System

Abstract

An object of the system according to the embodiment is to determine the authenticity of the description content of the salesperson and to check the inconsistency between the content of the contract document and the description content.SOLUTION: A system includes a contract document confirmation unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document confirmation unit confirms the contract document. The expression analysis unit analyzes the contents of the contract document confirmed by the contract document confirmation unit. The heart rate analysis part analyzes the expression of the salesman analyzed by the expression analysis part. The summarization part analyzes the heart rate of the salesman analyzed by the heart rate analysis part. The inconsistency checking unit checks the contract document and the description content summarized by the summarizing 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] With conventional technology, consumers have no choice but to judge the veracity of the explanation given by the salesperson based on their own intuition, and there is a risk that inconsistencies between the contents of the contract document and the explanation will be overlooked.

[0005] The system according to the embodiment aims to determine the authenticity of the explanation given by the salesperson and to check for inconsistencies between the contents of the contract document and the explanation given. [Means for solving the problem]

[0006] The system according to the embodiment includes a contract document confirmation unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document confirmation unit confirms the contract document. The facial expression analysis unit analyzes the content of the contract document confirmed by the contract document confirmation unit. The heart rate analysis unit analyzes the facial expression of the salesperson analyzed by the facial expression analysis unit. The summarization unit analyzes the heart rate of the salesperson analyzed by the heart rate analysis unit. The inconsistency confirmation unit confirms the contract document summarized by the summarization unit and the explanation content. [Effects of the Invention]

[0007] The system according to the embodiment can determine the authenticity of the explanation given by the salesperson and check for inconsistencies between the contents of the contract document and the explanation given. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A service according to an embodiment of the present invention enables consumers to more accurately determine the veracity of a salesperson's statements and quickly understand the contents of a contract. This service uses a generation AI to analyze information acquired from a smartphone camera, review the contract document, and analyze the salesperson's facial expressions and heart rate to determine in real time whether the salesperson is lying and present the results to the consumer as a percentage. It is also possible to summarize the contract document and the salesperson's explanation, and warn the user to check for inconsistencies that require additional questions before entering into the contract. This allows consumers to more accurately determine the veracity of a salesperson's statements and quickly understand the contents of the contract.

[0029] The service according to the embodiment includes a contract document verification unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document verification unit verifies the contract document. For example, the contract document verification unit reads the contract document using a smartphone camera and analyzes its contents. The contract document verification unit analyzes the contract contents based on image data of the contract document. The facial expression analysis unit analyzes the contents of the contract document verified by the contract document verification unit. For example, the facial expression analysis unit analyzes the facial expression of the salesperson in real time using the smartphone camera. The facial expression analysis unit determines the possibility of lying based on video data of the salesperson. The heart rate analysis unit analyzes the facial expression of the salesperson analyzed by the facial expression analysis unit. For example, the heart rate analysis unit analyzes the heart rate of the salesperson in real time using the smartphone camera. The heart rate analysis unit determines the possibility of lying based on heart rate data of the salesperson. The summarization unit analyzes the heart rate of the salesperson analyzed by the heart rate analysis unit. For example, the summarization unit summarizes the contract document and the explanations of the salesperson and provides them to the user. The summarization unit generates a summary based on image data of the contract document and voice data of the salesperson. The inconsistency checking unit checks the contract document summarized by the summarization unit and the explanations. For example, the inconsistency checking unit identifies inconsistencies between the contract document and the explanations of the salesperson and issues a warning to the user. The inconsistency checking unit identifies inconsistencies based on image data of the contract document and voice data of the salesperson. This allows the service according to the embodiment to enable consumers to more accurately determine the veracity of the salesperson's statements and quickly understand the contract contents. For example, when a consumer purchases a car or real estate, the service allows the consumer to check in real time whether the explanations of the salesperson are accurate and to identify inconsistencies in the contract document in advance. This allows consumers to make high-value transactions with peace of mind.

[0030] The contract verification unit understands the context of the contract and can identify high-risk clauses by comparing them with similar past contracts. For example, the generation AI analyzes the context of the contract and identifies high-risk clauses by comparing them with similar past contracts. For example, it focuses on analyzing clauses regarding cancellation conditions and penalty fees. In addition, to understand the context of the contract, the generation AI refers to past court decisions and legal documents to identify high-risk clauses. For example, it detects clauses that may violate consumer protection laws. This allows consumers to understand the risks of the contract in advance by identifying high-risk clauses.

[0031] The contract verification unit can compare the contents of the contract with the opinions of legal experts and automatically assess the legal risks. For example, the generation AI in the contract verification unit analyzes the contents of the contract and evaluates the legal risks by comparing them with the opinions of legal experts. For example, it checks whether the clauses in the contract violate the law. The contract verification unit also compares the contents of the contract with the opinions of legal experts and the generation AI automatically assesses the legal risks. For example, it performs risk assessment based on the Consumer Protection Act. This automatic assessment of legal risks allows consumers to understand the legal risks of the contract in advance.

