System

Abstract

An object of a system according to an embodiment is to evaluate a security vulnerability of a smart contract and a difference from advertisement content.SOLUTION: A system includes a code review unit and an evaluation unit. The code review unit analyzes the code of the smart contract. The evaluation unit evaluates a security vulnerability and a difference from advertisement content on the basis of the code of the smart contract analyzed by the code review unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for ordinary investors to distinguish between security vulnerabilities in smart contracts and discrepancies with advertised content.

[0005] The system according to the embodiment aims to evaluate the security vulnerabilities of smart contracts and discrepancies with their advertised content. [Means for solving the problem]

[0006] A system according to an embodiment includes a code review unit and an evaluation unit. The code review unit analyzes the code of a smart contract. The evaluation unit evaluates security vulnerabilities and discrepancies with advertised content based on the code of the smart contract analyzed by the code review unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the security vulnerabilities of smart contracts and discrepancies with their advertised content. [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 security assessment system according to an embodiment of the present invention is a system that reviews the code of smart contracts deployed on a blockchain and evaluates security vulnerabilities and discrepancies with advertised content. In this way, the security assessment system can analyze the code of smart contracts and protect the interests of investors.

[0029] A security evaluation system according to an embodiment includes a code review unit and an evaluation unit. The code review unit analyzes smart contract code. For example, the generation AI analyzes smart contract code deployed on a blockchain to detect security vulnerabilities. The generation AI can also identify bugs and areas of potential fraudulent operation in the code. The generation AI can also perform static analysis of the code to detect potential vulnerabilities. The evaluation unit evaluates security vulnerabilities and discrepancies with advertised content based on the smart contract code analyzed by the code review unit. For example, the generation AI evaluates whether the content of the smart contract matches the advertised content. The generation AI can also verify whether the services and functions provided by the smart contract operate as advertised. The generation AI can also detect whether advertising related to the smart contract is fraudulent. This allows the security evaluation system to analyze smart contract code and evaluate security vulnerabilities and discrepancies with advertised content.

[0030] The code review unit can analyze the change history of the smart contract's code and identify how past changes affect current vulnerabilities. The code review unit can, for example, use generative AI to analyze the change history of the smart contract's code and identify how past changes affect current vulnerabilities. For example, to detect whether a change introduced in a particular version causes a vulnerability in a later version. Generative AI can also be used to analyze the change history of the code and identify how past changes affect current vulnerabilities. For example, to identify whether a particular commit causes a vulnerability. Generative AI can also be used to analyze the change history of the code and identify how past changes affect current vulnerabilities. For example, to detect whether a specific change causes a vulnerability. This makes it possible to analyze the change history of the code and identify how past changes affect current vulnerabilities.

[0031] The code review unit can perform a code execution simulation to understand the semantics of the smart contract code and predict potential vulnerabilities. The code review unit can, for example, use a generative AI to perform a code execution simulation to understand the semantics of the smart contract code and predict potential vulnerabilities. For example, the code behavior under specific conditions is simulated to detect vulnerabilities. The code review unit can also perform a code execution simulation using a generative AI to understand the code semantics and predict potential vulnerabilities. For example, the code behavior for specific inputs is simulated to detect vulnerabilities. The code review unit can also perform a code execution simulation to understand the code semantics and predict potential vulnerabilities. For example, the code behavior under specific scenarios is simulated to detect vulnerabilities. This makes it possible to perform a code execution simulation to understand the code semantics and predict potential vulnerabilities.

[0032] The code review unit can simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. The code review unit can, for example, use generative AI to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Vyper to detect common vulnerabilities. Generative AI can also be used to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Rust to detect common vulnerabilities. Generative AI can also be used to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and JavaScript to detect common vulnerabilities. This makes it possible to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities.

[0033] The code review department can share the results of the code review with other blockchain networks to improve security across the network. For example, the generation AI can share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Ethereum and Binance Smart Chain networks. The generation AI can also be used to share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Polkadot and Cardano networks. The generation AI can also share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Solana and Avalanche networks. This allows the results of the code review to be shared with other blockchain networks to improve security across the network.

[0034] The code review unit can analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. The code review unit can, for example, use a generation AI to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be analyzed to confirm whether the advertised functions are operating correctly. The generation AI can also be used to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be used to confirm whether the advertised services are being provided. The generation AI can also be used to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be used to confirm whether the advertised functions are being executed correctly. This makes it possible to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content.

