System

Abstract

An object of a system according to an embodiment is to evaluate the credibility of a seller's statement and the accuracy of provided information in real time.SOLUTION: A system includes a remark analysis unit and an information verification unit. A statement analysis part analyzes the statement of the seller in real time and evaluates the credibility of the contents. The information verification unit analyzes the information provided by the seller based on the statement of the seller analyzed by the statement analysis unit, and verifies the accuracy of the information.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 the problem that it is difficult to evaluate the credibility of sellers' statements and the accuracy of the information they provide in real time.

[0005] The system according to the embodiment aims to evaluate the credibility of statements made by sellers and the accuracy of information provided in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a statement analysis unit and an information verification unit. The statement analysis unit analyzes statements made by sellers in real time and evaluates the credibility of the content. The information verification unit analyzes information provided by sellers based on the statements analyzed by the statement analysis unit and verifies the accuracy of the information. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the credibility of the seller's statements and the accuracy of the information provided in real time. [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 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 credibility evaluation system according to an embodiment of the present invention is a system that analyzes statements and provided information of sellers and evaluates their credibility when making expensive purchases, thereby protecting consumers from being deceived.

[0029] The credibility evaluation system according to the embodiment includes a statement analysis unit and an information verification unit. The statement analysis unit analyzes a seller's statements in real time and evaluates the credibility of the content. For example, the generation AI inputs the seller's statements as audio data and compares them with past data and market information to evaluate the credibility. The statement analysis unit can also input the seller's statements as text data and analyze them using natural language processing technology. For example, the generation AI inputs the seller's statements as text data and evaluates their consistency with past statements. The statement analysis unit can also input the seller's statements as video data and analyze them using video analysis technology. For example, the generation AI analyzes the seller's facial expressions and gestures to evaluate the credibility of the statements. The information verification unit analyzes information provided by the seller based on the seller's statements analyzed by the statement analysis unit and verifies its accuracy. For example, the generation AI inputs specifications and price information of products provided by the seller as text data and verifies their accuracy by comparing them with online databases and information from other retailers. The information verification unit can also input the provided information as image data and verify it using image analysis technology. For example, the generation AI analyzes images of products and evaluates the accuracy of the provided information. Furthermore, the information verification unit can input the provided information as video data and verify it using video analysis technology. For example, the generation AI analyzes promotional videos for products and evaluates the accuracy of the provided information. In this way, the credibility evaluation system according to the embodiment can evaluate the credibility of seller statements and provided information, preventing consumers from being deceived.

[0030] The statement analysis unit analyzes the intentions and motivations behind the seller's statements, further increasing the credibility of the statements. For example, if a seller says, "This product is a limited edition," the statement analysis unit analyzes the sales strategy and inventory status behind the statement to evaluate the credibility of the statement. The statement analysis unit can also analyze the seller's past statements and behavioral patterns to evaluate their intentions and motivations. For example, the generation AI analyzes the seller's past statement data to evaluate consistency and inconsistencies. Furthermore, the statement analysis unit can analyze the seller's industry and market trends to evaluate their intentions and motivations. For example, the generation AI analyzes industry trend data to evaluate the background of the seller's statements. This allows the intentions and motivations behind the seller's statements to be analyzed, further increasing the credibility of the statements.

[0031] When analyzing a seller's statements, the statement analysis unit can simultaneously analyze non-verbal elements to evaluate the credibility of the statements. For example, the statement analysis unit uses a generation AI to analyze the seller's tone of voice and facial expressions to evaluate the credibility of the statements. For example, if the tone of voice is high, the credibility of the statements may be low. The statement analysis unit can also analyze the seller's gestures and posture to evaluate non-verbal elements. For example, the generation AI can analyze the seller's hand movements and body position to evaluate the credibility of the statements. Furthermore, the statement analysis unit can analyze the seller's gaze and facial expressions to evaluate non-verbal elements. For example, the generation AI can analyze the seller's gaze movements and facial expressions to evaluate the credibility of the statements. In this way, the credibility of the statements can be evaluated by analyzing non-verbal elements.

