system

Abstract

The system according to the embodiment aims to automatically determine the evidence level of the text in the document and display it in a color-coded manner. [Solution] A system according to an embodiment includes an input unit, an analysis unit, and a display unit. The input unit inputs sentences from documents. The analysis unit analyzes the sentences input by the input unit and determines the evidence level. The display unit displays the sentences in different colors based on the evidence level determined by the analysis unit.

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 has the drawback that the task of manually determining the evidence level of text in documents and color-coding them is cumbersome and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to automatically determine the evidence level of the text in the document and display it in a color-coded manner. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a display unit. The input unit inputs sentences from a document. The analysis unit analyzes the sentences input by the input unit and determines the evidence level. The display unit displays the sentences in different colors based on the evidence level determined by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine the evidence level of the text in the document and display it in a color-coded manner. [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 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) An evidence level determination system according to an embodiment of the present invention automatically determines the evidence level of a sentence in a document and displays it in a color-coded manner. This evidence level determination system inputs a sentence from the document, determines the evidence level of the sentence using a generation AI (e.g., GPT-4 (registered trademark) or Gemini), and displays the sentence in a color-coded manner based on the determined evidence level. For example, the sentence in the document is input into the generation AI. A user can input the entire document at once. For example, the input can be from various sources, such as academic papers, government publications, expert commentaries, and personal blogs. The generation AI then determines the evidence level of the input sentence. The generation AI analyzes the input sentence and evaluates its reliability. For example, academic papers and government publications are determined to be highly reliable, while expert commentaries and information from reliable organizations are determined to be medium reliable. On the other hand, personal blogs and unfounded information are determined to be low reliable. The sentence is then displayed in a color-coded manner based on the determined evidence level. Specifically, highly reliable information is displayed in green, medium reliable information is displayed in yellow, and low reliable information is displayed in red. This allows users to understand the reliability of the information in a document at a glance. This tool allows users to quickly evaluate the reliability of the information in a document and make appropriate decisions. For example, users can prioritize academic papers and government agency announcements and avoid personal blogs and unsubstantiated information. Furthermore, for information with a medium level of reliability, additional verification can be performed to obtain more accurate information. This allows the evidence level determination system to quickly evaluate the reliability of the information in a document and make appropriate decisions.

[0029] The evidence level determination system according to the embodiment includes an input unit, an analysis unit, and a display unit. The input unit inputs text from a document. The text from the document includes, but is not limited to, academic papers, government publications, expert commentaries, and personal blogs. The input unit can input the entire document in one go. Alternatively, a user can manually input text from the input unit. The analysis unit uses a generation AI to analyze the text input by the input unit and determine its evidence level. The generation AI, for example, determines academic papers and government publications as highly reliable, expert commentaries and information from reliable organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit evaluates the reliability of the text using, for example, natural language processing technology. The display unit displays the text in different colors based on the evidence level determined by the analysis unit. For example, the display unit displays highly reliable information in green, medium reliable information in yellow, and low reliable information in red. This allows a user to grasp the reliability of the information in the document at a glance. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may display text in different colors using an AI model that inputs the evidence level determined by the analysis unit and outputs a color-coded display. This allows the evidence level determination system according to the embodiment to quickly evaluate the reliability of information in documents and make appropriate decisions.

[0030] The evidence level determination system includes a standard setting unit that sets an evidence level based on at least one information source among academic papers, government agency announcements, expert commentaries, and personal blogs. The standard setting unit sets the evidence level based on information sources such as academic papers, government agency announcements, expert commentaries, and personal blogs. Academic papers include, for example, peer-reviewed papers and conference papers. Government agency announcements include, for example, official reports and press releases. Expert commentaries include, for example, journal articles and expert blogs. Personal blogs include, for example, methods for evaluating reliability and methods for verifying content. The standard setting unit sets the evidence level based on these information sources. Thus, by setting standards for the evidence level, reliability can be evaluated. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or without AI. For example, the standard setting unit can set the evidence level using an AI model that evaluates the reliability of information sources.

[0031] The evidence level determination system includes a display setting unit that sets a visually easy-to-understand display method, such as at least one of color shading or font change. The display setting unit sets a visually easy-to-understand display method, such as color shading or font change. Color shading includes, for example, a level of shading and a range of colors used. Font change includes, for example, a change in the type of font used and font size. By making these settings, the display setting unit provides a visually easy-to-understand display method, which allows the user to intuitively understand the reliability of information. Some or all of the above-described processing in the display setting unit may be performed, for example, using AI or without AI. For example, the display setting unit can provide a visually easy-to-understand display method using an AI model that sets a display method based on the user's visual preferences.

[0032] The analysis unit can analyze the input text and evaluate its reliability. The analysis unit can use, for example, a generation AI to analyze the input text and evaluate its reliability. The generation AI can, for example, determine academic papers and announcements from government agencies as highly reliable, expert commentaries and information from reliable organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit can, for example, use natural language processing technology to evaluate the reliability of the text. This enables the determination of the evidence level by evaluating the reliability of the input text. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can analyze the input text and determine the evidence level using an AI model that evaluates its reliability.