[0035] The code review unit can analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. The code review unit can, for example, use generative AI to analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. For example, the code review unit can detect ambiguous expressions and make suggestions to replace them with clearer expressions. The code review unit can also use generative AI to analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. For example, the code review unit can detect misleading expressions and make suggestions to replace them with more specific expressions. In this way, the code review unit can analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions.

[0036] The code review unit can learn from past fraud cases and automatically detect advertisements with similar patterns. The code review unit can, for example, use generative AI to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, the code review unit can learn from the characteristics of past fraud cases and detect advertisements with similar characteristics. Generative AI can also be used to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, fraudulent advertisements are detected based on a dataset of fraud cases. Generative AI can also be used to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, text data of fraud cases is analyzed to detect advertisements with fraudulent elements. In this way, it is possible to learn from past fraud cases and automatically detect advertisements with similar patterns.

[0037] The code review unit can analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. The code review unit can, for example, use generative AI to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect phrases that promise excessive benefits and issue a warning. The generative AI can also be used to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect contradictory information and evaluate the possibility of fraud. The generative AI can also be used to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect exaggerated expressions and evaluate the risk of fraud. This makes it possible to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies.

[0038] The code review department can cross-check promotional content on different platforms and identify inconsistent information. The code review department can, for example, use generative AI to cross-check promotional content on different platforms and identify inconsistent information. For example, the promotional content on social media and the official website can be compared to detect inconsistencies. Generative AI can also be used to cross-check promotional content on different platforms and identify inconsistent information. For example, the content of advertisements and emails can be compared to detect inconsistencies. Generative AI can also be used to cross-check promotional content on different platforms and identify inconsistent information. For example, the content of blogs and forums can be compared to detect inconsistencies. This makes it possible to cross-check promotional content on different platforms and identify inconsistent information.

[0039] The code review department can generate an interactive report that explains in detail the detected vulnerabilities and differences. The code review department can, for example, use generative AI to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the specific location and impact of the vulnerability can be visually displayed. Generative AI can also be used to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the cause of the difference and how to fix it can be visually displayed. Generative AI can also be used to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the risk assessment and countermeasures for the vulnerability can be visually displayed. This makes it possible to generate an interactive report that explains in detail the detected vulnerabilities and differences.

[0040] The code review department can analyze the investor's past investment history and provide individually customized notifications. The code review department can, for example, use a generation AI to analyze the investor's past investment history and provide individually customized notifications. For example, notifying relevant vulnerability information based on a specific investment pattern. The generation AI can also be used to analyze the investor's past investment history and provide individually customized notifications. For example, notifying high-risk investments based on the past investment history. The generation AI can also be used to analyze the investor's past investment history and provide individually customized notifications. For example, notifying vulnerability information related to a specific investment. This makes it possible to analyze the investor's past investment history and provide individually customized notifications.

[0041] The code review department can simultaneously distribute notification content via different media (email, SMS, app notification). The code review department can, for example, use generation AI to simultaneously distribute notification content via different media (email, SMS, app notification). For example, important vulnerability information is notified simultaneously via multiple media. Generation AI can also be used to simultaneously distribute notification content via different media (email, SMS, app notification). For example, urgent security information is notified simultaneously via multiple media. Generation AI can also be used to simultaneously distribute notification content via different media (email, SMS, app notification). For example, important information is notified simultaneously via multiple media to quickly communicate important information to investors. This allows notification content to be simultaneously distributed via different media (email, SMS, app notification).

[0042] The code review department can analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. The code review department can, for example, use generation AI to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can detect whether a change introduced in a specific version causes a vulnerability in a later version. Generative AI can also be used to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can identify whether a specific commit causes a vulnerability. Generative AI can also be used to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can detect whether a specific change causes a vulnerability. This makes it possible to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities.

[0043] The code review unit can analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. The code review unit can, for example, use generative AI to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, newly deployed code can be immediately analyzed and vulnerabilities can be identified. Generative AI can also be used to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, it can detect whether a newly added function causes a vulnerability. Generative AI can also be used to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, it can immediately analyze the code of a newly released version and identify vulnerabilities. This makes it possible to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities.

[0044] The code review department can simultaneously monitor smart contracts on different blockchain networks. For example, the code review department can use generation AI to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Ethereum and Binance Smart Chain. Generation AI can also be used to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Polkadot and Cardano. Generation AI can also be used to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Solana and Avalanche. This makes it possible to simultaneously monitor smart contracts on different blockchain networks.

