System

Abstract

An object of a system according to an embodiment is to check the authenticity of a signature and exchange information between users.SOLUTION: A system according to an embodiment includes a signature determination unit, an authenticity verification unit, and an information exchange unit. The signature determination unit analyzes the image of the signature. The authenticity verification unit determines authenticity based on the image of the signature analyzed by the signature determination unit. The information exchange unit performs information exchange between users on the basis of information of the signature determined by the authenticity verification 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 to verify the authenticity of signatures and exchange information between users.

[0005] The system according to the embodiment aims to verify the authenticity of signatures and to facilitate information exchange between users. [Means for solving the problem]

[0006] The system according to the embodiment includes a signature determination unit, an authenticity confirmation unit, and an information exchange unit. The signature determination unit analyzes an image of a signature. The authenticity confirmation unit determines the authenticity based on the image of the signature analyzed by the signature determination unit. The information exchange unit exchanges information between users based on the information of the signature determined by the authenticity confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can verify the authenticity of signatures and enable users to exchange information with each other. [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 signature data management platform according to an embodiment of the present invention is a system with the functions of signature verification, authenticity verification, and information exchange, which can meet various needs related to celebrity signatures.

[0029] A signature data management platform according to an embodiment includes a signature determination unit, an authenticity verification unit, and an information exchange unit. The signature determination unit analyzes an image of a signature. For example, the signature determination unit digitally inputs an image of a signature, and a generation AI analyzes the image and compares it with a database. The signature determination unit can also upload an image of a signature, and the generation AI can analyze the image to identify the owner of the signature. For example, the generation AI can identify the owner of the signature using a text generation AI (e.g., LLM). The authenticity verification unit determines the authenticity based on the image of the signature analyzed by the signature determination unit. For example, the authenticity verification unit can determine the authenticity using image analysis technology. The authenticity verification unit can also analyze the image of a signature and determine the authenticity. For example, the generation AI can determine the authenticity using a multimodal generation AI. The information exchange unit exchanges information between users based on the information about the signature determined by the authenticity verification unit. For example, the information exchange unit allows users to share information about their signatures and interact with other users. The information exchange unit also allows the generation AI to analyze the content posted by users and perform appropriate matching. For example, the generation AI can suggest an appropriate recipient based on the content posted by users. This allows the signature data management platform according to the embodiment to meet various needs regarding celebrity signatures. For example, even if the owner of a signature is lost, the generation AI can easily determine the identity. Furthermore, by verifying the authenticity of a signature, the risk of being sold a counterfeit can be reduced. Furthermore, by allowing people with the same signature to exchange information and transfer unwanted signatures, the value of the signature can be maximized.

[0030] The signature determination unit inputs information about the place and date when the signature was written in addition to the image of the signature, and the generation AI comprehensively analyzes this information to improve the accuracy of the determination. For example, by inputting information about the place and date when the signature was written along with the image of the signature, the generation AI comprehensively analyzes this information and improves the accuracy of the determination. For example, by inputting information about the event where the signature was written, the system can compare it with a list of celebrities who attended the specific event. This improves the accuracy of the determination.

[0031] The signature determination unit analyzes the image of the signature, extracts features such as writing pressure and stroke order, and compares them with a database, thereby increasing the reliability of the determination. For example, the signature determination unit analyzes the image of the signature and extracts features such as writing pressure and stroke order. The generation AI compares these features with a database to increase the reliability of the determination. For example, it analyzes the strength of writing pressure and the pattern of stroke order. This increases the reliability of the determination.

[0032] The authenticity verification unit can improve the accuracy of authenticity determination by analyzing the image of the signature and by analyzing the paper quality and ink components on which the signature is written.The authenticity verification unit can improve the accuracy of authenticity determination by analyzing the image of the signature and by analyzing the paper quality and ink components on which the signature is written.For example, the fibrous structure of the paper quality and the chemical components of the ink are analyzed.This improves the accuracy of authenticity determination.