[0032] The statement analysis unit can also apply the seller's statement analysis to at least one text communication, such as online chat or email, to evaluate the credibility of the text-based statement. The statement analysis unit, for example, uses a generation AI to analyze the seller's statements in text communication, such as online chat or email, and evaluate their credibility. For example, the content of the text can be compared with past data. The statement analysis unit can also analyze the context of the text and the usage of words to evaluate the credibility. For example, the generation AI analyzes the context of the text and evaluates the consistency of the statement. Furthermore, the statement analysis unit can analyze the emotional nuances of the text to evaluate the credibility. For example, the generation AI analyzes the emotional nuances of the text and evaluates the credibility of the statement. This makes it possible to evaluate the credibility of the text-based statement.

[0033] The statement analysis unit can adapt the seller's statement analysis to different languages ​​or cultural areas and evaluate the credibility of the statements from a global perspective. The statement analysis unit, for example, uses a generation AI to build a seller's statement analysis system that supports different languages ​​and cultural areas. For example, it uses multilingual natural language processing technology. The statement analysis unit can also evaluate the credibility of statements taking into account different cultural backgrounds. For example, the generation AI analyzes statements taking into account cultural nuances and customs. Furthermore, the statement analysis unit can evaluate the credibility of statements taking into account trends in different markets and industries. For example, the generation AI analyzes data from different markets and evaluates the credibility of statements. This makes it possible to adapt to different languages ​​and cultural areas and evaluate the credibility of statements from a global perspective.

[0034] The information verification unit can analyze the data source behind the provided information and evaluate its reliability. The information verification unit can, for example, use the generation AI to analyze the data source behind the provided information and evaluate its reliability. For example, it can check the origin and source of the information. The information verification unit can also refer to third-party evaluations and reviews to evaluate the reliability of the data source. For example, the generation AI can analyze third-party evaluation data to evaluate the reliability of the data source. Furthermore, the information verification unit can analyze the past performance and history of the data source to evaluate its reliability. For example, the generation AI can analyze the past data of the data source and evaluate its consistency and accuracy. This makes it possible to analyze the data source behind the provided information and evaluate its reliability.

[0035] The information verification unit can evaluate the accuracy of the information by referring to market trends and trend information in real time when verifying the provided information. The information verification unit can, for example, use the generation AI to evaluate the accuracy of the information by referring to market trends and trend information in real time when verifying the provided information. For example, the latest market data is obtained. The information verification unit can also collect trend information in real time and evaluate the accuracy of the provided information. For example, the generation AI collects trend information from social media and news sites. Furthermore, the information verification unit can monitor market trends in real time and evaluate the accuracy of the provided information. For example, the generation AI analyzes market price trends in real time and evaluates the accuracy of the provided information. This makes it possible to evaluate the accuracy of the information by referring to market trends and trend information.

[0036] The information verification unit can apply the verification of the provided information to information in different industries and fields, and evaluate the accuracy of the information by referring to a wide range of databases. The information verification unit can apply the verification of the provided information to information in different industries and fields, for example, using the generation AI, and evaluate the accuracy of the information by referring to a wide range of databases. For example, multiple industry databases can be cross-referenced. The information verification unit can also evaluate the accuracy of information by referring to the opinions of experts in different industries and fields. For example, the generation AI can analyze expert reviews and evaluations to evaluate the accuracy of the information. Furthermore, the information verification unit can analyze trend data in different industries and fields to evaluate the accuracy of the information. For example, the generation AI can analyze trend data in different industries and evaluate the accuracy of the provided information. This makes it possible to apply the verification of the provided information to information in different industries and fields, and evaluate the accuracy of the information by referring to a wide range of databases.

