It can also group data and find patterns using clustering. Furthermore, it can analyze text data using natural language processing to extract important information. This improves the accuracy of the analysis results by using specific analytical methods. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can have a generative AI perform the analysis of the collected data.

[0072] The information provider can provide detailed information in response to user requests. This detailed information may include, but is not limited to, numerical data, graphs, and text reports. For example, the information provider can provide the latest financial results upon user request. It can also provide information on a company's business plans and new initiatives upon user request. Furthermore, it can provide relevant news articles and market trend information upon user request. By providing detailed information in response to user requests, the information provider can deliver information that meets the user's needs. Some or all of the above processing in the information provider may be performed using, for example, AI, or not. For example, the information provider can have a generating AI perform the task of providing information in response to user requests.

[0073] The data collection unit can collect information on recent financial results, business plans, and new initiatives. Recent financial results include, but are not limited to, revenue, profit, and income statements. For example, the data collection unit can collect a company's latest financial reports. It can also collect business plans, such as a company's medium-term plans and strategic objectives. Furthermore, it can collect information on new initiatives, such as new product development and marketing strategies. By collecting information on recent financial results, business plans, and new initiatives, the system can provide up-to-date investment information. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can have a generating AI perform the collection of a company's financial results.

[0074] The analysis unit can analyze the collected information and provide it to the user. The collected information includes, but is not limited to, text data, numerical data, and image data. For example, the analysis unit can analyze the collected text data and extract important information. It can also analyze the collected numerical data and find trends and patterns in the data. Furthermore, it can analyze the collected image data and extract visual information. In this way, by analyzing the collected information, useful information can be provided to the user. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can have a generative AI perform the analysis of the collected data.

[0075] The reception desk can estimate the user's emotions and adjust the request processing method based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the steps required to input the request. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable request method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and process the request quickly. This improves user convenience by adjusting the request processing method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can have a generative AI perform user emotion estimation.

[0076] The reception desk can analyze the user's past request history and select an appropriate reception method. For example, the reception desk can automatically display as suggestions the stocks and markets that the user has frequently requested in the past. The reception desk can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest stocks and markets to use at specific times based on the user's past request history. In this way, by analyzing the user's past request history, the reception desk can provide the optimal reception method. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have a generating AI perform the analysis of the user's past request history.

[0077] The reception desk can filter requests based on the user's current investment status and areas of interest. For example, it can refer to the user's current investment portfolio and prioritize displaying relevant stocks and markets. The reception desk can also filter requests based on the user's areas of interest (e.g., environmental, technology, etc.). Furthermore, the reception desk can suggest the optimal request method based on the user's investment goals (short-term, medium-term, long-term). This allows for the provision of highly relevant information by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can have a generative AI perform an analysis of the user's investment portfolio.

[0078] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is feeling anxious, the reception desk will prioritize important requests. If the user is relaxed, the reception desk can process requests with normal priority. Furthermore, if the user is in a hurry, the reception desk can prioritize urgent requests. This allows for the priority of important requests by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can have a generative AI perform user emotion estimation.

[0079] The reception desk can prioritize requests based on their relevance, taking into account the user's geographical location. For example, if a user is in a specific region, the reception desk can prioritize providing investment information relevant to that region. Furthermore, if a user is traveling, the reception desk can prioritize providing information about markets and stocks in their travel destination. Additionally, if a user is at home, the reception desk can prioritize providing information about local markets and stocks. This allows for the provision of highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk could have a generating AI perform the analysis of the user's geographical location.

[0080] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can filter requests based on stocks or markets that the user has shown interest in on social media. The reception unit can also analyze investment trends from the user's social media activity and prioritize accepting relevant requests. Furthermore, the reception unit can filter requests by referring to the investment activities of the user's social media followers and friends. This allows the reception unit to accept highly relevant requests by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can have a generative AI perform the analysis of the user's social media activity.

[0081] The data collection unit can estimate the user's emotions and adjust its information collection methods based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information from reliable sources. If the user is relaxed, the data collection unit can also collect information from a wide range of sources. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information from sources that can be obtained quickly. This allows for the collection of useful information for the user by adjusting the information collection methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have a generative AI perform user emotion estimation.

[0082] The data collection unit can evaluate the reliability of specific information sources and select appropriate sources during information gathering. For example, the data collection unit can evaluate the past performance of each information source and select reliable sources. The data collection unit can also evaluate the expertise and authority of information sources and select the most suitable sources. Furthermore, the data collection unit can evaluate the update frequency and recency of information sources and select the most suitable sources. In this way, reliable information can be collected by evaluating the reliability of specific information sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the reliability evaluation of information sources.

