System

Abstract

To provide a system which unitarily provides information of food and medicine, and issues warnings of allergies and contraindications.SOLUTION: A system includes a reception part, a retrieval part, a warning part, a provision part, and a display part. The reception part inputs information of food or medicine. The retrieval part retrieves nutrition information of food or components of medicine, based on the information inputted by the reception part. The warning part issues warnings of allergies and contraindications, in accordance with the information retrieved by the retrieval part. The provision part provides information on a place of origin of food or on a manufacturing method of medicine. The display part displays a user review or a user evaluation with regard to food or medicine.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, there is no sufficient system in place to provide information on foods and medicines in a centralized manner and to warn about allergies and contraindications, so there is room for improvement.

[0005] The system according to the embodiment aims to provide information on foods and medicines in a centralized manner and to issue warnings about allergies and contraindications. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a search unit, a warning unit, a provision unit, and a display unit. The reception unit inputs information about food or medicine. The search unit searches for nutritional information about the food or ingredients of the medicine based on the information input by the reception unit. The warning unit issues a warning about allergies or contraindications based on the information searched by the search unit. The provision unit provides information about the place of origin of the food or the manufacturing method of the medicine. The display unit displays user reviews or user ratings for the food or medicine. Effect of the Invention

[0007] The system according to the embodiment can provide information on foods and medicines in a centralized manner and issue warnings about allergies and contraindications. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information providing system according to the embodiment of the present invention is a system that provides information about food and medicine. This information providing system has a reception unit that inputs information about food or medicine by a user. There is a search unit that searches for nutritional information about food or ingredients of medicine based on the information input by the reception unit. There is a warning unit that issues a warning about allergies or contraindications based on the information searched by the search unit. There is also a provision unit that provides information about the place of origin of food or the manufacturing method of medicine. Finally, there is a display unit that displays user reviews or user ratings for food or medicine. For example, when a user wants to know information about a specific food, the user inputs the name of the food into the reception unit. The search unit searches for nutritional information about the food, and the warning unit checks the allergy information. The provision unit provides information about the place of origin of the food, and the display unit displays reviews from other users. The provision unit also provides information about the storage method of food and the expiration date of medicine. In addition, it provides information on the climate conditions and soil of the food's production area, information on the onset of allergies in specific areas, ethical standards and certification information of food producers and manufacturers, expiration date information from a prediction unit that predicts the rate at which food deteriorates, clinical trial data and side effect information for pharmaceutical products, advice on appropriate storage methods from a monitoring unit that monitors the storage conditions of food, and quality control information in the pharmaceutical manufacturing process.This system allows users to easily obtain detailed information about foods and pharmaceutical products, which can be useful for health management and safe consumption.The information provision system thus allows users to easily obtain detailed information about foods and pharmaceutical products, which can be useful for health management and safe consumption.

[0029] The information provision system according to the embodiment includes a reception unit, a search unit, a warning unit, a provision unit, and a display unit. The reception unit allows a user to input information about a food or a medicine. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and transmits it to the search unit. The search unit searches for nutritional information about the food or ingredients of the medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of the food. The search unit also searches for ingredients such as active ingredients, additives, and preservatives of the medicine. The search unit transmits the search results to the warning unit. The warning unit issues a warning about allergies or contraindications based on the information received from the search unit. For example, the warning unit warns about allergies to a specific ingredient or contraindications for a specific disease. The warning unit transmits the warning information to the provision unit. The provision unit provides information about the origin of the food or the manufacturing method of the medicine based on the information received from the warning unit. For example, the provision unit provides information about the origin of the food, the manufacturing process of the medicine, the technology used, and the quality control method. The providing unit transmits the provided information to the display unit. The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews or ratings by other users for the user to refer to. In this way, the information providing system according to the embodiment allows the user to easily obtain detailed information on the food or medicine and use it for health management and safe consumption.

[0030] The reception unit allows the user to input information about food or medicine. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and sends it to the search unit. Specifically, the user uses a smartphone or a PC to access a dedicated application or website and inputs information about the food or medicine. The input form has fields for inputting detailed information such as the name of the food, product code, ingredient list, manufacturing date, and expiration date. For medicines, information such as the drug name, ingredient name, dosage and administration, and manufacturing number can be input. The reception unit accurately receives this information and stores it in the database. In addition, the reception unit checks the format and content of the input information to check whether there is any missing or incorrect information. For example, if the input food name does not exist in the database, a message is displayed to prompt the user to re-input. In addition, if the name of the medicine's ingredients is incorrect, the reception unit also has a function to suggest the correct ingredient name. This allows the reception unit to support the user in inputting accurate and detailed information, thereby improving the reliability of the entire system.

[0031] The search unit searches for nutritional information of food or ingredients of medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of food. The search unit also searches for ingredients such as active ingredients, additives, and preservatives of medicine. The search unit transmits the search results to the warning unit. Specifically, the search unit accesses a database and acquires detailed information about the input food or medicine. In the case of food, it searches for nutritional ingredients such as calories, protein, lipids, carbohydrates, vitamins, minerals, and dietary fiber, and displays the content and recommended intake amount of each ingredient. In the case of medicine, it searches for information such as the name of the active ingredient, mechanism of action, indications, contraindications, interactions, types of additives and their roles, types of preservatives and their effects. The search unit quickly and accurately acquires this information and provides the user with the information he or she needs. The search unit also has a function of suggesting related information based on the user's past search history and preferences. For example, for a user who has input specific allergy information in the past, foods and medicines that do not contain allergenic ingredients are preferentially displayed. This enables the search unit to efficiently provide the information desired by the user, which can be useful for health management and safe consumption.

[0032] The warning unit issues a warning of allergies or contraindications based on the information received from the search unit. For example, the warning unit issues a warning of allergies to a specific ingredient or contraindications to a specific disease. The warning unit transmits the warning information to the provision unit. Specifically, the warning unit analyzes the ingredient information of the food or medicine received from the search unit and compares it with the allergy information or disease information input by the user. For example, if the user has a nut allergy, the warning unit checks whether the search results contain nut ingredients, and displays a warning message if they do. In addition, for users with a specific disease, the warning unit warns of medicines that contain contraindicated ingredients. For example, for users with high blood pressure, the warning unit warns of foods with high sodium content and medicines that contain ingredients that may increase blood pressure. The warning unit transmits these warning information to the provision unit and provides appropriate advice to the user. Furthermore, the warning unit can issue individually customized warnings based on the user's health condition and past warning history. For example, for users with specific allergies or diseases, the warning unit periodically provides related information to support health management. In this way, the warning unit can assist the user in safely selecting foods and medicines and minimize health risks.

[0033] The providing unit provides information on the place of origin of the food or the manufacturing method of the medicine based on the information received from the warning unit. For example, the providing unit provides information such as the place of origin information of the food, the manufacturing process of the medicine, the technology used, and the quality control method. The providing unit transmits the provided information to the display unit. Specifically, the providing unit provides detailed information such as the place of origin name, the producer name, the production method, the cultivation conditions, and the harvest time as the place of origin information of the food. This allows the user to confirm the safety and quality of the food. In the case of medicines, details of the manufacturing process, the technology used, the quality control method, and the certification information of the manufacturing facility are provided. For example, in the manufacturing process of medicines, each step from the procurement of raw materials to manufacturing, packaging, and shipping is explained in detail, and the technology used and the quality control method are also specifically described. This allows the user to confirm the safety and effectiveness of the medicine. In addition, the providing unit also has a function of suggesting related information according to the user's interests and needs. For example, when a user searches for information on the place of origin of a specific food, other foods from the same place of origin and other products provided by the same producer are suggested. In addition, for users who are interested in the manufacturing method of a drug, the provision department will provide other drugs that use the same technology and related research information. This allows the provision department to comprehensively provide the information users need and help them select foods and drugs.

[0034] The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews and ratings by other users so that the user can refer to them. Specifically, the display unit displays the user's ratings for the food or medicine as a number of stars or a score, and displays the review content in text format. The user can view reviews posted by other users and refer to their opinions on the quality and effectiveness of the product. Furthermore, the display unit also has a function for evaluating the reliability of the reviews. For example, the display unit displays the profile information of the reviewer and past review history, and preferentially displays reviews with high reliability. In addition, the user can give feedback on the review such as "helpful" or "helpful," which makes it easier for other users to find reviews that are easy to refer to. The display unit can display not only reviews and ratings, but also detailed information about the product, related news articles, expert opinions, and the like. For example, the display unit displays the latest research results on a specific food or articles on new methods of using a medicine so that the user can obtain more information. This allows the display unit to provide support for the user to comprehensively grasp information about food and medicine and make wiser choices.

[0035] The providing unit can provide information on a food preservation method or a medicine expiration date. For example, the providing unit provides information such as temperature conditions, humidity conditions, and storage period as a food preservation method. The providing unit also provides information such as a period from the date of manufacture or an expiration date based on storage conditions as a medicine expiration date. This allows the user to properly store food and use medicines. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information on a food preservation method or a medicine expiration date into an AI model and output an appropriate preservation method or expiration date.