[0037] The information verification unit can compare the verification of the provided information with past data on similar products and evaluate the consistency of the information. The information verification unit, for example, uses the generation AI to compare the verification of the provided information with past data on similar products and evaluate the consistency of the information. For example, it refers to past sales data and customer reviews. The information verification unit can also analyze specifications and price information of similar products in the past and evaluate the consistency of the provided information. For example, the generation AI analyzes specification data of similar products in the past and evaluates the consistency of the provided information. Furthermore, the information verification unit can analyze market trends of similar products in the past and evaluate the consistency of the provided information. For example, the generation AI analyzes past market data and evaluates the consistency of the provided information. This makes it possible to compare the provided information with data on similar products in the past and evaluate the consistency of the information.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The credibility evaluation system can also analyze a user's purchase history and evaluate credibility based on past purchasing patterns. For example, it can evaluate the credibility of similar products by referring to reviews and ratings of products the user has purchased in the past. It can also calculate the credibility of a specific seller from the user's purchase history and evaluate the credibility of the seller's statements and information provided. It can also analyze a user's purchase history and evaluate credibility by taking into account price fluctuations and market trends of specific products. This makes it possible to utilize a user's purchase history to perform more accurate credibility evaluations.

[0040] The credibility evaluation system can also analyze a seller's past transaction history to evaluate their credibility. For example, it can analyze what products a seller has sold in the past and what kind of reviews they received. It can also evaluate the credibility of a specific product from the seller's past transaction history. It can also analyze a seller's transaction history to calculate their credibility in a specific market or industry, and evaluate their credibility based on that credibility. This makes it possible to utilize a seller's past transaction history to perform a more accurate credibility evaluation.

[0041] The credibility evaluation system can also refer to third-party ratings and reviews to evaluate the credibility of the provided information. For example, it can collect third-party rating data from online review sites and social media to evaluate the credibility of the provided information. It can also analyze third-party rating data to evaluate the credibility of a specific product. It can also calculate the credibility of a specific seller or product based on the third-party rating data and evaluate the credibility based on that credibility. This makes it possible to utilize third-party ratings to perform more accurate credibility evaluations.

[0042] The credibility evaluation system can also refer to the opinions of experts in different industries and fields to evaluate the credibility of the provided information. For example, it can collect reviews and ratings from experts and evaluate the credibility of the provided information. It can also analyze the opinions of experts in different industries and fields to evaluate the credibility of a specific product. Furthermore, it can calculate the credibility of a specific seller or product based on the expert opinions and evaluate the credibility based on that credibility. This makes it possible to utilize expert opinions to perform more accurate credibility evaluations.

[0043] The credibility evaluation system can also compare the provided information with data on similar products in the past to evaluate the credibility of the provided information. For example, it can evaluate the credibility of the provided information by referring to past sales data and customer reviews. It can also analyze specifications and price information on similar products in the past to evaluate the consistency of the provided information. It can also analyze market trends of similar products in the past to evaluate the consistency of the provided information. This makes it possible to evaluate the credibility of the provided information by comparing it with data on similar products in the past.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The statement analysis unit analyzes the seller's statements in real time and evaluates the credibility of their content. For example, the generation AI inputs the seller's statements as audio data and compares them with past data and market information to evaluate their credibility. The statement analysis unit can also input the seller's statements as text data and analyze them using natural language processing technology. Furthermore, the statement analysis unit can input the seller's statements as video data and analyze them using video analysis technology. Step 2: The information verification unit analyzes the information provided by the seller based on the seller's statements analyzed by the statement analysis unit and verifies its accuracy. For example, the generation AI inputs product specifications and price information provided by the seller as text data and verifies its accuracy by comparing it with online databases and information from other retailers. The information verification unit can also input the provided information as image data and verify it using image analysis technology. Furthermore, the information verification unit can input the provided information as video data and verify it using video analysis technology.

[0046] (Example 2) The credibility evaluation system according to an embodiment of the present invention is a system that analyzes statements and provided information of sellers and evaluates their credibility when making expensive purchases, thereby protecting consumers from being deceived.