[0033] The display unit can display the sentences in different colors based on the determined evidence level. The display unit, for example, displays the sentences in different colors based on the evidence level determined by the analysis unit. For example, the display unit displays highly reliable information in green, medium reliability information in yellow, and low reliability information in red. This allows the user to grasp the reliability of the information in the document at a glance. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display the sentences in different colors using an AI model that receives the evidence level determined by the analysis unit as input and outputs a color-coded display. This allows the user to grasp the reliability of the information at a glance by color-coding the sentences based on the evidence level.

[0034] The input unit can analyze the user's past input history and select the optimal input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. The optimal input method includes, for example, the type of input device and the efficiency of the input method. For example, the input unit can automatically display sentence patterns that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest sentence patterns to be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can provide the optimal input method by analyzing the user's past input history and using an AI model that selects the optimal input method.

[0035] The input unit can filter text based on the user's current project or area of ​​interest when inputting the text. The input unit can filter text based on the user's current project or area of ​​interest when inputting the text. The filtering can include, for example, filtering conditions and an algorithm to be used. For example, information related to a project the user is currently working on can be preferentially input. Highly relevant information can also be filtered and input based on the user's area of ​​interest. Furthermore, related information can be suggested based on topics in which the user has previously shown interest. Thus, highly relevant information can be input by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input highly relevant information using an AI model that filters based on the user's current project or area of ​​interest.

[0036] When inputting a sentence, the input unit can prioritize inputting highly relevant sentences by taking into account the user's geographical location information. For example, when inputting a sentence, the input unit prioritizes inputting highly relevant sentences by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and the accuracy of location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is traveling, information related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, information related to the event can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input sentences using an AI model that prioritizes inputting highly relevant sentences by taking into account the user's geographical location information.

[0037] The input unit can analyze the user's social media activity and input related sentences when inputting sentences. For example, the input unit can analyze the user's social media activity and input related sentences when inputting sentences. Social media activity includes, for example, analyzing posted content and evaluating activity frequency. For example, related sentences can be input based on information shared by the user on social media. Related sentences can also be input based on information about accounts the user follows on social media. Furthermore, related sentences can be input based on topics in which the user has shown interest on social media. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input sentences using an AI model that analyzes the user's social media activity and inputs related sentences.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the text during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the text during analysis. The importance of the text includes, for example, the reliability of the information and the importance of the content. For example, important information is analyzed in detail. Information of medium importance can be analyzed with an appropriate level of detail. Furthermore, information of low importance can be analyzed in a simplified manner. In this way, by adjusting the level of detail of the analysis based on the importance of the text, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that adjusts the level of detail of the analysis based on the importance of the text.

[0039] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the text during analysis. Text categories include, for example, technical documents and news articles. For example, a specialized analysis algorithm can be applied to academic papers. A general analysis algorithm can also be applied to news articles. Furthermore, a simple analysis algorithm can be applied to personal blogs. In this way, by applying different analysis algorithms depending on the category of the text, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that applies different analysis algorithms depending on the category of the text.

[0040] The analysis unit can determine the analysis priority based on the submission time of the text during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text during analysis. The submission time includes, for example, the submission date and submission time. For example, the most recent information is analyzed first. Information submitted earlier can also be analyzed later. Furthermore, if the submission times are concentrated in a specific period, information from that period can be analyzed first. In this way, by determining the analysis priority based on the submission time of the text, the most recent information can be analyzed first. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that determines the analysis priority based on the submission time of the text.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the sentences during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the sentences during analysis. The relevance of the sentences includes, for example, similarity of content and related keywords. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, if the relevance is concentrated on a specific theme, information on that theme can be analyzed preferentially. In this way, by adjusting the order of analysis based on the relevance of the sentences, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that adjusts the order of analysis based on the relevance of the sentences.

[0042] The display unit can adjust the level of detail of the display based on the importance of the text when displaying the information. For example, the display unit adjusts the level of detail of the display based on the importance of the text when displaying the information. The level of detail of the display includes, for example, the amount of information to be displayed and the accuracy of the display. For example, important information can be displayed in detail. Information of medium importance can be displayed with an appropriate level of detail. Furthermore, information of low importance can be displayed in a simplified manner. In this way, by adjusting the level of detail of the display based on the importance of the text, an appropriate display can be achieved. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that adjusts the level of detail of the display based on the importance of the text.

[0043] The display unit can apply different display algorithms depending on the category of the text when displaying. The display unit, for example, applies different display algorithms depending on the category of the text when displaying. The display algorithm includes, for example, an information classification method and a display priority. For example, a specialized display algorithm can be applied to academic papers. A general display algorithm can also be applied to news articles. Furthermore, a simple display algorithm can be applied to personal blogs. In this way, by applying different display algorithms depending on the category of the text, appropriate display is possible. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that applies different display algorithms depending on the category of the text.

[0044] The display unit can determine the display priority based on the submission time of the text when displaying the information. The display unit, for example, determines the display priority based on the submission time of the text when displaying the information. The display priority includes, for example, the importance of the information and the submission time. For example, the latest information can be displayed preferentially. Information submitted earlier can also be displayed later. Furthermore, if the submission times are concentrated in a specific period, information from that period can also be displayed preferentially. In this way, by determining the display priority based on the submission time of the text, the latest information can be displayed preferentially. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that determines the display priority based on the submission time of the text.

[0045] The display unit can adjust the display order based on the relevance of the sentences when displaying them. The display unit, for example, adjusts the display order based on the relevance of the sentences when displaying them. The display order includes, for example, a relevance evaluation method and a display priority order. For example, highly relevant information can be displayed preferentially. Also, less relevant information can be displayed later. Furthermore, if the relevance is concentrated on a specific theme, information on that theme can be displayed preferentially. In this way, by adjusting the display order based on the relevance of the sentences, highly relevant information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that adjusts the display order based on the relevance of the sentences.

