System

Abstract

An object of a system according to an embodiment is to provide a consumer with detailed component information, effects, efficacies, and risks only by capturing an image of a product package.SOLUTION: A system includes an imaging unit, an analysis unit, and a provision unit. The photographing section photographs a package of a commodity. The analysis unit analyzes the image captured by the imaging unit. The providing unit provides the component information, the effect, the efficacy, and the risk analyzed by the analysis unit to the consumer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for consumers to obtain sufficient information from the information printed on the product packaging alone.

[0005] The system of the embodiment aims to provide consumers with detailed information on ingredients, effects, efficacy, and risks simply by taking a photo of the product packaging. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a provision unit. The photographing unit photographs the product packaging. The analysis unit analyzes the image photographed by the photographing unit. The provision unit provides the consumer with information on ingredients, effects, efficacy, and risks analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide consumers with detailed information on ingredients, effects, efficacy, and risks simply by taking a photo of the product packaging. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The system according to the embodiment of the present invention uses generative AI to analyze product ingredient information and provide information on its effects, efficacy, risks, etc., simply by taking a photo of the product packaging. This allows consumers to easily obtain detailed information and reviews that cannot be obtained from the limited information on the product packaging.

[0029] The system according to the embodiment includes a camera, an analyzer, and a provider. The camera takes a picture of a product package. For example, the camera can take a picture of the product package using a smartphone or camera. The camera can also automatically adjust the image resolution and focus. For example, the camera can automatically adjust the brightness when taking a picture in a dark location. The analyzer analyzes the image taken by the camera. For example, the generation AI recognizes letters and figures on the package and extracts them as text data. The generation AI also analyzes the effects, efficacy, and risks of a product based on its ingredient information. For example, if a product contains a specific ingredient, it provides information about the effects and risks of that ingredient. The generation AI can also collect and analyze online word-of-mouth information. For example, it collects reviews and ratings posted by product users and analyzes and summarizes their content. The provider provides the ingredient information, effects, efficacy, and risks analyzed by the analysis unit to consumers. For example, it displays the information as a text message via a smartphone app. It can also provide information via voice using a voice assistant. This allows consumers to receive information in a way that suits them best. As a result, the system according to the embodiment allows consumers to easily obtain detailed information on ingredients, effects, efficacy, and risks simply by taking a photo of the product packaging.

[0030] The analysis unit can recognize characters and figures written on the package and extract them as text data. For example, the analysis unit uses generative AI to analyze the design and color of the package and extract the product's characteristics. For example, color psychology can be used to evaluate the impact that specific colors have on consumers. The analysis unit also recognizes characters and figures written on the package and extracts them as text data. For example, OCR technology can be used to convert image data into text data. Furthermore, the analysis unit can analyze the product's characteristics from the package's design and color and evaluate the impact that visual elements have on consumers. This allows the information written on the package to be accurately extracted as text data.

[0031] If a specific ingredient is contained, the analysis unit can provide information about the effects and risks of that ingredient. For example, when the generation AI photographs a package, the analysis unit takes into account surrounding environmental information (e.g., lighting conditions and background) and performs optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing a package to obtain the optimal image. Furthermore, if a specific ingredient is contained, the analysis unit can provide information about the effects and risks of that ingredient. For example, the generation AI displays the latest research results on a specific ingredient. This makes it possible to provide information about the effects and risks of a specific ingredient.

[0032] The analysis unit can collect reviews or ratings posted by product users, and analyze and summarize their content. The analysis unit can, for example, use an emotion estimation function to analyze the emotions consumers have when taking photos of packaging, and customize the method of providing information based on those emotions. For example, the emotion estimation function can be used to analyze the facial expressions of consumers when taking photos of packaging, and estimate their emotions. The analysis unit can also collect reviews and ratings posted by product users, and analyze and summarize their content. For example, the generation AI can collect and analyze word-of-mouth information on the Internet. This makes it possible to provide a summary of product users' reviews and ratings.

[0033] The providing unit can display information as a text message through a smartphone app. For example, when photographing a product package, the providing unit also uses voice input, and the generation AI provides additional information based on the content of a verbal question asked by the consumer. For example, when photographing a product package, a function is added that allows the consumer to input a question by voice. For example, the consumer might ask, "What are the ingredients in this product?" The providing unit also displays information as a text message through a smartphone app. For example, ingredient information, effects, efficacy, and risks can be displayed as a text message through the smartphone app. This allows information to be displayed as a text message through the smartphone app.

