system

Abstract

The system according to this embodiment aims to efficiently collect, summarize, translate, and provide information that the user needs. [Solution] The system according to this embodiment comprises a reception unit, an information collection unit, a summarization unit, a translation unit, and a provision unit. The reception unit allows the user to specify the information they need. The information collection unit collects relevant information from the internet based on the information specified by the reception unit. The summarization unit summarizes the information collected by the information collection unit. The translation unit translates the information summarized by the summarization unit. The provision unit provides the information translated by the translation unit in HTML format.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently collect, summarize, and translate necessary information and provide it.

[0005] The system according to the embodiment aims to efficiently collect, summarize, and translate necessary information for the user and provide it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an information collection unit, a summarization unit, a translation unit, and a provision unit. The reception unit allows the user to specify the information they need. The information collection unit collects relevant information from the internet based on the information specified by the reception unit. The summarization unit summarizes the information collected by the information collection unit. The translation unit translates the information summarized by the summarization unit. The provision unit provides the information translated by the translation unit in HTML format. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect, summarize, translate, and provide the information that the user needs. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An information provision system according to an embodiment of the present invention is a system that automatically collects information needed by a user, summarizes and translates it using a generation AI, and presents it in an easy-to-understand HTML format. When a user specifies the information they need, the system automatically collects relevant information from the internet. Next, the collected information is summarized by the generation AI and translated as necessary. Finally, the summarized and translated information is provided to the user in HTML format. Furthermore, if the selected information is not what the user expected, the user can send feedback. The user can also instruct the generation AI in natural language what kind of information they want. This allows the system to provide information optimized for the individual based on the user's feedback and instructions. For example, when a user specifies the information they need, the information collection unit collects relevant information from the internet. Next, the collected information is summarized by the summarization unit and translated as necessary by the translation unit. The summarized and translated information is provided to the user in HTML format by the provision unit. Furthermore, if the user sends feedback, the feedback reception unit receives that feedback. If the user sends instructions in natural language, the instruction analysis unit analyzes those instructions. This allows the system to provide information optimized for the individual based on the user's feedback and instructions. This allows the information provision system to efficiently collect, summarize, translate, and provide users with the information they need.

[0029] The information provision system according to this embodiment comprises a reception unit, an information collection unit, a summarization unit, a translation unit, and a provision unit. The reception unit allows the user to specify the information they need. The information specified by the user includes, but is not limited to, text information, image information, video information, etc. The information collection unit collects relevant information from the internet based on the information specified by the reception unit. The information collection unit collects information based, for example, on a specific website or specific keywords. The summarization unit uses a generation AI to summarize the information collected by the information collection unit. The summarization is performed, for example, based on the length of the text or the importance of the information being summarized, but is not limited to such examples. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the information. The summarization unit can also use a multimodal generation AI to summarize the content of the information. The translation unit translates the information summarized by the summarization unit. The translation is performed, for example, based on the translation algorithm used or the accuracy of the translation, but is not limited to such examples. The provision unit provides the information translated by the translation unit in HTML format. The HTML format includes, but is not limited to, the usage of HTML tags and the layout of the display. This allows the information provision system according to the embodiment to efficiently collect, summarize, translate, and provide the information the user needs.

[0030] The service provider can provide users with summarized and translated information in HTML format. For example, the service provider can provide users with summarized and translated information in HTML format. HTML format includes, but is not limited to, the use of HTML tags and display layout. By providing summarized and translated information in HTML format, information can be conveyed to users in an easy-to-understand manner. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input summarized and translated information into a generating AI and have the generating AI produce output in HTML format.

[0031] The feedback receiving unit can receive feedback from users. For example, the feedback receiving unit receives feedback from users. This feedback includes, but is not limited to, text-based feedback or evaluation scores. This allows for system improvement by receiving feedback from users. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user feedback into a generating AI and have the generating AI perform analysis of the feedback.

[0032] The instruction analysis unit can analyze instructions from the user in natural language. For example, the instruction analysis unit analyzes instructions from the user in natural language. Instructions in natural language include, but are not limited to, questions or commands. By analyzing the user's instructions in natural language, it is possible to provide information optimized for the individual. Some or all of the above processing in the instruction analysis unit may be performed using AI, for example, or without AI. For example, the instruction analysis unit can input natural language instructions from the user into a generating AI and have the generating AI perform the analysis of the instructions.

[0033] The reception unit can analyze the user's past information specification history and propose the optimal specification method. For example, the reception unit analyzes the user's past information specification history and proposes the optimal specification method. Information specification history includes, but is not limited to, past search keywords and browsing history. Optimal specification methods include, but are not limited to, suggestions based on the user's behavior patterns. For example, it can automatically display information that the user has frequently specified in the past as a candidate. It can also prioritize suggesting specification methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that the user will use during specific time periods based on the user's past specification history. In this way, by analyzing the user's past information specification history, the optimal specification method can be proposed. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's past information specification history into a generating AI and have the generating AI propose the optimal specification method.

[0034] The reception unit can filter information based on the user's current areas of interest when the user specifies information. For example, the reception unit can filter information based on the user's current areas of interest when the user specifies information. Areas of interest include, but are not limited to, survey results or past search history. Filtering includes, but is not limited to, filtering based on specific keywords. For example, it can prioritize displaying relevant information based on keywords the user has recently searched for. It can also narrow down information candidates based on topics the user is interested in. Furthermore, it can analyze the user's social media activity and prioritize displaying relevant information. This allows for the provision of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0035] The reception unit can prioritize highly relevant information when the user specifies information, taking into account the user's geographical location. For example, the reception unit prioritizes highly relevant information when the user specifies information, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant information includes, but is not limited to, information that is geographically close or information specific to a particular region. For example, it can prioritize displaying information that is close to the user's current location. If the user has shown interest in a particular region, it can also prioritize displaying information related to that region. Furthermore, if the user is traveling, it can also prioritize displaying information related to their travel destination. In this way, highly relevant information can be provided by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI specify highly relevant information.

[0036] The reception unit can analyze the user's social media activity and specify relevant information when the user requests information. For example, the reception unit can analyze the user's social media activity and specify relevant information when the user requests information. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, trending information on social media. For example, it can prioritize displaying information related to posts the user has recently "liked." It can also prioritize displaying information based on posts from accounts the user follows. Furthermore, it can prioritize displaying information related to groups and communities the user participates in. This allows the reception unit to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI specify relevant information.