[0046] The reference setting unit can optimize the reference algorithm by referring to past reference data when setting the reference. For example, the reference setting unit optimizes the reference algorithm by referring to past reference data when setting the reference. The reference algorithm includes, for example, a method for using past data and a method for adjusting the algorithm. For example, the past reference data is analyzed to derive an optimal reference algorithm. The accuracy of the reference algorithm can also be improved based on the past reference data. Furthermore, parameters of the reference algorithm can be adjusted by referring to the past reference data. In this way, the accuracy of the reference algorithm can be improved by referring to the past reference data. Some or all of the above-mentioned processing in the reference setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference setting unit can set the reference using an AI model that optimizes the reference algorithm by referring to past reference data.

[0047] The criteria setting unit can weight the reference data based on the submission time of the information source when setting the criteria. For example, the criteria setting unit weights the reference data based on the submission time of the information source when setting the criteria. Weighting the reference data includes, for example, an evaluation method for the submission time and weighting criteria. For example, a newer information source can be weighted higher to evaluate its reliability. Also, an older information source can be weighted lower to evaluate its reliability. Furthermore, information sources whose submission times are concentrated in a specific period can be weighted appropriately. In this way, weighting the reference data based on the submission time of the information source enables evaluation of its reliability. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can set the criteria using an AI model that weights the reference data based on the submission time of the information source.

[0048] The display setting unit can select an optimal display method by referring to the user's past display history when setting the display. For example, the display setting unit can select an optimal display method by referring to the user's past display history when setting the display. The display history includes, for example, past display content and a display history storage method. For example, the display setting that the user has previously preferred can be automatically applied. The optimal display method can also be suggested based on the user's past display history. Furthermore, a customizable display method can be provided based on the display settings that the user has previously used. In this way, the optimal display method can be provided by referring to the user's past display history. Some or all of the above-described processing in the display setting unit can be performed, for example, using AI or without AI. For example, the display setting unit can provide a display method using an AI model that selects an optimal display method by referring to the user's past display history.

[0049] The display setting unit can select the optimal display method by taking into account the user's device information during display setting. For example, the display setting unit selects the optimal display method by taking into account the user's device information during display setting. The device information includes, for example, the device type and device performance. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the display setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the display setting unit can provide a display method using an AI model that selects the optimal display method by taking into account the user's device information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The evidence level determination system can further include a search history analysis unit that analyzes a user's past search history and prioritizes the display of related information. For example, highly relevant information can be displayed preferentially based on keywords the user has previously searched for or pages the user has viewed. Related information can also be automatically suggested based on topics the user frequently searches for. Furthermore, it can predict topics that the user will be interested in during a specific time period from the user's search history and display related information. This allows the system to provide more relevant information by analyzing the user's past search history.

[0052] The evidence level determination system can further include a feedback collection unit that collects user feedback and improves the accuracy of evidence level determination. For example, when a user evaluates the evidence level determination result, the system improves the determination algorithm based on the evaluation. The system can also analyze the feedback provided by the user and adjust the evidence level criteria. Furthermore, new evidence level categories can be added based on user feedback. In this way, the accuracy of evidence level determination can be improved by utilizing user feedback.

[0053] The evidence level determination system can further include a geographic information analysis unit that determines the evidence level by taking into account the user's geographic location information. For example, if the user is in a specific area, the reliability of information related to that area can be prioritized. Also, if the user is traveling, the reliability of information related to the travel destination can be evaluated. Furthermore, if the user is participating in a specific event, the reliability of information related to that event can be evaluated. In this way, by taking into account the user's geographic location information, it is possible to determine a more relevant evidence level.

[0054] The evidence level determination system may further include a social media analysis unit that analyzes a user's social media activity and evaluates the reliability of related information. For example, the reliability of information may be evaluated based on information shared by the user on social media. The reliability of related information may also be evaluated based on information on accounts the user follows. Furthermore, the reliability of related information may also be evaluated based on topics in which the user has shown interest. In this way, by analyzing a user's social media activity, the reliability of more relevant information may be evaluated.

[0055] The evidence level determination system can further include a device information analysis unit that adjusts the display method of the evidence level by taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0056] The evidence level determination system can further include a display history analysis unit that optimizes the display method of the evidence level by referring to the user's past display history. For example, the display settings that the user has used in the past are automatically applied. The system can also suggest the optimal display method based on the user's past display history. Furthermore, the system can provide a customizable display method based on the display settings that the user has used in the past. This makes it possible to provide the optimal display method by referring to the user's past display history.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The input section inputs the text from the source material. The text from the source material can include academic papers, government announcements, expert commentaries, personal blogs, etc. The input section can input the entire source material at once, or users can input the text manually. Step 2: The analysis unit uses the generation AI to analyze the text entered by the input unit and determine its level of evidence. The generation AI determines academic papers and announcements from government agencies as highly reliable, expert commentaries and information from trustworthy organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit uses natural language processing technology to evaluate the reliability of the text. Step 3: The display unit displays the text in different colors based on the evidence level determined by the analysis unit. The display unit displays highly reliable information in green, medium reliability information in yellow, and low reliability information in red. The processing in the display unit can also be performed using an AI model.