[0047] The credibility evaluation system according to the embodiment includes a statement analysis unit and an information verification unit. The statement analysis unit analyzes a seller's statements in real time and evaluates the credibility of the content. For example, the generation AI inputs the seller's statements as audio data and compares them with past data and market information to evaluate the credibility. The statement analysis unit can also input the seller's statements as text data and analyze them using natural language processing technology. For example, the generation AI inputs the seller's statements as text data and evaluates their consistency with past statements. The statement analysis unit can also input the seller's statements as video data and analyze them using video analysis technology. For example, the generation AI analyzes the seller's facial expressions and gestures to evaluate the credibility of the statements. The information verification unit analyzes information provided by the seller based on the seller's statements analyzed by the statement analysis unit and verifies its accuracy. For example, the generation AI inputs specifications and price information of products provided by the seller as text data and verifies their accuracy by comparing them with online databases and information from other retailers. The information verification unit can also input the provided information as image data and verify it using image analysis technology. For example, the generation AI analyzes images of products and evaluates the accuracy of the provided information. Furthermore, the information verification unit can input the provided information as video data and verify it using video analysis technology. For example, the generation AI analyzes promotional videos for products and evaluates the accuracy of the provided information. In this way, the credibility evaluation system according to the embodiment can evaluate the credibility of seller statements and provided information, preventing consumers from being deceived.

[0048] The statement analysis unit analyzes the intentions and motivations behind the seller's statements, further increasing the credibility of the statements. For example, if a seller says, "This product is a limited edition," the statement analysis unit analyzes the sales strategy and inventory status behind the statement to evaluate the credibility of the statement. The statement analysis unit can also analyze the seller's past statements and behavioral patterns to evaluate their intentions and motivations. For example, the generation AI analyzes the seller's past statement data to evaluate consistency and inconsistencies. Furthermore, the statement analysis unit can analyze the seller's industry and market trends to evaluate their intentions and motivations. For example, the generation AI analyzes industry trend data to evaluate the background of the seller's statements. This allows the intentions and motivations behind the seller's statements to be analyzed, further increasing the credibility of the statements.

[0049] When analyzing a seller's statements, the statement analysis unit can simultaneously analyze non-verbal elements to evaluate the credibility of the statements. For example, the statement analysis unit uses a generation AI to analyze the seller's tone of voice and facial expressions to evaluate the credibility of the statements. For example, if the tone of voice is high, the credibility of the statements may be low. The statement analysis unit can also analyze the seller's gestures and posture to evaluate non-verbal elements. For example, the generation AI can analyze the seller's hand movements and body position to evaluate the credibility of the statements. Furthermore, the statement analysis unit can analyze the seller's gaze and facial expressions to evaluate non-verbal elements. For example, the generation AI can analyze the seller's gaze movements and facial expressions to evaluate the credibility of the statements. In this way, the credibility of the statements can be evaluated by analyzing non-verbal elements.

[0050] The utterance analysis unit uses an emotion estimation function to analyze the consumer's emotional response to the seller's utterances in real time and can issue a warning if the consumer feels anxious. The utterance analysis unit, for example, uses a generation AI to analyze the consumer's emotional response to the seller's utterances in real time and can issue a warning if the consumer feels anxious. For example, it analyzes the consumer's facial expressions and tone of voice. The utterance analysis unit can also analyze the consumer's biometric data (heart rate and electrodermal activity) to evaluate the emotional response. For example, the generation AI analyzes the consumer's heart rate fluctuations and issues a warning if the consumer feels anxious. Furthermore, the utterance analysis unit can analyze the consumer's behavioral data (for example, eye movement and body movement) to evaluate the emotional response. For example, the generation AI analyzes the consumer's eye movement and body movement and issues a warning if the consumer feels anxious. In this way, it is possible to analyze the consumer's emotional response and issue a warning if the consumer feels anxious.

[0051] The statement analysis unit can also apply the seller's statement analysis to at least one text communication, such as online chat or email, to evaluate the credibility of the text-based statement. The statement analysis unit, for example, uses a generation AI to analyze the seller's statements in text communication, such as online chat or email, and evaluate their credibility. For example, the content of the text can be compared with past data. The statement analysis unit can also analyze the context of the text and the usage of words to evaluate the credibility. For example, the generation AI analyzes the context of the text and evaluates the consistency of the statement. Furthermore, the statement analysis unit can analyze the emotional nuances of the text to evaluate the credibility. For example, the generation AI analyzes the emotional nuances of the text and evaluates the credibility of the statement. This makes it possible to evaluate the credibility of the text-based statement.

