system

Abstract

We provide the system. [Solution] A means of collecting product data and sending it to the cloud, A generation module that analyzes product data on the cloud and evaluates the product's storage condition and manufacturing process, A means for generating personalized digital content based on analysis results, A means for recording the aforementioned digital content as a digital asset on a blockchain and assigning identification information to it, A system including means for providing content to a user using the aforementioned identification information.

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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 products such as high-grade tea and Japanese sake, it is difficult to guarantee authenticity and quality to consumers, and there is a lack of means to enhance their reliability. In particular, it is necessary to solve the problem that accurate information regarding the storage state and manufacturing process of products is difficult to convey to consumers, thereby preventing the full cultivation of consumers' understanding and attachment to the products.

Means for Solving the Problems

[0005] This invention solves this problem with a system equipped with a generation module that collects data on the storage conditions and manufacturing process of products and analyzes it in the cloud. Based on the obtained analysis results, personalized digital content is generated, and this content is recorded on the blockchain as a digital asset, thereby guaranteeing the authenticity and quality of the product. Furthermore, the system aims to enhance the consumer's experience with the product by providing content to users using identification information, and including music, visuals, and text information related to the product.

[0006] "Product data" refers to information about the characteristics and storage conditions of a product, including environmental parameters such as temperature, humidity, and pressure.

[0007] "Cloud" refers to a platform that utilizes computing resources hosted on remote servers over the internet for data storage and processing.

[0008] "Analysis" refers to the act of interpreting the structure and patterns of collected data and extracting meaningful results and insights.

[0009] A "generating module" refers to a software component that can automatically generate new information or content based on specific data that is taken as input.

[0010] "Personalized digital content" refers to digital information customized according to the user's characteristics and interests, and may include music, visuals, and text information.

[0011] "Digital assets" refer to assets held in digital format, specifically digital content whose ownership and scarcity are guaranteed.

[0012] "Blockchain" refers to a technology that uses distributed ledger technology to record multiple pieces of information within blocks and link them together in a chain, thereby guaranteeing the authenticity and security of the data.

[0013] "Identification information" refers to unique information used to individually identify digital content and its publisher, and is used as a key when a user accesses it. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that manages the storage conditions and manufacturing process of products on the cloud, guaranteeing the authenticity and quality of products to consumers. This system collects and analyzes product-related data, generates personalized digital content, and provides it to users. The system's operation is described in detail below.

[0036] First, the server acquires environmental information from data collection devices attached to the product. This information includes data about the external environment, such as storage temperature, humidity, and pressure. This data is acquired at regular intervals and sent to the cloud.

[0037] Next, the server analyzes the data received on the cloud. This analysis uses a generation module to evaluate whether the product's storage conditions and manufacturing process are appropriate. Based on this evaluation, the authenticity and quality of the product are confirmed.

[0038] Next, the server generates personalized digital content for the user based on the analysis results. This content includes detailed product reports, related stories, music, and visuals that enrich the user experience. This makes it possible to provide consumers with knowledge and appeal about the product.

[0039] The generated digital content is recorded on the blockchain as a digital asset. The server assigns identification information to this digital asset and provides it to the user as an NFT. Using this identification information, the user can securely access the content related to the product.

[0040] As a concrete example, consider a product from a high-end tea brand. This product is equipped with sensors that monitor temperature and humidity. A server receives this data and performs analysis. As a result, the server confirms that the tea leaves are stored under optimal conditions. It then generates content for consumers about the culture and history of the tea manufacturer, and delivers it as an NFT accompanied by related music. Users can access this content through their devices and enjoy the product while experiencing the tradition and individual value of the high-end tea brand.

[0041] Thus, this invention provides consumers with in-depth information and experiences related to the product, thereby achieving quality assurance and enhancing brand value.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server acquires product storage status and environmental information from a data collection device. This device is equipped with sensors for temperature, humidity, pressure, etc., and provides information via a protocol that sends data to the cloud at specific time intervals.

[0045] Step 2:

[0046] The server verifies the integrity and format of the data received on the cloud and records it in the database. If abnormal values ​​or missing data are detected during this process, they are corrected or removed, and the data is cleansed.

[0047] Step 3:

[0048] The server passes refined data to a generation module, which performs data analysis. This module evaluates whether the product is stored properly and sets a flag for alerts if there are any problems. At the same time, it extracts and analyzes product-specific features.

[0049] Step 4:

[0050] Based on the analysis results, the server generates personalized digital content for the user. This content includes reports based on the product's storage condition, documentation about the manufacturing process, and related music and visual content.

[0051] Step 5:

[0052] The server issues the generated digital content as NFTs using blockchain technology. In this process, unique identification information is assigned to the content, and it is transformed into a digital asset.

[0053] Step 6:

[0054] The server transfers the issued NFT to the user's wallet, making it accessible. This allows the user to access their owned NFTs and use their content through their device.

[0055] Step 7:

[0056] Users can access NFTs and related content using their identification information on their own devices, allowing them to view and watch them. This enables them to enjoy the product's quality and unique experiential value.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Properly managing product storage conditions and manufacturing processes, and guaranteeing product authenticity and quality to consumers, is a critical challenge in many industries. However, existing systems are inefficient in collecting and analyzing environmental data, and the generated information lacks transparency and reliability when provided to consumers. Furthermore, consumers often have limited opportunities to experience the cultural value and information associated with products, which is another troubling issue.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for collecting product data and transmitting it to a data processing server, a generation algorithm for analyzing the product data on the data processing server and evaluating the product's storage condition and manufacturing process, and means for generating personalized cultural information based on the analysis results. This makes it possible to provide consumers with a rich information experience while enhancing product quality assurance.

[0062] "Product data" refers to information about a product, including environmental data such as storage conditions and manufacturing processes.

[0063] A "data processing server" is a computing resource used to receive, analyze, and evaluate product data, and is built on the cloud.

[0064] A "generation algorithm" is a program procedure that evaluates analyzed product data and determines the product's storage condition and manufacturing process.

[0065] "Cultural information" refers to information such as history, culture, and stories related to a product, and is content that enriches the consumer experience.

[0066] "Digital assets" are data and content recorded in digital format, and are resources that can be traded because they possess identifying information.

[0067] A "distributed database" is a data recording system in which data is stored in multiple locations to ensure the integrity and transparency of information.

[0068] "Unique identifiers" are unique codes assigned to digital assets, used to identify and manage those assets.

[0069] This system manages the storage conditions and manufacturing process of products, guaranteeing their authenticity and quality to consumers. The main components of the system include data collection devices, data processing servers, generative AI models, and a digital asset management system.

[0070] The server collects data from sensors attached to the product. These sensors can measure environmental information such as temperature and humidity, and the acquired data is sent to the server at regular intervals. The server is located on a cloud infrastructure and has computing capabilities to process this data in real time.

[0071] Next, the server uses a generative AI model to analyze the received data. This AI model incorporates analytical algorithms to evaluate whether the product's storage conditions and manufacturing process meet the required standards. As a result, it is possible to generate cultural information such as the product's history, culture, and story.

[0072] Furthermore, the server registers the generated cultural information as digital assets in a distributed database and assigns unique identifiers to them. These digital assets are later made available to users. Users can access these digital assets using their own devices.

[0073] As a concrete example, let's consider the system operation for a product from a high-end tea brand. The server receives temperature and humidity data from sensors and analyzes this data to confirm that the tea leaves are stored under appropriate conditions. The generated cultural information includes the history of the tea and the brand's story, which is recorded on the blockchain as an NFT. Users can access this information via their devices and experience the deep knowledge and traditions of high-end tea.

[0074] As a concrete example of a prompt, one could give the generation AI model the instruction, "Generate cultural and historical content based on preservation data of high-grade tea."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server acquires environmental information from data collection devices attached to the product. Input data from the sensors includes temperature, humidity, and pressure. The server receives this data at regular intervals and sends it to the cloud. This data is recorded in raw format and analyzed in subsequent processing steps.

[0078] Step 2:

[0079] The server analyzes the raw data stored in the cloud. This process utilizes a generative AI model to execute algorithms for evaluating the raw data. The environmental information collected in step 1 is used as input data. The output is an evaluation of the product's storage condition, indicating whether the storage conditions are appropriate.

[0080] Step 3:

[0081] The server generates cultural information based on the analysis results. In this step, the generative AI model creates a history and cultural story related to the product. The input data used is the evaluation results obtained in step 2 and the cultural information pre-programmed into the model. The output is personalized text and audio information.

[0082] Step 4:

[0083] The server registers the generated cultural information as a digital asset. This process assigns unique identifiers to the information and records it in a distributed database. The input data is the cultural information generated in step 3. The output is a digital asset with an assigned identifier.

[0084] Step 5:

[0085] Users access digital assets using their own devices. The user's device connects to the server using unique identification information and retrieves the digital content to be displayed. The input data is identification information, and the output is cultural content provided in a format viewable by the user.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] Accurately guaranteeing the authenticity and quality of a product is a crucial factor in consumer purchasing behavior. However, conventional methods make it difficult for consumers to easily verify detailed information about product storage conditions and manufacturing processes, hindering their ability to choose reliable products. This invention aims to enable consumers to access detailed product quality information even in physical stores, thereby improving reliability and appealing to product value.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for collecting product information and transmitting it to a network system; a generating device for analyzing product information on the network system and evaluating the product's maintenance status and manufacturing history; and means for generating personalized electronic content based on the analysis results. This enables consumers in physical stores to directly and in real time check product information using a portable information terminal.

[0091] "Product information" refers to data related to the product's storage conditions and manufacturing process, including environmental information such as temperature, humidity, and pressure.

[0092] A "network system" is a communication infrastructure for sending and receiving data via the internet or dedicated lines, and a system for processing and analyzing information using cloud computing.

[0093] A "generation device" is a device that, based on analyzed data, performs product condition evaluations and generates reports, creating electronic content to be provided to consumers.

[0094] "Electronic assets" refer to digital information that has been given value and security by being stored in a database and having identification information attached to it.

[0095] A "portable information terminal" refers to an information processing device that consumers can carry with them, such as a smartphone or tablet.

[0096] This invention is a system that uses a network system, a generation device, and a portable information terminal to provide product information to consumers.

[0097] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product and transmits it to the cloud via a network system. On the cloud, a generator operates that analyzes this data and evaluates the product's maintenance status and manufacturing history. This generator assesses the product's authenticity and quality, and based on the results, generates personalized electronic content. This electronic content includes detailed product information, visual displays, music, and text.

[0098] The generated digital content is stored in a database as digital assets and assigned identification information using blockchain technology. This identification information enables secure access to the content.

[0099] Users can scan the QR code (registered trademark) of a product they are interested in at a physical store using their portable information terminal to obtain the corresponding identification information. This allows users to access detailed electronic content about that product in real time from a database in the cloud.

[0100] As a concrete example, with high-end tea products, users can scan a QR code on the tea leaves to access detailed information about their storage conditions and origin, as well as enjoy stories and music related to their cultural background. This allows consumers to gain a deeper understanding of the product's value and characteristics, leading to a more trustworthy purchase.

[0101] Examples of prompts to input into a generative AI model are as follows:

[0102] "Create personalized digital content that engages users, based on detailed information about the storage conditions, manufacturing process, and cultural background of specific products. The goal is to provide visually appealing content, including music, to help consumers gain a deeper understanding of the product's value."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product. The input is raw data obtained from the sensors, and the output is formatted environmental data. This data is processed at regular intervals by a data acquisition module and transmitted to the network system.

[0106] Step 2:

[0107] The server sends collected environmental data to the cloud, where it is analyzed using a generation device. The input is the environmental data sent to the cloud, and the output is evaluation data regarding the product's maintenance status and manufacturing history. This analysis uses a data analysis model and integrates it with a generation AI model to perform an accurate evaluation.

[0108] Step 3:

[0109] The generation device generates personalized electronic content based on evaluation data. In this process, a generation AI model is used with prompt text as input, and the output is electronic content including text information, visual displays, and music.

[0110] Step 4:

[0111] The server stores the generated electronic content as an electronic asset in a database and assigns identification information to it. The input is the generated electronic content, and the output is the electronic asset with the assigned identification information. This identification information is guaranteed using blockchain technology, ensuring secure record-keeping.

[0112] Step 5:

[0113] Users scan a product's QR code with a portable information terminal at a physical store to obtain identification information. The input is the QR code scanned by the user, and the output is an access key to a database in the cloud. This operation allows users to securely access electronic content related to the product.