[0032] The contract verification unit can combine the verification of the contract document with the reading out of the contract contents via voice input, making it possible to accommodate visually impaired persons. For example, the contract verification unit uses generation AI to analyze the contract document and provides a function to read out the contract contents via voice input. For example, this allows visually impaired persons to verify the contract contents by voice. The contract verification unit can also combine the verification of the contract document with voice input to build a system that also accommodates visually impaired persons. For example, a function to read out the contract contents by voice can be added. This allows visually impaired persons to verify the contract contents by voice.

[0033] The contract verification unit automatically translates the contents of a contract based on the laws of different jurisdictions, making it possible to support international transactions. For example, the contract verification unit uses generation AI to analyze a contract and automatically translate it based on the laws of different jurisdictions. For example, it provides contracts in multiple languages ​​to support international transactions. The contract verification unit also automatically translates the contents of a contract based on the laws of different jurisdictions, building a system that also supports international transactions. For example, it translates into multiple languages ​​such as English and Chinese. This makes it possible to provide contracts in multiple languages ​​to support international transactions.

[0034] The facial expression analysis unit analyzes the salesperson's tone of voice and speaking patterns, enabling it to determine with even greater accuracy the possibility that they are lying. For example, the facial expression analysis unit allows the generation AI to analyze the salesperson's tone of voice and speaking patterns to determine with greater accuracy the possibility that they are lying. For example, it detects voice tremors and changes in speaking style. The facial expression analysis unit also analyzes the salesperson's tone of voice and speaking patterns to determine with greater accuracy the possibility that they are lying. For example, it analyzes changes in voice pitch and speed. In this way, by analyzing the salesperson's tone of voice and speaking patterns, it is possible to determine with greater accuracy the possibility that they are lying.

[0035] The facial expression analysis unit can analyze the salesperson's past speech history and identify inconsistent speech. For example, the generation AI analyzes the salesperson's past speech history and identify inconsistent speech. For example, it compares past speech with current speech to detect inconsistencies. The facial expression analysis unit can also analyze the salesperson's past speech history and identify inconsistent speech. For example, it compares past speech with current speech to detect inconsistencies. In this way, by analyzing the salesperson's past speech history, inconsistent speech can be identified.

[0036] The facial expression analysis unit also includes the salesperson's physical movements and posture in its analysis, making it possible to determine the possibility of a lie from multiple angles. For example, the generation AI of the facial expression analysis unit analyzes the salesperson's physical movements and posture to determine the possibility of a lie from multiple angles. For example, it detects changes in hand movements and posture. The facial expression analysis unit also includes the salesperson's physical movements and posture in its analysis, making it possible to determine the possibility of a lie from multiple angles. For example, it detects changes in hand movements and posture. In this way, by including the salesperson's physical movements and posture in its analysis, it is possible to determine the possibility of a lie from multiple angles.

[0037] The facial expression analysis unit translates what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, the facial expression analysis unit uses a generation AI to translate what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, it translates into English or Chinese. The facial expression analysis unit also translates what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, it translates into English or Chinese. This makes it possible to accommodate users who speak foreign languages, making it possible to handle international transactions.

[0038] The summary section, when generating a summary of a contract document, can highlight important differences compared to summaries of similar past contracts. For example, the summary section may highlight important differences when the generation AI generates a summary of a contract document and compares it with summaries of similar past contracts. For example, it may clearly indicate differences in cancellation terms and penalty fees. Furthermore, when generating a summary of a contract document, the summary section may highlight important differences compared to summaries of similar past contracts. For example, it may clearly indicate differences in cancellation terms and penalty fees. This makes it easier for consumers to grasp the key points of the contract by highlighting important differences compared to similar past contracts.

[0039] The summarization unit can customize the summary content according to the user's level of understanding and replace technical terms with simpler language. For example, the generation AI generates a summary of a contract document and replaces technical terms with simpler language according to the user's level of understanding. For example, it converts legal terms into general language. The summarization unit also customizes the summary content according to the user's level of understanding and the generation AI replaces technical terms with simpler language. For example, it converts technical terms into simpler language. In this way, the summary content can be customized according to the user's level of understanding, making the contract content easier to understand.

[0040] The summarization unit can add a function to read the summary content aloud, making it possible to accommodate visually impaired people. For example, the summarization unit can add a function that allows the generation AI to generate a summary of a contract document and read it aloud. For example, this allows visually impaired people to check the contract content aloud. The summarization unit can also add a function to read the summary content aloud, making it possible for the generation AI to check the contract content aloud. For example, this allows visually impaired people to check the contract content aloud.

[0041] The summarization unit can automatically translate the summary content into different languages, making it possible to handle international transactions. For example, the summarization unit uses a generation AI to generate a summary of a contract document and automatically translate it into different languages. For example, the summary can be provided in multiple languages ​​to handle international transactions. The summarization unit can also automatically translate the summary content into different languages, making it possible for the generation AI to handle international transactions. For example, the summary can be translated into English or Chinese. This makes it possible to provide the summary in multiple languages ​​to handle international transactions.

