system

Abstract

The system according to this embodiment aims to ensure the reliability and traceability of AI-generated products. [Solution] The system according to the embodiment comprises a registration unit, a certification unit, and a traceability unit. The registration unit registers the products. The certification unit certifies the products registered by the registration unit. The traceability unit ensures the traceability of the products certified by the certification unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the reliability and traceability of AI products are not sufficiently ensured, which may cause anxiety to users.

[0005] The system according to the embodiment aims to ensure the reliability and traceability of AI products.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, a certification unit, and a traceability unit. The registration unit registers products. The certification unit certifies the products registered by the registration unit. The traceability unit ensures the traceability of the products certified by the certification unit.

Effects of the Invention

[0007] The system according to this embodiment can ensure the reliability and traceability of AI-generated products. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The product traceability system according to an embodiment of the present invention is a system that certifies AI products by blockchaining all products generated by generating AI, and establishes a traceable state that extends to the generating AI model and training data used. In this product traceability system, the generating AI creates a product, registers that product on the blockchain, and certifies the product. At this time, information related to the product (for example, the generating AI model and training data used) is also registered. This ensures the traceability of the product. For example, if the generating AI generates an image, the image is registered on the blockchain, and information on the generating AI model and training data used is also registered. This clarifies the origin and generation process of the product, eliminating suspicion. Furthermore, since information registered on the blockchain is difficult to tamper with, the reliability of the product is improved. As a result, users of the product can use it with peace of mind. In addition, the management of copyrights and intellectual property rights becomes easier because the origin and generation process of the product are clarified. This mechanism eliminates suspicion towards AI products and promotes more proactive and fair use of AI. For example, when a company uses generative AI to create advertising images, the increased reliability of the generated images enhances the effectiveness of the advertisement. Similarly, when a research institution uses generative AI to publish research results, the increased reliability of the generated images also enhances the credibility of the research itself. In this way, a product traceability system improves the reliability of AI-generated products, allowing users to use them with confidence.

[0029] The product traceability system according to the embodiment comprises a registration unit, a certification unit, and a traceability unit. The registration unit registers products. Products include, but are not limited to, digital content and physical products. The registration unit stores the products in a database, for example. The registration unit can also record the products on a blockchain. For example, the registration unit registers the products on a blockchain to prevent tampering with them. The certification unit certifies the products registered by the registration unit. Certification is performed by, but is not limited to, methods such as digital signatures and certificate issuance. For example, the certification unit affixes a digital signature to the product to verify its authenticity. The certification unit can also issue a certificate to the product to certify its origin and production process. The traceability unit ensures the traceability of the products certified by the certification unit. Traceability is performed by, but is not limited to, methods such as tracking systems and log recording. For example, the traceability unit identifies the origin and production process of the product to ensure its traceability. Furthermore, the traceability unit can record product logs and track the history of the products. This allows the product traceability system according to the embodiment to improve the reliability of AI products by ensuring product registration, certification, and traceability. For example, the product traceability system can determine registration priorities based on the importance of the product during registration. Additionally, the product traceability system can apply different registration methods depending on the product category during registration. This enables efficient management of the product traceability system.

[0030] The registration unit registers the generated products. These products include, but are not limited to, digital content and physical products. The registration unit stores the generated products in a database. Specifically, for digital content, it stores the metadata and the file itself in the database; for physical products, it records the product's serial number and manufacturing information. The registration unit can also record the generated products on a blockchain. Blockchain technology can prevent tampering with the generated products and enhance their reliability. For example, the registration unit calculates the hash value of the generated product and registers that hash value on the blockchain. This allows for the detection of tampering because if the content of the generated product changes, the hash value will no longer match. Furthermore, the registration unit can prioritize registration based on the importance of the generated product. For example, high-importance generated products can be registered quickly, while low-importance generated products can be postponed. This enables an efficient registration process. Additionally, different registration methods can be applied depending on the category of the generated product. For example, for digital content, detailed metadata is recorded, while for physical products, detailed records of the manufacturing process are recorded. This enables appropriate registration based on the characteristics of the product.

[0031] The certification unit certifies the products registered by the registration unit. Certification is performed by methods such as digital signatures and certificate issuance, but is not limited to these examples. Specifically, the certification unit affixes a digital signature to the product and verifies its authenticity. Digital signatures are generated using public-key cryptography and serve to prevent tampering with the product. For example, a digital signature is generated by calculating the hash value of the product and encrypting that hash value with a private key. The recipient can verify the digital signature using the public key to confirm that the product has not been tampered with. The certification unit can also issue certificates to the product, certifying its origin and creation process. Certificates contain detailed information about the product and the issuer, and serve to enhance the product's authenticity. For example, a certificate may include information such as the date and time of creation, the creator, and the technologies and methods used. This clarifies the origin and creation process of the product, improving its authenticity. Furthermore, the certification unit can automate the product certification process. For example, an efficient certification process can be achieved by automatically generating digital signatures and certificates at the same time as the product is registered. This allows the certification unit to quickly and accurately certify the product and ensure its reliability.

[0032] The traceability unit ensures the traceability of products certified by the certification unit. Traceability is achieved through methods such as tracking systems and log keeping, but is not limited to these examples. Specifically, the traceability unit identifies the origin and production process of products to ensure their traceability. For example, it records in detail the process by which the product was created and what materials and techniques were used. It can also track how the product was distributed and the routes it took to finally reach the consumer. This clarifies the product's history and improves its reliability. Furthermore, the traceability unit can also log the product and track its history. For example, it records information such as the date and time the product was registered, the date and time it was certified, the distribution route, and the final consumer as logs. This allows for centralized management of the product's entire history, which can be referenced as needed. In addition, the traceability unit can visualize the product's traceability information. For example, it can display the product's history as graphs and charts to make it easier to understand visually. This allows for quick understanding of the product's traceability information and appropriate action to be taken. This allows the traceability unit to record the history of the product in detail and ensure reliability, thereby improving the traceability of the product.