[0114] Step 6:

[0115] The device uses identification information to access electronic content in the cloud, allowing users to view product information. Input consists of identification information and user requests, while output is detailed product information and related cultural content provided to the user. This information is displayed on the device in real time, enhancing the user's purchasing experience.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention is a system that combines an emotion engine to enhance the user experience through the analysis of product data and the generation of digital content. The system not only manages the storage status and manufacturing process of products on the cloud, but also analyzes the user's emotions from their facial expressions, voice, and text, and provides personalized digital content based on that analysis.

[0118] First, the server receives real-time data about the product's storage conditions from a data collection device attached to the product. This data includes temperature, humidity, and pressure, and is transmitted to and stored in the cloud.

[0119] Next, the server uses data stored in the cloud to analyze the product's status via a generation module. This analysis allows for an assessment of the product's authenticity and quality. Following this, personalized digital content is generated based on the analysis results. This content includes a story related to the product, a storage status report, and even music and visuals that respond to the user's emotions.

[0120] Furthermore, the server uses an emotion engine to acquire data for analyzing the user's emotions. The user's device captures facial expressions and voice through its camera and microphone, and the server inputs this data into the emotion engine to determine what emotions the user is feeling towards the experience content.

[0121] The server adjusts the content of digital content based on the results of the user sentiment analysis. For example, if the user is relaxed, it will provide music that further enhances relaxation or present content with less information. If the server determines that the user is interested, it will enrich the experience by adding more information or a more detailed story.

[0122] Information related to a user's emotional state can be recorded on the blockchain as a digital asset and managed as a history of the user's emotions. In this process, identification information is assigned, and the emotional data, further analyzed, is used to construct the user's experience history.

[0123] As a concrete example, consider the usage scenario of a specific high-quality Japanese tea. The server analyzes whether the tea leaves are in good condition and, based on the results, creates content accompanied by relaxing music. The user watches this content while brewing the tea on their device. Simultaneously, an emotion engine analyzes the user's satisfaction level from their facial expressions and further customizes the content based on the analysis results.

[0124] This invention enables the provision of a deeper product-related experience and information tailored to consumer emotions. This strengthens product quality assurance and allows consumers to enjoy a more satisfying experience.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server periodically acquires environmental information such as temperature, humidity, and pressure from data collection devices installed on the product. This data is sent to the cloud and used for analysis of the product's storage conditions.

[0128] Step 2:

[0129] The server analyzes product data collected on the cloud to determine if the storage conditions are appropriate. This analysis utilizes a generation module to create detailed reports for evaluating product quality and authenticity.

[0130] Step 3:

[0131] Based on the analysis results, the server generates personalized digital content based on product information. This includes content such as the product's background story and storage tips, as well as music and visual materials to enrich the user experience.

[0132] Step 4:

[0133] The device collects the user's facial expressions and voice and sends them to the emotion engine. In this process, the device uses its camera and microphone to capture the user's reactions and sends that data to a server for processing.

[0134] Step 5:

[0135] The server activates an emotion engine to analyze the user's emotions. Based on this analysis, the aforementioned digital content is further customized according to the user's current emotions.

[0136] Step 6:

[0137] The server generates emotion-based customized content, assigns identification information to it, and records it on the blockchain as a digital asset. The content is then issued as an NFT and prepared for transfer to the user's wallet.

[0138] Step 7:

[0139] Users access the supplied content through their own devices. Here, they can view the displayed content and enjoy unique experiences related to the product. The system also manages the user's emotional history, which is used to improve future experiences.

[0140] (Example 2)

[0141] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0142] Modern consumers demand more than just the purchase of goods; they seek personalized digital information tailored to their emotions and experiences. However, traditional methods struggle to connect information based on product condition and manufacturing processes with user emotions, failing to provide a deeper consumer experience. Furthermore, the management of collected emotional data and the protection of private information are not well-established.

[0143] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0144] In this invention, the server includes means for collecting product information and transmitting it to a database, a generation module for analyzing the product information on the database and evaluating the product's condition and manufacturing process, and means for generating personalized digital information based on the analysis results. This makes it possible to provide users with personalized digital information that responds to their emotions.

[0145] "Product information" refers to data related to the storage conditions and manufacturing process of the product, and includes environmental data such as temperature, humidity, and pressure.

[0146] A "database" is a cloud-based data storage system used to collect, store, and later analyze product information.

[0147] A "generation module" is a software component used to analyze collected product information and evaluate the product's condition and quality.

[0148] "Personalized digital information" refers to digital content such as stories, music, and visual information that are customized according to each user's emotions and needs based on analysis results.

[0149] The "emotion analysis module" is a dedicated software engine that analyzes the user's facial expressions, voice, and other data to identify their emotional state.

[0150] "Digital record" refers to an electronic record that stores generated digital information and makes it accessible later.

[0151] A "distributed ledger" is a digital ledger that utilizes blockchain technology to store information securely and in a way that is resistant to tampering.

[0152] "Identifying information" refers to unique information that identifies generated digital information and related data, enabling personalized delivery.

[0153] The following methods are used as embodiments for carrying out the present invention.

[0154] The server uses a data collection device to gather product information and transmits environmental data such as temperature, humidity, and pressure obtained from this device to a cloud-based database. This database is a cloud storage system that enables the storage and rapid access of structured data.

[0155] The server uses a generative AI model to analyze the collected product information. This AI model evaluates the condition and quality of the products and generates reports based on the analysis results. This analysis utilizes existing data science techniques and neural networks.

[0156] The server uses an emotion analysis module to analyze the user's facial expressions and voice data transmitted from the device. This identifies the user's emotions, and personalized digital content is generated that corresponds to the user's emotions. The device used for this process includes a camera and microphone, and a process is in place to send the captured information to the server.

[0157] Digital records are used to store generated content using distributed ledger technology. This technology utilizes blockchain to enable secure and tamper-proof recording of data. Identification information is linked to this digital record, ensuring personalized access when the user accesses it again.

[0158] As a concrete example, consider the usage scenario of a specific premium beverage. The server analyzes the beverage's quality status using environmental data and generates digital content, such as music and visual information, that induces relaxation based on that status. This content is viewed by the user while they consume the beverage. Simultaneously, an emotion analysis module evaluates the user's satisfaction and further optimizes the content.

[0159] An example of a prompt to input into a generative AI model is, "How can personalized digital content be tailored to the user's emotions?"

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The terminal acquires environmental data such as temperature, humidity, and pressure in real time from data acquisition devices attached to the product. This data is collected directly through sensors, and the terminal immediately transmits it to a database in the cloud. It receives environmental data as input and generates formatted data for recording in the database as output.

[0163] Step 2:

[0164] The server receives environmental data stored in the cloud and analyzes the product's condition using a generated AI model. This analysis process utilizes the pattern recognition capabilities of the AI ​​model to detect anomalies and analyze trends in the data, thereby evaluating the product's quality and condition. The input here is formatted environmental data, and the output is a product quality evaluation report.

[0165] Step 3:

[0166] The server generates personalized digital content based on the quality evaluation report of the generated product. This process selects the most suitable narrative, music, and visual information based on data from similar past products and user profiles. The quality evaluation report and user profile are used as input, and the output is personalized digital content.

[0167] Step 4:

[0168] The device uses its built-in camera and microphone to capture the user's facial expressions and voice. The collected data is sent to a server to understand the user's current emotional state. The input here is the data captured by the camera and microphone, and the output is the input data for the emotion analysis module.

[0169] Step 5:

[0170] The server inputs facial and voice data from the terminal into an emotion analysis module. The module uses machine learning algorithms to analyze this data and identify the user's emotions. The input here is the user's facial and voice data, and the output is the analyzed emotional state of the user.

[0171] Step 6:

[0172] The server adjusts the previously generated digital content based on the user's sentiment analysis results. It enhances music and visual effects according to the user's emotions and appropriately adjusts the amount of information to provide the most suitable experience. Sentiment analysis results and initial digital content are used as input, and the adjusted digital content is provided as output.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In today's market, there is a demand to improve the consumer experience and increase product satisfaction. However, traditional methods of providing product information make it difficult to provide feedback that responds to the individual emotions of users, and the optimization of the consumer experience is not fully achieved. Therefore, it is important to analyze users' emotions in real time during the actual purchasing experience and realize the personalization of product information.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for collecting product information and transmitting it to the cloud; a generation module for analyzing the product information on the cloud and evaluating the product's storage condition and manufacturing process; means for generating personalized digital content based on the analysis results; means including an emotion engine for analyzing facial expression data and voice data and determining the user's emotions; and means for adjusting the content of the digital content based on the results of the user's emotion analysis. This makes it possible to provide optimal digital content that corresponds to the individual emotions of the user.

[0178] "Product information" refers to data related to the storage conditions and manufacturing process of a product, and includes information such as environmental conditions.

[0179] "Cloud" refers to a technological platform that provides data storage and applications via the internet, enabling remote storage and processing of information.

[0180] A "generation module" is a software or hardware configuration that has the functionality to analyze product information on the cloud and generate digital content based on the results.

[0181] "Digital content" refers to information provided in digital format, including audio, visuals, text, and other similar elements.

[0182] A "database" is a system designed to store information in a structured manner, allowing for efficient searching and management.

[0183] "Identification information" refers to unique codes or tags attached to digital content or product information, which make it possible to uniquely recognize specific data.

[0184] "Users" refers to the individuals or organizations that receive product information and digital content through this system.

[0185] "Facial expression data" refers to numerical data and signals obtained by analyzing the facial features of a user through a video device.

[0186] "Voice data" refers to numerical values ​​and signals obtained by analyzing the user's speech and surrounding sounds acquired through a microphone.

[0187] An "emotion engine" is software equipped with algorithms and technologies that analyze facial expression data and voice data to estimate the user's current emotional state.

[0188] To realize this invention, a system combining a server, smart devices, a cloud infrastructure, and an analysis engine is required.

[0189] The server is responsible for acquiring product information in real time from sensors attached to the product and transmitting it to the cloud. This information includes data indicating the product's storage status and is stored in the cloud. A generation module in the cloud analyzes the product information and generates personalized digital content based on the results. This digital content includes audio, visual, and text information and is optimized according to the user's needs.

[0190] Smart devices, such as smart glasses and smartphones, collect facial and voice data from users. This data is sent to a server and analyzed by an emotion engine. The emotion engine quantifies the facial and voice data to determine the user's emotions. As a result, the server dynamically adjusts the content of digital content to deliver a personalized experience.

[0191] As a concrete example, when a user purchases a product, they can use smart glasses to check detailed product information. If facial expression analysis determines that the user is favorable, it may be possible to provide additional digital content, such as "playing a video containing an interview with the product's creator and testimonials from users."

[0192] When using a generative AI model, an example of a prompt message might be: "If the user's sentiment analysis results indicate 'interested,' generate video content that provides a detailed explanation of the product's background and usage examples."

[0193] This system allows users to receive personalized information in real time, resulting in a more satisfying shopping experience.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The terminal acquires product information (temperature, humidity, pressure, etc.) from sensors installed on the product and transmits it to the cloud in real time. It receives raw data from sensors as input, converts it to a standardized format, and outputs it.

[0197] Step 2:

[0198] The server analyzes product information sent to the cloud. From the environmental data received as input, it calculates evaluation indicators for assessing storage conditions and manufacturing processes, and outputs them.

[0199] Step 3:

[0200] The server generates digital content using a generation module based on the analysis results. In this process, evaluation metrics are taken as input, and the target content (audio, video, text) is generated and output.

[0201] Step 4:

[0202] The device captures the user's facial expressions and voice data through its camera and microphone. It sends the real-time acquired video and audio as input to an emotion engine, which then extracts and outputs facial and vocal characteristics.

[0203] Step 5:

[0204] The server estimates the user's emotions based on facial expression and voice data analyzed by the emotion engine. It receives feature data as input, analyzes it using a machine learning model to determine the emotional state, and outputs the result.

[0205] Step 6:

[0206] The server adjusts existing digital content based on the estimated emotional state. It receives the emotional state and generated content as input, modifies it into personalized content, and outputs it.

[0207] Step 7:

[0208] Users view personalized digital content through their devices. This allows them to enjoy a digital experience optimized based on analytics results in real time.

[0209] In this way, by analyzing the data received at each step and generating and adjusting appropriate content, a series of processes is realized that provides users with the optimal experience.

