system

Abstract

The system according to the embodiment aims to analyze and recommend supplements suitable for an individual and eliminate supplements that are incompatible with other supplements. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and an exclusion unit. The reception unit inputs user information. The analysis unit analyzes the ingredients of supplements based on the information input by the reception unit. The recommendation unit recommends supplements suitable for the user based on the ingredient information analyzed by the analysis unit. The exclusion unit excludes supplements that are incompatible with other supplements from the supplements recommended by the recommendation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when selecting supplements suitable for an individual, there is room for improvement, as the analysis of ingredients and the elimination of supplements that cause poor interactions are not carried out sufficiently.

[0005] The system according to the embodiment aims to analyze and recommend supplements suitable for an individual and eliminate supplements that are incompatible with other supplements. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recommendation unit, and an exclusion unit. The reception unit inputs user information. The analysis unit analyzes the ingredients of supplements based on the information input by the reception unit. The recommendation unit recommends supplements suitable for the user based on the ingredient information analyzed by the analysis unit. The exclusion unit excludes supplements that are incompatible with other supplements from the supplements recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze and recommend supplements suitable for an individual and eliminate supplements that are incompatible with other supplements. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A supplement recommendation system according to an embodiment of the present invention provides advice on optimal supplements for individuals. In this supplement recommendation system, a user inputs a photo of a supplement or the manufacturer's URL, and a generation AI analyzes the information to identify the supplement's ingredients and raw materials. Furthermore, the user inputs information (e.g., age, gender, health status, allergies, current medication information, etc.). The generation AI then uses this information to provide an explanation of the supplement and recommend the optimal supplement for each individual user. The system also eliminates supplements that are incompatible with other supplements or may pose a health risk. For example, a user inputs a photo of a supplement or the manufacturer's URL. The user simply provides information about the supplements they use. For example, they can take and upload a photo of the supplement packaging or enter the URL of the manufacturer's official website. This information is then input into the generation AI. The generation AI then analyzes the input information and identifies the supplement's ingredients and raw materials. The generation AI then uses image recognition and web scraping technology to extract the supplement's ingredient list and raw materials. For example, the generation AI can use image recognition technology to read the ingredient list on the supplement packaging and identify the ingredients. It can also obtain ingredient information from the manufacturer's official website. Furthermore, user information is input. Users input their age, gender, health condition, allergy information, and information about medications they are currently taking. This information is input into the generation AI and used to recommend supplements. The generation AI then explains the supplements based on the supplement's ingredient information and the user's information, and recommends the supplements that are best suited to each individual user. For example, the generation AI can explain the effects of specific ingredients and recommend supplements tailored to the user's health condition. It can also eliminate supplements that are incompatible with other medications or that may pose a health risk. For example, for users taking certain medications, it can eliminate supplements that may interact with those medications. This allows users to easily find the best supplements for them and prevent health risks.For example, a user with allergies can avoid supplements containing allergens. Also, by selecting supplements tailored to specific health conditions, users can maintain and improve their health. This allows the supplement recommendation system to recommend optimal supplements based on the user's information and prevent health problems.

[0029] A supplement recommendation system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and an exclusion unit. The reception unit inputs user information. The user information may include, but is not limited to, age, gender, health status, allergy information, and information about currently taken medications. The reception unit, for example, allows the user to input information into an input form. The reception unit may also allow the user to input information using voice input. For example, using voice recognition technology, the reception unit may convert the user's voice into text data and input the information. The reception unit may also provide an auto-complete function based on the user's past input history. For example, the reception unit may automatically display candidates based on the user's past input information. The analysis unit analyzes the ingredients of the supplement. For example, the analysis unit may identify the ingredients of the supplement using image recognition technology. For example, the analysis unit may read the ingredient list on the supplement package using image recognition technology to identify the ingredients. The analysis unit may also obtain ingredient information of the supplement using web scraping technology. For example, the analysis unit may obtain ingredient information from the manufacturer's official website. The analysis unit may also perform a detailed analysis of the interactions between the ingredients of the supplement to more accurately identify the ingredients. For example, the system analyzes interactions between supplement ingredients and identifies combinations that maximize effectiveness. The recommendation unit recommends optimal supplements to the user. For example, the recommendation unit recommends supplements tailored to the user's health condition. For example, the system explains the effects of specific ingredients based on the user's health condition and recommends optimal supplements. The recommendation unit can also refer to the user's past health data to recommend more personalized supplements. For example, the system recommends optimal supplements based on the user's past health data. The exclusion unit eliminates supplements that are incompatible with other supplements. For example, the exclusion unit eliminates supplements that interact with specific medications. For example, for a user taking a specific medication, the system excludes supplements that may interact with that medication. The exclusion unit can also analyze interactions between supplement ingredients in detail to perform more accurate exclusions. For example, the system analyzes interactions between supplement ingredients and eliminates ingredients that may pose a health risk.As a result, the supplement recommendation system according to the embodiment can recommend optimal supplements based on the user's information and prevent health hazards.

[0030] The analysis unit can identify the ingredients of a supplement using image recognition technology. Image recognition technology includes, but is not limited to, machine learning algorithms and image processing technology. For example, the analysis unit reads the ingredient list on the supplement package using image recognition technology and identifies the ingredients. For example, the analysis unit uses a machine learning algorithm to extract the ingredient list from the supplement package image. The analysis unit can also analyze the supplement package image and identify the ingredients using image processing technology. For example, the analysis unit uses image processing technology to extract text information from the package image and identify the ingredient list. This allows the use of image recognition technology to accurately identify the ingredients of the supplement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the supplement package image into a generation AI and have the generation AI identify the ingredient list.

[0031] The analysis unit can acquire ingredient information for supplements using web scraping technology. Web scraping technology includes, but is not limited to, the programming language used and the data acquisition method. The analysis unit acquires ingredient information from, for example, a manufacturer's official website. For example, the analysis unit performs web scraping using Python to extract ingredient information from the manufacturer's official website. The analysis unit can also perform web scraping using JavaScript (registered trademark) to acquire ingredient information. For example, the analysis unit uses JavaScript to analyze the HTML structure of the manufacturer's official website and extract ingredient information. This allows for efficient acquisition of ingredient information for supplements using web scraping technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the URL of the manufacturer's official website into the generation AI and cause the generation AI to acquire ingredient information.