[0033] The registration unit can register information related to the product. This information may include, but is not limited to, the creation date and time, the creator, and the technology used. For example, the registration unit can record the creation date and time of the product to improve its traceability. It can also record the creator of the product to clarify its origin. Furthermore, the registration unit can record the technology used in the product to clarify the production process. This improves the traceability of the product by registering information related to it together. For example, the registration unit can record the creation date and time of the product on the blockchain to prevent tampering. It can also record the creator of the product on the blockchain to prove its origin. Furthermore, the registration unit can record the technology used in the product on the blockchain to prove the production process. This ensures the traceability of the product and improves its reliability.

[0034] The certification unit can certify the origin and production process of a product. The origin of a product includes, but is not limited to, the manufacturer or creator. The production process of a product includes, but is not limited to, details of the manufacturing process or algorithm. For example, the certification unit can certify the manufacturer of a product, thereby improving its reliability. The certification unit can also certify the creator of a product, clarifying its origin. Furthermore, the certification unit can certify the manufacturing process of a product, clarifying its production process. This improves the reliability of a product by certifying its origin and production process. For example, the certification unit can certify the manufacturer of a product with a digital signature, preventing tampering. The certification unit can also certify the creator of a product with a certificate, thus proving its origin. Furthermore, the certification unit can certify the manufacturing process of a product with a certificate, thus proving its production process. This improves the reliability of the product, allowing users to use it with confidence.

[0035] The traceability unit can identify the origin and production process of a product. The origin of a product includes, but is not limited to, the manufacturer or creator. The production process of a product includes, but is not limited to, details of the manufacturing process or algorithm. The traceability unit can, for example, identify the manufacturer of a product and ensure its traceability. The traceability unit can also identify the creator of a product and clarify its origin. Furthermore, the traceability unit can identify the manufacturing process of a product and clarify its production process. By identifying the origin and production process of a product, the traceability unit ensures the traceability of the product. For example, the traceability unit can log the manufacturer of a product to prevent tampering with the product. The traceability unit can also log the creator of a product to prove its origin. Furthermore, the traceability unit can log the manufacturing process of a product to prove its production process. This ensures the traceability of the product and improves its reliability.

[0036] The registration unit can register the generated product on the blockchain. Blockchains include, but are not limited to, public and private blockchains. For example, the registration unit can register the generated product on a public blockchain to prevent tampering. Alternatively, the registration unit can register the generated product on a private blockchain to improve its reliability. This ensures the traceability of the generated product and improves its reliability.

[0037] The certification unit can verify the reliability of the product. Reliability includes, but is not limited to, the error rate and the authentication process. For example, the certification unit can verify the error rate of the product to improve its reliability. The certification unit can also verify the authentication process of the product to improve its reliability. This allows users of the product to use it with confidence by verifying its reliability. For example, the certification unit can verify the error rate of the product to improve its reliability. The certification unit can also verify the authentication process of the product to improve its reliability. This improves the reliability of the product, allowing users of the product to use it with confidence.

[0038] The traceability unit can manage the traceability of the product. Traceability includes, but is not limited to, tracking systems and log recording. For example, the traceability unit can manage a product tracking system to ensure the traceability of the product. It can also record logs of the product to ensure its traceability. By managing the traceability of the product, the origin and production process of the product become clear. For example, the traceability unit can manage a product tracking system to ensure the traceability of the product. It can also record logs of the product to track its history. This ensures the traceability of the product and improves its reliability.

[0039] The registration unit can determine the registration priority of products based on their importance during the registration process. Product importance includes, but is not limited to, business impact and technological value. For example, the registration unit can prioritize the registration of high-importance products for faster verification. Alternatively, it can postpone the registration of low-importance products and prioritize the registration of high-importance products. Furthermore, the registration unit can adjust the timing of registration according to the importance of the products to ensure efficient registration. This allows for the priority registration of important products by determining registration priority based on their importance. For example, the registration unit can prioritize the registration of high-importance products for faster verification. Alternatively, it can postpone the registration of low-importance products and prioritize the registration of high-importance products. This ensures product traceability and improves product reliability.

[0040] The registration unit can apply different registration methods depending on the category of the product when registering it. Product categories include, but are not limited to, image products, text products, and audio products. For example, the registration unit can register image products using a dedicated registration method for efficient management. It can also register text products using a different registration method and provide appropriate certification. Furthermore, it can register audio products using yet another registration method to ensure traceability. This allows for efficient management by applying different registration methods depending on the product category. For example, the registration unit can register image products using a dedicated registration method for efficient management. It can also register text products using a different registration method and provide appropriate certification. This ensures product traceability and improves the reliability of the products.

[0041] The registration unit can prioritize the registration of highly relevant products by considering their geographical distribution during the registration process. The geographical distribution of products includes, but is not limited to, regional data and geographic information systems (GIS). For example, the registration unit can prioritize the registration of geographically close products to ensure regional traceability. The registration unit can also group together geographically relevant products for efficient management. Furthermore, the registration unit can adjust the registration order of products based on their geographical distribution. This ensures regional traceability by considering the geographical distribution of products. For example, the registration unit can prioritize the registration of geographically close products to ensure regional traceability. The registration unit can also group together geographically relevant products for efficient management. This ensures the traceability of products and improves their reliability.

[0042] The registration unit can improve the accuracy of product registration by referring to relevant literature when registering a product. Relevant literature includes, but is not limited to, academic papers and patent documents. For example, the registration unit can automatically refer to literature related to the product to verify the accuracy of the registration information. Furthermore, the registration unit can add detailed information based on relevant literature when registering a product. This ensures the traceability of the product and improves its reliability.

