System

Abstract

An object of a system according to an embodiment is to enable a receiver to accurately understand information.SOLUTION: A system includes a designation unit, a conversion unit, an evaluation unit, and a provision unit. The designation unit designates the type or format of information that the receiver wants to receive. The conversion section analyzes the information designated by the designation section and converts the information into a form easily understood by a receiver. The evaluation unit evaluates reliability of the information converted by the conversion unit. The providing unit provides the information evaluated by the evaluating unit to the receiver.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology is prone to problems caused by misunderstandings or lack of understanding of information, so there is room for improvement.

[0005] The system according to the embodiment aims to enable the recipient to accurately understand the information. [Means for solving the problem]

[0006] The system according to the embodiment includes a designation unit, a conversion unit, an evaluation unit, and a provision unit. The designation unit designates the type or format of information that the recipient wishes to receive. The conversion unit analyzes the information designated by the designation unit and converts it into a form that is easy for the recipient to understand. The evaluation unit evaluates the reliability of the information converted by the conversion unit. The provision unit provides the information evaluated by the evaluation unit to the recipient. [Effects of the Invention]

[0007] The system according to the embodiment can enable the recipient to accurately understand the information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) In an embodiment of the present invention, an information conversion system allows a recipient to specify the type and format of information they wish to receive, and a generation AI analyzes and converts the information into a format that is easy for the recipient to understand, evaluates its reliability, and provides it. The information conversion system allows a recipient to specify the type and format of information they wish to receive, and a generation AI analyzes and converts the information into a format that is easy for the recipient to understand, evaluates its reliability, and provides it. For example, the information conversion system allows a recipient to specify the type and format of information they wish to receive. For example, it can convert text information into a concise summary or replace technical terms with simpler language. Next, the information conversion system uses a generation AI to analyze the specified information and convert it into a format that is easy for the recipient to understand. The input to the generation AI is the specified information itself, and the generation AI performs the conversion based on that content. For example, the generation AI receives a prompt such as "Please summarize this sentence concisely" and summarizes the information. Next, the information conversion system evaluates the reliability of the information converted by the generation AI. The reliability evaluation includes the reliability of the information source and the accuracy of the data. For example, the generation AI evaluates the reliability of the information source and calculates a reliability score. Next, the information conversion system provides the evaluated information to the recipient. For example, it provides only reliable information to the recipient. This allows the information conversion system to help recipients understand the true nature of information and avoid misunderstandings and trouble. This allows the information conversion system to promote accurate understanding of information and avoid trouble. For example, in response to problems such as filter bubbles and echo chambers, where slanderous and biased information accumulate on social media, generative AI can analyze information and convert it into a form that is easy for recipients to understand, making it easier for recipients to understand the true nature of the information and avoiding misunderstandings and trouble. Furthermore, even today, when youth filtering is mandatory, its adoption rate is low due to convenience concerns. Using generative AI can promote accurate understanding of information without sacrificing convenience. This allows the information conversion system to contribute to the promotion of public welfare.

[0029] An information conversion system according to an embodiment includes a designation unit, a conversion unit, an evaluation unit, and a provision unit. The designation unit designates the type and format of information the recipient desires to receive. Examples of the type and format of information the recipient desires to receive include, but are not limited to, text, images, audio, and video. The designation unit, for example, can designate the recipient to convert text information into a concise summary. The designation unit can also designate the recipient to replace technical terms with plain language. The conversion unit uses a generation AI to analyze the designated information and convert it into a form that is easy for the recipient to understand. The generation AI analyzes and converts the information using technology such as Transformer. For example, the conversion unit can cause the generation AI to receive a prompt such as "Please summarize this sentence concisely" and then summarize the information. The conversion unit can also cause the generation AI to replace technical terms with plain language. The evaluation unit evaluates the reliability of the information converted by the conversion unit. The evaluation of reliability includes, for example, the reliability of the information source, the accuracy of the data, and the frequency of updates. For example, the evaluation unit causes the generation AI to evaluate the reliability of the information source and calculate a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. The provision unit provides the information evaluated by the evaluation unit to the recipient. For example, the provision unit provides only highly reliable information to the recipient. The provision unit can also select an appropriate method for providing the evaluated information to the recipient. For example, the provision unit provides the evaluated information to the recipient via email, notification, dashboard, etc. This allows the information conversion system according to the embodiment to promote accurate understanding of information by the recipient and avoid problems. For example, the information conversion system can make it easier for the recipient to understand the essence of information and avoid misunderstandings and problems. The information conversion system can also contribute to the promotion of public welfare.

[0030] The designation unit can use a generation AI to analyze information and convert it into a form that is easy for the recipient to understand. Examples of generation AI include, but are not limited to, Transformer. For example, the designation unit can cause the generation AI to receive a prompt such as "Please summarize this sentence concisely" and summarize the information. The designation unit can also cause the generation AI to replace technical terms with simpler language. For example, the designation unit can cause the generation AI to receive a prompt such as "Please replace this technical term with simpler language" and convert the information. This improves the accuracy of information analysis and conversion by using the generation AI. Some or all of the above-mentioned processing in the designation unit can be performed using, for example, AI, or can be performed without using AI. For example, the designation unit inputs information into the generation AI, and the generation AI analyzes and converts the information.

[0031] The conversion unit can use a generation AI to analyze information and convert it into a form that is easy for the recipient to understand. Examples of generation AI include, but are not limited to, Transformer. For example, the generation AI receives a prompt such as "Please summarize this sentence concisely" and summarizes the information. The conversion unit can also replace technical terms with simpler language. For example, the generation AI receives a prompt such as "Please replace this technical term with simpler language" and converts the information. This improves the accuracy of information analysis and conversion by using a generation AI. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information into the generation AI, which analyzes and converts the information.

