system

Abstract

The system according to this embodiment aims to make effective use of the user's waiting time and provide information that will interest the user. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a display unit. The analysis unit analyzes the user's most recent input characters and attributes. The generation unit generates trivia and personal mottos based on the information analyzed by the analysis unit. The display unit displays the information generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 conventional technology, means for effectively utilizing the waiting time of the user are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively utilize the waiting time of the user and provide information that attracts the user's interest.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a display unit. The analysis unit analyzes the user's previous input characters and attributes. The generation unit generates miscellaneous knowledge and mottos based on the information analyzed by the analysis unit. The display unit displays the information generated by the generation unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can effectively utilize the user's waiting time and provide information that will interest the user. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An API Bot system according to an embodiment of the present invention is a system that displays trivia or a motto during the waiting time for time-consuming processes, such as waiting for a response from a generating AI. In this API Bot system, the user sends a request to the generating AI, which receives the request and starts processing, but this processing may take time. During this waiting time, the API Bot operates. The API Bot first analyzes the most recent input characters and user attributes. Next, the API Bot displays the generated trivia or motto to the user. This allows the user to obtain interesting information during the waiting time and encourages them to continue waiting without abandoning the service. This mechanism can improve the user experience during the waiting time for the generating AI. For example, the user can obtain useful information or interesting trivia during the waiting time, increasing their motivation to continue using the service. Furthermore, because the API Bot provides information tailored to the user's needs, it can provide a personalized experience for each individual user. In this way, the API Bot system can provide interesting information during the user's waiting time and improve the user experience.

[0029] The API Bot system according to this embodiment comprises an analysis unit, a generation unit, and a display unit. The analysis unit analyzes the user's most recent input characters and attributes. For example, the analysis unit analyzes the text entered by the user and attribute information such as the user's age and gender. The analysis unit can analyze the text using natural language processing technology to understand the user's intent and interests. For example, the analysis unit tokenizes the text entered by the user and analyzes the meaning of each token. The analysis unit can also analyze the user's attribute information using statistical analysis technology to grasp the user's tendencies. The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. For example, the generation unit generates trivia and mottos using a generation AI. The generation AI can generate trivia and mottos that match the user's interests using a text generation AI (e.g., LLM). For example, the generation unit inputs the user's input text as a prompt to the generation AI, and the generation AI generates trivia and mottos. Furthermore, the generation unit can randomly generate trivia or mottos using algorithmic generation technology. The display unit displays the information generated by the generation unit to the user. The display unit can, for example, use text display technology to display the generated trivia or mottos on the user's screen. The display unit can also use graphical display technology to display the information in a visually appealing format. For example, the display unit can display the generated information with a colorful background and icons to attract the user's interest. As a result, the API Bot system according to the embodiment can improve the user experience by analyzing the user's most recent input characters and attributes and displaying the generated information.

[0030] The analysis unit analyzes the user's most recent input characters and attributes. Specifically, it analyzes the text entered by the user using natural language processing technology to understand the user's intent and interests. For example, if a user enters "What's the weather like today?", the analysis unit tokenizes this text and extracts keywords such as "today" and "weather". Furthermore, the analysis unit analyzes attribute information such as the user's age, gender, and past input history using statistical analysis technology to understand the user's tendencies. For example, if a young user asks many sports-related questions, the analysis unit can determine that the user is interested in sports. In this way, the analysis unit can comprehensively analyze the user's input text and attribute information to understand the user's intent and interests with high accuracy. In addition, the analysis unit can also predict future behavior from the user's input patterns and attribute information using machine learning algorithms. For example, based on past data, it can prepare to provide relevant information in advance to users who tend to ask specific questions at specific times of day. In this way, the analysis unit can anticipate user needs and provide more personalized services.

[0031] The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. Specifically, it uses a generation AI to generate trivia and mottos that match the user's interests. For example, if the user inputs "I want to increase my motivation," the generation unit inputs this text as a prompt to the generation AI, which then generates a motto such as "Success is the accumulation of small efforts." The generation AI uses a text generation AI (e.g., LLM) and has learned from a large amount of text data, enabling it to generate diverse expressions and content. The generation unit can also generate trivia and mottos randomly using algorithmic generation technology. For example, it can combine randomly selected information from a database related to a specific keyword to generate new trivia. This allows the generation unit to provide diverse information that meets the user's interests and needs. Furthermore, the generation unit has a feedback loop to evaluate the quality of the generated information, allowing it to continuously improve the generation algorithm based on user evaluations and reactions. This ensures that the generation unit always provides high-quality and useful information to the user.

[0032] The display unit displays information generated by the generation unit to the user. Specifically, it uses text display technology to display generated trivia and mottos on the user's screen. For example, if the user is using a smartphone, the generated information will be displayed as a notification bar or pop-up window. The display unit can also use graphical display technology to display information in a visually appealing format. For example, generated mottos can be displayed with a colorful background and icons to create a design that attracts the user's interest. Furthermore, the display unit can optimize the display format according to the user's device and usage environment. For example, for users using a desktop computer, the information can be displayed as a browser sidebar or widget, and for users using a smartwatch, the information can be displayed as short text and icons. In this way, the display unit can provide information in a format that is optimal for the user's device and usage environment, improving the user experience. In addition, the display unit can collect user feedback and use it to improve the display format and content. For example, if a user prefers a particular display format, the display unit can adjust the display method based on that information to provide a more user-friendly interface. In this way, the display unit can provide flexible display methods that meet user needs, improving the user experience.

[0033] The generation unit includes a source display unit that shows the sources and citations of the generated information. The generation unit can enhance the reliability of the information by displaying the sources and citations of generated trivia or mottos, for example. The source display unit displays source information such as books, websites, and academic papers. The source display unit displays source information below the generated information, allowing users to verify its reliability. For example, the source display unit displays information such as "Source: XX Book" or "Cited from: XX Website" below the generated trivia. The source display unit can also display source information as a link, allowing users to click and view more detailed information. In this way, the generation unit can enhance the reliability of the information by displaying the sources and citations of the generated information.

[0034] The generation unit includes a needs understanding unit that generates information based on user needs. For example, the generation unit analyzes the user's most recent input characters and attribute information to understand user needs. For example, the needs understanding unit understands user needs based on survey results and user behavior history. The needs understanding unit can analyze user needs using generation AI and generate information that is optimal for the user. For example, the needs understanding unit analyzes keywords the user has searched for in the past and browsing history to generate trivia or mottos related to topics the user is interested in. The needs understanding unit can also generate information appropriate to the user's age and gender based on the user's attribute information. As a result, the generation unit can provide a personalized experience by generating information based on user needs.