[0037] The information gathering unit can analyze the user's past information gathering history and select the optimal gathering method when gathering information. For example, the information gathering unit can analyze the user's past information gathering history and select the optimal gathering method when gathering information. Information gathering history includes, but is not limited to, past search keywords and browsing history. Optimal gathering methods include, but are not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize the collection of information that the user has frequently collected in the past. It can also prioritize the use of gathering methods (websites, APIs, etc.) that the user has used in the past. Furthermore, it can predict and collect information to be collected at specific time periods based on the user's past gathering history. This allows the optimal gathering method to be selected by analyzing the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or without AI. For example, the information gathering unit can input the user's past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

[0038] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. Projects include, but are not limited to, research projects and business projects. Areas of interest include, but are not limited to, survey results and past search history. Filtering includes, but is not limited to, filtering based on specific keywords. For example, it can prioritize the collection of information related to projects the user is currently working on. It can also filter information based on topics the user has shown interest in. Furthermore, it can analyze the user's social media activity and prioritize the collection of relevant information. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or not using AI. For example, the information gathering unit can input the user's project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0039] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location information during information gathering. For example, the information gathering unit prioritizes the collection of highly relevant information by considering the user's geographical location information during information gathering. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant information includes, but is not limited to, information that is geographically close or information specific to a particular region. For example, it prioritizes the collection of information that is close to the user's current location. If the user has shown interest in a particular region, it can also prioritize the collection of information related to that region. Furthermore, if the user is traveling, it can also prioritize the collection of information related to their travel destination. In this way, highly relevant information can be collected by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0040] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, trending information on social media. For example, it can prioritize collecting information related to posts that the user has recently "liked." It can also collect information based on posts from accounts that the user follows. Furthermore, it can prioritize collecting information related to groups and communities that the user participates in. In this way, by analyzing a user's social media activity, it is possible to collect highly relevant information. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or not using AI. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0041] The summarization unit can adjust the level of detail of a summary based on the importance of the information during summary generation. For example, the summarization unit can adjust the level of detail of a summary based on the importance of the information during summary generation. The importance of information includes, but is not limited to, the reliability and impact of the information. The level of detail of a summary includes, but is not limited to, detailed summaries and concise summaries. For example, important information is summarized in detail, and less important information is summarized concisely. The level of detail of a summary can also be adjusted according to the category of information. Furthermore, the level of detail of a summary can also be adjusted based on the reliability of the information. By adjusting the level of detail of a summary based on the importance of the information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the summary.

[0042] The summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. Categories of information include, but are not limited to, news, technical information, and entertainment. Summarization algorithms include, but are not limited to, abstract summaries and extractive summaries. For example, in the case of news articles, the summarization unit can apply a summarization algorithm that highlights important points. In the case of technical documents, the summarization unit can also apply a summarization algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the summarization unit can also apply an attention-grabbing summarization algorithm. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information category data into a generating AI and have the generating AI perform the application of a summarization algorithm.

[0043] The summarization unit can determine the priority of summaries based on when the information was collected during summary generation. For example, the summarization unit can determine the priority of summaries based on when the information was collected during summary generation. The information collection period includes, but is not limited to, the latest information or historical information. The priority of summaries includes, but is not limited to, the importance or relevance of the information. For example, the latest information may be summarized preferentially. Historical information may also be summarized concisely, while the latest information may be summarized in detail. Furthermore, the order of summaries can be adjusted based on when the information was collected. This allows for the provision of more appropriate summaries by determining the priority of summaries based on when the information was collected. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information collection period data into a generation AI and have the generation AI perform the determination of the summary priority.

[0044] The summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. For example, the summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. Relevance of information includes, but is not limited to, common keywords or related topics. Order of summaries includes, but is not limited to, order of importance or relevance. For example, highly relevant information can be summarized preferentially. Less relevant information can be summarized concisely, while highly relevant information can be summarized in detail. Furthermore, the order of summaries can be adjusted based on the relevance of the information. This allows for the provision of more appropriate summaries by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of summaries.

[0045] The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. For example, the translation unit can adjust the level of detail of the translation based on the importance of the information during translation. The importance of the information includes, but is not limited to, the reliability and impact of the information. The level of detail of the translation includes, but is not limited to, detailed translations and concise translations. For example, important information can be translated in detail, and less important information can be translated concisely. The level of detail of the translation can also be adjusted according to the category of the information. Furthermore, the level of detail of the translation can also be adjusted based on the reliability of the information. In this way, by adjusting the level of detail of the translation based on the importance of the information, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the translation.

[0046] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, the translation unit can apply different translation algorithms depending on the category of information during translation. Categories of information include, but are not limited to, news, technical information, and entertainment. Translation algorithms include, but are not limited to, machine translation and technical terminology translation. For example, in the case of a news article, the translation unit can apply a translation algorithm that highlights important points. In the case of a technical document, the translation unit can also apply a translation algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the translation unit can also apply an engaging translation algorithm. By applying different translation algorithms depending on the category of information, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information category data into a generating AI and have the generating AI perform the application of translation algorithms.

[0047] The translation unit can determine translation priorities based on when the information was collected. For example, the translation unit can determine translation priorities based on when the information was collected. The information collection period includes, but is not limited to, the latest information or historical information. Translation priorities include, but are not limited to, the importance or relevance of the information. For example, the latest information may be translated first. Historical information may be translated concisely, while the latest information may be translated in detail. Furthermore, the order of translations can be adjusted based on when the information was collected. This allows for the provision of more appropriate translations by determining translation priorities based on when the information was collected. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information collection period data into a generating AI and have the generating AI perform the determination of translation priorities.

[0048] The translation unit can adjust the order of translations based on the relevance of the information during translation. For example, the translation unit can adjust the order of translations based on the relevance of the information during translation. Relevance of information includes, but is not limited to, common keywords or related topics. Order of translation includes, but is not limited to, order of importance or relevance. For example, highly relevant information can be translated preferentially. Less relevant information can be translated concisely, while highly relevant information can be translated in detail. Furthermore, the order of translations can be adjusted based on the relevance of the information. This allows for the provision of more appropriate translations by adjusting the order of translations based on the relevance of the information. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the translation order.