[0032] The recommendation unit can recommend supplements tailored to the user's health condition. Health conditions include, but are not limited to, medical history, current health condition, and medical records. For example, the recommendation unit can explain the effects of specific ingredients based on the user's health condition and recommend optimal supplements. For example, the recommendation unit can explain the effects of specific ingredients based on the user's medical history and recommend optimal supplements. The recommendation unit can also recommend optimal supplements based on the user's current health condition. For example, the recommendation unit can recommend optimal supplements based on the user's medical records. This allows for more appropriate supplements to be provided by recommending supplements tailored to the user's health condition. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input data on the user's health condition into a generation AI and have the generation AI recommend optimal supplements.

[0033] The exclusion unit can exclude supplements that interact with specific medications. Examples of specific medications include, but are not limited to, the medication's ingredients, effects, and side effects. For example, the exclusion unit excludes supplements that may interact with a specific medication for a user taking that medication. For example, the exclusion unit analyzes interactions between medication ingredients and supplement ingredients to exclude supplements that may pose health risks. The exclusion unit can also exclude interacting supplements by taking into account the medication's effects and side effects. For example, the exclusion unit excludes interacting supplements based on the medication's side effects. This eliminates supplements that may interact with specific medications, thereby preventing health risks. Some or all of the above-described processing by the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input medication ingredient information into the generation AI and cause the generation AI to exclude interacting supplements.

[0034] The reception unit can input information about the user's age, gender, health condition, allergy information, and current medications. Examples of user information include, but are not limited to, age, gender, health condition, allergy information, and current medications. The reception unit, for example, allows the user to input information into an input form. For example, the reception unit provides an input form for the user to input information about their age, gender, health condition, allergy information, and current medications. The reception unit can also allow the user to input information using voice input. For example, speech recognition technology can be used to convert the user's voice into text data and input the information. Furthermore, the reception unit can provide an auto-completion function based on the user's past input history. For example, candidates can be automatically displayed based on information previously entered by the user. This allows for more accurate supplement recommendations by entering detailed user information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input information into a generation AI and have the generation AI analyze and complete the information.

[0035] The reception unit can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. The reception unit, for example, automatically displays candidates based on information previously input by the user. For example, the reception unit prioritizes displaying information frequently input by the user, simplifying input. The reception unit can also analyze the user's past input patterns and suggest optimal input candidates. For example, the reception unit automatically displays optimal input candidates based on the user's past input history. This allows the auto-completion function to be provided based on the past input history, reducing the effort required for input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and cause the generation AI to provide the auto-completion function.

[0036] The reception unit can collect data on the user's lifestyle habits and daily activity to improve the accuracy of the input information. The reception unit, for example, acquires daily activity data from the user's smartwatch and reflects the data in the input information. For example, the reception unit acquires data from the user's food recording app and uses it to recommend supplements. The reception unit can also collect data to recommend appropriate supplements taking the user's exercise habits into consideration. For example, the reception unit can recommend optimal supplements based on the user's exercise habits. By collecting lifestyle and daily activity data, more accurate supplement recommendations are possible. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's lifestyle and daily activity data into the generation AI and cause the generation AI to improve the accuracy of the input information.

[0037] The reception unit can provide an option to input region-specific health information taking into account the user's geographical location information. The reception unit, for example, inputs information taking into account health risks specific to the region in which the user lives. For example, the reception unit can suggest region-specific supplements based on the user's location information. The reception unit can also suggest supplements suitable for the region's climate and environment based on the user's location information. For example, the reception unit can suggest supplements suitable for the region's climate and environment based on the user's location information. This allows for more appropriate supplement recommendations by taking region-specific health information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input region-specific health information.

[0038] The reception unit can analyze the user's social media activity and automatically input related health information. The reception unit automates input based on, for example, health information shared by the user on social media. For example, the reception unit extracts health-related concerns from the user's social media posts and reflects them in the input. The reception unit can also infer the user's health status from the user's social media activity and suggest appropriate supplements. For example, the reception unit analyzes the user's social media activity and suggests optimal supplements. This analysis of social media activity can reduce the effort of inputting information and provide more accurate information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI input related health information.

[0039] During analysis, the analysis unit can perform a detailed analysis of the interactions between the supplement's ingredients to identify ingredients with greater accuracy. The analysis unit, for example, analyzes the interactions between the supplement's ingredients to identify a combination that maximizes effectiveness. For example, the analysis unit analyzes how the supplement's ingredients interact with other ingredients and suggests an optimal combination. The analysis unit can also analyze how the supplement's ingredients affect a specific health condition to identify appropriate ingredients. For example, the analysis unit analyzes ingredient interactions and identifies optimal ingredients based on the health condition. This enables more accurate ingredient identification by analyzing ingredient interactions in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input supplement ingredient data into a generation AI and have the generation AI analyze ingredient interactions.

[0040] The analysis unit can perform the analysis while taking into account additional information such as the manufacturer and manufacturing date of the supplement. The analysis unit, for example, performs the analysis while taking into account the reliability of the supplement manufacturer. For example, the analysis unit analyzes the deterioration and alteration of ingredients while taking into account the manufacturing date of the supplement. The analysis unit can also perform the analysis while taking into account the quality control information of the supplement manufacturer. For example, the analysis unit evaluates the reliability of ingredients based on the manufacturer's quality control information. This enables more accurate analysis by taking into account additional information such as the manufacturer and manufacturing date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the manufacturer and manufacturing date of the supplement into the generation AI and have the generation AI perform the analysis.

[0041] The analysis unit can take into account visual information such as the package design and color of the supplement during analysis. The analysis unit, for example, extracts ingredient information from the package design of the supplement and reflects it in the analysis. For example, the analysis unit analyzes the color and design of the supplement package to help identify the ingredients. The analysis unit can also identify ingredients based on visual information from the supplement package. For example, the analysis unit identifies ingredients based on the package design and color. This enables more accurate analysis by taking visual information such as package design and color into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the package design and color of the supplement into the generation AI and have the generation AI perform the analysis.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the supplement. The analysis unit, for example, refers to the latest research data on the ingredients of the supplement and reflects it in the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the ingredients of the supplement. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the ingredients of the supplement. For example, the analysis unit identifies ingredients based on related literature and research data. By referring to related literature and research data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature and research data related to the supplement into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] When making a recommendation, the recommendation unit can refer to the user's past health data to recommend a more personalized supplement. The recommendation unit, for example, recommends the optimal supplement based on the user's past health data. For example, the recommendation unit recommends supplements tailored to a specific health condition based on the user's past health data. The recommendation unit can also analyze the user's past health data and recommend the most effective supplement. For example, the recommendation unit recommends personalized supplements based on past health data. This makes it possible to recommend more personalized supplements by referring to past health data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past health data into the generation AI and have the generation AI recommend personalized supplements.