[0083] The data collection unit can prioritize collecting highly relevant information by referring to the user's past investment history during information gathering. For example, the data collection unit can prioritize collecting information related to stocks and markets that the user has invested in in the past. The data collection unit can also prioritize collecting information related to specific industries or sectors from the user's past investment history. Furthermore, the data collection unit can analyze the user's investment patterns and prioritize collecting highly relevant information. This allows for the collection of highly relevant information by referring to the user's past investment history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of the user's investment history.

[0084] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important information. If the user is relaxed, the data collection unit can also collect information with normal priority. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information of highest urgency. In this way, by prioritizing information according to the user's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have a generative AI perform user emotion estimation.

[0085] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit will prioritize collecting investment information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting information about markets and stocks in the travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting information about local markets and stocks. This allows for the collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generating AI perform the analysis of the user's geographical location.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information based on stocks or markets that the user has shown interest in on social media. The data collection unit can also analyze investment trends from the user's social media activity and prioritize the collection of relevant information. Furthermore, the data collection unit can collect information by referring to the investment activities of the user's social media followers and friends. In this way, highly relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of the user's social media activity.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing highly reliable data. If the user is relaxed, the analysis unit can also analyze a wide range of data. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing data that can be analyzed quickly. By adjusting the analysis method according to the user's emotions, useful analysis results can be provided to the user. 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 without AI. For example, the analysis unit can have a generative AI perform user emotion estimation.

[0088] The analysis unit can evaluate the reliability of the collected information during analysis and improve the accuracy of the analysis results. For example, the analysis unit can evaluate the reliability of each information source and prioritize the analysis of highly reliable data. The analysis unit can also evaluate the consistency of the collected information and analyze data that is consistent. Furthermore, the analysis unit can evaluate the timeliness of the collected information and prioritize the analysis of the most recent data. By evaluating the reliability of the collected information, the accuracy of the analysis results is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the reliability evaluation of the information.

[0089] The analysis unit can prioritize providing highly relevant analysis results by referring to the user's past investment history during analysis. For example, the analysis unit can prioritize providing analysis results related to stocks or markets in which the user has previously invested. Furthermore, the analysis unit can prioritize providing analysis results related to specific industries or sectors based on the user's past investment history. In addition, the analysis unit can analyze the user's investment patterns and prioritize providing highly relevant analysis results. This allows the analysis unit to provide highly relevant results by referring to the user's past investment history. 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 have a generating AI perform the analysis of the user's investment history.

[0090] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display is possible for the user. 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 without AI. For example, the analysis unit can have a generative AI perform user emotion estimation.

[0091] The analysis unit can prioritize providing highly relevant analysis results by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit will prioritize providing analysis results related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize providing analysis results related to the market or brands of their travel destination. Additionally, if the user is at home, the analysis unit can prioritize providing analysis results related to local markets or brands. This allows for the provision of highly relevant analysis results by considering the user's geographical location. 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 have a generating AI perform the analysis of the user's geographical location.

[0092] The analysis unit can analyze a user's social media activity during analysis and provide relevant analysis results. For example, the analysis unit can provide analysis results based on stocks or markets that the user has shown interest in on social media. The analysis unit can also analyze investment trends from the user's social media activity and prioritize providing relevant analysis results. Furthermore, the analysis unit can provide analysis results by referring to the investment activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, highly relevant analysis results can be provided. 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 have a generative AI perform the analysis of the user's social media activity.

[0093] The information provider can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider can provide a simple and highly visible method of information delivery. If the user is relaxed, the information provider can also provide a method of information delivery that includes detailed information. Furthermore, if the user is in a hurry, the information provider can provide a concise method of information delivery. By adjusting the method of information delivery according to the user's emotions, it becomes possible to provide information that is highly visible to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can have a generative AI perform user emotion estimation.

[0094] The information provider can select an appropriate method of providing information by referring to the user's past investment history. For example, the provider may prioritize providing information related to stocks or markets in which the user has previously invested. It can also prioritize providing information related to specific industries or sectors based on the user's past investment history. Furthermore, the provider may analyze the user's investment patterns and prioritize providing highly relevant information. This allows the provider to select the optimal method of providing information by referring to the user's past investment history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider may have a generating AI perform the analysis of the user's investment history.

[0095] The information provider can customize the means of providing information based on the user's current investment status. For example, the provider can refer to the user's current investment portfolio and prioritize providing relevant information. The provider can also suggest the optimal method of providing information based on the user's investment goals (short-term, medium-term, long-term). Furthermore, the provider can customize the means of providing information based on the user's investment risk tolerance. This allows for the provision of optimal information to the user by customizing the means of providing information based on the user's current investment status. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can have a generating AI perform an analysis of the user's investment status.