[0032] The evaluation unit can evaluate the reliability of the information. Evaluation of reliability includes, for example, reliability of the information source, accuracy of the data, update frequency, etc., but is not limited to these examples. For example, the evaluation unit has the generation AI evaluate the reliability of the information source and calculate a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. For example, the evaluation unit has the generation AI evaluate the reliability of the information source and calculate a reliability score. In this way, by evaluating the reliability of the information, the recipient can receive highly reliable information. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs information to the generation AI, and the generation AI evaluates the reliability of the information.

[0033] The providing unit can provide the evaluated information to the recipient. For example, the providing unit provides only highly reliable information to the recipient. The providing unit can also select an appropriate method for providing the evaluated information to the recipient. For example, the providing unit provides the evaluated information to the recipient via email, notification, dashboard, etc. In this way, by providing the evaluated information, the recipient can receive highly reliable information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the evaluated information to the generating AI, and the generating AI provides the information.

[0034] The designation unit can analyze past designation history and predict an information format based on the user's preferences. For example, the designation unit preferentially suggests information formats that the user has frequently selected in the past. The designation unit can also predict and suggest an information format preferred during a specific time period based on the user's past designation history. The designation unit can also analyze the user's past designation history and suggest an optimal information format according to a specific situation. In this way, by analyzing the past designation history, it is possible to provide an information format that suits the user's preferences. Some or all of the above-mentioned processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit inputs past designation history data to a generation AI, which then predicts an information format.

[0035] The designation unit can dynamically change the format of the information based on the user's current situation. For example, if the user receives information at night, the designation unit can suggest a dark mode format that is easy on the eyes. The designation unit can also suggest audio format information if the user is on the move. The designation unit can also suggest an information format appropriate for a specific location if the user is in a specific location (e.g., an office or home). This allows more appropriate information to be provided by changing the format of the information according to the user's current situation. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's current situation data to the generation AI, and the generation AI dynamically changes the information format.

[0036] The designation unit can adjust the level of detail of the information according to the user's level of expertise. For example, if the user is a beginner, the designation unit can suggest a format that provides basic information. Furthermore, if the user is an intermediate user, the designation unit can also suggest a format that includes detailed information. Furthermore, if the user is an expert, the designation unit can also suggest a format that includes technical terms and detailed data. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided. Some or all of the above-mentioned processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit inputs the user's level of expertise data to the generation AI, and the generation AI adjusts the level of detail of the information.

[0037] The designation unit can propose an information format specialized for a region by taking into account the user's geographical location information. For example, when the user is in a specific region, the designation unit can prioritize displaying information related to that region. Furthermore, when the user is traveling, the designation unit can also prioritize displaying regional information for the user's travel destination. Furthermore, when the user is at home, the designation unit can also prioritize displaying local news and event information. In this way, by taking the user's geographical location information into account, it is possible to provide information specialized for a region. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's geographical location information data to a generation AI, which then proposes an information format specialized for the region.

[0038] The designation unit can analyze the user's social media activity and suggest relevant information formats. For example, the designation unit can prioritize and suggest information formats that the user frequently shares on social media. The designation unit can also analyze the content of the user's social media posts and suggest relevant information formats. The designation unit can also suggest relevant information formats by referring to the activities of the user's friends on social media. In this way, relevant information formats can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's social media activity data into a generation AI, which then suggests relevant information formats.

[0039] The designation unit can customize the method for designating an information format by reflecting the user's past feedback. The designation unit can, for example, suggest an optimal information format based on feedback provided by the user in the past. The designation unit can also preferentially suggest a specific information format based on the user's past feedback. The designation unit can also analyze the user's feedback and improve the method for designating an information format. In this way, the method for designating an information format can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's past feedback data into the generation AI, and the generation AI customizes the method for designating an information format.

[0040] The conversion unit can adjust the level of detail of the conversion based on the importance of the information. For example, the conversion unit converts information of high importance in detail and information of low importance in a concise manner. The conversion unit can also add additional annotations or explanations to information of high importance. The conversion unit can also extract and convert only the main points of information of low importance. In this way, by adjusting the level of detail of the conversion based on the importance of the information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information importance data to a generation AI, and the generation AI adjusts the level of detail of the conversion.

[0041] The conversion unit can apply different conversion algorithms depending on the category of information. For example, the conversion unit can apply a summarization algorithm to news information to generate a detailed summary. The conversion unit can also apply an algorithm to replace technical jargon with simpler language to technical information. The conversion unit can also apply an algorithm to convert entertainment information into a visually appealing format. In this way, by applying different conversion algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information category data to a generation AI, which then applies different conversion algorithms.

[0042] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit suggests an optimal conversion method based on the user's preferred conversion results in the past. The conversion unit can also extract specific patterns from the user's past conversion results to improve the conversion accuracy. The conversion unit can also improve the conversion algorithm by reflecting user feedback. In this way, the conversion accuracy can be improved by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs the user's past conversion result data into a generation AI, which then improves the conversion accuracy.

[0043] The conversion unit can determine the priority of conversion based on the time of submission of information. The conversion unit, for example, prioritizes conversion of information with high urgency. The conversion unit can also postpone information that was submitted earlier. The conversion unit can also prioritize conversion of information that is due to be submitted soon. In this way, by determining the priority of conversion based on the time of submission of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs data on the time of submission of information to the generation AI, and the generation AI determines the priority of conversion.

[0044] The conversion unit can adjust the order of conversion based on the relevance of the information. For example, the conversion unit prioritizes conversion of highly relevant information. The conversion unit can also postpone conversion of less relevant information. The conversion unit can also group and convert highly relevant information. This makes it possible to provide more appropriate information by adjusting the order of conversion based on the relevance of the information. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI, for example. For example, the conversion unit inputs information relevance data to a generation AI, and the generation AI adjusts the order of conversion.

[0045] The conversion unit can adjust the use of technical terms in the converted information according to the user's level of expertise. For example, if the user is a beginner, the conversion unit replaces technical terms with simpler language. Furthermore, if the user is an intermediate user, the conversion unit can use technical terms moderately. Furthermore, if the user is an expert, the conversion unit can use technical terms extensively. This allows for more appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms.