[0210] 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.

[0211] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] 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.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0217] 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.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0219] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] 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.

[0221] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The 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.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is a system that manages the storage conditions and manufacturing process of products on the cloud, guaranteeing the authenticity and quality of products to consumers. This system collects and analyzes product-related data, generates personalized digital content, and provides it to users. The system's operation is described in detail below.

[0227] First, the server acquires environmental information from data collection devices attached to the product. This information includes data about the external environment, such as storage temperature, humidity, and pressure. This data is acquired at regular intervals and sent to the cloud.

[0228] Next, the server analyzes the data received on the cloud. This analysis uses a generation module to evaluate whether the product's storage conditions and manufacturing process are appropriate. Based on this evaluation, the authenticity and quality of the product are confirmed.

[0229] Next, the server generates personalized digital content for the user based on the analysis results. This content includes detailed product reports, related stories, music, and visuals that enrich the user experience. This makes it possible to provide consumers with knowledge and appeal about the product.

[0230] The generated digital content is recorded on the blockchain as a digital asset. The server assigns identification information to this digital asset and provides it to the user as an NFT. Using this identification information, the user can securely access the content related to the product.

[0231] As a concrete example, consider a product from a high-end tea brand. This product is equipped with sensors that monitor temperature and humidity. A server receives this data and performs analysis. As a result, the server confirms that the tea leaves are stored under optimal conditions. It then generates content for consumers about the culture and history of the tea manufacturer, and delivers it as an NFT accompanied by related music. Users can access this content through their devices and enjoy the product while experiencing the tradition and individual value of the high-end tea brand.

[0232] Thus, this invention provides consumers with in-depth information and experiences related to the product, thereby achieving quality assurance and enhancing brand value.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The server acquires product storage status and environmental information from a data collection device. This device is equipped with sensors for temperature, humidity, pressure, etc., and provides information via a protocol that sends data to the cloud at specific time intervals.

[0236] Step 2:

[0237] The server verifies the integrity and format of the data received on the cloud and records it in the database. If abnormal values ​​or missing data are detected during this process, they are corrected or removed, and the data is cleansed.

[0238] Step 3:

[0239] The server passes refined data to a generation module, which performs data analysis. This module evaluates whether the product is stored properly and sets a flag for alerts if there are any problems. At the same time, it extracts and analyzes product-specific features.

[0240] Step 4:

[0241] Based on the analysis results, the server generates personalized digital content for the user. This content includes reports based on the product's storage condition, documentation about the manufacturing process, and related music and visual content.

[0242] Step 5:

[0243] The server issues the generated digital content as NFTs using blockchain technology. In this process, unique identification information is assigned to the content, and it is transformed into a digital asset.

[0244] Step 6:

[0245] The server transfers the issued NFT to the user's wallet, making it accessible. This allows the user to access their owned NFTs and use their content through their device.

[0246] Step 7:

[0247] Users can access NFTs and related content using their identification information on their own devices, allowing them to view and watch them. This enables them to enjoy the product's quality and unique experiential value.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] Properly managing product storage conditions and manufacturing processes, and guaranteeing product authenticity and quality to consumers, is a critical challenge in many industries. However, existing systems are inefficient in collecting and analyzing environmental data, and the generated information lacks transparency and reliability when provided to consumers. Furthermore, consumers often have limited opportunities to experience the cultural value and information associated with products, which is another troubling issue.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for collecting product data and transmitting it to a data processing server, a generation algorithm for analyzing the product data on the data processing server and evaluating the product's storage condition and manufacturing process, and means for generating personalized cultural information based on the analysis results. This makes it possible to provide consumers with a rich information experience while enhancing product quality assurance.

[0253] "Product data" refers to information about a product, including environmental data such as storage conditions and manufacturing processes.

[0254] A "data processing server" is a computing resource used to receive, analyze, and evaluate product data, and is built on the cloud.

[0255] A "generation algorithm" is a program procedure that evaluates analyzed product data and determines the product's storage condition and manufacturing process.

[0256] "Cultural information" refers to information such as history, culture, and stories related to a product, and is content that enriches the consumer experience.

[0257] "Digital assets" are data and content recorded in digital format, and are resources that can be traded because they possess identifying information.

[0258] A "distributed database" is a data recording system in which data is stored in multiple locations to ensure the integrity and transparency of information.

[0259] "Unique identifiers" are unique codes assigned to digital assets, used to identify and manage those assets.

[0260] This system manages the storage conditions and manufacturing process of products, guaranteeing their authenticity and quality to consumers. The main components of the system include data collection devices, data processing servers, generative AI models, and a digital asset management system.

[0261] The server collects data from sensors attached to the product. These sensors can measure environmental information such as temperature and humidity, and the acquired data is sent to the server at regular intervals. The server is located on a cloud infrastructure and has computing capabilities to process this data in real time.

[0262] Next, the server uses a generative AI model to analyze the received data. This AI model incorporates analytical algorithms to evaluate whether the product's storage conditions and manufacturing process meet the required standards. As a result, it is possible to generate cultural information such as the product's history, culture, and story.

[0263] Furthermore, the server registers the generated cultural information as digital assets in a distributed database and assigns unique identifiers to them. These digital assets are later made available to users. Users can access these digital assets using their own devices.

[0264] As a concrete example, let's consider the system operation for a product from a high-end tea brand. The server receives temperature and humidity data from sensors and analyzes this data to confirm that the tea leaves are stored under appropriate conditions. The generated cultural information includes the history of the tea and the brand's story, which is recorded on the blockchain as an NFT. Users can access this information via their devices and experience the deep knowledge and traditions of high-end tea.

[0265] As a concrete example of a prompt, one could give the generation AI model the instruction, "Generate cultural and historical content based on preservation data of high-grade tea."

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The server acquires environmental information from data collection devices attached to the product. Input data from the sensors includes temperature, humidity, and pressure. The server receives this data at regular intervals and sends it to the cloud. This data is recorded in raw format and analyzed in subsequent processing steps.

[0269] Step 2:

[0270] The server analyzes the raw data stored in the cloud. This process utilizes a generative AI model to execute algorithms for evaluating the raw data. The environmental information collected in step 1 is used as input data. The output is an evaluation of the product's storage condition, indicating whether the storage conditions are appropriate.

[0271] Step 3:

[0272] The server generates cultural information based on the analysis results. In this step, the generative AI model creates a history and cultural story related to the product. The input data used is the evaluation results obtained in step 2 and the cultural information pre-programmed into the model. The output is personalized text and audio information.

[0273] Step 4:

[0274] The server registers the generated cultural information as a digital asset. This process assigns unique identifiers to the information and records it in a distributed database. The input data is the cultural information generated in step 3. The output is a digital asset with an assigned identifier.

[0275] Step 5:

[0276] Users access digital assets using their own devices. The user's device connects to the server using unique identification information and retrieves the digital content to be displayed. The input data is identification information, and the output is cultural content provided in a format viewable by the user.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Accurately guaranteeing the authenticity and quality of products is an important factor in consumers' purchasing behavior. However, with conventional methods, it is difficult for consumers to easily confirm detailed information regarding the storage state and manufacturing process of products, which hinders reliable product selection. The present invention aims to enable consumers to access detailed product quality information even in physical stores, thereby achieving improved reliability and appealing to the value of products.

[0280] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0281] In this invention, the server includes means for collecting information regarding products and transmitting it to the network system, a generation device for analyzing information regarding products on the network system and evaluating the maintenance state and manufacturing history of the products, and means for generating individualized electronic content based on the analysis results. As a result, consumers in physical stores can directly and in real time confirm product information using a portable information terminal.

[0282] "Information regarding products" refers to data related to the storage state and manufacturing process of products, and includes environmental information such as temperature, humidity, and pressure.

[0283] "Network system" is a communication infrastructure for transmitting and receiving data via the Internet or a dedicated line, and is a system for processing and analyzing information using cloud computing.

[0284] "Generation device" is a device that creates electronic content for providing consumers with product state evaluation and report generation based on the analyzed data.

[0285] "Electronic asset" is digital information to which value and security are imparted by storing the generated digital content on a database and assigning identification information.

[0286] The "portable information terminal" refers to an information processing device that consumers can carry, and refers to portable devices such as smartphones and tablets.

[0287] This invention is a system that uses a network system, a generation device, and a portable information terminal to provide product information to consumers.

[0288] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product and transmits this data to the cloud via a network system. On the cloud, a generation device that analyzes these data and evaluates the maintenance status and manufacturing history of the product operates. This generation device performs authenticity and quality evaluation of the product and generates individualized electronic content based on the results. This electronic content includes detailed information about the product, visual displays, music, and text information.

[0289] The generated electronic content is stored in a database as an electronic asset, and identification information is assigned to it by blockchain technology. This identification information enables secure access to the content.

[0290] The user can scan the QR code of the product of interest at a physical store with a portable information terminal and obtain the corresponding identification information. As a result, the user can view the detailed electronic content related to the product from the database on the cloud in real time.

[0291] As a specific example, taking a high-class tea product, the user can scan the QR code of the tea leaves to enjoy detailed information about its storage state and origin, as well as the story and music of the corresponding cultural background. As a result, consumers can deeply understand the value and characteristics of the product and make a purchase in a reliable manner.

[0292] Examples of prompt sentences input to the generation AI model are as follows:

[0293] "Create personalized digital content that engages users, based on detailed information about the storage conditions, manufacturing process, and cultural background of specific products. The goal is to provide visually appealing content, including music, to help consumers gain a deeper understanding of the product's value."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product. The input is raw data obtained from the sensors, and the output is formatted environmental data. This data is processed at regular intervals by a data acquisition module and transmitted to the network system.

[0297] Step 2:

[0298] The server sends collected environmental data to the cloud, where it is analyzed using a generation device. The input is the environmental data sent to the cloud, and the output is evaluation data regarding the product's maintenance status and manufacturing history. This analysis uses a data analysis model and integrates it with a generation AI model to perform an accurate evaluation.

[0299] Step 3:

[0300] The generation device generates personalized electronic content based on evaluation data. In this process, a generation AI model is used with prompt text as input, and the output is electronic content including text information, visual displays, and music.

[0301] Step 4:

[0302] The server stores the generated electronic content in the database as an electronic asset and assigns identification information. The input is the generated electronic content, and the output is the electronic asset with the assigned identification information. This identification information is guaranteed using blockchain technology, and secure recording is performed.

[0303] Step 5:

[0304] The user scans the QR code of the product at the physical store with a portable information terminal to obtain the identification information. The input is the QR code scanned by the user, and the output is the access key to the database on the cloud. By this operation, the user can safely access the electronic content related to the product.

[0305] Step 6:

[0306] The terminal accesses the electronic content on the cloud using the identification information, and the user views the product information. The inputs are the identification information and the user's request, and the outputs are the detailed information of the product and related cultural content provided to the user. This information is displayed on the terminal in real time, improving the user's purchase experience.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0308] The present invention is a system that combines an emotion engine to enhance the user experience through the analysis of product data and the generated digital content. The system not only manages the storage status and manufacturing process of the product on the cloud, but also analyzes the emotion from the user's expression, voice, and text, and provides personalized digital content based on it.

[0309] First, the server receives real-time data about the product's storage conditions from a data collection device attached to the product. This data includes temperature, humidity, and pressure, and is transmitted to and stored in the cloud.

[0310] Next, the server uses data stored in the cloud to analyze the product's status via a generation module. This analysis allows for an assessment of the product's authenticity and quality. Following this, personalized digital content is generated based on the analysis results. This content includes a story related to the product, a storage status report, and even music and visuals that respond to the user's emotions.

[0311] Furthermore, the server uses an emotion engine to acquire data for analyzing the user's emotions. The user's device captures facial expressions and voice through its camera and microphone, and the server inputs this data into the emotion engine to determine what emotions the user is feeling towards the experience content.

[0312] The server adjusts the content of digital content based on the results of the user sentiment analysis. For example, if the user is relaxed, it will provide music that further enhances relaxation or present content with less information. If the server determines that the user is interested, it will enrich the experience by adding more information or a more detailed story.

[0313] Information related to a user's emotional state can be recorded on the blockchain as a digital asset and managed as a history of the user's emotions. In this process, identification information is assigned, and the emotional data, further analyzed, is used to construct the user's experience history.