[0036] The providing unit can provide climatic conditions and soil information of the food production area. For example, the providing unit provides information such as temperature, precipitation, and sunshine hours as climatic conditions. The providing unit also provides information such as soil type, nutritional components, and pH value as soil information. This allows the user to understand the quality and characteristics of the food. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input climatic conditions and soil information of the food production area into an AI model and output appropriate information.

[0037] The warning unit can provide allergy onset information in a specific region in real time. For example, the warning unit provides information such as the incidence rate, cause of onset, and onset time in a specific region. This allows the user to avoid allergy risks. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input allergy onset information in a specific region into an AI model and provide information in real time.

[0038] The providing unit can provide the ethical standards and certification information of food producers and manufacturers. For example, the providing unit provides information such as working conditions, environmental protection, and animal welfare as the ethical standards. The providing unit also provides information such as organic certification, fair trade certification, and ISO certification as the certification information. This allows the user to select highly reliable food. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the ethical standards and certification information of food producers and manufacturers into an AI model and output appropriate information.

[0039] The prediction unit is provided with a prediction unit, and the providing unit can provide information on the expiration date based on the prediction result by the prediction unit. The prediction unit predicts the deterioration rate of the food. For example, the prediction unit predicts the deterioration rate based on data such as chemical changes, microbial growth, and physical changes. The providing unit provides information on the expiration date based on the prediction result by the prediction unit. For example, the providing unit provides information on the expiration date based on the period from the production date and storage conditions. This allows the user to consume the food appropriately. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the deterioration rate of the food to the generation AI and cause the generation AI to predict the expiration date. The generation AI may be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0040] The providing unit can provide clinical trial data and side effect information of the drug. For example, the providing unit provides information such as the scale of the trial, the period of the trial, and the results of the trial as the clinical trial data. In addition, the providing unit provides information such as the type of side effect, the frequency of the side effect, and the severity of the side effect as the side effect information. This allows the user to understand the safety of the drug. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the clinical trial data and side effect information of the drug into an AI model and output appropriate information.

[0041] The monitoring unit is provided, and the providing unit can advise on an appropriate storage method based on the monitoring results by the monitoring unit. The monitoring unit monitors the storage conditions of the food. For example, the monitoring unit collects data such as temperature, humidity, and storage period. The providing unit advises on an appropriate storage method based on the monitoring results by the monitoring unit. For example, the providing unit provides information such as refrigerated storage, frozen storage, and room temperature storage. This allows the user to maintain the quality of the food. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the storage conditions of the food into an AI model and cause the generating AI to execute advice on an appropriate storage method.

[0042] The providing unit can provide quality control information in the manufacturing process of the pharmaceutical product. The providing unit provides, for example, quality control information in the manufacturing process of the pharmaceutical product. For example, the providing unit provides information such as a method for monitoring the manufacturing process, a quality inspection standard, and a quality assurance method. This allows the user to understand the quality of the pharmaceutical product. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input quality control information in the manufacturing process of the pharmaceutical product to the generation AI and output appropriate information. The generation AI may be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0043] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays information on foods and medicines that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest information on foods and medicines to be used in a specific time period from the user's past input history. In this way, the optimal input method is provided by analyzing the user's past input history, improving user convenience. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into an AI model and cause the generation AI to select the optimal input method.

[0044] The reception unit can filter information based on the user's current health condition and areas of interest when inputting information. For example, if the user has an allergy, the reception unit automatically excludes information on foods and medicines containing allergenic ingredients. In addition, if the user is interested in a specific nutrient, the reception unit can preferentially display information on foods that contain a large amount of that nutrient. Furthermore, the reception unit can filter and provide information on appropriate foods and medicines based on the user's health condition. This provides more appropriate information by filtering information based on the user's health condition and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and areas of interest into an AI model and have the generation AI perform filtering.

[0045] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting information. For example, when the user is in a specific area, the reception unit preferentially displays information on foods and medicines available in that area. In addition, when the user is traveling, the reception unit can also preferentially provide allergy information and taboo information at the travel destination. Furthermore, when the user lives in a specific area, the reception unit can also provide food information based on the climate and soil conditions of the area. In this way, highly relevant information is provided by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to an AI model and cause the generation AI to execute the preferential input of highly relevant information.

[0046] The reception unit can analyze the user's social media activity at the time of inputting information and input related information. For example, if the user mentions a specific food or medicine on social media, the reception unit preferentially displays that information. The reception unit can also provide information from health-related accounts that the user follows on social media. Furthermore, the reception unit can predict and suggest information on foods and medicines of interest from the user's social media activity. In this way, related information is provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into an AI model and cause the generation AI to input related information.

[0047] The search unit can adjust the level of detail of the search results based on the importance of the information during the search. For example, if nutritional information is important, the search unit displays a detailed nutritional table. If allergy information is important, the search unit can also display details of allergens. If the storage method is important, the search unit can also display detailed storage methods. This provides important information to the user by adjusting the level of detail of the search results based on the importance of the information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the search results.

[0048] The search unit can apply different search algorithms depending on the category of information during the search. For example, when searching for nutritional information, the search unit applies a search algorithm specialized for nutritional components. In addition, when searching for allergy information, the search unit can also apply a search algorithm specialized for allergenic components. In addition, when searching for a storage method, the search unit can also apply a search algorithm specialized for the storage method. In this way, by applying different search algorithms depending on the category of information, more appropriate search results are provided. Part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the category of information into an AI model and cause the generation AI to apply the search algorithm.

[0049] The search unit can determine the priority of search results based on the time of information submission during the search. For example, the search unit can prioritize displaying the latest nutrition information. The search unit can also prioritize displaying the latest allergy information. Furthermore, the search unit can also prioritize displaying the latest storage method. In this way, the priority of search results is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of search results.

[0050] The search unit can adjust the order of search results based on the relevance of information during a search. For example, the search unit can preferentially display nutrition information that is most relevant to the user's search query. The search unit can also preferentially display allergy information that is most relevant to the user's search query. Furthermore, the search unit can also preferentially display a saving method that is most relevant to the user's search query. This provides the most relevant information to the user by adjusting the order of search results based on the relevance of information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of search results.

[0051] The warning unit can adjust the level of detail of the warning based on the importance of the allergy information at the time of warning. For example, the warning unit displays a detailed warning in the case of serious allergy information. The warning unit can also display a concise warning in the case of mild allergy information. Furthermore, the warning unit can adjust the level of detail of the warning according to the importance of the allergy information. As a result, important information is provided to the user by adjusting the level of detail of the warning based on the importance of the allergy information. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the importance of the allergy information into an AI model and cause the generation AI to adjust the level of detail of the warning.

[0052] The warning unit can apply different warning algorithms depending on the category of allergy information when issuing a warning. For example, in the case of a food allergy, the warning unit applies a warning algorithm specialized for food ingredients. In addition, in the case of a drug allergy, the warning unit can also apply a warning algorithm specialized for drug ingredients. Furthermore, the warning unit can apply different warning algorithms depending on the category of allergy information. In this way, by applying different warning algorithms depending on the category of allergy information, a more appropriate warning is provided. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the category of allergy information into an AI model and cause the generation AI to apply the warning algorithm.

[0053] The warning unit can determine the priority of the warning based on the time of submission of the allergy information at the time of warning. For example, the warning unit preferentially displays the latest allergy information. The warning unit can also postpone old allergy information. Furthermore, the warning unit can determine the priority of the warning based on the time of submission of the allergy information. In this way, the latest information is provided by determining the priority of the warning based on the time of submission of the allergy information. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the time of submission of the allergy information into an AI model and cause the generation AI to determine the priority of the warning.

[0054] The warning unit can adjust the order of warnings based on the relevance of allergy information at the time of warning. For example, the warning unit preferentially displays information most relevant to the user's allergies. The warning unit can also postpone information less relevant to the user's allergies. Furthermore, the warning unit can adjust the order of warnings based on the relevance of allergy information. In this way, by adjusting the order of warnings based on the relevance of allergy information, the most relevant information for the user is provided. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the relevance of allergy information into an AI model and cause the generation AI to adjust the order of warnings.

[0055] The providing unit can adjust the level of detail of the information provided based on the importance of the information at the time of providing. For example, if nutritional information is important, the providing unit provides a detailed nutritional table. Also, if allergy information is important, the providing unit can provide details of allergens. Furthermore, if the storage method is important, the providing unit can provide detailed storage methods. In this way, by adjusting the level of detail of the information provided based on the importance of the information, important information for the user is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the information provided.