[0059] (Example 2) An evidence level determination system according to an embodiment of the present invention automatically determines the evidence level of a sentence in a document and displays it in a color-coded manner. This evidence level determination system inputs a sentence from the document, determines the evidence level of the sentence using a generation AI (e.g., GPT-4 or Gemini), and displays the sentence in a color-coded manner based on the determined evidence level. For example, the sentence in the document is input into the generation AI. A user can input the entire document at once. For example, the input can be from various sources, such as academic papers, government publications, expert commentaries, and personal blogs. The generation AI then determines the evidence level of the input sentence. The generation AI analyzes the input sentence and evaluates its reliability. For example, academic papers and government publications are determined to be highly reliable, while expert commentaries and information from reliable organizations are determined to be medium reliable. On the other hand, personal blogs and unfounded information are determined to be low reliable. The sentence is then displayed in a color-coded manner based on the determined evidence level. Specifically, highly reliable information is displayed in green, medium reliable information is displayed in yellow, and low reliable information is displayed in red. This allows users to understand the reliability of the information in a document at a glance. This tool allows users to quickly evaluate the reliability of the information in a document and make appropriate decisions. For example, users can prioritize academic papers and government agency announcements and avoid personal blogs and unsubstantiated information. Furthermore, for information with a medium level of reliability, additional verification can be performed to obtain more accurate information. This allows the evidence level determination system to quickly evaluate the reliability of the information in a document and make appropriate decisions.

[0060] The evidence level determination system according to the embodiment includes an input unit, an analysis unit, and a display unit. The input unit inputs text from a document. The text from the document includes, but is not limited to, academic papers, government publications, expert commentaries, and personal blogs. The input unit can input the entire document in one go. Alternatively, a user can manually input text from the input unit. The analysis unit uses a generation AI to analyze the text input by the input unit and determine its evidence level. The generation AI, for example, determines academic papers and government publications as highly reliable, expert commentaries and information from reliable organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit evaluates the reliability of the text using, for example, natural language processing technology. The display unit displays the text in different colors based on the evidence level determined by the analysis unit. For example, the display unit displays highly reliable information in green, medium reliable information in yellow, and low reliable information in red. This allows a user to grasp the reliability of the information in the document at a glance. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may display text in different colors using an AI model that inputs the evidence level determined by the analysis unit and outputs a color-coded display. This allows the evidence level determination system according to the embodiment to quickly evaluate the reliability of information in documents and make appropriate decisions.

[0061] The evidence level determination system includes a standard setting unit that sets an evidence level based on at least one information source among academic papers, government agency announcements, expert commentaries, and personal blogs. The standard setting unit sets the evidence level based on information sources such as academic papers, government agency announcements, expert commentaries, and personal blogs. Academic papers include, for example, peer-reviewed papers and conference papers. Government agency announcements include, for example, official reports and press releases. Expert commentaries include, for example, journal articles and expert blogs. Personal blogs include, for example, methods for evaluating reliability and methods for verifying content. The standard setting unit sets the evidence level based on these information sources. Thus, by setting standards for the evidence level, reliability can be evaluated. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or without AI. For example, the standard setting unit can set the evidence level using an AI model that evaluates the reliability of information sources.

[0062] The evidence level determination system includes a display setting unit that sets a visually easy-to-understand display method, such as at least one of color shading or font change. The display setting unit sets a visually easy-to-understand display method, such as color shading or font change. Color shading includes, for example, a level of shading and a range of colors used. Font change includes, for example, a change in the type of font used and font size. By making these settings, the display setting unit provides a visually easy-to-understand display method, which allows the user to intuitively understand the reliability of information. Some or all of the above-described processing in the display setting unit may be performed, for example, using AI or without AI. For example, the display setting unit can provide a visually easy-to-understand display method using an AI model that sets a display method based on the user's visual preferences.

[0063] The analysis unit can analyze the input text and evaluate its reliability. The analysis unit can use, for example, a generation AI to analyze the input text and evaluate its reliability. The generation AI can, for example, determine academic papers and announcements from government agencies as highly reliable, expert commentaries and information from reliable organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit can, for example, use natural language processing technology to evaluate the reliability of the text. This enables the determination of the evidence level by evaluating the reliability of the input text. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can analyze the input text and determine the evidence level using an AI model that evaluates its reliability.

[0064] The display unit can display the sentences in different colors based on the determined evidence level. The display unit, for example, displays the sentences in different colors based on the evidence level determined by the analysis unit. For example, the display unit displays highly reliable information in green, medium reliability information in yellow, and low reliability information in red. This allows the user to grasp the reliability of the information in the document at a glance. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can display the sentences in different colors using an AI model that receives the evidence level determined by the analysis unit as input and outputs a color-coded display. This allows the user to grasp the reliability of the information at a glance by color-coding the sentences based on the evidence level.

[0065] The input unit can estimate a user's emotions and adjust the timing of sentence input based on the estimated user emotions. The input unit, for example, estimates a user's emotions and adjusts the timing of sentence input based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. Adjusting the input timing includes, for example, adjusting the input delay time and timing. For example, if the user is stressed, the input interface can be simplified to minimize input steps. Also, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable quick sentence input. This allows for more appropriate input by adjusting the input timing according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can adjust the timing of sentence input using an AI model that estimates a user's emotions and adjusts the input timing based on the estimated emotions.