[0042] The inconsistency confirmation unit cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, the generation AI cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. The inconsistency confirmation unit also cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. This makes it possible to automatically identify inconsistencies by cross-referencing the contract document with the explanation given by the salesperson.

[0043] When identifying inconsistencies, the inconsistency checking unit can prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, the inconsistency checking unit uses the generation AI to identify inconsistencies and prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, it prioritizes warning of inconsistencies in significant penalty clauses. Furthermore, when identifying inconsistencies, the inconsistency checking unit uses the generation AI to prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, it prioritizes warning of inconsistencies in significant penalty clauses. In this way, by prioritizing warning of inconsistencies that are riskier, consumers can understand the risks of the contract in advance.

[0044] The inconsistency checking unit can add a function to warn of inconsistent content by voice, making it possible to accommodate visually impaired people. The inconsistency checking unit can add a function, for example, in which the generation AI identifies inconsistent content and warns of it by voice. For example, this allows visually impaired people to confirm inconsistent content by voice. The inconsistency checking unit can also add a function to warn of inconsistent content by voice, making it possible for the generation AI to accommodate visually impaired people. For example, it warns of inconsistent content by voice. This allows visually impaired people to confirm inconsistent content by voice.

[0045] The inconsistency checking unit automatically translates inconsistent content into a different language, making it possible to support international transactions. For example, the generation AI identifies inconsistent content and automatically translates it into a different language. For example, the inconsistent content is provided in multiple languages ​​to support international transactions. The inconsistency checking unit also automatically translates inconsistent content into a different language, making it possible for the generation AI to support international transactions. For example, the inconsistency checking unit translates inconsistent content into English or Chinese. This makes it possible to provide inconsistent content in multiple languages ​​to support international transactions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The contract verification unit can automatically translate the contents of a contract based on the laws of different jurisdictions, making it possible to support international transactions. For example, a generation AI analyzes a contract and automatically translates it based on the laws of different jurisdictions. For example, contracts can be provided in multiple languages ​​to support international transactions. The contract verification unit can also automatically translate the contents of a contract based on the laws of different jurisdictions, building a system that can also support international transactions. For example, it can translate into multiple languages, such as English and Chinese. This makes it possible to provide contracts in multiple languages ​​to support international transactions.

[0048] The facial expression analysis unit analyzes the salesperson's tone of voice and speaking patterns, enabling even more accurate determination of the possibility that they are lying. For example, the generation AI analyzes the salesperson's tone of voice and speaking patterns to determine with high accuracy the possibility that they are lying. For example, it detects voice tremors and changes in speaking style. The facial expression analysis unit also analyzes the salesperson's tone of voice and speaking patterns to determine with high accuracy the possibility that they are lying. For example, it analyzes changes in voice pitch and speed. In this way, by analyzing the salesperson's tone of voice and speaking patterns, it is possible to determine with high accuracy the possibility that they are lying.

[0049] When generating a summary of a contract document, the summary section can highlight important differences by comparing it with summaries of similar past contracts. For example, the generation AI may generate a summary of a contract document and highlight important differences by comparing it with summaries of similar past contracts. For example, differences in cancellation terms and penalty fees may be clearly indicated. Also, when generating a summary of a contract document, the generation AI may highlight important differences by comparing it with summaries of similar past contracts. For example, differences in cancellation terms and penalty fees may be clearly indicated. This makes it easier for consumers to grasp the key points of the contract by highlighting important differences by comparing it with summaries of similar past contracts.

[0050] The inconsistency checking unit cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, the generation AI cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. The inconsistency checking unit also cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. This makes it possible to automatically identify inconsistencies by cross-referencing the contract document with the explanation given by the salesperson.

[0051] The contract verification unit can combine the verification of the contract document with the reading of the contract contents via voice input, making it possible to accommodate visually impaired people. For example, the generation AI analyzes the contract document and provides a function to read the contract contents via voice input. For example, this allows visually impaired people to verify the contract contents by voice. The contract verification unit can also combine the verification of the contract document with voice input to create a system that also accommodates visually impaired people. For example, a function to read the contract contents by voice can be added. This allows visually impaired people to verify the contract contents by voice.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The contract document verification unit verifies the contract document. For example, the contract document verification unit reads the contract document using a smartphone camera and analyzes its contents. The contract document verification unit analyzes the contract contents based on the image data of the contract document. Step 2: The facial expression analysis unit analyzes the contents of the contract document confirmed by the contract document confirmation unit. For example, the facial expression analysis unit analyzes the salesperson's facial expressions in real time using a smartphone camera. The facial expression analysis unit determines the possibility of a lie based on the salesperson's video data. Step 3: The heart rate analysis unit analyzes the salesperson's facial expression analyzed by the facial expression analysis unit. For example, the heart rate analysis unit analyzes the salesperson's heart rate in real time using a smartphone camera. The heart rate analysis unit determines the possibility of a lie based on the salesperson's heart rate data. Step 4: The summarization unit analyzes the salesperson's heart rate analyzed by the heart rate analysis unit. For example, the summarization unit summarizes the contract document and the salesperson's explanation and provides it to the user. The summarization unit generates a summary based on image data of the contract document and voice data of the salesperson. Step 5: The inconsistency checking unit checks the contract document summarized by the summarizing unit and the explanation. For example, if there are inconsistencies in the contract document or the salesperson's explanation, the inconsistency checking unit identifies them and issues a warning to the user. The inconsistency checking unit identifies inconsistencies based on the image data of the contract document and the salesperson's voice data.