[0056] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. For example, when providing nutritional information, the providing unit applies a providing algorithm specialized for nutritional components. In addition, when providing allergy information, the providing unit can also apply a providing algorithm specialized for allergenic components. Furthermore, when providing a storage method, the providing unit can also apply a providing algorithm specialized for the storage method. In this way, by applying different providing algorithms depending on the category of information, more appropriate information is provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of information into an AI model and cause the generation AI to apply the providing algorithm.

[0057] The providing unit can determine the priority of provision based on the time of information submission at the time of provision. For example, the providing unit can provide the latest nutritional information with priority. The providing unit can also provide the latest allergy information with priority. Furthermore, the providing unit can provide the latest storage method with priority. In this way, the latest information is provided by determining the priority of provision based on the time of information submission. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of provision.

[0058] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit preferentially provides nutritional information most relevant to the user's interest. The providing unit can also preferentially provide allergy information most relevant to the user's interest. Furthermore, the providing unit can also preferentially provide a storage method most relevant to the user's interest. In this way, the order of provision is adjusted based on the relevance of information, thereby providing information most relevant to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of provision.

[0059] The display unit can adjust the level of detail of the display based on the importance of the information when displaying. For example, if nutritional information is important, the display unit displays a detailed nutritional table. Also, if allergy information is important, the display unit can display details of allergens. Furthermore, if the storage method is important, the display unit can display detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the display based on the importance of the information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the display.

[0060] The display unit can apply different display algorithms depending on the category of information when displaying. For example, when displaying nutritional information, the display unit applies a display algorithm specialized for nutritional components. Furthermore, when displaying allergy information, the display unit can also apply a display algorithm specialized for allergenic components. Furthermore, when displaying a storage method, the display unit can also apply a display algorithm specialized for the storage method. In this way, by applying different display algorithms depending on the category of information, more appropriate information is provided. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the category of information into an AI model and cause the generation AI to apply the display algorithm.

[0061] The display unit can determine the display priority based on the time of information submission at the time of display. For example, the display unit can prioritize displaying the latest nutrition information. The display unit can also prioritize displaying the latest allergy information. Furthermore, the display unit can also prioritize displaying the latest storage method. In this way, the display priority is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the time of information submission into an AI model and cause the generation AI to determine the display priority.

[0062] The display unit can adjust the display order based on the relevance of information when displaying. For example, the display unit preferentially displays nutritional information that is most relevant to the user's interest. The display unit can also preferentially display allergy information that is most relevant to the user's interest. Furthermore, the display unit can also preferentially display a storage method that is most relevant to the user's interest. In this way, the display order is adjusted based on the relevance of information, thereby providing the most relevant information to the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the relevance of information into an AI model and cause the generation AI to adjust the display order.

[0063] The prediction unit can adjust the level of detail of the prediction based on the importance of the information at the time of prediction. For example, if the nutritional information is important, the prediction unit predicts a detailed nutritional table. In addition, if the allergy information is important, the prediction unit can also predict details of allergens. Furthermore, if the storage method is important, the prediction unit can also predict detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the prediction based on the importance of the information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the prediction.

[0064] The prediction unit can apply different prediction algorithms depending on the category of information at the time of prediction. For example, when predicting nutritional information, the prediction unit applies a prediction algorithm specialized for nutritional components. Furthermore, when predicting allergy information, the prediction unit can also apply a prediction algorithm specialized for allergenic components. Furthermore, when predicting a storage method, the prediction unit can also apply a prediction algorithm specialized for the storage method. In this way, by applying different prediction algorithms depending on the category of information, more appropriate prediction results are provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the category of information into an AI model and cause the generation AI to apply the prediction algorithm.

[0065] The prediction unit can determine the priority of prediction based on the time of information submission at the time of prediction. For example, the prediction unit preferentially predicts the latest nutritional information. The prediction unit can also preferentially predict the latest allergy information. Furthermore, the prediction unit can also preferentially predict the latest storage method. In this way, the priority of prediction is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of prediction.

[0066] The prediction unit can adjust the order of prediction based on the relevance of information at the time of prediction. For example, the prediction unit preferentially predicts nutritional information most relevant to the user's interest. The prediction unit can also preferentially predict allergy information most relevant to the user's interest. Furthermore, the prediction unit can also preferentially predict a storage method most relevant to the user's interest. In this way, the order of prediction is adjusted based on the relevance of information, thereby providing information most relevant to the user. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of prediction.

[0067] The monitoring unit can adjust the level of detail of the monitoring based on the importance of the information during monitoring. For example, if nutritional information is important, the monitoring unit monitors a detailed nutritional table. Also, if allergy information is important, the monitoring unit can monitor details of allergens. Furthermore, if the storage method is important, the monitoring unit can monitor detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the monitoring based on the importance of the information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the monitoring.

[0068] The monitoring unit can apply different monitoring algorithms depending on the category of information during monitoring. For example, when monitoring nutritional information, the monitoring unit applies a monitoring algorithm specialized for nutritional components. In addition, when monitoring allergy information, the monitoring unit can also apply a monitoring algorithm specialized for allergenic components. In addition, when monitoring a storage method, the monitoring unit can also apply a monitoring algorithm specialized for the storage method. In this way, by applying different monitoring algorithms depending on the category of information, more appropriate monitoring results are provided. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the category of information into an AI model and cause the generation AI to apply the monitoring algorithm.

[0069] The monitoring unit can determine the priority of monitoring based on the time of information submission during monitoring. For example, the monitoring unit prioritizes monitoring the latest nutritional information. The monitoring unit can also prioritize monitoring the latest allergy information. Furthermore, the monitoring unit can also prioritize monitoring the latest storage method. In this way, the monitoring priority is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the time of information submission into an AI model and cause the generation AI to determine the monitoring priority.

[0070] The monitoring unit can adjust the monitoring order based on the relevance of the information during monitoring. For example, the monitoring unit preferentially monitors nutritional information that is most relevant to the user's interest. The monitoring unit can also preferentially monitor allergy information that is most relevant to the user's interest. Furthermore, the monitoring unit can also preferentially monitor the storage method that is most relevant to the user's interest. In this way, the monitoring order is adjusted based on the relevance of the information, thereby providing the most relevant information to the user. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of the information into an AI model and cause the generation AI to adjust the monitoring order.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The information providing system may further include a history unit that records the user's dietary history. The history unit may store information on foods and medicines previously input by the user, and automatically display the information as candidates when the user inputs the same information again. For example, if the user has previously searched for nutritional information on a particular food, the history unit may record the information and provide the information quickly the next time the user searches for the same food. The history unit may also analyze the user's dietary patterns and provide advice for health management. Furthermore, the history unit may record foods and medicines to which the user has had an allergic reaction in the past, and provide the information to the warning unit to issue a warning to avoid allergy risks.

[0073] The providing unit can provide personalized nutrition advice based on the user's dietary history. For example, the providing unit can analyze the balance of nutrients that the user has taken in the past, and if a specific nutrient is lacking, can suggest foods that contain a lot of that nutrient. The providing unit can also provide an appropriate meal plan based on the user's health goals (e.g., weight loss, muscle building, etc.). Furthermore, the providing unit can also suggest recommended seasonal recipes based on the user's dietary history. This allows the user to receive personalized nutrition advice and manage their health more effectively.

[0074] The providing unit can provide information on foods and medicines specific to a region, taking into account the geographical location information of the user. For example, when the user is in a specific region, the providing unit preferentially displays information on foods and medicines available in that region. In addition, when the user is traveling, the providing unit can also provide allergy information and contraindication information for the travel destination. Furthermore, when the user lives in a specific region, the providing unit can also provide food information based on the climate and soil conditions of the region. In this way, highly relevant information is provided by taking into account the geographical location information of the user.

[0075] The search unit can analyze the user's past search history and provide optimal search results. For example, the search unit can automatically display information on foods and medicines that the user has frequently searched for in the past as candidates. The search unit can also preferentially suggest search methods (voice, text, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest information on foods and medicines to be used during a specific time period from the user's past search history. In this way, optimal search results can be provided by analyzing the user's past search history, improving user convenience.

[0076] The providing unit can analyze the user's social media activity and provide related information. For example, when the user mentions a specific food or medicine on social media, the providing unit preferentially displays that information. The providing unit can also provide information from health-related accounts that the user follows on social media. Furthermore, the providing unit can predict and suggest information on foods and medicines of interest from the user's social media activity. In this way, related information is provided by analyzing the user's social media activity.

[0077] The warning unit can monitor the user's health condition and issue a warning based on the health condition. For example, if the user has a specific disease, the warning unit provides information on foods and medicines related to the disease and warns of contraindications. The warning unit can also issue a warning regarding over-intake or deficiency of a specific nutrient based on the user's health condition. Furthermore, the warning unit can monitor the user's health condition in real time and immediately issue a warning if an abnormality is detected. This supports health management by issuing appropriate warnings based on the user's health condition.

[0078] The process flow of the first embodiment will be briefly described below.