[0043] The certification unit can adjust the level of detail in the certification based on the importance of the product. The importance of the product includes, but is not limited to, business impact and technical value. For example, the certification unit can provide detailed certificates for highly important products to ensure reliability. Alternatively, it can provide concise certificates for less important products for efficient certification. Furthermore, the certification unit can adjust the level of detail in the certificate according to the importance of the product. This ensures reliability by adjusting the level of detail in the certification based on the importance of the product. For example, the certification unit can provide detailed certificates for highly important products to ensure reliability. Alternatively, it can provide concise certificates for less important products for efficient certification. This ensures the traceability of the product and improves its reliability.

[0044] The proof unit can apply different proof algorithms depending on the category of the product. Product categories include, but are not limited to, image products, text products, and audio products. For example, the proof unit can apply a dedicated proof algorithm to image products to provide accurate proof. It can also apply a different proof algorithm to text products to provide appropriate proof. Furthermore, it can apply yet another proof algorithm to audio products to ensure reliability. This ensures accurate proof by applying different proof algorithms depending on the category of the product. For example, the proof unit can apply a dedicated proof algorithm to image products to provide accurate proof. It can also apply a different proof algorithm to text products to provide appropriate proof. This ensures the traceability of the products and improves their reliability.

[0045] The proofing unit can determine the priority of proofs based on the submission timing of the products. The submission timing of the products includes, but is not limited to, the submission date and time. For example, the proofing unit can prioritize proving products submitted earlier to ensure a quick response. Alternatively, the proofing unit can prioritize the proof of earlier products, postponing later products. Furthermore, the proofing unit can adjust the order of proofs based on the submission timing. This allows for a quick response by prioritizing proofs based on the submission timing of the products. For example, the proofing unit can prioritize proving products submitted earlier to ensure a quick response. Alternatively, the proofing unit can prioritize the proof of earlier products, postponing later products. This ensures the traceability of the products and improves their reliability.

[0046] The proof unit can adjust the order of proof based on the relevance of the products. Relevance of products includes, but is not limited to, technical or business relevance. For example, the proof unit can prioritize proving highly relevant products, thereby achieving an efficient proof. Alternatively, the proof unit can prioritize proving highly relevant products while delaying less relevant products. Furthermore, the proof unit can adjust the order of proof based on the relevance of the products. This allows for an efficient proof by adjusting the order of proof based on the relevance of the products. For example, the proof unit can prioritize proving highly relevant products, achieving an efficient proof. Alternatively, the proof unit can prioritize proving highly relevant products while delaying less relevant products. This ensures the traceability of the products and improves their reliability.

[0047] The traceability unit can predict current traceability by referring to past traceability data. Past traceability data includes, but is not limited to, past log data and historical data. For example, the traceability unit can predict current traceability based on past traceability data and perform efficient management. The traceability unit can also identify problems with current traceability by referring to past traceability data. Furthermore, the traceability unit can analyze past traceability data and propose areas for improvement in current traceability. This ensures the traceability of the product and improves its reliability.

[0048] The traceability unit can apply different traceability analysis methods to each category of product. Product categories include, but are not limited to, image products, text products, and audio products. For example, the traceability unit can apply a dedicated traceability analysis method to image products to ensure accurate traceability. It can also apply a different traceability analysis method to text products to ensure appropriate traceability. Furthermore, it can apply yet another traceability analysis method to audio products to ensure reliability. This ensures accurate traceability by applying different traceability analysis methods to each category of product. For example, the traceability unit can apply a dedicated traceability analysis method to image products to ensure accurate traceability. It can also apply a different traceability analysis method to text products to ensure appropriate traceability. This ensures the traceability of the products and improves their reliability.

[0049] The traceability department can analyze changes in traceability based on the submission date of the product. The submission date of the product includes, but is not limited to, the submission date and time. For example, the traceability department can prioritize the traceability of products submitted earlier and respond quickly. Alternatively, the traceability department can prioritize the traceability of products submitted earlier and postpone those submitted later. Furthermore, the traceability department can analyze changes in traceability based on the submission date and implement efficient management. This ensures the traceability of the product and improves its reliability.

[0050] The traceability department can analyze traceability by referring to relevant market data for the product. This relevant market data includes, but is not limited to, market research data and sales data. For example, the traceability department can analyze traceability based on relevant market data for the product and implement efficient management. Furthermore, the traceability department can identify traceability issues by referring to relevant market data for the product. In addition, the traceability department can analyze relevant market data for the product and propose improvements to traceability. This ensures the traceability of the product and improves its reliability.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The product traceability system can automatically retrieve relevant legal information about a product and add it to the registration information when the product is registered. For example, it can automatically retrieve patent and copyright information to enhance the legal protection of the product. It can also retrieve relevant regulatory information when the product is registered to verify legal compliance. Furthermore, it can retrieve relevant contract information and verify contract terms when the product is registered. In this way, legal protection and compliance can be ensured by automatically retrieving the legal information of the product.

[0053] The product traceability system can perform a quality assessment of a product upon registration and add the assessment results to the registration information. For example, it can set criteria for evaluating product quality and perform the assessment based on those criteria. It can also record the product quality assessment results on the blockchain to prevent tampering. Furthermore, it can provide the product quality assessment results to users to improve the reliability of the product. In this way, the reliability of the product can be improved by performing a quality assessment.

[0054] The product traceability system can perform an environmental impact assessment of a product when it is registered and add the assessment results to the registration information. For example, it can assess the environmental burden during the manufacturing process of a product and record the assessment results. It can also record the environmental impact assessment results of a product on a blockchain to prevent tampering. Furthermore, it can provide the environmental impact assessment results of a product to users, promoting the selection of environmentally conscious products. In this way, by conducting environmental impact assessments of products, the use of environmentally conscious products can be promoted.

[0055] The product traceability system can perform a market value assessment of a product upon registration and add the assessment result to the registration information. For example, it can set criteria for assessing the market value of a product and perform the assessment based on those criteria. It can also record the market value assessment result of the product on the blockchain to prevent tampering. Furthermore, it can provide the market value assessment result of the product to the user, clarifying the economic value of the product. In this way, the economic value of a product can be clarified by performing a market value assessment.