[0054] (Example 2) A service according to an embodiment of the present invention enables consumers to more accurately determine the veracity of a salesperson's statements and quickly understand the contents of a contract. This service uses a generation AI to analyze information acquired from a smartphone camera, review the contract document, and analyze the salesperson's facial expressions and heart rate to determine in real time whether the salesperson is lying and present the results to the consumer as a percentage. It is also possible to summarize the contract document and the salesperson's explanation, and warn the user to check for inconsistencies that require additional questions before entering into the contract. This allows consumers to more accurately determine the veracity of a salesperson's statements and quickly understand the contents of the contract.

[0055] The service according to the embodiment includes a contract document verification unit, a facial expression analysis unit, a heart rate analysis unit, a summarization unit, and an inconsistency confirmation unit. The contract document verification unit verifies the contract document. For example, the contract document verification unit reads the contract document using a smartphone camera and analyzes its contents. The contract document verification unit analyzes the contract contents based on image data of the contract document. The facial expression analysis unit analyzes the contents of the contract document verified by the contract document verification unit. For example, the facial expression analysis unit analyzes the facial expression of the salesperson in real time using the smartphone camera. The facial expression analysis unit determines the possibility of lying based on video data of the salesperson. The heart rate analysis unit analyzes the facial expression of the salesperson analyzed by the facial expression analysis unit. For example, the heart rate analysis unit analyzes the heart rate of the salesperson in real time using the smartphone camera. The heart rate analysis unit determines the possibility of lying based on heart rate data of the salesperson. The summarization unit analyzes the heart rate of the salesperson analyzed by the heart rate analysis unit. For example, the summarization unit summarizes the contract document and the explanations of the salesperson and provides them to the user. The summarization unit generates a summary based on image data of the contract document and voice data of the salesperson. The inconsistency checking unit checks the contract document summarized by the summarization unit and the explanations. For example, the inconsistency checking unit identifies inconsistencies between the contract document and the explanations of the salesperson and issues a warning to the user. The inconsistency checking unit identifies inconsistencies based on image data of the contract document and voice data of the salesperson. This allows the service according to the embodiment to enable consumers to more accurately determine the veracity of the salesperson's statements and quickly understand the contract contents. For example, when a consumer purchases a car or real estate, the service allows the consumer to check in real time whether the explanations of the salesperson are accurate and to identify inconsistencies in the contract document in advance. This allows consumers to make high-value transactions with peace of mind.

[0056] The contract verification unit understands the context of the contract and can identify high-risk clauses by comparing them with similar past contracts. For example, the generation AI analyzes the context of the contract and identifies high-risk clauses by comparing them with similar past contracts. For example, it focuses on analyzing clauses regarding cancellation conditions and penalty fees. In addition, to understand the context of the contract, the generation AI refers to past court decisions and legal documents to identify high-risk clauses. For example, it detects clauses that may violate consumer protection laws. This allows consumers to understand the risks of the contract in advance by identifying high-risk clauses.

[0057] The contract verification unit can compare the contents of the contract with the opinions of legal experts and automatically assess the legal risks. For example, the generation AI in the contract verification unit analyzes the contents of the contract and evaluates the legal risks by comparing them with the opinions of legal experts. For example, it checks whether the clauses in the contract violate the law. The contract verification unit also compares the contents of the contract with the opinions of legal experts and the generation AI automatically assesses the legal risks. For example, it performs risk assessment based on the Consumer Protection Act. This automatic assessment of legal risks allows consumers to understand the legal risks of the contract in advance.

[0058] The contract document verification unit uses the emotion estimation function to analyze the emotional reactions of users who read the contract document and can highlight parts that cause particular anxiety. In the contract document verification unit, for example, the generation AI analyzes the contract document and uses the emotion estimation function to analyze the user's emotional reactions. For example, clauses that cause anxiety are highlighted. The contract document verification unit also uses the emotion estimation function to analyze the emotional reactions of users who read the contract document in real time and highlight parts that cause anxiety. For example, clauses regarding cancellation conditions and penalty fees are highlighted. In this way, by highlighting parts that cause anxiety to the user, consumers can more clearly understand the risks of the contract.

