System

Abstract

An object of a system according to an embodiment is to efficiently determine the authenticity of a speech of a politician and improve the reliability of information.SOLUTION: A system includes a text input unit, a voice input unit, an analysis unit, and an evaluation unit. The text input unit receives an utterance in a text format. The voice input unit receives an utterance in a voice format. The analysis unit analyzes the speech received by the text input unit and the speech input unit. The evaluation unit evaluates the reliability of the statement analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced challenges such as difficulty in efficiently determining the veracity of politicians' statements and a lack of means to prevent the spread of false information.

[0005] The system according to the embodiment aims to efficiently determine the truthfulness of statements made by politicians and improve the reliability of information. [Means for solving the problem]

[0006] A system according to an embodiment includes a text input unit, a voice input unit, an analysis unit, and an evaluation unit. The text input unit accepts utterances in text format. The voice input unit accepts utterances in voice format. The analysis unit analyzes the utterances accepted by the text input unit and the voice input unit. The evaluation unit evaluates the reliability of the utterances analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently determine the truthfulness of statements made by politicians and improve the reliability of information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The statement credibility assessment system according to an embodiment of the present invention is a system in which a generation AI analyzes statements entered by users and determines their credibility. This enables the statement credibility assessment system to prevent the spread of false information and improve the reliability of information in society.

[0029] The statement credibility evaluation system according to the embodiment includes a text input unit, a voice input unit, an analysis unit, and an evaluation unit. The text input unit accepts statements in text format. For example, it can accept statements input in the form of chat messages, emails, document files, etc. The voice input unit accepts statements in voice format. For example, it can accept recorded voice or real-time voice input. The analysis unit analyzes the statements accepted by the text input unit and the voice input unit. For example, it analyzes the text using natural language processing technology and analyzes the voice using voice recognition technology. The evaluation unit evaluates the credibility of the statements analyzed by the analysis unit. For example, it evaluates the credibility based on the consistency of the statements and the speaker's past credibility score. As a result, the statement credibility evaluation system according to the embodiment can analyze statements in text and voice format and evaluate their credibility.

[0030] The analysis unit can refer to past statements and policy documents to understand the context of a statement. For example, when the generative AI analyzes text input, the analysis unit refers to related past statements and policy documents to understand the context of the statement. For example, if a politician says, "The economic growth rate has increased by 2% compared to last year," the analysis unit can understand the context by referring to past economic growth rate data and related policy documents. This allows the analysis unit to refer to past statements and policy documents to understand the context of the statement.

[0031] The analysis unit can evaluate the reliability of a statement based on the speaker's past reliability score. For example, when the generation AI analyzes a statement, the analysis unit evaluates the reliability of the statement by taking into account the speaker's past reliability score. For example, statements made by politicians who have made many highly reliable statements in the past are given a high rating. This makes it possible to evaluate the reliability of a statement by taking into account the speaker's past reliability score.

[0032] The analysis unit can output the analysis results of the text input as infographics that are visually easy to understand. For example, the analysis unit outputs the analysis results of the text input as infographics that are visually easy to understand. For example, economic growth rate data can be displayed in graphs or charts to visually indicate the reliability of statements. This allows the analysis results of the text input to be output as infographics that are visually easy to understand.

[0033] The analysis unit can automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective. The analysis unit, for example, can automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective. For example, it can translate a statement in English into Japanese and evaluate the reliability. This makes it possible to automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective.

[0034] The voice input unit can analyze the tone and speed of the speaker's voice and evaluate the reliability of the statement. For example, when analyzing the voice input, the generation AI analyzes the tone and speed of the speaker's voice and evaluates the reliability of the statement. For example, if the tone of the voice is calm, it is evaluated as being highly reliable. This makes it possible to analyze the tone and speed of the speaker's voice and evaluate the reliability of the statement.

[0035] The voice input unit can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content. For example, the voice input unit can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content. For example, when analyzing speech in a noisy environment, noise canceling technology is used. This can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content.

[0036] The voice input unit displays the analysis results of the voice input as subtitles in real time, making it easier to understand visually. The voice input unit, for example, displays the analysis results of the voice input as subtitles in real time, making it easier to understand visually. For example, the speech of a politician can be displayed as subtitles in real time, making it easier to confirm visually. In this way, the analysis results of the voice input can be displayed as subtitles in real time, making it easier to understand visually.