[0066] The input unit can analyze the user's past input history and select the optimal input method. The input unit, for example, analyzes the user's past input history and selects the optimal input method. The optimal input method includes, for example, the type of input device and the efficiency of the input method. For example, the input unit can automatically display sentence patterns that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest sentence patterns to be used in a specific time period from the user's past input history. In this way, by analyzing the past input history, it is possible to provide the user with the optimal input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can provide the optimal input method by analyzing the user's past input history and using an AI model that selects the optimal input method.

[0067] The input unit can filter text based on the user's current project or area of ​​interest when inputting the text. The input unit can filter text based on the user's current project or area of ​​interest when inputting the text. The filtering can include, for example, filtering conditions and an algorithm to be used. For example, information related to a project the user is currently working on can be preferentially input. Highly relevant information can also be filtered and input based on the user's area of ​​interest. Furthermore, related information can be suggested based on topics in which the user has previously shown interest. Thus, highly relevant information can be input by filtering based on the user's project or area of ​​interest. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input highly relevant information using an AI model that filters based on the user's current project or area of ​​interest.

[0068] The input unit can estimate the user's emotions and determine the priority of sentences to be input based on the estimated user emotions. The input unit, for example, estimates the user's emotions and determines the priority of sentences to be input based on the estimated user emotions. The priority of sentences to be input includes, for example, an importance evaluation method and a priority setting standard. For example, if the user is nervous, important information can be input preferentially. Also, if the user is relaxed, detailed information can be input preferentially. Furthermore, if the user is in a hurry, information that highlights the main points can be input preferentially. In this way, by determining the priority of sentences to be input according to the user's emotions, more appropriate information can be input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can determine the priority of sentences using an AI model that estimates the user's emotions and determines the priority of sentences to be input based on the estimated emotions.

[0069] When inputting a sentence, the input unit can prioritize inputting highly relevant sentences by taking into account the user's geographical location information. For example, when inputting a sentence, the input unit prioritizes inputting highly relevant sentences by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and the accuracy of location information. For example, if the user is in a specific area, information related to that area can be prioritized. Also, if the user is traveling, information related to the travel destination can be prioritized. Furthermore, if the user is participating in a specific event, information related to the event can be prioritized. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input sentences using an AI model that prioritizes inputting highly relevant sentences by taking into account the user's geographical location information.

[0070] The input unit can analyze the user's social media activity and input related sentences when inputting sentences. For example, the input unit can analyze the user's social media activity and input related sentences when inputting sentences. Social media activity includes, for example, analyzing posted content and evaluating activity frequency. For example, related sentences can be input based on information shared by the user on social media. Related sentences can also be input based on information about accounts the user follows on social media. Furthermore, related sentences can be input based on topics in which the user has shown interest on social media. In this way, highly relevant information can be input by analyzing the user's social media activity. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or can be performed without using AI. For example, the input unit can input sentences using an AI model that analyzes the user's social media activity and inputs related sentences.

[0071] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user's emotions. The presentation method of the analysis includes, for example, the type of graph or the text presentation method. For example, if the user is nervous, a simple and highly visible analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a summary analysis result can be provided. In this way, by adjusting the presentation method of the analysis according to the user's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated emotions.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the text during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the text during analysis. The importance of the text includes, for example, the reliability of the information and the importance of the content. For example, important information is analyzed in detail. Information of medium importance can be analyzed with an appropriate level of detail. Furthermore, information of low importance can be analyzed in a simplified manner. In this way, by adjusting the level of detail of the analysis based on the importance of the text, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that adjusts the level of detail of the analysis based on the importance of the text.

[0073] The analysis unit can apply different analysis algorithms depending on the category of the text during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the text during analysis. Text categories include, for example, technical documents and news articles. For example, a specialized analysis algorithm can be applied to academic papers. A general analysis algorithm can also be applied to news articles. Furthermore, a simple analysis algorithm can be applied to personal blogs. In this way, by applying different analysis algorithms depending on the category of the text, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that applies different analysis algorithms depending on the category of the text.

[0074] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The length of the analysis can include, for example, adjusting the level of detail of the analysis and the analysis time. For example, if the user is in a hurry, a short and concise analysis can be performed. If the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is excited, an analysis with visually stimulating effects can be performed. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can provide analysis results using an AI model that estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions.

[0075] The analysis unit can determine the analysis priority based on the submission time of the text during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the text during analysis. The submission time includes, for example, the submission date and submission time. For example, the most recent information is analyzed first. Information submitted earlier can also be analyzed later. Furthermore, if the submission times are concentrated in a specific period, information from that period can be analyzed first. In this way, by determining the analysis priority based on the submission time of the text, the most recent information can be analyzed first. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that determines the analysis priority based on the submission time of the text.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the sentences during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the sentences during analysis. The relevance of the sentences includes, for example, similarity of content and related keywords. For example, highly relevant information is analyzed preferentially. Also, less relevant information can be analyzed later. Furthermore, if the relevance is concentrated on a specific theme, information on that theme can be analyzed preferentially. In this way, by adjusting the order of analysis based on the relevance of the sentences, highly relevant information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can provide analysis results using an AI model that adjusts the order of analysis based on the relevance of the sentences.