[0049] The information provider can adjust the level of detail displayed based on the importance of the information at the time of provision. For example, the information provider can adjust the level of detail displayed based on the importance of the information at the time of provision. The importance of the information includes, but is not limited to, the reliability and impact of the information. The level of detail displayed includes, but is not limited to, detailed or concise displays. For example, important information is displayed in detail, and less important information is displayed concisely. The level of detail can also be adjusted according to the category of the information. Furthermore, the level of detail can also be adjusted based on the reliability of the information. By adjusting the level of detail displayed based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, AI, or without using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0050] The information provider can apply different display algorithms depending on the category of information at the time of delivery. For example, the information provider can apply different display algorithms depending on the category of information at the time of delivery. Categories of information include, but are not limited to, news, technical information, and entertainment. Display algorithms include, but are not limited to, graph displays and list displays. For example, in the case of a news article, the provider can apply a display algorithm that highlights important points. In the case of a technical document, the provider can also apply a display algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the provider can also apply an attention-grabbing display algorithm. By applying different display algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information category data into a generating AI and have the generating AI perform the application of display algorithms.

[0051] The information provider can determine the display priority based on the information collection timing at the time of provision. For example, the information provider can determine the display priority based on the information collection timing at the time of provision. The information collection timing includes, but is not limited to, the latest information and past information. The display priority includes, but is not limited to, the importance and relevance of the information. For example, the latest information can be displayed preferentially. Past information can also be displayed concisely, while the latest information can be displayed in detail. Furthermore, the display order can be adjusted based on the information collection timing. This allows for the provision of more appropriate information by determining the display priority based on the information collection timing. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information collection timing data into a generating AI and have the generating AI perform the determination of the display priority.

[0052] The information provider can adjust the display order based on the relevance of the information at the time of provision. For example, the information provider can adjust the display order based on the relevance of the information at the time of provision. The relevance of the information includes, but is not limited to, common keywords or related topics. The display order includes, but is not limited to, order by importance or relevance. For example, highly relevant information can be displayed preferentially. Less relevant information can also be displayed concisely, while highly relevant information can be displayed in detail. Furthermore, the display order can be adjusted based on the relevance of the information. This allows for the provision of more appropriate information by adjusting the display order based on the relevance of the information. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0053] The feedback receiving unit can analyze the user's past feedback history and select the optimal receiving method when receiving feedback. For example, the feedback receiving unit can analyze the user's past feedback history and select the optimal receiving method. The feedback history includes, but is not limited to, past feedback content and evaluation scores. The optimal receiving method includes, but is not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize receiving feedback in formats that the user has frequently sent in the past. It can also prioritize receiving feedback methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and accept feedback to be sent at specific time periods based on the user's past feedback history. In this way, the optimal receiving method can be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using, for example, AI, or not using AI. For example, the feedback receiving unit can input the user's past feedback history into a generating AI and have the generating AI select the optimal receiving method.

[0054] The feedback receiving unit can prioritize receiving highly relevant feedback by considering the user's geographical location information when receiving feedback. For example, the feedback receiving unit prioritizes receiving highly relevant feedback by considering the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant feedback includes, but is not limited to, feedback that is geographically close or feedback that is specific to a particular region. For example, it can prioritize receiving feedback related to the user's current location. If the user has shown interest in a particular region, it can also prioritize receiving feedback related to that region. Furthermore, if the user is traveling, it can also prioritize receiving feedback related to their travel destination. In this way, by considering the user's geographical location information, it is possible to receive highly relevant feedback. Some or all of the above processing in the feedback receiving unit may be performed using, for example, AI, or not using AI. For example, the feedback receiving unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant feedback.

[0055] The instruction analysis unit can analyze the user's past instruction history and select the optimal analysis method during instruction analysis. For example, the instruction analysis unit can analyze the user's past instruction history and select the optimal analysis method. The instruction history includes, but is not limited to, past instruction content and frequency. The optimal analysis method includes, but is not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize analyzing the format of instructions the user has frequently sent in the past. It can also prioritize analyzing instruction methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and analyze instructions to be sent during specific time periods based on the user's past instruction history. In this way, the optimal analysis method can be selected by analyzing the user's past instruction history. Some or all of the above processing in the instruction analysis unit may be performed using, for example, AI, or without AI. For example, the instruction analysis unit can input the user's past instruction history into a generating AI and have the generating AI select the optimal analysis method.

[0056] The instruction analysis unit can prioritize the analysis of highly relevant instructions by considering the user's geographical location information during instruction analysis. For example, the instruction analysis unit prioritizes the analysis of highly relevant instructions by considering the user's geographical location information during instruction analysis. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant instructions include, but are not limited to, instructions that are geographically close or instructions specific to a particular region. For example, it prioritizes the analysis of instructions related to the user's current location. If the user has shown interest in a particular region, it can also prioritize the analysis of instructions related to that region. Furthermore, if the user is traveling, it can also prioritize the analysis of instructions related to their travel destination. In this way, highly relevant instructions can be analyzed by considering the user's geographical location information. Some or all of the above processing in the instruction analysis unit may be performed using, for example, AI, or without AI. For example, the instruction analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant instructions.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can analyze a user's past feedback history and propose the most suitable information delivery method. For example, it can prioritize suggesting information delivery methods that the user has previously given high ratings to. It can also avoid information delivery methods that the user has previously expressed dissatisfaction with. Furthermore, it can predict and propose information delivery methods suitable for specific time periods based on the user's feedback history. In this way, by analyzing a user's past feedback history, the reception desk can propose the most suitable information delivery method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past feedback history into a generating AI and have the generating AI propose the most suitable information delivery method.

[0059] The information gathering unit can adjust the timing of information collection considering the user's current activity status. For example, if the user is working, the information gathering unit will prioritize collecting work-related information. If the user is on a break, it can also collect information that helps them relax. Furthermore, if the user is traveling, it can also collect information related to their destination. This allows for the collection of more appropriate information by considering the user's current activity status. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user activity data into a generating AI and have the generating AI adjust the timing of information collection.

[0060] The summarization unit can adjust the level of detail in the summary based on the reliability of the information. For example, reliable information is summarized in detail, while less reliable information is summarized concisely. The order of the summaries can also be adjusted according to the reliability of the information. Furthermore, less reliable information can be excluded from the summary. This allows for the provision of more appropriate summaries by adjusting the level of detail based on the reliability of the information. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0061] The translation unit can adjust the difficulty of the translation based on the user's language skills. For example, if the user is a beginner, it can provide a translation using simple expressions. If the user is an advanced user, it can provide a detailed translation that includes specialized terminology. Furthermore, it can adjust the order of translations according to the user's language skills. This allows for the provision of more appropriate translations by adjusting the difficulty of the translation based on the user's language skills. Some or all of the above processes in the translation unit may be performed using AI or not. For example, the translation unit can input the user's language skill data into a generating AI and have the generating AI perform the adjustment of the translation difficulty.

