System

Abstract

An object of a system according to an embodiment is to centrally manage the state and position information of perishable foods.SOLUTION: A system according to an embodiment includes an assignment unit, a tracking unit, an update unit, and an access unit. The imparting unit imparts a unique identification code to the perishable food. The tracking unit tracks the state and the position information of the food using the identification code assigned by the assigning unit. The updating unit updates the information tracked by the tracking unit to the database on the cloud in real time. The access unit accesses the information updated by the update unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to centrally manage the condition and location information of fresh food, and there is room for improvement.

[0005] The system according to the embodiment aims to centrally manage the status and location information of fresh food. [Means for solving the problem]

[0006] The system according to the embodiment includes an assigning unit, a tracking unit, an updating unit, and an accessing unit. The assigning unit assigns a unique identification code to fresh food. The tracking unit tracks the status and location information of the food using the identification code assigned by the assigning unit. The updating unit updates the information tracked by the tracking unit to a database on the cloud in real time. The accessing unit accesses the information updated by the updating unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage the status and location information of fresh food. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A centralized supply chain system for fresh food according to an embodiment of the present invention assigns unique identification codes to fresh food, tracks the food's condition and location using the identification codes, and updates the information in a cloud-based database in real time, making it accessible to interested parties. For example, a producer assigns a unique identification code to each fresh food. This identification code is affixed to the food in the form of a barcode or two-dimensional code (e.g., a QR code (registered trademark)). Next, as the shipped food passes through the distribution process, the identification code is scanned at each stage to record the food's condition and location. For example, when the food is transported by truck, the truck's location information and temperature control status can be recorded. Furthermore, the information recorded during the distribution process is updated in real time to a cloud-based database. Interested parties can access this database to check the food's condition and location. For example, a retailer can check the arrival status of tomatoes, or a consumer can check the production history of the tomatoes they purchased. This improves quality control and traceability of fresh food, enabling consumers to receive safe and fresh food. As a result, a centralized supply chain system for fresh food can improve quality control and traceability of fresh food, providing consumers with safe and fresh food. For example, by checking that food is kept at the appropriate temperature, deterioration of quality can be prevented. In addition, by checking the production history of food, it is possible to respond quickly in the unlikely event of a problem occurring.

[0029] The centralized supply chain system for fresh food management according to the embodiment includes an assigning unit, a tracking unit, an updating unit, and an accessing unit. The assigning unit assigns a unique identification code to fresh food. Examples of identification codes include, but are not limited to, barcodes, two-dimensional codes, and RFID tags. The assigning unit assigns the identification code using, for example, a barcode. Alternatively, the assigning unit can assign the identification code using a two-dimensional code. Alternatively, the assigning unit can assign the identification code using an RFID tag. For example, the assigning unit prints a barcode and affixes it to the food. Alternatively, the two-dimensional code can be printed directly on the food packaging. Alternatively, the RFID tag can be embedded in the food packaging. The tracking unit tracks the status and location information of the food using the identification code assigned by the assigning unit. For example, the tracking unit scans the identification code using a smartphone and transmits the data to the cloud via Wi-Fi or a mobile network. Alternatively, the tracking unit can scan the identification code using a dedicated scanner device and transmit the data to the cloud. Alternatively, the tracking unit can obtain location information using GPS and transmit it to the cloud. For example, the tracking unit scans a barcode using a smartphone camera and transmits the data to the cloud. Dedicated scanner devices include handheld scanners and fixed scanners. Wi-Fi standards include 802.11ac, 2.4GHz, and 5GHz. Mobile networks include 4G, 5G, and LTE. The update unit updates the information tracked by the tracking unit to a database on the cloud in real time. The update unit manages the database using cloud services such as AWS (registered trademark), Google (registered trademark), and Azure (registered trademark). The update unit can also encrypt data. For example, the update unit encrypts data using encryption algorithms such as AES, RSA, and SHA-256. The access unit accesses the information updated by the update unit. For example, the access unit sets access permissions for each relevant party so that they can view only the information they need.For example, the access unit sets roles such as administrator, viewer, editor, etc., and sets access rights according to each role. As a result, the unified management supply chain system for fresh food according to the embodiment can improve the quality control and traceability of fresh food, and provide safe and fresh food to consumers.

[0030] The assigning unit can assign the identification code using a barcode or a two-dimensional code. Examples of barcodes include, but are not limited to, EAN, UPC, and Code 128. Examples of QR codes include, but are not limited to, Model 1, Model 2, and Micro QR. The assigning unit, for example, prints a barcode and attaches it to the food. The assigning unit can also print a two-dimensional code directly on the food packaging. This makes it easier to assign an identification code and improves traceability.

[0031] The tracking unit can scan the identification code using a smartphone or a dedicated scanner device and transmit the data to the cloud via Wi-Fi or a mobile network. Examples of smartphones include, but are not limited to, operating systems such as iOS and Android. Examples of dedicated scanner devices include, but are not limited to, handheld scanners and fixed scanners. Examples of Wi-Fi include, but are not limited to, standards such as 802.11ac, 2.4 GHz, and 5 GHz. Examples of mobile networks include, but are not limited to, standards such as 4G, 5G, and LTE. For example, the tracking unit can scan the barcode using a smartphone camera and transmit the data to the cloud. Alternatively, the tracking unit can scan the barcode using a dedicated scanner device and transmit the data to the cloud. This allows for efficient scanning of the identification code and data transmission.

[0032] The update unit can update data in a cloud database in real time. Examples of cloud databases include, but are not limited to, cloud services such as AWS, Google Cloud, and Azure. The update unit updates data in real time using, for example, AWS's database service. The update unit can also update data in real time using Google Cloud's database service. This allows data to be updated in real time, ensuring that the latest information is always available.