[0035] The analysis unit can analyze a user's past input history and select the optimal analysis method. For example, the analysis unit can analyze keywords and text previously entered by the user and provide related trivia or personal mottos. Based on past input history, the analysis unit can analyze topics and themes that the user is interested in. For example, the analysis unit can cluster keywords previously entered by the user and analyze that the user is interested in a particular theme. The analysis unit can also extract specific patterns from the user's past input history and select the optimal analysis method based on those patterns. For example, the analysis unit can analyze patterns of information that the user has liked in the past and provide information with similar patterns. In this way, the analysis unit can select the optimal analysis method by analyzing the user's past input history and provide information that is suitable for the user.

[0036] The analytics unit can perform analyses while considering the user's current interests and trends. For example, the analytics unit can analyze topics the user has recently searched for and their social media activity, and provide relevant trivia and mottos. To understand the user's current interests and trends, the analytics unit can collect and analyze the latest news articles and social media posts on the internet. For example, the analytics unit can provide relevant information based on keywords the user has recently searched for. The analytics unit can also analyze the user's social media activity and provide information related to their current interests. For example, the analytics unit can analyze posts the user has "liked" and shared on social media, and provide relevant trivia and mottos. Furthermore, the analytics unit can analyze the latest trend information and provide information relevant to the user. For example, the analytics unit can collect trend information on the internet and provide information that matches the user's interests. In this way, the analytics unit can provide highly relevant information by considering the user's current interests and trends.

[0037] The analysis unit can prioritize analyzing highly relevant information by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can provide trivia or mottos related to that region. Based on the user's location, the analysis unit can provide information related to regional trends. For example, if the user is traveling, the analysis unit can prioritize providing information related to their travel destination. Furthermore, based on the user's geographical location, the analysis unit can provide information related to regional culture and history. For example, if the user is in a specific city, the analysis unit can provide trivia about the city's history and tourist attractions. In this way, the analysis unit can provide highly relevant information by considering the user's geographical location.

[0038] The analytics unit can analyze a user's social media activity and extract relevant information during the analysis process. For example, it can provide relevant trivia and mottos based on information shared by the user on social media. The analytics unit can also analyze topics of interest to the user's social media followers and provide relevant information. For example, it can provide relevant information based on posts that the user has "liked" on social media. Furthermore, the analytics unit can analyze a user's social media activity to understand the topics and themes that the user is interested in. For example, it can analyze topics that the user frequently shares and interactions with followers to provide relevant trivia and mottos. In this way, the analytics unit can provide highly relevant information by analyzing the user's social media activity.

[0039] The generation unit can adjust the level of detail generated based on the importance of the information during generation. For example, for highly important information, the generation unit will generate trivia or mottos that include detailed explanations. The generation unit can evaluate the importance of information and adjust the level of detail of the generated content according to its importance. For example, the generation unit evaluates the importance of information based on user interest and the reliability of the information, and adds detailed explanations to highly important information. The generation unit can also generate concise trivia or mottos for less important information. For example, the generation unit will generate content that includes only short explanations or key points for less important information. In this way, the generation unit can provide appropriate information by adjusting the level of detail generated based on the importance of the information.

[0040] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, in the case of trivia related to science, the generation unit will apply an algorithm that generates based on scientific data. The generation unit can classify the categories of information and select the most suitable generation algorithm according to the category. For example, in the case of trivia related to history, the generation unit will apply an algorithm that generates based on historical data. Also, in the case of trivia related to health, the generation unit can apply an algorithm that generates based on health-related data. For example, the generation unit will collect information related to health and apply an algorithm that generates health-related trivia. In this way, the generation unit can provide appropriate information by applying different generation algorithms depending on the category of information.

[0041] The generation unit can determine the generation priority based on the information submission date during generation. For example, the generation unit may prioritize generating the latest information. The generation unit can evaluate the information submission date and determine the priority of the content to be generated based on the submission date. For example, the generation unit may prioritize generating information with a recent submission date and time, and generate older information as needed. The generation unit can also adjust the priority of the information to be generated based on the submission date. For example, the generation unit may prioritize generating the latest trend information and postpone generating past information. In this way, the generation unit can provide appropriate information by determining the generation priority based on the information submission date.

[0042] The generation unit can adjust the order of generation based on the relevance of the information during generation. For example, the generation unit can prioritize generating information related to the user's interests. The generation unit can evaluate the relevance of the information and adjust the order of content to be generated based on that relevance. For example, the generation unit can determine the order of information based on the user's level of interest and the relevance of the information. The generation unit can also postpone the generation of less relevant information. For example, the generation unit can prioritize generating information related to the user's interests and postpone less relevant information. In this way, the generation unit can provide appropriate information by adjusting the order of generation based on the relevance of the information.

[0043] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the display method the user has preferred in the past. The display unit can also select a display method that is highly visible to the user based on their past operation history. For example, the display unit can analyze the history of links the user has clicked and pages they have viewed in the past and select the optimal display method. Furthermore, the display unit can also provide the optimal display method based on the device the user has used in the past. For example, if the user is using a smartphone, the display unit will provide a display method optimized for smartphones. In this way, the display unit can select the optimal display method by referring to the user's past operation history and provide information that is appropriate for the user.

[0044] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can select the optimal display method based on the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. For example, the display unit displays information concisely to fit the small screen of a smartwatch. In this way, the display unit can provide the optimal display method by taking the user's device information into consideration.

[0045] The display unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the display unit can display trivia or mottos related to that region. The display unit can also display information related to regional trends based on the user's location. For example, if the user is traveling, the display unit can prioritize displaying information related to the travel destination. Furthermore, the display unit can also display information related to regional culture and history based on the user's geographical location. For example, if the user is in a specific city, the display unit can display trivia about the city's history and tourist attractions. In this way, the display unit can provide highly relevant information by considering the user's geographical location.

[0046] The display unit can analyze the user's social media activity and display relevant information when it is displayed. For example, the display unit can display relevant trivia or mottos based on information the user has shared on social media. The display unit can also display relevant information based on topics that the user's social media followers are interested in. For example, the display unit can display relevant information based on posts the user has "liked" on social media. Furthermore, the display unit can analyze the user's social media activity and understand the topics and themes the user is interested in. For example, the display unit can analyze topics the user frequently shares and interactions with followers and display relevant trivia or mottos. In this way, the display unit can provide highly relevant information by analyzing the user's social media activity.

[0047] The source attribution section can adjust the level of detail of the source based on the reliability of the information when displaying the source. For example, the source attribution section will display detailed source information for highly reliable information. The source attribution section can evaluate the reliability of the information and adjust the level of detail of the source according to its reliability. For example, the source attribution section will display concise source information for less reliable information. The source attribution section can also adjust the level of detail of the source according to the reliability of the information. For example, the source attribution section will display detailed explanations and links to the source for highly reliable information, and concise explanations for less reliable information. In this way, the source attribution section can provide appropriate information by adjusting the level of detail of the source based on the reliability of the information.