[0062] The information provider can adjust how information is displayed depending on the user's device. For example, if a smartphone is being used, the provider can provide a mobile-friendly display. If a desktop is being used, the provider can also display detailed information. Furthermore, if a tablet is being used, the provider can provide an interactive display. By adjusting how information is displayed according to the user's device, more relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user device data into a generating AI and have the generating AI perform the adjustment of how information is displayed.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives the information the user needs. This information may include text, images, and videos. Step 2: The Information Gathering Department collects relevant information from the internet based on the information specified by the Reception Department. The Information Gathering Department collects information based on specific websites or specific keywords. Step 3: The summarization unit uses a generation AI to summarize the information collected by the information gathering unit. Summarization is performed based on the length of the text and the importance of the information being summarized. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to concisely summarize the information. Step 4: The translation unit translates the information summarized by the summarization unit. The translation is performed based on the translation algorithm used and the accuracy of the translation. Step 5: The provider provides the translated information in HTML format. This includes how to use HTML tags and the display layout.

[0065] (Example of form 2) An information provision system according to an embodiment of the present invention is a system that automatically collects information needed by a user, summarizes and translates it using a generation AI, and presents it in an easy-to-understand HTML format. When a user specifies the information they need, the system automatically collects relevant information from the internet. Next, the collected information is summarized by the generation AI and translated as necessary. Finally, the summarized and translated information is provided to the user in HTML format. Furthermore, if the selected information is not what the user expected, the user can send feedback. The user can also instruct the generation AI in natural language what kind of information they want. This allows the system to provide information optimized for the individual based on the user's feedback and instructions. For example, when a user specifies the information they need, the information collection unit collects relevant information from the internet. Next, the collected information is summarized by the summarization unit and translated as necessary by the translation unit. The summarized and translated information is provided to the user in HTML format by the provision unit. Furthermore, if the user sends feedback, the feedback reception unit receives that feedback. If the user sends instructions in natural language, the instruction analysis unit analyzes those instructions. This allows the system to provide information optimized for the individual based on the user's feedback and instructions. This allows the information provision system to efficiently collect, summarize, translate, and provide users with the information they need.

[0066] The information provision system according to this embodiment comprises a reception unit, an information collection unit, a summarization unit, a translation unit, and a provision unit. The reception unit allows the user to specify the information they need. The information specified by the user includes, but is not limited to, text information, image information, video information, etc. The information collection unit collects relevant information from the internet based on the information specified by the reception unit. The information collection unit collects information based, for example, on a specific website or specific keywords. The summarization unit uses a generation AI to summarize the information collected by the information collection unit. The summarization is performed, for example, based on the length of the text or the importance of the information being summarized, but is not limited to such examples. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the information. The summarization unit can also use a multimodal generation AI to summarize the content of the information. The translation unit translates the information summarized by the summarization unit. The translation is performed, for example, based on the translation algorithm used or the accuracy of the translation, but is not limited to such examples. The provision unit provides the information translated by the translation unit in HTML format. The HTML format includes, but is not limited to, the usage of HTML tags and the layout of the display. This allows the information provision system according to the embodiment to efficiently collect, summarize, translate, and provide the information the user needs.

[0067] The service provider can provide users with summarized and translated information in HTML format. For example, the service provider can provide users with summarized and translated information in HTML format. HTML format includes, but is not limited to, the use of HTML tags and display layout. By providing summarized and translated information in HTML format, information can be conveyed to users in an easy-to-understand manner. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input summarized and translated information into a generating AI and have the generating AI produce output in HTML format.

[0068] The feedback receiving unit can receive feedback from users. For example, the feedback receiving unit receives feedback from users. This feedback includes, but is not limited to, text-based feedback or evaluation scores. This allows for system improvement by receiving feedback from users. Some or all of the above processing in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user feedback into a generating AI and have the generating AI perform analysis of the feedback.

[0069] The instruction analysis unit can analyze instructions from the user in natural language. For example, the instruction analysis unit analyzes instructions from the user in natural language. Instructions in natural language include, but are not limited to, questions or commands. By analyzing the user's instructions in natural language, it is possible to provide information optimized for the individual. Some or all of the above processing in the instruction analysis unit may be performed using AI, for example, or without AI. For example, the instruction analysis unit can input natural language instructions from the user into a generating AI and have the generating AI perform the analysis of the instructions.

[0070] The reception desk can estimate the user's emotions and adjust the method of specifying information based on the estimated emotions. For example, the reception desk can estimate the user's emotions and adjust the method of specifying information based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Methods of specifying information include, but are not limited to, keyword search and category selection. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the steps required to specify information. If the user is relaxed, the reception desk can provide detailed specification options and suggest a customizable method of specification. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information specification. This allows for the provision of more appropriate information by adjusting the method of specifying information according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0071] The reception unit can analyze the user's past information specification history and propose the optimal specification method. For example, the reception unit analyzes the user's past information specification history and proposes the optimal specification method. Information specification history includes, but is not limited to, past search keywords and browsing history. Optimal specification methods include, but are not limited to, suggestions based on the user's behavior patterns. For example, it can automatically display information that the user has frequently specified in the past as a candidate. It can also prioritize suggesting specification methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest information that the user will use during specific time periods based on the user's past specification history. In this way, by analyzing the user's past information specification history, the optimal specification method can be proposed. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's past information specification history into a generating AI and have the generating AI propose the optimal specification method.

[0072] The reception unit can filter information based on the user's current areas of interest when the user specifies information. For example, the reception unit can filter information based on the user's current areas of interest when the user specifies information. Areas of interest include, but are not limited to, survey results or past search history. Filtering includes, but is not limited to, filtering based on specific keywords. For example, it can prioritize displaying relevant information based on keywords the user has recently searched for. It can also narrow down information candidates based on topics the user is interested in. Furthermore, it can analyze the user's social media activity and prioritize displaying relevant information. This allows for the provision of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0073] The reception unit can estimate the user's emotions and determine the priority of the information to be specified based on the estimated emotions. For example, the reception unit can estimate the user's emotions and determine the priority of the information to be specified based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Information priority includes, but is not limited to, importance and relevance. For example, if the user is stressed, the reception unit will prioritize displaying important information. If the user is relaxed, the reception unit may also prioritize displaying detailed information. Furthermore, if the user is in a hurry, the reception unit may also prioritize displaying information that can be retrieved quickly. In this way, more appropriate information can be provided by prioritizing information according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input user emotion data into a generating AI and have the AI ​​determine the priority of the information.