[0033] The access unit can set access permissions for each person involved, allowing them to view only the information they need. Access permissions include, but are not limited to, roles such as administrator, viewer, and editor. For example, the access unit can set permissions for administrators that allow them to access all information. The access unit can also set permissions for viewers that allow them to only view information. This allows appropriate information to be provided to each person involved, improving security.

[0034] The update unit can encrypt the data. The encryption algorithms include, but are not limited to, AES, RSA, and SHA-256. For example, the update unit encrypts the data using AES. The update unit can also encrypt the data using RSA. This improves the security of the data.

[0035] When assigning an identification code, the assigning unit can generate different identification codes depending on the type and quality of the food. Types of food include, but are not limited to, fruits, vegetables, meat, seafood, etc. Quality includes, but is not limited to, evaluation criteria such as freshness, nutritional value, and appearance. For example, the assigning unit assigns a special identification code to high-quality food and includes information indicating the quality. The assigning unit can also generate identification codes in different formats for different types of food. The assigning unit can also include warning information in the identification code if the quality of the food deteriorates. This improves traceability by generating identification codes depending on the type and quality of the food.

[0036] The assigning unit can simultaneously record food production environment information when assigning an identification code. Production environment information includes, for example, items such as temperature, humidity, and light intensity, but is not limited to these examples. For example, the assigning unit can include temperature information at the time of production in the identification code. The assigning unit can also include humidity information at the time of production in the identification code. The assigning unit can also include light intensity information at the time of production in the identification code. This allows for improved food quality control by recording production environment information.

[0037] The assigning unit can automatically link information about the food producer when assigning an identification code. Producer information includes, but is not limited to, items such as name, location, and contact information. For example, the assigning unit can include the producer's name in the identification code. The assigning unit can also include the producer's contact information in the identification code. The assigning unit can also include the producer's authentication information in the identification code. This improves traceability by linking the producer information.

[0038] When assigning an identification code, the assigning unit can attach the code to an optimal position taking into consideration the packaging condition of the food. Packaging condition includes, but is not limited to, items such as the degree of sealing, the type of packaging material, and the packaging method. For example, if the packaging is transparent, the assigning unit can attach the code to an easily visible position. Furthermore, if the packaging is opaque, the assigning unit can also attach the code to an easily openable position. Furthermore, if the packaging is complex, the assigning unit can attach the code to an easily scannable position. In this way, scanning becomes easier by attaching the code to an optimal position depending on the packaging condition.

[0039] When assigning an identification code, the assigning unit can adjust the code generation algorithm by referring to the production history of the food. The production history includes, but is not limited to, items such as the production date, the raw materials used, and the manufacturing process. For example, the assigning unit generates a code corresponding to a specific production method based on the production history. The assigning unit can also generate a code corresponding to a specific production region based on the production history. The assigning unit can also generate a code corresponding to a specific production period based on the production history. In this way, generating codes based on the production history improves traceability.

[0040] When assigning an identification code, the assigning unit can generate a different identification code depending on the means of transportation of the food. Examples of means of transportation include, but are not limited to, trucks, ships, and airplanes. For example, in the case of truck transportation, the assigning unit can generate a code including temperature control information. In addition, in the case of ship transportation, the assigning unit can also generate a code including humidity control information. In addition, in the case of air transportation, the assigning unit can also generate a code including vibration control information. In this way, by generating an identification code depending on the means of transportation, traceability is improved.

[0041] The tracking unit can monitor the condition of the food in real time during tracking. The condition of the food includes, but is not limited to, items such as temperature, humidity, and freshness. For example, the tracking unit can monitor the temperature of the food in real time using a temperature sensor. The tracking unit can also monitor the humidity of the food in real time using a humidity sensor. The tracking unit can also monitor the vibration of the food in real time using a vibration sensor. This improves quality control by monitoring the condition of the food in real time.

[0042] The tracking unit can collect data for optimizing the transportation route of the food during tracking. The transportation route can be optimized by, for example, the shortest route, cost minimization, time minimization, and other optimization methods, but is not limited to these examples. The tracking unit can record the transportation route of the food in real time using, for example, GPS data. The tracking unit can also collect traffic information and propose an optimal transportation route. The tracking unit can also analyze past transportation data and predict an optimal transportation route. As a result, collecting data for optimizing the transportation route enables efficient transportation.

[0043] The tracking unit can apply an algorithm to predict a change in quality of food during tracking. Examples of algorithms for predicting a change in quality include, but are not limited to, methods such as machine learning models and statistical analysis. For example, the tracking unit can apply an algorithm to predict a change in quality of food based on a change in temperature. The tracking unit can also apply an algorithm to predict a change in quality of food based on a change in humidity. The tracking unit can also apply an algorithm to predict a change in quality of food based on vibration data. This improves quality control by predicting quality changes.

[0044] The tracking unit can apply different tracking methods depending on the means of transportation of the food during tracking. Transport means include, but are not limited to, trucks, ships, and aircraft. For example, in the case of truck transport, the tracking unit tracks location information using GPS. In addition, in the case of ship transport, the tracking unit can also track location information using satellite communication. In addition, in the case of air transport, the tracking unit can also track location information using aircraft flight data. This enables efficient tracking by applying a tracking method depending on the means of transportation.

[0045] The tracking unit can use different tracking devices depending on the storage location of the food when tracking. Storage locations include, but are not limited to, types such as a warehouse, a refrigerator, and a freezer. For example, the tracking unit uses a fixed tracking device when the food is stored in a warehouse. Furthermore, the tracking unit can use a tracking device with a temperature sensor when the food is stored in a refrigerator. Furthermore, the tracking unit can use a portable tracking device when the food is stored in a mobile location. This allows for efficient tracking by using a tracking device appropriate for the storage location.

[0046] During tracking, the tracking unit can collect environmental data during the transportation of food and transmit it to the cloud. Environmental data includes, but is not limited to, items such as temperature, humidity, and vibration. For example, the tracking unit can use a temperature sensor to collect temperature data during transportation and transmit it to the cloud. The tracking unit can also use a humidity sensor to collect humidity data during transportation and transmit it to the cloud. The tracking unit can also use a vibration sensor to collect vibration data during transportation and transmit it to the cloud. In this way, collecting environmental data during transportation improves quality control.