[0048] The source attribution section can apply different source attribution algorithms depending on the category of information when displaying sources. For example, in the case of scientific information, the source attribution section applies an algorithm that displays sources based on scientific data. The source attribution section can classify information categories and select the most appropriate source attribution algorithm according to the category. For example, in the case of historical information, the source attribution section applies an algorithm that displays sources based on historical data. Also, in the case of health information, the source attribution section can apply an algorithm that displays sources based on health-related data. For example, the source attribution section collects health-related information and applies an algorithm that displays health-related sources. In this way, the source attribution section can provide appropriate information by applying different source attribution algorithms depending on the category of information.

[0049] The source display section can adjust the display order of sources based on when the information was submitted. For example, the source display section prioritizes displaying sources for the most recent information. The source display section can evaluate when the information was submitted and adjust the display order of sources based on that. For example, the source display section prioritizes displaying sources for information with a more recent submission date and displays sources for older information as needed. The source display section can also adjust the display order of sources based on the submission date. For example, the source display section prioritizes displaying sources for the latest trend information and postpones displaying sources for older information. In this way, the source display section can provide appropriate information by adjusting the display order of sources based on when the information was submitted.

[0050] The source display section can adjust the display order of sources based on the relevance of the information when displaying sources. For example, the source display section can prioritize displaying sources related to the user's interests. The source display section can evaluate the relevance of the information and adjust the display order of sources based on that relevance. For example, the source display section can determine the order of sources based on the user's level of interest and the relevance of the information. The source display section can also display sources for less relevant information later. For example, the source display section can prioritize displaying sources for information related to the user's interests and postpone the display of sources for less relevant information. In this way, the source display section can provide appropriate information by adjusting the display order of sources based on the relevance of the information.

[0051] The needs understanding unit can select the optimal needs understanding method by referring to the user's past behavioral history when understanding user needs. For example, the needs understanding unit can select the optimal needs understanding method based on patterns of information the user has previously preferred. The needs understanding unit can understand topics and themes that the user is interested in based on past behavioral history. For example, the needs understanding unit can analyze the history of links the user has clicked and pages they have viewed in the past to select the optimal needs understanding method. The needs understanding unit can also select the optimal needs understanding method based on the devices the user has previously used. For example, if the user is using a smartphone, the needs understanding unit will select a needs understanding method optimized for smartphones. In this way, the needs understanding unit can select the optimal needs understanding method by referring to the user's past behavioral history and provide information that is appropriate for the user.

[0052] The needs understanding unit can understand a user's needs by considering their current interests and trends. For example, it can analyze topics a user has recently searched for and their social media activity to understand their need for relevant information. To grasp a user's current interests and trends, the needs understanding unit can collect and analyze the latest news articles and social media posts on the internet. For example, it can understand the need for relevant information based on keywords a user has recently searched for. It can also analyze a user's social media activity to understand their need for information related to their current interests. For example, it can analyze posts a user has "liked" and shared on social media to understand their need for relevant information. Furthermore, it can analyze the latest trend information to understand the user's need for relevant information. For example, it can collect trend information on the internet to understand the need for information that matches the user's interests. As a result, the needs understanding unit can provide appropriate information by considering the user's current interests and trends.

[0053] The needs understanding unit can prioritize understanding highly relevant needs by considering the user's geographical location when understanding needs. For example, if the user is in a specific region, the needs understanding unit will prioritize understanding the need for information related to that region. Based on the user's location, the needs understanding unit can understand the need for information related to regional trends. For example, if the user is traveling, the needs understanding unit will prioritize understanding the need for information related to their travel destination. Furthermore, based on the user's geographical location, the needs understanding unit can also understand the need for information related to regional culture and history. For example, if the user is in a specific city, the needs understanding unit will understand the need for information about the city's history and tourist attractions. In this way, the needs understanding unit can provide appropriate information by considering the user's geographical location.

[0054] The needs understanding unit can analyze a user's social media activity and understand related needs. For example, it can understand the need for relevant information based on information a user has shared on social media. It can also understand the need for relevant information based on topics of interest to a user's social media followers. For example, it can understand the need for relevant information based on posts a user has "liked" on social media. Furthermore, the needs understanding unit can analyze a user's social media activity to grasp topics and themes that the user is interested in. For example, it can analyze topics a user frequently shares and interactions with followers to understand the need for relevant information. As a result, the needs understanding unit can provide appropriate information by analyzing a 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] The analytics unit can analyze not only the user's most recent input characters and attributes, but also their past search and browsing history. For example, the analytics unit can analyze keywords the user has searched for in the past and the content of pages they have viewed, allowing for a deeper understanding of the user's interests and preferences. Furthermore, the analytics unit can predict future behavior based on the user's past behavioral patterns. For instance, if a user tends to search for specific topics during certain time periods, the analytics unit can prioritize providing information relevant to those times. This allows the analytics unit to provide more personalized information by considering the user's past behavioral history.

[0057] The generation unit not only includes a needs understanding unit that generates information based on user needs, but can also generate information while considering the user's current interests and trends. For example, the needs understanding unit can analyze topics the user has recently searched for and their activity on social media, and generate relevant trivia and mottos. Furthermore, the needs understanding unit can collect the latest news articles and trend information from the internet and provide information tailored to the user's interests. This allows the needs understanding unit to provide more relevant information by considering the user's current interests and trends.

[0058] The analysis unit can not only analyze the user's past input history and select the optimal analysis method, but can also perform analysis while considering the user's geographical location. For example, if the user is in a specific region, the analysis unit can provide trivia or mottos related to that region. Furthermore, based on the user's location, the analysis unit can provide information related to regional trends. In this way, by considering the user's geographical location, the analysis unit can provide more relevant information.

[0059] The analytics unit can perform analyses that consider the user's current interests and trends, as well as analyze the user's social media activity. For example, the analytics unit can provide relevant trivia and mottos based on information the user has shared and posts they have "liked" on social media. Furthermore, the analytics unit can analyze topics of interest to the user's social media followers and provide relevant information. This allows the analytics unit to provide more relevant information by analyzing the user's social media activity.

[0060] The analysis unit can not only prioritize analyzing highly relevant information by considering the user's geographical location during analysis, but can also perform analysis while considering the user's device information. For example, if the user is using a smartphone, the analysis unit can provide information optimized for the smartphone. Furthermore, if the user is using a tablet or smartwatch, the analysis unit can provide information optimized for each respective device. In this way, the analysis unit can provide more appropriate information by considering the user's device information.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the user's most recent input characters and attributes. For example, the analysis unit analyzes the text entered by the user and attribute information such as the user's age and gender. The analysis unit can analyze the text using natural language processing technology to understand the user's intent and interests. For example, the analysis unit tokenizes the text entered by the user and analyzes the meaning of each token. The analysis unit can also analyze the user's attribute information using statistical analysis technology to understand the user's trends. Step 2: The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. The generation unit generates trivia and mottos using, for example, a generation AI. The generation AI can use a text generation AI (e.g., LLM) to generate trivia and mottos that match the user's interests. For example, the generation unit inputs the user's input text as a prompt to the generation AI, and the generation AI generates trivia and mottos. The generation unit can also generate trivia and mottos randomly using algorithmic generation technology. Step 3: The display unit displays the information generated by the generation unit to the user. The display unit can, for example, use text display technology to display generated trivia or mottos on the user's screen. The display unit can also use graphical display technology to display the information in a visually appealing format. For example, the display unit can display the generated information with a colorful background and icons to attract the user's interest.