[0096] The information provider can estimate the user's emotions and determine the priority of information delivery based on the estimated emotions. For example, if the user is feeling anxious, the information provider will prioritize providing important information. If the user is relaxed, the information provider can also provide information with normal priority. Furthermore, if the user is in a hurry, the information provider can prioritize providing highly urgent information. In this way, by determining the priority of information delivery according to the user's emotions, important information can be delivered preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the information provider can have a generative AI perform user emotion estimation.

[0097] The information provider can select an appropriate method of information delivery by considering the user's geographical location. For example, if the user is in a specific region, the provider can prioritize providing investment information related to that region. Furthermore, if the user is traveling, the provider can prioritize providing information about markets and stocks in the travel destination. Additionally, if the user is at home, the provider can prioritize providing information about local markets and stocks. This allows the provider to select the optimal method of information delivery by considering the user's geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can have a generating AI perform the analysis of the user's geographical location.

[0098] The information provider can analyze the user's social media activity and provide relevant information when providing information. For example, the provider can provide information based on stocks or markets that the user has shown interest in on social media. The provider can also analyze investment trends from the user's social media activity and prioritize providing relevant information. Furthermore, the provider can provide information by referring to the investment activities of the user's social media followers and friends. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can have a generative AI perform the analysis of the user's social media activity.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] When receiving a user's request, the reception desk can refer to the user's past investment history and automatically display relevant stocks and markets as suggestions. For example, it can prioritize displaying stocks and markets that the user has frequently requested in the past. The reception desk can also prioritize suggesting request methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest stocks and markets to use at specific times based on the user's past request history. In this way, by analyzing the user's past request history, the reception desk can provide the most optimal reception method.

[0101] The data collection unit can evaluate the reliability of information sources when collecting data from specific sources and prioritize the selection of highly reliable sources. For example, it can evaluate the past performance of each information source and select the most reliable one. The data collection unit can also evaluate the expertise and authority of information sources and select the most suitable one. Furthermore, the data collection unit can evaluate the update frequency and recency of information sources and select the most suitable one. In this way, by evaluating the reliability of specific information sources, highly reliable information can be collected.

[0102] The analysis unit, when analyzing information using specific analytical methods, can refer to the user's past investment history and prioritize providing highly relevant analysis results. For example, it can prioritize providing analysis results related to stocks or markets the user has invested in in the past. Furthermore, the analysis unit can prioritize providing analysis results related to specific industries or sectors based on the user's past investment history. In addition, the analysis unit can analyze the user's investment patterns and prioritize providing highly relevant analysis results. This allows the system to provide highly relevant analysis results by referencing the user's past investment history.

[0103] The service provider can filter detailed information based on the user's current investment status and areas of interest when providing it in response to user requests. For example, it can refer to the user's current investment portfolio and prioritize displaying relevant stocks and markets. The service provider can also filter requests based on the user's areas of interest (e.g., environmental, technology, etc.). Furthermore, the service provider can suggest the most suitable request method based on the user's investment goals (short-term, medium-term, long-term). This allows for the provision of highly relevant information by filtering based on the user's current investment status and areas of interest.

[0104] The data collection unit can estimate the user's emotions when gathering information on recent financial results, business plans, and new initiatives, and adjust its information collection methods based on these estimated emotions. For example, if the user is feeling anxious, it will prioritize collecting information from highly reliable sources. Conversely, if the user is relaxed, the data collection unit can collect information from a wide range of sources. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information from sources that can be obtained quickly. By adjusting the information collection method according to the user's emotions, it is possible to collect information that is useful to the user.

[0105] The analysis unit can estimate the user's emotions when analyzing collected information and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, it will prioritize analyzing highly reliable data. If the user is relaxed, the analysis unit can also analyze a wide range of data. Furthermore, if the user is in a hurry, it can prioritize analyzing data that can be processed quickly. By adjusting the analysis method according to the user's emotions, it can provide the user with useful analysis results.

[0106] The information delivery system can estimate the user's emotions when providing detailed information in response to user requests, and adjust the method of information delivery based on those estimated emotions. For example, if the user is feeling anxious, it can provide a simple and highly visible method of information delivery. If the user is relaxed, the system can also provide a more detailed method of information delivery. Furthermore, if the user is in a hurry, it can provide a concise and to-the-point method of information delivery. By adjusting the method of information delivery according to the user's emotions, it becomes possible to provide information that is highly visible to the user.