[0047] When updating data, the update unit can apply an algorithm that compares the data with past data to detect anomalies. Examples of algorithms for detecting anomalies include, but are not limited to, methods such as machine learning models and statistical analysis. For example, the update unit can apply an algorithm that compares the data with past temperature data to detect abnormal temperature changes. The update unit can also apply an algorithm that compares the data with past humidity data to detect abnormal humidity changes. The update unit can also apply an algorithm that compares the data with past vibration data to detect abnormal vibrations. This improves quality control by detecting abnormalities by comparing the data with past data.

[0048] The update unit can add a check function to check the consistency of data when updating data. Data consistency includes, but is not limited to, methods such as checking the consistency of data and detecting duplicate data. For example, the update unit can add a check function to check the consistency of data when updating data. The update unit can also add a check function to check the completeness of data when updating data. The update unit can also add a check function to check the accuracy of data when updating data. This improves the reliability of the data by checking the consistency of the data.

[0049] The update unit can automatically create a backup of the data when updating the data. Data backup methods include, but are not limited to, periodic backups and incremental backups. For example, the update unit can automatically create a backup on the cloud when updating the data. The update unit can also automatically create a backup in local storage when updating the data. The update unit can also automatically create a backup in external storage when updating the data. This automatically creates a backup of the data, thereby improving data protection.

[0050] When updating data, the update unit can integrate and update information from different data sources. Examples of different data sources include, but are not limited to, sensor data, user input data, and external API data. For example, the update unit integrates and updates data from different sensors. The update unit can also integrate and update data from different devices. The update unit can also integrate and update data from different cloud services. This improves data consistency by integrating information from different data sources.

[0051] The update unit can add a function to automatically convert the data format when updating data. Examples of data formats include, but are not limited to, CSV, JSON, and XML. The update unit, for example, converts data from different sensors into a unified format and updates the data. The update unit can also convert data from different devices into a unified format and update the data. The update unit can also convert data from different cloud services into a unified format and update the data. This automatically converting the data format improves data compatibility.

[0052] The update unit can adjust the encryption level of the data when updating the data. The encryption level includes, but is not limited to, settings such as AES-128 and AES-256. For example, the update unit can apply a high encryption level to highly confidential data. The update unit can also apply a low encryption level to less confidential data. The update unit can also dynamically adjust the encryption level depending on the importance of the data. In this way, adjusting the encryption level of the data improves data security.

[0053] When accessing, the access unit can provide optimal information by referring to the user's past access history. The past access history includes, but is not limited to, items such as access date and time, access content, and access source IP. The access unit, for example, preferentially displays information that the user has frequently accessed in the past. The access unit can also suggest highly relevant information based on the user's past access history. The access unit can also analyze the user's past access patterns and provide optimal information. In this way, optimal information can be provided to the user by referring to the past access history.

[0054] The access unit can optimize the display content by taking into account the user's device information when accessing the device. The device information includes, but is not limited to, items such as the device type, OS, and browser. For example, when using a smartphone, the access unit provides display content tailored to the screen size. Furthermore, when using a tablet, the access unit can provide display content optimized for a large screen. Furthermore, when using a smartwatch, the access unit can provide display content that is concise and highly visible. This improves usability by optimizing the display content according to the device information.

[0055] The access unit can improve the access interface by reflecting user feedback at the time of access. User feedback can include, but is not limited to, methods such as questionnaires, reviews, and usage logs. The access unit can improve the interface design based on the user feedback, for example. The access unit can also add or modify functions based on the user feedback. The access unit can also improve operability based on the user feedback. In this way, the ease of use of the interface can be improved by reflecting user feedback.

[0056] The access unit can provide highly relevant information by taking into account the user's geographical location information at the time of access. Examples of geographical location information include, but are not limited to, methods of obtaining the information, such as GPS, beacons, and Wi-Fi location information. For example, the access unit can preferentially display information close to the user's current location. The access unit can also suggest related information based on the user's geographical location information. The access unit can also suggest an optimal route based on the user's geographical location information. This improves usability by providing highly relevant information based on the geographical location information.

[0057] The access unit can customize the display content according to the user's level of expertise when accessing the site. Examples of expertise levels include, but are not limited to, evaluation criteria such as beginner, intermediate, and advanced. For example, the access unit can provide detailed technical information to users with high levels of expertise. The access unit can also provide concise, easy-to-understand information to users with low levels of expertise. The access unit can also suggest optimal display content based on the user's level of expertise. Customizing the display content according to the level of expertise improves usability.

[0058] The access unit can make the display content multilingual according to the user's language setting when accessing the site. The language setting includes, but is not limited to, methods of acquisition such as the language setting of the browser and the language setting of the user profile. The access unit automatically sets the display content based on, for example, the language setting of the user's device. The access unit can also provide a language switching function when the user uses multiple languages. The access unit can also provide the display content in a specific language when the user selects that language. This improves usability by making the display content multilingual according to the language setting.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The assigning unit can automatically link nutritional information of the food when assigning the identification code. Nutritional information includes, but is not limited to, items such as calories, protein, fat, and carbohydrates. For example, the assigning unit can include calorie information in the identification code. The assigning unit can also include protein information in the identification code. The assigning unit can also include fat information in the identification code. This allows consumers to easily check nutritional information of foods, which can be useful for health management.

[0061] The update unit can apply an algorithm for evaluating the reliability of the data when updating the data. Examples of algorithms for evaluating reliability include, but are not limited to, methods such as checking the consistency of the data and verifying the origin of the data. For example, the update unit applies an algorithm for evaluating the consistency of the data. The update unit can also apply an algorithm for verifying the origin of the data. The update unit can also apply an algorithm for scoring the reliability of the data. This improves the quality of the data by evaluating the reliability of the data.