[0037] The speech input unit can automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech. For example, the speech input unit can automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech. For example, the speech input unit can analyze a speech in Kansai dialect and evaluate the reliability. This makes it possible to automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech.

[0038] The evaluation unit can check whether the content of a statement matches past data or reliable information sources. For example, when the generation AI evaluates the reliability of a statement, the evaluation unit checks whether the content of the statement matches past data or reliable information sources. For example, it checks whether a statement about economic growth rates matches past economic data. This makes it possible to check whether the content of the statement matches past data or reliable information sources.

[0039] The evaluation unit can evaluate the reliability of a statement based on the speaker's past actions and track record. For example, when evaluating the reliability of a statement, the generation AI considers the speaker's past actions and track record to evaluate reliability. For example, it gives a high rating to statements made by politicians who have performed many highly reliable actions in the past. This makes it possible to evaluate the reliability of a statement by considering the speaker's past actions and track record.

[0040] The evaluation unit can output the statement reliability evaluation results as a graph or chart that is visually easy to understand. The evaluation unit outputs, for example, the statement reliability evaluation results as a graph or chart that is visually easy to understand. For example, economic growth rate data is displayed in a graph or chart to visually show the reliability of the statement. This allows the statement reliability evaluation results to be output as a graph or chart that is visually easy to understand.

[0041] The evaluation unit can evaluate the reliability of a statement from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective. For example, the evaluation unit can evaluate the reliability of a statement from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective. For example, a statement in English can be translated into Japanese and its reliability evaluated. This allows the reliability of a statement to be evaluated from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can also take into account the speaker's social network when evaluating the reliability of a statement. For example, if the speaker has many connections with highly reliable people, the statement can be evaluated as highly reliable. On the other hand, if the speaker has frequently interacted with less reliable people in the past, the statement can be evaluated as less reliable. Furthermore, the extent and influence of the speaker's social network can also be taken into account in the evaluation.

[0044] The evaluation department can also take into account the speaker's expertise and qualifications when assessing the credibility of a statement. For example, if a speaker has a high level of expertise in a particular field, the statement can be assessed as highly credible. Also, if the speaker has relevant qualifications or certifications, the statement can be assessed as highly credible. Furthermore, the speaker's past research results and achievements can also be taken into account in the evaluation.

[0045] The analysis unit can also take into account the timing and context of a statement when assessing its credibility. For example, if a statement is made immediately after a specific event or occurrence, it can be assessed as highly credible. Also, if a statement is made during an emergency or important meeting, it can be assessed as highly credible. Furthermore, the analysis unit can also take into account whether the statement is consistent under certain circumstances.

[0046] When assessing the reliability of a statement, the evaluation department can also check whether the content of the statement is based on scientific evidence and data. For example, if a statement is based on scientific research results or statistical data, the statement can be evaluated as highly reliable. Also, if a statement is based on expert opinions or reviews, the statement can be evaluated as highly reliable. Furthermore, whether the statement is supported by multiple reliable sources can be added to the evaluation.

[0047] The analysis unit can also take into account the format and expression of a statement when evaluating its credibility. For example, if a statement uses clear and specific language, it can be evaluated as highly credible. Also, if a statement has a logical and consistent structure, it can be evaluated as highly credible. Furthermore, the evaluation can also take into account whether the statement uses technical terms and expressions appropriately.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The text input unit accepts comments in text format, such as chat messages, emails, document files, and the like. Step 2: The voice input unit accepts voice-based speech, such as recorded speech or real-time voice input. Step 3: The analysis unit analyzes the utterances received by the text input unit and the voice input unit. For example, the analysis unit analyzes the text using natural language processing technology and the voice using voice recognition technology. Step 4: The evaluation unit evaluates the reliability of the statements analyzed by the analysis unit, for example, based on the consistency of the statements and the speaker's past reliability score.

[0050] (Example 2) The statement credibility assessment system according to an embodiment of the present invention is a system in which a generation AI analyzes statements entered by users and determines their credibility. This enables the statement credibility assessment system to prevent the spread of false information and improve the reliability of information in society.