[0063] (Example of form 2) An API Bot system according to an embodiment of the present invention is a system that displays trivia or a motto during the waiting time for time-consuming processes, such as waiting for a response from a generating AI. In this API Bot system, the user sends a request to the generating AI, which receives the request and starts processing, but this processing may take time. During this waiting time, the API Bot operates. The API Bot first analyzes the most recent input characters and user attributes. Next, the API Bot displays the generated trivia or motto to the user. This allows the user to obtain interesting information during the waiting time and encourages them to continue waiting without abandoning the service. This mechanism can improve the user experience during the waiting time for the generating AI. For example, the user can obtain useful information or interesting trivia during the waiting time, increasing their motivation to continue using the service. Furthermore, because the API Bot provides information tailored to the user's needs, it can provide a personalized experience for each individual user. In this way, the API Bot system can provide interesting information during the user's waiting time and improve the user experience.

[0064] The API Bot system according to this embodiment comprises an analysis unit, a generation unit, and a display unit. The analysis unit analyzes the user's most recent input characters and attributes. For example, the analysis unit analyzes the text entered by the user and attribute information such as the user's age and gender. The analysis unit can analyze the text using natural language processing technology to understand the user's intent and interests. For example, the analysis unit tokenizes the text entered by the user and analyzes the meaning of each token. The analysis unit can also analyze the user's attribute information using statistical analysis technology to grasp the user's tendencies. The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. For example, the generation unit generates trivia and mottos using a generation AI. The generation AI can generate trivia and mottos that match the user's interests using a text generation AI (e.g., LLM). For example, the generation unit inputs the user's input text as a prompt to the generation AI, and the generation AI generates trivia and mottos. Furthermore, the generation unit can randomly generate trivia or mottos using algorithmic generation technology. The display unit displays the information generated by the generation unit to the user. The display unit can, for example, use text display technology to display the generated trivia or mottos on the user's screen. The display unit can also use graphical display technology to display the information in a visually appealing format. For example, the display unit can display the generated information with a colorful background and icons to attract the user's interest. As a result, the API Bot system according to the embodiment can improve the user experience by analyzing the user's most recent input characters and attributes and displaying the generated information.

[0065] The analysis unit analyzes the user's most recent input characters and attributes. Specifically, it analyzes the text entered by the user using natural language processing technology to understand the user's intent and interests. For example, if a user enters "What's the weather like today?", the analysis unit tokenizes this text and extracts keywords such as "today" and "weather". Furthermore, the analysis unit analyzes attribute information such as the user's age, gender, and past input history using statistical analysis technology to understand the user's tendencies. For example, if a young user asks many sports-related questions, the analysis unit can determine that the user is interested in sports. In this way, the analysis unit can comprehensively analyze the user's input text and attribute information to understand the user's intent and interests with high accuracy. In addition, the analysis unit can also predict future behavior from the user's input patterns and attribute information using machine learning algorithms. For example, based on past data, it can prepare to provide relevant information in advance to users who tend to ask specific questions at specific times of day. In this way, the analysis unit can anticipate user needs and provide more personalized services.

[0066] The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. Specifically, it uses a generation AI to generate trivia and mottos that match the user's interests. For example, if the user inputs "I want to increase my motivation," the generation unit inputs this text as a prompt to the generation AI, which then generates a motto such as "Success is the accumulation of small efforts." The generation AI uses a text generation AI (e.g., LLM) and has learned from a large amount of text data, enabling it to generate diverse expressions and content. The generation unit can also generate trivia and mottos randomly using algorithmic generation technology. For example, it can combine randomly selected information from a database related to a specific keyword to generate new trivia. This allows the generation unit to provide diverse information that meets the user's interests and needs. Furthermore, the generation unit has a feedback loop to evaluate the quality of the generated information, allowing it to continuously improve the generation algorithm based on user evaluations and reactions. This ensures that the generation unit always provides high-quality and useful information to the user.

[0067] The display unit displays information generated by the generation unit to the user. Specifically, it uses text display technology to display generated trivia and mottos on the user's screen. For example, if the user is using a smartphone, the generated information will be displayed as a notification bar or pop-up window. The display unit can also use graphical display technology to display information in a visually appealing format. For example, generated mottos can be displayed with a colorful background and icons to create a design that attracts the user's interest. Furthermore, the display unit can optimize the display format according to the user's device and usage environment. For example, for users using a desktop computer, the information can be displayed as a browser sidebar or widget, and for users using a smartwatch, the information can be displayed as short text and icons. In this way, the display unit can provide information in a format that is optimal for the user's device and usage environment, improving the user experience. In addition, the display unit can collect user feedback and use it to improve the display format and content. For example, if a user prefers a particular display format, the display unit can adjust the display method based on that information to provide a more user-friendly interface. In this way, the display unit can provide flexible display methods that meet user needs, improving the user experience.

[0068] The generation unit includes a source display unit that shows the sources and citations of the generated information. The generation unit can enhance the reliability of the information by displaying the sources and citations of generated trivia or mottos, for example. The source display unit displays source information such as books, websites, and academic papers. The source display unit displays source information below the generated information, allowing users to verify its reliability. For example, the source display unit displays information such as "Source: XX Book" or "Cited from: XX Website" below the generated trivia. The source display unit can also display source information as a link, allowing users to click and view more detailed information. In this way, the generation unit can enhance the reliability of the information by displaying the sources and citations of the generated information.

[0069] The generation unit includes a needs understanding unit that generates information based on user needs. For example, the generation unit analyzes the user's most recent input characters and attribute information to understand user needs. For example, the needs understanding unit understands user needs based on survey results and user behavior history. The needs understanding unit can analyze user needs using generation AI and generate information that is optimal for the user. For example, the needs understanding unit analyzes keywords the user has searched for in the past and browsing history to generate trivia or mottos related to topics the user is interested in. The needs understanding unit can also generate information appropriate to the user's age and gender based on the user's attribute information. As a result, the generation unit can provide a personalized experience by generating information based on user needs.