[0079] Step 1: The reception unit receives the input information of a food or medicine from the user. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and transmits it to the search unit. Step 2: The search unit searches for nutritional information of food or ingredients of medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of food. Also, the search unit searches for ingredients such as active ingredients, additives, and preservatives of medicine. The search unit transmits the search results to the warning unit. Step 3: The warning unit issues a warning of allergies or contraindications based on the information received from the search unit. For example, the warning unit issues a warning of allergies to a specific ingredient or contraindications for a specific disease. The warning unit transmits the warning information to the provision unit. Step 4: The providing unit provides information on the origin of the food or the manufacturing method of the drug based on the information received from the warning unit. For example, the providing unit provides information on the origin of the food, the manufacturing process of the drug, the technology used, the quality control method, etc. The providing unit transmits the provided information to the display unit. Step 5: The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews or ratings by other users for the user's reference.

[0080] (Example 2) The information providing system according to the embodiment of the present invention is a system that provides information about food and medicine. This information providing system has a reception unit that inputs information about food or medicine by a user. There is a search unit that searches for nutritional information about food or ingredients of medicine based on the information input by the reception unit. There is a warning unit that issues a warning about allergies or contraindications based on the information searched by the search unit. There is also a provision unit that provides information about the place of origin of food or the manufacturing method of medicine. Finally, there is a display unit that displays user reviews or user ratings for food or medicine. For example, when a user wants to know information about a specific food, the user inputs the name of the food into the reception unit. The search unit searches for nutritional information about the food, and the warning unit checks the allergy information. The provision unit provides information about the place of origin of the food, and the display unit displays reviews from other users. The provision unit also provides information about the storage method of food and the expiration date of medicine. In addition, it provides information on the climate conditions and soil of the food's production area, information on the onset of allergies in specific areas, ethical standards and certification information of food producers and manufacturers, expiration date information from a prediction unit that predicts the rate at which food deteriorates, clinical trial data and side effect information for pharmaceutical products, advice on appropriate storage methods from a monitoring unit that monitors the storage conditions of food, and quality control information in the pharmaceutical manufacturing process.This system allows users to easily obtain detailed information about foods and pharmaceutical products, which can be useful for health management and safe consumption.The information provision system thus allows users to easily obtain detailed information about foods and pharmaceutical products, which can be useful for health management and safe consumption.

[0081] The information provision system according to the embodiment includes a reception unit, a search unit, a warning unit, a provision unit, and a display unit. The reception unit allows a user to input information about a food or a medicine. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and transmits it to the search unit. The search unit searches for nutritional information about the food or ingredients of the medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of the food. The search unit also searches for ingredients such as active ingredients, additives, and preservatives of the medicine. The search unit transmits the search results to the warning unit. The warning unit issues a warning about allergies or contraindications based on the information received from the search unit. For example, the warning unit warns about allergies to a specific ingredient or contraindications for a specific disease. The warning unit transmits the warning information to the provision unit. The provision unit provides information about the origin of the food or the manufacturing method of the medicine based on the information received from the warning unit. For example, the provision unit provides information about the origin of the food, the manufacturing process of the medicine, the technology used, and the quality control method. The providing unit transmits the provided information to the display unit. The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews or ratings by other users for the user to refer to. In this way, the information providing system according to the embodiment allows the user to easily obtain detailed information on the food or medicine and use it for health management and safe consumption.

[0082] The reception unit allows the user to input information about food or medicine. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and sends it to the search unit. Specifically, the user uses a smartphone or a PC to access a dedicated application or website and inputs information about the food or medicine. The input form has fields for inputting detailed information such as the name of the food, product code, ingredient list, manufacturing date, and expiration date. For medicines, information such as the drug name, ingredient name, dosage and administration, and manufacturing number can be input. The reception unit accurately receives this information and stores it in the database. In addition, the reception unit checks the format and content of the input information to check whether there is any missing or incorrect information. For example, if the input food name does not exist in the database, a message is displayed to prompt the user to re-input. In addition, if the name of the medicine's ingredients is incorrect, the reception unit also has a function to suggest the correct ingredient name. This allows the reception unit to support the user in inputting accurate and detailed information, thereby improving the reliability of the entire system.

[0083] The search unit searches for nutritional information of food or ingredients of medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of food. The search unit also searches for ingredients such as active ingredients, additives, and preservatives of medicine. The search unit transmits the search results to the warning unit. Specifically, the search unit accesses a database and acquires detailed information about the input food or medicine. In the case of food, it searches for nutritional ingredients such as calories, protein, lipids, carbohydrates, vitamins, minerals, and dietary fiber, and displays the content and recommended intake amount of each ingredient. In the case of medicine, it searches for information such as the name of the active ingredient, mechanism of action, indications, contraindications, interactions, types of additives and their roles, types of preservatives and their effects. The search unit quickly and accurately acquires this information and provides the user with the information he or she needs. The search unit also has a function of suggesting related information based on the user's past search history and preferences. For example, for a user who has input specific allergy information in the past, foods and medicines that do not contain allergenic ingredients are preferentially displayed. This enables the search unit to efficiently provide the information desired by the user, which can be useful for health management and safe consumption.

[0084] The warning unit issues a warning of allergies or contraindications based on the information received from the search unit. For example, the warning unit issues a warning of allergies to a specific ingredient or contraindications to a specific disease. The warning unit transmits the warning information to the provision unit. Specifically, the warning unit analyzes the ingredient information of the food or medicine received from the search unit and compares it with the allergy information or disease information input by the user. For example, if the user has a nut allergy, the warning unit checks whether the search results contain nut ingredients, and displays a warning message if they do. In addition, for users with a specific disease, the warning unit warns of medicines that contain contraindicated ingredients. For example, for users with high blood pressure, the warning unit warns of foods with high sodium content and medicines that contain ingredients that may increase blood pressure. The warning unit transmits these warning information to the provision unit and provides appropriate advice to the user. Furthermore, the warning unit can issue individually customized warnings based on the user's health condition and past warning history. For example, for users with specific allergies or diseases, the warning unit periodically provides related information to support health management. In this way, the warning unit can assist the user in safely selecting foods and medicines and minimize health risks.

[0085] The providing unit provides information on the place of origin of the food or the manufacturing method of the medicine based on the information received from the warning unit. For example, the providing unit provides information such as the place of origin information of the food, the manufacturing process of the medicine, the technology used, and the quality control method. The providing unit transmits the provided information to the display unit. Specifically, the providing unit provides detailed information such as the place of origin name, the producer name, the production method, the cultivation conditions, and the harvest time as the place of origin information of the food. This allows the user to confirm the safety and quality of the food. In the case of medicines, details of the manufacturing process, the technology used, the quality control method, and the certification information of the manufacturing facility are provided. For example, in the manufacturing process of medicines, each step from the procurement of raw materials to manufacturing, packaging, and shipping is explained in detail, and the technology used and the quality control method are also specifically described. This allows the user to confirm the safety and effectiveness of the medicine. In addition, the providing unit also has a function of suggesting related information according to the user's interests and needs. For example, when a user searches for information on the place of origin of a specific food, other foods from the same place of origin and other products provided by the same producer are suggested. In addition, for users who are interested in the manufacturing method of a drug, the provision department will provide other drugs that use the same technology and related research information. This allows the provision department to comprehensively provide the information users need and help them select foods and drugs.

[0086] The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews and ratings by other users so that the user can refer to them. Specifically, the display unit displays the user's ratings for the food or medicine as a number of stars or a score, and displays the review content in text format. The user can view reviews posted by other users and refer to their opinions on the quality and effectiveness of the product. Furthermore, the display unit also has a function for evaluating the reliability of the reviews. For example, the display unit displays the profile information of the reviewer and past review history, and preferentially displays reviews with high reliability. In addition, the user can give feedback on the review such as "helpful" or "helpful," which makes it easier for other users to find reviews that are easy to refer to. The display unit can display not only reviews and ratings, but also detailed information about the product, related news articles, expert opinions, and the like. For example, the display unit displays the latest research results on a specific food or articles on new methods of using a medicine so that the user can obtain more information. This allows the display unit to provide support for the user to comprehensively grasp information about food and medicine and make wiser choices.

[0087] The providing unit can provide information on a food preservation method or a medicine expiration date. For example, the providing unit provides information such as temperature conditions, humidity conditions, and storage period as a food preservation method. The providing unit also provides information such as a period from the date of manufacture or an expiration date based on storage conditions as a medicine expiration date. This allows the user to properly store food and use medicines. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input information on a food preservation method or a medicine expiration date into an AI model and output an appropriate preservation method or expiration date.

[0088] The providing unit can provide climatic conditions and soil information of the food production area. For example, the providing unit provides information such as temperature, precipitation, and sunshine hours as climatic conditions. The providing unit also provides information such as soil type, nutritional components, and pH value as soil information. This allows the user to understand the quality and characteristics of the food. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input climatic conditions and soil information of the food production area into an AI model and output appropriate information.