[0062] The assigning unit can automatically link allergen information of a food product when assigning the identification code. The allergen information includes, for example, items such as dairy products, eggs, wheat, and nuts, but is not limited to these examples. For example, the assigning unit can include dairy allergen information in the identification code. The assigning unit can also include egg allergen information in the identification code. The assigning unit can also include wheat allergen information in the identification code. This allows consumers to easily check food allergen information, which can be useful in taking measures against allergies.

[0063] During tracking, the tracking unit can collect vibration data during transportation of the food and apply an algorithm to predict quality changes. The vibration data includes, but is not limited to, items such as vibration intensity, frequency, and duration. The tracking unit can apply an algorithm to predict quality changes based on, for example, vibration intensity. The tracking unit can also apply an algorithm to predict quality changes based on vibration frequency. The tracking unit can also apply an algorithm to predict quality changes based on vibration duration. This improves quality control by collecting vibration data and predicting quality changes.

[0064] When accessing, the access unit can provide related information by referring to the user's purchase history. The purchase history includes, but is not limited to, items such as purchase date and time, purchased items, and purchase amount. For example, the access unit can preferentially display information related to items the user has purchased in the past. The access unit can also suggest highly relevant items based on the user's purchase history. The access unit can also analyze the user's purchasing patterns and provide optimal information. This allows the optimal information to be provided to the user by referring to the purchase history.

[0065] The assigning unit can automatically link information regarding the food storage method when assigning the identification code. Storage methods include, for example, refrigerated storage, frozen storage, room temperature storage, and the like, but are not limited to these examples. For example, the assigning unit can include refrigerated storage information in the identification code. The assigning unit can also include frozen storage information in the identification code. The assigning unit can also include room temperature storage information in the identification code. This allows consumers to easily check the food storage method and ensures appropriate storage.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The assigning unit assigns a unique identification code to fresh food. Identification codes include barcodes, two-dimensional codes, and RFID tags. For example, a barcode can be printed and attached to the food, a two-dimensional code can be printed directly on the food packaging, or an RFID tag can be embedded in the food packaging. Step 2: The tracking unit tracks the status and location of the food using the identification code assigned by the assigning unit. For example, the tracking unit scans the identification code using a smartphone or a dedicated scanner device and sends the data to the cloud via Wi-Fi or a mobile network. Alternatively, location information can be obtained using GPS and sent to the cloud. Step 3: The update unit updates the information tracked by the tracking unit to a cloud database in real time. For example, the database can be managed using a cloud service such as AWS, Google Cloud, or Azure, and data encryption can also be performed. Step 4: The access unit accesses the information updated by the update unit. For example, access rights are set for each person involved, and access rights are set according to roles such as administrator, viewer, and editor.

[0068] (Example 2) A centralized supply chain system for fresh food according to an embodiment of the present invention assigns unique identification codes to fresh food, tracks the food's condition and location using the identification codes, and updates the information in a cloud-based database in real time, making it accessible to interested parties. For example, a producer assigns a unique identification code to each fresh food. This identification code is affixed to the food in the form of a barcode or two-dimensional code (e.g., a QR code). Then, as the shipped food passes through the distribution process, the identification code is scanned at each stage to record the food's condition and location. For example, when the food is transported by truck, the truck's location information and temperature control status can be recorded. Furthermore, the information recorded during the distribution process is updated in real time to a cloud-based database. Interested parties can access this database to check the food's condition and location. For example, a retail store employee can check the arrival status of tomatoes, or a consumer can check the production history of the tomatoes they purchased. This improves the quality control and traceability of fresh food, enabling consumers to receive safe and fresh food. This centralized supply chain system for fresh food improves the quality control and traceability of fresh food, enabling consumers to receive safe and fresh food. For example, by checking whether food is being stored at the appropriate temperature, deterioration of quality can be prevented. Also, by checking the production history of food, it is possible to respond quickly in the unlikely event of a problem occurring.

[0069] The centralized supply chain system for fresh food management according to the embodiment includes an assigning unit, a tracking unit, an updating unit, and an accessing unit. The assigning unit assigns a unique identification code to fresh food. Examples of identification codes include, but are not limited to, barcodes, two-dimensional codes, and RFID tags. The assigning unit assigns the identification code using, for example, a barcode. Alternatively, the assigning unit can assign the identification code using a two-dimensional code. Alternatively, the assigning unit can assign the identification code using an RFID tag. For example, the assigning unit prints a barcode and affixes it to the food. Alternatively, the two-dimensional code can be printed directly on the food packaging. Alternatively, the RFID tag can be embedded in the food packaging. The tracking unit tracks the status and location information of the food using the identification code assigned by the assigning unit. For example, the tracking unit scans the identification code using a smartphone and transmits the data to the cloud via Wi-Fi or a mobile network. Alternatively, the tracking unit can scan the identification code using a dedicated scanner device and transmit the data to the cloud. Alternatively, the tracking unit can obtain location information using GPS and transmit it to the cloud. For example, the tracking unit scans barcodes using a smartphone camera and sends the data to the cloud. Dedicated scanner devices include handheld scanners and fixed scanners. Wi-Fi standards include 802.11ac, 2.4GHz, and 5GHz. Mobile networks include 4G, 5G, and LTE. The update unit updates the information tracked by the tracking unit to a database on the cloud in real time. The update unit manages the database using cloud services such as AWS, Google Cloud, and Azure. The update unit can also encrypt data. For example, the update unit encrypts data using encryption algorithms such as AES, RSA, and SHA-256. The access unit accesses the information updated by the update unit. For example, the access unit sets access permissions for each relevant party so that they can view only the information they need. For example, the access unit sets roles such as administrator, viewer, and editor, and sets access permissions according to each role.As a result, the centralized management supply chain system for fresh food according to the embodiment can improve the quality control and traceability of fresh food, and provide safe and fresh food to consumers.