[0314] As a concrete example, consider the usage scenario of a specific high-quality Japanese tea. The server analyzes whether the tea leaves are in good condition and, based on the results, creates content accompanied by relaxing music. The user watches this content while brewing the tea on their device. Simultaneously, an emotion engine analyzes the user's satisfaction level from their facial expressions and further customizes the content based on the analysis results.

[0315] This invention enables the provision of a deeper product-related experience and information tailored to consumer emotions. This strengthens product quality assurance and allows consumers to enjoy a more satisfying experience.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The server periodically acquires environmental information such as temperature, humidity, and pressure from data collection devices installed on the product. This data is sent to the cloud and used for analysis of the product's storage conditions.

[0319] Step 2:

[0320] The server analyzes product data collected on the cloud to determine if the storage conditions are appropriate. This analysis utilizes a generation module to create detailed reports for evaluating product quality and authenticity.

[0321] Step 3:

[0322] Based on the analysis results, the server generates personalized digital content based on product information. This includes content such as the product's background story and storage tips, as well as music and visual materials to enrich the user experience.

[0323] Step 4:

[0324] The device collects the user's facial expressions and voice and sends them to the emotion engine. In this process, the device uses its camera and microphone to capture the user's reactions and sends that data to a server for processing.

[0325] Step 5:

[0326] The server activates an emotion engine to analyze the user's emotions. Based on this analysis, the aforementioned digital content is further customized according to the user's current emotions.

[0327] Step 6:

[0328] The server generates emotion-based customized content, assigns identification information to it, and records it on the blockchain as a digital asset. The content is then issued as an NFT and prepared for transfer to the user's wallet.

[0329] Step 7:

[0330] Users access the supplied content through their own devices. Here, they can view the displayed content and enjoy unique experiences related to the product. The system also manages the user's emotional history, which is used to improve future experiences.

[0331] (Example 2)

[0332] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0333] Modern consumers demand more than just the purchase of goods; they seek personalized digital information tailored to their emotions and experiences. However, traditional methods struggle to connect information based on product condition and manufacturing processes with user emotions, failing to provide a deeper consumer experience. Furthermore, the management of collected emotional data and the protection of private information are not well-established.

[0334] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0335] In this invention, the server includes means for collecting product information and transmitting it to a database, a generation module for analyzing the product information on the database and evaluating the product's condition and manufacturing process, and means for generating personalized digital information based on the analysis results. This makes it possible to provide users with personalized digital information that responds to their emotions.

[0336] "Product information" refers to data related to the storage conditions and manufacturing process of the product, and includes environmental data such as temperature, humidity, and pressure.

[0337] A "database" is a cloud-based data storage system used to collect, store, and later analyze product information.

[0338] A "generation module" is a software component used to analyze collected product information and evaluate the product's condition and quality.

[0339] "Personalized digital information" refers to digital content such as stories, music, and visual information that are customized according to each user's emotions and needs based on analysis results.

[0340] The "emotion analysis module" is a dedicated software engine that analyzes the user's facial expressions, voice, and other data to identify their emotional state.

[0341] "Digital record" refers to an electronic record that stores generated digital information and makes it accessible later.

[0342] A "distributed ledger" is a digital ledger that utilizes blockchain technology to store information securely and in a way that is resistant to tampering.

[0343] "Identifying information" refers to unique information that identifies generated digital information and related data, enabling personalized delivery.

[0344] The following methods are used as embodiments for carrying out the present invention.

[0345] The server uses a data collection device to gather product information and transmits environmental data such as temperature, humidity, and pressure obtained from this device to a cloud-based database. This database is a cloud storage system that enables the storage and rapid access of structured data.

[0346] The server uses a generative AI model to analyze the collected product information. This AI model evaluates the condition and quality of the products and generates reports based on the analysis results. This analysis utilizes existing data science techniques and neural networks.

[0347] The server uses an emotion analysis module to analyze the user's facial expressions and voice data transmitted from the device. This identifies the user's emotions, and personalized digital content is generated that corresponds to the user's emotions. The device used for this process includes a camera and microphone, and a process is in place to send the captured information to the server.

[0348] Digital records are used to store generated content using distributed ledger technology. This technology utilizes blockchain to enable secure and tamper-proof recording of data. Identification information is linked to this digital record, ensuring personalized access when the user accesses it again.

[0349] As a concrete example, consider the usage scenario of a specific premium beverage. The server analyzes the beverage's quality status using environmental data and generates digital content, such as music and visual information, that induces relaxation based on that status. This content is viewed by the user while they consume the beverage. Simultaneously, an emotion analysis module evaluates the user's satisfaction and further optimizes the content.

[0350] An example of a prompt to input into a generative AI model is, "How can personalized digital content be tailored to the user's emotions?"

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The terminal acquires environmental data such as temperature, humidity, and pressure in real time from data acquisition devices attached to the product. This data is collected directly through sensors, and the terminal immediately transmits it to a database in the cloud. It receives environmental data as input and generates formatted data for recording in the database as output.

[0354] Step 2:

[0355] The server receives environmental data stored in the cloud and analyzes the product's condition using a generated AI model. This analysis process utilizes the pattern recognition capabilities of the AI ​​model to detect anomalies and analyze trends in the data, thereby evaluating the product's quality and condition. The input here is formatted environmental data, and the output is a product quality evaluation report.

[0356] Step 3:

[0357] The server generates personalized digital content based on the quality evaluation report of the generated product. This process selects the most suitable narrative, music, and visual information based on data from similar past products and user profiles. The quality evaluation report and user profile are used as input, and the output is personalized digital content.

[0358] Step 4:

[0359] The device uses its built-in camera and microphone to capture the user's facial expressions and voice. The collected data is sent to a server to understand the user's current emotional state. The input here is the data captured by the camera and microphone, and the output is the input data for the emotion analysis module.

[0360] Step 5:

[0361] The server inputs facial and voice data from the terminal into an emotion analysis module. The module uses machine learning algorithms to analyze this data and identify the user's emotions. The input here is the user's facial and voice data, and the output is the analyzed emotional state of the user.

[0362] Step 6:

[0363] The server adjusts the previously generated digital content based on the user's sentiment analysis results. It enhances music and visual effects according to the user's emotions and appropriately adjusts the amount of information to provide the most suitable experience. Sentiment analysis results and initial digital content are used as input, and the adjusted digital content is provided as output.

[0364] (Application Example 2)

[0365] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0366] In today's market, there is a demand to improve the consumer experience and increase product satisfaction. However, traditional methods of providing product information make it difficult to provide feedback that responds to the individual emotions of users, and the optimization of the consumer experience is not fully achieved. Therefore, it is important to analyze users' emotions in real time during the actual purchasing experience and realize the personalization of product information.

[0367] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0368] In this invention, the server includes means for collecting product information and transmitting it to the cloud; a generation module for analyzing the product information on the cloud and evaluating the product's storage condition and manufacturing process; means for generating personalized digital content based on the analysis results; means including an emotion engine for analyzing facial expression data and voice data and determining the user's emotions; and means for adjusting the content of the digital content based on the results of the user's emotion analysis. This makes it possible to provide optimal digital content that corresponds to the individual emotions of the user.

[0369] "Product information" refers to data related to the storage conditions and manufacturing process of a product, and includes information such as environmental conditions.

[0370] "Cloud" refers to a technological platform that provides data storage and applications via the internet, enabling remote storage and processing of information.

[0371] A "generation module" is a software or hardware configuration that has the functionality to analyze product information on the cloud and generate digital content based on the results.

[0372] "Digital content" refers to information provided in digital format, including audio, visuals, text, and other similar elements.

[0373] A "database" is a system designed to store information in a structured manner, allowing for efficient searching and management.

[0374] "Identification information" refers to unique codes or tags attached to digital content or product information, which make it possible to uniquely recognize specific data.

[0375] "Users" refers to the individuals or organizations that receive product information and digital content through this system.

[0376] "Facial expression data" refers to numerical data and signals obtained by analyzing the facial features of a user through a video device.

[0377] "Voice data" refers to numerical values ​​and signals obtained by analyzing the user's speech and surrounding sounds acquired through a microphone.

[0378] An "emotion engine" is software equipped with algorithms and technologies that analyze facial expression data and voice data to estimate the user's current emotional state.

[0379] To realize this invention, a system combining a server, smart devices, a cloud infrastructure, and an analysis engine is required.

[0380] The server is responsible for acquiring product information in real time from sensors attached to the product and transmitting it to the cloud. This information includes data indicating the product's storage status and is stored in the cloud. A generation module in the cloud analyzes the product information and generates personalized digital content based on the results. This digital content includes audio, visual, and text information and is optimized according to the user's needs.

[0381] Smart devices, such as smart glasses and smartphones, collect facial and voice data from users. This data is sent to a server and analyzed by an emotion engine. The emotion engine quantifies the facial and voice data to determine the user's emotions. As a result, the server dynamically adjusts the content of digital content to deliver a personalized experience.

[0382] As a concrete example, when a user purchases a product, they can use smart glasses to check detailed product information. If facial expression analysis determines that the user is favorable, it may be possible to provide additional digital content, such as "playing a video containing an interview with the product's creator and testimonials from users."

[0383] When using a generative AI model, an example of a prompt message might be: "If the user's sentiment analysis results indicate 'interested,' generate video content that provides a detailed explanation of the product's background and usage examples."

[0384] This system allows users to receive personalized information in real time, resulting in a more satisfying shopping experience.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The terminal acquires product information (temperature, humidity, pressure, etc.) from sensors installed on the product and transmits it to the cloud in real time. It receives raw data from sensors as input, converts it to a standardized format, and outputs it.

[0388] Step 2:

[0389] The server analyzes product information sent to the cloud. From the environmental data received as input, it calculates evaluation indicators for assessing storage conditions and manufacturing processes, and outputs them.

[0390] Step 3:

[0391] The server generates digital content using a generation module based on the analysis results. In this process, evaluation metrics are taken as input, and the target content (audio, video, text) is generated and output.

[0392] Step 4:

[0393] The device captures the user's facial expressions and voice data through its camera and microphone. It sends the real-time acquired video and audio as input to an emotion engine, which then extracts and outputs facial and vocal characteristics.

[0394] Step 5:

[0395] The server estimates the user's emotions based on facial expression and voice data analyzed by the emotion engine. It receives feature data as input, analyzes it using a machine learning model to determine the emotional state, and outputs the result.

[0396] Step 6:

[0397] The server adjusts existing digital content based on the estimated emotional state. It receives the emotional state and generated content as input, modifies it into personalized content, and outputs it.

[0398] Step 7:

[0399] Users view personalized digital content through their devices. This allows them to enjoy a digital experience optimized based on analytics results in real time.

[0400] In this way, by analyzing the data received at each step and generating and adjusting appropriate content, a series of processes is realized that provides users with the optimal experience.

[0401] 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.

[0402] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] 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.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0408] 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.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0410] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] 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.

[0412] 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.

[0413] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0414] The 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.

[0415] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0417] This invention is a system that manages the storage conditions and manufacturing process of products on the cloud, guaranteeing the authenticity and quality of products to consumers. This system collects and analyzes product-related data, generates personalized digital content, and provides it to users. The system's operation is described in detail below.

[0418] First, the server acquires environmental information from data collection devices attached to the product. This information includes data about the external environment, such as storage temperature, humidity, and pressure. This data is acquired at regular intervals and sent to the cloud.

[0419] Next, the server analyzes the data received on the cloud. This analysis uses a generation module to evaluate whether the product's storage conditions and manufacturing process are appropriate. Based on this evaluation, the authenticity and quality of the product are confirmed.

[0420] Next, the server generates personalized digital content for the user based on the analysis results. This content includes detailed product reports, related stories, music, and visuals that enrich the user experience. This makes it possible to provide consumers with knowledge and appeal about the product.

[0421] The generated digital content is recorded on the blockchain as a digital asset. The server assigns identification information to this digital asset and provides it to the user as an NFT. Using this identification information, the user can securely access the content related to the product.

[0422] As a concrete example, consider a product from a high-end tea brand. This product is equipped with sensors that monitor temperature and humidity. A server receives this data and performs analysis. As a result, the server confirms that the tea leaves are stored under optimal conditions. It then generates content for consumers about the culture and history of the tea manufacturer, and delivers it as an NFT accompanied by related music. Users can access this content through their devices and enjoy the product while experiencing the tradition and individual value of the high-end tea brand.