[0034] If a consumer has a specific allergy, the providing unit can provide information with particular emphasis on whether the product contains ingredients related to the allergy. For example, the providing unit acquires additional digital information by scanning a barcode or QR code of the product at the same time as photographing the product package. For example, a function for automatically scanning a barcode or QR code may be added when photographing the product package. For example, scanning may automatically start when the camera detects a barcode. Furthermore, if a consumer has a specific allergy, the providing unit provides information with particular emphasis on whether the product contains ingredients related to the allergy. For example, if a consumer has a specific allergy, the providing unit displays information with particular emphasis on whether the product contains ingredients related to the allergy. This makes it possible to provide information on ingredients related to the consumer's allergy with emphasis.

[0035] The analysis unit can analyze the package design and color and extract product characteristics. For example, using a generative AI, the analysis unit can analyze product characteristics from the package design and color as well and evaluate the impact of visual elements on consumers. For example, the generative AI can analyze the package design and color and extract product characteristics. For example, using color psychology to evaluate the impact that specific colors have on consumers. The analysis unit can also analyze the package design and color and extract product characteristics. For example, the generative AI can analyze the package design and color and extract product characteristics. This makes it possible to extract product characteristics from the package design and color.

[0036] The analysis unit takes into account information about the surrounding environment when photographing the package and can perform optimal image analysis. For example, the generation AI takes into account information about the surrounding environment (e.g., lighting conditions and background) when photographing the package and can perform optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing the package and can obtain optimal images. The analysis unit also takes into account information about the surrounding environment when photographing the package and can perform optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing the package and can obtain optimal images. This allows optimal image analysis to be performed by taking into account information about the surrounding environment.

[0037] The provision unit allows the generation AI to provide additional information based on the content of a question verbally asked by a consumer. For example, when photographing a product package, the provision unit also uses voice input, and the generation AI provides additional information based on the content of the question verbally asked by the consumer. For example, when photographing a product package, a function is added that allows the consumer to input a question by voice. For example, the question is, "What are the ingredients in this product?" The provision unit also allows the generation AI to provide additional information based on the content of a question verbally asked by the consumer. For example, the generation AI provides additional information based on the content of a question verbally asked by the consumer. This makes it possible to provide additional information based on the content of a question verbally asked by the consumer.

[0038] The providing unit can scan the barcode or QR code of a product to obtain additional digital information. For example, the providing unit scans the barcode or QR code of a product at the same time as photographing the product package to obtain the additional digital information. For example, a function for automatically scanning the barcode or QR code when photographing the product package is added. For example, scanning automatically starts when the camera detects a barcode. The providing unit also scans the barcode or QR code of a product to obtain the additional digital information. For example, the providing unit scans the barcode or QR code of a product to obtain the additional digital information. This makes it possible to scan the barcode or QR code of a product to obtain the additional digital information.

[0039] When analyzing ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added that automatically refers to past research data and academic papers. For example, the latest research results on a specific ingredient can be displayed. Furthermore, when analyzing ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added that automatically refers to past research data and academic papers. This makes it possible to provide information that is based on scientific evidence when analyzing ingredient information.

[0040] The analysis unit can customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice. The analysis unit, for example, customizes the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provides personalized advice. For example, a function is added to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle. For example, ingredient information is provided based on allergy information and dietary restrictions. The analysis unit can also customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice. For example, a function is added to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle. This makes it possible to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice.

[0041] The analysis unit can visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. The analysis unit can, for example, visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of the ingredient information as graphs or charts. For example, the effects and risks of ingredients can be visually displayed. The analysis unit can also visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of the ingredient information as graphs or charts. This allows the analysis results of the ingredient information to be visualized and provided to make it easier for consumers to understand intuitively.

[0042] The analysis unit can automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. The analysis unit can, for example, automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. For example, a function is added to automatically translate the analysis results of the ingredient information into different languages. For example, translation into multiple languages ​​such as English, French, and Chinese. The analysis unit can also automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. For example, a function is added to automatically translate the analysis results of the ingredient information into different languages. This allows the analysis results of the ingredient information to be automatically translated into different languages ​​to accommodate international consumers.

[0043] When analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, a function to evaluate the reliability of the poster is added. For example, reliability is evaluated based on the poster's past review history and rating score. Furthermore, when analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, a function to evaluate the reliability of the poster is added. As a result, when analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially.

[0044] The analysis unit can customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. The analysis unit can, for example, customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. For example, a function is added to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history. For example, word-of-mouth information related to products purchased in the past is preferentially displayed. The analysis unit can also customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. For example, a function is added to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history. This makes it possible to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice.

[0045] The analysis unit can visualize and provide the word-of-mouth information to make it easier for consumers to understand intuitively. The analysis unit, for example, visualizes and provides the word-of-mouth information to make it easier for consumers to understand intuitively. For example, a function for visualizing the word-of-mouth information as a graph or chart can be added. For example, the distribution and trends of reviews can be visually displayed. The analysis unit can also visualize and provide the word-of-mouth information to make it easier for consumers to understand intuitively. For example, a function for visualizing the word-of-mouth information as a graph or chart can be added. This allows the word-of-mouth information to be visualized and provided to make it easier for consumers to understand intuitively.