[0051] The statement credibility evaluation system according to the embodiment includes a text input unit, a voice input unit, an analysis unit, and an evaluation unit. The text input unit accepts statements in text format. For example, it can accept statements input in the form of chat messages, emails, document files, etc. The voice input unit accepts statements in voice format. For example, it can accept recorded voice or real-time voice input. The analysis unit analyzes the statements accepted by the text input unit and the voice input unit. For example, it analyzes the text using natural language processing technology and analyzes the voice using voice recognition technology. The evaluation unit evaluates the credibility of the statements analyzed by the analysis unit. For example, it evaluates the credibility based on the consistency of the statements and the speaker's past credibility score. As a result, the statement credibility evaluation system according to the embodiment can analyze statements in text and voice format and evaluate their credibility.

[0052] The analysis unit can refer to past statements and policy documents to understand the context of a statement. For example, when the generative AI analyzes text input, the analysis unit refers to related past statements and policy documents to understand the context of the statement. For example, if a politician says, "The economic growth rate has increased by 2% compared to last year," the analysis unit can understand the context by referring to past economic growth rate data and related policy documents. This allows the analysis unit to refer to past statements and policy documents to understand the context of the statement.

[0053] The analysis unit can evaluate the reliability of a statement based on the speaker's past reliability score. For example, when the generation AI analyzes a statement, the analysis unit evaluates the reliability of the statement by taking into account the speaker's past reliability score. For example, statements made by politicians who have made many highly reliable statements in the past are given a high rating. This makes it possible to evaluate the reliability of a statement by taking into account the speaker's past reliability score.

[0054] The analysis unit can use the emotion estimation function to analyze the emotional nuances of a statement and distinguish between positive and negative statements. For example, the analysis unit can use the emotion estimation function to analyze the emotional nuances of a statement and distinguish between emotionally positive and negative statements. For example, it can analyze whether the statement "The economic growth rate increased by 2% compared to last year" has a positive emotion. This makes it possible to analyze the emotional nuances of a statement and distinguish between positive and negative statements.

[0055] The analysis unit can output the analysis results of the text input as infographics that are visually easy to understand. For example, the analysis unit outputs the analysis results of the text input as infographics that are visually easy to understand. For example, economic growth rate data can be displayed in graphs or charts to visually indicate the reliability of statements. This allows the analysis results of the text input to be output as infographics that are visually easy to understand.

[0056] The analysis unit can automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective. The analysis unit, for example, can automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective. For example, it can translate a statement in English into Japanese and evaluate the reliability. This makes it possible to automatically translate statements in different languages ​​and evaluate the reliability of the statements from an international perspective.

[0057] The analysis unit can use the emotion estimation function to evaluate the emotional impact of a statement and provide feedback to elicit positive emotions. The analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact of a statement and provide feedback to elicit positive emotions. For example, the analysis unit makes suggestions to change negative statements to positive ones. This makes it possible to evaluate the emotional impact of a statement and provide feedback to elicit positive emotions.

[0058] The voice input unit can analyze the tone and speed of the speaker's voice and evaluate the reliability of the statement. For example, when analyzing the voice input, the generation AI analyzes the tone and speed of the speaker's voice and evaluates the reliability of the statement. For example, if the tone of the voice is calm, it is evaluated as being highly reliable. This makes it possible to analyze the tone and speed of the speaker's voice and evaluate the reliability of the statement.

[0059] The voice input unit can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content. For example, the voice input unit can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content. For example, when analyzing speech in a noisy environment, noise canceling technology is used. This can remove background sounds and noise when analyzing voice data, thereby improving the accuracy of the speech content.

[0060] The voice input unit can use the emotion estimation function to analyze the speaker's emotional state and distinguish between positive and negative statements. The voice input unit can, for example, use the emotion estimation function to analyze the speaker's emotional state and distinguish between emotionally positive and negative statements. For example, if the tone of the voice is bright, it is determined to be a positive statement. This makes it possible to analyze the speaker's emotional state and distinguish between positive and negative statements.