[0107] The reception desk can estimate the user's emotions when receiving a request and adjust the request processing method based on that estimation. For example, if the user is feeling anxious, it can provide a simple and intuitive interface and minimize the steps required to enter the request. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable request method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input and process the request quickly. This improves user convenience by adjusting the request processing method according to the user's emotions.

[0108] The data collection unit can prioritize collecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize collecting investment information related to that region. Furthermore, if the user is traveling, the data collection unit can prioritize collecting information about markets and stocks in their travel destination. Additionally, if the user is at home, the data collection unit can prioritize collecting information about local markets and stocks. This allows for the collection of highly relevant information by considering the user's geographical location.

[0109] The service provider can analyze a user's social media activity and provide relevant information when offering detailed information in response to a user's request. For example, it can provide information based on stocks or markets the user has shown interest in on social media. Furthermore, the service provider can analyze investment trends from the user's social media activity and prioritize providing relevant information. It can also provide information based on the investment activities of the user's social media followers and friends. This allows the service provider to provide highly relevant information by analyzing the user's social media activity.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives user requests. User requests can include, for example, text, audio, or specific questions. For example, a user might enter a request such as, "I'm considering investing 500,000 yen in a NISA account. Please tell me about investment information ranging from tourism to airlines." Step 2: The collection unit collects information based on requests received by the reception unit. Information collection includes, for example, web scraping and data retrieval using APIs. The collection unit collects data from sources such as news sites, government databases, and company websites. Step 3: The analysis unit analyzes the information collected by the data collection unit. Analysis includes, for example, statistical analysis and the use of machine learning algorithms. The analysis unit analyzes the information using techniques such as regression analysis, clustering, and natural language processing. Step 4: The service provider provides the analysis results obtained by the analysis service provider. This includes, for example, report formats, dashboard displays, and notifications. The service provider provides detailed information upon user request.

[0112] 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] 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.

[0115] For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user requests. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects information using web scraping or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using statistical analysis or machine learning algorithms. The provision unit is implemented by the output device 40 of the smart device 14 and provides the analysis results in report format or dashboard display. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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.

[0131] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives user requests. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects information using web scraping or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using statistical analysis or machine learning algorithms. The delivery unit is implemented by the speaker 240 of the smart glasses 214 and provides the analysis results in voice or text. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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.

[0147] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives user requests. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects information using web scraping or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using statistical analysis or machine learning algorithms. The provision unit is implemented by the display 343 of the headset terminal 314 and provides the analysis results visually. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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.

[0164] For example, the reception unit is implemented by the microphone 238 of the robot 414, which receives user requests. The data collection unit is implemented by the specific processing unit 290 of the data processing device 12, for example, which collects information using web scraping or APIs. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, for example, which analyzes the collected information using statistical analysis or machine learning algorithms. The data provision unit is implemented by the speaker 240 or display device of the robot 414, for example, which provides the analysis results by voice or visually. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that accepts user requests, A collection unit that collects information based on requests received by the aforementioned reception unit, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data from specific sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze information using specific analytical methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide detailed information upon user request. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Gather the latest financial results, business plans, and information on new initiatives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The collected information is analyzed and provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are processed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past request history and select the appropriate method of receiving the request. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a request is received, it is filtered based on the user's current investment status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and adjusts the information gathering method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When gathering information, evaluate the reliability of specific information sources and select appropriate sources. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When gathering information, the system prioritizes collecting highly relevant information by referring to the user's past investment history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the reliability of the collected information is evaluated, and the accuracy of the analysis results is improved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the system prioritizes providing highly relevant analysis results by referencing the user's past investment history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the system prioritizes providing highly relevant analysis results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, the system analyzes the user's social media activity and provides relevant analytical results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, the appropriate method of information provision is selected by referring to the user's past investment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing information, the method of providing information will be customized based on the user's current investment status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, the appropriate method of information provision will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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. A reception desk that accepts user requests, A collection unit that collects information based on requests received by the aforementioned reception unit, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data from specific sources. The system according to feature 1.

3. The aforementioned analysis unit, Analyze information using specific analytical methods. The system according to feature 1.

4. The aforementioned supply unit is, Provide detailed information upon user request. The system according to feature 1.

5. The aforementioned collection unit is Gather the latest financial results, business plans, and information on new initiatives. The system according to feature 1.

6. The aforementioned analysis unit, The collected information is analyzed and provided to the user. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts how requests are processed based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past request history and select the appropriate method of receiving the request. The system according to feature 1.

9. The aforementioned reception unit is When a request is received, it is filtered based on the user's current investment status and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system according to feature 1.

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