System

Abstract

An object of a system according to an embodiment is to accurately determine whether or not a revenue stamp is necessary for a contract and the amount of money.SOLUTION: A system includes a sentence analysis part, a determination part, an amount calculation part, and a law confirmation part. The text analysis unit analyzes the text of the contract. A determination part determines the necessity of the revenue stamp on the basis of the content of the contract analyzed by the sentence analysis part. The amount calculation unit calculates an amount when the determination unit determines that the revenue stamp is necessary. A law confirmation part confirms whether the determination result and the amount of money comply with laws.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the decision on whether or not a revenue stamp is required for a contract and the amount of the stamp is left to the discretion of the person in charge, which can lead to errors.

[0005] The system according to the embodiment aims to accurately determine whether or not a revenue stamp is required for a contract, and the amount of the stamp. [Means for solving the problem]

[0006] The system according to the embodiment includes a text analysis unit, a determination unit, an amount calculation unit, and a law confirmation unit. The text analysis unit analyzes the text of the contract. The determination unit determines whether a revenue stamp is required based on the content of the contract analyzed by the text analysis unit. The amount calculation unit calculates the amount of a revenue stamp if the determination unit determines that a revenue stamp is required. The law confirmation unit confirms whether the determination result and the amount comply with laws and regulations. [Effects of the Invention]

[0007] The system according to the embodiment can accurately determine whether or not a revenue stamp is required for a contract, and the amount of the stamp. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The contract stamp determination app according to the embodiment of the present invention is a system that determines whether or not a revenue stamp is required simply by copying the text of a contract. As a result, the contract stamp determination app reduces the burden on personnel, and can achieve compliance with laws and regulations and business efficiency.

[0029] A contract stamp determination app according to an embodiment includes a text analysis unit, a determination unit, an amount calculation unit, and a legal compliance confirmation unit. The text analysis unit analyzes the text of a contract. For example, the generation AI understands the content of the contract and extracts information for determining whether a revenue stamp is required. The generation AI performs analysis based on the text of the contract. The determination unit determines whether a revenue stamp is required based on the content of the contract analyzed by the text analysis unit. For example, the generation AI outputs a determination result such as "This contract requires a revenue stamp" or "This contract does not require a revenue stamp." The amount calculation unit calculates the amount when the determination unit determines that a revenue stamp is required. For example, the generation AI presents the amount such as "This contract requires a 1,000 yen revenue stamp." The legal compliance confirmation unit checks whether the determination result and the amount comply with the law. For example, the generation AI confirms the result by saying, "This determination result is based on the latest laws and regulations." As a result, the contract stamp determination app of the embodiment analyzes the text of the contract, automatically determines whether or not a revenue stamp is required and the amount, and confirms compliance with laws and regulations, thereby reducing the burden on staff and improving work efficiency.

[0030] The text analysis unit can analyze not only the contract text, but also attached documents and past contract history. For example, the text analysis unit uses generative AI to analyze not only the contract text but also related attached documents. For example, it analyzes the drawings and specifications attached to the contract to understand the details of the contract content. The text analysis unit also analyzes past contract history. For example, it refers to a database of past contracts and analyzes the content of similar contracts. This allows for more accurate judgments by analyzing not only the contract text, but also related attached documents and past contract history.

[0031] The text analysis unit can also perform analysis taking into account the intentions of the contract creator and background information. For example, the text analysis unit refers to the creator's comments and notes to analyze the intentions of the contract creator. For example, it analyzes notes and annotations attached to the contract to understand the creator's intentions. The text analysis unit also performs analysis taking into account background information. For example, it analyzes the contents of the contract by referring to the purpose of the contract and related business information. This makes it possible to perform analysis that is more in line with the context by taking into account the intentions of the contract creator and background information.

[0032] The text analysis unit makes it possible to analyze contracts in different languages ​​and also handle international contracts. For example, the text analysis unit develops a multilingual generation AI to analyze contracts in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, when analyzing contracts in different languages, the text analysis unit takes into account the characteristics of the language. For example, it reflects differences in grammar and expressions between languages ​​in the analysis. This makes it possible to analyze contracts in different languages ​​and also handle international contracts.

[0033] The text analysis unit can convert the analysis results into visual notes or mind maps to make them easier to understand visually. For example, the text analysis unit can convert the text analysis results of a contract into visual notes to visually display important points. For example, the main points of the contract can be shown using diagrams or icons. The text analysis unit can also convert the analysis results into mind maps. For example, the relationships between the contents of the contract can be represented using nodes and edges to make them easier to understand visually. In this way, visually displaying the analysis results makes it easier for users to understand.