[0059] The contract verification unit can combine the verification of the contract document with the reading out of the contract contents via voice input, making it possible to accommodate visually impaired persons. For example, the contract verification unit uses generation AI to analyze the contract document and provides a function to read out the contract contents via voice input. For example, this allows visually impaired persons to verify the contract contents by voice. The contract verification unit can also combine the verification of the contract document with voice input to build a system that also accommodates visually impaired persons. For example, a function to read out the contract contents by voice can be added. This allows visually impaired persons to verify the contract contents by voice.

[0060] The contract verification unit automatically translates the contents of a contract based on the laws of different jurisdictions, making it possible to support international transactions. For example, the contract verification unit uses generation AI to analyze a contract and automatically translate it based on the laws of different jurisdictions. For example, it provides contracts in multiple languages ​​to support international transactions. The contract verification unit also automatically translates the contents of a contract based on the laws of different jurisdictions, building a system that also supports international transactions. For example, it translates into multiple languages ​​such as English and Chinese. This makes it possible to provide contracts in multiple languages ​​to support international transactions.

[0061] The contract document confirmation unit can use the emotion estimation function to monitor the user's emotions regarding the contents of the contract document in real time and make suggestions that elicit positive emotions. For example, the contract document confirmation unit uses the generation AI to analyze the contract document and the emotion estimation function to monitor the user's emotions in real time. For example, it makes suggestions that elicit positive emotions. The contract document confirmation unit also uses the emotion estimation function to monitor the user's emotions regarding the contents of the contract in real time and make suggestions that elicit positive emotions. For example, it emphasizes the positive aspects of the contract content. In this way, by monitoring the user's emotions in real time and making suggestions that elicit positive emotions, it is possible to provide a sense of security regarding the contract.

[0062] The facial expression analysis unit analyzes the salesperson's tone of voice and speaking patterns, enabling it to determine with even greater accuracy the possibility that they are lying. For example, the facial expression analysis unit allows the generation AI to analyze the salesperson's tone of voice and speaking patterns to determine with greater accuracy the possibility that they are lying. For example, it detects voice tremors and changes in speaking style. The facial expression analysis unit also analyzes the salesperson's tone of voice and speaking patterns to determine with greater accuracy the possibility that they are lying. For example, it analyzes changes in voice pitch and speed. In this way, by analyzing the salesperson's tone of voice and speaking patterns, it is possible to determine with greater accuracy the possibility that they are lying.

[0063] The facial expression analysis unit can analyze the salesperson's past speech history and identify inconsistent speech. For example, the generation AI analyzes the salesperson's past speech history and identify inconsistent speech. For example, it compares past speech with current speech to detect inconsistencies. The facial expression analysis unit can also analyze the salesperson's past speech history and identify inconsistent speech. For example, it compares past speech with current speech to detect inconsistencies. In this way, by analyzing the salesperson's past speech history, inconsistent speech can be identified.

[0064] The facial expression analysis unit can use the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user based on changes in the salesperson's facial expression and heart rate. For example, the generative AI in the facial expression analysis unit analyzes changes in the salesperson's facial expression and heart rate, and then uses the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user. For example, it detects increases in heart rate and changes in facial expression. The facial expression analysis unit also uses the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user based on changes in the salesperson's facial expression and heart rate. For example, it detects increases in heart rate and changes in facial expression. This allows the consumer to more clearly understand the risks of the contract by providing real-time feedback on the anxiety and doubt felt by the user.

[0065] The facial expression analysis unit also includes the salesperson's physical movements and posture in its analysis, making it possible to determine the possibility of a lie from multiple angles. For example, the generation AI of the facial expression analysis unit analyzes the salesperson's physical movements and posture to determine the possibility of a lie from multiple angles. For example, it detects changes in hand movements and posture. The facial expression analysis unit also includes the salesperson's physical movements and posture in its analysis, making it possible to determine the possibility of a lie from multiple angles. For example, it detects changes in hand movements and posture. In this way, by including the salesperson's physical movements and posture in its analysis, it is possible to determine the possibility of a lie from multiple angles.

[0066] The facial expression analysis unit translates what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, the facial expression analysis unit uses a generation AI to translate what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, it translates into English or Chinese. The facial expression analysis unit also translates what the salesperson says in real time, making it possible to accommodate users who speak foreign languages. For example, it translates into English or Chinese. This makes it possible to accommodate users who speak foreign languages, making it possible to handle international transactions.

[0067] The facial expression analysis unit uses the emotion estimation function to provide important information to the user at a time when they feel most at ease, based on changes in the salesperson's facial expression and heart rate. For example, the generation AI in the facial expression analysis unit analyzes changes in the salesperson's facial expression and heart rate, and uses the emotion estimation function to provide important information to the user at a time when they feel most at ease. For example, important information is presented when the heart rate is stable. The facial expression analysis unit also uses the emotion estimation function to provide important information to the user at a time when they feel most at ease, based on changes in the salesperson's facial expression and heart rate. For example, important information is presented when the heart rate is stable. This provides important information to the user at a time when they feel most at ease, giving them peace of mind about the contract.