[0046] The evaluation unit can optimize the reliability evaluation algorithm by referring to past evaluation data. The evaluation unit, for example, improves the reliability evaluation algorithm based on past evaluation data. The evaluation unit can also extract specific patterns from past evaluation data and optimize the algorithm. The evaluation unit can also analyze past evaluation data and revise the evaluation criteria. In this way, the reliability evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit inputs past evaluation data into a generation AI, and the generation AI optimizes the reliability evaluation algorithm.

[0047] The evaluation unit can evaluate reliability by taking into account attribute information of the information submitter. The evaluation unit can evaluate reliability by taking into account, for example, the submitter's level of expertise. The evaluation unit can also evaluate reliability based on the submitter's past performance. The evaluation unit can also evaluate reliability by taking into account the submitter's affiliated institution or organization. In this way, by taking into account the attribute information of the information submitter, highly reliable information can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs attribute information data of the submitter into the generation AI, and the generation AI evaluates reliability.

[0048] The evaluation unit can weight the reliability based on the frequency of information submission. For example, the evaluation unit can rate information that is submitted frequently as highly reliable. The evaluation unit can also rate information that is submitted infrequently as less reliable. The evaluation unit can also adjust the reliability weighting according to the submission frequency. In this way, by weighting the reliability based on the frequency of information submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit inputs information submission frequency data to the generation AI, and the generation AI weights the reliability.

[0049] The evaluation unit can evaluate the reliability of information by taking into account the geographical distribution of the information. For example, the evaluation unit can evaluate the reliability of geographically close information sources as high. The evaluation unit can also evaluate the reliability of geographically distant information sources as low. The evaluation unit can also adjust the weighting of the reliability based on the geographical distribution. This makes it possible to provide highly reliable information by taking the geographical distribution of the information into consideration. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs geographical distribution data of the information to the generation AI, and the generation AI evaluates the reliability.

[0050] The evaluation unit can improve the accuracy of the reliability evaluation by referring to related literature of the information. The evaluation unit, for example, evaluates reliability based on related literature. The evaluation unit can also evaluate reliability by taking into account the number of citations of related literature. The evaluation unit can also evaluate based on the reliability of the author of the related literature. In this way, by referring to related literature of the information, the accuracy of the reliability evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs related literature data to a generation AI, and the generation AI evaluates reliability.

[0051] The evaluation unit can evaluate the reliability taking into account the market value of the information. For example, the evaluation unit can evaluate information with high market value as highly reliable. The evaluation unit can also evaluate information with low market value as less reliable. The evaluation unit can also adjust the weighting of the reliability based on the market value. This makes it possible to provide highly reliable information by taking the market value of the information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs market value data of the information to the generation AI, and the generation AI evaluates the reliability.

[0052] The providing unit can select the optimal providing method by referring to the past providing history. For example, the providing unit preferentially selects a providing method that the user has previously preferred. The providing unit can also select the optimal providing method according to a specific situation from the user's past providing history. The providing unit can also analyze the user's past providing history and improve the providing method. In this way, the optimal providing method can be selected by referring to the past providing history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs past providing history data to a generating AI, and the generating AI selects the optimal providing method.

[0053] The providing unit can customize the content to be provided according to the user's current task. For example, when the user is at work, the providing unit can prioritize providing information related to work. Furthermore, when the user is on a break, the providing unit can also provide information that helps the user relax. Furthermore, when the user is traveling, the providing unit can also provide information related to traveling. In this way, by customizing the content to be provided according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's current task data into the generating AI, and the generating AI customizes the content to be provided.

[0054] The providing unit can improve the delivery method by reflecting user feedback. The providing unit improves the delivery method based on, for example, feedback provided by the user regarding the delivery method. The providing unit can also preferentially select a specific delivery method based on the user feedback. The providing unit can also analyze the user feedback and improve the delivery method algorithm. In this way, the delivery method can be improved by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs user feedback data into a generation AI, and the generation AI improves the delivery method.

[0055] The providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is simple and highly visible. This allows the optimal providing method to be selected by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs the user's device information data into the generating AI, which then selects the optimal providing method.

[0056] The providing unit can make the provided content multilingual according to the user's language setting. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. This makes it possible to provide more appropriate information by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the user's language setting data to a generating AI, which then makes the provided content multilingual.

[0057] The providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's social media activity data into a generation AI, which then provides the related information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The designation unit can analyze the user's past behavioral patterns and predict future information needs. For example, if the user has frequently requested a specific type of information during a specific time period in the past, similar information can be automatically suggested for that time period. The designation unit can also learn what information the user needs during specific events or situations and provide appropriate information when a similar situation occurs. Furthermore, the designation unit can predict future information needs based on the user's behavioral patterns and prepare in advance. This allows the provision of more appropriate information by suggesting the type and format of information based on the user's behavioral patterns.

[0060] The converter can enhance the visual aspects of the information. For example, it can convert textual information into infographics to make it easier to understand visually. The converter can also convert data into graphs and charts to visually emphasize key points of the information. Furthermore, the converter can generate videos and animations to present information in a dynamic format. This can enhance the visual aspects of the information, facilitating comprehension and allowing the recipient to use the information more effectively.

[0061] When assessing the reliability of information, the evaluation unit can take into account the transparency of the source of the information. For example, it can evaluate the process through which the information was generated and the background of the source. The evaluation unit can also assess reliability based on data and evidence made public by the source of the information. Furthermore, the evaluation unit can also make an assessment based on the reliability of information provided in the past by the source of the information. In this way, by taking into account the transparency of the source of the information, it is possible to provide more reliable information.

[0062] The conversion unit can convert information while taking into account its context. For example, when converting information related to a specific industry or field, it takes into account the terminology and background knowledge of that industry or field. The conversion unit can also convert information into a form that is easy for the recipient to understand, taking into account the background of the recipient. Furthermore, the conversion unit can select an appropriate format or expression method depending on the context of the information. This allows the provision of more appropriate information by taking into account the context of the information.