[0061] The voice input unit displays the analysis results of the voice input as subtitles in real time, making it easier to understand visually. The voice input unit, for example, displays the analysis results of the voice input as subtitles in real time, making it easier to understand visually. For example, the speech of a politician can be displayed as subtitles in real time, making it easier to confirm visually. In this way, the analysis results of the voice input can be displayed as subtitles in real time, making it easier to understand visually.

[0062] The speech input unit can automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech. For example, the speech input unit can automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech. For example, the speech input unit can analyze a speech in Kansai dialect and evaluate the reliability. This makes it possible to automatically recognize different accents and dialects when analyzing speech data and evaluate the reliability of the speech.

[0063] The voice input unit can use the emotion estimation function to evaluate the emotional impact of the speaker and provide feedback to elicit positive emotions. The voice input unit can, for example, use the emotion estimation function to evaluate the emotional impact of the speaker and provide feedback to elicit positive emotions. For example, the voice input unit can make suggestions to change negative comments into positive ones. This makes it possible to evaluate the emotional impact of the speaker and provide feedback to elicit positive emotions.

[0064] The evaluation unit can check whether the content of a statement matches past data or reliable information sources. For example, when the generation AI evaluates the reliability of a statement, the evaluation unit checks whether the content of the statement matches past data or reliable information sources. For example, it checks whether a statement about economic growth rates matches past economic data. This makes it possible to check whether the content of the statement matches past data or reliable information sources.

[0065] The evaluation unit can evaluate the reliability of a statement based on the speaker's past actions and track record. For example, when evaluating the reliability of a statement, the generation AI considers the speaker's past actions and track record to evaluate reliability. For example, it gives a high rating to statements made by politicians who have performed many highly reliable actions in the past. This makes it possible to evaluate the reliability of a statement by considering the speaker's past actions and track record.

[0066] The evaluation unit can use the emotion estimation function to evaluate the emotional impact of a statement and distinguish between positive and negative statements. For example, the evaluation unit can use the emotion estimation function to evaluate the emotional impact of a statement and distinguish between emotionally positive and negative statements. For example, the evaluation unit can analyze whether the statement "The economic growth rate increased by 2% compared to last year" has a positive emotion. This makes it possible to evaluate the emotional impact of a statement and distinguish between positive and negative statements.

[0067] The evaluation unit can output the statement reliability evaluation results as a graph or chart that is visually easy to understand. The evaluation unit outputs, for example, the statement reliability evaluation results as a graph or chart that is visually easy to understand. For example, economic growth rate data is displayed in a graph or chart to visually show the reliability of the statement. This allows the statement reliability evaluation results to be output as a graph or chart that is visually easy to understand.

[0068] The evaluation unit can evaluate the reliability of a statement from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective. For example, the evaluation unit can evaluate the reliability of a statement from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective. For example, a statement in English can be translated into Japanese and its reliability evaluated. This allows the reliability of a statement to be evaluated from the perspective of different regions and cultural spheres, and provide an evaluation from a global perspective.

[0069] The evaluation unit can use the emotion estimation function to evaluate the emotional impact of the utterance and provide feedback to elicit positive emotions. The evaluation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the utterance and provide feedback to elicit positive emotions. For example, the evaluation unit makes suggestions to change negative utterances into positive ones. This makes it possible to evaluate the emotional impact of the utterance and provide feedback to elicit positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The analysis unit can also take into account the speaker's social network when evaluating the reliability of a statement. For example, if the speaker has many connections with highly reliable people, the statement can be evaluated as highly reliable. On the other hand, if the speaker has frequently interacted with less reliable people in the past, the statement can be evaluated as less reliable. Furthermore, the extent and influence of the speaker's social network can also be taken into account in the evaluation.

[0072] The evaluation department can also take into account the speaker's expertise and qualifications when assessing the credibility of a statement. For example, if a speaker has a high level of expertise in a particular field, the statement can be assessed as highly credible. Also, if the speaker has relevant qualifications or certifications, the statement can be assessed as highly credible. Furthermore, the speaker's past research results and achievements can also be taken into account in the evaluation.

[0073] The analysis unit can also take into account the timing and context of a statement when assessing its credibility. For example, if a statement is made immediately after a specific event or occurrence, it can be assessed as highly credible. Also, if a statement is made during an emergency or important meeting, it can be assessed as highly credible. Furthermore, the analysis unit can also take into account whether the statement is consistent under certain circumstances.