[0034] The judgment unit can also refer to past court cases and legal amendment information when determining whether revenue stamps are required. For example, the judgment unit uses generation AI to refer to past court cases when determining whether revenue stamps are required. For example, the judgment unit makes a judgment based on past court cases and administrative decisions. The judgment unit also refers to legal amendment information. For example, it obtains the latest legal amendment information and reflects it in the judgment. In this way, by referring to past court cases and legal amendment information, more accurate judgments can be made.

[0035] When determining whether or not a revenue stamp is required, the determination unit can take into account not only the contents of the contract but also the purpose and background of the contract. For example, when determining whether or not a revenue stamp is required, the determination unit considers the purpose of the contract. For example, it determines whether the contract is a sales contract or a rental contract and makes a determination based on that. The determination unit also considers the background of the contract. For example, it references the history of the contract and related business information and reflects this in its determination. This allows for a more appropriate determination by taking into account the purpose and background of the contract.

[0036] The determination unit can adapt the determination of whether revenue stamps are required to the laws of different jurisdictions and countries, making it applicable to international contracts. For example, the determination unit develops a generation AI that supports multiple jurisdictions to accommodate the laws of different jurisdictions and countries. For example, it supports multiple jurisdictions such as Japan, the United States, and Europe. The determination unit also references the laws of different jurisdictions and countries. For example, it references the legal databases of each country and reflects this in its determination. This makes it possible to accommodate the laws of different jurisdictions and countries, making it applicable to international contracts.

[0037] The determination unit can add an educational function to interactively explain to the user the determination result of whether or not a revenue stamp is required, thereby deepening their understanding. The determination unit, for example, adds an educational function to interactively explain the determination result of whether or not a revenue stamp is required. For example, the reason for the determination result is explained in detail. The determination unit also employs an interactive explanation method to deepen the user's understanding. For example, when the user inputs a question, the generation AI provides an answer. This allows the determination result to be explained interactively, thereby deepening the user's understanding.

[0038] The amount calculation unit can also take into account past transaction data and market trends when calculating the amount of revenue stamps. For example, the amount calculation unit uses generation AI to refer to past transaction data when calculating the amount of revenue stamps. For example, it calculates the amount based on the amounts of similar contracts in the past. The amount calculation unit also takes market trends into account. For example, it refers to the latest market research reports and economic indicators and reflects them in the amount. In this way, by taking into account past transaction data and market trends, it is possible to present a more appropriate amount.

[0039] The amount calculation unit can calculate the amount of revenue stamps for different currencies and economic zones, making it applicable to international contracts. For example, the amount calculation unit develops a multi-currency generation AI to support different currencies. For example, it can support multiple currencies such as dollars, euros, and yen. The amount calculation unit can also support different economic zones. For example, it can reference regional economic characteristics and economic indicators and reflect them in the amount. This makes it possible to support different currencies and economic zones, making it applicable to international contracts.

[0040] The amount calculation unit can add an educational function to interactively explain the calculation result of the revenue stamp amount to the user and deepen their understanding. The amount calculation unit, for example, adds an educational function to interactively explain the calculation result of the revenue stamp amount. For example, the reason for the calculation of the amount is explained in detail. The amount calculation unit also employs an interactive explanation method to deepen the user's understanding. For example, when the user inputs a question, the generation AI provides an answer. This allows the user's understanding to be deepened by interactively explaining the calculation result of the amount.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The contract stamp determination app can also include a summary section that summarizes the contents of the contract. The summary section extracts the main points of the contract and summarizes them concisely. For example, it may summarize the purpose of the contract, key clauses, and important deadlines. The summary section can also adjust the level of detail of the summary depending on the length and complexity of the contract. This allows users to quickly grasp the overall picture of the contract and make efficient decisions.

[0043] The contract stamp verification app can further include a risk assessment unit that performs risk assessment of the contract. The risk assessment unit analyzes the contents of the contract and identifies potential risks. For example, it detects ambiguity in contract clauses or unfavorable conditions. The risk assessment unit can also evaluate the severity of the risk and present details of the risk and countermeasures to the user. This allows the user to understand the risks in the contract in advance and take appropriate measures.