[0077] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the display method based on the estimated user's emotion. The display method can include, for example, a display format and a display order. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This enables a more appropriate display by adjusting the display method according to the user's emotion. Some or all of the above-described processing in the display unit can be performed, for example, using AI, or can be performed without using AI. For example, the display unit can provide a display method using an AI model that estimates the user's emotion and adjusts the display method based on the estimated emotion.

[0078] The display unit can adjust the level of detail of the display based on the importance of the text when displaying the information. For example, the display unit adjusts the level of detail of the display based on the importance of the text when displaying the information. The level of detail of the display includes, for example, the amount of information to be displayed and the accuracy of the display. For example, important information can be displayed in detail. Information of medium importance can be displayed with an appropriate level of detail. Furthermore, information of low importance can be displayed in a simplified manner. In this way, by adjusting the level of detail of the display based on the importance of the text, an appropriate display can be achieved. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that adjusts the level of detail of the display based on the importance of the text.

[0079] The display unit can apply different display algorithms depending on the category of the text when displaying. The display unit, for example, applies different display algorithms depending on the category of the text when displaying. The display algorithm includes, for example, an information classification method and a display priority. For example, a specialized display algorithm can be applied to academic papers. A general display algorithm can also be applied to news articles. Furthermore, a simple display algorithm can be applied to personal blogs. In this way, by applying different display algorithms depending on the category of the text, appropriate display is possible. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that applies different display algorithms depending on the category of the text.

[0080] The display unit can estimate the user's emotion and adjust the display length based on the estimated user's emotion. The display unit, for example, estimates the user's emotion and adjusts the display length based on the estimated user's emotion. The display length includes, for example, the display time and the amount of display content. For example, if the user is in a hurry, a short, to-the-point display can be provided. If the user is relaxed, a detailed display can be provided. Furthermore, if the user is excited, a display with a visually stimulating effect can be provided. This allows for a more appropriate display by adjusting the display length according to the user's emotion. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that estimates the user's emotion and adjusts the display length based on the estimated emotion.

[0081] The display unit can determine the display priority based on the submission time of the text when displaying the information. The display unit, for example, determines the display priority based on the submission time of the text when displaying the information. The display priority includes, for example, the importance of the information and the submission time. For example, the latest information can be displayed preferentially. Information submitted earlier can also be displayed later. Furthermore, if the submission times are concentrated in a specific period, information from that period can also be displayed preferentially. In this way, by determining the display priority based on the submission time of the text, the latest information can be displayed preferentially. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that determines the display priority based on the submission time of the text.

[0082] The display unit can adjust the display order based on the relevance of the sentences when displaying them. The display unit, for example, adjusts the display order based on the relevance of the sentences when displaying them. The display order includes, for example, a relevance evaluation method and a display priority order. For example, highly relevant information can be displayed preferentially. Also, less relevant information can be displayed later. Furthermore, if the relevance is concentrated on a specific theme, information on that theme can be displayed preferentially. In this way, by adjusting the display order based on the relevance of the sentences, highly relevant information can be displayed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can provide a display method using an AI model that adjusts the display order based on the relevance of the sentences.

[0083] The standard setting unit can estimate the user's emotions and adjust the evidence level criteria based on the estimated user emotions. The standard setting unit, for example, estimates the user's emotions and adjusts the evidence level criteria based on the estimated user emotions. The evidence level criteria include, for example, a reliability evaluation method and a standard setting method. For example, if the user is nervous, strict criteria can be applied to prioritize highly reliable information. Alternatively, if the user is relaxed, flexible criteria can be applied to accept a wide range of information. Furthermore, if the user is in a hurry, criteria that allow for quick judgment can be applied. This enables more appropriate criteria setting by adjusting the evidence level criteria according to the user's emotions. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without AI. For example, the standard setting unit can set the criteria using an AI model that estimates the user's emotions and adjusts the evidence level criteria based on the estimated emotions.

[0084] The reference setting unit can optimize the reference algorithm by referring to past reference data when setting the reference. For example, the reference setting unit optimizes the reference algorithm by referring to past reference data when setting the reference. The reference algorithm includes, for example, a method for using past data and a method for adjusting the algorithm. For example, the past reference data is analyzed to derive an optimal reference algorithm. The accuracy of the reference algorithm can also be improved based on the past reference data. Furthermore, parameters of the reference algorithm can be adjusted by referring to the past reference data. In this way, the accuracy of the reference algorithm can be improved by referring to the past reference data. Some or all of the above-mentioned processing in the reference setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reference setting unit can set the reference using an AI model that optimizes the reference algorithm by referring to past reference data.

[0085] The standard setting unit can estimate the user's emotions and adjust the frequency of standard setting based on the estimated user emotions. The standard setting unit, for example, estimates the user's emotions and adjusts the frequency of standard setting based on the estimated user emotions. The frequency of standard setting includes, for example, the timing of setting and the frequency standard. For example, if the user is nervous, the standard can be set frequently to ensure reliability. Also, if the user is relaxed, the frequency of standard setting can be reduced to provide flexibility. Furthermore, if the user is in a hurry, the standard can be set quickly to emphasize efficiency. This allows for more appropriate standard setting by adjusting the frequency of standard setting according to the user's emotions. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without AI. For example, the standard setting unit can set standards using an AI model that estimates the user's emotions and adjusts the frequency of standard setting based on the estimated emotions.