[0056] The product traceability system can automatically acquire relevant market data for a product when it is registered and add it to the registration information. For example, it can automatically acquire market trends and competitive information for the product and evaluate its market value. It can also formulate market strategies based on relevant market data when the product is registered. Furthermore, it can make market forecasts based on relevant market data when the product is registered. In this way, by automatically acquiring market data for a product, it is possible to evaluate its market value and formulate strategies.

[0057] The product traceability system can automatically acquire relevant technical information about a product when it is registered and add it to the registration information. For example, it can automatically acquire the technical specifications and technical literature of the product and evaluate its technical value. It can also perform a technical evaluation based on the relevant technical information when the product is registered. Furthermore, it can perform a technical prediction based on the relevant technical information when the product is registered. In this way, by automatically acquiring the technical information of the product, it is possible to evaluate and predict its technical value.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The registration unit registers the generated product. Generated products include digital content and physical products. The registration unit can store the generated product in a database or record it on the blockchain. For example, registering the generated product on the blockchain prevents tampering with the product. Step 2: The certification unit certifies the products registered by the registration unit. Certification is carried out by methods such as digital signatures and certificate issuance. For example, a digital signature is attached to the product to verify its authenticity. Alternatively, a certificate can be issued to the product to prove its origin and generation process. Step 3: The traceability unit ensures the traceability of the product certified by the certification unit. Traceability is achieved through methods such as tracking systems and log keeping. For example, the origin and production process of the product are identified to ensure its traceability. Logs of the product can also be recorded to track its history.

[0060] (Example of form 2) The product traceability system according to an embodiment of the present invention is a system that certifies AI products by blockchaining all products generated by generating AI, and establishes a traceable state that extends to the generating AI model and training data used. In this product traceability system, the generating AI creates a product, registers that product on the blockchain, and certifies the product. At this time, information related to the product (for example, the generating AI model and training data used) is also registered. This ensures the traceability of the product. For example, if the generating AI generates an image, the image is registered on the blockchain, and information on the generating AI model and training data used is also registered. This clarifies the origin and generation process of the product, eliminating suspicion. Furthermore, since information registered on the blockchain is difficult to tamper with, the reliability of the product is improved. As a result, users of the product can use it with peace of mind. In addition, the management of copyrights and intellectual property rights becomes easier because the origin and generation process of the product are clarified. This mechanism eliminates suspicion towards AI products and promotes more proactive and fair use of AI. For example, when a company uses generative AI to create advertising images, the increased reliability of the generated images enhances the effectiveness of the advertisement. Similarly, when a research institution uses generative AI to publish research results, the increased reliability of the generated images also enhances the credibility of the research itself. In this way, a product traceability system improves the reliability of AI-generated products, allowing users to use them with confidence.

[0061] The product traceability system according to the embodiment comprises a registration unit, a certification unit, and a traceability unit. The registration unit registers products. Products include, but are not limited to, digital content and physical products. The registration unit stores the products in a database, for example. The registration unit can also record the products on a blockchain. For example, the registration unit registers the products on a blockchain to prevent tampering with them. The certification unit certifies the products registered by the registration unit. Certification is performed by, but is not limited to, methods such as digital signatures and certificate issuance. For example, the certification unit affixes a digital signature to the product to verify its authenticity. The certification unit can also issue a certificate to the product to certify its origin and production process. The traceability unit ensures the traceability of the products certified by the certification unit. Traceability is performed by, but is not limited to, methods such as tracking systems and log recording. For example, the traceability unit identifies the origin and production process of the product to ensure its traceability. Furthermore, the traceability unit can record product logs and track the history of the products. This allows the product traceability system according to the embodiment to improve the reliability of AI products by ensuring product registration, certification, and traceability. For example, the product traceability system can determine registration priorities based on the importance of the product during registration. Additionally, the product traceability system can apply different registration methods depending on the product category during registration. This enables efficient management of the product traceability system.

[0062] The registration unit registers the generated products. These products include, but are not limited to, digital content and physical products. The registration unit stores the generated products in a database. Specifically, for digital content, it stores the metadata and the file itself in the database; for physical products, it records the product's serial number and manufacturing information. The registration unit can also record the generated products on a blockchain. Blockchain technology can prevent tampering with the generated products and enhance their reliability. For example, the registration unit calculates the hash value of the generated product and registers that hash value on the blockchain. This allows for the detection of tampering because if the content of the generated product changes, the hash value will no longer match. Furthermore, the registration unit can prioritize registration based on the importance of the generated product. For example, high-importance generated products can be registered quickly, while low-importance generated products can be postponed. This enables an efficient registration process. Additionally, different registration methods can be applied depending on the category of the generated product. For example, for digital content, detailed metadata is recorded, while for physical products, detailed records of the manufacturing process are recorded. This enables appropriate registration based on the characteristics of the product.

[0063] The certification unit certifies the products registered by the registration unit. Certification is performed by methods such as digital signatures and certificate issuance, but is not limited to these examples. Specifically, the certification unit affixes a digital signature to the product and verifies its authenticity. Digital signatures are generated using public-key cryptography and serve to prevent tampering with the product. For example, a digital signature is generated by calculating the hash value of the product and encrypting that hash value with a private key. The recipient can verify the digital signature using the public key to confirm that the product has not been tampered with. The certification unit can also issue certificates to the product, certifying its origin and creation process. Certificates contain detailed information about the product and the issuer, and serve to enhance the product's authenticity. For example, a certificate may include information such as the date and time of creation, the creator, and the technologies and methods used. This clarifies the origin and creation process of the product, improving its authenticity. Furthermore, the certification unit can automate the product certification process. For example, an efficient certification process can be achieved by automatically generating digital signatures and certificates at the same time as the product is registered. This allows the certification unit to quickly and accurately certify the product and ensure its reliability.