[0033] The authenticity verification unit can analyze not only the handwriting of the signature but also other handwritten elements around the signature to comprehensively determine its authenticity. For example, the authenticity verification unit can analyze not only the handwriting of the signature but also other handwritten elements around the signature (e.g., message and date) to comprehensively determine its authenticity. For example, it can analyze the content of the message and the way the date is written. This improves the accuracy of authenticity determination.

[0034] The information exchange unit can analyze the user's past exchange history and ratings in addition to the content posted by the user, and perform more appropriate matching. The information exchange unit can, for example, analyze the user's past exchange history and ratings in addition to the content posted by the user, and perform more appropriate matching. For example, it can prioritize matching with users who have a history of successful exchanges in the past or who have high ratings. This enables more appropriate matching.

[0035] The information exchange section can add a function that allows users to share not only images of autographs but also stories and background information related to the autographs, thereby increasing the depth of interaction.The information exchange section can add a function that allows users to share not only images of autographs but also stories and background information related to the autographs, thereby increasing the depth of interaction.For example, users can post stories and background information about when they received an autograph.This increases the depth of interaction.

[0036] The information exchange unit can apply the information exchange and exchange function to other collector's items, enabling the exchange and exchange of a wide range of items. For example, the information exchange unit can apply the information exchange and exchange function to other collector's items (e.g., trading cards and figurines), enabling the exchange and exchange of a wide range of items. For example, trading cards and figurines can be exchanged. This makes it possible to exchange and exchange a wide range of items.

[0037] The information exchange unit can provide the results of the information exchange and sharing together with related item and event information customized based on the user's preferences. For example, the information exchange unit can provide the results of the information exchange and sharing together with related item and event information customized based on the user's preferences. For example, it can provide information about items and events that interest the user. This makes it possible to provide information based on the user's preferences.

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

[0039] The signature recognition unit inputs information about the location and date when the signature was written, in addition to the image of the signature, and the AI ​​generation analyzes this information comprehensively to improve the accuracy of the recognition. For example, by inputting information about the event where the signature was written, the AI ​​can compare it with a list of celebrities who attended that particular event. This improves the accuracy of the recognition.

[0040] The signature determination unit analyzes the image of the signature, extracts features such as writing pressure and stroke order, and compares them with a database to increase the reliability of the determination. For example, it analyzes the strength of writing pressure and the pattern of stroke order. This increases the reliability of the determination.

[0041] In addition to analyzing the image of the signature, the authentication unit can also analyze the paper quality and ink components used to write the signature, improving the accuracy of authenticity determination. For example, it analyzes the fiber structure of the paper and the chemical components of the ink. This improves the accuracy of authenticity determination.

[0042] The authentication unit can comprehensively determine the authenticity of a signature by analyzing not only the handwriting of the signature but also other handwritten elements around the signature, such as the content of the message or the way the date is written. This improves the accuracy of the authentication.

[0043] The information exchange unit analyzes the user's past exchange history and ratings in addition to the content posted by the user, allowing for more appropriate matching. For example, it prioritizes matching with users who have a history of successful exchanges in the past or who have high ratings. This allows for more appropriate matching.

[0044] The Information Exchange Department will add a function that allows users to share not only images of autographs, but also stories and background information related to the autographs, thereby deepening the depth of interactions. For example, users can post stories and background information about when they received an autograph. This will deepen the depth of interactions.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The signature determination unit analyzes the image of the signature. For example, the signature determination unit inputs a digital image of the signature, and the generation AI analyzes the image and compares it with a database. The signature determination unit can also upload an image of the signature, and the generation AI analyzes the image to identify the owner of the signature. For example, the generation AI uses a text generation AI (e.g., LLM) to identify the owner of the signature. Step 2: The authenticity verification unit determines the authenticity based on the image of the signature analyzed by the signature determination unit. For example, the authenticity verification unit determines the authenticity of the signature using image analysis technology. The authenticity verification unit can also determine the authenticity by having the generation AI analyze the image of the signature. For example, the generation AI determines the authenticity of the signature using multimodal generation AI. Step 3: The information exchange unit exchanges information between users based on the signature information determined by the authenticity verification unit. For example, the information exchange unit allows users to share information about their signatures and interact with other users. The information exchange unit also allows the generation AI to analyze the content posted by users and perform appropriate matching. For example, the generation AI can suggest an appropriate transfer recipient based on the content posted by users.