[0044] The recommendation unit can provide detailed information about the effects and side effects of supplements when making a recommendation. For example, the recommendation unit can provide detailed information about the effects of supplements and encourage the user to make an appropriate selection. For example, the recommendation unit can provide information about the side effects of supplements and warn the user. The recommendation unit can also provide balanced information about the effects and side effects of supplements. For example, the recommendation unit can prompt the user to select an appropriate supplement based on information about the effects and side effects. This allows the user to make an appropriate selection by providing detailed information about the effects and side effects of supplements. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input data about the effects and side effects of supplements into the generation AI and have the generation AI provide the information.

[0045] When making a recommendation, the recommendation unit can recommend the optimal supplement by taking into consideration the user's living environment and eating habits. The recommendation unit, for example, considers the user's living environment to recommend the optimal supplement. For example, the recommendation unit considers the user's eating habits to recommend supplements that complement nutritional balance. The recommendation unit can also recommend the optimal supplement by comprehensively considering the user's living environment and eating habits. For example, the recommendation unit suggests the optimal supplement based on the living environment and eating habits. This makes it possible to recommend more appropriate supplements by taking into consideration the living environment and eating habits. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living environment and eating habits into the generation AI and have the generation AI recommend the optimal supplement.

[0046] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend relevant supplements. For example, the recommendation unit extracts health-related concerns from the user's social media posts and recommends appropriate supplements. For example, the recommendation unit infers the user's health status from the user's social media activity and recommends appropriate supplements. The recommendation unit can also analyze the user's social media activity and recommend optimal supplements. For example, the recommendation unit suggests optimal supplements based on the social media activity. This makes it possible to recommend more appropriate supplements by analyzing social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media activity data into a generation AI and have the generation AI recommend relevant supplements.

[0047] The exclusion unit can perform more accurate exclusion by analyzing the interactions between supplement ingredients in detail during exclusion. For example, the exclusion unit can analyze the interactions between supplement ingredients and exclude ingredients that may pose a health hazard. For example, the exclusion unit can analyze how supplement ingredients interact with other ingredients and exclude appropriate ingredients. The exclusion unit can also analyze how supplement ingredients affect specific health conditions and exclude appropriate ingredients. For example, the exclusion unit can analyze ingredient interactions and exclude appropriate ingredients based on the health condition. This enables more accurate exclusion by analyzing ingredient interactions in detail. Some or all of the above-described processing in the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input supplement ingredient data into a generation AI and have the generation AI analyze ingredient interactions.

[0048] When excluding ingredients, the elimination unit can refer to the user's past health data and perform elimination based on a specific health condition. The elimination unit, for example, eliminates ingredients that may pose a health hazard based on the user's past health data. For example, the elimination unit may eliminate ingredients that have a negative impact on a specific health condition from the user's past health data. The elimination unit can also analyze the user's past health data and eliminate the most appropriate ingredients. For example, the elimination unit performs elimination based on a specific health condition based on the past health data. This makes it possible to perform elimination based on a specific health condition by referring to the past health data. Some or all of the above-described processing in the elimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the elimination unit can input the user's past health data into the generation AI and cause the generation AI to perform elimination based on a specific health condition.

[0049] When excluding, the exclusion unit can consider additional information such as the manufacturer and manufacturing date of the supplement. The exclusion unit, for example, considers the reliability of the supplement manufacturer when excluding. For example, the exclusion unit considers the manufacturing date of the supplement to eliminate deterioration or alteration of ingredients. The exclusion unit can also consider the quality control information of the supplement manufacturer when excluding. For example, the exclusion unit evaluates the reliability of ingredients based on the manufacturer's quality control information. This allows for more accurate exclusion by considering additional information such as the manufacturer and manufacturing date. Some or all of the above-mentioned processing in the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input data on the manufacturer and manufacturing date of the supplement into the generation AI and have the generation AI perform the exclusion.

[0050] The exclusion unit can improve the accuracy of exclusion by referring to related literature and research data on the supplements when excluding ingredients. The exclusion unit, for example, refers to the latest research data on the ingredients of the supplements and reflects this in the exclusion. For example, the exclusion unit can improve the accuracy of exclusion by referring to related literature on the ingredients of the supplements. The exclusion unit can also improve the accuracy of exclusion by referring to past research data on the ingredients of the supplements. For example, the exclusion unit excludes ingredients based on related literature and research data. By referring to related literature and research data, the accuracy of exclusion is improved. Some or all of the above-described processing in the exclusion unit may be performed using, for example, AI, or may be performed without using AI. For example, the exclusion unit can input related literature and research data on the supplements into the generation AI and cause the generation AI to improve the accuracy of exclusion.

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

[0052] The reception unit can collect data on the user's lifestyle habits and daily activities to improve the accuracy of the input information. For example, the reception unit can obtain daily activity data from the user's smartwatch and reflect it in the input information. This allows for more appropriate supplement recommendations based on data such as the user's exercise volume and sleep patterns. The reception unit can also obtain data from the user's food recording app and recommend supplements that take nutritional balance into consideration. Furthermore, the reception unit can measure the user's stress level and suggest supplements that are useful for stress reduction. This allows for more accurate supplement recommendations by collecting lifestyle habits and daily activity data.

[0053] The recommendation unit can analyze a user's social media activity and recommend relevant supplements. For example, it can extract health-related interests from a user's social media posts and recommend appropriate supplements. This makes it possible to provide supplements based on health topics that interest the user. It can also infer a user's health condition from the user's social media activity and recommend appropriate supplements. It can also analyze a user's social media activity and recommend optimal supplements. This makes it possible to recommend more appropriate supplements by analyzing social media activity.