[0070] The assigning unit can assign the identification code using a barcode or a two-dimensional code. Examples of barcodes include, but are not limited to, EAN, UPC, and Code 128. Examples of QR codes include, but are not limited to, Model 1, Model 2, and Micro QR. The assigning unit, for example, prints a barcode and attaches it to the food. The assigning unit can also print a two-dimensional code directly on the food packaging. This makes it easier to assign an identification code and improves traceability.

[0071] The tracking unit can scan the identification code using a smartphone or a dedicated scanner device and transmit the data to the cloud via Wi-Fi or a mobile network. Examples of smartphones include, but are not limited to, operating systems such as iOS and Android. Examples of dedicated scanner devices include, but are not limited to, handheld scanners and fixed scanners. Examples of Wi-Fi include, but are not limited to, standards such as 802.11ac, 2.4 GHz, and 5 GHz. Examples of mobile networks include, but are not limited to, standards such as 4G, 5G, and LTE. For example, the tracking unit can scan the barcode using a smartphone camera and transmit the data to the cloud. Alternatively, the tracking unit can scan the barcode using a dedicated scanner device and transmit the data to the cloud. This allows for efficient scanning of the identification code and data transmission.

[0072] The update unit can update data in a cloud database in real time. Examples of cloud databases include, but are not limited to, cloud services such as AWS, Google Cloud, and Azure. The update unit updates data in real time using, for example, AWS's database service. The update unit can also update data in real time using Google Cloud's database service. This allows data to be updated in real time, ensuring that the latest information is always available.

[0073] The access unit can set access permissions for each person involved, allowing them to view only the information they need. Access permissions include, but are not limited to, roles such as administrator, viewer, and editor. For example, the access unit can set permissions for administrators that allow them to access all information. The access unit can also set permissions for viewers that allow them to only view information. This allows appropriate information to be provided to each person involved, improving security.

[0074] The update unit can encrypt the data. The encryption algorithms include, but are not limited to, AES, RSA, and SHA-256. For example, the update unit encrypts the data using AES. The update unit can also encrypt the data using RSA. This improves the security of the data.

[0075] The assigning unit can estimate the user's emotions and adjust the timing of assigning the identification code based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is stressed, the assigning unit can automate the assignment of the identification code to minimize manual operation. Alternatively, if the producer is relaxed, the assigning unit can provide an option to manually assign the identification code, allowing for fine adjustments. Alternatively, if the producer is in a hurry, the assigning unit can provide a simplified procedure for quickly assigning the identification code. This allows the timing of assigning the identification code to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] When assigning an identification code, the assigning unit can generate different identification codes depending on the type and quality of the food. Types of food include, but are not limited to, fruits, vegetables, meat, seafood, etc. Quality includes, but is not limited to, evaluation criteria such as freshness, nutritional value, and appearance. For example, the assigning unit assigns a special identification code to high-quality food and includes information indicating the quality. The assigning unit can also generate identification codes in different formats for different types of food. The assigning unit can also include warning information in the identification code if the quality of the food deteriorates. This improves traceability by generating identification codes depending on the type and quality of the food.

[0077] The assigning unit can simultaneously record food production environment information when assigning an identification code. Production environment information includes, for example, items such as temperature, humidity, and light intensity, but is not limited to these examples. For example, the assigning unit can include temperature information at the time of production in the identification code. The assigning unit can also include humidity information at the time of production in the identification code. The assigning unit can also include light intensity information at the time of production in the identification code. This allows for improved food quality control by recording production environment information.

[0078] The assigning unit can automatically link information about the food producer when assigning an identification code. Producer information includes, but is not limited to, items such as name, location, and contact information. For example, the assigning unit can include the producer's name in the identification code. The assigning unit can also include the producer's contact information in the identification code. The assigning unit can also include the producer's authentication information in the identification code. This improves traceability by linking the producer information.

[0079] The assigning unit can estimate the user's emotion and select the format of the identification code based on the estimated user emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is stressed, the assigning unit selects a two-dimensional code that is easy to scan. Alternatively, if the producer is relaxed, the assigning unit can select a barcode that includes detailed information. Alternatively, if the producer is in a hurry, the assigning unit can select a simple code that can be generated quickly. This allows the optimal format of the identification code to be selected depending on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When assigning an identification code, the assigning unit can attach the code to an optimal position taking into consideration the packaging condition of the food. Packaging condition includes, but is not limited to, items such as the degree of sealing, the type of packaging material, and the packaging method. For example, if the packaging is transparent, the assigning unit can attach the code to an easily visible position. Furthermore, if the packaging is opaque, the assigning unit can also attach the code to an easily openable position. Furthermore, if the packaging is complex, the assigning unit can attach the code to an easily scannable position. In this way, scanning becomes easier by attaching the code to an optimal position depending on the packaging condition.

[0081] When assigning an identification code, the assigning unit can adjust the code generation algorithm by referring to the production history of the food. The production history includes, but is not limited to, items such as the production date, the raw materials used, and the manufacturing process. For example, the assigning unit generates a code corresponding to a specific production method based on the production history. The assigning unit can also generate a code corresponding to a specific production region based on the production history. The assigning unit can also generate a code corresponding to a specific production period based on the production history. In this way, generating codes based on the production history improves traceability.

[0082] When assigning an identification code, the assigning unit can generate a different identification code depending on the means of transportation of the food. Examples of means of transportation include, but are not limited to, trucks, ships, and airplanes. For example, in the case of truck transportation, the assigning unit can generate a code including temperature control information. In addition, in the case of ship transportation, the assigning unit can also generate a code including humidity control information. In addition, in the case of air transportation, the assigning unit can also generate a code including vibration control information. In this way, by generating an identification code depending on the means of transportation, traceability is improved.