[0064] The traceability unit ensures the traceability of products certified by the certification unit. Traceability is achieved through methods such as tracking systems and log keeping, but is not limited to these examples. Specifically, the traceability unit identifies the origin and production process of products to ensure their traceability. For example, it records in detail the process by which the product was created and what materials and techniques were used. It can also track how the product was distributed and the routes it took to finally reach the consumer. This clarifies the product's history and improves its reliability. Furthermore, the traceability unit can also log the product and track its history. For example, it records information such as the date and time the product was registered, the date and time it was certified, the distribution route, and the final consumer as logs. This allows for centralized management of the product's entire history, which can be referenced as needed. In addition, the traceability unit can visualize the product's traceability information. For example, it can display the product's history as graphs and charts to make it easier to understand visually. This allows for quick understanding of the product's traceability information and appropriate action to be taken. This allows the traceability unit to record the history of the product in detail and ensure reliability, thereby improving the traceability of the product.

[0065] The registration unit can register information related to the product. This information may include, but is not limited to, the creation date and time, the creator, and the technology used. For example, the registration unit can record the creation date and time of the product to improve its traceability. It can also record the creator of the product to clarify its origin. Furthermore, the registration unit can record the technology used in the product to clarify the production process. This improves the traceability of the product by registering information related to it together. For example, the registration unit can record the creation date and time of the product on the blockchain to prevent tampering. It can also record the creator of the product on the blockchain to prove its origin. Furthermore, the registration unit can record the technology used in the product on the blockchain to prove the production process. This ensures the traceability of the product and improves its reliability.

[0066] The certification unit can certify the origin and production process of a product. The origin of a product includes, but is not limited to, the manufacturer or creator. The production process of a product includes, but is not limited to, details of the manufacturing process or algorithm. For example, the certification unit can certify the manufacturer of a product, thereby improving its reliability. The certification unit can also certify the creator of a product, clarifying its origin. Furthermore, the certification unit can certify the manufacturing process of a product, clarifying its production process. This improves the reliability of a product by certifying its origin and production process. For example, the certification unit can certify the manufacturer of a product with a digital signature, preventing tampering. The certification unit can also certify the creator of a product with a certificate, thus proving its origin. Furthermore, the certification unit can certify the manufacturing process of a product with a certificate, thus proving its production process. This improves the reliability of the product, allowing users to use it with confidence.

[0067] The traceability unit can identify the origin and production process of a product. The origin of a product includes, but is not limited to, the manufacturer or creator. The production process of a product includes, but is not limited to, details of the manufacturing process or algorithm. The traceability unit can, for example, identify the manufacturer of a product and ensure its traceability. The traceability unit can also identify the creator of a product and clarify its origin. Furthermore, the traceability unit can identify the manufacturing process of a product and clarify its production process. By identifying the origin and production process of a product, the traceability unit ensures the traceability of the product. For example, the traceability unit can log the manufacturer of a product to prevent tampering with the product. The traceability unit can also log the creator of a product to prove its origin. Furthermore, the traceability unit can log the manufacturing process of a product to prove its production process. This ensures the traceability of the product and improves its reliability.

[0068] The registration unit can register the generated product on the blockchain. Blockchains include, but are not limited to, public and private blockchains. For example, the registration unit can register the generated product on a public blockchain to prevent tampering. Alternatively, the registration unit can register the generated product on a private blockchain to improve its reliability. This ensures the traceability of the generated product and improves its reliability.

[0069] The certification unit can verify the reliability of the product. Reliability includes, but is not limited to, the error rate and the authentication process. For example, the certification unit can verify the error rate of the product to improve its reliability. The certification unit can also verify the authentication process of the product to improve its reliability. This allows users of the product to use it with confidence by verifying its reliability. For example, the certification unit can verify the error rate of the product to improve its reliability. The certification unit can also verify the authentication process of the product to improve its reliability. This improves the reliability of the product, allowing users of the product to use it with confidence.

[0070] The traceability unit can manage the traceability of the product. Traceability includes, but is not limited to, tracking systems and log recording. For example, the traceability unit can manage a product tracking system to ensure the traceability of the product. It can also record logs of the product to ensure its traceability. By managing the traceability of the product, the origin and production process of the product become clear. For example, the traceability unit can manage a product tracking system to ensure the traceability of the product. It can also record logs of the product to track its history. This ensures the traceability of the product and improves its reliability.

[0071] The registration unit can estimate the user's emotions and adjust the timing of product registration based on the estimated emotions. For example, if the user is stressed, the registration unit can register the product quickly to reduce the user's burden. If the user is relaxed, the registration unit can register the product slowly, allowing for detailed confirmation. Furthermore, if the user is in a hurry, the registration unit can register the product immediately, enabling a quick response. In this way, the user's burden is reduced by adjusting the timing of product registration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the registration unit can collect user emotion data with sensors and estimate emotions using an emotion estimation algorithm. This allows the timing of product registration to be adjusted based on the user's emotions.

[0072] The registration unit can determine the registration priority of products based on their importance during the registration process. Product importance includes, but is not limited to, business impact and technological value. For example, the registration unit can prioritize the registration of high-importance products for faster verification. Alternatively, it can postpone the registration of low-importance products and prioritize the registration of high-importance products. Furthermore, the registration unit can adjust the timing of registration according to the importance of the products to ensure efficient registration. This allows for the priority registration of important products by determining registration priority based on their importance. For example, the registration unit can prioritize the registration of high-importance products for faster verification. Alternatively, it can postpone the registration of low-importance products and prioritize the registration of high-importance products. This ensures product traceability and improves product reliability.

[0073] The registration unit can apply different registration methods depending on the category of the product when registering it. Product categories include, but are not limited to, image products, text products, and audio products. For example, the registration unit can register image products using a dedicated registration method for efficient management. It can also register text products using a different registration method and provide appropriate certification. Furthermore, it can register audio products using yet another registration method to ensure traceability. This allows for efficient management by applying different registration methods depending on the product category. For example, the registration unit can register image products using a dedicated registration method for efficient management. It can also register text products using a different registration method and provide appropriate certification. This ensures product traceability and improves the reliability of the products.