[0054] The reception unit can provide an option to input region-specific health information taking into account the user's geographic location information. For example, the user can input information taking into account health risks specific to the region where the user lives. This can then suggest region-specific supplements. Furthermore, based on the user's location information, it can also suggest supplements suitable for the region's climate and environment. Furthermore, based on the user's location information, it can also suggest supplements suitable for the region's climate and environment. This allows for more appropriate supplement recommendations by taking into account region-specific health information.

[0055] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the supplement. For example, the latest research data on the ingredients of the supplement can be referenced and reflected in the analysis. This enables analysis based on the latest scientific knowledge. The accuracy of the analysis can also be improved by referring to literature related to the ingredients of the supplement. Furthermore, the accuracy of the analysis can also be improved by referring to past research data on the ingredients of the supplement. This improves the accuracy of the analysis by referring to related literature and research data.

[0056] The reception unit can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, candidates can be automatically displayed based on information previously input by the user. This allows information frequently input by the user to be preferentially displayed, simplifying input. The reception unit can also analyze the user's past input patterns and suggest optimal input candidates. Furthermore, the reception unit can automatically display optimal input candidates based on the user's past input history. This reduces the effort required for input by providing an auto-completion function based on the user's past input history.

[0057] The analysis unit can take into account additional information such as the manufacturer and manufacturing date of the supplement during analysis. For example, the analysis can be performed taking into account the reliability of the supplement manufacturer. This makes it possible to recommend highly reliable supplements. It can also analyze the deterioration and alteration of ingredients by taking into account the manufacturing date of the supplement. Furthermore, it can also perform analysis taking into account the quality control information of the supplement manufacturer. This allows for more accurate analysis by taking into account additional information such as the manufacturer and manufacturing date.

[0058] When making recommendations, the recommendation unit can recommend optimal supplements taking into account the user's living environment and eating habits. For example, the recommendation unit can recommend optimal supplements taking into account the user's living environment. This makes it possible to provide supplements suited to the user's living environment. It can also recommend supplements that complement nutritional balance taking into account the user's eating habits. Furthermore, it can recommend optimal supplements taking into account the user's living environment and eating habits comprehensively. This makes it possible to recommend more appropriate supplements by taking into account the user's living environment and eating habits.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit inputs user information, including age, gender, health status, allergy information, and information on medications currently being taken. The reception unit allows users to enter information into an input form and also accepts voice input using voice recognition technology. It also provides an auto-complete function based on past input history. Step 2: The analysis unit analyzes the supplement ingredients based on the information entered by the reception unit. The analysis unit identifies the supplement ingredients using image recognition technology and obtains ingredient information using web scraping technology. It also analyzes the interactions between ingredients in detail to identify combinations that maximize effectiveness. Step 3: The recommendation unit recommends supplements suitable for the user based on the ingredient information analyzed by the analysis unit. The recommendation unit recommends supplements tailored to the user's health condition and recommends personalized supplements by referring to past health data. Step 4: The Exclusion Department eliminates incompatible supplements from the supplements recommended by the Recommendation Department. The Exclusion Department eliminates supplements that interact with specific medications and performs detailed analysis of interactions between ingredients to eliminate ingredients that may pose a health risk.

[0061] (Example 2) A supplement recommendation system according to an embodiment of the present invention provides advice on optimal supplements for individuals. In this supplement recommendation system, a user inputs a photo of a supplement or the manufacturer's URL, and a generation AI analyzes the information to identify the supplement's ingredients and raw materials. Furthermore, the user inputs information (e.g., age, gender, health status, allergies, current medication information, etc.). The generation AI then uses this information to provide an explanation of the supplement and recommend the optimal supplement for each individual user. The system also eliminates supplements that are incompatible with other supplements or may pose a health risk. For example, a user inputs a photo of a supplement or the manufacturer's URL. The user simply provides information about the supplements they use. For example, they can take and upload a photo of the supplement packaging or enter the URL of the manufacturer's official website. This information is then input into the generation AI. The generation AI then analyzes the input information and identifies the supplement's ingredients and raw materials. The generation AI then uses image recognition and web scraping technology to extract the supplement's ingredient list and raw materials. For example, the generation AI can use image recognition technology to read the ingredient list on the supplement packaging and identify the ingredients. It can also obtain ingredient information from the manufacturer's official website. Furthermore, user information is input. Users input their age, gender, health condition, allergy information, and information about medications they are currently taking. This information is input into the generation AI and used to recommend supplements. The generation AI then explains the supplements based on the supplement's ingredient information and the user's information, and recommends the supplements that are best suited to each individual user. For example, the generation AI can explain the effects of specific ingredients and recommend supplements tailored to the user's health condition. It can also eliminate supplements that are incompatible with other medications or that may pose a health risk. For example, for users taking certain medications, it can eliminate supplements that may interact with those medications. This allows users to easily find the best supplements for them and prevent health risks.For example, a user with allergies can avoid supplements containing allergens. Also, by selecting supplements tailored to specific health conditions, users can maintain and improve their health. This allows the supplement recommendation system to recommend optimal supplements based on the user's information and prevent health problems.

[0062] A supplement recommendation system according to an embodiment includes a reception unit, an analysis unit, a recommendation unit, and an exclusion unit. The reception unit inputs user information. The user information may include, but is not limited to, age, gender, health status, allergy information, and information about currently taken medications. The reception unit, for example, allows the user to input information into an input form. The reception unit may also allow the user to input information using voice input. For example, using voice recognition technology, the reception unit may convert the user's voice into text data and input the information. The reception unit may also provide an auto-complete function based on the user's past input history. For example, the reception unit may automatically display candidates based on the user's past input information. The analysis unit analyzes the ingredients of the supplement. For example, the analysis unit may identify the ingredients of the supplement using image recognition technology. For example, the analysis unit may read the ingredient list on the supplement package using image recognition technology to identify the ingredients. The analysis unit may also obtain ingredient information of the supplement using web scraping technology. For example, the analysis unit may obtain ingredient information from the manufacturer's official website. The analysis unit may also perform a detailed analysis of the interactions between the ingredients of the supplement to more accurately identify the ingredients. For example, the system analyzes interactions between supplement ingredients and identifies combinations that maximize effectiveness. The recommendation unit recommends optimal supplements to the user. For example, the recommendation unit recommends supplements tailored to the user's health condition. For example, the system explains the effects of specific ingredients based on the user's health condition and recommends optimal supplements. The recommendation unit can also refer to the user's past health data to recommend more personalized supplements. For example, the system recommends optimal supplements based on the user's past health data. The exclusion unit eliminates supplements that are incompatible with other supplements. For example, the exclusion unit eliminates supplements that interact with specific medications. For example, for a user taking a specific medication, the system excludes supplements that may interact with that medication. The exclusion unit can also analyze interactions between supplement ingredients in detail to perform more accurate exclusions. For example, the system analyzes interactions between supplement ingredients and eliminates ingredients that may pose a health risk.As a result, the supplement recommendation system according to the embodiment can recommend optimal supplements based on the user's information and prevent health hazards.