[0045] The code review department can link the monitoring results with other security systems to provide comprehensive security measures. The code review department can, for example, use generative AI to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with a firewall or IDS to strengthen vulnerability countermeasures. Generative AI can also be used to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with a SIEM system to share threat information. Generative AI can also be used to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with an endpoint security system to strengthen vulnerability countermeasures. This makes it possible to link the monitoring results with other security systems to provide comprehensive security measures.

[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 security assessment system can also share vulnerability information between different blockchain networks to improve security across the entire network. For example, vulnerability information can be shared between the Ethereum and Binance Smart Chain networks. It can also share vulnerability information between the Polkadot and Cardano networks. This will promote cooperation between different blockchain networks and improve overall security.

[0048] The security assessment system can also simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Vyper to detect common vulnerabilities. It can also analyze code written in Solidity and Rust to detect common vulnerabilities. This makes it possible to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities.

[0049] The security evaluation system can further analyze the execution log of a smart contract to verify whether its actual behavior matches the advertised content. For example, it can analyze the execution log to verify whether the advertised functions are working correctly. It can also verify whether the advertised services are being provided based on the execution log. This makes it possible to analyze the execution log of a smart contract to verify whether its actual behavior matches the advertised content.

[0050] The security evaluation system can also learn from past fraud cases and automatically detect advertisements with similar patterns. For example, it can learn the characteristics of past fraud cases and detect advertisements with similar characteristics. It can also detect fraudulent advertisements based on a dataset of fraud cases. This allows it to learn from past fraud cases and automatically detect advertisements with similar patterns.

[0051] The security assessment system can further analyze the text of the promotional content to identify excessive promises or inconsistencies. For example, it can detect phrases that promise excessive benefits and issue a warning. It can also detect contradictory information and assess the likelihood of fraud. This allows it to analyze the text of the promotional content to identify phrases that promise excessive benefits or inconsistencies.

[0052] The security assessment system can also cross-check advertising content across different platforms to identify inconsistencies. For example, it can compare advertising content on social media and official websites to detect inconsistencies. It can also compare advertising content with email content to detect inconsistencies. This allows it to cross-check advertising content across different platforms to identify inconsistencies.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The code review unit analyzes the smart contract code. For example, the generation AI analyzes the code of a smart contract deployed on the blockchain to detect security vulnerabilities. The generation AI can also identify bugs in the code and areas where unauthorized manipulation is possible. Furthermore, the generation AI can perform static analysis of the code to detect potential vulnerabilities. Step 2: The evaluation unit evaluates the smart contract code analyzed by the code review unit for security vulnerabilities and discrepancies with the advertised content. For example, the generation AI evaluates whether the content of the smart contract matches the advertised content. The generation AI can also verify whether the services and functions provided by the smart contract work as advertised. Furthermore, the generation AI can detect whether the advertising related to the smart contract is deceptive.

[0055] (Example 2) A security assessment system according to an embodiment of the present invention is a system that reviews the code of smart contracts deployed on a blockchain and evaluates security vulnerabilities and discrepancies with advertised content. In this way, the security assessment system can analyze the code of smart contracts and protect the interests of investors.

[0056] A security evaluation system according to an embodiment includes a code review unit and an evaluation unit. The code review unit analyzes smart contract code. For example, the generation AI analyzes smart contract code deployed on a blockchain to detect security vulnerabilities. The generation AI can also identify bugs and areas of potential fraudulent operation in the code. The generation AI can also perform static analysis of the code to detect potential vulnerabilities. The evaluation unit evaluates security vulnerabilities and discrepancies with advertised content based on the smart contract code analyzed by the code review unit. For example, the generation AI evaluates whether the content of the smart contract matches the advertised content. The generation AI can also verify whether the services and functions provided by the smart contract operate as advertised. The generation AI can also detect whether advertising related to the smart contract is fraudulent. This allows the security evaluation system to analyze smart contract code and evaluate security vulnerabilities and discrepancies with advertised content.