[0423] Thus, this invention provides consumers with in-depth information and experiences related to the product, thereby achieving quality assurance and enhancing brand value.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The server acquires product storage status and environmental information from a data collection device. This device is equipped with sensors for temperature, humidity, pressure, etc., and provides information via a protocol that sends data to the cloud at specific time intervals.

[0427] Step 2:

[0428] The server verifies the integrity and format of the data received on the cloud and records it in the database. If abnormal values ​​or missing data are detected during this process, they are corrected or removed, and the data is cleansed.

[0429] Step 3:

[0430] The server passes refined data to a generation module, which performs data analysis. This module evaluates whether the product is stored properly and sets a flag for alerts if there are any problems. At the same time, it extracts and analyzes product-specific features.

[0431] Step 4:

[0432] Based on the analysis results, the server generates personalized digital content for the user. This content includes reports based on the product's storage condition, documentation about the manufacturing process, and related music and visual content.

[0433] Step 5:

[0434] The server issues the generated digital content as NFTs using blockchain technology. In this process, unique identification information is assigned to the content, and it is transformed into a digital asset.

[0435] Step 6:

[0436] The server transfers the issued NFT to the user's wallet, making it accessible. This allows the user to access their owned NFTs and use their content through their device.

[0437] Step 7:

[0438] Users can access NFTs and related content using their identification information on their own devices, allowing them to view and watch them. This enables them to enjoy the product's quality and unique experiential value.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] Properly managing product storage conditions and manufacturing processes, and guaranteeing product authenticity and quality to consumers, is a critical challenge in many industries. However, existing systems are inefficient in collecting and analyzing environmental data, and the generated information lacks transparency and reliability when provided to consumers. Furthermore, consumers often have limited opportunities to experience the cultural value and information associated with products, which is another troubling issue.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for collecting product data and transmitting it to a data processing server, a generation algorithm for analyzing the product data on the data processing server and evaluating the product's storage condition and manufacturing process, and means for generating personalized cultural information based on the analysis results. This makes it possible to provide consumers with a rich information experience while enhancing product quality assurance.

[0444] "Product data" refers to information about a product, including environmental data such as storage conditions and manufacturing processes.

[0445] A "data processing server" is a computing resource used to receive, analyze, and evaluate product data, and is built on the cloud.

[0446] A "generation algorithm" is a program procedure that evaluates analyzed product data and determines the product's storage condition and manufacturing process.

[0447] "Cultural information" refers to information such as history, culture, and stories related to a product, and is content that enriches the consumer experience.

[0448] "Digital assets" are data and content recorded in digital format, and are resources that can be traded because they possess identifying information.

[0449] A "distributed database" is a data recording system in which data is stored in multiple locations to ensure the integrity and transparency of information.

[0450] "Unique identifiers" are unique codes assigned to digital assets, used to identify and manage those assets.

[0451] This system manages the storage conditions and manufacturing process of products, guaranteeing their authenticity and quality to consumers. The main components of the system include data collection devices, data processing servers, generative AI models, and a digital asset management system.

[0452] The server collects data from sensors attached to the product. These sensors can measure environmental information such as temperature and humidity, and the acquired data is sent to the server at regular intervals. The server is located on a cloud infrastructure and has computing capabilities to process this data in real time.

[0453] Next, the server uses a generative AI model to analyze the received data. This AI model incorporates analytical algorithms to evaluate whether the product's storage conditions and manufacturing process meet the required standards. As a result, it is possible to generate cultural information such as the product's history, culture, and story.

[0454] Furthermore, the server registers the generated cultural information as digital assets in a distributed database and assigns unique identifiers to them. These digital assets are later made available to users. Users can access these digital assets using their own devices.

[0455] As a concrete example, let's consider the system operation for a product from a high-end tea brand. The server receives temperature and humidity data from sensors and analyzes this data to confirm that the tea leaves are stored under appropriate conditions. The generated cultural information includes the history of the tea and the brand's story, which is recorded on the blockchain as an NFT. Users can access this information via their devices and experience the deep knowledge and traditions of high-end tea.

[0456] As a concrete example of a prompt, one could give the generation AI model the instruction, "Generate cultural and historical content based on preservation data of high-grade tea."

[0457] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0458] Step 1:

[0459] The server acquires environmental information from data collection devices attached to the product. Input data from the sensors includes temperature, humidity, and pressure. The server receives this data at regular intervals and sends it to the cloud. This data is recorded in raw format and analyzed in subsequent processing steps.

[0460] Step 2:

[0461] The server analyzes the raw data stored in the cloud. This process utilizes a generative AI model to execute algorithms for evaluating the raw data. The environmental information collected in step 1 is used as input data. The output is an evaluation of the product's storage condition, indicating whether the storage conditions are appropriate.

[0462] Step 3:

[0463] The server generates cultural information based on the analysis results. In this step, the generative AI model creates a history and cultural story related to the product. The input data used is the evaluation results obtained in step 2 and the cultural information pre-programmed into the model. The output is personalized text and audio information.

[0464] Step 4:

[0465] The server registers the generated cultural information as a digital asset. This process assigns unique identifiers to the information and records it in a distributed database. The input data is the cultural information generated in step 3. The output is a digital asset with an assigned identifier.

[0466] Step 5:

[0467] Users access digital assets using their own devices. The user's device connects to the server using unique identification information and retrieves the digital content to be displayed. The input data is identification information, and the output is cultural content provided in a format viewable by the user.

[0468] (Application Example 1)

[0469] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] Accurately guaranteeing the authenticity and quality of a product is a crucial factor in consumer purchasing behavior. However, conventional methods make it difficult for consumers to easily verify detailed information about product storage conditions and manufacturing processes, hindering their ability to choose reliable products. This invention aims to enable consumers to access detailed product quality information even in physical stores, thereby improving reliability and appealing to product value.

[0471] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0472] In this invention, the server includes means for collecting product information and transmitting it to a network system; a generating device for analyzing product information on the network system and evaluating the product's maintenance status and manufacturing history; and means for generating personalized electronic content based on the analysis results. This enables consumers in physical stores to directly and in real time check product information using a portable information terminal.

[0473] "Product information" refers to data related to the product's storage conditions and manufacturing process, including environmental information such as temperature, humidity, and pressure.

[0474] A "network system" is a communication infrastructure for sending and receiving data via the internet or dedicated lines, and a system for processing and analyzing information using cloud computing.

[0475] A "generation device" is a device that, based on analyzed data, performs product condition evaluations and generates reports, creating electronic content to be provided to consumers.

[0476] "Electronic assets" refer to digital information that has been given value and security by being stored in a database and having identification information attached to it.

[0477] A "portable information terminal" refers to an information processing device that consumers can carry with them, such as a smartphone or tablet.

[0478] This invention is a system that uses a network system, a generation device, and a portable information terminal to provide product information to consumers.

[0479] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product and transmits it to the cloud via a network system. On the cloud, a generator operates that analyzes this data and evaluates the product's maintenance status and manufacturing history. This generator assesses the product's authenticity and quality, and based on the results, generates personalized electronic content. This electronic content includes detailed product information, visual displays, music, and text.

[0480] The generated digital content is stored in a database as digital assets and assigned identification information using blockchain technology. This identification information enables secure access to the content.

[0481] Users can scan QR codes of products they are interested in at physical stores using their portable information terminals to obtain corresponding identification information. This allows users to access detailed electronic content about those products in real time from a database in the cloud.

[0482] As a concrete example, with high-end tea products, users can scan a QR code on the tea leaves to access detailed information about their storage conditions and origin, as well as enjoy stories and music related to their cultural background. This allows consumers to gain a deeper understanding of the product's value and characteristics, leading to a more trustworthy purchase.

[0483] Examples of prompts to input into a generative AI model are as follows:

[0484] "Create personalized digital content that engages users, based on detailed information about the storage conditions, manufacturing process, and cultural background of specific products. The goal is to provide visually appealing content, including music, to help consumers gain a deeper understanding of the product's value."

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product. The input is raw data obtained from the sensors, and the output is formatted environmental data. This data is processed at regular intervals by a data acquisition module and transmitted to the network system.

[0488] Step 2:

[0489] The server sends collected environmental data to the cloud, where it is analyzed using a generation device. The input is the environmental data sent to the cloud, and the output is evaluation data regarding the product's maintenance status and manufacturing history. This analysis uses a data analysis model and integrates it with a generation AI model to perform an accurate evaluation.

[0490] Step 3:

[0491] The generation device generates personalized electronic content based on evaluation data. In this process, a generation AI model is used with prompt text as input, and the output is electronic content including text information, visual displays, and music.

[0492] Step 4:

[0493] The server stores the generated electronic content as an electronic asset in a database and assigns identification information to it. The input is the generated electronic content, and the output is the electronic asset with the assigned identification information. This identification information is guaranteed using blockchain technology, ensuring secure record-keeping.

[0494] Step 5:

[0495] Users scan a product's QR code with a portable information terminal at a physical store to obtain identification information. The input is the QR code scanned by the user, and the output is an access key to a database in the cloud. This operation allows users to securely access electronic content related to the product.

[0496] Step 6:

[0497] The device uses identification information to access electronic content in the cloud, allowing users to view product information. Input consists of identification information and user requests, while output is detailed product information and related cultural content provided to the user. This information is displayed on the device in real time, enhancing the user's purchasing experience.

[0498] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0499] This invention is a system that combines an emotion engine to enhance the user experience through the analysis of product data and the generation of digital content. The system not only manages the storage status and manufacturing process of products on the cloud, but also analyzes the user's emotions from their facial expressions, voice, and text, and provides personalized digital content based on that analysis.

[0500] First, the server receives real-time data about the product's storage conditions from a data collection device attached to the product. This data includes temperature, humidity, and pressure, and is transmitted to and stored in the cloud.

[0501] Next, the server uses data stored in the cloud to analyze the product's status via a generation module. This analysis allows for an assessment of the product's authenticity and quality. Following this, personalized digital content is generated based on the analysis results. This content includes a story related to the product, a storage status report, and even music and visuals that respond to the user's emotions.

[0502] Furthermore, the server uses an emotion engine to acquire data for analyzing the user's emotions. The user's device captures facial expressions and voice through its camera and microphone, and the server inputs this data into the emotion engine to determine what emotions the user is feeling towards the experience content.

[0503] The server adjusts the content of digital content based on the results of the user sentiment analysis. For example, if the user is relaxed, it will provide music that further enhances relaxation or present content with less information. If the server determines that the user is interested, it will enrich the experience by adding more information or a more detailed story.

[0504] Information related to a user's emotional state can be recorded on the blockchain as a digital asset and managed as a user's emotional history. In this process, identification information is assigned, and the emotional data, further analyzed, is used to construct the user's experience history.

[0505] As a concrete example, consider the usage scenario of a specific high-quality Japanese tea. The server analyzes whether the tea leaves are in good condition and, based on the results, creates content accompanied by relaxing music. The user watches this content while brewing the tea on their device. Simultaneously, an emotion engine analyzes the user's satisfaction level from their facial expressions and further customizes the content based on the analysis results.

[0506] This invention enables the provision of a deeper product-related experience and information tailored to consumer emotions. This strengthens product quality assurance and allows consumers to enjoy a more satisfying experience.

[0507] The following describes the processing flow.

[0508] Step 1:

[0509] The server periodically acquires environmental information such as temperature, humidity, and pressure from data collection devices installed on the product. This data is sent to the cloud and used for analysis of the product's storage conditions.

[0510] Step 2:

[0511] The server analyzes product data collected on the cloud to determine if the storage conditions are appropriate. This analysis utilizes a generation module to create detailed reports for evaluating product quality and authenticity.

[0512] Step 3:

[0513] Based on the analysis results, the server generates personalized digital content based on product information. This includes content such as the product's background story and storage tips, as well as music and visual materials to enrich the user experience.

[0514] Step 4:

[0515] The device collects the user's facial expressions and voice and sends them to the emotion engine. In this process, the device uses its camera and microphone to capture the user's reactions and sends that data to a server for processing.

[0516] Step 5:

[0517] The server activates an emotion engine to analyze the user's emotions. Based on this analysis, the aforementioned digital content is further customized according to the user's current emotions.

[0518] Step 6:

[0519] The server generates emotion-based customized content, assigns identification information to it, and records it on the blockchain as a digital asset. The content is then issued as an NFT and prepared for transfer to the user's wallet.