[0083] The tracking unit can estimate the user's emotions and adjust the frequency of tracking data collection based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the tracking unit can set the data collection frequency low to reduce the burden. Alternatively, if the producer is relaxed, the tracking unit can set the data collection frequency high to collect more detailed information. Alternatively, if the producer is in a hurry, the tracking unit can set the data collection frequency to a medium level to achieve a balance. This allows the frequency of tracking data collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The tracking unit can monitor the condition of the food in real time during tracking. The condition of the food includes, but is not limited to, items such as temperature, humidity, and freshness. For example, the tracking unit can monitor the temperature of the food in real time using a temperature sensor. The tracking unit can also monitor the humidity of the food in real time using a humidity sensor. The tracking unit can also monitor the vibration of the food in real time using a vibration sensor. This improves quality control by monitoring the condition of the food in real time.

[0085] The tracking unit can collect data for optimizing the transportation route of the food during tracking. The transportation route can be optimized by, for example, the shortest route, cost minimization, time minimization, and other optimization methods, but is not limited to these examples. The tracking unit can record the transportation route of the food in real time using, for example, GPS data. The tracking unit can also collect traffic information and propose an optimal transportation route. The tracking unit can also analyze past transportation data and predict an optimal transportation route. As a result, collecting data for optimizing the transportation route enables efficient transportation.

[0086] The tracking unit can apply an algorithm to predict a change in quality of food during tracking. Examples of algorithms for predicting a change in quality include, but are not limited to, methods such as machine learning models and statistical analysis. For example, the tracking unit can apply an algorithm to predict a change in quality of food based on a change in temperature. The tracking unit can also apply an algorithm to predict a change in quality of food based on a change in humidity. The tracking unit can also apply an algorithm to predict a change in quality of food based on vibration data. This improves quality control by predicting quality changes.

[0087] The tracking unit can estimate the user's emotions and adjust the display method of the tracking data based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the tracking unit can provide a simple, highly visible display method. If the producer is relaxed, the tracking unit can also provide a display method that includes detailed information. If the producer is in a hurry, the tracking unit can also provide a display method that focuses on the main points. This allows the display method of the tracking data to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The tracking unit can apply different tracking methods depending on the means of transportation of the food during tracking. Transport means include, but are not limited to, trucks, ships, and aircraft. For example, in the case of truck transport, the tracking unit tracks location information using GPS. In addition, in the case of ship transport, the tracking unit can also track location information using satellite communication. In addition, in the case of air transport, the tracking unit can also track location information using aircraft flight data. This enables efficient tracking by applying a tracking method depending on the means of transportation.

[0089] The tracking unit can use different tracking devices depending on the storage location of the food when tracking. Storage locations include, but are not limited to, types such as a warehouse, a refrigerator, and a freezer. For example, the tracking unit uses a fixed tracking device when the food is stored in a warehouse. Furthermore, the tracking unit can use a tracking device with a temperature sensor when the food is stored in a refrigerator. Furthermore, the tracking unit can use a portable tracking device when the food is stored in a mobile location. This allows for efficient tracking by using a tracking device appropriate for the storage location.

[0090] During tracking, the tracking unit can collect environmental data during the transportation of food and transmit it to the cloud. Environmental data includes, but is not limited to, items such as temperature, humidity, and vibration. For example, the tracking unit can use a temperature sensor to collect temperature data during transportation and transmit it to the cloud. The tracking unit can also use a humidity sensor to collect humidity data during transportation and transmit it to the cloud. The tracking unit can also use a vibration sensor to collect vibration data during transportation and transmit it to the cloud. In this way, collecting environmental data during transportation improves quality control.

[0091] The update unit can estimate the user's emotions and adjust the timing of data updates based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the update unit can set the data update frequency low to reduce the burden. Furthermore, if the producer is relaxed, the update unit can set the data update frequency high to collect more detailed information. Furthermore, if the producer is in a hurry, the update unit can set the data update frequency to a medium level to achieve a balance. This allows the timing of data updates to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] When updating data, the update unit can apply an algorithm that compares the data with past data to detect anomalies. Examples of algorithms for detecting anomalies include, but are not limited to, methods such as machine learning models and statistical analysis. For example, the update unit can apply an algorithm that compares the data with past temperature data to detect abnormal temperature changes. The update unit can also apply an algorithm that compares the data with past humidity data to detect abnormal humidity changes. The update unit can also apply an algorithm that compares the data with past vibration data to detect abnormal vibrations. This improves quality control by detecting abnormalities by comparing the data with past data.

[0093] The update unit can add a check function to check the consistency of data when updating data. Data consistency includes, but is not limited to, methods such as checking the consistency of data and detecting duplicate data. For example, the update unit can add a check function to check the consistency of data when updating data. The update unit can also add a check function to check the completeness of data when updating data. The update unit can also add a check function to check the accuracy of data when updating data. This improves the reliability of the data by checking the consistency of the data.

[0094] The update unit can automatically create a backup of the data when updating the data. Data backup methods include, but are not limited to, periodic backups and incremental backups. For example, the update unit can automatically create a backup on the cloud when updating the data. The update unit can also automatically create a backup in local storage when updating the data. The update unit can also automatically create a backup in external storage when updating the data. This automatically creates a backup of the data, thereby improving data protection.

[0095] The update unit can estimate the user's emotions and determine the priority of data updates based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the update unit prioritizes updating only important data. Furthermore, if the producer is relaxed, the update unit can also update all data evenly. Furthermore, if the producer is in a hurry, the update unit can prioritize updating data that needs to be updated quickly. This allows the priority of data updates to be optimized according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When updating data, the update unit can integrate and update information from different data sources. Examples of different data sources include, but are not limited to, sensor data, user input data, and external API data. For example, the update unit integrates and updates data from different sensors. The update unit can also integrate and update data from different devices. The update unit can also integrate and update data from different cloud services. This improves data consistency by integrating information from different data sources.