[0057] The code review unit can analyze the change history of the smart contract's code and identify how past changes affect current vulnerabilities. The code review unit can, for example, use generative AI to analyze the change history of the smart contract's code and identify how past changes affect current vulnerabilities. For example, to detect whether a change introduced in a particular version causes a vulnerability in a later version. Generative AI can also be used to analyze the change history of the code and identify how past changes affect current vulnerabilities. For example, to identify whether a particular commit causes a vulnerability. Generative AI can also be used to analyze the change history of the code and identify how past changes affect current vulnerabilities. For example, to detect whether a specific change causes a vulnerability. This makes it possible to analyze the change history of the code and identify how past changes affect current vulnerabilities.

[0058] The code review unit can perform a code execution simulation to understand the semantics of the smart contract code and predict potential vulnerabilities. The code review unit can, for example, use a generative AI to perform a code execution simulation to understand the semantics of the smart contract code and predict potential vulnerabilities. For example, the code behavior under specific conditions is simulated to detect vulnerabilities. The code review unit can also perform a code execution simulation using a generative AI to understand the code semantics and predict potential vulnerabilities. For example, the code behavior for specific inputs is simulated to detect vulnerabilities. The code review unit can also perform a code execution simulation to understand the code semantics and predict potential vulnerabilities. For example, the code behavior under specific scenarios is simulated to detect vulnerabilities. This makes it possible to perform a code execution simulation to understand the code semantics and predict potential vulnerabilities.

[0059] The code review unit can simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. The code review unit can, for example, use generative AI to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Vyper to detect common vulnerabilities. Generative AI can also be used to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Rust to detect common vulnerabilities. Generative AI can also be used to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and JavaScript to detect common vulnerabilities. This makes it possible to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities.

[0060] The code review department can share the results of the code review with other blockchain networks to improve security across the network. For example, the generation AI can share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Ethereum and Binance Smart Chain networks. The generation AI can also be used to share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Polkadot and Cardano networks. The generation AI can also share the results of the code review with other blockchain networks to improve security across the network. For example, vulnerability information can be shared between the Solana and Avalanche networks. This allows the results of the code review to be shared with other blockchain networks to improve security across the network.

[0061] The code review unit can analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. The code review unit can, for example, use a generation AI to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be analyzed to confirm whether the advertised functions are operating correctly. The generation AI can also be used to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be used to confirm whether the advertised services are being provided. The generation AI can also be used to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content. For example, the execution log can be used to confirm whether the advertised functions are being executed correctly. This makes it possible to analyze the execution log of a smart contract and verify whether the actual behavior matches the advertised content.

[0062] The code review unit can analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. The code review unit can, for example, use generative AI to analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. For example, the code review unit can detect ambiguous expressions and make suggestions to replace them with clearer expressions. The code review unit can also use generative AI to analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions. For example, the code review unit can detect misleading expressions and make suggestions to replace them with more specific expressions. In this way, the code review unit can analyze the text of the promotional content using natural language processing to identify ambiguous or misleading expressions.

[0063] The code review unit can learn from past fraud cases and automatically detect advertisements with similar patterns. The code review unit can, for example, use generative AI to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, the code review unit can learn from the characteristics of past fraud cases and detect advertisements with similar characteristics. Generative AI can also be used to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, fraudulent advertisements are detected based on a dataset of fraud cases. Generative AI can also be used to learn from past fraud cases and automatically detect advertisements with similar patterns. For example, text data of fraud cases is analyzed to detect advertisements with fraudulent elements. In this way, it is possible to learn from past fraud cases and automatically detect advertisements with similar patterns.

[0064] The code review unit can analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. The code review unit can, for example, use generative AI to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect phrases that promise excessive benefits and issue a warning. The generative AI can also be used to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect contradictory information and evaluate the possibility of fraud. The generative AI can also be used to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies. For example, the code review unit can detect exaggerated expressions and evaluate the risk of fraud. This makes it possible to analyze the text of the promotional content and identify expressions that promise excessive benefits or inconsistencies.

[0065] The code review department can cross-check promotional content on different platforms and identify inconsistent information. The code review department can, for example, use generative AI to cross-check promotional content on different platforms and identify inconsistent information. For example, the promotional content on social media and the official website can be compared to detect inconsistencies. Generative AI can also be used to cross-check promotional content on different platforms and identify inconsistent information. For example, the content of advertisements and emails can be compared to detect inconsistencies. Generative AI can also be used to cross-check promotional content on different platforms and identify inconsistent information. For example, the content of blogs and forums can be compared to detect inconsistencies. This makes it possible to cross-check promotional content on different platforms and identify inconsistent information.