[0070] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also estimate emotions from the user's facial expressions using facial recognition technology. For example, the analysis unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The analysis unit can also analyze the user's input text and estimate emotions from the text. For example, the analysis unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the analysis unit can adjust the accuracy of the analysis based on the estimated emotions of the user. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis to provide more appropriate trivia or mottos. If the user is relaxed, the analysis unit can adjust the accuracy of the analysis to provide information that matches their relaxed state. In this way, the analysis unit can provide more appropriate information by adjusting the accuracy of the analysis based on the user's emotions.

[0071] The analysis unit can analyze a user's past input history and select the optimal analysis method. For example, the analysis unit can analyze keywords and text previously entered by the user and provide related trivia or personal mottos. Based on past input history, the analysis unit can analyze topics and themes that the user is interested in. For example, the analysis unit can cluster keywords previously entered by the user and analyze that the user is interested in a particular theme. The analysis unit can also extract specific patterns from the user's past input history and select the optimal analysis method based on those patterns. For example, the analysis unit can analyze patterns of information that the user has liked in the past and provide information with similar patterns. In this way, the analysis unit can select the optimal analysis method by analyzing the user's past input history and provide information that is suitable for the user.

[0072] The analytics unit can perform analyses while considering the user's current interests and trends. For example, the analytics unit can analyze topics the user has recently searched for and their social media activity, and provide relevant trivia and mottos. To understand the user's current interests and trends, the analytics unit can collect and analyze the latest news articles and social media posts on the internet. For example, the analytics unit can provide relevant information based on keywords the user has recently searched for. The analytics unit can also analyze the user's social media activity and provide information related to their current interests. For example, the analytics unit can analyze posts the user has "liked" and shared on social media, and provide relevant trivia and mottos. Furthermore, the analytics unit can analyze the latest trend information and provide information relevant to the user. For example, the analytics unit can collect trend information on the internet and provide information that matches the user's interests. In this way, the analytics unit can provide highly relevant information by considering the user's current interests and trends.

[0073] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also estimate emotions from the user's facial expressions using facial recognition technology. For example, the analysis unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The analysis unit can also analyze the user's input text and estimate emotions from the text. For example, the analysis unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the analysis unit can prioritize the analysis results based on the estimated emotions of the user. For example, if the user is stressed, the analysis unit will prioritize providing information that helps them relax. Also, if the user is excited, the analysis unit will prioritize providing information that helps alleviate the excitement. In this way, the analysis unit can provide more appropriate information by prioritizing the analysis results based on the user's emotions.

[0074] The analysis unit can prioritize analyzing highly relevant information by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can provide trivia or mottos related to that region. Based on the user's location, the analysis unit can provide information related to regional trends. For example, if the user is traveling, the analysis unit can prioritize providing information related to their travel destination. Furthermore, based on the user's geographical location, the analysis unit can provide information related to regional culture and history. For example, if the user is in a specific city, the analysis unit can provide trivia about the city's history and tourist attractions. In this way, the analysis unit can provide highly relevant information by considering the user's geographical location.

[0075] The analytics unit can analyze a user's social media activity and extract relevant information during the analysis process. For example, it can provide relevant trivia and mottos based on information shared by the user on social media. The analytics unit can also analyze topics of interest to the user's social media followers and provide relevant information. For example, it can provide relevant information based on posts that the user has "liked" on social media. Furthermore, the analytics unit can analyze a user's social media activity to understand the topics and themes that the user is interested in. For example, it can analyze topics that the user frequently shares and interactions with followers to provide relevant trivia and mottos. In this way, the analytics unit can provide highly relevant information by analyzing the user's social media activity.

[0076] The generation unit can estimate the user's emotions and adjust the content of the trivia and mottos it generates based on the estimated emotions. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also estimate emotions from the user's facial expression using facial recognition technology. For example, the generation unit can analyze the user's facial features, such as smiles and frown lines, to estimate whether the user is relaxed or stressed. The generation unit can also analyze the user's input text and estimate emotions from the text. For example, the generation unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the generation unit can adjust the content of the trivia and mottos it generates based on the estimated emotions of the user. For example, if the user is stressed, the generation unit can generate relaxing trivia and mottos. If the user is relaxed, the generation unit can generate interesting trivia and mottos. In this way, the generation unit can provide more appropriate information by adjusting the content it generates based on the user's emotions.

[0077] The generation unit can adjust the level of detail generated based on the importance of the information during generation. For example, for highly important information, the generation unit will generate trivia or mottos that include detailed explanations. The generation unit can evaluate the importance of information and adjust the level of detail of the generated content according to its importance. For example, the generation unit evaluates the importance of information based on user interest and the reliability of the information, and adds detailed explanations to highly important information. The generation unit can also generate concise trivia or mottos for less important information. For example, the generation unit will generate content that includes only short explanations or key points for less important information. In this way, the generation unit can provide appropriate information by adjusting the level of detail generated based on the importance of the information.

[0078] The generation unit can apply different generation algorithms depending on the category of information during generation. For example, in the case of trivia related to science, the generation unit will apply an algorithm that generates based on scientific data. The generation unit can classify the categories of information and select the most suitable generation algorithm according to the category. For example, in the case of trivia related to history, the generation unit will apply an algorithm that generates based on historical data. Also, in the case of trivia related to health, the generation unit can apply an algorithm that generates based on health-related data. For example, the generation unit will collect information related to health and apply an algorithm that generates health-related trivia. In this way, the generation unit can provide appropriate information by applying different generation algorithms depending on the category of information.

[0079] The generation unit can estimate the user's emotions and adjust the length of the information it generates based on those estimated emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also estimate emotions from the user's facial expressions using facial recognition technology. For example, the generation unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The generation unit can also analyze the user's input text and estimate emotions from the text. For example, the generation unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the generation unit can adjust the length of the information it generates based on the estimated emotions of the user. For example, if the user is in a hurry, the generation unit can generate short, concise trivia or mottos. If the user is relaxed, the generation unit can generate longer trivia or mottos that include detailed explanations. In this way, the generation unit can provide appropriate information by adjusting the length of the information it generates based on the user's emotions.

[0080] The generation unit can determine the generation priority based on the information submission date during generation. For example, the generation unit may prioritize generating the latest information. The generation unit can evaluate the information submission date and determine the priority of the content to be generated based on the submission date. For example, the generation unit may prioritize generating information with a recent submission date and time, and generate older information as needed. The generation unit can also adjust the priority of the information to be generated based on the submission date. For example, the generation unit may prioritize generating the latest trend information and postpone generating past information. In this way, the generation unit can provide appropriate information by determining the generation priority based on the information submission date.

[0081] The generation unit can adjust the order of generation based on the relevance of the information during generation. For example, the generation unit can prioritize generating information related to the user's interests. The generation unit can evaluate the relevance of the information and adjust the order of content to be generated based on that relevance. For example, the generation unit can determine the order of information based on the user's level of interest and the relevance of the information. The generation unit can also postpone the generation of less relevant information. For example, the generation unit can prioritize generating information related to the user's interests and postpone less relevant information. In this way, the generation unit can provide appropriate information by adjusting the order of generation based on the relevance of the information.