[0046] The analysis unit can automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. The analysis unit can, for example, automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. For example, a function for automatically translating word-of-mouth information into different languages ​​is added. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The analysis unit can also automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. For example, a function for automatically translating word-of-mouth information into different languages ​​is added. This allows word-of-mouth information to be automatically translated into different languages ​​to accommodate international consumers.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When analyzing the ingredient information listed on product packaging, the analysis unit can provide personalized advice by taking into account the consumer's past purchase history and preferences. For example, the analysis unit can compare the ingredient information of a current product with the ingredient information contained in products the consumer has previously purchased, providing useful information to the consumer. The analysis results of the ingredient information can also be customized based on the consumer's preferences, and information that is likely to interest the consumer can be displayed preferentially. Furthermore, the analysis unit can emphasize ingredient information related to allergies or dietary restrictions based on the consumer's past purchase history.

[0049] If a consumer is allergic to a particular ingredient, the information provider can provide information that highlights whether that ingredient is included. For example, if the consumer registers their allergy information in the app in advance, the information provider can analyze the product's ingredient information and display a warning if the product contains an ingredient related to the allergy. Furthermore, if the product does not contain any ingredients related to the allergy, the information provider can clearly state this to provide peace of mind. Furthermore, it is also possible to suggest alternative products based on the allergy information.

[0050] When analyzing a product's ingredient information, the provision unit can refer to past research data and academic papers to provide information based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added to automatically refer to past research data and academic papers. This makes it possible to display the latest research results and scientific evidence for specific ingredients and provide consumers with highly reliable information. Furthermore, providing the results of ingredient information analysis based on scientific evidence can deepen consumer understanding.

[0051] The providing unit can visualize and provide ingredient information about products, making it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of ingredient information as graphs or charts. This allows the effects and risks of ingredients to be visually displayed, making it easier for consumers to understand intuitively. Furthermore, visualizing the analysis results of ingredient information makes it easier for consumers to quickly grasp the information. Furthermore, using visualized information, it is possible to provide information to consumers more effectively.

[0052] The providing unit can automatically translate ingredient information of products into different languages ​​to accommodate international consumers. For example, a function for automatically translating the analysis results of ingredient information into different languages ​​can be added. This allows translation into multiple languages ​​such as English, French, and Chinese to accommodate international consumers. Furthermore, providing ingredient information translated into different languages ​​can be used as a reference for international consumers when selecting products. Furthermore, using information translated into different languages, more effective information provision can be made to international consumers.

[0053] When analyzing the ingredient information of a product, the providing unit can customize it based on the consumer's health condition and lifestyle and provide individualized advice. For example, a function is added to customize the analysis results of ingredient information based on the consumer's health condition and lifestyle. This makes it possible to provide ingredient information based on allergy information and dietary restrictions. Furthermore, by customizing the analysis results of ingredient information based on the consumer's health condition and lifestyle and providing individualized advice, it is possible to provide more useful information to consumers. Furthermore, by customizing the analysis results of ingredient information based on the consumer's health condition and lifestyle, it is possible to provide more effective information to consumers.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The camera unit takes a picture of the product packaging. For example, the product packaging can be photographed using a smartphone or camera. The camera unit can also have a function to automatically adjust the image resolution and focus. For example, the camera unit can automatically adjust the brightness when taking a picture in a dark place. Step 2: The analysis unit analyzes the image captured by the photography unit. For example, the generation AI recognizes the letters and figures on the packaging and extracts them as text data. The generation AI also analyzes the effects, efficacy, and risks of the product based on its ingredient information. Furthermore, the generation AI can collect and analyze word-of-mouth information on the Internet. For example, it collects reviews and ratings posted by product users, analyzes their content, and summarizes it. Step 3: The provision unit provides the ingredient information, effects, efficacy, and risks analyzed by the analysis unit to the consumer. For example, this can be displayed as a text message via a smartphone app. The information can also be provided audibly using a voice assistant. This allows consumers to receive information in a way that suits them best.

[0056] (Example 2) The system according to the embodiment of the present invention uses generative AI to analyze product ingredient information and provide information on its effects, efficacy, risks, etc., simply by taking a photo of the product packaging. This allows consumers to easily obtain detailed information and reviews that cannot be obtained from the limited information on the product packaging.