[0066] The code review department can generate an interactive report that explains in detail the detected vulnerabilities and differences. The code review department can, for example, use generative AI to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the specific location and impact of the vulnerability can be visually displayed. Generative AI can also be used to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the cause of the difference and how to fix it can be visually displayed. Generative AI can also be used to generate an interactive report that explains in detail the detected vulnerabilities and differences. For example, the risk assessment and countermeasures for the vulnerability can be visually displayed. This makes it possible to generate an interactive report that explains in detail the detected vulnerabilities and differences.

[0067] The code review department can analyze the investor's past investment history and provide individually customized notifications. The code review department can, for example, use a generation AI to analyze the investor's past investment history and provide individually customized notifications. For example, notifying relevant vulnerability information based on a specific investment pattern. The generation AI can also be used to analyze the investor's past investment history and provide individually customized notifications. For example, notifying high-risk investments based on the past investment history. The generation AI can also be used to analyze the investor's past investment history and provide individually customized notifications. For example, notifying vulnerability information related to a specific investment. This makes it possible to analyze the investor's past investment history and provide individually customized notifications.

[0068] The code review department can simultaneously distribute notification content via different media (email, SMS, app notification). The code review department can, for example, use generation AI to simultaneously distribute notification content via different media (email, SMS, app notification). For example, important vulnerability information is notified simultaneously via multiple media. Generation AI can also be used to simultaneously distribute notification content via different media (email, SMS, app notification). For example, urgent security information is notified simultaneously via multiple media. Generation AI can also be used to simultaneously distribute notification content via different media (email, SMS, app notification). For example, important information is notified simultaneously via multiple media to quickly communicate important information to investors. This allows notification content to be simultaneously distributed via different media (email, SMS, app notification).

[0069] The code review department can analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. The code review department can, for example, use generation AI to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can detect whether a change introduced in a specific version causes a vulnerability in a later version. Generative AI can also be used to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can identify whether a specific commit causes a vulnerability. Generative AI can also be used to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities. For example, it can detect whether a specific change causes a vulnerability. This makes it possible to analyze the update history of a smart contract and identify how past changes affect current vulnerabilities.

[0070] The code review unit can analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. The code review unit can, for example, use generative AI to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, newly deployed code can be immediately analyzed and vulnerabilities can be identified. Generative AI can also be used to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, it can detect whether a newly added function causes a vulnerability. Generative AI can also be used to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities. For example, it can immediately analyze the code of a newly released version and identify vulnerabilities. This makes it possible to analyze newly deployed smart contracts in real time and immediately detect vulnerabilities.

[0071] The code review department can simultaneously monitor smart contracts on different blockchain networks. For example, the code review department can use generation AI to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Ethereum and Binance Smart Chain. Generation AI can also be used to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Polkadot and Cardano. Generation AI can also be used to simultaneously monitor smart contracts on different blockchain networks. For example, it can simultaneously monitor smart contracts on Solana and Avalanche. This makes it possible to simultaneously monitor smart contracts on different blockchain networks.

[0072] The code review department can link the monitoring results with other security systems to provide comprehensive security measures. The code review department can, for example, use generative AI to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with a firewall or IDS to strengthen vulnerability countermeasures. Generative AI can also be used to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with a SIEM system to share threat information. Generative AI can also be used to link the monitoring results with other security systems to provide comprehensive security measures. For example, it can link with an endpoint security system to strengthen vulnerability countermeasures. This makes it possible to link the monitoring results with other security systems to provide comprehensive security measures.

[0073] The code review unit can use the emotion estimation function to analyze the emotional state of a developer performing a code review and evaluate the impact of stress and fatigue on vulnerability detection. The code review unit, for example, uses the emotion estimation function to analyze the emotional state of a developer performing a code review and evaluate the impact of stress and fatigue on vulnerability detection. For example, to investigate how the developer's emotional state affects the accuracy of vulnerability detection. The emotion estimation function can also be used to analyze the emotional state of a developer performing a code review and evaluate the impact of stress and fatigue on vulnerability detection. For example, to investigate how the developer's emotional state affects the efficiency of vulnerability detection. The emotion estimation function can also be used to analyze the emotional state of a developer performing a code review and evaluate the impact of stress and fatigue on vulnerability detection. For example, to investigate how the developer's emotional state affects the results of vulnerability detection. In this way, the emotion estimation function can be used to analyze the emotional state of a developer performing a code review and evaluate the impact of stress and fatigue on vulnerability detection.