[0082] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The display unit can also estimate emotions from the user's facial expression using facial recognition technology. For example, the display unit can analyze the user's facial features, such as a smile or frown lines, to estimate whether the user is relaxed or stressed. The display unit can also analyze the user's input text and estimate emotions from the text. For example, the display unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the display unit can adjust the display method based on the estimated emotions of the user. For example, if the user is tense, the display unit can display information with a calm color scheme. Conversely, if the user is relaxed, the display unit can display information with a bright color scheme. In this way, the display unit can provide appropriate information by adjusting the display method based on the user's emotions.

[0083] The display unit can select the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit can provide the optimal display method based on the display method the user has preferred in the past. The display unit can also select a display method that is highly visible to the user based on their past operation history. For example, the display unit can analyze the history of links the user has clicked and pages they have viewed in the past and select the optimal display method. Furthermore, the display unit can also provide the optimal display method based on the device the user has used in the past. For example, if the user is using a smartphone, the display unit will provide a display method optimized for smartphones. In this way, the display unit can select the optimal display method by referring to the user's past operation history and provide information that is appropriate for the user.

[0084] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. The display unit can select the optimal display method based on the user's device information. For example, if the user is using a tablet, the display unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. For example, the display unit displays information concisely to fit the small screen of a smartwatch. In this way, the display unit can provide the optimal display method by taking the user's device information into consideration.

[0085] The display unit can estimate the user's emotions and determine the priority of information to display based on the estimated emotions. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The display unit can also estimate emotions from the user's facial expression using facial recognition technology. For example, the display unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The display unit can also analyze the user's input text and estimate emotions from the text. For example, the display unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the display unit can determine the priority of information to display based on the estimated emotions of the user. For example, if the user is stressed, the display unit will prioritize displaying information that helps them relax. Also, if the user is excited, the display unit can prioritize displaying information that helps alleviate the excitement. In this way, the display unit can provide appropriate information by determining the priority of information to display based on the user's emotions.

[0086] The display unit can prioritize displaying highly relevant information by considering the user's geographical location. For example, if the user is in a specific region, the display unit can display trivia or mottos related to that region. The display unit can also display information related to regional trends based on the user's location. For example, if the user is traveling, the display unit can prioritize displaying information related to the travel destination. Furthermore, the display unit can also display information related to regional culture and history based on the user's geographical location. For example, if the user is in a specific city, the display unit can display trivia about the city's history and tourist attractions. In this way, the display unit can provide highly relevant information by considering the user's geographical location.

[0087] The display unit can analyze the user's social media activity and display relevant information when it is displayed. For example, the display unit can display relevant trivia or mottos based on information the user has shared on social media. The display unit can also display relevant information based on topics that the user's social media followers are interested in. For example, the display unit can display relevant information based on posts the user has "liked" on social media. Furthermore, the display unit can analyze the user's social media activity and understand the topics and themes the user is interested in. For example, the display unit can analyze topics the user frequently shares and interactions with followers and display relevant trivia or mottos. In this way, the display unit can provide highly relevant information by analyzing the user's social media activity.

[0088] The source display unit can estimate the user's emotions and adjust the way sources are displayed based on those estimated emotions. For example, the source display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The source display unit can also estimate emotions from the user's facial expression using facial recognition technology. For example, the source display unit can analyze the user's facial features, such as smiles and frown lines, to estimate whether the user is relaxed or stressed. The source display unit can also analyze the user's input text and estimate emotions from the text. For example, the source display unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the source display unit can adjust the way sources are displayed based on the estimated emotions of the user. For example, if the user is tense, the source display unit can provide a simple and highly visible source display method. If the user is relaxed, the source display unit can provide detailed source information. In this way, the source display unit can provide appropriate information by adjusting the way sources are displayed based on the user's emotions.

[0089] The source attribution section can adjust the level of detail of the source based on the reliability of the information when displaying the source. For example, the source attribution section will display detailed source information for highly reliable information. The source attribution section can evaluate the reliability of the information and adjust the level of detail of the source according to its reliability. For example, the source attribution section will display concise source information for less reliable information. The source attribution section can also adjust the level of detail of the source according to the reliability of the information. For example, the source attribution section will display detailed explanations and links to the source for highly reliable information, and concise explanations for less reliable information. In this way, the source attribution section can provide appropriate information by adjusting the level of detail of the source based on the reliability of the information.

[0090] The source attribution section can apply different source attribution algorithms depending on the category of information when displaying sources. For example, in the case of scientific information, the source attribution section applies an algorithm that displays sources based on scientific data. The source attribution section can classify information categories and select the most appropriate source attribution algorithm according to the category. For example, in the case of historical information, the source attribution section applies an algorithm that displays sources based on historical data. Also, in the case of health information, the source attribution section can apply an algorithm that displays sources based on health-related data. For example, the source attribution section collects health-related information and applies an algorithm that displays health-related sources. In this way, the source attribution section can provide appropriate information by applying different source attribution algorithms depending on the category of information.

[0091] The source display unit can estimate the user's emotions and determine the priority of sources based on the estimated emotions. For example, the source display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The source display unit can also estimate emotions from the user's facial expression using facial recognition technology. For example, the source display unit can analyze the user's facial features, such as a smile or frown lines, to estimate whether the user is relaxed or stressed. The source display unit can also analyze the user's input text and estimate emotions from the text. For example, the source display unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the source display unit can determine the priority of sources based on the estimated emotions of the user. For example, if the user is stressed, the source display unit will prioritize displaying sources of information that can help them relax. Also, if the user is agitated, the source display unit can prioritize displaying sources of information that can alleviate the agitation. This allows the source display section to prioritize sources based on the user's sentiment, thereby providing appropriate information.

[0092] The source display section can adjust the display order of sources based on when the information was submitted. For example, the source display section prioritizes displaying sources for the most recent information. The source display section can evaluate when the information was submitted and adjust the display order of sources based on that. For example, the source display section prioritizes displaying sources for information with a more recent submission date and displays sources for older information as needed. The source display section can also adjust the display order of sources based on the submission date. For example, the source display section prioritizes displaying sources for the latest trend information and postpones displaying sources for older information. In this way, the source display section can provide appropriate information by adjusting the display order of sources based on when the information was submitted.

[0093] The source display section can adjust the display order of sources based on the relevance of the information when displaying sources. For example, the source display section can prioritize displaying sources related to the user's interests. The source display section can evaluate the relevance of the information and adjust the display order of sources based on that relevance. For example, the source display section can determine the order of sources based on the user's level of interest and the relevance of the information. The source display section can also display sources for less relevant information later. For example, the source display section can prioritize displaying sources for information related to the user's interests and postpone the display of sources for less relevant information. In this way, the source display section can provide appropriate information by adjusting the display order of sources based on the relevance of the information.