[0052] The statement analysis unit can adapt the seller's statement analysis to different languages ​​or cultural areas and evaluate the credibility of the statements from a global perspective. The statement analysis unit, for example, uses a generation AI to build a seller's statement analysis system that supports different languages ​​and cultural areas. For example, it uses multilingual natural language processing technology. The statement analysis unit can also evaluate the credibility of statements taking into account different cultural backgrounds. For example, the generation AI analyzes statements taking into account cultural nuances and customs. Furthermore, the statement analysis unit can evaluate the credibility of statements taking into account trends in different markets and industries. For example, the generation AI analyzes data from different markets and evaluates the credibility of statements. This makes it possible to adapt to different languages ​​and cultural areas and evaluate the credibility of statements from a global perspective.

[0053] The statement analysis unit can use the emotion estimation function to collect other consumers' emotional reactions to the seller's statements and evaluate credibility based on collective emotional data. The statement analysis unit can, for example, use a generation AI to collect other consumers' emotional reactions to the seller's statements and evaluate credibility based on collective emotional data. For example, it can aggregate the emotional scores of multiple consumers. The statement analysis unit can also collect emotional data from social media and online review sites to evaluate credibility. For example, the generation AI can analyze social media posts and reviews to collect emotional data. Furthermore, the statement analysis unit can collect emotional data using surveys and feedback forms to evaluate credibility. For example, the generation AI can analyze survey results and evaluate credibility based on collective emotional data. This makes it possible to evaluate credibility based on collective emotional data.

[0054] The information verification unit can analyze the data source behind the provided information and evaluate its reliability. The information verification unit can, for example, use the generation AI to analyze the data source behind the provided information and evaluate its reliability. For example, it can check the origin and source of the information. The information verification unit can also refer to third-party evaluations and reviews to evaluate the reliability of the data source. For example, the generation AI can analyze third-party evaluation data to evaluate the reliability of the data source. Furthermore, the information verification unit can analyze the past performance and history of the data source to evaluate its reliability. For example, the generation AI can analyze the past data of the data source and evaluate its consistency and accuracy. This makes it possible to analyze the data source behind the provided information and evaluate its reliability.

[0055] The information verification unit can evaluate the accuracy of the information by referring to market trends and trend information in real time when verifying the provided information. The information verification unit can, for example, use the generation AI to evaluate the accuracy of the information by referring to market trends and trend information in real time when verifying the provided information. For example, the latest market data is obtained. The information verification unit can also collect trend information in real time and evaluate the accuracy of the provided information. For example, the generation AI collects trend information from social media and news sites. Furthermore, the information verification unit can monitor market trends in real time and evaluate the accuracy of the provided information. For example, the generation AI analyzes market price trends in real time and evaluates the accuracy of the provided information. This makes it possible to evaluate the accuracy of the information by referring to market trends and trend information.

[0056] The information verification unit can use the emotion estimation function to analyze the consumer's emotional response to the provided information and issue a warning if the information causes anxiety. The information verification unit can, for example, use the generation AI to analyze the consumer's emotional response to the provided information and issue a warning if the information causes anxiety. For example, the information verification unit analyzes the consumer's facial expression and tone of voice. The information verification unit can also analyze the consumer's biometric data (heart rate and electrodermal activity) to evaluate the emotional response. For example, the generation AI can analyze the consumer's heart rate fluctuations and issue a warning if the consumer feels anxious. Furthermore, the information verification unit can analyze the consumer's behavioral data (for example, eye movement and body movement) to evaluate the emotional response. For example, the generation AI can analyze the consumer's eye movement and body movement and issue a warning if the consumer feels anxious. In this way, the information verification unit can analyze the consumer's emotional response to the provided information and issue a warning if the information causes anxiety.

[0057] The information verification unit can apply the verification of the provided information to information in different industries and fields, and evaluate the accuracy of the information by referring to a wide range of databases. The information verification unit can apply the verification of the provided information to information in different industries and fields, for example, using the generation AI, and evaluate the accuracy of the information by referring to a wide range of databases. For example, multiple industry databases can be cross-referenced. The information verification unit can also evaluate the accuracy of information by referring to the opinions of experts in different industries and fields. For example, the generation AI can analyze expert reviews and evaluations to evaluate the accuracy of the information. Furthermore, the information verification unit can analyze trend data in different industries and fields to evaluate the accuracy of the information. For example, the generation AI can analyze trend data in different industries and evaluate the accuracy of the provided information. This makes it possible to apply the verification of the provided information to information in different industries and fields, and evaluate the accuracy of the information by referring to a wide range of databases.