[0068] The summary section, when generating a summary of a contract document, can highlight important differences compared to summaries of similar past contracts. For example, the summary section may highlight important differences when the generation AI generates a summary of a contract document and compares it with summaries of similar past contracts. For example, it may clearly indicate differences in cancellation terms and penalty fees. Furthermore, when generating a summary of a contract document, the summary section may highlight important differences compared to summaries of similar past contracts. For example, it may clearly indicate differences in cancellation terms and penalty fees. This makes it easier for consumers to grasp the key points of the contract by highlighting important differences compared to similar past contracts.

[0069] The summarization unit can customize the summary content according to the user's level of understanding and replace technical terms with simpler language. For example, the generation AI generates a summary of a contract document and replaces technical terms with simpler language according to the user's level of understanding. For example, it converts legal terms into general language. The summarization unit also customizes the summary content according to the user's level of understanding and the generation AI replaces technical terms with simpler language. For example, it converts technical terms into simpler language. In this way, the summary content can be customized according to the user's level of understanding, making the contract content easier to understand.

[0070] The summarization unit uses the emotion estimation function to analyze the user's emotional response to the summary content and can provide a detailed explanation of any parts that cause particular anxiety. For example, the summarization unit generates a summary of a contract document using the generation AI and analyzes the user's emotional response using the emotion estimation function. For example, it provides a detailed explanation of any parts that cause anxiety. The summarization unit also uses the emotion estimation function to analyze the user's emotional response to the summary content in real time and provides a detailed explanation of any parts that cause anxiety. For example, it provides a detailed explanation of cancellation conditions and penalty clauses. This makes it easier for the user to understand the contract content by providing a detailed explanation of any parts that cause anxiety.

[0071] The summarization unit can add a function to read the summary content aloud, making it possible to accommodate visually impaired people. For example, the summarization unit can add a function that allows the generation AI to generate a summary of a contract document and read it aloud. For example, this allows visually impaired people to check the contract content aloud. The summarization unit can also add a function to read the summary content aloud, making it possible for the generation AI to check the contract content aloud. For example, this allows visually impaired people to check the contract content aloud.

[0072] The summarization unit can automatically translate the summary content into different languages, making it possible to handle international transactions. For example, the summarization unit uses a generation AI to generate a summary of a contract document and automatically translate it into different languages. For example, the summary can be provided in multiple languages ​​to handle international transactions. The summarization unit can also automatically translate the summary content into different languages, making it possible for the generation AI to handle international transactions. For example, the summary can be translated into English or Chinese. This makes it possible to provide the summary in multiple languages ​​to handle international transactions.

[0073] The summarization unit can use the emotion estimation function to monitor the user's emotions regarding the summary content in real time and make suggestions that elicit positive emotions. For example, the summarization unit uses the generation AI to generate a summary of a contract document and monitors the user's emotions in real time using the emotion estimation function. For example, it makes suggestions that elicit positive emotions. The summarization unit also uses the emotion estimation function to monitor the user's emotions regarding the summary content in real time and make suggestions that elicit positive emotions. For example, it emphasizes the positive aspects of the contract content. In this way, by monitoring the user's emotions in real time and making suggestions that elicit positive emotions, it is possible to provide a sense of security regarding the contract.

[0074] The inconsistency confirmation unit cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, the generation AI cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. The inconsistency confirmation unit also cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. This makes it possible to automatically identify inconsistencies by cross-referencing the contract document with the explanation given by the salesperson.

[0075] When identifying inconsistencies, the inconsistency checking unit can prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, the inconsistency checking unit uses the generation AI to identify inconsistencies and prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, it prioritizes warning of inconsistencies in significant penalty clauses. Furthermore, when identifying inconsistencies, the inconsistency checking unit uses the generation AI to prioritize warning of inconsistencies that are riskier compared to similar cases in the past. For example, it prioritizes warning of inconsistencies in significant penalty clauses. In this way, by prioritizing warning of inconsistencies that are riskier, consumers can understand the risks of the contract in advance.

[0076] The inconsistency confirmation unit can use the emotion estimation function to analyze the user's emotional reaction to inconsistent content and highlight parts that cause particular anxiety. For example, the inconsistency confirmation unit uses the generation AI to identify inconsistent content and analyze the user's emotional reaction using the emotion estimation function. For example, it highlights parts that cause anxiety. The inconsistency confirmation unit also uses the emotion estimation function to analyze the user's emotional reaction to inconsistent content in real time and highlights parts that cause anxiety. For example, it highlights inconsistencies in serious penalty clauses. In this way, by highlighting parts that cause the user anxiety, consumers can more clearly understand the risks of the contract.