[0520] Step 7:

[0521] Users access the supplied content through their own devices. Here, they can view the displayed content and enjoy unique experiences related to the product. The system also manages the user's emotional history, which is used to improve future experiences.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Modern consumers demand more than just the purchase of goods; they seek personalized digital information tailored to their emotions and experiences. However, traditional methods struggle to connect information based on product condition and manufacturing processes with user emotions, failing to provide a deeper consumer experience. Furthermore, the management of collected emotional data and the protection of private information are not well-established.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes means for collecting product information and transmitting it to a database, a generation module for analyzing the product information on the database and evaluating the product's condition and manufacturing process, and means for generating personalized digital information based on the analysis results. This makes it possible to provide users with personalized digital information that responds to their emotions.

[0527] "Product information" refers to data related to the storage conditions and manufacturing process of the product, and includes environmental data such as temperature, humidity, and pressure.

[0528] A "database" is a cloud-based data storage system used to collect, store, and later analyze product information.

[0529] A "generation module" is a software component used to analyze collected product information and evaluate the product's condition and quality.

[0530] "Personalized digital information" refers to digital content such as stories, music, and visual information that are customized according to each user's emotions and needs based on analysis results.

[0531] The "emotion analysis module" is a dedicated software engine that analyzes the user's facial expressions, voice, and other data to identify their emotional state.

[0532] "Digital record" refers to an electronic record that stores generated digital information and makes it accessible later.

[0533] A "distributed ledger" is a digital ledger that utilizes blockchain technology to store information securely and in a way that is resistant to tampering.

[0534] "Identifying information" refers to unique information that identifies generated digital information and related data, enabling personalized delivery.

[0535] The following methods are used as embodiments for carrying out the present invention.

[0536] The server uses a data collection device to gather product information and transmits environmental data such as temperature, humidity, and pressure obtained from this device to a cloud-based database. This database is a cloud storage system that enables the storage and rapid access of structured data.

[0537] The server uses a generative AI model to analyze the collected product information. This AI model evaluates the condition and quality of the products and generates reports based on the analysis results. This analysis utilizes existing data science techniques and neural networks.

[0538] The server uses an emotion analysis module to analyze the user's facial expressions and voice data transmitted from the device. This identifies the user's emotions, and personalized digital content is generated that corresponds to the user's emotions. The device used for this process includes a camera and microphone, and a process is in place to send the captured information to the server.

[0539] Digital records are used to store generated content using distributed ledger technology. This technology utilizes blockchain to record data securely and with low tamper resistance. Identification information is linked to this digital record, ensuring personalized access when the user accesses it again.

[0540] As a concrete example, consider the usage scenario of a specific premium beverage. The server analyzes the beverage's quality status using environmental data and generates digital content, such as music and visual information, that induces relaxation based on that status. This content is viewed by the user while they consume the beverage. Simultaneously, an emotion analysis module evaluates the user's satisfaction and further optimizes the content.

[0541] An example of a prompt to input into a generative AI model is, "How can personalized digital content be tailored to the user's emotions?"

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] The terminal acquires environmental data such as temperature, humidity, and pressure in real time from data acquisition devices attached to the product. This data is collected directly through sensors, and the terminal immediately transmits it to a database in the cloud. It receives environmental data as input and generates formatted data for recording in the database as output.

[0545] Step 2:

[0546] The server receives environmental data stored in the cloud and analyzes the product's condition using a generated AI model. This analysis process utilizes the pattern recognition capabilities of the AI ​​model to detect anomalies and analyze trends in the data, thereby evaluating the product's quality and condition. The input here is formatted environmental data, and the output is a product quality evaluation report.

[0547] Step 3:

[0548] The server generates personalized digital content based on the quality evaluation report of the generated product. This process selects the most suitable narrative, music, and visual information based on data from similar past products and user profiles. The quality evaluation report and user profile are used as input, and the output is personalized digital content.

[0549] Step 4:

[0550] The device uses its built-in camera and microphone to capture the user's facial expressions and voice. The collected data is sent to a server to understand the user's current emotional state. The input here is the data captured by the camera and microphone, and the output is the input data for the emotion analysis module.

[0551] Step 5:

[0552] The server inputs facial and voice data from the terminal into an emotion analysis module. The module uses machine learning algorithms to analyze this data and identify the user's emotions. The input here is the user's facial and voice data, and the output is the analyzed emotional state of the user.

[0553] Step 6:

[0554] The server adjusts the previously generated digital content based on the user's sentiment analysis results. It enhances music and visual effects according to the user's emotions and appropriately adjusts the amount of information to provide the most suitable experience. Sentiment analysis results and initial digital content are used as input, and the adjusted digital content is provided as output.

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] In today's market, there is a demand to improve the consumer experience and increase product satisfaction. However, traditional methods of providing product information make it difficult to provide feedback that responds to the individual emotions of users, and the optimization of the consumer experience is not fully achieved. Therefore, it is important to analyze users' emotions in real time during the actual purchasing experience and realize the personalization of product information.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for collecting product information and transmitting it to the cloud; a generation module for analyzing the product information on the cloud and evaluating the product's storage condition and manufacturing process; means for generating personalized digital content based on the analysis results; means including an emotion engine for analyzing facial expression data and voice data and determining the user's emotions; and means for adjusting the content of the digital content based on the results of the user's emotion analysis. This makes it possible to provide optimal digital content that corresponds to the individual emotions of the user.

[0560] "Product information" refers to data related to the storage conditions and manufacturing process of a product, and includes information such as environmental conditions.

[0561] "Cloud" refers to a technological platform that provides data storage and applications via the internet, enabling remote storage and processing of information.

[0562] A "generation module" is a software or hardware configuration that has the functionality to analyze product information on the cloud and generate digital content based on the results.

[0563] "Digital content" refers to information provided in digital format, including audio, visuals, text, and other similar elements.

[0564] A "database" is a system designed to store information in a structured manner, allowing for efficient searching and management.

[0565] "Identification information" refers to unique codes or tags attached to digital content or product information, which make it possible to uniquely recognize specific data.

[0566] "Users" refers to the individuals or organizations that receive product information and digital content through this system.

[0567] "Facial expression data" refers to numerical data and signals obtained by analyzing the facial features of a user through a video device.

[0568] "Voice data" refers to numerical values ​​and signals obtained by analyzing the user's speech and surrounding sounds acquired through a microphone.

[0569] An "emotion engine" is software equipped with algorithms and technologies that analyze facial expression data and voice data to estimate the user's current emotional state.

[0570] To realize this invention, a system combining a server, smart devices, a cloud infrastructure, and an analysis engine is required.

[0571] The server is responsible for acquiring product information in real time from sensors attached to the product and transmitting it to the cloud. This information includes data indicating the product's storage status and is stored in the cloud. A generation module in the cloud analyzes the product information and generates personalized digital content based on the results. This digital content includes audio, visual, and text information and is optimized according to the user's needs.

[0572] Smart devices, such as smart glasses and smartphones, collect facial and voice data from users. This data is sent to a server and analyzed by an emotion engine. The emotion engine quantifies the facial and voice data to determine the user's emotions. As a result, the server dynamically adjusts the content of digital content to deliver a personalized experience.

[0573] As a concrete example, when a user purchases a product, they can use smart glasses to check detailed product information. If facial expression analysis determines that the user is favorable, it may be possible to provide additional digital content, such as "playing a video containing an interview with the product's creator and testimonials from users."

[0574] When using a generative AI model, an example of a prompt message could be: "If the user's sentiment analysis result is determined to be 'interested,' generate video content that provides a detailed introduction to the product's background and usage examples."

[0575] This system allows users to receive personalized information in real time, resulting in a more satisfying shopping experience.

[0576] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0577] Step 1:

[0578] The terminal acquires product information (temperature, humidity, pressure, etc.) from sensors installed on the product and transmits it to the cloud in real time. It receives raw data from sensors as input, converts it to a standardized format, and outputs it.

[0579] Step 2:

[0580] The server analyzes product information sent to the cloud. From the environmental data received as input, it calculates evaluation indicators for assessing storage conditions and manufacturing processes, and outputs them.

[0581] Step 3:

[0582] The server generates digital content using a generation module based on the analysis results. In this process, evaluation metrics are taken as input, and the target content (audio, video, text) is generated and output.

[0583] Step 4:

[0584] The device captures the user's facial expressions and voice data through its camera and microphone. It sends the real-time acquired video and audio as input to an emotion engine, which then extracts and outputs facial and vocal characteristics.

[0585] Step 5:

[0586] The server estimates the user's emotions based on facial expression and voice data analyzed by the emotion engine. It receives feature data as input, analyzes it using a machine learning model to determine the emotional state, and outputs the result.

[0587] Step 6:

[0588] The server adjusts existing digital content based on the estimated emotional state. It receives the emotional state and generated content as input, modifies it into personalized content, and outputs it.

[0589] Step 7:

[0590] Users view personalized digital content through their devices. This allows them to enjoy a digital experience optimized based on analytics results in real time.

[0591] In this way, by analyzing the data received at each step and generating and adjusting appropriate content, a series of processes is realized that provides users with the optimal experience.

[0592] 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.

[0593] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] 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.

[0598] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0599] 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.

[0600] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0601] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0602] 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.

[0603] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0604] 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.

[0605] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0606] The 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.

[0607] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0608] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] This invention is a system that manages the storage conditions and manufacturing process of products on the cloud, guaranteeing the authenticity and quality of products to consumers. This system collects and analyzes product-related data, generates personalized digital content, and provides it to users. The system's operation is described in detail below.

[0610] First, the server acquires environmental information from data collection devices attached to the product. This information includes data about the external environment, such as storage temperature, humidity, and pressure. This data is acquired at regular intervals and sent to the cloud.

[0611] Next, the server analyzes the data received on the cloud. This analysis uses a generation module to evaluate whether the product's storage conditions and manufacturing process are appropriate. Based on this evaluation, the authenticity and quality of the product are confirmed.

[0612] Next, the server generates personalized digital content for the user based on the analysis results. This content includes detailed product reports, related stories, music, and visuals that enrich the user experience. This makes it possible to provide consumers with knowledge and appeal about the product.

[0613] The generated digital content is recorded on the blockchain as a digital asset. The server assigns identification information to this digital asset and provides it to the user as an NFT. Using this identification information, the user can securely access the content related to the product.

[0614] As a concrete example, consider a product from a high-end tea brand. This product is equipped with sensors that monitor temperature and humidity. A server receives this data and performs analysis. As a result, the server confirms that the tea leaves are stored under optimal conditions. It then generates content for consumers about the culture and history of the tea manufacturer, and delivers it as an NFT accompanied by related music. Users can access this content through their devices and enjoy the product while experiencing the tradition and individual value of the high-end tea brand.

[0615] Thus, this invention provides consumers with in-depth information and experiences related to the product, thereby achieving quality assurance and enhancing brand value.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server acquires product storage status and environmental information from a data collection device. This device is equipped with sensors for temperature, humidity, pressure, etc., and provides information via a protocol that sends data to the cloud at specific time intervals.

[0619] Step 2:

[0620] The server verifies the integrity and format of the data received on the cloud and records it in the database. If abnormal values ​​or missing data are detected during this process, they are corrected or removed, and the data is cleansed.

[0621] Step 3:

[0622] The server passes refined data to a generation module, which performs data analysis. This module evaluates whether the product is stored properly and sets a flag for alerts if there are any problems. At the same time, it extracts and analyzes product-specific features.

[0623] Step 4:

[0624] Based on the analysis results, the server generates personalized digital content for the user. This content includes reports based on the product's storage condition, documentation about the manufacturing process, and related music and visual content.

[0625] Step 5:

[0626] The server issues the generated digital content as NFTs using blockchain technology. In this process, unique identification information is assigned to the content, and it is transformed into a digital asset.

[0627] Step 6:

[0628] The server transfers the issued NFT to the user's wallet, making it accessible. This allows the user to access their owned NFTs and use their content through their device.

[0629] Step 7:

[0630] Users can access NFTs and related content using their identification information on their own devices, allowing them to view and watch them. This enables them to enjoy the product's quality and unique experiential value.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Properly managing product storage conditions and manufacturing processes, and guaranteeing product authenticity and quality to consumers, is a critical challenge in many industries. However, existing systems are inefficient in collecting and analyzing environmental data, and the generated information lacks transparency and reliability when provided to consumers. Furthermore, consumers often have limited opportunities to experience the cultural value and information associated with products, which is another troubling issue.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes means for collecting product data and transmitting it to a data processing server, a generation algorithm for analyzing the product data on the data processing server and evaluating the product's storage condition and manufacturing process, and means for generating personalized cultural information based on the analysis results. This makes it possible to provide consumers with a rich information experience while enhancing product quality assurance.