[0044] The contract stamp determination app can further include an amendment suggestion unit that automatically amends contract clauses. The amendment suggestion unit analyzes the contents of the contract and proposes amendments based on laws and industry standards. For example, it can propose specific wording to clarify ambiguous clauses. The amendment suggestion unit can also propose amendments customized according to the user's intentions and purposes. This allows users to improve the quality of their contracts.

[0045] The contract stamp verification app can further include a comparison unit that compares the contents of a contract with other contracts. The comparison unit compares the contents of a contract with other contracts in a database to identify similarities and differences. For example, by comparing it with past contracts, it can identify changes to clauses and new risks. The comparison unit can also suggest areas for improvement in the contract by comparing it with industry standards and best practices. This allows users to objectively evaluate the contents of a contract and make appropriate revisions.

[0046] The contract stamp verification app can further include a visualization unit that visually displays the contents of the contract. The visualization unit visually displays the contents of the contract using graphs, charts, infographics, etc. For example, the visualization unit can color-code the main clauses and risks of the contract. The visualization unit can also display the contents of the contract in chronological order, allowing users to visually grasp important deadlines and events. This makes it easier for users to intuitively understand the contents of the contract.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The text analysis unit analyzes the contract text. For example, the generation AI understands the contents of the contract and extracts information to determine whether or not revenue stamps are required. The generation AI performs analysis based on the contract text. Step 2: The judgment unit determines whether revenue stamps are required based on the content of the contract analyzed by the text analysis unit. For example, the generation AI outputs a judgment result such as "This contract requires revenue stamps" or "This contract does not require revenue stamps." Step 3: If the judgment unit determines that a revenue stamp is required, the amount calculation unit calculates the amount. For example, the generation AI may present the amount as, "This contract requires a revenue stamp of 1,000 yen." Step 4: The legal verification unit checks whether the judgment result and amount comply with the law. For example, the generation AI will confirm by saying, "This judgment result is based on the latest laws and regulations."

[0049] (Example 2) The contract stamp determination app according to the embodiment of the present invention is a system that determines whether or not a revenue stamp is required simply by copying the text of a contract. As a result, the contract stamp determination app reduces the burden on personnel, and can achieve compliance with laws and regulations and business efficiency.

[0050] A contract stamp determination app according to an embodiment includes a text analysis unit, a determination unit, an amount calculation unit, and a legal compliance confirmation unit. The text analysis unit analyzes the text of a contract. For example, the generation AI understands the content of the contract and extracts information for determining whether a revenue stamp is required. The generation AI performs analysis based on the text of the contract. The determination unit determines whether a revenue stamp is required based on the content of the contract analyzed by the text analysis unit. For example, the generation AI outputs a determination result such as "This contract requires a revenue stamp" or "This contract does not require a revenue stamp." The amount calculation unit calculates the amount when the determination unit determines that a revenue stamp is required. For example, the generation AI presents the amount such as "This contract requires a 1,000 yen revenue stamp." The legal compliance confirmation unit checks whether the determination result and the amount comply with the law. For example, the generation AI confirms the result by saying, "This determination result is based on the latest laws and regulations." As a result, the contract stamp determination app of the embodiment analyzes the text of the contract, automatically determines whether or not a revenue stamp is required and the amount, and confirms compliance with laws and regulations, thereby reducing the burden on staff and improving work efficiency.

[0051] The text analysis unit can analyze not only the contract text, but also attached documents and past contract history. For example, the text analysis unit uses generative AI to analyze not only the contract text but also related attached documents. For example, it analyzes the drawings and specifications attached to the contract to understand the details of the contract content. The text analysis unit also analyzes past contract history. For example, it refers to a database of past contracts and analyzes the content of similar contracts. This allows for more accurate judgments by analyzing not only the contract text, but also related attached documents and past contract history.

[0052] The text analysis unit can also perform analysis taking into account the intentions of the contract creator and background information. For example, the text analysis unit refers to the creator's comments and notes to analyze the intentions of the contract creator. For example, it analyzes notes and annotations attached to the contract to understand the creator's intentions. The text analysis unit also performs analysis taking into account background information. For example, it analyzes the contents of the contract by referring to the purpose of the contract and related business information. This makes it possible to perform analysis that is more in line with the context by taking into account the intentions of the contract creator and background information.

[0053] The text analysis unit can use the emotion estimation function to estimate the emotion of the creator from the text of the contract and adjust the analysis results based on that emotion. The text analysis unit, for example, uses the emotion estimation function to estimate the emotion of the creator from the text of the contract. For example, it analyzes emotion from the tone and expression of the text and identifies positive and negative emotions. The text analysis unit also adjusts the analysis results based on the estimated emotion. For example, if negative emotion is strong, it evaluates the risk higher. This makes it possible to provide more appropriate analysis results by taking the creator's emotion into consideration.