[0097] The update unit can add a function to automatically convert the data format when updating data. Examples of data formats include, but are not limited to, CSV, JSON, and XML. The update unit, for example, converts data from different sensors into a unified format and updates the data. The update unit can also convert data from different devices into a unified format and update the data. The update unit can also convert data from different cloud services into a unified format and update the data. This automatically converting the data format improves data compatibility.

[0098] The update unit can adjust the encryption level of the data when updating the data. The encryption level includes, but is not limited to, settings such as AES-128 and AES-256. For example, the update unit can apply a high encryption level to highly confidential data. The update unit can also apply a low encryption level to less confidential data. The update unit can also dynamically adjust the encryption level depending on the importance of the data. In this way, adjusting the encryption level of the data improves data security.

[0099] The access unit can estimate the user's emotions and adjust access permissions based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the access unit can simplify the access permissions and display only the minimum necessary information. Furthermore, if the producer is relaxed, the access unit can provide permissions to access detailed information. Furthermore, if the producer is in a hurry, the access unit can provide permissions to access information quickly. This allows the access permissions to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When accessing, the access unit can provide optimal information by referring to the user's past access history. The past access history includes, but is not limited to, items such as access date and time, access content, and access source IP. The access unit, for example, preferentially displays information that the user has frequently accessed in the past. The access unit can also suggest highly relevant information based on the user's past access history. The access unit can also analyze the user's past access patterns and provide optimal information. In this way, optimal information can be provided to the user by referring to the past access history.

[0101] The access unit can optimize the display content by taking into account the user's device information when accessing the device. The device information includes, but is not limited to, items such as the device type, OS, and browser. For example, when using a smartphone, the access unit provides display content tailored to the screen size. Furthermore, when using a tablet, the access unit can provide display content optimized for a large screen. Furthermore, when using a smartwatch, the access unit can provide display content that is concise and highly visible. This improves usability by optimizing the display content according to the device information.

[0102] The access unit can improve the access interface by reflecting user feedback at the time of access. User feedback can include, but is not limited to, methods such as questionnaires, reviews, and usage logs. The access unit can improve the interface design based on the user feedback, for example. The access unit can also add or modify functions based on the user feedback. The access unit can also improve operability based on the user feedback. In this way, the ease of use of the interface can be improved by reflecting user feedback.

[0103] The access unit can estimate the user's emotions and adjust the display method of the access interface based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the access unit can provide a simple, highly visible interface. If the producer is relaxed, the access unit can also provide an interface containing detailed information. If the producer is in a hurry, the access unit can also provide an interface that focuses on the main points. This allows the display method to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The access unit can provide highly relevant information by taking into account the user's geographical location information at the time of access. Examples of geographical location information include, but are not limited to, methods of obtaining the information, such as GPS, beacons, and Wi-Fi location information. For example, the access unit can preferentially display information close to the user's current location. The access unit can also suggest related information based on the user's geographical location information. The access unit can also suggest an optimal route based on the user's geographical location information. This improves usability by providing highly relevant information based on the geographical location information.

[0105] The access unit can customize the display content according to the user's level of expertise when accessing the site. Examples of expertise levels include, but are not limited to, evaluation criteria such as beginner, intermediate, and advanced. For example, the access unit can provide detailed technical information to users with high levels of expertise. The access unit can also provide concise, easy-to-understand information to users with low levels of expertise. The access unit can also suggest optimal display content based on the user's level of expertise. Customizing the display content according to the level of expertise improves usability.

[0106] The access unit can make the display content multilingual according to the user's language setting when accessing the site. The language setting includes, but is not limited to, methods of acquisition such as the language setting of the browser and the language setting of the user profile. The access unit automatically sets the display content based on, for example, the language setting of the user's device. The access unit can also provide a language switching function when the user uses multiple languages. The access unit can also provide the display content in a specific language when the user selects that language. This improves usability by making the display content multilingual according to the language setting. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned assigning unit, tracking unit, updating unit, and accessing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the assigning unit is realized by the control unit 46A of the smart device 14 and prints and attaches a barcode or two-dimensional code to food. The tracking unit scans the identification code using the camera 42 of the smart device 14 and transmits the data to the cloud via Wi-Fi or a mobile network. The updating unit is realized by the specific processing unit 290 of the data processing device 12 and updates information in a database on the cloud in real time. The accessing unit is realized by the specific processing unit 290 of the data processing device 12 and sets access permissions for each relevant person so that only necessary information can be viewed. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned assigning unit, tracking unit, updating unit, and accessing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the assigning unit is realized by the control unit 46A of the smart glasses 214 and prints and attaches a barcode or two-dimensional code to food. The tracking unit scans the identification code using the camera 42 of the smart glasses 214 and transmits the data to the cloud via Wi-Fi or a mobile network. The updating unit is realized by the specific processing unit 290 of the data processing device 12 and updates information in a database on the cloud in real time. The accessing unit is realized by the specific processing unit 290 of the data processing device 12 and sets access permissions for each relevant person, allowing them to view only the information they need. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned assigning unit, tracking unit, updating unit, and accessing unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the assigning unit is realized by the control unit 46A of the headset terminal 314, and prints and attaches a barcode or two-dimensional code to food. The tracking unit scans the identification code using the camera 42 of the headset terminal 314 and transmits the data to the cloud via Wi-Fi or a mobile network. The updating unit is realized by the specific processing unit 290 of the data processing device 12, and updates information in a database on the cloud in real time. The accessing unit is realized by the specific processing unit 290 of the data processing device 12, and sets access permissions for each relevant person so that only necessary information can be viewed. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned assigning unit, tracking unit, updating unit, and accessing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the assigning unit is realized by the control unit 46A of the robot 414, and prints and attaches a barcode or two-dimensional code to food. The tracking unit scans the identification code using the camera 42 of the robot 414 and transmits the data to the cloud via Wi-Fi or a mobile network. The updating unit is realized by the specific processing unit 290 of the data processing device 12, and updates information in a database on the cloud in real time. The accessing unit is realized by the specific processing unit 290 of the data processing device 12, and sets access permissions for each relevant person so that only necessary information can be viewed.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The assigning unit can automatically link nutritional information of the food when assigning the identification code. Nutritional information includes, but is not limited to, items such as calories, protein, fat, and carbohydrates. For example, the assigning unit can include calorie information in the identification code. The assigning unit can also include protein information in the identification code. The assigning unit can also include fat information in the identification code. This allows consumers to easily check nutritional information of foods, which can be useful for health management.