[0063] The evaluation unit can take into account the update frequency of the information when evaluating the reliability of the information. For example, information that is updated frequently is evaluated as highly reliable, and information that is updated infrequently is evaluated as less reliable. The evaluation unit can also evaluate the reliability based on the recency of the information. Furthermore, the evaluation unit can analyze the update history of the information and evaluate the reliability based on the content of past updates. In this way, by taking the update frequency of the information into consideration, more reliable information can be provided.

[0064] The evaluation unit can take into account the reliability of the source of the information when evaluating the reliability of the information. For example, information from a highly reliable source can be rated high, and information from a less reliable source can be rated low. The evaluation unit can also evaluate reliability based on the source's past performance. Furthermore, the evaluation unit can evaluate reliability by taking into account the source's expertise and experience. In this way, by taking into account the reliability of the source of the information, more reliable information can be provided.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The specifying unit specifies the type and format of information the recipient wants to receive. The type and format of information the recipient wants to receive can include, for example, text, images, audio, video, etc. The specifying unit can specify that the recipient should convert text information into a concise summary or replace technical terms with plain language. Step 2: The conversion unit uses the generation AI to analyze the specified information and convert it into a form that is easy for the recipient to understand. The generation AI analyzes and converts information using technologies such as Transformer. For example, the conversion unit allows the generation AI to receive a prompt such as "Please summarize this sentence concisely" and summarize the information. The conversion unit also allows the generation AI to replace technical terms with simpler language. Step 3: The evaluation unit evaluates the reliability of the information converted by the conversion unit. The evaluation of reliability includes, for example, the reliability of the information source, the accuracy of the data, and the update frequency. For example, the evaluation unit evaluates the reliability of the information source by the generation AI and calculates a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. Step 4: The providing unit provides the information evaluated by the evaluating unit to the receiver. For example, the providing unit provides only reliable information to the receiver. The providing unit can also select an appropriate method for providing the evaluated information to the receiver. For example, the providing unit provides the evaluated information to the receiver via email, notification, dashboard, etc.

[0067] (Example 2) In an embodiment of the present invention, an information conversion system allows a recipient to specify the type and format of information they wish to receive, and a generation AI analyzes and converts the information into a format that is easy for the recipient to understand, evaluates its reliability, and provides it. The information conversion system allows a recipient to specify the type and format of information they wish to receive, and a generation AI analyzes and converts the information into a format that is easy for the recipient to understand, evaluates its reliability, and provides it. For example, the information conversion system allows a recipient to specify the type and format of information they wish to receive. For example, it can convert text information into a concise summary or replace technical terms with simpler language. Next, the information conversion system uses a generation AI to analyze the specified information and convert it into a format that is easy for the recipient to understand. The input to the generation AI is the specified information itself, and the generation AI performs the conversion based on that content. For example, the generation AI receives a prompt such as "Please summarize this sentence concisely" and summarizes the information. Next, the information conversion system evaluates the reliability of the information converted by the generation AI. The reliability evaluation includes the reliability of the information source and the accuracy of the data. For example, the generation AI evaluates the reliability of the information source and calculates a reliability score. Next, the information conversion system provides the evaluated information to the recipient. For example, it provides only reliable information to the recipient. This allows the information conversion system to help recipients understand the true nature of information and avoid misunderstandings and trouble. This allows the information conversion system to promote accurate understanding of information and avoid trouble. For example, in response to problems such as filter bubbles and echo chambers, where slanderous and biased information accumulate on social media, generative AI can analyze information and convert it into a form that is easy for recipients to understand, making it easier for recipients to understand the true nature of the information and avoiding misunderstandings and trouble. Furthermore, even today, when youth filtering is mandatory, its adoption rate is low due to convenience concerns. Using generative AI can promote accurate understanding of information without sacrificing convenience. This allows the information conversion system to contribute to the promotion of public welfare.

[0068] An information conversion system according to an embodiment includes a designation unit, a conversion unit, an evaluation unit, and a provision unit. The designation unit designates the type and format of information the recipient desires to receive. Examples of the type and format of information the recipient desires to receive include, but are not limited to, text, images, audio, and video. The designation unit, for example, can designate the recipient to convert text information into a concise summary. The designation unit can also designate the recipient to replace technical terms with plain language. The conversion unit uses a generation AI to analyze the designated information and convert it into a form that is easy for the recipient to understand. The generation AI analyzes and converts the information using technology such as Transformer. For example, the conversion unit can cause the generation AI to receive a prompt such as "Please summarize this sentence concisely" and then summarize the information. The conversion unit can also cause the generation AI to replace technical terms with plain language. The evaluation unit evaluates the reliability of the information converted by the conversion unit. The evaluation of reliability includes, for example, the reliability of the information source, the accuracy of the data, and the frequency of updates. For example, the evaluation unit causes the generation AI to evaluate the reliability of the information source and calculate a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. The provision unit provides the information evaluated by the evaluation unit to the recipient. For example, the provision unit provides only highly reliable information to the recipient. The provision unit can also select an appropriate method for providing the evaluated information to the recipient. For example, the provision unit provides the evaluated information to the recipient via email, notification, dashboard, etc. This allows the information conversion system according to the embodiment to promote accurate understanding of information by the recipient and avoid problems. For example, the information conversion system can make it easier for the recipient to understand the essence of information and avoid misunderstandings and problems. The information conversion system can also contribute to the promotion of public welfare.

[0069] The designation unit can use a generation AI to analyze information and convert it into a form that is easy for the recipient to understand. Examples of generation AI include, but are not limited to, Transformer. For example, the designation unit can cause the generation AI to receive a prompt such as "Please summarize this sentence concisely" and summarize the information. The designation unit can also cause the generation AI to replace technical terms with simpler language. For example, the designation unit can cause the generation AI to receive a prompt such as "Please replace this technical term with simpler language" and convert the information. This improves the accuracy of information analysis and conversion by using the generation AI. Some or all of the above-mentioned processing in the designation unit can be performed using, for example, AI, or can be performed without using AI. For example, the designation unit inputs information into the generation AI, and the generation AI analyzes and converts the information.