[0077] The inconsistency checking unit can add a function to warn of inconsistent content by voice, making it possible to accommodate visually impaired people. The inconsistency checking unit can add a function, for example, in which the generation AI identifies inconsistent content and warns of it by voice. For example, this allows visually impaired people to confirm inconsistent content by voice. The inconsistency checking unit can also add a function to warn of inconsistent content by voice, making it possible for the generation AI to accommodate visually impaired people. For example, it warns of inconsistent content by voice. This allows visually impaired people to confirm inconsistent content by voice.

[0078] The inconsistency checking unit automatically translates inconsistent content into a different language, making it possible to support international transactions. For example, the generation AI identifies inconsistent content and automatically translates it into a different language. For example, the inconsistent content is provided in multiple languages ​​to support international transactions. The inconsistency checking unit also automatically translates inconsistent content into a different language, making it possible for the generation AI to support international transactions. For example, the inconsistency checking unit translates inconsistent content into English or Chinese. This makes it possible to provide inconsistent content in multiple languages ​​to support international transactions.

[0079] The inconsistency confirmation unit can use the emotion estimation function to monitor the user's emotions regarding inconsistent content in real time and make suggestions that elicit positive emotions. For example, the inconsistency confirmation unit uses the emotion estimation function to identify inconsistent content using the generation AI and monitor the user's emotions in real time. For example, it makes suggestions that elicit positive emotions. The inconsistency confirmation unit also uses the emotion estimation function to monitor the user's emotions regarding inconsistent content in real time and make suggestions that elicit positive emotions. For example, it emphasizes the positive aspects of the inconsistent content. In this way, by monitoring the user's emotions in real time and making suggestions that elicit positive emotions, it is possible to provide a sense of security regarding the contract.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The contract verification unit can automatically translate the contents of a contract based on the laws of different jurisdictions, making it possible to support international transactions. For example, a generation AI analyzes a contract and automatically translates it based on the laws of different jurisdictions. For example, contracts can be provided in multiple languages ​​to support international transactions. The contract verification unit can also automatically translate the contents of a contract based on the laws of different jurisdictions, building a system that can also support international transactions. For example, it can translate into multiple languages, such as English and Chinese. This makes it possible to provide contracts in multiple languages ​​to support international transactions.

[0082] The facial expression analysis unit analyzes the salesperson's tone of voice and speaking patterns, enabling even more accurate determination of the possibility that they are lying. For example, the generation AI analyzes the salesperson's tone of voice and speaking patterns to determine with high accuracy the possibility that they are lying. For example, it detects voice tremors and changes in speaking style. The facial expression analysis unit also analyzes the salesperson's tone of voice and speaking patterns to determine with high accuracy the possibility that they are lying. For example, it analyzes changes in voice pitch and speed. In this way, by analyzing the salesperson's tone of voice and speaking patterns, it is possible to determine with high accuracy the possibility that they are lying.

[0083] When generating a summary of a contract document, the summary section can highlight important differences by comparing it with summaries of similar past contracts. For example, the generation AI may generate a summary of a contract document and highlight important differences by comparing it with summaries of similar past contracts. For example, differences in cancellation terms and penalty fees may be clearly indicated. Also, when generating a summary of a contract document, the generation AI may highlight important differences by comparing it with summaries of similar past contracts. For example, differences in cancellation terms and penalty fees may be clearly indicated. This makes it easier for consumers to grasp the key points of the contract by highlighting important differences by comparing it with summaries of similar past contracts.

[0084] The inconsistency checking unit cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, the generation AI cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. The inconsistency checking unit also cross-references the contract document with the explanation given by the salesperson and can automatically identify inconsistencies. For example, it detects cases where the clauses in the contract do not match the explanation given by the salesperson. This makes it possible to automatically identify inconsistencies by cross-referencing the contract document with the explanation given by the salesperson.

[0085] The contract document verification unit uses the emotion estimation function to analyze the emotional reactions of users who read the contract document and highlights parts that cause particular anxiety. For example, the generation AI analyzes the contract document and uses the emotion estimation function to analyze the user's emotional reactions. For example, clauses that cause anxiety are highlighted. The contract document verification unit also uses the emotion estimation function to analyze the emotional reactions of users who read the contract document in real time and highlight parts that cause anxiety. For example, clauses regarding cancellation conditions and penalty fees are highlighted. In this way, by highlighting parts that cause anxiety to the user, consumers can more clearly understand the risks of the contract.

[0086] The facial expression analysis unit can use the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user based on changes in the salesperson's facial expression and heart rate. For example, the generation AI analyzes the salesperson's facial expression and changes in heart rate, and uses the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user. For example, it detects increases in heart rate and changes in facial expression. The facial expression analysis unit also uses the emotion estimation function to provide real-time feedback on the anxiety and doubt felt by the user based on changes in the salesperson's facial expression and heart rate. For example, it detects increases in heart rate and changes in facial expression. This allows the consumer to more clearly understand the risks of the contract by providing real-time feedback on the anxiety and doubt felt by the user.