[0058] The information verification unit can compare the verification of the provided information with past data on similar products and evaluate the consistency of the information. The information verification unit, for example, uses the generation AI to compare the verification of the provided information with past data on similar products and evaluate the consistency of the information. For example, it refers to past sales data and customer reviews. The information verification unit can also analyze specifications and price information of similar products in the past and evaluate the consistency of the provided information. For example, the generation AI analyzes specification data of similar products in the past and evaluates the consistency of the provided information. Furthermore, the information verification unit can analyze market trends of similar products in the past and evaluate the consistency of the provided information. For example, the generation AI analyzes past market data and evaluates the consistency of the provided information. This makes it possible to compare the provided information with data on similar products in the past and evaluate the consistency of the information.

[0059] The information verification unit can use the emotion estimation function to collect other consumers' emotional reactions to the provided information and evaluate the accuracy of the information based on collective emotional data. The information verification unit can, for example, use the generation AI to collect other consumers' emotional reactions to the provided information and evaluate the accuracy of the information based on collective emotional data. For example, the information verification unit can aggregate the emotional scores of multiple consumers. The information verification unit can also collect emotional data from social media or online review sites and evaluate the accuracy of the information. For example, the generation AI can analyze social media posts and reviews to collect emotional data. Furthermore, the information verification unit can collect emotional data using questionnaire surveys or feedback forms and evaluate the accuracy of the information. For example, the generation AI can analyze questionnaire results and evaluate the accuracy of the information based on collective emotional data. This makes it possible to evaluate the accuracy of the information based on collective emotional data.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The credibility evaluation system can also analyze a user's purchase history and evaluate credibility based on past purchasing patterns. For example, it can evaluate the credibility of similar products by referring to reviews and ratings of products the user has purchased in the past. It can also calculate the credibility of a specific seller from the user's purchase history and evaluate the credibility of the seller's statements and information provided. It can also analyze a user's purchase history and evaluate credibility by taking into account price fluctuations and market trends of specific products. This makes it possible to utilize a user's purchase history to perform more accurate credibility evaluations.

[0062] The credibility assessment system can also estimate the user's emotions and assess credibility based on the estimated user emotions. For example, if a user feels anxiety or doubt about a seller's statement, the system can analyze that emotion and reflect it in the assessment of credibility. It can also analyze how users felt about similar statements in the past and use that information in assessing credibility. Furthermore, it can collect user emotion data and assess credibility based on collective emotion data. This makes it possible to assess credibility taking user emotions into account.

[0063] The credibility evaluation system can also analyze a seller's past transaction history to evaluate their credibility. For example, it can analyze what products a seller has sold in the past and what kind of reviews they received. It can also evaluate the credibility of a specific product from the seller's past transaction history. It can also analyze a seller's transaction history to calculate their credibility in a specific market or industry, and evaluate their credibility based on that credibility. This makes it possible to utilize a seller's past transaction history to perform a more accurate credibility evaluation.

[0064] The credibility assessment system can also estimate the user's emotions and evaluate the credibility of the provided information based on the estimated user emotions. For example, if a user feels anxiety or doubt about the provided information, the system can analyze those emotions and reflect them in the assessment of credibility. It can also analyze the emotions the user has felt toward similar information in the past and use this information in assessing credibility. Furthermore, it can collect user emotion data and evaluate credibility based on collective emotion data. This makes it possible to evaluate credibility taking user emotions into account.

[0065] The credibility evaluation system can also refer to third-party ratings and reviews to evaluate the credibility of the provided information. For example, it can collect third-party rating data from online review sites and social media to evaluate the credibility of the provided information. It can also analyze third-party rating data to evaluate the credibility of a specific product. It can also calculate the credibility of a specific seller or product based on the third-party rating data and evaluate the credibility based on that credibility. This makes it possible to utilize third-party ratings to perform more accurate credibility evaluations.