[0089] The warning unit can provide allergy onset information in a specific region in real time. For example, the warning unit provides information such as the incidence rate, cause of onset, and onset time in a specific region. This allows the user to avoid allergy risks. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input allergy onset information in a specific region into an AI model and provide information in real time.

[0090] The providing unit can provide the ethical standards and certification information of food producers and manufacturers. For example, the providing unit provides information such as working conditions, environmental protection, and animal welfare as the ethical standards. The providing unit also provides information such as organic certification, fair trade certification, and ISO certification as the certification information. This allows the user to select highly reliable food. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the ethical standards and certification information of food producers and manufacturers into an AI model and output appropriate information.

[0091] The prediction unit is provided with a prediction unit, and the providing unit can provide information on the expiration date based on the prediction result by the prediction unit. The prediction unit predicts the deterioration rate of the food. For example, the prediction unit predicts the deterioration rate based on data such as chemical changes, microbial growth, and physical changes. The providing unit provides information on the expiration date based on the prediction result by the prediction unit. For example, the providing unit provides information on the expiration date based on the period from the production date and storage conditions. This allows the user to consume the food appropriately. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the prediction unit can input the deterioration rate of the food to the generation AI and cause the generation AI to predict the expiration date. The generation AI may be a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] The providing unit can provide clinical trial data and side effect information of the drug. For example, the providing unit provides information such as the scale of the trial, the period of the trial, and the results of the trial as the clinical trial data. In addition, the providing unit provides information such as the type of side effect, the frequency of the side effect, and the severity of the side effect as the side effect information. This allows the user to understand the safety of the drug. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the clinical trial data and side effect information of the drug into an AI model and output appropriate information.

[0093] The monitoring unit is provided, and the providing unit can advise on an appropriate storage method based on the monitoring results by the monitoring unit. The monitoring unit monitors the storage conditions of the food. For example, the monitoring unit collects data such as temperature, humidity, and storage period. The providing unit advises on an appropriate storage method based on the monitoring results by the monitoring unit. For example, the providing unit provides information such as refrigerated storage, frozen storage, and room temperature storage. This allows the user to maintain the quality of the food. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the storage conditions of the food into an AI model and cause the generating AI to execute advice on an appropriate storage method.

[0094] The providing unit can provide quality control information in the manufacturing process of the pharmaceutical product. The providing unit provides, for example, quality control information in the manufacturing process of the pharmaceutical product. For example, the providing unit provides information such as a method for monitoring the manufacturing process, a quality inspection standard, and a quality assurance method. This allows the user to understand the quality of the pharmaceutical product. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input quality control information in the manufacturing process of the pharmaceutical product to the generation AI and output appropriate information. The generation AI may be, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0095] The reception unit can estimate the user's emotion and adjust the timing of information input based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. In addition, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input and enable the user to input information quickly. This improves user convenience by adjusting the timing of information input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of information input.

[0096] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit automatically displays information on foods and medicines that the user frequently inputs in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest information on foods and medicines to be used in a specific time period from the user's past input history. In this way, the optimal input method is provided by analyzing the user's past input history, improving user convenience. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into an AI model and cause the generation AI to select the optimal input method.

[0097] The reception unit can filter information based on the user's current health condition and areas of interest when inputting information. For example, if the user has an allergy, the reception unit automatically excludes information on foods and medicines containing allergenic ingredients. In addition, if the user is interested in a specific nutrient, the reception unit can preferentially display information on foods that contain a large amount of that nutrient. Furthermore, the reception unit can filter and provide information on appropriate foods and medicines based on the user's health condition. This provides more appropriate information by filtering information based on the user's health condition and areas of interest. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's health condition and areas of interest into an AI model and have the generation AI perform filtering.

[0098] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit preferentially inputs important information. Furthermore, when the user is relaxed, the reception unit can also input detailed information. Furthermore, when the user is in a hurry, the reception unit can also preferentially input the minimum necessary information. This improves user convenience by determining the priority of information to be input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0099] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting information. For example, when the user is in a specific area, the reception unit preferentially displays information on foods and medicines available in that area. In addition, when the user is traveling, the reception unit can also preferentially provide allergy information and taboo information at the travel destination. Furthermore, when the user lives in a specific area, the reception unit can also provide food information based on the climate and soil conditions of the area. In this way, highly relevant information is provided by considering the geographical location information of the user. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the geographical location information of the user to an AI model and cause the generation AI to execute the preferential input of highly relevant information.

[0100] The reception unit can analyze the user's social media activity at the time of inputting information and input related information. For example, if the user mentions a specific food or medicine on social media, the reception unit preferentially displays that information. The reception unit can also provide information from health-related accounts that the user follows on social media. Furthermore, the reception unit can predict and suggest information on foods and medicines of interest from the user's social media activity. In this way, related information is provided by analyzing the user's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into an AI model and cause the generation AI to input related information.

[0101] The search unit can estimate the user's emotion and adjust the display method of the search results based on the estimated user's emotion. For example, when the user is nervous, the search unit displays a simple and highly visible search result. In addition, when the user is relaxed, the search unit can also display a search result including detailed information. Furthermore, when the user is in a hurry, the search unit can also display a search result that covers the main points. This improves the user's convenience by adjusting the display method of the search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the search results.

[0102] The search unit can adjust the level of detail of the search results based on the importance of the information during the search. For example, if nutritional information is important, the search unit displays a detailed nutritional table. If allergy information is important, the search unit can also display details of allergens. If the storage method is important, the search unit can also display detailed storage methods. This provides important information to the user by adjusting the level of detail of the search results based on the importance of the information. Some or all of the above-mentioned processing in the search unit may be performed using, for example, AI, or may be performed without using AI. For example, the search unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the search results.

[0103] The search unit can apply different search algorithms depending on the category of information during the search. For example, when searching for nutritional information, the search unit applies a search algorithm specialized for nutritional components. In addition, when searching for allergy information, the search unit can also apply a search algorithm specialized for allergenic components. In addition, when searching for a storage method, the search unit can also apply a search algorithm specialized for the storage method. In this way, by applying different search algorithms depending on the category of information, more appropriate search results are provided. Part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the category of information into an AI model and cause the generation AI to apply the search algorithm.

[0104] The search unit can estimate the user's emotion and adjust the length of the search results based on the estimated user's emotion. For example, when the user is in a hurry, the search unit displays a short search result that is to the point. In addition, when the user is relaxed, the search unit can also display a longer search result that includes a detailed explanation. Furthermore, when the user is excited, the search unit can also display a search result with a visually stimulating effect. This improves the user's convenience by adjusting the length of the search results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the search unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the search unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the search results.

[0105] The search unit can determine the priority of search results based on the time of information submission during the search. For example, the search unit can prioritize displaying the latest nutrition information. The search unit can also prioritize displaying the latest allergy information. Furthermore, the search unit can also prioritize displaying the latest storage method. In this way, the priority of search results is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI, or may be performed without using AI. For example, the search unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of search results.

[0106] The search unit can adjust the order of search results based on the relevance of information during a search. For example, the search unit can preferentially display nutrition information that is most relevant to the user's search query. The search unit can also preferentially display allergy information that is most relevant to the user's search query. Furthermore, the search unit can also preferentially display a saving method that is most relevant to the user's search query. This provides the most relevant information to the user by adjusting the order of search results based on the relevance of information. A part or all of the above-mentioned processing in the search unit may be performed, for example, using AI or may be performed without using AI. For example, the search unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of search results.

[0107] The warning unit can estimate the user's emotion and adjust the display method of the warning based on the estimated user's emotion. For example, when the user is nervous, the warning unit displays a simple and highly visible warning. Also, when the user is relaxed, the warning unit can display a warning including detailed information. Furthermore, when the user is in a hurry, the warning unit can display a warning that covers the main points. This improves the user's convenience by adjusting the display method of the warning according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the warning.

[0108] The warning unit can adjust the level of detail of the warning based on the importance of the allergy information at the time of warning. For example, the warning unit displays a detailed warning in the case of serious allergy information. The warning unit can also display a concise warning in the case of mild allergy information. Furthermore, the warning unit can adjust the level of detail of the warning according to the importance of the allergy information. As a result, important information is provided to the user by adjusting the level of detail of the warning based on the importance of the allergy information. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the importance of the allergy information into an AI model and cause the generation AI to adjust the level of detail of the warning.

[0109] The warning unit can apply different warning algorithms depending on the category of allergy information when issuing a warning. For example, in the case of a food allergy, the warning unit applies a warning algorithm specialized for food ingredients. In addition, in the case of a drug allergy, the warning unit can also apply a warning algorithm specialized for drug ingredients. Furthermore, the warning unit can apply different warning algorithms depending on the category of allergy information. In this way, by applying different warning algorithms depending on the category of allergy information, a more appropriate warning is provided. Some or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the category of allergy information into an AI model and cause the generation AI to apply the warning algorithm.