[0086] The criteria setting unit can weight the reference data based on the submission time of the information source when setting the criteria. For example, the criteria setting unit weights the reference data based on the submission time of the information source when setting the criteria. Weighting the reference data includes, for example, an evaluation method for the submission time and weighting criteria. For example, a newer information source can be weighted higher to evaluate its reliability. Also, an older information source can be weighted lower to evaluate its reliability. Furthermore, information sources whose submission times are concentrated in a specific period can be weighted appropriately. In this way, weighting the reference data based on the submission time of the information source enables evaluation of its reliability. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can set the criteria using an AI model that weights the reference data based on the submission time of the information source.

[0087] The display setting unit can estimate a user's emotions and adjust the display setting method based on the estimated user's emotions. The display setting unit, for example, estimates a user's emotions and adjusts the display setting method based on the estimated user's emotions. The display setting method includes, for example, a display format selection method and display priority. For example, if a user is nervous, a display setting with subdued colors can be provided. Also, if a user is relaxed, a display setting with bright colors can be provided. Furthermore, if a user is in a hurry, a simple, highly visible display setting can be provided. This enables a more appropriate display by adjusting the display setting method according to the user's emotions. Some or all of the above-described processing in the display setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the display setting unit can provide a display method using an AI model that estimates a user's emotions and adjusts the display setting method based on the estimated emotions.

[0088] The display setting unit can select an optimal display method by referring to the user's past display history when setting the display. For example, the display setting unit can select an optimal display method by referring to the user's past display history when setting the display. The display history includes, for example, past display content and a display history storage method. For example, the display setting that the user has previously preferred can be automatically applied. The optimal display method can also be suggested based on the user's past display history. Furthermore, a customizable display method can be provided based on the display settings that the user has previously used. In this way, the optimal display method can be provided by referring to the user's past display history. Some or all of the above-described processing in the display setting unit can be performed, for example, using AI or without AI. For example, the display setting unit can provide a display method using an AI model that selects an optimal display method by referring to the user's past display history.

[0089] The display setting unit can estimate a user's emotions and determine the priority of display settings based on the estimated user's emotions. The display setting unit, for example, estimates a user's emotions and determines the priority of display settings based on the estimated user's emotions. The priority of display settings includes, for example, the importance of display content and the user's level of interest. For example, if a user is nervous, important information can be displayed preferentially. Also, if a user is relaxed, detailed information can be displayed preferentially. Furthermore, if a user is in a hurry, information that highlights the main points can be displayed preferentially. This enables more appropriate display by determining the priority of display settings according to the user's emotions. Some or all of the above-described processing in the display setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the display setting unit can provide a display method using an AI model that estimates a user's emotions and determines the priority of display settings based on the estimated emotions.

[0090] The display setting unit can select the optimal display method by taking into account the user's device information during display setting. For example, the display setting unit selects the optimal display method by taking into account the user's device information during display setting. The device information includes, for example, the device type and device performance. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a display method that is simple and highly visible can be provided. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-described processing in the display setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the display setting unit can provide a display method using an AI model that selects the optimal display method by taking into account the user's device information. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, analysis unit, display unit, standard setting unit, and display setting unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is implemented by the reception device 38 of the smart device 14 and inputs text from documents. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and determines its evidence level. The display unit is implemented, for example, by the output device 40 of the smart device 14 and displays the text in a different color based on the determined evidence level. The standard setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and sets the evidence level based on information sources such as academic papers and government publications. The display setting unit is implemented, for example, by the control unit 46A of the smart device 14 and sets a visually easy-to-understand display method, such as changing color shading or font. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, display unit, standard setting unit, and display setting unit, described above, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214 and inputs text from a document. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and determines its evidence level. The display unit is implemented, for example, by the speaker 240 of the smart glasses 214 and displays the text in a different color based on the determined evidence level. The standard setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and sets the evidence level based on information sources such as academic papers and government publications. The display setting unit is implemented, for example, by the control unit 46A of the smart glasses 214 and sets a visually easy-to-understand display method, such as changing color shading or font. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, analysis unit, display unit, standard setting unit, and display setting unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is implemented by the microphone 238 of the headset terminal 314 and inputs text from a document. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and determines its evidence level. The display unit is implemented, for example, by the display 343 of the headset terminal 314 and displays the text in different colors based on the determined evidence level. The standard setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and sets the evidence level based on information sources such as academic papers and government publications. The display setting unit is implemented, for example, by the control unit 46A of the headset terminal 314 and sets a visually easy-to-understand display method, such as changing color shading or font. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, analysis unit, display unit, standard setting unit, and display setting unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is implemented by the microphone 238 of the robot 414 and inputs text from a document. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input text using a generation AI and determines its evidence level. The display unit is implemented, for example, by the speaker 240 of the robot 414 and displays the text in a color-coded format based on the determined evidence level. The standard setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and sets the evidence level based on information sources such as academic papers and government publications. The display setting unit is implemented, for example, by the control unit 46A of the robot 414 and sets a visually easy-to-understand display method, such as changing color shading or font.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The evidence level determination system can further include a search history analysis unit that analyzes a user's past search history and prioritizes the display of related information. For example, highly relevant information can be displayed preferentially based on keywords the user has previously searched for or pages the user has viewed. Related information can also be automatically suggested based on topics the user frequently searches for. Furthermore, it can predict topics that the user will be interested in during a specific time period from the user's search history and display related information. This allows the system to provide more relevant information by analyzing the user's past search history.