[0070] The conversion unit can use a generation AI to analyze information and convert it into a form that is easy for the recipient to understand. Examples of generation AI include, but are not limited to, Transformer. For example, the generation AI receives a prompt such as "Please summarize this sentence concisely" and summarizes the information. The conversion unit can also replace technical terms with simpler language. For example, the generation AI receives a prompt such as "Please replace this technical term with simpler language" and converts the information. This improves the accuracy of information analysis and conversion by using a generation AI. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information into the generation AI, which analyzes and converts the information.

[0071] The evaluation unit can evaluate the reliability of the information. Evaluation of reliability includes, for example, reliability of the information source, accuracy of the data, update frequency, etc., but is not limited to these examples. For example, the evaluation unit has the generation AI evaluate the reliability of the information source and calculate a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. For example, the evaluation unit has the generation AI evaluate the reliability of the information source and calculate a reliability score. In this way, by evaluating the reliability of the information, the recipient can receive highly reliable information. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit inputs information to the generation AI, and the generation AI evaluates the reliability of the information.

[0072] The providing unit can provide the evaluated information to the recipient. For example, the providing unit provides only highly reliable information to the recipient. The providing unit can also select an appropriate method for providing the evaluated information to the recipient. For example, the providing unit provides the evaluated information to the recipient via email, notification, dashboard, etc. In this way, by providing the evaluated information, the recipient can receive highly reliable information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the evaluated information to the generating AI, and the generating AI provides the information.

[0073] The information conversion system includes a designation unit that estimates a user's emotions and automatically suggests a type and format of information based on the estimated user's emotions. For example, if the user is feeling stressed, the designation unit may suggest a relaxing information format (e.g., a concise summary or a visual infographic). If the user is excited, the designation unit may also suggest a format containing detailed information or in-depth content. If the user is tired, the designation unit may also suggest a short, concise information format. This allows for more appropriate information to be provided by suggesting a type and format of information based on the user's emotions. The designation unit may use an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the designation unit may be performed using, for example, an AI. For example, the designation unit inputs the user's emotion data into the generation AI, which then suggests a type and format of information.

[0074] The designation unit can analyze past designation history and predict an information format based on the user's preferences. For example, the designation unit preferentially suggests information formats that the user has frequently selected in the past. The designation unit can also predict and suggest an information format preferred during a specific time period based on the user's past designation history. The designation unit can also analyze the user's past designation history and suggest an optimal information format according to a specific situation. In this way, by analyzing the past designation history, it is possible to provide an information format that suits the user's preferences. Some or all of the above-mentioned processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit inputs past designation history data to a generation AI, which then predicts an information format.

[0075] The designation unit can dynamically change the format of the information based on the user's current situation. For example, if the user receives information at night, the designation unit can suggest a dark mode format that is easy on the eyes. The designation unit can also suggest audio format information if the user is on the move. The designation unit can also suggest an information format appropriate for a specific location if the user is in a specific location (e.g., an office or home). This allows more appropriate information to be provided by changing the format of the information according to the user's current situation. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's current situation data to the generation AI, and the generation AI dynamically changes the information format.

[0076] The designation unit can adjust the level of detail of the information according to the user's level of expertise. For example, if the user is a beginner, the designation unit can suggest a format that provides basic information. Furthermore, if the user is an intermediate user, the designation unit can also suggest a format that includes detailed information. Furthermore, if the user is an expert, the designation unit can also suggest a format that includes technical terms and detailed data. In this way, by adjusting the level of detail of the information according to the user's level of expertise, more appropriate information can be provided. Some or all of the above-mentioned processing in the designation unit may be performed using, for example, AI, or may be performed without using AI. For example, the designation unit inputs the user's level of expertise data to the generation AI, and the generation AI adjusts the level of detail of the information.

[0077] The designation unit can estimate the user's emotions and determine the priority of information based on the estimated user emotions. For example, if the user is feeling stressed, the designation unit can prioritize displaying relaxing information. Furthermore, if the user is excited, the designation unit can prioritize displaying detailed information. Furthermore, if the user is tired, the designation unit can prioritize displaying short, concise information. This allows for more appropriate information to be provided by determining the priority of information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the designation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the designation unit inputs the user's emotion data into the generation AI, which then determines the priority of the information.

[0078] The designation unit can propose an information format specialized for a region by taking into account the user's geographical location information. For example, when the user is in a specific region, the designation unit can prioritize displaying information related to that region. Furthermore, when the user is traveling, the designation unit can also prioritize displaying regional information for the user's travel destination. Furthermore, when the user is at home, the designation unit can also prioritize displaying local news and event information. In this way, by taking the user's geographical location information into account, it is possible to provide information specialized for a region. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's geographical location information data to a generation AI, which then proposes an information format specialized for the region.

[0079] The designation unit can analyze the user's social media activity and suggest relevant information formats. For example, the designation unit can prioritize and suggest information formats that the user frequently shares on social media. The designation unit can also analyze the content of the user's social media posts and suggest relevant information formats. The designation unit can also suggest relevant information formats by referring to the activities of the user's friends on social media. In this way, relevant information formats can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's social media activity data into a generation AI, which then suggests relevant information formats.

[0080] The designation unit can customize the method for designating an information format by reflecting the user's past feedback. The designation unit can, for example, suggest an optimal information format based on feedback provided by the user in the past. The designation unit can also preferentially suggest a specific information format based on the user's past feedback. The designation unit can also analyze the user's feedback and improve the method for designating an information format. In this way, the method for designating an information format can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the designation unit may be performed using AI, for example, or may be performed without using AI. For example, the designation unit inputs the user's past feedback data into the generation AI, and the generation AI customizes the method for designating an information format.