[0074] The registration unit can estimate the user's emotions and determine the priority of the products to register based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize registering high-importance products. If the user is relaxed, the registration unit can register products sequentially, including those of lower importance. Furthermore, if the user is in a hurry, the registration unit can immediately register the most important products. This reduces the user's burden by determining the priority of products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the registration unit can collect user emotion data using sensors and estimate emotions using an emotion estimation algorithm. This allows the system to determine the priority of products based on the user's emotions.

[0075] The registration unit can prioritize the registration of highly relevant products by considering their geographical distribution during the registration process. The geographical distribution of products includes, but is not limited to, regional data and geographic information systems (GIS). For example, the registration unit can prioritize the registration of geographically close products to ensure regional traceability. The registration unit can also group together geographically relevant products for efficient management. Furthermore, the registration unit can adjust the registration order of products based on their geographical distribution. This ensures regional traceability by considering the geographical distribution of products. For example, the registration unit can prioritize the registration of geographically close products to ensure regional traceability. The registration unit can also group together geographically relevant products for efficient management. This ensures the traceability of products and improves their reliability.

[0076] The registration unit can improve the accuracy of product registration by referring to relevant literature when registering a product. Relevant literature includes, but is not limited to, academic papers and patent documents. For example, the registration unit can automatically refer to literature related to the product to verify the accuracy of the registration information. Furthermore, the registration unit can add detailed information based on relevant literature when registering a product. This ensures the traceability of the product and improves its reliability.

[0077] The certification unit can estimate the user's emotions and adjust the presentation of the certification based on the estimated emotions. For example, if the user is nervous, the certification unit can provide a simple and highly visible certification. If the user is relaxed, the certification unit can also provide a certification containing detailed information. Furthermore, if the user is in a hurry, the certification unit can provide a concise certification that gets straight to the point. This reduces the user's burden by adjusting the presentation of the certification according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the certification unit can collect user emotion data with sensors and estimate emotions using an emotion estimation algorithm. This allows the presentation of the certification to be adjusted based on the user's emotions.

[0078] The certification unit can adjust the level of detail in the certification based on the importance of the product. The importance of the product includes, but is not limited to, business impact and technical value. For example, the certification unit can provide detailed certificates for highly important products to ensure reliability. Alternatively, it can provide concise certificates for less important products for efficient certification. Furthermore, the certification unit can adjust the level of detail in the certificate according to the importance of the product. This ensures reliability by adjusting the level of detail in the certification based on the importance of the product. For example, the certification unit can provide detailed certificates for highly important products to ensure reliability. Alternatively, it can provide concise certificates for less important products for efficient certification. This ensures the traceability of the product and improves its reliability.

[0079] The proof unit can apply different proof algorithms depending on the category of the product. Product categories include, but are not limited to, image products, text products, and audio products. For example, the proof unit can apply a dedicated proof algorithm to image products to provide accurate proof. It can also apply a different proof algorithm to text products to provide appropriate proof. Furthermore, it can apply yet another proof algorithm to audio products to ensure reliability. This ensures accurate proof by applying different proof algorithms depending on the category of the product. For example, the proof unit can apply a dedicated proof algorithm to image products to provide accurate proof. It can also apply a different proof algorithm to text products to provide appropriate proof. This ensures the traceability of the products and improves their reliability.

[0080] The certification unit can estimate the user's emotions and adjust the length of the certification based on the estimated emotions. For example, if the user is nervous, the certification unit can provide a short, to-the-point certification. If the user is relaxed, the certification unit can provide a longer certification with a more detailed explanation. Furthermore, if the user is in a hurry, the certification unit can provide a short certification that can be quickly reviewed. This reduces the user's burden by adjusting the length of the certification according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the certification unit can collect user emotion data using sensors and estimate emotions using an emotion estimation algorithm. This allows the length of the certification to be adjusted based on the user's emotions.

[0081] The proofing unit can determine the priority of proofs based on the submission timing of the products. The submission timing of the products includes, but is not limited to, the submission date and time. For example, the proofing unit can prioritize proving products submitted earlier to ensure a quick response. Alternatively, the proofing unit can prioritize the proof of earlier products, postponing later products. Furthermore, the proofing unit can adjust the order of proofs based on the submission timing. This allows for a quick response by prioritizing proofs based on the submission timing of the products. For example, the proofing unit can prioritize proving products submitted earlier to ensure a quick response. Alternatively, the proofing unit can prioritize the proof of earlier products, postponing later products. This ensures the traceability of the products and improves their reliability.

[0082] The proof unit can adjust the order of proof based on the relevance of the products. Relevance of products includes, but is not limited to, technical or business relevance. For example, the proof unit can prioritize proving highly relevant products, thereby achieving an efficient proof. Alternatively, the proof unit can prioritize proving highly relevant products while delaying less relevant products. Furthermore, the proof unit can adjust the order of proof based on the relevance of the products. This allows for an efficient proof by adjusting the order of proof based on the relevance of the products. For example, the proof unit can prioritize proving highly relevant products, achieving an efficient proof. Alternatively, the proof unit can prioritize proving highly relevant products while delaying less relevant products. This ensures the traceability of the products and improves their reliability.

[0083] The traceability unit can estimate the user's emotions and adjust the traceability display method based on the estimated emotions. For example, if the user is tense, the traceability unit can provide a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise display method. This reduces the user's burden by adjusting the traceability display method according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the traceability unit can collect user emotion data using sensors and estimate emotions using an emotion estimation algorithm. This allows the traceability display method to be adjusted based on the user's emotions.

[0084] The traceability unit can predict current traceability by referring to past traceability data. Past traceability data includes, but is not limited to, past log data and historical data. For example, the traceability unit can predict current traceability based on past traceability data and perform efficient management. The traceability unit can also identify problems with current traceability by referring to past traceability data. Furthermore, the traceability unit can analyze past traceability data and propose areas for improvement in current traceability. This ensures the traceability of the product and improves its reliability.