[0094] The needs understanding unit can estimate the user's emotions and adjust the accuracy of its needs understanding based on the estimated emotions. For example, the needs understanding unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The needs understanding unit can also estimate emotions from the user's facial expressions using facial recognition technology. For example, the needs understanding unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The needs understanding unit can also analyze the user's input text and estimate emotions from the text. For example, the needs understanding unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the needs understanding unit can adjust the accuracy of its needs understanding based on the estimated emotions of the user. For example, if the user is stressed, the needs understanding unit will prioritize understanding the need for information that can help them relax. Also, if the user is agitated, the needs understanding unit will prioritize understanding the need for information that can alleviate the agitation. In this way, the needs understanding unit can provide appropriate information by adjusting the accuracy of its needs understanding based on the user's emotions.

[0095] The needs understanding unit can select the optimal needs understanding method by referring to the user's past behavioral history when understanding user needs. For example, the needs understanding unit can select the optimal needs understanding method based on patterns of information the user has previously preferred. The needs understanding unit can understand topics and themes that the user is interested in based on past behavioral history. For example, the needs understanding unit can analyze the history of links the user has clicked and pages they have viewed in the past to select the optimal needs understanding method. The needs understanding unit can also select the optimal needs understanding method based on the devices the user has previously used. For example, if the user is using a smartphone, the needs understanding unit will select a needs understanding method optimized for smartphones. In this way, the needs understanding unit can select the optimal needs understanding method by referring to the user's past behavioral history and provide information that is appropriate for the user.

[0096] The needs understanding unit can understand a user's needs by considering their current interests and trends. For example, it can analyze topics a user has recently searched for and their social media activity to understand their need for relevant information. To grasp a user's current interests and trends, the needs understanding unit can collect and analyze the latest news articles and social media posts on the internet. For example, it can understand the need for relevant information based on keywords a user has recently searched for. It can also analyze a user's social media activity to understand their need for information related to their current interests. For example, it can analyze posts a user has "liked" and shared on social media to understand their need for relevant information. Furthermore, it can analyze the latest trend information to understand the user's need for relevant information. For example, it can collect trend information on the internet to understand the need for information that matches the user's interests. As a result, the needs understanding unit can provide appropriate information by considering the user's current interests and trends.

[0097] The needs understanding unit can estimate the user's emotions and prioritize needs based on those estimated emotions. For example, the needs understanding unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The needs understanding unit can also estimate emotions from the user's facial expressions using facial recognition technology. For example, the needs understanding unit can analyze facial features such as the user's smile or frown lines to estimate whether the user is relaxed or stressed. The needs understanding unit can also analyze the user's input text and estimate emotions from the text. For example, the needs understanding unit can analyze emotional expressions and keywords contained in the user's input text to estimate the user's emotions. Furthermore, the needs understanding unit can prioritize needs based on the estimated user emotions. For example, if the user is stressed, the needs understanding unit will prioritize the need for information that can help them relax. If the user is agitated, the needs understanding unit will prioritize the need for information that can alleviate the agitation. In this way, the needs understanding unit can provide appropriate information by prioritizing needs based on the user's emotions.

[0098] The needs understanding unit can prioritize understanding highly relevant needs by considering the user's geographical location when understanding needs. For example, if the user is in a specific region, the needs understanding unit will prioritize understanding the need for information related to that region. Based on the user's location, the needs understanding unit can understand the need for information related to regional trends. For example, if the user is traveling, the needs understanding unit will prioritize understanding the need for information related to their travel destination. Furthermore, based on the user's geographical location, the needs understanding unit can also understand the need for information related to regional culture and history. For example, if the user is in a specific city, the needs understanding unit will understand the need for information about the city's history and tourist attractions. In this way, the needs understanding unit can provide appropriate information by considering the user's geographical location.

[0099] The needs understanding unit can analyze a user's social media activity and understand related needs. For example, it can understand the need for relevant information based on information a user has shared on social media. It can also understand the need for relevant information based on topics of interest to a user's social media followers. For example, it can understand the need for relevant information based on posts a user has "liked" on social media. Furthermore, the needs understanding unit can analyze a user's social media activity to grasp topics and themes that the user is interested in. For example, it can analyze topics a user frequently shares and interactions with followers to understand the need for relevant information. As a result, the needs understanding unit can provide appropriate information by analyzing a user's social media activity.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The analytics unit can analyze not only the user's most recent input characters and attributes, but also their past search and browsing history. For example, the analytics unit can analyze keywords the user has searched for in the past and the content of pages they have viewed, allowing for a deeper understanding of the user's interests and preferences. Furthermore, the analytics unit can predict future behavior based on the user's past behavioral patterns. For instance, if a user tends to search for specific topics during certain time periods, the analytics unit can prioritize providing information relevant to those times. This allows the analytics unit to provide more personalized information by considering the user's past behavioral history.

[0102] The generation unit not only includes a source display unit that shows the source and citations of the generated information, but can also estimate the user's emotions and adjust the way the sources are displayed based on those emotions. For example, if the user is relaxed, the source display unit can display detailed source information, while if the user is stressed, it can display concise source information. The source display unit can also adjust the display position and font size of the source information according to the user's emotions. In this way, the source display unit can provide the user with the most optimal information by adjusting the way the sources are displayed based on the user's emotions.

[0103] The generation unit not only includes a needs understanding unit that generates information based on user needs, but can also generate information while considering the user's current interests and trends. For example, the needs understanding unit can analyze topics the user has recently searched for and their activity on social media, and generate relevant trivia and mottos. Furthermore, the needs understanding unit can collect the latest news articles and trend information from the internet and provide information tailored to the user's interests. This allows the needs understanding unit to provide more relevant information by considering the user's current interests and trends.

[0104] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. Furthermore, it can adjust how the analysis results are displayed according to the user's emotions. For example, if the user is relaxed, the analysis unit can display detailed results; if the user is stressed, it can display concise results. The analysis unit can also adjust the display position and font size of the analysis results according to the user's emotions. This allows the analysis unit to provide the user with the most relevant information by adjusting how the analysis results are displayed based on their emotions.

[0105] The analysis unit can not only analyze the user's past input history and select the optimal analysis method, but can also perform analysis while considering the user's geographical location. For example, if the user is in a specific region, the analysis unit can provide trivia or mottos related to that region. Furthermore, based on the user's location, the analysis unit can provide information related to regional trends. In this way, by considering the user's geographical location, the analysis unit can provide more relevant information.

[0106] The analytics unit can perform analyses that consider the user's current interests and trends, as well as analyze the user's social media activity. For example, the analytics unit can provide relevant trivia and mottos based on information the user has shared and posts they have "liked" on social media. Furthermore, the analytics unit can analyze topics of interest to the user's social media followers and provide relevant information. This allows the analytics unit to provide more relevant information by analyzing the user's social media activity.