[0074] The reception unit can prioritize highly relevant information when the user specifies information, taking into account the user's geographical location. For example, the reception unit prioritizes highly relevant information when the user specifies information, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant information includes, but is not limited to, information that is geographically close or information specific to a particular region. For example, it can prioritize displaying information that is close to the user's current location. If the user has shown interest in a particular region, it can also prioritize displaying information related to that region. Furthermore, if the user is traveling, it can also prioritize displaying information related to their travel destination. In this way, highly relevant information can be provided by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI specify highly relevant information.

[0075] The reception unit can analyze the user's social media activity and specify relevant information when the user requests information. For example, the reception unit can analyze the user's social media activity and specify relevant information when the user requests information. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, trending information on social media. For example, it can prioritize displaying information related to posts the user has recently "liked." It can also prioritize displaying information based on posts from accounts the user follows. Furthermore, it can prioritize displaying information related to groups and communities the user participates in. This allows the reception unit to provide highly relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI specify relevant information.

[0076] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, the information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Timing of information gathering includes, but is not limited to, the user's activity time and changes in emotions. For example, if the user is relaxed, the information gathering unit can gather information slowly. If the user is in a hurry, the information gathering unit can gather information quickly. Furthermore, if the user is stressed, the information gathering unit can prioritize the collection of important information. In this way, more appropriate information can be collected by adjusting the timing of information gathering according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or not using AI. For example, the information gathering unit can input user emotion data into a generating AI and have the generating AI adjust the timing of information gathering.

[0077] The information gathering unit can analyze the user's past information gathering history and select the optimal gathering method when gathering information. For example, the information gathering unit can analyze the user's past information gathering history and select the optimal gathering method when gathering information. Information gathering history includes, but is not limited to, past search keywords and browsing history. Optimal gathering methods include, but are not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize the collection of information that the user has frequently collected in the past. It can also prioritize the use of gathering methods (websites, APIs, etc.) that the user has used in the past. Furthermore, it can predict and collect information to be collected at specific time periods based on the user's past gathering history. This allows the optimal gathering method to be selected by analyzing the user's past information gathering history. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or without AI. For example, the information gathering unit can input the user's past information gathering history into a generating AI and have the generating AI select the optimal gathering method.

[0078] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. Projects include, but are not limited to, research projects and business projects. Areas of interest include, but are not limited to, survey results and past search history. Filtering includes, but is not limited to, filtering based on specific keywords. For example, it can prioritize the collection of information related to projects the user is currently working on. It can also filter information based on topics the user has shown interest in. Furthermore, it can analyze the user's social media activity and prioritize the collection of relevant information. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or not using AI. For example, the information gathering unit can input the user's project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0079] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Information prioritization includes, but is not limited to, importance and relevance. For example, if the user is stressed, the information gathering unit will prioritize collecting important information. If the user is relaxed, the information gathering unit may also prioritize collecting detailed information. Furthermore, if the user is in a hurry, the information gathering unit may also prioritize collecting information that can be obtained quickly. In this way, more appropriate information can be collected by determining the priority of information according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or without AI. For example, the information gathering unit can input user emotion data into a generating AI and have the generating AI determine the priority of the information.

[0080] The information gathering unit can prioritize the collection of highly relevant information by considering the user's geographical location information during information gathering. For example, the information gathering unit prioritizes the collection of highly relevant information by considering the user's geographical location information during information gathering. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant information includes, but is not limited to, information that is geographically close or information specific to a particular region. For example, it prioritizes the collection of information that is close to the user's current location. If the user has shown interest in a particular region, it can also prioritize the collection of information related to that region. Furthermore, if the user is traveling, it can also prioritize the collection of information related to their travel destination. In this way, highly relevant information can be collected by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0081] The information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze a user's social media activity and collect relevant information during the information gathering process. Social media activity includes, but is not limited to, posts and follower counts. Relevant information includes, but is not limited to, trending information on social media. For example, it can prioritize collecting information related to posts that the user has recently "liked." It can also collect information based on posts from accounts that the user follows. Furthermore, it can prioritize collecting information related to groups and communities that the user participates in. In this way, by analyzing a user's social media activity, it is possible to collect highly relevant information. Some or all of the above processing in the information gathering unit may be performed using, for example, AI, or not using AI. For example, the information gathering unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant information.

[0082] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, the summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition or voice analysis. The way the summary is presented includes, but is not limited to, formal or casual expressions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, the summarization unit can provide a concise summary. Furthermore, if the user is stressed, the summarization unit can provide a summary that highlights the important points. In this way, by adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to these examples. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input user sentiment data into the generating AI and have the generating AI adjust the way the summary is expressed.

[0083] The summarization unit can adjust the level of detail of a summary based on the importance of the information during summary generation. For example, the summarization unit can adjust the level of detail of a summary based on the importance of the information during summary generation. The importance of information includes, but is not limited to, the reliability and impact of the information. The level of detail of a summary includes, but is not limited to, detailed summaries and concise summaries. For example, important information is summarized in detail, and less important information is summarized concisely. The level of detail of a summary can also be adjusted according to the category of information. Furthermore, the level of detail of a summary can also be adjusted based on the reliability of the information. By adjusting the level of detail of a summary based on the importance of the information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the summary.

[0084] The summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. For example, the summarization unit can apply different summarization algorithms depending on the category of information when generating summaries. Categories of information include, but are not limited to, news, technical information, and entertainment. Summarization algorithms include, but are not limited to, abstract summaries and extractive summaries. For example, in the case of news articles, the summarization unit can apply a summarization algorithm that highlights important points. In the case of technical documents, the summarization unit can also apply a summarization algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the summarization unit can also apply an attention-grabbing summarization algorithm. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information category data into a generating AI and have the generating AI perform the application of a summarization algorithm.

[0085] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, the summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition or voice analysis. Summary length includes, but is not limited to, short or long summaries. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can provide a longer summary with more detailed explanations. Furthermore, if the user is stressed, the summarization unit can provide a short summary that highlights the important points. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input user sentiment data into a generating AI and have the AI ​​adjust the length of the summary.

[0086] The summarization unit can determine the priority of summaries based on when the information was collected during summary generation. For example, the summarization unit can determine the priority of summaries based on when the information was collected during summary generation. The information collection period includes, but is not limited to, the latest information or historical information. The priority of summaries includes, but is not limited to, the importance or relevance of the information. For example, the latest information may be summarized preferentially. Historical information may also be summarized concisely, while the latest information may be summarized in detail. Furthermore, the order of summaries can be adjusted based on when the information was collected. This allows for the provision of more appropriate summaries by determining the priority of summaries based on when the information was collected. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information collection period data into a generation AI and have the generation AI perform the determination of the summary priority.