[0085] The traceability unit can apply different traceability analysis methods to each category of product. Product categories include, but are not limited to, image products, text products, and audio products. For example, the traceability unit can apply a dedicated traceability analysis method to image products to ensure accurate traceability. It can also apply a different traceability analysis method to text products to ensure appropriate traceability. Furthermore, it can apply yet another traceability analysis method to audio products to ensure reliability. This ensures accurate traceability by applying different traceability analysis methods to each category of product. For example, the traceability unit can apply a dedicated traceability analysis method to image products to ensure accurate traceability. It can also apply a different traceability analysis method to text products to ensure appropriate traceability. This ensures the traceability of the products and improves their reliability.

[0086] The traceability unit can estimate the user's emotions and adjust the importance of traceability based on the estimated emotions. For example, if the user is stressed, the traceability unit will prioritize tracing high-importance products. If the user is relaxed, the traceability unit can sequentially trace products, including those of lower importance. Furthermore, if the user is in a hurry, the traceability unit can immediately trace the most important products. This reduces the user's burden by adjusting the importance of traceability according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the traceability unit can collect user emotion data using sensors and estimate emotions using an emotion estimation algorithm. This allows the importance of traceability to be adjusted based on the user's emotions.

[0087] The traceability department can analyze changes in traceability based on the submission date of the product. The submission date of the product includes, but is not limited to, the submission date and time. For example, the traceability department can prioritize the traceability of products submitted earlier and respond quickly. Alternatively, the traceability department can prioritize the traceability of products submitted earlier and postpone those submitted later. Furthermore, the traceability department can analyze changes in traceability based on the submission date and implement efficient management. This ensures the traceability of the product and improves its reliability.

[0088] The traceability department can analyze traceability by referring to relevant market data for the product. This relevant market data includes, but is not limited to, market research data and sales data. For example, the traceability department can analyze traceability based on relevant market data for the product and implement efficient management. Furthermore, the traceability department can identify traceability issues by referring to relevant market data for the product. In addition, the traceability department can analyze relevant market data for the product and propose improvements to traceability. This ensures the traceability of the product and improves its reliability.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The product traceability system can further estimate the user's emotions and adjust the product certification method based on those emotions. For example, if the user is feeling anxious, the certification unit can provide a certificate with detailed explanations to increase the user's sense of security. If the user is in a hurry, the certification unit can provide a concise and quickly verifiable certificate. Furthermore, if the user is relaxed, the certification unit can provide a visually appealing certificate to improve the user experience. In this way, by providing a certification method that responds to the user's emotions, user satisfaction can be increased.

[0091] The product traceability system can automatically retrieve relevant legal information about a product and add it to the registration information when the product is registered. For example, it can automatically retrieve patent and copyright information to enhance the legal protection of the product. It can also retrieve relevant regulatory information when the product is registered to verify legal compliance. Furthermore, it can retrieve relevant contract information and verify contract terms when the product is registered. In this way, legal protection and compliance can be ensured by automatically retrieving the legal information of the product.

[0092] The product traceability system can perform a quality assessment of a product upon registration and add the assessment results to the registration information. For example, it can set criteria for evaluating product quality and perform the assessment based on those criteria. It can also record the product quality assessment results on the blockchain to prevent tampering. Furthermore, it can provide the product quality assessment results to users to improve the reliability of the product. In this way, the reliability of the product can be improved by performing a quality assessment.

[0093] The product traceability system can perform an environmental impact assessment of a product when it is registered and add the assessment results to the registration information. For example, it can assess the environmental burden during the manufacturing process of a product and record the assessment results. It can also record the environmental impact assessment results of a product on a blockchain to prevent tampering. Furthermore, it can provide the environmental impact assessment results of a product to users, promoting the selection of environmentally conscious products. In this way, by conducting environmental impact assessments of products, the use of environmentally conscious products can be promoted.

[0094] The product traceability system can perform a market value assessment of a product upon registration and add the assessment result to the registration information. For example, it can set criteria for assessing the market value of a product and perform the assessment based on those criteria. It can also record the market value assessment result of the product on the blockchain to prevent tampering. Furthermore, it can provide the market value assessment result of the product to the user, clarifying the economic value of the product. In this way, the economic value of a product can be clarified by performing a market value assessment.

[0095] The product traceability system can estimate the user's emotions and adjust how product traceability information is displayed based on those emotions. For example, if the user is feeling anxious, the traceability unit can provide detailed information to increase the user's sense of security. If the user is in a hurry, the traceability unit can provide concise and easily visible information. Furthermore, if the user is relaxed, the traceability unit can provide visually appealing information to improve the user experience. In this way, by providing traceability information displayed in a way that suits the user's emotions, user satisfaction can be improved.

[0096] The product traceability system can automatically acquire relevant market data for a product when it is registered and add it to the registration information. For example, it can automatically acquire market trends and competitive information for the product and evaluate its market value. It can also formulate market strategies based on relevant market data when the product is registered. Furthermore, it can make market forecasts based on relevant market data when the product is registered. In this way, by automatically acquiring market data for a product, it is possible to evaluate its market value and formulate strategies.

[0097] The product traceability system can estimate the user's emotions and adjust the product registration procedure based on those emotions. For example, if the user is stressed, the registration unit can simplify the procedure and register the product quickly. If the user is relaxed, the registration unit can prompt detailed confirmation to ensure accurate registration. Furthermore, if the user is in a hurry, the registration unit can complete the registration immediately and provide a quick response. This reduces the burden on the user by providing a registration procedure that is tailored to their emotions.