[0109] The tracking unit can estimate the user's emotions and adjust the notification method of the tracking data based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the tracking unit can send a concise notification if the producer is stressed. The tracking unit can also send a detailed notification if the producer is relaxed. The tracking unit can also send a notification that focuses on the main points if the producer is in a hurry. This makes it possible to provide an optimal notification method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The update unit can apply an algorithm for evaluating the reliability of the data when updating the data. Examples of algorithms for evaluating reliability include, but are not limited to, methods such as checking the consistency of the data and verifying the origin of the data. For example, the update unit applies an algorithm for evaluating the consistency of the data. The update unit can also apply an algorithm for verifying the origin of the data. The update unit can also apply an algorithm for scoring the reliability of the data. This improves the quality of the data by evaluating the reliability of the data.

[0111] The access unit can estimate the user's emotions and adjust the access log analysis method based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is stressed, the access unit can provide a concise analysis result. If the producer is relaxed, the access unit can also provide a detailed analysis result. If the producer is in a hurry, the access unit can also provide a summary analysis result. This makes it possible to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0112] The assigning unit can automatically link allergen information of a food product when assigning the identification code. The allergen information includes, for example, items such as dairy products, eggs, wheat, and nuts, but is not limited to these examples. For example, the assigning unit can include dairy allergen information in the identification code. The assigning unit can also include egg allergen information in the identification code. The assigning unit can also include wheat allergen information in the identification code. This allows consumers to easily check food allergen information, which can be useful in taking measures against allergies.

[0113] During tracking, the tracking unit can collect vibration data during transportation of the food and apply an algorithm to predict quality changes. The vibration data includes, but is not limited to, items such as vibration intensity, frequency, and duration. The tracking unit can apply an algorithm to predict quality changes based on, for example, vibration intensity. The tracking unit can also apply an algorithm to predict quality changes based on vibration frequency. The tracking unit can also apply an algorithm to predict quality changes based on vibration duration. This improves quality control by collecting vibration data and predicting quality changes.

[0114] The update unit can estimate the user's emotions and adjust the data storage method based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is stressed, the update unit can provide a concise data storage method. If the producer is relaxed, the update unit can also provide a detailed data storage method. If the producer is in a hurry, the update unit can also provide a quick data storage method. This makes it possible to provide an optimal data storage method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When accessing, the access unit can provide related information by referring to the user's purchase history. The purchase history includes, but is not limited to, items such as purchase date and time, purchased items, and purchase amount. For example, the access unit can preferentially display information related to items the user has purchased in the past. The access unit can also suggest highly relevant items based on the user's purchase history. The access unit can also analyze the user's purchasing patterns and provide optimal information. This allows the optimal information to be provided to the user by referring to the purchase history.

[0116] The assigning unit can automatically link information regarding the food storage method when assigning the identification code. Storage methods include, for example, refrigerated storage, frozen storage, room temperature storage, and the like, but are not limited to these examples. For example, the assigning unit can include refrigerated storage information in the identification code. The assigning unit can also include frozen storage information in the identification code. The assigning unit can also include room temperature storage information in the identification code. This allows consumers to easily check the food storage method and ensures appropriate storage.

[0117] The tracking unit can estimate the user's emotions and adjust the visualization method of the tracking data based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the producer is feeling stressed, the tracking unit can provide a simple, highly visible visualization method. If the producer is relaxed, the tracking unit can also provide a visualization method that includes detailed information. If the producer is in a hurry, the tracking unit can also provide a visualization method that focuses on the main points. This makes it possible to provide an optimal visualization method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The assigning unit assigns a unique identification code to fresh food. Identification codes include barcodes, two-dimensional codes, and RFID tags. For example, a barcode can be printed and attached to the food, a two-dimensional code can be printed directly on the food packaging, or an RFID tag can be embedded in the food packaging. Step 2: The tracking unit tracks the status and location of the food using the identification code assigned by the assigning unit. For example, the tracking unit scans the identification code using a smartphone or a dedicated scanner device and sends the data to the cloud via Wi-Fi or a mobile network. Alternatively, location information can be obtained using GPS and sent to the cloud. Step 3: The update unit updates the information tracked by the tracking unit to a cloud database in real time. For example, the database can be managed using a cloud service such as AWS, Google Cloud, or Azure, and data encryption can also be performed. Step 4: The access unit accesses the information updated by the update unit. For example, access rights are set for each person involved, and access rights are set according to roles such as administrator, viewer, and editor.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an assigning unit that assigns a unique identification code to the fresh food; a tracking unit that tracks the state and location information of the food using the identification code assigned by the assigning unit; an update unit that updates the information tracked by the tracking unit to a database on a cloud in real time; an access unit that accesses the information updated by the update unit; A system characterized by:

2. The granting unit Assign an identification code using a barcode or QR code 2. The system of claim 1.

3. The tracking unit The identification code is scanned using a smartphone or a dedicated scanner device, and the data is sent to the cloud via Wi-Fi or mobile networks.

2. The system of claim 1.

4. The update unit Update data in real time to a cloud database 2. The system of claim 1.

5. The access unit is Set access rights for each person involved so they can only see the information they need 2. The system of claim 1.

6. The update unit Encrypting data 2. The system of claim 1.

7. The granting unit The user's emotions are estimated, and the timing of assigning the identification code is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The granting unit When assigning an identification code, different identification codes are generated depending on the type and quality of the food.

2. The system of claim 1.

Images