[0087] The summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. For example, the summarization unit can adjust the order of summaries based on the relevance of the information during summary generation. Relevance of information includes, but is not limited to, common keywords or related topics. Order of summaries includes, but is not limited to, order of importance or relevance. For example, highly relevant information can be summarized preferentially. Less relevant information can be summarized concisely, while highly relevant information can be summarized in detail. Furthermore, the order of summaries can be adjusted based on the relevance of the information. This allows for the provision of more appropriate summaries by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using, for example, AI, or not using AI. For example, the summarization unit can input information relevance data into a generation AI and have the generation AI perform the adjustment of the order of summaries.

[0088] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, the translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and speech analysis. Translation expression includes, but is not limited to, formal or casual expressions. For example, if the user is relaxed, the translation unit will provide a detailed translation. If the user is in a hurry, the translation unit may provide a concise translation. Furthermore, if the user is stressed, the translation unit may provide a translation that emphasizes important points. In this way, by adjusting the translation's expression according to the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input user emotion data into a generating AI and have the AI ​​adjust the way the translation is expressed.

[0089] The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. For example, the translation unit can adjust the level of detail of the translation based on the importance of the information during translation. The importance of the information includes, but is not limited to, the reliability and impact of the information. The level of detail of the translation includes, but is not limited to, detailed translations and concise translations. For example, important information can be translated in detail, and less important information can be translated concisely. The level of detail of the translation can also be adjusted according to the category of the information. Furthermore, the level of detail of the translation can also be adjusted based on the reliability of the information. In this way, by adjusting the level of detail of the translation based on the importance of the information, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the translation.

[0090] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, the translation unit can apply different translation algorithms depending on the category of information during translation. Categories of information include, but are not limited to, news, technical information, and entertainment. Translation algorithms include, but are not limited to, machine translation and technical terminology translation. For example, in the case of a news article, the translation unit can apply a translation algorithm that highlights important points. In the case of a technical document, the translation unit can also apply a translation algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the translation unit can also apply an engaging translation algorithm. By applying different translation algorithms depending on the category of information, a more appropriate translation can be provided. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information category data into a generating AI and have the generating AI perform the application of translation algorithms.

[0091] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotions. For example, the translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition or speech analysis. Translation length includes, but is not limited to, short or long translations. For example, if the user is in a hurry, the translation unit can provide a short, concise translation. If the user is relaxed, the translation unit can provide a longer translation with more detailed explanations. Furthermore, if the user is stressed, the translation unit can provide a short translation that highlights important points. This allows for the provision of more appropriate translations by adjusting the length of the translation according to the user's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the translation.

[0092] The translation unit can determine translation priorities based on when the information was collected. For example, the translation unit can determine translation priorities based on when the information was collected. The information collection period includes, but is not limited to, the latest information or historical information. Translation priorities include, but are not limited to, the importance or relevance of the information. For example, the latest information may be translated first. Historical information may be translated concisely, while the latest information may be translated in detail. Furthermore, the order of translations can be adjusted based on when the information was collected. This allows for the provision of more appropriate translations by determining translation priorities based on when the information was collected. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information collection period data into a generating AI and have the generating AI perform the determination of translation priorities.

[0093] The translation unit can adjust the order of translations based on the relevance of the information during translation. For example, the translation unit can adjust the order of translations based on the relevance of the information during translation. Relevance of information includes, but is not limited to, common keywords or related topics. Order of translation includes, but is not limited to, order of importance or relevance. For example, highly relevant information can be translated preferentially. Less relevant information can be translated concisely, while highly relevant information can be translated in detail. Furthermore, the order of translations can be adjusted based on the relevance of the information. This allows for the provision of more appropriate translations by adjusting the order of translations based on the relevance of the information. Some or all of the above processing in the translation unit may be performed using, for example, AI, or not using AI. For example, the translation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the translation order.

[0094] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. The method of displaying information includes, but is not limited to, text display and graphical display. For example, if the user is relaxed, the service provider can display detailed information. If the user is in a hurry, the service provider can also display concise information. Furthermore, if the user is stressed, the service provider can display information that highlights important points. In this way, by adjusting the way information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​adjust how the information is displayed.

[0095] The information provider can adjust the level of detail displayed based on the importance of the information at the time of provision. For example, the information provider can adjust the level of detail displayed based on the importance of the information at the time of provision. The importance of the information includes, but is not limited to, the reliability and impact of the information. The level of detail displayed includes, but is not limited to, detailed or concise displays. For example, important information is displayed in detail, and less important information is displayed concisely. The level of detail can also be adjusted according to the category of the information. Furthermore, the level of detail can also be adjusted based on the reliability of the information. By adjusting the level of detail displayed based on the importance of the information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, AI, or without using AI. For example, the information provider can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0096] The information provider can apply different display algorithms depending on the category of information at the time of delivery. For example, the information provider can apply different display algorithms depending on the category of information at the time of delivery. Categories of information include, but are not limited to, news, technical information, and entertainment. Display algorithms include, but are not limited to, graph displays and list displays. For example, in the case of a news article, the provider can apply a display algorithm that highlights important points. In the case of a technical document, the provider can also apply a display algorithm that includes detailed explanations. Furthermore, in the case of entertainment information, the provider can also apply an attention-grabbing display algorithm. By applying different display algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information category data into a generating AI and have the generating AI perform the application of display algorithms.

[0097] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, the service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Information priority includes, but is not limited to, importance and relevance. For example, if the user is stressed, the service provider will prioritize displaying important information. If the user is relaxed, the service provider may also prioritize displaying detailed information. Furthermore, if the user is in a hurry, the service provider may also prioritize displaying information that can be retrieved quickly. In this way, more appropriate information can be provided by prioritizing information according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generating AI and have the AI ​​determine the priority of the information.

[0098] The information provider can determine the display priority based on the information collection timing at the time of provision. For example, the information provider can determine the display priority based on the information collection timing at the time of provision. The information collection timing includes, but is not limited to, the latest information and past information. The display priority includes, but is not limited to, the importance and relevance of the information. For example, the latest information can be displayed preferentially. Past information can also be displayed concisely, while the latest information can be displayed in detail. Furthermore, the display order can be adjusted based on the information collection timing. This allows for the provision of more appropriate information by determining the display priority based on the information collection timing. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information collection timing data into a generating AI and have the generating AI perform the determination of the display priority.