[0066] The credibility assessment system can also estimate the user's emotions and evaluate the credibility of the seller's statements based on the estimated user emotions. For example, if a user feels anxiety or doubt about a seller's statement, the system can analyze that emotion and reflect it in the credibility assessment. It can also analyze the emotions users have felt in the past regarding similar statements and use this information in the credibility assessment. Furthermore, it can collect user emotion data and evaluate credibility based on collective emotion data. This makes it possible to evaluate credibility taking user emotions into account.

[0067] The credibility evaluation system can also refer to the opinions of experts in different industries and fields to evaluate the credibility of the provided information. For example, it can collect reviews and ratings from experts and evaluate the credibility of the provided information. It can also analyze the opinions of experts in different industries and fields to evaluate the credibility of a specific product. Furthermore, it can calculate the credibility of a specific seller or product based on the expert opinions and evaluate the credibility based on that credibility. This makes it possible to utilize expert opinions to perform more accurate credibility evaluations.

[0068] The credibility assessment system can also estimate the user's emotions and evaluate the credibility of the provided information based on the estimated user emotions. For example, if a user feels anxiety or doubt about the provided information, the system can analyze those emotions and reflect them in the assessment of credibility. It can also analyze the emotions the user has felt toward similar information in the past and use this information in assessing credibility. Furthermore, it can collect user emotion data and evaluate credibility based on collective emotion data. This makes it possible to evaluate credibility taking user emotions into account.

[0069] The credibility evaluation system can also compare the provided information with data on similar products in the past to evaluate the credibility of the provided information. For example, it can evaluate the credibility of the provided information by referring to past sales data and customer reviews. It can also analyze specifications and price information on similar products in the past to evaluate the consistency of the provided information. It can also analyze market trends of similar products in the past to evaluate the consistency of the provided information. This makes it possible to evaluate the credibility of the provided information by comparing it with data on similar products in the past.

[0070] The credibility assessment system can also estimate the user's emotions and evaluate the credibility of the provided information based on the estimated user emotions. For example, if a user feels anxiety or doubt about the provided information, the system can analyze those emotions and reflect them in the assessment of credibility. It can also analyze the emotions the user has felt toward similar information in the past and use this information in assessing credibility. Furthermore, it can collect user emotion data and evaluate credibility based on collective emotion data. This makes it possible to evaluate credibility taking user emotions into account.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The statement analysis unit analyzes the seller's statements in real time and evaluates the credibility of their content. For example, the generation AI inputs the seller's statements as audio data and compares them with past data and market information to evaluate their credibility. The statement analysis unit can also input the seller's statements as text data and analyze them using natural language processing technology. Furthermore, the statement analysis unit can input the seller's statements as video data and analyze them using video analysis technology. Step 2: The information verification unit analyzes the information provided by the seller based on the seller's statements analyzed by the statement analysis unit and verifies its accuracy. For example, the generation AI inputs product specifications and price information provided by the seller as text data and verifies its accuracy by comparing it with online databases and information from other retailers. The information verification unit can also input the provided information as image data and verify it using image analysis technology. Furthermore, the information verification unit can input the provided information as video data and verify it using video analysis technology.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 statement analysis unit that analyzes the seller's statements in real time and evaluates the credibility of the content; an information verification unit that analyzes information provided by the seller based on the seller's statement analyzed by the statement analysis unit and verifies the accuracy of the information. A system characterized by:

2. The utterance analysis unit When analyzing the seller's statements, non-verbal elements are also analyzed at the same time to evaluate the credibility of the statements.

2. The system of claim 1.

3. The utterance analysis unit The analysis of the seller's statements is also applied to at least one text communication of online chat or email to evaluate the credibility of the text-based statements.

2. The system of claim 1.

4. The information verification unit Analyze the data sources behind the information provided and evaluate their reliability 2. The system of claim 1.

5. The utterance analysis unit Using an emotion estimation function, the system analyzes the consumer's emotional response to the seller's statements in real time and issues a warning if the consumer feels uneasy.

2. The system of claim 1.

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