[0110] The warning unit can estimate the user's emotion and determine the priority of the warning based on the estimated user's emotion. For example, when the user is nervous, the warning unit can preferentially display an important warning. Furthermore, when the user is relaxed, the warning unit can also display a detailed warning. Furthermore, when the user is in a hurry, the warning unit can preferentially display a minimum necessary warning. This improves the user's convenience by determining the priority of the warning according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the warning.

[0111] The warning unit can determine the priority of the warning based on the time of submission of the allergy information at the time of warning. For example, the warning unit preferentially displays the latest allergy information. The warning unit can also postpone old allergy information. Furthermore, the warning unit can determine the priority of the warning based on the time of submission of the allergy information. In this way, the latest information is provided by determining the priority of the warning based on the time of submission of the allergy information. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the time of submission of the allergy information into an AI model and cause the generation AI to determine the priority of the warning.

[0112] The warning unit can adjust the order of warnings based on the relevance of allergy information at the time of warning. For example, the warning unit preferentially displays information most relevant to the user's allergies. The warning unit can also postpone information less relevant to the user's allergies. Furthermore, the warning unit can adjust the order of warnings based on the relevance of allergy information. In this way, by adjusting the order of warnings based on the relevance of allergy information, the most relevant information for the user is provided. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI or may be performed without using AI. For example, the warning unit can input the relevance of allergy information into an AI model and cause the generation AI to adjust the order of warnings.

[0113] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This improves the user's convenience by adjusting the display method of the information to be provided according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the information.

[0114] The providing unit can adjust the level of detail of the information provided based on the importance of the information at the time of providing. For example, if nutritional information is important, the providing unit provides a detailed nutritional table. Also, if allergy information is important, the providing unit can provide details of allergens. Furthermore, if the storage method is important, the providing unit can provide detailed storage methods. In this way, by adjusting the level of detail of the information provided based on the importance of the information, important information for the user is provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the information provided.

[0115] The providing unit can apply different providing algorithms depending on the category of information at the time of providing. For example, when providing nutritional information, the providing unit applies a providing algorithm specialized for nutritional components. In addition, when providing allergy information, the providing unit can also apply a providing algorithm specialized for allergenic components. Furthermore, when providing a storage method, the providing unit can also apply a providing algorithm specialized for the storage method. In this way, by applying different providing algorithms depending on the category of information, more appropriate information is provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of information into an AI model and cause the generation AI to apply the providing algorithm.

[0116] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide important information with priority. Furthermore, when the user is relaxed, the providing unit can also provide detailed information with priority. Furthermore, when the user is in a hurry, the providing unit can also provide the minimum necessary information with priority. This improves the user's convenience by determining the priority of information to be provided according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0117] The providing unit can determine the priority of provision based on the time of information submission at the time of provision. For example, the providing unit can provide the latest nutritional information with priority. The providing unit can also provide the latest allergy information with priority. Furthermore, the providing unit can provide the latest storage method with priority. In this way, the latest information is provided by determining the priority of provision based on the time of information submission. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of provision.

[0118] The providing unit can adjust the order of provision based on the relevance of information at the time of provision. For example, the providing unit preferentially provides nutritional information most relevant to the user's interest. The providing unit can also preferentially provide allergy information most relevant to the user's interest. Furthermore, the providing unit can also preferentially provide a storage method most relevant to the user's interest. In this way, the order of provision is adjusted based on the relevance of information, thereby providing information most relevant to the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of provision.

[0119] The display unit can estimate the user's emotion and adjust the display method based on the estimated user's emotion. For example, when the user is nervous, the display unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the display unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can also provide a display method that focuses on the main points. This improves the user's convenience by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0120] The display unit can adjust the level of detail of the display based on the importance of the information when displaying. For example, if nutritional information is important, the display unit displays a detailed nutritional table. Also, if allergy information is important, the display unit can display details of allergens. Furthermore, if the storage method is important, the display unit can display detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the display based on the importance of the information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the display.

[0121] The display unit can apply different display algorithms depending on the category of information when displaying. For example, when displaying nutritional information, the display unit applies a display algorithm specialized for nutritional components. Furthermore, when displaying allergy information, the display unit can also apply a display algorithm specialized for allergenic components. Furthermore, when displaying a storage method, the display unit can also apply a display algorithm specialized for the storage method. In this way, by applying different display algorithms depending on the category of information, more appropriate information is provided. Part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the category of information into an AI model and cause the generation AI to apply the display algorithm.

[0122] The display unit can estimate the user's emotion and determine the display priority based on the estimated user's emotion. For example, when the user is nervous, the display unit preferentially displays important information. Also, when the user is relaxed, the display unit can display detailed information. Furthermore, when the user is in a hurry, the display unit can preferentially display the minimum necessary information. This improves the user's convenience by determining the display priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the display priority.

[0123] The display unit can determine the display priority based on the time of information submission at the time of display. For example, the display unit can prioritize displaying the latest nutrition information. The display unit can also prioritize displaying the latest allergy information. Furthermore, the display unit can also prioritize displaying the latest storage method. In this way, the display priority is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the time of information submission into an AI model and cause the generation AI to determine the display priority.

[0124] The display unit can adjust the display order based on the relevance of information when displaying. For example, the display unit preferentially displays nutritional information that is most relevant to the user's interest. The display unit can also preferentially display allergy information that is most relevant to the user's interest. Furthermore, the display unit can also preferentially display a storage method that is most relevant to the user's interest. In this way, the display order is adjusted based on the relevance of information, thereby providing the most relevant information to the user. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the relevance of information into an AI model and cause the generation AI to adjust the display order.

[0125] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, when the user is nervous, the prediction unit displays a simple and highly visible prediction result. In addition, when the user is relaxed, the prediction unit can also display a prediction result including detailed information. Furthermore, when the user is in a hurry, the prediction unit can also display a prediction result that covers the main points. This improves the user's convenience by adjusting the display method of the prediction result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the prediction result.

[0126] The prediction unit can adjust the level of detail of the prediction based on the importance of the information at the time of prediction. For example, if the nutritional information is important, the prediction unit predicts a detailed nutritional table. In addition, if the allergy information is important, the prediction unit can also predict details of allergens. Furthermore, if the storage method is important, the prediction unit can also predict detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the prediction based on the importance of the information. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the prediction.

[0127] The prediction unit can apply different prediction algorithms depending on the category of information at the time of prediction. For example, when predicting nutritional information, the prediction unit applies a prediction algorithm specialized for nutritional components. Furthermore, when predicting allergy information, the prediction unit can also apply a prediction algorithm specialized for allergenic components. Furthermore, when predicting a storage method, the prediction unit can also apply a prediction algorithm specialized for the storage method. In this way, by applying different prediction algorithms depending on the category of information, more appropriate prediction results are provided. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the category of information into an AI model and cause the generation AI to apply the prediction algorithm.

[0128] The prediction unit can estimate the user's emotion and determine the priority of the prediction results based on the estimated user's emotion. For example, when the user is nervous, the prediction unit preferentially displays important prediction results. In addition, the prediction unit can also display detailed prediction results when the user is relaxed. Furthermore, when the user is in a hurry, the prediction unit can also preferentially display the minimum necessary prediction results. This improves user convenience by determining the priority of the prediction results according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the prediction results.

[0129] The prediction unit can determine the priority of prediction based on the time of information submission at the time of prediction. For example, the prediction unit preferentially predicts the latest nutritional information. The prediction unit can also preferentially predict the latest allergy information. Furthermore, the prediction unit can also preferentially predict the latest storage method. In this way, the priority of prediction is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the time of information submission into an AI model and cause the generation AI to determine the priority of prediction.

[0130] The prediction unit can adjust the order of prediction based on the relevance of information at the time of prediction. For example, the prediction unit preferentially predicts nutritional information most relevant to the user's interest. The prediction unit can also preferentially predict allergy information most relevant to the user's interest. Furthermore, the prediction unit can also preferentially predict a storage method most relevant to the user's interest. In this way, the order of prediction is adjusted based on the relevance of information, thereby providing information most relevant to the user. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the relevance of information into an AI model and cause the generation AI to adjust the order of prediction.

[0131] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring result based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit displays a simple and highly visible monitoring result. In addition, when the user is relaxed, the monitoring unit can also display a monitoring result including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can also display a monitoring result with the main points. This improves the user's convenience by adjusting the display method of the monitoring result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the monitoring result.

[0132] The monitoring unit can adjust the level of detail of the monitoring based on the importance of the information during monitoring. For example, if nutritional information is important, the monitoring unit monitors a detailed nutritional table. Also, if allergy information is important, the monitoring unit can monitor details of allergens. Furthermore, if the storage method is important, the monitoring unit can monitor detailed storage methods. In this way, important information for the user is provided by adjusting the level of detail of the monitoring based on the importance of the information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the importance of the information into an AI model and cause the generation AI to adjust the level of detail of the monitoring.