[0099] The information provider can adjust the display order based on the relevance of the information at the time of provision. For example, the information provider can adjust the display order based on the relevance of the information at the time of provision. The relevance of the information includes, but is not limited to, common keywords or related topics. The display order includes, but is not limited to, order by importance or relevance. For example, highly relevant information can be displayed preferentially. Less relevant information can also be displayed concisely, while highly relevant information can be displayed in detail. Furthermore, the display order can be adjusted based on the relevance of the information. This allows for the provision of more appropriate information by adjusting the display order based on the relevance of the information. Some or all of the above processing in the information provider may be performed using, for example, AI, or not using AI. For example, the information provider can input information relevance data into a generating AI and have the generating AI perform the adjustment of the display order.

[0100] The feedback receiving unit can estimate the user's emotions and adjust the feedback receiving method based on the estimated emotions. For example, the feedback receiving unit can estimate the user's emotions and adjust the feedback receiving method based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Feedback receiving methods include, but are not limited to, online forms and email. For example, if the user is relaxed, the feedback receiving unit can accept detailed feedback. If the user is in a hurry, the feedback receiving unit can accept concise feedback. Furthermore, if the user is stressed, the feedback receiving unit can accept feedback that highlights important points. In this way, by adjusting the feedback receiving method according to the user's emotions, more appropriate feedback can be received. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the feedback receiving unit may be performed using, for example, AI, or not using AI. For example, the feedback receiving unit can input user emotion data into a generating AI and have the generating AI adjust the feedback receiving method.

[0101] The feedback receiving unit can analyze the user's past feedback history and select the optimal receiving method when receiving feedback. For example, the feedback receiving unit can analyze the user's past feedback history and select the optimal receiving method. The feedback history includes, but is not limited to, past feedback content and evaluation scores. The optimal receiving method includes, but is not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize receiving feedback in formats that the user has frequently sent in the past. It can also prioritize receiving feedback methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and accept feedback to be sent at specific time periods based on the user's past feedback history. In this way, the optimal receiving method can be selected by analyzing the user's past feedback history. Some or all of the above processing in the feedback receiving unit may be performed using, for example, AI, or not using AI. For example, the feedback receiving unit can input the user's past feedback history into a generating AI and have the generating AI select the optimal receiving method.

[0102] The feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Prioritization of feedback includes, but is not limited to, importance and relevance. For example, if the user is stressed, the feedback receiving unit will prioritize important feedback. If the user is relaxed, the feedback receiving unit may also prioritize detailed feedback. Furthermore, if the user is in a hurry, the feedback receiving unit may also prioritize feedback that can be obtained quickly. In this way, by determining the priority of feedback according to the user's emotions, more appropriate feedback can be received. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback receiving unit may be performed using AI, for example, or without AI. For example, the feedback receiving unit can input user emotion data into a generating AI and have the generating AI determine the priority of the feedback.

[0103] The feedback receiving unit can prioritize receiving highly relevant feedback by considering the user's geographical location information when receiving feedback. For example, the feedback receiving unit prioritizes receiving highly relevant feedback by considering the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant feedback includes, but is not limited to, feedback that is geographically close or feedback that is specific to a particular region. For example, it can prioritize receiving feedback related to the user's current location. If the user has shown interest in a particular region, it can also prioritize receiving feedback related to that region. Furthermore, if the user is traveling, it can also prioritize receiving feedback related to their travel destination. In this way, by considering the user's geographical location information, it is possible to receive highly relevant feedback. Some or all of the above processing in the feedback receiving unit may be performed using, for example, AI, or not using AI. For example, the feedback receiving unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of receiving highly relevant feedback.

[0104] The instruction analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. For example, the instruction analysis unit can estimate the user's emotions and adjust the instruction analysis method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Instruction analysis methods include, but are not limited to, natural language processing and keyword analysis. For example, if the user is relaxed, the instruction analysis unit will analyze detailed instructions. If the user is in a hurry, the instruction analysis unit can also analyze concise instructions. Furthermore, if the user is stressed, the instruction analysis unit can analyze instructions that highlight important points. In this way, by adjusting the instruction analysis method according to the user's emotions, more appropriate instructions can be analyzed. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the instruction analysis unit may be performed using AI, for example, or without AI. For example, the instruction analysis unit can input user emotion data into a generating AI and have the generating AI adjust the method of analyzing the instructions.

[0105] The instruction analysis unit can analyze the user's past instruction history and select the optimal analysis method during instruction analysis. For example, the instruction analysis unit can analyze the user's past instruction history and select the optimal analysis method. The instruction history includes, but is not limited to, past instruction content and frequency. The optimal analysis method includes, but is not limited to, suggestions based on the user's behavior patterns. For example, it can prioritize analyzing the format of instructions the user has frequently sent in the past. It can also prioritize analyzing instruction methods the user has used in the past (voice, text, etc.). Furthermore, it can predict and analyze instructions to be sent during specific time periods based on the user's past instruction history. In this way, the optimal analysis method can be selected by analyzing the user's past instruction history. Some or all of the above processing in the instruction analysis unit may be performed using, for example, AI, or without AI. For example, the instruction analysis unit can input the user's past instruction history into a generating AI and have the generating AI select the optimal analysis method.

[0106] The instruction analysis unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, the instruction analysis unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. Emotion estimation includes, but is not limited to, facial recognition and voice analysis. Instruction priority includes, but is not limited to, importance and relevance. For example, if the user is stressed, the instruction analysis unit will prioritize analyzing important instructions. If the user is relaxed, the instruction analysis unit may also prioritize analyzing detailed instructions. Furthermore, if the user is in a hurry, the instruction analysis unit may also prioritize analyzing instructions that can be obtained quickly. In this way, by determining the priority of instructions according to the user's emotions, more appropriate instructions can be analyzed. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the instruction analysis unit may be performed using, for example, AI, or without AI. For example, the instruction analysis unit can input user emotion data into the generating AI and have the generating AI determine the priority of instructions.