[0054] The text analysis unit makes it possible to analyze contracts in different languages ​​and also handle international contracts. For example, the text analysis unit develops a multilingual generation AI to analyze contracts in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. In addition, when analyzing contracts in different languages, the text analysis unit takes into account the characteristics of the language. For example, it reflects differences in grammar and expressions between languages ​​in the analysis. This makes it possible to analyze contracts in different languages ​​and also handle international contracts.

[0055] The text analysis unit can convert the analysis results into visual notes or mind maps to make them easier to understand visually. For example, the text analysis unit can convert the text analysis results of a contract into visual notes to visually display important points. For example, the main points of the contract can be shown using diagrams or icons. The text analysis unit can also convert the analysis results into mind maps. For example, the relationships between the contents of the contract can be represented using nodes and edges to make them easier to understand visually. In this way, visually displaying the analysis results makes it easier for users to understand.

[0056] The text analysis unit can use the emotion estimation function to monitor the user's emotions in real time and adjust the analysis results to match the user's emotions. For example, the text analysis unit uses the emotion estimation function to monitor the user's emotions in real time when analyzing the text of a contract. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The text analysis unit also adjusts the analysis results to match the user's emotions. For example, if the user is feeling stressed, it summarizes the analysis results concisely. In this way, by adjusting the analysis results according to the user's emotions, more appropriate results can be provided.

[0057] The judgment unit can also refer to past court cases and legal amendment information when determining whether revenue stamps are required. For example, the judgment unit uses generation AI to refer to past court cases when determining whether revenue stamps are required. For example, the judgment unit makes a judgment based on past court cases and administrative decisions. The judgment unit also refers to legal amendment information. For example, it obtains the latest legal amendment information and reflects it in the judgment. In this way, by referring to past court cases and legal amendment information, more accurate judgments can be made.

[0058] When determining whether or not a revenue stamp is required, the determination unit can take into account not only the contents of the contract but also the purpose and background of the contract. For example, when determining whether or not a revenue stamp is required, the determination unit considers the purpose of the contract. For example, it determines whether the contract is a sales contract or a rental contract and makes a determination based on that. The determination unit also considers the background of the contract. For example, it references the history of the contract and related business information and reflects this in its determination. This allows for a more appropriate determination by taking into account the purpose and background of the contract.

[0059] The determination unit can analyze the user's emotions using the emotion estimation function and present a determination result that elicits positive emotions. The determination unit, for example, uses the emotion estimation function to analyze the user's emotions and present a determination result that elicits positive emotions. For example, the determination unit analyzes the user's facial expressions and voice and calculates an emotion score. The determination unit also devise a determination result to elicit positive emotions. For example, the determination result is presented using positive expressions. This makes it possible to provide a more positive determination result by taking the user's emotions into consideration.

[0060] The determination unit can adapt the determination of whether revenue stamps are required to the laws of different jurisdictions and countries, making it applicable to international contracts. For example, the determination unit develops a generation AI that supports multiple jurisdictions to accommodate the laws of different jurisdictions and countries. For example, it supports multiple jurisdictions such as Japan, the United States, and Europe. The determination unit also references the laws of different jurisdictions and countries. For example, it references the legal databases of each country and reflects this in its determination. This makes it possible to accommodate the laws of different jurisdictions and countries, making it applicable to international contracts.

[0061] The determination unit can add an educational function to interactively explain to the user the determination result of whether or not a revenue stamp is required, thereby deepening their understanding. The determination unit, for example, adds an educational function to interactively explain the determination result of whether or not a revenue stamp is required. For example, the reason for the determination result is explained in detail. The determination unit also employs an interactive explanation method to deepen the user's understanding. For example, when the user inputs a question, the generation AI provides an answer. This allows the determination result to be explained interactively, thereby deepening the user's understanding.

[0062] The determination unit can use the emotion estimation function to collect the user's emotional reactions to the determination result and improve the accuracy of the determination algorithm. The determination unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the determination result. For example, the determination unit analyzes the user's facial expressions and voice and calculates an emotion score. The determination unit also improves the accuracy of the determination algorithm based on the collected emotional reactions. For example, the determination unit reflects the user's emotional reactions in the algorithm as feedback. In this way, the accuracy of the determination algorithm can be improved by collecting the user's emotional reactions.