[0636] "Product data" refers to information about a product, including environmental data such as storage conditions and manufacturing processes.

[0637] A "data processing server" is a computing resource used to receive, analyze, and evaluate product data, and is built on the cloud.

[0638] A "generation algorithm" is a program procedure that evaluates analyzed product data and determines the product's storage condition and manufacturing process.

[0639] "Cultural information" refers to information such as history, culture, and stories related to a product, and is content that enriches the consumer experience.

[0640] "Digital assets" are data and content recorded in digital format, and are resources that can be traded because they possess identifying information.

[0641] A "distributed database" is a data recording system in which data is stored in multiple locations to ensure the integrity and transparency of information.

[0642] "Unique identifiers" are unique codes assigned to digital assets, used to identify and manage those assets.

[0643] This system manages the storage conditions and manufacturing process of products, guaranteeing their authenticity and quality to consumers. The main components of the system include data collection devices, data processing servers, generative AI models, and a digital asset management system.

[0644] The server collects data from sensors attached to the product. These sensors can measure environmental information such as temperature and humidity, and the acquired data is sent to the server at regular intervals. The server is located on a cloud infrastructure and has computing capabilities to process this data in real time.

[0645] Next, the server uses a generative AI model to analyze the received data. This AI model incorporates analytical algorithms to evaluate whether the product's storage conditions and manufacturing process meet the required standards. As a result, it is possible to generate cultural information such as the product's history, culture, and story.

[0646] Furthermore, the server registers the generated cultural information as digital assets in a distributed database and assigns unique identifiers to them. These digital assets are later made available to users. Users can access these digital assets using their own devices.

[0647] As a concrete example, let's consider the system operation for a product from a high-end tea brand. The server receives temperature and humidity data from sensors and analyzes this data to confirm that the tea leaves are stored under appropriate conditions. The generated cultural information includes the history of the tea and the brand's story, which is recorded on the blockchain as an NFT. Users can access this information via their devices and experience the deep knowledge and traditions of high-end tea.

[0648] As a concrete example of a prompt, one could give the generation AI model the instruction, "Generate cultural and historical content based on preservation data of high-grade tea."

[0649] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0650] Step 1:

[0651] The server acquires environmental information from data collection devices attached to the product. Input data from the sensors includes temperature, humidity, and pressure. The server receives this data at regular intervals and sends it to the cloud. This data is recorded in raw format and analyzed in subsequent processing steps.

[0652] Step 2:

[0653] The server analyzes the raw data stored in the cloud. This process utilizes a generative AI model to execute algorithms for evaluating the raw data. The environmental information collected in step 1 is used as input data. The output is an evaluation of the product's storage condition, indicating whether the storage conditions are appropriate.

[0654] Step 3:

[0655] The server generates cultural information based on the analysis results. In this step, the generative AI model creates a history and cultural story related to the product. The input data used is the evaluation results obtained in step 2 and the cultural information pre-programmed into the model. The output is personalized text and audio information.

[0656] Step 4:

[0657] The server registers the generated cultural information as a digital asset. This process assigns unique identifiers to the information and records it in a distributed database. The input data is the cultural information generated in step 3. The output is a digital asset with an assigned identifier.

[0658] Step 5:

[0659] Users access digital assets using their own devices. The user's device connects to the server using unique identification information and retrieves the digital content to be displayed. The input data is identification information, and the output is cultural content provided in a format viewable by the user.

[0660] (Application Example 1)

[0661] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0662] Accurately guaranteeing the authenticity and quality of a product is a crucial factor in consumer purchasing behavior. However, conventional methods make it difficult for consumers to easily verify detailed information about product storage conditions and manufacturing processes, hindering their ability to choose reliable products. This invention aims to enable consumers to access detailed product quality information even in physical stores, thereby improving reliability and appealing to product value.

[0663] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0664] In this invention, the server includes means for collecting product information and transmitting it to a network system; a generating device for analyzing product information on the network system and evaluating the product's maintenance status and manufacturing history; and means for generating personalized electronic content based on the analysis results. This enables consumers in physical stores to directly and in real time check product information using a portable information terminal.

[0665] "Product information" refers to data related to the product's storage conditions and manufacturing process, including environmental information such as temperature, humidity, and pressure.

[0666] A "network system" is a communication infrastructure for sending and receiving data via the internet or dedicated lines, and a system for processing and analyzing information using cloud computing.

[0667] A "generation device" is a device that, based on analyzed data, performs product condition evaluations and generates reports, creating electronic content to be provided to consumers.

[0668] "Electronic assets" refer to digital information that has been given value and security by being stored in a database and having identification information attached to it.

[0669] A "portable information terminal" refers to an information processing device that consumers can carry with them, such as a smartphone or tablet.

[0670] This invention is a system that uses a network system, a generation device, and a portable information terminal to provide product information to consumers.

[0671] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product and transmits it to the cloud via a network system. On the cloud, a generator operates that analyzes this data and evaluates the product's maintenance status and manufacturing history. This generator assesses the product's authenticity and quality, and based on the results, generates personalized electronic content. This electronic content includes detailed product information, visual displays, music, and text.

[0672] The generated digital content is stored in a database as digital assets and assigned identification information using blockchain technology. This identification information enables secure access to the content.

[0673] Users can scan QR codes of products they are interested in at physical stores using their portable information terminals to obtain corresponding identification information. This allows users to access detailed electronic content about those products in real time from a database in the cloud.

[0674] As a concrete example, with high-end tea products, users can scan a QR code on the tea leaves to access detailed information about their storage conditions and origin, as well as enjoy stories and music related to their cultural background. This allows consumers to gain a deeper understanding of the product's value and characteristics, leading to a more trustworthy purchase.

[0675] Examples of prompts to input into a generative AI model are as follows:

[0676] "Create personalized digital content that engages users, based on detailed information about the storage conditions, manufacturing process, and cultural background of specific products. The goal is to provide visually appealing content, including music, to help consumers gain a deeper understanding of the product's value."

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The server collects environmental data such as temperature, humidity, and pressure from sensors attached to the product. The input is raw data obtained from the sensors, and the output is formatted environmental data. This data is processed at regular intervals by a data acquisition module and transmitted to the network system.

[0680] Step 2:

[0681] The server sends collected environmental data to the cloud, where it is analyzed using a generation device. The input is the environmental data sent to the cloud, and the output is evaluation data regarding the product's maintenance status and manufacturing history. This analysis uses a data analysis model and integrates it with a generation AI model to perform an accurate evaluation.

[0682] Step 3:

[0683] The generation device generates personalized electronic content based on evaluation data. In this process, a generation AI model is used with prompt text as input, and the output is electronic content including text information, visual displays, and music.

[0684] Step 4:

[0685] The server stores the generated electronic content as an electronic asset in a database and assigns identification information to it. The input is the generated electronic content, and the output is the electronic asset with the assigned identification information. This identification information is guaranteed using blockchain technology, ensuring secure record-keeping.

[0686] Step 5:

[0687] Users scan a product's QR code with a portable information terminal at a physical store to obtain identification information. The input is the QR code scanned by the user, and the output is an access key to a database in the cloud. This operation allows users to securely access electronic content related to the product.

[0688] Step 6:

[0689] The device uses identification information to access electronic content in the cloud, allowing users to view product information. Input consists of identification information and user requests, while output is detailed product information and related cultural content provided to the user. This information is displayed on the device in real time, enhancing the user's purchasing experience.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention is a system that combines an emotion engine to enhance the user experience through the analysis of product data and the generation of digital content. The system not only manages the storage status and manufacturing process of products on the cloud, but also analyzes the user's emotions from their facial expressions, voice, and text, and provides personalized digital content based on that analysis.

[0692] First, the server receives real-time data about the product's storage conditions from a data collection device attached to the product. This data includes temperature, humidity, and pressure, and is transmitted to and stored in the cloud.

[0693] Next, the server uses data stored in the cloud to analyze the product's status via a generation module. This analysis allows for an assessment of the product's authenticity and quality. Following this, personalized digital content is generated based on the analysis results. This content includes a story related to the product, a storage status report, and even music and visuals that respond to the user's emotions.

[0694] Furthermore, the server uses an emotion engine to acquire data for analyzing the user's emotions. The user's device captures facial expressions and voice through its camera and microphone, and the server inputs this data into the emotion engine to determine what emotions the user is feeling towards the experience content.

[0695] The server adjusts the content of digital content based on the results of the user sentiment analysis. For example, if the user is relaxed, it will provide music that further enhances relaxation or present content with less information. If the server determines that the user is interested, it will enrich the experience by adding more information or a more detailed story.

[0696] Information related to a user's emotional state can be recorded on the blockchain as a digital asset and managed as a user's emotional history. In this process, identification information is assigned, and the emotional data, further analyzed, is used to construct the user's experience history.

[0697] As a concrete example, consider the usage scenario of a specific high-quality Japanese tea. The server analyzes whether the tea leaves are in good condition and, based on the results, creates content accompanied by relaxing music. The user watches this content while brewing the tea on their device. Simultaneously, an emotion engine analyzes the user's satisfaction level from their facial expressions and further customizes the content based on the analysis results.

[0698] This invention enables the provision of a deeper product-related experience and information tailored to consumer emotions. This strengthens product quality assurance and allows consumers to enjoy a more satisfying experience.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The server periodically acquires environmental information such as temperature, humidity, and pressure from data collection devices installed on the product. This data is sent to the cloud and used for analysis of the product's storage conditions.

[0702] Step 2:

[0703] The server analyzes product data collected on the cloud to determine if the storage conditions are appropriate. This analysis utilizes a generation module to create detailed reports for evaluating product quality and authenticity.

[0704] Step 3:

[0705] Based on the analysis results, the server generates personalized digital content based on product information. This includes content such as the product's background story and storage tips, as well as music and visual materials to enrich the user experience.

[0706] Step 4:

[0707] The device collects the user's facial expressions and voice and sends them to the emotion engine. In this process, the device uses its camera and microphone to capture the user's reactions and sends that data to a server for processing.

[0708] Step 5:

[0709] The server activates an emotion engine to analyze the user's emotions. Based on this analysis, the aforementioned digital content is further customized according to the user's current emotions.

[0710] Step 6:

[0711] The server generates emotion-based customized content, assigns identification information to it, and records it on the blockchain as a digital asset. The content is then issued as an NFT and prepared for transfer to the user's wallet.

[0712] Step 7:

[0713] Users access the supplied content through their own devices. Here, they can view the displayed content and enjoy unique experiences related to the product. The system also manages the user's emotional history, which is used to improve future experiences.

[0714] (Example 2)

[0715] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] Modern consumers demand more than just the purchase of goods; they seek personalized digital information tailored to their emotions and experiences. However, traditional methods struggle to connect information based on product condition and manufacturing processes with user emotions, failing to provide a deeper consumer experience. Furthermore, the management of collected emotional data and the protection of private information are not well-established.

[0717] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0718] In this invention, the server includes means for collecting product information and transmitting it to a database, a generation module for analyzing the product information on the database and evaluating the product's condition and manufacturing process, and means for generating personalized digital information based on the analysis results. This makes it possible to provide users with personalized digital information that responds to their emotions.

[0719] "Product information" refers to data related to the storage conditions and manufacturing process of the product, and includes environmental data such as temperature, humidity, and pressure.

[0720] A "database" is a cloud-based data storage system used to collect, store, and later analyze product information.

[0721] A "generation module" is a software component used to analyze collected product information and evaluate the product's condition and quality.

[0722] "Personalized digital information" refers to digital content such as stories, music, and visual information that are customized according to each user's emotions and needs based on analysis results.

[0723] The "emotion analysis module" is a dedicated software engine that analyzes the user's facial expressions, voice, and other data to identify their emotional state.

[0724] "Digital record" refers to an electronic record that stores generated digital information and makes it accessible later.

[0725] A "distributed ledger" is a digital ledger that utilizes blockchain technology to store information securely and in a way that is resistant to tampering.

[0726] "Identifying information" refers to unique information that identifies generated digital information and related data, enabling personalized delivery.

[0727] The following methods are used as embodiments for carrying out the present invention.