[0081] The conversion unit can estimate the user's emotions and adjust the presentation method of the converted information based on the estimated user emotions. For example, if the user is relaxed, the generation AI of the conversion unit generates information that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation AI of the conversion unit can generate information that emphasizes the shortest route. Furthermore, if the user is excited, the generation AI of the conversion unit can generate information that adds visually stimulating effects. This allows for more appropriate information to be provided by adjusting the presentation method of information based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be 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-described processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit inputs the user's emotion data into the generation AI, which then adjusts the presentation method of the information.

[0082] The conversion unit can adjust the level of detail of the conversion based on the importance of the information. For example, the conversion unit converts information of high importance in detail and information of low importance in a concise manner. The conversion unit can also add additional annotations or explanations to information of high importance. The conversion unit can also extract and convert only the main points of information of low importance. In this way, by adjusting the level of detail of the conversion based on the importance of the information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information importance data to a generation AI, and the generation AI adjusts the level of detail of the conversion.

[0083] The conversion unit can apply different conversion algorithms depending on the category of information. For example, the conversion unit can apply a summarization algorithm to news information to generate a detailed summary. The conversion unit can also apply an algorithm to replace technical jargon with simpler language to technical information. The conversion unit can also apply an algorithm to convert entertainment information into a visually appealing format. In this way, by applying different conversion algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs information category data to a generation AI, which then applies different conversion algorithms.

[0084] The conversion unit can improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit suggests an optimal conversion method based on the user's preferred conversion results in the past. The conversion unit can also extract specific patterns from the user's past conversion results to improve the conversion accuracy. The conversion unit can also improve the conversion algorithm by reflecting user feedback. In this way, the conversion accuracy can be improved by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs the user's past conversion result data into a generation AI, which then improves the conversion accuracy.

[0085] The conversion unit can estimate the user's emotions and adjust the length of the converted information based on the estimated user emotions. For example, if the user is in a hurry, the conversion unit generates short, to-the-point information using the generation AI. Alternatively, if the user is relaxed, the conversion unit can generate longer information with detailed explanations. Alternatively, if the user is excited, the conversion unit can generate information with visually stimulating effects. This allows for more appropriate information to be provided by adjusting the length of the information based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be 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-described processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit inputs the user's emotion data into the generation AI, which then adjusts the length of the information.

[0086] The conversion unit can determine the priority of conversion based on the time of submission of information. The conversion unit, for example, prioritizes conversion of information with high urgency. The conversion unit can also postpone information that was submitted earlier. The conversion unit can also prioritize conversion of information that is due to be submitted soon. In this way, by determining the priority of conversion based on the time of submission of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs data on the time of submission of information to the generation AI, and the generation AI determines the priority of conversion.

[0087] The conversion unit can adjust the order of conversion based on the relevance of the information. For example, the conversion unit prioritizes conversion of highly relevant information. The conversion unit can also postpone conversion of less relevant information. The conversion unit can also group and convert highly relevant information. This makes it possible to provide more appropriate information by adjusting the order of conversion based on the relevance of the information. Some or all of the above-described processing in the conversion unit may be performed using, or without, AI, for example. For example, the conversion unit inputs information relevance data to a generation AI, and the generation AI adjusts the order of conversion.

[0088] The conversion unit can adjust the use of technical terms in the converted information according to the user's level of expertise. For example, if the user is a beginner, the conversion unit replaces technical terms with simpler language. Furthermore, if the user is an intermediate user, the conversion unit can use technical terms moderately. Furthermore, if the user is an expert, the conversion unit can use technical terms extensively. This allows for more appropriate information to be provided by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit inputs the user's level of expertise data into the generation AI, which then adjusts the use of technical terms.

[0089] The evaluation unit can estimate the user's emotions and adjust the reliability evaluation criteria for information based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can prioritize highly reliable information. Furthermore, if the user is relaxed, the evaluation unit can evaluate a wide range of information. Furthermore, if the user is in a hurry, the evaluation unit can apply criteria that allow for quick evaluation. This allows for adjusting the reliability evaluation criteria based on the user's emotions to provide more appropriate information. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit inputs user emotion data into the generation AI, which then adjusts the reliability evaluation criteria.

[0090] The evaluation unit can optimize the reliability evaluation algorithm by referring to past evaluation data. The evaluation unit, for example, improves the reliability evaluation algorithm based on past evaluation data. The evaluation unit can also extract specific patterns from past evaluation data and optimize the algorithm. The evaluation unit can also analyze past evaluation data and revise the evaluation criteria. In this way, the reliability evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit inputs past evaluation data into a generation AI, and the generation AI optimizes the reliability evaluation algorithm.

[0091] The evaluation unit can evaluate reliability by taking into account attribute information of the information submitter. The evaluation unit can evaluate reliability by taking into account, for example, the submitter's level of expertise. The evaluation unit can also evaluate reliability based on the submitter's past performance. The evaluation unit can also evaluate reliability by taking into account the submitter's affiliated institution or organization. In this way, by taking into account the attribute information of the information submitter, highly reliable information can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs attribute information data of the submitter into the generation AI, and the generation AI evaluates reliability.

[0092] The evaluation unit can weight the reliability based on the frequency of information submission. For example, the evaluation unit can rate information that is submitted frequently as highly reliable. The evaluation unit can also rate information that is submitted infrequently as less reliable. The evaluation unit can also adjust the reliability weighting according to the submission frequency. In this way, by weighting the reliability based on the frequency of information submission, more appropriate information can be provided. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit inputs information submission frequency data to the generation AI, and the generation AI weights the reliability.

[0093] The evaluation unit can estimate the user's emotions and adjust the order in which the reliability evaluation results are displayed based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can prioritize displaying highly reliable information. Furthermore, if the user is relaxed, the evaluation unit can also display a wide range of information. Furthermore, if the user is in a hurry, the evaluation unit can quickly display evaluation results. By adjusting the order in which the reliability evaluation results are displayed based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the evaluation unit inputs user emotion data into the generation AI, and adjusts the order in which the generation AI displays the reliability evaluation results.