[0107] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on those emotions. Furthermore, it can adjust the content of the analysis results according to the user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed information; if the user is stressed, it can provide concise information. The analysis unit can also adjust how the analysis results are displayed according to the user's emotions. This allows the analysis unit to provide the user with the most relevant information by adjusting the content of the analysis results based on their emotions.

[0108] The analysis unit can not only prioritize analyzing highly relevant information by considering the user's geographical location during analysis, but can also perform analysis while considering the user's device information. For example, if the user is using a smartphone, the analysis unit can provide information optimized for the smartphone. Furthermore, if the user is using a tablet or smartwatch, the analysis unit can provide information optimized for each respective device. In this way, the analysis unit can provide more appropriate information by considering the user's device information.

[0109] The analysis unit not only analyzes the user's social media activity and related information during analysis, but can also estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis, while if the user is stressed, it can perform a concise analysis. The analysis unit can also adjust how the analysis results are displayed according to the user's emotions. In this way, the analysis unit can provide the user with the most optimal information by adjusting the accuracy of the analysis based on the user's emotions.

[0110] The generation unit can estimate the user's emotions and adjust the content of the trivia and mottos it generates based on those emotions. It can also adjust the format of the information generated according to the user's emotions. For example, if the user is relaxed, the generation unit can generate information with detailed explanations; if the user is stressed, it can generate concise information. Furthermore, the generation unit can adjust how the information is displayed according to the user's emotions. This allows the generation unit to provide the user with the most relevant information by adjusting the format of the information generated based on their emotions.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The analysis unit analyzes the user's most recent input characters and attributes. For example, the analysis unit analyzes the text entered by the user and attribute information such as the user's age and gender. The analysis unit can analyze the text using natural language processing technology to understand the user's intent and interests. For example, the analysis unit tokenizes the text entered by the user and analyzes the meaning of each token. The analysis unit can also analyze the user's attribute information using statistical analysis technology to understand the user's trends. Step 2: The generation unit generates trivia and mottos based on the information analyzed by the analysis unit. The generation unit generates trivia and mottos using, for example, a generation AI. The generation AI can use a text generation AI (e.g., LLM) to generate trivia and mottos that match the user's interests. For example, the generation unit inputs the user's input text as a prompt to the generation AI, and the generation AI generates trivia and mottos. The generation unit can also generate trivia and mottos randomly using algorithmic generation technology. Step 3: The display unit displays the information generated by the generation unit to the user. The display unit can, for example, use text display technology to display generated trivia or mottos on the user's screen. The display unit can also use graphical display technology to display the information in a visually appealing format. For example, the display unit can display the generated information with a colorful background and icons to attract the user's interest.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the analysis unit, generation unit, display unit, source display unit, and needs understanding unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the user's most recent input characters and attributes. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates trivia or mottos based on the analyzed information. The display unit is implemented by the control unit 46A of the smart device 14 and displays the generated information to the user. The source display unit is implemented by the identification processing unit 290 of the data processing device 12 and displays the source and citation of the generated information. The needs understanding unit is implemented by the processor 46 of the smart device 14 and generates information based on the user's needs. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, generation unit, display unit, source display unit, and needs understanding unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the user's most recent input characters and attributes. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates trivia or mottos based on the analyzed information. The display unit is implemented by the control unit 46A of the smart glasses 214 and displays the generated information to the user. The source display unit is implemented by the identification processing unit 290 of the data processing device 12 and displays the source and citation of the generated information. The needs understanding unit is implemented by the processor 46 of the smart glasses 214 and generates information based on the user's needs. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, generation unit, display unit, source display unit, and needs understanding unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the user's most recent input characters and attributes. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates trivia or mottos based on the analyzed information. The display unit is implemented by the control unit 46A of the headset terminal 314 and displays the generated information to the user. The source display unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays the source and citation of the generated information. The needs understanding unit is implemented by the processor 46 of the headset terminal 314 and generates information based on the user's needs. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the analysis unit, generation unit, display unit, source display unit, and needs understanding unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the user's most recent input characters and attributes. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates trivia or mottos based on the analyzed information. The display unit is implemented by the control unit 46A of the robot 414 and displays the generated information to the user. The source display unit is implemented by the specific processing unit 290 of the data processing unit 12 and displays the source and citation of the generated information. The needs understanding unit is implemented by the processor 46 of the robot 414 and generates information based on the user's needs. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) An analysis unit that analyzes the user's most recent input characters and attributes, A generation unit generates trivia and personal mottos based on the information analyzed by the aforementioned analysis unit, The system includes a display unit that displays the information generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is It includes a source display section that shows the source and citations of the generated information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes a needs understanding unit that generates information based on user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the user's past input history and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, During the analysis, the analysis will take into account the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed, and relevant information is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the content of the generated trivia and mottos based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, different generation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the length of the information generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the generation priority is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the order of generation is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and determines the priority of the information to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying information, the system prioritizes showing more relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned source indication section is, It estimates the user's sentiment and adjusts how sources are displayed based on that estimated sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned source indication section is, When citing sources, adjust the level of detail based on the reliability of the information. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned source indication section is, When citing sources, different citation algorithms are applied depending on the category of the information. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned source indication section is, It estimates the user's sentiment and determines the priority of sources based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned source indication section is, When displaying sources, adjust the display order of sources based on when the information was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned source indication section is, When displaying sources, the display order of sources is adjusted based on the relevance of the information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned needs understanding unit is It estimates the user's emotions and adjusts the accuracy of understanding their needs based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned needs understanding unit is When understanding user needs, the optimal method for understanding those needs is selected by referring to the user's past behavioral history. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned needs understanding unit is When understanding needs, take into account the user's current interests and trends. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned needs understanding unit is It estimates the user's emotions and prioritizes their needs based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned needs understanding unit is When understanding user needs, prioritize understanding the most relevant needs by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned needs understanding unit is When understanding needs, we analyze users' social media activity to understand related needs. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0185] 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. An analysis unit that analyzes the user's most recent input characters and attributes, A generation unit generates trivia and personal mottos based on the information analyzed by the aforementioned analysis unit, The system includes a display unit that displays the information generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is It includes a source display section that shows the source and citations of the generated information. The system according to feature 1.

3. The generating unit is It includes a needs understanding unit that generates information based on user needs. The system according to feature 1.

4. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the user's past input history and select the optimal analysis method. The system according to feature 1.

6. The aforementioned analysis unit, During the analysis, the analysis will take into account the user's current interests and trends. The system according to feature 1.

7. The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system according to feature 1.

9. The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed, and relevant information is analyzed. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the content of the generated trivia and mottos based on those estimated emotions. The system according to feature 1.

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