[0074] The code review unit can use the emotion estimation function to collect investors' emotional reactions to the results of the code review and improve the reliability of the review. The code review unit, for example, uses the emotion estimation function to collect investors' emotional reactions to the results of the code review and improve the reliability of the review. For example, the emotional reactions of the investors are analyzed and the reliability of the review results is evaluated. The emotion estimation function can also be used to collect investors' emotional reactions to the results of the code review and improve the reliability of the review. For example, the review results are adjusted based on the investors' emotional reactions. The emotion estimation function can also be used to collect investors' emotional reactions to the results of the code review and improve the reliability of the review. For example, areas for improvement in the review results are identified based on the investors' emotional reactions. In this way, the emotion estimation function can be used to collect investors' emotional reactions to the results of the code review and improve the reliability of the review.

[0075] The code review unit can use the emotion estimation function to analyze users' emotional reactions to the promotional content and evaluate whether there are many positive reactions. The code review unit, for example, uses the emotion estimation function to analyze users' emotional reactions to the promotional content and evaluate whether there are many positive reactions. For example, the code review unit evaluates the promotional content based on the user's emotion score. The emotion estimation function can also be used to analyze users' emotional reactions to the promotional content and evaluate whether there are many positive reactions. For example, the promotional content is evaluated based on user emotion data. The emotion estimation function can also be used to analyze users' emotional reactions to the promotional content and evaluate whether there are many positive reactions. For example, areas for improvement in the promotional content are identified based on the user's emotion score. In this way, the emotion estimation function can be used to analyze users' emotional reactions to the promotional content and evaluate whether there are many positive reactions.

[0076] The code review unit can use the emotion estimation function to analyze users' emotional reactions to deceptive advertising and evaluate whether there are many negative reactions. The code review unit, for example, uses the emotion estimation function to analyze users' emotional reactions to deceptive advertising and evaluate whether there are many negative reactions. For example, the code review unit evaluates the possibility of fraud based on the user's emotion score. The emotion estimation function can also be used to analyze users' emotional reactions to deceptive advertising and evaluate whether there are many negative reactions. For example, the emotion estimation function can evaluate the risk of fraud based on user emotion data. The emotion estimation function can also be used to analyze users' emotional reactions to deceptive advertising and evaluate whether there are many negative reactions. For example, the emotion estimation function can identify signs of fraud based on the user's emotion score. As a result, the emotion estimation function can be used to analyze users' emotional reactions to deceptive advertising and evaluate whether there are many negative reactions.

[0077] The code review unit can use the emotion estimation function to analyze investors' emotional reactions to the monitoring results and improve the reliability of monitoring. The code review unit, for example, uses the emotion estimation function to analyze investors' emotional reactions to the monitoring results and improve the reliability of monitoring. For example, the reliability of the monitoring results is evaluated based on the investor's emotion score. The emotion estimation function can also be used to analyze investors' emotional reactions to the monitoring results and improve the reliability of monitoring. For example, the monitoring results are adjusted based on investor emotion data. The emotion estimation function can also be used to analyze investors' emotional reactions to the monitoring results and improve the reliability of monitoring. For example, areas for improvement in the monitoring results are identified based on the investor's emotion score. In this way, the emotion estimation function can be used to analyze investors' emotional reactions to the monitoring results and improve the reliability of monitoring.

[0078] The code review unit can use the emotion estimation function to monitor investors' emotional reactions to the monitoring results in real time and issue early warnings. The code review unit can, for example, use the emotion estimation function to monitor investors' emotional reactions to the monitoring results in real time and issue early warnings. For example, a warning is issued if there are many negative emotional reactions. The emotion estimation function can also be used to monitor investors' emotional reactions to the monitoring results in real time and issue early warnings. For example, a warning is issued based on investor emotion data. The emotion estimation function can also be used to monitor investors' emotional reactions to the monitoring results in real time and issue early warnings. For example, a warning is issued if the negative emotion score is high. In this way, the emotion estimation function can be used to monitor investors' emotional reactions to the monitoring results in real time and issue early warnings.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The security evaluation system can further estimate the user's emotions and customize the results of the security evaluation based on the estimated user emotions. For example, if the user feels anxious, it can provide a more detailed explanation or additional security measures. If the user is satisfied, it can provide a concise report. This allows for flexible responses according to the user's emotions and improves user trust.