[0047] (Example 2) The signature data management platform according to an embodiment of the present invention is a system with the functions of signature verification, authenticity verification, and information exchange, which can meet various needs related to celebrity signatures.

[0048] A signature data management platform according to an embodiment includes a signature determination unit, an authenticity verification unit, and an information exchange unit. The signature determination unit analyzes an image of a signature. For example, the signature determination unit digitally inputs an image of a signature, and a generation AI analyzes the image and compares it with a database. The signature determination unit can also upload an image of a signature, and the generation AI can analyze the image to identify the owner of the signature. For example, the generation AI can identify the owner of the signature using a text generation AI (e.g., LLM). The authenticity verification unit determines the authenticity based on the image of the signature analyzed by the signature determination unit. For example, the authenticity verification unit can determine the authenticity using image analysis technology. The authenticity verification unit can also analyze the image of a signature and determine the authenticity. For example, the generation AI can determine the authenticity using a multimodal generation AI. The information exchange unit exchanges information between users based on the information about the signature determined by the authenticity verification unit. For example, the information exchange unit allows users to share information about their signatures and interact with other users. The information exchange unit also allows the generation AI to analyze the content posted by users and perform appropriate matching. For example, the generation AI can suggest an appropriate recipient based on the content posted by users. This allows the signature data management platform according to the embodiment to meet various needs regarding celebrity signatures. For example, even if the owner of a signature is lost, the generation AI can easily determine the identity. Furthermore, by verifying the authenticity of a signature, the risk of being sold a counterfeit can be reduced. Furthermore, by allowing people with the same signature to exchange information and transfer unwanted signatures, the value of the signature can be maximized.

[0049] The signature determination unit inputs information about the place and date when the signature was written in addition to the image of the signature, and the generation AI comprehensively analyzes this information to improve the accuracy of the determination. For example, by inputting information about the place and date when the signature was written along with the image of the signature, the generation AI comprehensively analyzes this information and improves the accuracy of the determination. For example, by inputting information about the event where the signature was written, the system can compare it with a list of celebrities who attended the specific event. This improves the accuracy of the determination.

[0050] The signature determination unit analyzes the image of the signature, extracts features such as writing pressure and stroke order, and compares them with a database, thereby increasing the reliability of the determination. For example, the signature determination unit analyzes the image of the signature and extracts features such as writing pressure and stroke order. The generation AI compares these features with a database to increase the reliability of the determination. For example, it analyzes the strength of writing pressure and the pattern of stroke order. This increases the reliability of the determination.

[0051] The signature determination unit can use the emotion estimation function to analyze the emotion of the user when receiving an autograph and identify the owner of the autograph based on the emotion data. The signature determination unit, for example, analyzes the emotion of the user when receiving an autograph and identifies the owner of the autograph based on the emotion data. For example, it analyzes the emotion of joy or surprise the user felt when receiving an autograph and identifies the owner of the autograph based on the emotion data. This improves the accuracy of identifying the owner of the autograph.

[0052] The authenticity verification unit can improve the accuracy of authenticity determination by analyzing the image of the signature and by analyzing the paper quality and ink components on which the signature is written.The authenticity verification unit can improve the accuracy of authenticity determination by analyzing the image of the signature and by analyzing the paper quality and ink components on which the signature is written.For example, the fibrous structure of the paper quality and the chemical components of the ink are analyzed.This improves the accuracy of authenticity determination.

[0053] The authenticity verification unit can analyze not only the handwriting of the signature but also other handwritten elements around the signature to comprehensively determine its authenticity. For example, the authenticity verification unit can analyze not only the handwriting of the signature but also other handwritten elements around the signature (e.g., message and date) to comprehensively determine its authenticity. For example, it can analyze the content of the message and the way the date is written. This improves the accuracy of authenticity determination.