[0074] When assessing the reliability of a statement, the evaluation department can also check whether the content of the statement is based on scientific evidence and data. For example, if a statement is based on scientific research results or statistical data, the statement can be evaluated as highly reliable. Also, if a statement is based on expert opinions or reviews, the statement can be evaluated as highly reliable. Furthermore, whether the statement is supported by multiple reliable sources can be added to the evaluation.

[0075] The analysis unit can also take into account the format and expression of a statement when evaluating its credibility. For example, if a statement uses clear and specific language, it can be evaluated as highly credible. Also, if a statement has a logical and consistent structure, it can be evaluated as highly credible. Furthermore, the evaluation can also take into account whether the statement uses technical terms and expressions appropriately.

[0076] The evaluation unit can estimate the user's emotions and evaluate the reliability of the statement based on the estimated user emotions. For example, if the user has positive emotions toward the statement, the reliability of the statement can be evaluated as high. On the other hand, if the user has negative emotions toward the statement, the reliability of the statement can be evaluated as low. Furthermore, changes in the user's emotions can be tracked and reflected in the reliability evaluation of the statement.

[0077] The analysis unit can estimate the user's emotions and evaluate the influence of a comment based on the estimated user emotions. For example, if the user has strong emotions about a comment, the comment can be evaluated as having a high influence. On the other hand, if the user has indifferent emotions about a comment, the comment can be evaluated as having a low influence. Furthermore, the strength of the user's emotions can be measured and reflected in the evaluation of the comment's influence.

[0078] The evaluation unit can estimate the user's emotions and visually indicate the reliability of a statement based on the estimated user emotions. For example, if the user has positive emotions toward a statement, the reliability of the statement can be displayed in green. On the other hand, if the user has negative emotions toward a statement, the reliability of the statement can be displayed in red. Furthermore, changes in the user's emotions can be visually indicated using graphs and charts.

[0079] The analysis unit can estimate the user's emotions and update the reliability of the statement in real time based on the estimated user emotions. For example, if the user continues to have positive emotions toward the statement, the reliability of the statement can be continuously evaluated as high. On the other hand, if the user continues to have negative emotions toward the statement, the reliability of the statement can be continuously evaluated as low. Furthermore, changes in the user's emotions can be tracked in real time and reflected in the reliability evaluation of the statement.

[0080] The evaluation unit can estimate the user's emotions and provide feedback to evaluate the reliability of the statement based on the estimated user emotions. For example, if the user has positive emotions toward the statement, feedback can be provided to evaluate the reliability of the statement as high. On the other hand, if the user has negative emotions toward the statement, feedback can be provided to evaluate the reliability of the statement as low. Furthermore, changes in the user's emotions can be tracked and feedback can be provided to reflect the changes in the reliability evaluation of the statement.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The text input unit accepts comments in text format, such as chat messages, emails, document files, and the like. Step 2: The voice input unit accepts voice-based speech, such as recorded speech or real-time voice input. Step 3: The analysis unit analyzes the utterances received by the text input unit and the voice input unit. For example, the analysis unit analyzes the text using natural language processing technology and the voice using voice recognition technology. Step 4: The evaluation unit evaluates the reliability of the statements analyzed by the analysis unit, for example, based on the consistency of the statements and the speaker's past reliability score.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a text input unit for accepting text-based statements; a voice input unit for accepting voice-based utterances; an analysis unit that analyzes the utterances received by the text input unit and the voice input unit; an evaluation unit that evaluates the reliability of the utterances analyzed by the analysis unit; A system characterized by:

2. The analysis unit Evaluate the trustworthiness of a statement based on the speaker's past trustworthiness scores 2. The system of claim 1.

3. The analysis unit Outputting the analysis results of text input as visually easy-to-understand infographics The system of claim 1 .

4. The voice input unit Analyzing the tone or rate of a speaker's voice to assess the credibility of said statement 2. The system of claim 1.

5. The evaluation unit Verify whether the statements are consistent with historical data or the above-mentioned reliable sources.

2. The system of claim 1.

6. The analysis unit Using emotion estimation, the emotional nuances of the statements are analyzed to distinguish between positive and negative statements.

2. The system of claim 1.

Images