[0728] The server uses a data collection device to gather product information and transmits environmental data such as temperature, humidity, and pressure obtained from this device to a cloud-based database. This database is a cloud storage system that enables the storage and rapid access of structured data.

[0729] The server uses a generative AI model to analyze the collected product information. This AI model evaluates the condition and quality of the products and generates reports based on the analysis results. This analysis utilizes existing data science techniques and neural networks.

[0730] The server uses an emotion analysis module to analyze the user's facial expressions and voice data transmitted from the device. This identifies the user's emotions, and personalized digital content is generated that corresponds to the user's emotions. The device used for this process includes a camera and microphone, and a process is in place to send the captured information to the server.

[0731] Digital records are used to store generated content using distributed ledger technology. This technology utilizes blockchain to record data securely and with low tamper resistance. Identification information is linked to this digital record, ensuring personalized access when the user accesses it again.

[0732] As a concrete example, consider the usage scenario of a specific premium beverage. The server analyzes the beverage's quality status using environmental data and generates digital content, such as music and visual information, that induces relaxation based on that status. This content is viewed by the user while they consume the beverage. Simultaneously, an emotion analysis module evaluates the user's satisfaction and further optimizes the content.

[0733] An example of a prompt to input into a generative AI model is, "How can personalized digital content be tailored to the user's emotions?"

[0734] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0735] Step 1:

[0736] The terminal acquires environmental data such as temperature, humidity, and pressure in real time from data acquisition devices attached to the product. This data is collected directly through sensors, and the terminal immediately transmits it to a database in the cloud. It receives environmental data as input and generates formatted data for recording in the database as output.

[0737] Step 2:

[0738] The server receives environmental data stored in the cloud and analyzes the product's condition using a generated AI model. This analysis process utilizes the pattern recognition capabilities of the AI ​​model to detect anomalies and analyze trends in the data, thereby evaluating the product's quality and condition. The input here is formatted environmental data, and the output is a product quality evaluation report.

[0739] Step 3:

[0740] The server generates personalized digital content based on the quality evaluation report of the generated product. This process selects the most suitable narrative, music, and visual information based on data from similar past products and user profiles. The quality evaluation report and user profile are used as input, and the output is personalized digital content.

[0741] Step 4:

[0742] The device uses its built-in camera and microphone to capture the user's facial expressions and voice. The collected data is sent to a server to understand the user's current emotional state. The input here is the data captured by the camera and microphone, and the output is the input data for the emotion analysis module.

[0743] Step 5:

[0744] The server inputs facial and voice data from the terminal into an emotion analysis module. The module uses machine learning algorithms to analyze this data and identify the user's emotions. The input here is the user's facial and voice data, and the output is the analyzed emotional state of the user.

[0745] Step 6:

[0746] The server adjusts the previously generated digital content based on the user's sentiment analysis results. It enhances music and visual effects according to the user's emotions and appropriately adjusts the amount of information to provide the most suitable experience. Sentiment analysis results and initial digital content are used as input, and the adjusted digital content is provided as output.

[0747] (Application Example 2)

[0748] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] In today's market, there is a demand to improve the consumer experience and increase product satisfaction. However, traditional methods of providing product information make it difficult to provide feedback that responds to the individual emotions of users, and the optimization of the consumer experience is not fully achieved. Therefore, it is important to analyze users' emotions in real time during the actual purchasing experience and realize the personalization of product information.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0751] In this invention, the server includes means for collecting product information and transmitting it to the cloud; a generation module for analyzing the product information on the cloud and evaluating the product's storage condition and manufacturing process; means for generating personalized digital content based on the analysis results; means including an emotion engine for analyzing facial expression data and voice data and determining the user's emotions; and means for adjusting the content of the digital content based on the results of the user's emotion analysis. This makes it possible to provide optimal digital content that corresponds to the individual emotions of the user.

[0752] "Product information" refers to data related to the storage conditions and manufacturing process of a product, and includes information such as environmental conditions.

[0753] "Cloud" refers to a technological platform that provides data storage and applications via the internet, enabling remote storage and processing of information.

[0754] A "generation module" is a software or hardware configuration that has the functionality to analyze product information on the cloud and generate digital content based on the results.

[0755] "Digital content" refers to information provided in digital format, including audio, visuals, text, and other similar elements.

[0756] A "database" is a system designed to store information in a structured manner, allowing for efficient searching and management.

[0757] "Identification information" refers to unique codes or tags attached to digital content or product information, which make it possible to uniquely recognize specific data.

[0758] "Users" refers to the individuals or organizations that receive product information and digital content through this system.

[0759] "Facial expression data" refers to numerical data and signals obtained by analyzing the facial features of a user through a video device.

[0760] "Voice data" refers to numerical values ​​and signals obtained by analyzing the user's speech and surrounding sounds acquired through a microphone.

[0761] An "emotion engine" is software equipped with algorithms and technologies that analyze facial expression data and voice data to estimate the user's current emotional state.

[0762] To realize this invention, a system combining a server, smart devices, a cloud infrastructure, and an analysis engine is required.

[0763] The server is responsible for acquiring product information in real time from sensors attached to the product and transmitting it to the cloud. This information includes data indicating the product's storage status and is stored in the cloud. A generation module in the cloud analyzes the product information and generates personalized digital content based on the results. This digital content includes audio, visual, and text information and is optimized according to the user's needs.

[0764] Smart devices, such as smart glasses and smartphones, collect facial and voice data from users. This data is sent to a server and analyzed by an emotion engine. The emotion engine quantifies the facial and voice data to determine the user's emotions. As a result, the server dynamically adjusts the content of digital content to deliver a personalized experience.

[0765] As a concrete example, when a user purchases a product, they can use smart glasses to check detailed product information. If facial expression analysis determines that the user is favorable, it may be possible to provide additional digital content, such as "playing a video containing an interview with the product's creator and testimonials from users."

[0766] When using a generative AI model, an example of a prompt message could be: "If the user's sentiment analysis result is determined to be 'interested,' generate video content that provides a detailed introduction to the product's background and usage examples."

[0767] This system allows users to receive personalized information in real time, resulting in a more satisfying shopping experience.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] The terminal acquires product information (temperature, humidity, pressure, etc.) from sensors installed on the product and transmits it to the cloud in real time. It receives raw data from sensors as input, converts it to a standardized format, and outputs it.

[0771] Step 2:

[0772] The server analyzes product information sent to the cloud. From the environmental data received as input, it calculates evaluation indicators for assessing storage conditions and manufacturing processes, and outputs them.

[0773] Step 3:

[0774] The server generates digital content using a generation module based on the analysis results. In this process, evaluation metrics are taken as input, and the target content (audio, video, text) is generated and output.

[0775] Step 4:

[0776] The device captures the user's facial expressions and voice data through its camera and microphone. It sends the real-time acquired video and audio as input to an emotion engine, which then extracts and outputs facial and vocal characteristics.

[0777] Step 5:

[0778] The server estimates the user's emotions based on facial expression and voice data analyzed by the emotion engine. It receives feature data as input, analyzes it using a machine learning model to determine the emotional state, and outputs the result.

[0779] Step 6:

[0780] The server adjusts existing digital content based on the estimated emotional state. It receives the emotional state and generated content as input, modifies it into personalized content, and outputs it.

[0781] Step 7:

[0782] Users view personalized digital content through their devices. This allows them to enjoy a digital experience optimized based on analytics results in real time.

[0783] In this way, by analyzing the data received at each step and generating and adjusting appropriate content, a series of processes is realized that provides users with the optimal experience.

[0784] 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.

[0785] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0787] 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.

[0788] Figure 9 shows an 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.

[0789] 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.

[0790] 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.

[0791] 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, motorcycles, etc., 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, for example, based 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.

[0792] 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."

[0793] 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.

[0794] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0795] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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 the like 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.

[0804] 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.

[0805] The following is further disclosed regarding the embodiments described above.

[0806] (Claim 1)

[0807] A means of collecting product data and sending it to the cloud,

[0808] A generation module that analyzes product data on the cloud and evaluates the product's storage condition and manufacturing process,

[0809] A means for generating personalized digital content based on analysis results,

[0810] A means for recording the aforementioned digital content as a digital asset on a blockchain and assigning identification information to it,

[0811] A system including means for providing content to a user using the aforementioned identification information.

[0812] (Claim 2)

[0813] The system according to claim 1, comprising means for recording environmental information such as temperature, humidity, and pressure when collecting product data.

[0814] (Claim 3)

[0815] The system according to claim 1, comprising means for creating music, visuals, and text information related to the generated digital content and customizing them according to the characteristics of the product.

[0816] "Example 1"

[0817] (Claim 1)

[0818] A means of collecting product data and sending it to a data processing server,

[0819] A generation algorithm that analyzes product data on the aforementioned data processing server and evaluates the product's storage condition and manufacturing process,

[0820] A means of generating personalized cultural information based on analysis results,

[0821] A means for recording the aforementioned cultural information as a digital asset in a distributed database and assigning unique identification information to it,

[0822] A system including means for providing information to users using the aforementioned uniquely identified information.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for recording environmental information such as thermal conditions, humidity conditions, and pressure conditions when collecting product data.

[0825] (Claim 3)

[0826] The system according to claim 1, comprising means for creating acoustic media, visual media, and textual information related to the generated cultural information, and for editing them according to the characteristics of the product.

[0827] "Application Example 1"

[0828] (Claim 1)

[0829] A means for collecting product information and transmitting it to a network system,

[0830] A generating device that analyzes product information on a network system and evaluates the product's maintenance status and manufacturing history,

[0831] A means for generating personalized electronic content based on analysis results,

[0832] Means for storing the aforementioned electronic content as an electronic asset in a database and assigning identification data to it,

[0833] A means of providing content to consumers using the aforementioned identification data,

[0834] A system that includes means of accessing product information via a consumer's portable information terminal.

[0835] (Claim 2)

[0836] The system according to claim 1, comprising means for recording environmental conditions such as temperature, humidity, and pressure when collecting information about a product.

[0837] (Claim 3)

[0838] The system according to claim 1, comprising means for producing music, visual displays, and text information related to the generated electronic content, and for personalizing them according to the characteristics of the product.

[0839] "Example 2 of combining an emotion engine"

[0840] (Claim 1)

[0841] A means of collecting product information and sending it to a database,

[0842] A generation module that analyzes product information in a database and evaluates the product's condition and manufacturing process,

[0843] A means for generating personalized digital information based on analysis results,

[0844] It includes an emotion analysis module for analyzing the user's emotions, and means for adjusting digital information based on the emotion analysis results,

[0845] Means for storing the aforementioned digital information as a digital record in a distributed ledger and assigning identification information to it,

[0846] A system including means for providing information to a user using the aforementioned identification information.

[0847] (Claim 2)

[0848] The system according to claim 1, comprising means for recording environmental data such as temperature, humidity, and pressure when collecting product information.

[0849] (Claim 3)

[0850] The system according to claim 1, comprising means for creating acoustic, visual, and textual information related to generated digital information and customizing them according to product characteristics and user emotions.

[0851] "Application example 2 when combining with an emotional engine"

[0852] (Claim 1)

[0853] A means of collecting product information and sending it to the cloud,

[0854] A generation module that analyzes product information on the cloud and evaluates the product's storage condition and manufacturing process,

[0855] A means for generating personalized digital content based on analysis results,

[0856] A means for recording the aforementioned digital content as a digital asset in a database and assigning identification information to it,

[0857] A means of providing content to a user using the aforementioned identification information,

[0858] A means including an emotion engine that analyzes facial expression data and voice data to determine the user's emotions,

[0859] A means of adjusting the content of digital content based on the results of user sentiment analysis,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for recording environmental information such as temperature, humidity, and pressure when collecting product information.

[0863] (Claim 3)

[0864] The system according to claim 1, comprising means for creating audio, visual, and text information related to the generated digital content and customizing them according to the characteristics of the product. [Explanation of Symbols]

[0865] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting product data and sending it to the cloud, A generation module that analyzes product data on the cloud and evaluates the product's storage condition and manufacturing process, A means for generating personalized digital content based on analysis results, A means for recording the aforementioned digital content as a digital asset on a blockchain and assigning identification information to it, A system including means for providing content to a user using the aforementioned identification information.

2. The system according to claim 1, comprising means for recording environmental information such as temperature, humidity, and pressure when collecting product data.

3. The system according to claim 1, comprising means for creating music, visuals, and text information related to the generated digital content and customizing them according to the characteristics of the product.

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