[0094] The evaluation unit can evaluate the reliability of information by taking into account the geographical distribution of the information. For example, the evaluation unit can evaluate the reliability of geographically close information sources as high. The evaluation unit can also evaluate the reliability of geographically distant information sources as low. The evaluation unit can also adjust the weighting of the reliability based on the geographical distribution. This makes it possible to provide highly reliable information by taking the geographical distribution of the information into consideration. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs geographical distribution data of the information to the generation AI, and the generation AI evaluates the reliability.

[0095] The evaluation unit can improve the accuracy of the reliability evaluation by referring to related literature of the information. The evaluation unit, for example, evaluates reliability based on related literature. The evaluation unit can also evaluate reliability by taking into account the number of citations of related literature. The evaluation unit can also evaluate based on the reliability of the author of the related literature. In this way, by referring to related literature of the information, the accuracy of the reliability evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs related literature data to a generation AI, and the generation AI evaluates reliability.

[0096] The evaluation unit can evaluate the reliability taking into account the market value of the information. For example, the evaluation unit can evaluate information with high market value as highly reliable. The evaluation unit can also evaluate information with low market value as less reliable. The evaluation unit can also adjust the weighting of the reliability based on the market value. This makes it possible to provide highly reliable information by taking the market value of the information into consideration. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit inputs market value data of the information to the generation AI, and the generation AI evaluates the reliability.

[0097] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide voice guidance in a calm voice. If the user is relaxed, the providing unit can also provide voice guidance in a cheerful voice. If the user is in a hurry, the providing unit can also provide quick and concise voice guidance. This allows for adjusting the information provision method based on the user's emotions to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the information provision method.

[0098] The providing unit can select the optimal providing method by referring to the past providing history. For example, the providing unit preferentially selects a providing method that the user has previously preferred. The providing unit can also select the optimal providing method according to a specific situation from the user's past providing history. The providing unit can also analyze the user's past providing history and improve the providing method. In this way, the optimal providing method can be selected by referring to the past providing history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs past providing history data to a generating AI, and the generating AI selects the optimal providing method.

[0099] The providing unit can customize the content to be provided according to the user's current task. For example, when the user is at work, the providing unit can prioritize providing information related to work. Furthermore, when the user is on a break, the providing unit can also provide information that helps the user relax. Furthermore, when the user is traveling, the providing unit can also provide information related to traveling. In this way, by customizing the content to be provided according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's current task data into the generating AI, and the generating AI customizes the content to be provided.

[0100] The providing unit can improve the delivery method by reflecting user feedback. The providing unit improves the delivery method based on, for example, feedback provided by the user regarding the delivery method. The providing unit can also preferentially select a specific delivery method based on the user feedback. The providing unit can also analyze the user feedback and improve the delivery method algorithm. In this way, the delivery method can be improved by reflecting the user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs user feedback data into a generation AI, and the generation AI improves the delivery method.

[0101] The providing unit can estimate the user's emotions and adjust the information provision procedure based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information provision procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed information provision procedures. Furthermore, if the user is in a hurry, the providing unit can provide information provision procedures that focus on the main points. This allows for adjusting the information provision procedure based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be 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-mentioned processing in the providing unit may be performed using AI, for example, or without AI. For example, the providing unit inputs user emotion data into the generation AI, and the generation AI adjusts the information provision procedure.

[0102] The providing unit can select the optimal providing method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is simple and highly visible. This allows the optimal providing method to be selected by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit inputs the user's device information data into the generating AI, which then selects the optimal providing method.

[0103] The providing unit can make the provided content multilingual according to the user's language setting. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. This makes it possible to provide more appropriate information by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs the user's language setting data to a generating AI, which then makes the provided content multilingual.

[0104] The providing unit can analyze the user's social media activity and provide related information. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. The providing unit can also provide information about related places and events by referring to the activities of the user's friends on social media. In this way, related information can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs the user's social media activity data into a generation AI, which then provides the related information. === Hard Collateral 1-1 === Each of the multiple elements, including the designation unit, conversion unit, evaluation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the designation unit is realized by the control unit 46A of the smart device 14 and specifies the type and format of information the recipient wants to receive. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI and converts it into a form that is easy for the recipient to understand. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the converted information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the evaluated information to the recipient. The designation unit, for example, has a function of estimating the user's emotions and suggesting the type and format of information based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned designation unit, conversion unit, evaluation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the designation unit is realized by the control unit 46A of the smart glasses 214 and designates the type and format of information the recipient wishes to receive. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI and converts it into a form that is easy for the recipient to understand. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the converted information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the evaluated information to the recipient. The designation unit, for example, has a function of estimating the user's emotions and suggesting the type and format of information based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned designation unit, conversion unit, evaluation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the designation unit is realized by the control unit 46A of the headset-type terminal 314 and specifies the type and format of information the recipient wants to receive. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI and converts it into a form that is easy for the recipient to understand. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the converted information. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the evaluated information to the recipient. The designation unit has a function, for example, of estimating the user's emotions and suggesting the type and format of information based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned designation unit, conversion unit, evaluation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the designation unit is realized by the control unit 46A of the robot 414 and specifies the type and format of information the recipient wants to receive. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes information using a generation AI and converts it into a form that is easy for the recipient to understand. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the reliability of the converted information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the evaluated information to the recipient. The designation unit has a function, for example, of estimating the user's emotions and suggesting the type and format of information based on the estimated emotions.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The designation unit can analyze the user's past behavioral patterns and predict future information needs. For example, if the user has frequently requested a specific type of information during a specific time period in the past, similar information can be automatically suggested for that time period. The designation unit can also learn what information the user needs during specific events or situations and provide appropriate information when a similar situation occurs. Furthermore, the designation unit can predict future information needs based on the user's behavioral patterns and prepare in advance. This allows the provision of more appropriate information by suggesting the type and format of information based on the user's behavioral patterns.