[0054] The authenticity verification unit can use the emotion estimation function to analyze the emotion felt by the user when receiving the autograph and determine the authenticity based on the emotion data. For example, the authenticity verification unit uses the emotion estimation function to analyze the emotion felt by the user when receiving the autograph and determine the authenticity based on the emotion data. For example, the authenticity verification unit analyzes the emotion felt by the user when receiving the autograph, such as joy or surprise, and determines the authenticity based on the emotion data. This improves the accuracy of authenticity determination.

[0055] The information exchange unit can analyze the user's past exchange history and ratings in addition to the content posted by the user, and perform more appropriate matching. The information exchange unit can, for example, analyze the user's past exchange history and ratings in addition to the content posted by the user, and perform more appropriate matching. For example, it can prioritize matching with users who have a history of successful exchanges in the past or who have high ratings. This enables more appropriate matching.

[0056] The information exchange section can add a function that allows users to share not only images of autographs but also stories and background information related to the autographs, thereby increasing the depth of interaction.The information exchange section can add a function that allows users to share not only images of autographs but also stories and background information related to the autographs, thereby increasing the depth of interaction.For example, users can post stories and background information about when they received an autograph.This increases the depth of interaction.

[0057] The information exchange unit can use the emotion estimation function to analyze other users' emotional reactions to the content posted by the user and preferentially display posts that are likely to resonate emotionally. The information exchange unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the content posted by the user and preferentially display posts that are likely to resonate emotionally. For example, posts with a large number of positive emotional reactions are preferentially displayed. This makes it possible to preferentially display posts that are likely to resonate emotionally.

[0058] The information exchange unit can apply the information exchange and exchange function to other collector's items, enabling the exchange and exchange of a wide range of items. For example, the information exchange unit can apply the information exchange and exchange function to other collector's items (e.g., trading cards and figurines), enabling the exchange and exchange of a wide range of items. For example, trading cards and figurines can be exchanged. This makes it possible to exchange and exchange a wide range of items.

[0059] The information exchange unit can provide the results of the information exchange and sharing together with related item and event information customized based on the user's preferences. For example, the information exchange unit can provide the results of the information exchange and sharing together with related item and event information customized based on the user's preferences. For example, it can provide information about items and events that interest the user. This makes it possible to provide information based on the user's preferences.

[0060] The information exchange unit can use the emotion estimation function to analyze the user's emotion regarding information exchange and exchange, and suggest related items and events based on that emotion. For example, the information exchange unit can use the emotion estimation function to analyze the user's emotion regarding information exchange and exchange, and suggest related items and events based on that emotion. For example, if the user shows positive emotion regarding information exchange and exchange, related items and events are suggested based on that emotion. This makes it possible to make suggestions based on the user's emotion.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The signature recognition unit inputs information about the location and date when the signature was written, in addition to the image of the signature, and the AI ​​generation analyzes this information comprehensively to improve the accuracy of the recognition. For example, by inputting information about the event where the signature was written, the AI ​​can compare it with a list of celebrities who attended that particular event. This improves the accuracy of the recognition.

[0063] The signature determination unit analyzes the image of the signature, extracts features such as writing pressure and stroke order, and compares them with a database to increase the reliability of the determination. For example, it analyzes the strength of writing pressure and the pattern of stroke order. This increases the reliability of the determination.

[0064] The signature determination unit uses the emotion estimation function to analyze the emotion expressed by the user when receiving the signature and identify the owner of the signature based on the emotion data. For example, it can analyze the emotion of joy or surprise expressed by the user when receiving the signature and identify the owner of the signature based on the emotion data. This improves the accuracy of identifying the owner of the signature.

[0065] In addition to analyzing the image of the signature, the authentication unit can also analyze the paper quality and ink components used to write the signature, improving the accuracy of authenticity determination. For example, it analyzes the fiber structure of the paper and the chemical components of the ink. This improves the accuracy of authenticity determination.

[0066] The authentication unit can comprehensively determine the authenticity of a signature by analyzing not only the handwriting of the signature but also other handwritten elements around the signature, such as the content of the message or the way the date is written. This improves the accuracy of the authentication.