[0063] The analysis unit can identify the ingredients of a supplement using image recognition technology. Image recognition technology includes, but is not limited to, machine learning algorithms and image processing technology. For example, the analysis unit reads the ingredient list on the supplement package using image recognition technology and identifies the ingredients. For example, the analysis unit uses a machine learning algorithm to extract the ingredient list from the supplement package image. The analysis unit can also analyze the supplement package image and identify the ingredients using image processing technology. For example, the analysis unit uses image processing technology to extract text information from the package image and identify the ingredient list. This allows the use of image recognition technology to accurately identify the ingredients of the supplement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the supplement package image into a generation AI and have the generation AI identify the ingredient list.

[0064] The analysis unit can acquire ingredient information for supplements using web scraping technology. Web scraping technology includes, but is not limited to, the programming language used and the data acquisition method. The analysis unit acquires ingredient information from, for example, a manufacturer's official website. For example, the analysis unit performs web scraping using Python to extract ingredient information from the manufacturer's official website. The analysis unit can also perform web scraping using JavaScript to acquire ingredient information. For example, the analysis unit uses JavaScript to analyze the HTML structure of the manufacturer's official website and extract ingredient information. This allows for efficient acquisition of ingredient information for supplements using web scraping technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the URL of the manufacturer's official website into the generation AI and cause the generation AI to acquire ingredient information.

[0065] The recommendation unit can recommend supplements tailored to the user's health condition. Health conditions include, but are not limited to, medical history, current health condition, and medical records. For example, the recommendation unit can explain the effects of specific ingredients based on the user's health condition and recommend optimal supplements. For example, the recommendation unit can explain the effects of specific ingredients based on the user's medical history and recommend optimal supplements. The recommendation unit can also recommend optimal supplements based on the user's current health condition. For example, the recommendation unit can recommend optimal supplements based on the user's medical records. This allows for more appropriate supplements to be provided by recommending supplements tailored to the user's health condition. Some or all of the above-described processing in the recommendation unit can be performed using, or without, AI. For example, the recommendation unit can input data on the user's health condition into a generation AI and have the generation AI recommend optimal supplements.

[0066] The exclusion unit can exclude supplements that interact with specific medications. Examples of specific medications include, but are not limited to, the medication's ingredients, effects, and side effects. For example, the exclusion unit excludes supplements that may interact with a specific medication for a user taking that medication. For example, the exclusion unit analyzes interactions between medication ingredients and supplement ingredients to exclude supplements that may pose health risks. The exclusion unit can also exclude interacting supplements by taking into account the medication's effects and side effects. For example, the exclusion unit excludes interacting supplements based on the medication's side effects. This eliminates supplements that may interact with specific medications, thereby preventing health risks. Some or all of the above-described processing by the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input medication ingredient information into the generation AI and cause the generation AI to exclude interacting supplements.

[0067] The reception unit can input information about the user's age, gender, health condition, allergy information, and current medications. Examples of user information include, but are not limited to, age, gender, health condition, allergy information, and current medications. The reception unit, for example, allows the user to input information into an input form. For example, the reception unit provides an input form for the user to input information about their age, gender, health condition, allergy information, and current medications. The reception unit can also allow the user to input information using voice input. For example, speech recognition technology can be used to convert the user's voice into text data and input the information. Furthermore, the reception unit can provide an auto-completion function based on the user's past input history. For example, candidates can be automatically displayed based on information previously entered by the user. This allows for more accurate supplement recommendations by entering detailed user information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can input the user's input information into a generation AI and have the generation AI analyze and complete the information.

[0068] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input to enable quick information input. For example, the reception unit uses voice recognition technology to convert the user's voice into text data and input information. This reduces input stress by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI adjust the interface.

[0069] The reception unit can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. The reception unit, for example, automatically displays candidates based on information previously input by the user. For example, the reception unit prioritizes displaying information frequently input by the user, simplifying input. The reception unit can also analyze the user's past input patterns and suggest optimal input candidates. For example, the reception unit automatically displays optimal input candidates based on the user's past input history. This allows the auto-completion function to be provided based on the past input history, reducing the effort required for input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into a generation AI and cause the generation AI to provide the auto-completion function.

[0070] The reception unit can collect data on the user's lifestyle habits and daily activity to improve the accuracy of the input information. The reception unit, for example, acquires daily activity data from the user's smartwatch and reflects the data in the input information. For example, the reception unit acquires data from the user's food recording app and uses it to recommend supplements. The reception unit can also collect data to recommend appropriate supplements taking the user's exercise habits into consideration. For example, the reception unit can recommend optimal supplements based on the user's exercise habits. By collecting lifestyle and daily activity data, more accurate supplement recommendations are possible. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's lifestyle and daily activity data into the generation AI and cause the generation AI to improve the accuracy of the input information.

[0071] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes input of important information and postpones input of other information. For example, if the user is relaxed, the reception unit prompts the user to input detailed information. Furthermore, if the user is in a hurry, the reception unit minimizes the input of information. For example, the reception unit uses speech recognition technology to convert the user's voice into text data and input the information. This allows input prioritization based on the user's emotions, thereby improving input efficiency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into a generation AI and have the generation AI determine the input priorities.