[0107] The converter can enhance the visual aspects of the information. For example, it can convert textual information into infographics to make it easier to understand visually. The converter can also convert data into graphs and charts to visually emphasize key points of the information. Furthermore, the converter can generate videos and animations to present information in a dynamic format. This can enhance the visual aspects of the information, facilitating comprehension and allowing the recipient to use the information more effectively.

[0108] When assessing the reliability of information, the evaluation unit can take into account the transparency of the source of the information. For example, it can evaluate the process through which the information was generated and the background of the source. The evaluation unit can also assess reliability based on data and evidence made public by the source of the information. Furthermore, the evaluation unit can also make an assessment based on the reliability of information provided in the past by the source of the information. In this way, by taking into account the transparency of the source of the information, it is possible to provide more reliable information.

[0109] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is feeling stressed, information can be provided at a time when the user is able to relax. Also, if the user is concentrating, information can be provided at a time that does not disturb the user's concentration. Furthermore, if the user is relaxed, information can be provided at a time when the user is most likely to receive it. In this way, by adjusting the timing of providing information based on the user's emotions, more appropriate information can be provided.

[0110] The designation unit can estimate the user's emotions and adjust the information filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, information that helps the user relax can be displayed preferentially. If the user is excited, detailed information can be displayed preferentially. Furthermore, if the user is tired, short, to-the-point information can be displayed preferentially. In this way, by adjusting the information filtering criteria based on the user's emotions, more appropriate information can be provided.

[0111] The conversion unit can convert information while taking into account its context. For example, when converting information related to a specific industry or field, it takes into account the terminology and background knowledge of that industry or field. The conversion unit can also convert information into a form that is easy for the recipient to understand, taking into account the background of the recipient. Furthermore, the conversion unit can select an appropriate format or expression method depending on the context of the information. This allows the provision of more appropriate information by taking into account the context of the information.

[0112] The evaluation unit can take into account the update frequency of the information when evaluating the reliability of the information. For example, information that is updated frequently is evaluated as highly reliable, and information that is updated infrequently is evaluated as less reliable. The evaluation unit can also evaluate the reliability based on the recency of the information. Furthermore, the evaluation unit can analyze the update history of the information and evaluate the reliability based on the content of past updates. In this way, by taking the update frequency of the information into consideration, more reliable information can be provided.

[0113] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is feeling stressed, information can be provided in a visually relaxing format (e.g., infographics or videos). If the user is excited, detailed text information can be provided. Furthermore, if the user is tired, information can be provided in a short, to-the-point format. In this way, by adjusting the format of information provision based on the user's emotions, more appropriate information can be provided.

[0114] The designation unit can estimate the user's emotions and determine the priority of information based on the estimated user's emotions. For example, if the user is feeling stressed, information that helps the user relax can be displayed preferentially. If the user is excited, detailed information can be displayed preferentially. Furthermore, if the user is tired, short, to-the-point information can be displayed preferentially. Thus, by determining the priority of information based on the user's emotions, more appropriate information can be provided.

[0115] The evaluation unit can take into account the reliability of the source of the information when evaluating the reliability of the information. For example, information from a highly reliable source can be rated high, and information from a less reliable source can be rated low. The evaluation unit can also evaluate reliability based on the source's past performance. Furthermore, the evaluation unit can evaluate reliability by taking into account the source's expertise and experience. In this way, by taking into account the reliability of the source of the information, more reliable information can be provided.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The specifying unit specifies the type and format of information the recipient wants to receive. The type and format of information the recipient wants to receive can include, for example, text, images, audio, video, etc. The specifying unit can specify that the recipient should convert text information into a concise summary or replace technical terms with plain language. Step 2: The conversion unit uses the generation AI to analyze the specified information and convert it into a form that is easy for the recipient to understand. The generation AI analyzes and converts information using technologies such as Transformer. For example, the conversion unit allows the generation AI to receive a prompt such as "Please summarize this sentence concisely" and summarize the information. The conversion unit also allows the generation AI to replace technical terms with simpler language. Step 3: The evaluation unit evaluates the reliability of the information converted by the conversion unit. The evaluation of reliability includes, for example, the reliability of the information source, the accuracy of the data, and the update frequency. For example, the evaluation unit evaluates the reliability of the information source by the generation AI and calculates a reliability score. The evaluation unit can also evaluate the accuracy of the data and calculate a reliability score. Step 4: The providing unit provides the information evaluated by the evaluating unit to the receiver. For example, the providing unit provides only reliable information to the receiver. The providing unit can also select an appropriate method for providing the evaluated information to the receiver. For example, the providing unit provides the evaluated information to the receiver via email, notification, dashboard, etc.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] 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.

[0181] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a designation section for designating the type or format of information that the recipient wishes to receive; a conversion unit that analyzes the information designated by the designation unit and converts it into a format that is easy for a recipient to understand; an evaluation unit that evaluates the reliability of the information converted by the conversion unit; a providing unit that provides the information evaluated by the evaluating unit to a recipient. A system characterized by:

2. The designation unit Generative AI is used to analyze information and convert it into a form that is easy for the recipient to understand.

2. The system of claim 1.

3. The conversion unit Generative AI is used to analyze information and convert it into a form that is easy for the recipient to understand.

2. The system of claim 1.

4. The evaluation unit Evaluate the reliability of information 2. The system of claim 1.

5. The providing unit Providing evaluated information to recipients 2. The system of claim 1.

6. The designation unit Estimates the user's emotions and automatically suggests the type and format of information based on the estimated user emotions.

2. The system of claim 1.

7. The designation unit Analyzes past selection history and predicts information format based on user preferences 2. The system of claim 1.

8. The designation unit Dynamically change the format of information based on the user's current situation 2. The system of claim 1.

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