[0107] The instruction analysis unit can prioritize the analysis of highly relevant instructions by considering the user's geographical location information during instruction analysis. For example, the instruction analysis unit prioritizes the analysis of highly relevant instructions by considering the user's geographical location information during instruction analysis. Geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant instructions include, but are not limited to, instructions that are geographically close or instructions specific to a particular region. For example, it prioritizes the analysis of instructions related to the user's current location. If the user has shown interest in a particular region, it can also prioritize the analysis of instructions related to that region. Furthermore, if the user is traveling, it can also prioritize the analysis of instructions related to their travel destination. In this way, highly relevant instructions can be analyzed by considering the user's geographical location information. Some or all of the above processing in the instruction analysis unit may be performed using, for example, AI, or without AI. For example, the instruction analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis of highly relevant instructions. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, information collection unit, summarization unit, translation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user specifies the information they need. The information collection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where relevant information is collected from the internet. The summarization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the collected information is summarized using a generation AI. The translation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the summarized information is translated. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, where the translated information is provided in HTML format. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, information gathering unit, summarization unit, translation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user specifies the information they need. The information gathering unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where relevant information is collected from the internet. The summarization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the collected information is summarized using a generation AI. The translation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where the summarized information is translated. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, where the translated information is provided in HTML format. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, information gathering unit, summarization unit, translation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user specifies the information they need. The information gathering unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where relevant information is collected from the internet. The summarization unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the collected information is summarized using a generation AI. The translation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the summarized information is translated. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, where the translated information is provided in HTML format. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, information gathering unit, summarization unit, translation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user specifies the information they need. The information gathering unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where relevant information is collected from the internet. The summarization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the collected information is summarized using a generation AI. The translation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the summarized information is translated. The provision unit is implemented by, for example, the control unit 46A of the robot 414, where the translated information is provided in HTML format.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The reception desk can analyze a user's past feedback history and propose the most suitable information delivery method. For example, it can prioritize suggesting information delivery methods that the user has previously given high ratings to. It can also avoid information delivery methods that the user has previously expressed dissatisfaction with. Furthermore, it can predict and propose information delivery methods suitable for specific time periods based on the user's feedback history. In this way, by analyzing a user's past feedback history, the reception desk can propose the most suitable information delivery method. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past feedback history into a generating AI and have the generating AI propose the most suitable information delivery method.

[0110] The information gathering unit can adjust the timing of information collection considering the user's current activity status. For example, if the user is working, the information gathering unit will prioritize collecting work-related information. If the user is on a break, it can also collect information that helps them relax. Furthermore, if the user is traveling, it can also collect information related to their destination. This allows for the collection of more appropriate information by considering the user's current activity status. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user activity data into a generating AI and have the generating AI adjust the timing of information collection.

[0111] The summarization unit can adjust the level of detail in the summary based on the reliability of the information. For example, reliable information is summarized in detail, while less reliable information is summarized concisely. The order of the summaries can also be adjusted according to the reliability of the information. Furthermore, less reliable information can be excluded from the summary. This allows for the provision of more appropriate summaries by adjusting the level of detail based on the reliability of the information. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0112] The translation unit can adjust the difficulty of the translation based on the user's language skills. For example, if the user is a beginner, it can provide a translation using simple expressions. If the user is an advanced user, it can provide a detailed translation that includes specialized terminology. Furthermore, it can adjust the order of translations according to the user's language skills. This allows for the provision of more appropriate translations by adjusting the difficulty of the translation based on the user's language skills. Some or all of the above processes in the translation unit may be performed using AI or not. For example, the translation unit can input the user's language skill data into a generating AI and have the generating AI perform the adjustment of the translation difficulty.

[0113] The information provider can adjust how information is displayed depending on the user's device. For example, if a smartphone is being used, the provider can provide a mobile-friendly display. If a desktop is being used, the provider can also display detailed information. Furthermore, if a tablet is being used, the provider can provide an interactive display. By adjusting how information is displayed according to the user's device, more relevant information can be provided. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input user device data into a generating AI and have the generating AI perform the adjustment of how information is displayed.

[0114] The reception desk can estimate the user's emotions and adjust how information is specified based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the steps required to specify information. If the user is relaxed, the reception desk can provide detailed specification options and suggest a customizable specification method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick information specification. This allows for the provision of more appropriate information by adjusting how information is specified according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is relaxed, the information gathering unit will gather information slowly. If the user is in a hurry, the information gathering unit can gather information quickly. Furthermore, if the user is stressed, the information gathering unit can prioritize the collection of important information. In this way, by adjusting the timing of information gathering according to the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI adjust the timing of information gathering.

[0116] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is relaxed, the summarization unit can provide a detailed summary. If the user is in a hurry, the summarization unit can provide a concise summary. Furthermore, if the user is stressed, the summarization unit can provide a summary that highlights the important points. In this way, by adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into the generative AI and have the generative AI adjust the way the summary is presented.

[0117] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is relaxed, the translation unit can provide a detailed translation. If the user is in a hurry, the translation unit can provide a concise translation. Furthermore, if the user is stressed, the translation unit can provide a translation that emphasizes important points. This allows for more appropriate translations by adjusting the translation's expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0118] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed information. If the user is in a hurry, the service provider can display concise information. Furthermore, if the user is stressed, the service provider can display information that highlights important points. In this way, by adjusting how information is displayed according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the information is displayed.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the information the user needs. This information may include text, images, and videos. Step 2: The Information Gathering Department collects relevant information from the internet based on the information specified by the Reception Department. The Information Gathering Department collects information based on specific websites or specific keywords. Step 3: The summarization unit uses a generation AI to summarize the information collected by the information gathering unit. Summarization is performed based on the length of the text and the importance of the information being summarized. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to concisely summarize the information. Step 4: The translation unit translates the information summarized by the summarization unit. The translation is performed based on the translation algorithm used and the accuracy of the translation. Step 5: The provider provides the translated information in HTML format. This includes how to use HTML tags and the display layout.

[0121] 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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.

[0173] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where the user specifies the information they need, An information gathering unit that collects relevant information from the internet based on the information specified by the reception unit, A summarization unit that summarizes the information collected by the aforementioned information collection unit, A translation unit that translates the information summarized by the aforementioned summarization unit, A provisioning unit that provides the information translated by the aforementioned translation unit in HTML format, Equipped with A system characterized by the following features.

2. The aforementioned supply unit is, The summarized and translated information is provided to the user in HTML format. The system according to feature 1.

3. It includes a feedback reception section for receiving user feedback. The system according to feature 1.

4. It includes an instruction analysis unit that analyzes natural language instructions from the user. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts how information is specified based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past information specification history and proposes the optimal specification method. The system according to feature 1.

7. The aforementioned reception unit is When specifying information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to specify based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is When specifying information, the system prioritizes highly relevant information by considering the user's geographical location. The system according to feature 1.

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