[0072] The reception unit can provide an option to input region-specific health information taking into account the user's geographical location information. The reception unit, for example, inputs information taking into account health risks specific to the region in which the user lives. For example, the reception unit can suggest region-specific supplements based on the user's location information. The reception unit can also suggest supplements suitable for the region's climate and environment based on the user's location information. For example, the reception unit can suggest supplements suitable for the region's climate and environment based on the user's location information. This allows for more appropriate supplement recommendations by taking region-specific health information into account. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to input region-specific health information.

[0073] The reception unit can analyze the user's social media activity and automatically input related health information. The reception unit automates input based on, for example, health information shared by the user on social media. For example, the reception unit extracts health-related concerns from the user's social media posts and reflects them in the input. The reception unit can also infer the user's health status from the user's social media activity and suggest appropriate supplements. For example, the reception unit analyzes the user's social media activity and suggests optimal supplements. This analysis of social media activity can reduce the effort of inputting information and provide more accurate information. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's social media activity data into a generation AI and have the generation AI input related health information.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit adjusts the display method of the analysis results according to the user's emotions. This can facilitate understanding of the analysis results by providing a display method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0075] During analysis, the analysis unit can perform a detailed analysis of the interactions between the supplement's ingredients to identify ingredients with greater accuracy. The analysis unit, for example, analyzes the interactions between the supplement's ingredients to identify a combination that maximizes effectiveness. For example, the analysis unit analyzes how the supplement's ingredients interact with other ingredients and suggests an optimal combination. The analysis unit can also analyze how the supplement's ingredients affect a specific health condition to identify appropriate ingredients. For example, the analysis unit analyzes ingredient interactions and identifies optimal ingredients based on the health condition. This enables more accurate ingredient identification by analyzing ingredient interactions in detail. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input supplement ingredient data into a generation AI and have the generation AI analyze ingredient interactions.

[0076] The analysis unit can perform the analysis while taking into account additional information such as the manufacturer and manufacturing date of the supplement. The analysis unit, for example, performs the analysis while taking into account the reliability of the supplement manufacturer. For example, the analysis unit analyzes the deterioration and alteration of ingredients while taking into account the manufacturing date of the supplement. The analysis unit can also perform the analysis while taking into account the quality control information of the supplement manufacturer. For example, the analysis unit evaluates the reliability of ingredients based on the manufacturer's quality control information. This enables more accurate analysis by taking into account additional information such as the manufacturer and manufacturing date. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the manufacturer and manufacturing date of the supplement into the generation AI and have the generation AI perform the analysis.

[0077] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes important analyses and postpones other analyses. For example, if the user is relaxed, the analysis unit performs detailed analyses. Furthermore, if the user is in a hurry, the analysis unit minimizes the amount of analysis required. For example, the analysis unit determines the analysis priority according to the user's emotions. This improves the efficiency of analysis by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0078] The analysis unit can take into account visual information such as the package design and color of the supplement during analysis. The analysis unit, for example, extracts ingredient information from the package design of the supplement and reflects it in the analysis. For example, the analysis unit analyzes the color and design of the supplement package to help identify the ingredients. The analysis unit can also identify ingredients based on visual information from the supplement package. For example, the analysis unit identifies ingredients based on the package design and color. This enables more accurate analysis by taking visual information such as package design and color into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the package design and color of the supplement into the generation AI and have the generation AI perform the analysis.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the supplement. The analysis unit, for example, refers to the latest research data on the ingredients of the supplement and reflects it in the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the ingredients of the supplement. The analysis unit can also improve the accuracy of the analysis by referring to past research data on the ingredients of the supplement. For example, the analysis unit identifies ingredients based on related literature and research data. By referring to related literature and research data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input literature and research data related to the supplement into the generation AI and have the generation AI improve the accuracy of the analysis.

[0080] The recommendation unit can estimate the user's emotions and adjust the recommended expression method based on the estimated user emotions. For example, if the user is nervous, the recommendation unit provides a simple, highly visible display method. For example, if the user is relaxed, the recommendation unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the recommendation unit can also provide a display method that focuses on the main points. For example, the recommendation unit adjusts the recommended expression method according to the user's emotions. This can promote understanding of the recommendations by providing an expression method that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 recommendation unit can be performed using an AI, for example, or without an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the recommended expression method.

[0081] When making a recommendation, the recommendation unit can refer to the user's past health data to recommend a more personalized supplement. The recommendation unit, for example, recommends the optimal supplement based on the user's past health data. For example, the recommendation unit recommends supplements tailored to a specific health condition based on the user's past health data. The recommendation unit can also analyze the user's past health data and recommend the most effective supplement. For example, the recommendation unit recommends personalized supplements based on past health data. This makes it possible to recommend more personalized supplements by referring to past health data. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past health data into the generation AI and have the generation AI recommend personalized supplements.

[0082] The recommendation unit can provide detailed information about the effects and side effects of supplements when making a recommendation. For example, the recommendation unit can provide detailed information about the effects of supplements and encourage the user to make an appropriate selection. For example, the recommendation unit can provide information about the side effects of supplements and warn the user. The recommendation unit can also provide balanced information about the effects and side effects of supplements. For example, the recommendation unit can prompt the user to select an appropriate supplement based on information about the effects and side effects. This allows the user to make an appropriate selection by providing detailed information about the effects and side effects of supplements. Some or all of the above-mentioned processing in the recommendation unit can be performed, for example, using AI or without AI. For example, the recommendation unit can input data about the effects and side effects of supplements into the generation AI and have the generation AI provide the information.

[0083] The recommendation unit can estimate the user's emotions and prioritize recommendations based on the estimated user emotions. For example, if the user is feeling stressed, the recommendation unit prioritizes important recommendations and postpones other recommendations. For example, if the user is relaxed, the recommendation unit provides detailed recommendations. Furthermore, if the user is in a hurry, the recommendation unit minimizes recommendations. For example, the recommendation unit prioritizes recommendations based on the user's emotions. This improves recommendation efficiency by prioritizing recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of recommendations.

[0084] When making a recommendation, the recommendation unit can recommend the optimal supplement by taking into consideration the user's living environment and eating habits. The recommendation unit, for example, considers the user's living environment to recommend the optimal supplement. For example, the recommendation unit considers the user's eating habits to recommend supplements that complement nutritional balance. The recommendation unit can also recommend the optimal supplement by comprehensively considering the user's living environment and eating habits. For example, the recommendation unit suggests the optimal supplement based on the living environment and eating habits. This makes it possible to recommend more appropriate supplements by taking into consideration the living environment and eating habits. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the user's living environment and eating habits into the generation AI and have the generation AI recommend the optimal supplement.