[0087] The summarization unit uses the emotion estimation function to analyze the user's emotional response to the summary content and can provide a detailed explanation of any parts that cause particular anxiety. For example, the generation AI generates a summary of a contract document and uses the emotion estimation function to analyze the user's emotional response. For example, it provides a detailed explanation of any parts that cause anxiety. The summarization unit also uses the emotion estimation function to analyze the user's emotional response to the summary content in real time and provides a detailed explanation of any parts that cause anxiety. For example, it provides a detailed explanation of cancellation conditions and penalty clauses. This makes the contract content easier to understand by providing a detailed explanation of any parts that cause anxiety to the user.

[0088] The inconsistency confirmation unit can use the emotion estimation function to analyze the user's emotional reaction to inconsistent content and highlight parts that cause particular anxiety. For example, the generation AI identifies inconsistent content and uses the emotion estimation function to analyze the user's emotional reaction. For example, parts that cause anxiety are highlighted. The inconsistency confirmation unit also uses the emotion estimation function to analyze the user's emotional reaction to inconsistent content in real time and highlight parts that cause anxiety. For example, it highlights inconsistencies in serious penalty clauses. In this way, by highlighting parts that cause anxiety to the user, consumers can more clearly understand the risks of the contract.

[0089] The inconsistency confirmation unit can use the emotion estimation function to monitor the user's emotions regarding inconsistent content in real time and make suggestions that elicit positive emotions. For example, the generation AI identifies inconsistent content and uses the emotion estimation function to monitor the user's emotions in real time. For example, it makes suggestions that elicit positive emotions. The inconsistency confirmation unit also uses the emotion estimation function to monitor the user's emotions regarding inconsistent content in real time and make suggestions that elicit positive emotions. For example, it emphasizes the positive aspects of the inconsistent content. In this way, by monitoring the user's emotions in real time and making suggestions that elicit positive emotions, it is possible to provide a sense of security regarding the contract.

[0090] The contract verification unit can combine the verification of the contract document with the reading of the contract contents via voice input, making it possible to accommodate visually impaired people. For example, the generation AI analyzes the contract document and provides a function to read the contract contents via voice input. For example, this allows visually impaired people to verify the contract contents by voice. The contract verification unit can also combine the verification of the contract document with voice input to create a system that also accommodates visually impaired people. For example, a function to read the contract contents by voice can be added. This allows visually impaired people to verify the contract contents by voice.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The contract document verification unit verifies the contract document. For example, the contract document verification unit reads the contract document using a smartphone camera and analyzes its contents. The contract document verification unit analyzes the contract contents based on the image data of the contract document. Step 2: The facial expression analysis unit analyzes the contents of the contract document confirmed by the contract document confirmation unit. For example, the facial expression analysis unit analyzes the salesperson's facial expressions in real time using a smartphone camera. The facial expression analysis unit determines the possibility of a lie based on the salesperson's video data. Step 3: The heart rate analysis unit analyzes the salesperson's facial expression analyzed by the facial expression analysis unit. For example, the heart rate analysis unit analyzes the salesperson's heart rate in real time using a smartphone camera. The heart rate analysis unit determines the possibility of a lie based on the salesperson's heart rate data. Step 4: The summarization unit analyzes the salesperson's heart rate analyzed by the heart rate analysis unit. For example, the summarization unit summarizes the contract document and the salesperson's explanation and provides it to the user. The summarization unit generates a summary based on image data of the contract document and voice data of the salesperson. Step 5: The inconsistency checking unit checks the contract document summarized by the summarizing unit and the explanation. For example, if there are inconsistencies in the contract document or the salesperson's explanation, the inconsistency checking unit identifies them and issues a warning to the user. The inconsistency checking unit identifies inconsistencies based on the image data of the contract document and the salesperson's voice data.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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 contract document verification department that verifies the contract document; a facial expression analysis unit that analyzes the content of the contract document confirmed by the contract document confirmation unit; a heart rate analysis unit that analyzes the facial expression of the salesperson analyzed by the facial expression analysis unit; a summarizing unit that analyzes the heart rate of the salesperson analyzed by the heart rate analyzing unit; and an inconsistency checking unit that checks the contract document summarized by the summarizing unit against the contents of the explanation. A system characterized by:

2. The contract document verification unit Understand the context of the contract and compare it with similar contracts to identify high-risk clauses 2. The system of claim 1.

3. The contract document verification unit The contents of the contract are compared with the opinions of legal experts to automatically assess legal risks.

2. The system of claim 1.

4. The contract document verification unit Analyze the emotional response of the user who reads the contract document and highlight any parts that cause anxiety 2. The system of claim 1.

5. The contract document verification unit The confirmation of the contract document is combined with voice input to read out the contract contents, making it suitable for visually impaired people.

2. The system of claim 1.

6. The contract document verification unit The contents of the contract document are automatically translated based on the laws of the different jurisdictions, and international transactions are also supported.

2. The system of claim 1.

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