[0081] The security assessment system can also share vulnerability information between different blockchain networks to improve security across the entire network. For example, vulnerability information can be shared between the Ethereum and Binance Smart Chain networks. It can also share vulnerability information between the Polkadot and Cardano networks. This will promote cooperation between different blockchain networks and improve overall security.

[0082] The security evaluation system can further estimate the user's emotions and prioritize security evaluations based on the estimated user emotions. For example, if the user feels very anxious, the system can prioritize reporting the most important vulnerabilities. On the other hand, if the user feels safe, the system can provide an overall evaluation. This allows for appropriate responses according to the user's emotions, thereby improving user satisfaction.

[0083] The security assessment system can also simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities. For example, it can analyze code written in Solidity and Vyper to detect common vulnerabilities. It can also analyze code written in Solidity and Rust to detect common vulnerabilities. This makes it possible to simultaneously analyze smart contract code written in different programming languages ​​to identify common vulnerabilities.

[0084] The security evaluation system can further estimate the user's emotions and provide security evaluation feedback based on the estimated user emotions. For example, if the user feels anxious, it can provide detailed explanations or additional support. If the user is satisfied, it can provide concise feedback. This allows for flexible responses according to the user's emotions and improves user trust.

[0085] The security evaluation system can further analyze the execution log of a smart contract to verify whether its actual behavior matches the advertised content. For example, it can analyze the execution log to verify whether the advertised functions are working correctly. It can also verify whether the advertised services are being provided based on the execution log. This makes it possible to analyze the execution log of a smart contract to verify whether its actual behavior matches the advertised content.

[0086] The security evaluation system can further estimate the user's emotions and customize the results of the security evaluation based on the estimated user emotions. For example, if the user feels anxious, it can provide a more detailed explanation or additional security measures. If the user is satisfied, it can provide a concise report. This allows for flexible responses according to the user's emotions and improves user trust.

[0087] The security evaluation system can also learn from past fraud cases and automatically detect advertisements with similar patterns. For example, it can learn the characteristics of past fraud cases and detect advertisements with similar characteristics. It can also detect fraudulent advertisements based on a dataset of fraud cases. This allows it to learn from past fraud cases and automatically detect advertisements with similar patterns.

[0088] The security assessment system can further analyze the text of the promotional content to identify excessive promises or inconsistencies. For example, it can detect phrases that promise excessive benefits and issue a warning. It can also detect contradictory information and assess the likelihood of fraud. This allows it to analyze the text of the promotional content to identify phrases that promise excessive benefits or inconsistencies.

[0089] The security assessment system can also cross-check advertising content across different platforms to identify inconsistencies. For example, it can compare advertising content on social media and official websites to detect inconsistencies. It can also compare advertising content with email content to detect inconsistencies. This allows it to cross-check advertising content across different platforms to identify inconsistencies.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The code review unit analyzes the smart contract code. For example, the generation AI analyzes the code of a smart contract deployed on the blockchain to detect security vulnerabilities. The generation AI can also identify bugs in the code and areas where unauthorized manipulation is possible. Furthermore, the generation AI can perform static analysis of the code to detect potential vulnerabilities. Step 2: The evaluation unit evaluates the smart contract code analyzed by the code review unit for security vulnerabilities and discrepancies with the advertised content. For example, the generation AI evaluates whether the content of the smart contract matches the advertised content. The generation AI can also verify whether the services and functions provided by the smart contract work as advertised. Furthermore, the generation AI can detect whether the advertising related to the smart contract is deceptive.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 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 code review department that analyzes smart contract code; and an evaluation unit that evaluates security vulnerabilities and discrepancies with advertised content based on the code of the smart contract analyzed by the code review unit. A system characterized by:

2. The code review unit Analyzing the change history of the smart contract's code to identify how past changes affect the current vulnerability 2. The system of claim 1.

3. The code review unit Simulating the execution of the smart contract code to understand its semantics and predict potential vulnerabilities.

2. The system of claim 1.

4. The code review unit Simultaneously analyze the smart contract code written in different programming languages ​​to identify common vulnerabilities.

2. The system of claim 1.

5. The code review unit The results of the code review will be shared with other blockchain networks to improve security across the network.

2. The system of claim 1.

6. The code review unit Analyzing the execution log of the smart contract and verifying whether the actual behavior matches the advertised content 2. The system of claim 1.

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