[0085] When making a recommendation, the recommendation unit can analyze the user's social media activity and recommend relevant supplements. For example, the recommendation unit extracts health-related concerns from the user's social media posts and recommends appropriate supplements. For example, the recommendation unit infers the user's health status from the user's social media activity and recommends appropriate supplements. The recommendation unit can also analyze the user's social media activity and recommend optimal supplements. For example, the recommendation unit suggests optimal supplements based on the social media activity. This makes it possible to recommend more appropriate supplements by analyzing social media activity. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's social media activity data into a generation AI and have the generation AI recommend relevant supplements.

[0086] The rejection unit can estimate the user's emotions and adjust the rejection criteria based on the estimated user emotions. For example, if the user is stressed, the rejection unit can perform rejection using strict criteria. For example, if the user is relaxed, the rejection unit can perform rejection using flexible criteria. The rejection unit can also perform rejection quickly if the user is in a hurry. For example, the rejection unit adjusts the rejection criteria according to the user's emotions. This allows for improved rejection accuracy by providing rejection criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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 rejection unit can be performed using AI, for example, or without AI. For example, the rejection unit can input the user's emotion data into the generation AI and have the generation AI adjust the rejection criteria.

[0087] The exclusion unit can perform more accurate exclusion by analyzing the interactions between supplement ingredients in detail during exclusion. For example, the exclusion unit can analyze the interactions between supplement ingredients and exclude ingredients that may pose a health hazard. For example, the exclusion unit can analyze how supplement ingredients interact with other ingredients and exclude appropriate ingredients. The exclusion unit can also analyze how supplement ingredients affect specific health conditions and exclude appropriate ingredients. For example, the exclusion unit can analyze ingredient interactions and exclude appropriate ingredients based on the health condition. This enables more accurate exclusion by analyzing ingredient interactions in detail. Some or all of the above-described processing in the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input supplement ingredient data into a generation AI and have the generation AI analyze ingredient interactions.

[0088] When excluding ingredients, the elimination unit can refer to the user's past health data and perform elimination based on a specific health condition. The elimination unit, for example, eliminates ingredients that may pose a health hazard based on the user's past health data. For example, the elimination unit may eliminate ingredients that have a negative impact on a specific health condition from the user's past health data. The elimination unit can also analyze the user's past health data and eliminate the most appropriate ingredients. For example, the elimination unit performs elimination based on a specific health condition based on the past health data. This makes it possible to perform elimination based on a specific health condition by referring to the past health data. Some or all of the above-described processing in the elimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the elimination unit can input the user's past health data into the generation AI and cause the generation AI to perform elimination based on a specific health condition.

[0089] The elimination unit can estimate the user's emotions and determine the priority of elimination based on the estimated user's emotions. For example, if the user is feeling stressed, the elimination unit prioritizes important elimination and postpones other eliminations. For example, if the user is relaxed, the elimination unit performs detailed elimination. Furthermore, if the user is in a hurry, the elimination unit minimizes elimination. For example, the elimination unit determines the priority of elimination according to the user's emotions. This improves the efficiency of elimination by determining the priority of elimination according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the elimination unit may be performed using, for example, an AI. For example, the elimination unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of elimination.

[0090] When excluding, the exclusion unit can consider additional information such as the manufacturer and manufacturing date of the supplement. The exclusion unit, for example, considers the reliability of the supplement manufacturer when excluding. For example, the exclusion unit considers the manufacturing date of the supplement to eliminate deterioration or alteration of ingredients. The exclusion unit can also consider the quality control information of the supplement manufacturer when excluding. For example, the exclusion unit evaluates the reliability of ingredients based on the manufacturer's quality control information. This allows for more accurate exclusion by considering additional information such as the manufacturer and manufacturing date. Some or all of the above-mentioned processing in the exclusion unit may be performed using, or without, AI. For example, the exclusion unit can input data on the manufacturer and manufacturing date of the supplement into the generation AI and have the generation AI perform the exclusion.

[0091] The exclusion unit can improve the accuracy of exclusion by referring to related literature and research data on the supplements when excluding ingredients. The exclusion unit, for example, refers to the latest research data on the ingredients of the supplements and reflects this in the exclusion. For example, the exclusion unit can improve the accuracy of exclusion by referring to related literature on the ingredients of the supplements. The exclusion unit can also improve the accuracy of exclusion by referring to past research data on the ingredients of the supplements. For example, the exclusion unit excludes ingredients based on related literature and research data. By referring to related literature and research data, the accuracy of exclusion is improved. Some or all of the above-described processing in the exclusion unit may be performed using, for example, AI, or may be performed without using AI. For example, the exclusion unit can input related literature and research data on the supplements into the generation AI and cause the generation AI to improve the accuracy of exclusion. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and exclusion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input user information using the reception device 38 of the smart device 14. The analysis unit, for example, analyzes the ingredients of supplements using the specific processing unit 290 of the data processing device 12. The recommendation unit, for example, recommends optimal supplements to the user using the specific processing unit 290 of the data processing device 12. The exclusion unit, for example, eliminates supplements that are incompatible with other supplements using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, recommendation unit, and exclusion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the smart glasses 214. The analysis unit, for example, analyzes the ingredients of supplements using the specific processing unit 290 of the data processing device 12. The recommendation unit, for example, recommends optimal supplements to the user using the specific processing unit 290 of the data processing device 12. The exclusion unit, for example, excludes supplements that are incompatible with other supplements using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and exclusion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the headset-type terminal 314. The analysis unit, for example, analyzes the ingredients of supplements using the specific processing unit 290 of the data processing device 12. The recommendation unit, for example, recommends optimal supplements to the user using the specific processing unit 290 of the data processing device 12. The exclusion unit, for example, eliminates supplements that are incompatible with other supplements using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, recommendation unit, and exclusion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input user information using the microphone 238 of the robot 414. The analysis unit, for example, analyzes the ingredients of supplements using the specific processing unit 290 of the data processing device 12. The recommendation unit, for example, recommends optimal supplements to the user using the specific processing unit 290 of the data processing device 12. The exclusion unit, for example, eliminates supplements that are incompatible with other supplements using the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can collect data on the user's lifestyle habits and daily activities to improve the accuracy of the input information. For example, the reception unit can obtain daily activity data from the user's smartwatch and reflect it in the input information. This allows for more appropriate supplement recommendations based on data such as the user's exercise volume and sleep patterns. The reception unit can also obtain data from the user's food recording app and recommend supplements that take nutritional balance into consideration. Furthermore, the reception unit can measure the user's stress level and suggest supplements that are useful for stress reduction. This allows for more accurate supplement recommendations by collecting lifestyle habits and daily activity data.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided, making it easier for the user to understand the information. If the user is relaxed, a display method including detailed information can be provided, allowing the user to understand more deeply. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided, allowing the user to quickly obtain the necessary information. In this way, by providing a display method that corresponds to the user's emotions, it is possible to promote understanding of the analysis results.