[0133] The monitoring unit can apply different monitoring algorithms depending on the category of information during monitoring. For example, when monitoring nutritional information, the monitoring unit applies a monitoring algorithm specialized for nutritional components. In addition, when monitoring allergy information, the monitoring unit can also apply a monitoring algorithm specialized for allergenic components. In addition, when monitoring a storage method, the monitoring unit can also apply a monitoring algorithm specialized for the storage method. In this way, by applying different monitoring algorithms depending on the category of information, more appropriate monitoring results are provided. Part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI. For example, the monitoring unit can input the category of information into an AI model and cause the generation AI to apply the monitoring algorithm.

[0134] The monitoring unit can estimate the user's emotion and determine the priority of the monitoring results based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit preferentially displays important monitoring results. In addition, the monitoring unit can also display detailed monitoring results when the user is relaxed. Furthermore, when the user is in a hurry, the monitoring unit can also preferentially display the minimum necessary monitoring results. This improves the user's convenience by determining the priority of the monitoring results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the monitoring unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the monitoring unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the monitoring results.

[0135] The monitoring unit can determine the priority of monitoring based on the time of information submission during monitoring. For example, the monitoring unit prioritizes monitoring the latest nutritional information. The monitoring unit can also prioritize monitoring the latest allergy information. Furthermore, the monitoring unit can also prioritize monitoring the latest storage method. In this way, the monitoring priority is determined based on the time of information submission, thereby providing the latest information. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the time of information submission into an AI model and cause the generation AI to determine the monitoring priority.

[0136] The monitoring unit can adjust the monitoring order based on the relevance of the information during monitoring. For example, the monitoring unit preferentially monitors nutritional information that is most relevant to the user's interest. The monitoring unit can also preferentially monitor allergy information that is most relevant to the user's interest. Furthermore, the monitoring unit can also preferentially monitor the storage method that is most relevant to the user's interest. In this way, the monitoring order is adjusted based on the relevance of the information, thereby providing the most relevant information to the user. A part or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI. For example, the monitoring unit can input the relevance of the information into an AI model and cause the generation AI to adjust the monitoring order.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The information providing system may further include a history unit that records the user's dietary history. The history unit may store information on foods and medicines previously input by the user, and automatically display the information as candidates when the user inputs the same information again. For example, if the user has previously searched for nutritional information on a particular food, the history unit may record the information and provide the information quickly the next time the user searches for the same food. The history unit may also analyze the user's dietary patterns and provide advice for health management. Furthermore, the history unit may record foods and medicines to which the user has had an allergic reaction in the past, and provide the information to the warning unit to issue a warning to avoid allergy risks.

[0139] The providing unit can provide personalized nutrition advice based on the user's dietary history. For example, the providing unit can analyze the balance of nutrients that the user has taken in the past, and if a specific nutrient is lacking, can suggest foods that contain a lot of that nutrient. The providing unit can also provide an appropriate meal plan based on the user's health goals (e.g., weight loss, muscle building, etc.). Furthermore, the providing unit can also suggest recommended seasonal recipes based on the user's dietary history. This allows the user to receive personalized nutrition advice and manage their health more effectively.

[0140] The search unit can estimate the user's emotions and adjust the display method of the search results based on the estimated user's emotions. For example, when the user is nervous, the search unit displays search results that are simple and highly visible. When the user is relaxed, the search unit can also display search results that include detailed information. Furthermore, when the user is in a hurry, the search unit can display search results that focus on the main points. In this way, the display method of the search results is adjusted according to the user's emotions, thereby improving user convenience.

[0141] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. When the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. In this way, the display method of the information to be provided is adjusted according to the user's emotions, thereby improving user convenience.

[0142] The warning unit can estimate the user's emotions and adjust the display method of the warning based on the estimated user's emotions. For example, when the user is nervous, the warning unit displays a simple and highly visible warning. When the user is relaxed, the warning unit can also display a warning including detailed information. Furthermore, when the user is in a hurry, the warning unit can also display a warning that focuses on the main points. In this way, the convenience for the user is improved by adjusting the display method of the warning according to the user's emotions.

[0143] The providing unit can provide information on foods and medicines specific to a region, taking into account the geographical location information of the user. For example, when the user is in a specific region, the providing unit preferentially displays information on foods and medicines available in that region. In addition, when the user is traveling, the providing unit can also provide allergy information and contraindication information for the travel destination. Furthermore, when the user lives in a specific region, the providing unit can also provide food information based on the climate and soil conditions of the region. In this way, highly relevant information is provided by taking into account the geographical location information of the user.

[0144] The search unit can analyze the user's past search history and provide optimal search results. For example, the search unit can automatically display information on foods and medicines that the user has frequently searched for in the past as candidates. The search unit can also preferentially suggest search methods (voice, text, etc.) that the user has used in the past. Furthermore, the search unit can predict and suggest information on foods and medicines to be used during a specific time period from the user's past search history. In this way, optimal search results can be provided by analyzing the user's past search history, improving user convenience.

[0145] The providing unit can analyze the user's social media activity and provide related information. For example, when the user mentions a specific food or medicine on social media, the providing unit preferentially displays that information. The providing unit can also provide information from health-related accounts that the user follows on social media. Furthermore, the providing unit can predict and suggest information on foods and medicines of interest from the user's social media activity. In this way, related information is provided by analyzing the user's social media activity.

[0146] The warning unit can monitor the user's health condition and issue a warning based on the health condition. For example, if the user has a specific disease, the warning unit provides information on foods and medicines related to the disease and warns of contraindications. The warning unit can also issue a warning regarding over-intake or deficiency of a specific nutrient based on the user's health condition. Furthermore, the warning unit can monitor the user's health condition in real time and immediately issue a warning if an abnormality is detected. This supports health management by issuing appropriate warnings based on the user's health condition.

[0147] The providing unit can estimate the user's emotions and determine the priority order of information to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide important information with priority. Also, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide the minimum necessary information with priority. In this way, the priority order of information to be provided is determined according to the user's emotions, thereby improving user convenience.

[0148] The process flow of the second embodiment will be briefly described below.

[0149] Step 1: The reception unit receives the input information of a food or medicine from the user. For example, the user can input the name of a food or the ingredients of a medicine. The reception unit receives the input information and transmits it to the search unit. Step 2: The search unit searches for nutritional information of food or ingredients of medicine based on the information received from the reception unit. For example, the search unit searches for nutritional information such as calories, vitamins, and minerals of food. Also, the search unit searches for ingredients such as active ingredients, additives, and preservatives of medicine. The search unit transmits the search results to the warning unit. Step 3: The warning unit issues a warning of allergies or contraindications based on the information received from the search unit. For example, the warning unit issues a warning of allergies to a specific ingredient or contraindications for a specific disease. The warning unit transmits the warning information to the provision unit. Step 4: The providing unit provides information on the origin of the food or the manufacturing method of the drug based on the information received from the warning unit. For example, the providing unit provides information on the origin of the food, the manufacturing process of the drug, the technology used, the quality control method, etc. The providing unit transmits the provided information to the display unit. Step 5: The display unit displays user reviews or user ratings for the food or medicine based on the information received from the providing unit. For example, the display unit displays reviews or ratings by other users for the user's reference.

[0150] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0151] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0152] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0153] For example, the reception unit is realized by the reception device 38 of the smart device 14, and allows the user to input information on food or medicine. The search unit is realized by the specific processing unit 290 of the data processing device 12, and searches for nutritional information on food or ingredients of medicine based on the input information. The warning unit is realized by the specific processing unit 290 of the data processing device 12, and issues a warning of allergies or contraindications. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides information on the origin of food or the manufacturing method of medicine. The display unit is realized by the output device 40 of the smart device 14, and displays user reviews or user ratings. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the deterioration speed of food. The monitoring unit monitors the storage status of food using the camera 42 and sensors of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 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.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0157] 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.

[0158] 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 the voice according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0160] 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 performed in a secure state.

[0161] Fig. 4 shows an example of 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.

[0162] 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.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0165] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0166] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0167] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0168] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0169] For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and allows the user to input information on food or medicine. The search unit is realized by the specific processing unit 290 of the data processing device 12, and searches for nutritional information on food or ingredients of medicine based on the input information. The warning unit is realized by the specific processing unit 290 of the data processing device 12, and issues a warning of allergies or contraindications. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides information on the origin of food or the manufacturing method of medicine. The display unit is realized by the speaker 240 of the smart glasses 214, and displays user reviews or user ratings. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the deterioration speed of food. The monitoring unit monitors the storage status of food using the camera 42 and sensors of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 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.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0173] 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.

[0174] 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 the voice according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0176] 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 performed in a secure state.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0181] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] 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 voice indicating a user input for 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.