[0063] The amount calculation unit can also take into account past transaction data and market trends when calculating the amount of revenue stamps. For example, the amount calculation unit uses generation AI to refer to past transaction data when calculating the amount of revenue stamps. For example, it calculates the amount based on the amounts of similar contracts in the past. The amount calculation unit also takes market trends into account. For example, it refers to the latest market research reports and economic indicators and reflects them in the amount. In this way, by taking into account past transaction data and market trends, it is possible to present a more appropriate amount.

[0064] The amount calculation unit can use the emotion estimation function to analyze the user's emotions and present an amount that elicits positive emotions. The amount calculation unit, for example, uses the emotion estimation function to analyze the user's emotions and presents an amount that elicits positive emotions. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The amount calculation unit also devise ways to present an amount that elicits positive emotions. For example, it presents an amount using positive expressions. This makes it possible to present a more positive amount by taking the user's emotions into consideration.

[0065] The amount calculation unit can calculate the amount of revenue stamps for different currencies and economic zones, making it applicable to international contracts. For example, the amount calculation unit develops a multi-currency generation AI to support different currencies. For example, it can support multiple currencies such as dollars, euros, and yen. The amount calculation unit can also support different economic zones. For example, it can reference regional economic characteristics and economic indicators and reflect them in the amount. This makes it possible to support different currencies and economic zones, making it applicable to international contracts.

[0066] The amount calculation unit can add an educational function to interactively explain the calculation result of the revenue stamp amount to the user and deepen their understanding. The amount calculation unit, for example, adds an educational function to interactively explain the calculation result of the revenue stamp amount. For example, the reason for the calculation of the amount is explained in detail. The amount calculation unit also employs an interactive explanation method to deepen the user's understanding. For example, when the user inputs a question, the generation AI provides an answer. This allows the user's understanding to be deepened by interactively explaining the calculation result of the amount.

[0067] The amount calculation unit can use the emotion estimation function to collect the user's emotional response to the amount calculation result and improve the accuracy of the calculation algorithm. The amount calculation unit, for example, uses the emotion estimation function to collect the user's emotional response to the amount calculation result. For example, it analyzes the user's facial expression and voice to calculate an emotion score. The amount calculation unit also improves the accuracy of the calculation algorithm based on the collected emotional response. For example, it reflects the user's emotional response in the algorithm as feedback. In this way, by collecting the user's emotional response, the accuracy of the calculation algorithm can be improved.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The contract stamp determination app can also include a summary section that summarizes the contents of the contract. The summary section extracts the main points of the contract and summarizes them concisely. For example, it may summarize the purpose of the contract, key clauses, and important deadlines. The summary section can also adjust the level of detail of the summary depending on the length and complexity of the contract. This allows users to quickly grasp the overall picture of the contract and make efficient decisions.

[0070] The contract stamp verification app can further include a risk assessment unit that performs risk assessment of the contract. The risk assessment unit analyzes the contents of the contract and identifies potential risks. For example, it detects ambiguity in contract clauses or unfavorable conditions. The risk assessment unit can also evaluate the severity of the risk and present details of the risk and countermeasures to the user. This allows the user to understand the risks in the contract in advance and take appropriate measures.

[0071] The contract stamp determination app can further include an amendment suggestion unit that automatically amends contract clauses. The amendment suggestion unit analyzes the contents of the contract and proposes amendments based on laws and industry standards. For example, it can propose specific wording to clarify ambiguous clauses. The amendment suggestion unit can also propose amendments customized according to the user's intentions and purposes. This allows users to improve the quality of their contracts.

[0072] The contract stamp verification app can further include a comparison unit that compares the contents of a contract with other contracts. The comparison unit compares the contents of a contract with other contracts in a database to identify similarities and differences. For example, by comparing it with past contracts, it can identify changes to clauses and new risks. The comparison unit can also suggest areas for improvement in the contract by comparing it with industry standards and best practices. This allows users to objectively evaluate the contents of a contract and make appropriate revisions.

[0073] The contract stamp verification app can further include a visualization unit that visually displays the contents of the contract. The visualization unit visually displays the contents of the contract using graphs, charts, infographics, etc. For example, the visualization unit can color-code the main clauses and risks of the contract. The visualization unit can also display the contents of the contract in chronological order, allowing users to visually grasp important deadlines and events. This makes it easier for users to intuitively understand the contents of the contract.