[0098] The product traceability system can automatically acquire relevant technical information about a product when it is registered and add it to the registration information. For example, it can automatically acquire the technical specifications and technical literature of the product and evaluate its technical value. It can also perform a technical evaluation based on the relevant technical information when the product is registered. Furthermore, it can perform a technical prediction based on the relevant technical information when the product is registered. In this way, by automatically acquiring the technical information of the product, it is possible to evaluate and predict its technical value.

[0099] The product traceability system can estimate the user's emotions and adjust the frequency of product traceability information updates based on those emotions. For example, if the user is feeling anxious, the traceability unit will update the information frequently to increase the user's sense of security. Conversely, if the user is relaxed, the traceability unit can update the information at a moderate frequency to reduce the user's burden. Furthermore, if the user is in a hurry, the traceability unit can update the information quickly to provide a prompt response. By providing traceability information updates that match the user's emotions, user satisfaction can be improved.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The registration unit registers the generated product. Generated products include digital content and physical products. The registration unit can store the generated product in a database or record it on the blockchain. For example, registering the generated product on the blockchain prevents tampering with the product. Step 2: The certification unit certifies the products registered by the registration unit. Certification is carried out by methods such as digital signatures and certificate issuance. For example, a digital signature is attached to the product to verify its authenticity. Alternatively, a certificate can be issued to the product to prove its origin and generation process. Step 3: The traceability unit ensures the traceability of the product certified by the certification unit. Traceability is achieved through methods such as tracking systems and log keeping. For example, the origin and production process of the product are identified to ensure its traceability. Logs of the product can also be recorded to track its history.

[0102] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0105] For example, each of the multiple elements, including the registration unit, certification unit, and traceability unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 or the identification processing unit 290 of the data processing unit 12, and registers the product on the blockchain. The certification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs digital signatures and issues certificates for the product. The traceability unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and tracks the origin and generation process of the product. Furthermore, the registration unit can estimate the user's emotions and adjust the timing of product registration based on the estimated emotions. Emotion estimation is implemented, for example, using sensors or an emotion engine of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] As shown in Figure 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.

[0108] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0114] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0115] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0116] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0118] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0120] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0121] For example, each of the multiple elements, including the registration unit, certification unit, and traceability unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 or the identification processing unit 290 of the data processing unit 12, and registers the product on the blockchain. The certification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs digital signatures and issues certificates for the product. The traceability unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and tracks the origin and generation process of the product. Furthermore, the registration unit can estimate the user's emotions and adjust the timing of product registration based on the estimated emotions. Emotion estimation is implemented, for example, using sensors or an emotion engine in the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] For example, each of the multiple elements, including the registration unit, certification unit, and traceability unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 or the identification processing unit 290 of the data processing unit 12, and registers the product on the blockchain. The certification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and performs digital signatures and issues certificates for the product. The traceability unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and tracks the origin and generation process of the product. Furthermore, the registration unit can estimate the user's emotions and adjust the timing of product registration based on the estimated emotions. Emotion estimation is implemented, for example, using sensors or an emotion engine in the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 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.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] For example, each of the multiple elements, including the registration unit, certification unit, and traceability unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 or the identification processing unit 290 of the data processing unit 12, and registers the product on the blockchain. The certification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and performs digital signatures and issues certificates for the product. The traceability unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and tracks the origin and generation process of the product. Furthermore, the registration unit can estimate the user's emotions and adjust the timing of product registration based on the estimated emotions. Emotion estimation is implemented, for example, using sensors or an emotion engine of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0155] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] (Note 1) A registration unit for registering the products, A certification unit that certifies the product registered by the registration unit, The system includes a traceability unit that ensures the traceability of the product certified by the certification unit. A system characterized by the following features. (Note 2) The aforementioned registration unit is Register information related to the product together. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned certification unit is To prove the origin and formation process of the product. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned traceability unit is Identify the origin and formation process of the product. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned registration unit is Register the generated product on the blockchain. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned certification unit is Verify the reliability of the product. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned traceability unit is Managing the traceability of the products The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of product registration based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is Prioritize registration based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is Apply different registration methods depending on the category of the product. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is It estimates the user's emotions and determines the priority of the generated items to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is Prioritize registering highly relevant products, taking into account their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned registration unit is Referencing relevant literature on the product will improve the accuracy of registration. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned certification unit is We estimate the user's emotions and adjust the presentation of the proof based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned certification unit is Adjust the level of detail in the proof based on the importance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned certification unit is Apply different proof algorithms depending on the category of the product. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned certification unit is The system estimates the user's emotions and adjusts the length of the proof based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned certification unit is Prioritizing proofs based on the submission date of the product. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned certification unit is Adjust the order of the proof based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned traceability unit is It estimates the user's emotions and adjusts how traceability is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned traceability unit is Predicting current traceability by referring to past traceability data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned traceability unit is Apply different traceability analysis methods to each product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned traceability unit is It estimates the user's emotions and adjusts the importance of traceability based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned traceability unit is Analyze changes in traceability based on the submission date of the product. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned traceability unit is Analyze traceability by referring to relevant market data for the product. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration unit for registering the products, A certification unit that certifies the product registered by the registration unit, The system includes a traceability unit that ensures the traceability of the product certified by the certification unit. A system characterized by the following features.

2. The aforementioned registration unit is Register information related to the product together. The system according to feature 1.

3. The aforementioned certification unit is To prove the origin and formation process of the product. The system according to feature 1.

4. The aforementioned traceability unit is Identify the origin and formation process of the product. The system according to feature 1.

5. The aforementioned registration unit is Register the generated product on the blockchain. The system according to feature 1.

6. The aforementioned certification unit is Verify the reliability of the product. The system according to feature 1.

7. The aforementioned traceability unit is Managing the traceability of the products The system according to feature 1.

8. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of product registration based on the estimated user emotions. The system according to feature 1.

9. The aforementioned registration unit is Prioritize registration based on the importance of the product. The system according to feature 1.

10. The aforementioned registration unit is Apply different registration methods depending on the category of the product. The system according to feature 1.

Images