[0067] The authenticity verification unit can use the emotion estimation function to analyze the emotion the user felt when receiving the autograph and determine the authenticity based on that emotion data. For example, it can analyze the joy or surprise the user felt when receiving the autograph and determine the authenticity based on that emotion data. This improves the accuracy of authenticity determination.

[0068] The information exchange unit analyzes the user's past exchange history and ratings in addition to the content posted by the user, allowing for more appropriate matching. For example, it prioritizes matching with users who have a history of successful exchanges in the past or who have high ratings. This allows for more appropriate matching.

[0069] The Information Exchange Department will add a function that allows users to share not only images of autographs, but also stories and background information related to the autographs, thereby deepening the depth of interactions. For example, users can post stories and background information about when they received an autograph. This will deepen the depth of interactions.

[0070] The information exchange unit uses the emotion estimation function to analyze other users' emotional reactions to the content posted by the user, and can prioritize displaying posts that are likely to evoke emotional empathy. For example, posts with a large number of positive emotional reactions can be prioritized. This allows posts that are likely to evoke emotional empathy to be prioritized.

[0071] The information exchange unit uses the emotion estimation function to analyze the user's emotions regarding information exchange and can suggest related items and events based on those emotions. For example, if the user expresses positive emotions regarding information exchange and can suggest related items and events based on those emotions, this makes it possible to make suggestions based on the user's emotions.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The signature determination unit analyzes the image of the signature. For example, the signature determination unit inputs a digital image of the signature, and the generation AI analyzes the image and compares it with a database. The signature determination unit can also upload an image of the signature, and the generation AI analyzes the image to identify the owner of the signature. For example, the generation AI uses a text generation AI (e.g., LLM) to identify the owner of the signature. Step 2: The authenticity verification unit determines the authenticity based on the image of the signature analyzed by the signature determination unit. For example, the authenticity verification unit determines the authenticity of the signature using image analysis technology. The authenticity verification unit can also determine the authenticity by having the generation AI analyze the image of the signature. For example, the generation AI determines the authenticity of the signature using multimodal generation AI. Step 3: The information exchange unit exchanges information between users based on the signature information determined by the authenticity verification unit. For example, the information exchange unit allows users to share information about their signatures and interact with other users. The information exchange unit also allows the generation AI to analyze the content posted by users and perform appropriate matching. For example, the generation AI can suggest an appropriate transfer recipient based on the content posted by users.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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, in order to avoid confusion and to 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.

[0140] 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]

[0141] 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 signature determination unit that analyzes an image of a signature; an authenticity confirmation unit that determines the authenticity of the signature based on the image of the signature analyzed by the signature determination unit; an information exchange unit that exchanges information between users based on the information of the signature determined by the authenticity verification unit. A system characterized by:

2. The signature determination unit In addition to the image of the signature, information on the location and date when the signature was written is input, and the generating AI comprehensively analyzes this information to improve the accuracy of the judgment.

2. The system of claim 1.

3. The authenticity verification unit In addition to analyzing the image of the signature, the quality of the paper on which the signature is written and the composition of the ink will be analyzed to improve the accuracy of authenticity determination.

2. The system of claim 1.

4. The information exchange unit In addition to the content posted by the user, the system analyzes the user's past interaction history and ratings to perform more appropriate matching.

2. The system of claim 1.

5. The signature determination unit The emotion of the user when receiving the autograph is analyzed, and the owner of the autograph is identified based on the emotion data.

2. The system of claim 1.

6. The authenticity verification unit The emotions of the user when receiving the autograph are analyzed, and the authenticity is determined based on the emotional data.

2. The system of claim 1.

7. The information exchange unit Analyzing the emotional reactions of other users to the content posted by the user, and preferentially displaying posts that are likely to evoke emotional empathy 2. The system of claim 1.

8. The information exchange unit Analyze the user's feelings regarding information exchange and suggest related items and events based on those feelings.

2. The system of claim 1.

Images