[0183] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0185] For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and the user can input information on food or medicine. The search unit is realized by the specific processing unit 290 of the data processing device 12, and searches for nutritional information of food or ingredients of medicine based on the input information. The warning unit is realized by the specific processing unit 290 of the data processing device 12, and issues a warning of allergies or contraindications. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides information on the origin of food or the manufacturing method of medicine. The display unit is realized by the display 343 of the headset terminal 314, and displays user reviews or user ratings. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the deterioration speed of food. The monitoring unit monitors the storage status of food using the camera 42 and sensors of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a 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.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0189] 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.

[0190] 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 the voice according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0192] 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 performed in a secure state.

[0193] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0194] 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.

[0195] 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.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0197] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0198] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0199] 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 a voice indicating a user input for 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.

[0200] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0201] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0202] For example, the reception unit is realized by the microphone 238 of the robot 414, and the user can input information on food or medicine. The search unit is realized by the specific processing unit 290 of the data processing device 12, and searches for nutritional information of food or ingredients of medicine based on the input information. The warning unit is realized by the specific processing unit 290 of the data processing device 12, and issues a warning of allergies or contraindications. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides information on the origin of food or the manufacturing method of medicine. The display unit is realized by the speaker 240 of the robot 414, and displays user reviews or user ratings. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the deterioration speed of food. The monitoring unit monitors the storage status of food using the camera 42 and sensors of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0204] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0206] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0208] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0210] 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.

[0211] 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 Universal Serial Bus (USB) 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.

[0212] In addition, 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 upon request from the data processing device 12.

[0213] 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.

[0214] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0215] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0216] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0217] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0219] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0221] (Appendix 1) a reception unit for inputting information on food or medicine; a search unit that searches for nutritional information of the food or ingredients of the drug based on the information input by the reception unit; a warning unit that issues a warning about an allergy or contraindication based on the information searched by the search unit; A provision unit that provides information on the origin of the food or the manufacturing method of the drug; A display unit that displays user reviews or user ratings for the food or the medicine. A system characterized by: (Appendix 2) The providing unit is Providing information on how to store said food or the expiration date of said medicine 2. The system of claim 1, (Appendix 3) The providing unit is Provide information on the climatic conditions and soil conditions of the area where the food is produced 2. The system of claim 1, (Appendix 4) The system described in Appendix 1, characterized in that it provides information on allergy onset in a specific area. (Appendix 5) The providing unit is Provide information about the ethical standards and credentials of the producers and manufacturers of said foods; 2. The system of claim 1, (Appendix 6) A prediction unit is provided for predicting a deterioration rate of food, The providing unit is Provide information on the expiration date based on the prediction result by the prediction unit 2. The system of claim 1, (Appendix 7) The providing unit is Provide clinical trial data and side effect information for said pharmaceuticals 2. The system of claim 1, (Appendix 8) Equipped with a monitoring unit that monitors the food preservation status, The providing unit is Advise on appropriate storage methods based on the results of monitoring by the monitoring department. 2. The system of claim 1, (Appendix 9) The providing unit is Provide quality control information for the manufacturing process of said pharmaceuticals 2. The system of claim 1, (Appendix 10) The reception unit is To estimate a user's emotion and adjust the timing of information input based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is Analyze the user's past input history and select the optimal input method 2. The system of claim 1, (Appendix 12) The reception unit is As information is entered, it is filtered based on the user's current health status and areas of interest. 2. The system of claim 1, (Appendix 13) The reception unit is Estimate the user's emotions and prioritize the information to be input based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The reception unit is When entering information, the system takes into account the user's geographic location information and prioritizes entering information that is more relevant 2. The system of claim 1, (Appendix 15) The reception unit is When you enter information, analyze your social media activity and enter relevant information 2. The system of claim 1, (Appendix 16) The search unit is Infer user sentiment and adjust how search results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The search unit is When searching, adjust the detail of your search results based on how important the information is to you 2. The system of claim 1, (Appendix 18) The search unit is When searching, apply different search algorithms depending on the category of information 2. The system of claim 1, (Appendix 19) The search unit is Infer user sentiment and adjust the length of search results based on the inferred sentiment 2. The system of claim 1, (Appendix 20) The search unit is When searching, prioritize search results based on when information was submitted 2. The system of claim 1, (Appendix 21) The search unit is When searching, adjust the order of search results based on the relevance of the information 2. The system of claim 1, (Appendix 22) The warning unit is Infer user emotions and adjust how alerts are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The warning unit is When alerting, adjust the verbosity of the alert based on the importance of the allergy information 2. The system of claim 1, (Appendix 24) The warning unit is When alerting, apply different alerting algorithms depending on the category of allergy information 2. The system of claim 1, (Appendix 25) The warning unit is Infer user emotions and prioritize alerts based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The warning unit is At the time of alert, prioritize alerts based on when allergy information was submitted 2. The system of claim 1, (Appendix 27) The warning unit is When alerting, adjust the order of alerts based on the relevance of allergy information 2. The system of claim 1, (Appendix 28) The providing unit is Estimate the user's emotions and adjust the way information is displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The providing unit is When providing information, adjust the level of detail provided based on the importance of the information 2. The system of claim 1, (Appendix 30) The providing unit is When serving, different serving algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 31) The providing unit is Estimate the user's emotions and prioritize the information to be provided based on the estimated user's emotions. 2. The system of claim 1, (Appendix 32) The providing unit is At the time of submission, the information will be prioritized based on when it was submitted. 2. The system of claim 1, (Appendix 33) The providing unit is When serving, adjust the order of serving based on the relevance of the information 2. The system of claim 1, (Appendix 34) The display unit is Estimate user's emotions and adjust the display method based on the estimated user's emotions 2. The system of claim 1, (Appendix 35) The display unit is When viewing, adjust the level of detail shown based on the importance of the information 2. The system of claim 1, (Appendix 36) The display unit is When displayed, apply different display algorithms depending on the category of information 2. The system of claim 1, (Appendix 37) The display unit is Estimate the user's emotions and determine the display priority based on the estimated user's emotions 2. The system of claim 1, (Appendix 38) The display unit is When displaying, prioritize display based on when the information was submitted 2. The system of claim 1, (Appendix 39) The display unit is At display time, adjust the order of display based on the relevance of the information 2. The system of claim 1, (Appendix 40) The prediction unit is Infer user emotions and adjust how prediction results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 41) The prediction unit is When forecasting, adjust the forecast detail based on the importance of the information 2. The system of claim 1, (Appendix 42) The prediction unit is When making predictions, apply different prediction algorithms depending on the category of information 2. The system of claim 1, (Appendix 43) The prediction unit is Infer user emotions and prioritize prediction results based on the inferred user emotions 2. The system of claim 1, (Appendix 44) The prediction unit is During forecasting, prioritize forecasts based on when information is submitted 2. The system of claim 1, (Appendix 45) The prediction unit is When forecasting, adjust the order of predictions based on the relevance of information 2. The system of claim 1, (Appendix 46) The monitoring unit is To estimate a user's emotion and adjust a display method of a monitoring result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 47) The monitoring unit is When monitoring, adjust the level of detail of your monitoring based on the importance of the information 2. The system of claim 1, (Appendix 48) The monitoring unit is During monitoring, different monitoring algorithms are applied depending on the category of information. 2. The system of claim 1, (Appendix 49) The monitoring unit is Estimate user emotions and prioritize monitoring results based on the estimated user emotions. 2. The system of claim 1, (Appendix 50) The monitoring unit is During monitoring, prioritize monitoring based on when information is submitted 2. The system of claim 1, (Appendix 51) The monitoring unit is When monitoring, adjust the order of monitoring based on the relevance of the information 2. The system of claim 1, [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting information on food or medicine; a search unit that searches for nutritional information of the food or ingredients of the drug based on the information input by the reception unit; a warning unit that issues a warning about an allergy or contraindication based on the information searched by the search unit; A provision unit that provides information on the origin of the food or the manufacturing method of the drug; A display unit that displays user reviews or user ratings for the food or the medicine. A system characterized by:

2. The providing unit is Providing information on how to store said food or the expiration date of said medicine 2. The system of claim 1 .

3. The providing unit is Provide information on the climatic conditions and soil conditions of the area where the food is produced 2. The system of claim 1 .

4. The system according to claim 1, which provides information on allergy onset in a specific region.

5. The providing unit is Provide information about the ethical standards and credentials of the producers and manufacturers of said foods; 2. The system of claim 1 .

6. A prediction unit for predicting a deterioration rate of the food, The providing unit is Provide information on the expiration date based on the prediction result by the prediction unit 2. The system of claim 1 .

7. The providing unit is Provide clinical trial data and side effect information for said pharmaceuticals 2. The system of claim 1 .

8. A monitoring unit that monitors the storage condition of the food, The providing unit is Advise on appropriate storage methods based on the results of monitoring by the monitoring department.

2. The system of claim 1 .

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