[0095] The recommendation unit can analyze a user's social media activity and recommend relevant supplements. For example, it can extract health-related interests from a user's social media posts and recommend appropriate supplements. This makes it possible to provide supplements based on health topics that interest the user. It can also infer a user's health condition from the user's social media activity and recommend appropriate supplements. It can also analyze a user's social media activity and recommend optimal supplements. This makes it possible to recommend more appropriate supplements by analyzing social media activity.

[0096] The exclusion unit can estimate the user's emotions and adjust the exclusion criteria based on the estimated user's emotions. For example, if the user is feeling stressed, strict criteria can be used for exclusion. This allows the user to select supplements with peace of mind. Also, if the user is relaxed, flexible criteria can be used for exclusion. Furthermore, if the user is in a hurry, quick exclusion can be used. This allows the accuracy of exclusion to be improved by providing exclusion criteria according to the user's emotions.

[0097] The reception unit can provide an option to input region-specific health information taking into account the user's geographic location information. For example, the user can input information taking into account health risks specific to the region where the user lives. This can then suggest region-specific supplements. Furthermore, based on the user's location information, it can also suggest supplements suitable for the region's climate and environment. Furthermore, based on the user's location information, it can also suggest supplements suitable for the region's climate and environment. This allows for more appropriate supplement recommendations by taking into account region-specific health information.

[0098] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the supplement. For example, the latest research data on the ingredients of the supplement can be referenced and reflected in the analysis. This enables analysis based on the latest scientific knowledge. The accuracy of the analysis can also be improved by referring to literature related to the ingredients of the supplement. Furthermore, the accuracy of the analysis can also be improved by referring to past research data on the ingredients of the supplement. This improves the accuracy of the analysis by referring to related literature and research data.

[0099] The recommendation unit can estimate the user's emotions and adjust the way recommendations are presented based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided, making it easier for the user to understand the information. If the user is relaxed, a display method including detailed information can be provided, allowing the user to understand more deeply. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided, allowing the user to quickly obtain the necessary information. In this way, by providing a display method that corresponds to the user's emotions, it is possible to promote understanding of recommendations.

[0100] The reception unit can analyze the user's past input history and provide an auto-completion function to reduce the effort required for input. For example, candidates can be automatically displayed based on information previously input by the user. This allows information frequently input by the user to be preferentially displayed, simplifying input. The reception unit can also analyze the user's past input patterns and suggest optimal input candidates. Furthermore, the reception unit can automatically display optimal input candidates based on the user's past input history. This reduces the effort required for input by providing an auto-completion function based on the user's past input history.

[0101] The analysis unit can take into account additional information such as the manufacturer and manufacturing date of the supplement during analysis. For example, the analysis can be performed taking into account the reliability of the supplement manufacturer. This makes it possible to recommend highly reliable supplements. It can also analyze the deterioration and alteration of ingredients by taking into account the manufacturing date of the supplement. Furthermore, it can also perform analysis taking into account the quality control information of the supplement manufacturer. This allows for more accurate analysis by taking into account additional information such as the manufacturer and manufacturing date.

[0102] When making recommendations, the recommendation unit can recommend optimal supplements taking into account the user's living environment and eating habits. For example, the recommendation unit can recommend optimal supplements taking into account the user's living environment. This makes it possible to provide supplements suited to the user's living environment. It can also recommend supplements that complement nutritional balance taking into account the user's eating habits. Furthermore, it can recommend optimal supplements taking into account the user's living environment and eating habits comprehensively. This makes it possible to recommend more appropriate supplements by taking into account the user's living environment and eating habits.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit inputs user information, including age, gender, health status, allergy information, and information on medications currently being taken. The reception unit allows users to enter information into an input form and also accepts voice input using voice recognition technology. It also provides an auto-complete function based on past input history. Step 2: The analysis unit analyzes the supplement ingredients based on the information entered by the reception unit. The analysis unit identifies the supplement ingredients using image recognition technology and obtains ingredient information using web scraping technology. It also analyzes the interactions between ingredients in detail to identify combinations that maximize effectiveness. Step 3: The recommendation unit recommends supplements suitable for the user based on the ingredient information analyzed by the analysis unit. The recommendation unit recommends supplements tailored to the user's health condition and recommends personalized supplements by referring to past health data. Step 4: The Exclusion Department eliminates incompatible supplements from the supplements recommended by the Recommendation Department. The Exclusion Department eliminates supplements that interact with specific medications and performs detailed analysis of interactions between ingredients to eliminate ingredients that may pose a health risk.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 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 user information; an analysis unit that analyzes the ingredients of the supplement based on the information input by the reception unit; A recommendation unit that recommends supplements suitable for the user based on the component information analyzed by the analysis unit; An exclusion unit that excludes incompatible supplements from the supplements recommended by the recommendation unit A system characterized by:

2. The analysis unit Identifying supplement ingredients using image recognition technology The system of claim 1 .

3. The analysis unit Obtaining supplement ingredient information using web scraping technology The system of claim 1 .

4. The recommendation unit Recommend supplements tailored to the user's health condition The system of claim 1 .

5. The exclusion unit Eliminate supplements that interact with certain medications The system of claim 1 .

6. The reception unit Enter the user's age, gender, health condition, allergy information, and current medication information. The system of claim 1 .

7. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyzes the user's input history and provides an auto-complete function to reduce the effort required for input. The system of claim 1 .

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