[0093] The evidence level determination system can further include a feedback collection unit that collects user feedback and improves the accuracy of evidence level determination. For example, when a user evaluates the evidence level determination result, the system improves the determination algorithm based on the evaluation. The system can also analyze the feedback provided by the user and adjust the evidence level criteria. Furthermore, new evidence level categories can be added based on user feedback. In this way, the accuracy of evidence level determination can be improved by utilizing user feedback.

[0094] The evidence level determination system can further estimate the user's emotions and adjust the display method of the evidence level based on the estimated emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the evidence level according to the user's emotions.

[0095] The evidence level determination system can further include a geographic information analysis unit that determines the evidence level by taking into account the user's geographic location information. For example, if the user is in a specific area, the reliability of information related to that area can be prioritized. Also, if the user is traveling, the reliability of information related to the travel destination can be evaluated. Furthermore, if the user is participating in a specific event, the reliability of information related to that event can be evaluated. In this way, by taking into account the user's geographic location information, it is possible to determine a more relevant evidence level.

[0096] The evidence level determination system can further estimate the user's emotions and adjust the evidence level criteria based on the estimated emotions. For example, if the user is nervous, strict criteria can be applied to prioritize highly reliable information. Alternatively, if the user is relaxed, flexible criteria can be applied to accept a wide range of information. Furthermore, if the user is in a hurry, criteria that allow for quick judgment can be applied. This allows for more appropriate criteria to be set by adjusting the evidence level criteria according to the user's emotions.

[0097] The evidence level determination system may further include a social media analysis unit that analyzes a user's social media activity and evaluates the reliability of related information. For example, the reliability of information may be evaluated based on information shared by the user on social media. The reliability of related information may also be evaluated based on information on accounts the user follows. Furthermore, the reliability of related information may also be evaluated based on topics in which the user has shown interest. In this way, by analyzing a user's social media activity, the reliability of more relevant information may be evaluated.

[0098] The evidence level determination system can further estimate the user's emotions and adjust the display order of evidence levels based on the estimated emotions. For example, if the user is nervous, important information can be displayed with priority. If the user is relaxed, detailed information can be displayed with priority. Furthermore, if the user is in a hurry, information that covers the main points can be displayed with priority. In this way, by adjusting the display order of evidence levels according to the user's emotions, more appropriate information can be provided.

[0099] The evidence level determination system can further include a device information analysis unit that adjusts the display method of the evidence level by taking into account the user's device information. For example, if the user is using a smartphone, a display method tailored to the screen size can be provided. Also, if the user is using a tablet, a display method optimized for a large screen can be provided. Furthermore, if the user is using a smartwatch, a simple and highly visible display method can be provided. In this way, the optimal display method can be provided by taking into account the user's device information.

[0100] The evidence level determination system can further estimate the user's emotions and adjust the frequency of evidence level determination based on the estimated emotions. For example, if the user is nervous, the evidence level can be determined frequently to ensure reliability. Also, if the user is relaxed, the determination frequency can be reduced to provide flexibility. Furthermore, if the user is in a hurry, the evidence level can be determined quickly to emphasize efficiency. In this way, more appropriate determinations can be made by adjusting the frequency of evidence level determination according to the user's emotions.

[0101] The evidence level determination system can further include a display history analysis unit that optimizes the display method of the evidence level by referring to the user's past display history. For example, the display settings that the user has used in the past are automatically applied. The system can also suggest the optimal display method based on the user's past display history. Furthermore, the system can provide a customizable display method based on the display settings that the user has used in the past. This makes it possible to provide the optimal display method by referring to the user's past display history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The input section inputs the text from the source material. The text from the source material can include academic papers, government announcements, expert commentaries, personal blogs, etc. The input section can input the entire source material at once, or users can input the text manually. Step 2: The analysis unit uses the generation AI to analyze the text entered by the input unit and determine its level of evidence. The generation AI determines academic papers and announcements from government agencies as highly reliable, expert commentaries and information from trustworthy organizations as medium reliable, and personal blogs and unfounded information as low reliable. The analysis unit uses natural language processing technology to evaluate the reliability of the text. Step 3: The display unit displays the text in different colors based on the evidence level determined by the analysis unit. The display unit displays highly reliable information in green, medium reliability information in yellow, and low reliability information in red. The processing in the display unit can also be performed using an AI model.

[0104] 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.

[0105] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

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

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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, in order to avoid confusion and to 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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. an input section for inputting text in the document; an analysis unit that analyzes the sentences input by the input unit and determines an evidence level; a display unit that displays sentences in different colors based on the evidence level determined by the analysis unit; Equipped with A system characterized by:

2. It has a standard setting section that sets the level of evidence based on at least one of the following sources: academic papers or government publications, expert commentary articles, and personal blogs.

2. The system of claim 1.

3. A display setting unit is provided for setting at least one of color shading and font change, and for setting a visually easy-to-understand display method.

2. The system of claim 1.

4. The analysis unit Analyze the input text and evaluate its reliability 2. The system of claim 1.

5. The display unit Text is color-coded based on the determined evidence level 2. The system of claim 1.

6. The input unit Estimate the user's emotions and adjust the timing of sentence input based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

8. The input unit Filtering based on your current projects and interests as you type 2. The system of claim 1.

9. The input unit Estimate the user's emotions and prioritize the sentences to be input based on the estimated user emotions.

2. The system of claim 1.

Images