[0074] The contract stamp judgment app can further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of the contract based on the estimated emotions. The emotion adjustment unit analyzes the user's emotions and adjusts the content of the contract to elicit positive emotions. For example, if the user is feeling stressed, the emotion adjustment unit can summarize the content of the contract in a concise manner. The emotion adjustment unit can also evaluate the risks of the contract and suggest modifications based on the user's emotions. This allows the user to more comfortably understand the contract and make appropriate decisions.

[0075] The contract stamp verification app may further include an emotional risk assessment unit that estimates the user's emotions and adjusts the risk assessment of the contract based on the estimated emotions. The emotional risk assessment unit analyzes the user's emotions and assesses the risk highly if the user's emotions are strong and negative. For example, if the user feels anxious, the emotional risk assessment unit may perform a detailed risk assessment and present specific risk countermeasures. The emotional risk assessment unit may also visually display the results of the risk assessment according to the user's emotions. This allows the user to more appropriately understand and respond to risks.

[0076] The contract stamp determination app may further include an emotion correction suggestion unit that estimates the user's emotion and suggests modifications to the contract based on the estimated emotion. The emotion correction suggestion unit analyzes the user's emotion and suggests modifications to elicit positive emotions. For example, if the user is feeling anxious, the emotion correction suggestion unit suggests modifications that give the user a sense of security. The emotion correction suggestion unit can also adjust the level of detail of the suggested modifications depending on the user's emotion. This allows the user to make modifications to the contract with greater peace of mind.

[0077] The contract stamp verification app may further include an emotion summarization unit that estimates the user's emotion and summarizes the contract based on the estimated emotion. The emotion summarization unit analyzes the user's emotion and summarizes the contract in a way that elicits positive emotions. For example, if the user is feeling stressed, the summary will be concise. The emotion summarization unit can also adjust the level of detail of the summary depending on the user's emotion. This allows the user to more comfortably grasp the main points of the contract.

[0078] The contract stamp judgment app can further include an emotion comparison unit that estimates the user's emotion and adjusts the contract comparison results based on the estimated emotion. The emotion comparison unit analyzes the user's emotion and adjusts the comparison results to elicit positive emotions. For example, if the user is feeling anxious, the comparison results can be presented in detail to provide a sense of security. The emotion comparison unit can also adjust the display method of the comparison results according to the user's emotion. This allows the user to understand the contract comparison results with greater peace of mind.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The text analysis unit analyzes the contract text. For example, the generation AI understands the contents of the contract and extracts information to determine whether or not revenue stamps are required. The generation AI performs analysis based on the contract text. Step 2: The judgment unit determines whether revenue stamps are required based on the content of the contract analyzed by the text analysis unit. For example, the generation AI outputs a judgment result such as "This contract requires revenue stamps" or "This contract does not require revenue stamps." Step 3: If the judgment unit determines that a revenue stamp is required, the amount calculation unit calculates the amount. For example, the generation AI may present the amount as, "This contract requires a revenue stamp of 1,000 yen." Step 4: The legal verification unit checks whether the judgment result and amount comply with the law. For example, the generation AI will confirm by saying, "This judgment result is based on the latest laws and regulations."

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a text analysis unit that analyzes the text of the contract; a determination unit that determines whether or not a revenue stamp is required based on the content of the contract analyzed by the text analysis unit; an amount calculation unit that calculates the amount of revenue stamps when the determination unit determines that revenue stamps are necessary; and a legal confirmation unit that confirms whether the determination result and the amount comply with the law. A system characterized by:

2. The sentence analysis unit Analyze not only the text of the contract, but also attached documents and past contract history 2. The system of claim 1.

3. The sentence analysis unit It is also possible to analyze contracts in different languages, and to handle international contracts.

2. The system of claim 1.

4. The determination unit When determining whether or not the revenue stamp is required, past court decisions and legal amendments are also referred to.

2. The system of claim 1.

5. The amount calculation unit When calculating the amount of the revenue stamp, past transaction data and market trends are also taken into consideration.

2. The system of claim 1.

6. The sentence analysis unit An emotion estimation function is used to estimate the emotion of the creator of the contract from the text, and the analysis results are adjusted based on the emotion.

2. The system of claim 1.

7. The determination unit Analyze the user's emotions using the emotion estimation function and present the judgment results that elicit positive emotions.

2. The system of claim 1.

8. The amount calculation unit Analyzes user emotions using emotion estimation function and presents prices that elicit positive emotions 2. The system of claim 1.

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