[0057] The system according to the embodiment includes a camera, an analyzer, and a provider. The camera takes a picture of a product package. For example, the camera can take a picture of the product package using a smartphone or camera. The camera can also automatically adjust the image resolution and focus. For example, the camera can automatically adjust the brightness when taking a picture in a dark location. The analyzer analyzes the image taken by the camera. For example, the generation AI recognizes letters and figures on the package and extracts them as text data. The generation AI also analyzes the effects, efficacy, and risks of a product based on its ingredient information. For example, if a product contains a specific ingredient, it provides information about the effects and risks of that ingredient. The generation AI can also collect and analyze online word-of-mouth information. For example, it collects reviews and ratings posted by product users and analyzes and summarizes their content. The provider provides the ingredient information, effects, efficacy, and risks analyzed by the analysis unit to consumers. For example, it displays the information as a text message via a smartphone app. It can also provide information via voice using a voice assistant. This allows consumers to receive information in a way that suits them best. As a result, the system according to the embodiment allows consumers to easily obtain detailed information on ingredients, effects, efficacy, and risks simply by taking a photo of the product packaging.

[0058] The analysis unit can recognize characters and figures written on the package and extract them as text data. For example, the analysis unit uses generative AI to analyze the design and color of the package and extract the product's characteristics. For example, color psychology can be used to evaluate the impact that specific colors have on consumers. The analysis unit also recognizes characters and figures written on the package and extracts them as text data. For example, OCR technology can be used to convert image data into text data. Furthermore, the analysis unit can analyze the product's characteristics from the package's design and color and evaluate the impact that visual elements have on consumers. This allows the information written on the package to be accurately extracted as text data.

[0059] If a specific ingredient is contained, the analysis unit can provide information about the effects and risks of that ingredient. For example, when the generation AI photographs a package, the analysis unit takes into account surrounding environmental information (e.g., lighting conditions and background) and performs optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing a package to obtain the optimal image. Furthermore, if a specific ingredient is contained, the analysis unit can provide information about the effects and risks of that ingredient. For example, the generation AI displays the latest research results on a specific ingredient. This makes it possible to provide information about the effects and risks of a specific ingredient.

[0060] The analysis unit can collect reviews or ratings posted by product users, and analyze and summarize their content. The analysis unit can, for example, use an emotion estimation function to analyze the emotions consumers have when taking photos of packaging, and customize the method of providing information based on those emotions. For example, the emotion estimation function can be used to analyze the facial expressions of consumers when taking photos of packaging, and estimate their emotions. The analysis unit can also collect reviews and ratings posted by product users, and analyze and summarize their content. For example, the generation AI can collect and analyze word-of-mouth information on the Internet. This makes it possible to provide a summary of product users' reviews and ratings.

[0061] The providing unit can display information as a text message through a smartphone app. For example, when photographing a product package, the providing unit also uses voice input, and the generation AI provides additional information based on the content of a verbal question asked by the consumer. For example, when photographing a product package, a function is added that allows the consumer to input a question by voice. For example, the consumer might ask, "What are the ingredients in this product?" The providing unit also displays information as a text message through a smartphone app. For example, ingredient information, effects, efficacy, and risks can be displayed as a text message through the smartphone app. This allows information to be displayed as a text message through the smartphone app.

[0062] If a consumer has a specific allergy, the providing unit can provide information with particular emphasis on whether the product contains ingredients related to the allergy. For example, the providing unit acquires additional digital information by scanning a barcode or QR code of the product at the same time as photographing the product package. For example, a function for automatically scanning a barcode or QR code may be added when photographing the product package. For example, scanning may automatically start when the camera detects a barcode. Furthermore, if a consumer has a specific allergy, the providing unit provides information with particular emphasis on whether the product contains ingredients related to the allergy. For example, if a consumer has a specific allergy, the providing unit displays information with particular emphasis on whether the product contains ingredients related to the allergy. This makes it possible to provide information on ingredients related to the consumer's allergy with emphasis.

[0063] The analysis unit can analyze the package design and color and extract product characteristics. For example, using a generative AI, the analysis unit can analyze product characteristics from the package design and color as well and evaluate the impact of visual elements on consumers. For example, the generative AI can analyze the package design and color and extract product characteristics. For example, using color psychology to evaluate the impact that specific colors have on consumers. The analysis unit can also analyze the package design and color and extract product characteristics. For example, the generative AI can analyze the package design and color and extract product characteristics. This makes it possible to extract product characteristics from the package design and color.

[0064] The analysis unit takes into account information about the surrounding environment when photographing the package and can perform optimal image analysis. For example, the generation AI takes into account information about the surrounding environment (e.g., lighting conditions and background) when photographing the package and can perform optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing the package and can obtain optimal images. The analysis unit also takes into account information about the surrounding environment when photographing the package and can perform optimal image analysis. For example, the generation AI automatically adjusts lighting conditions when photographing the package and can obtain optimal images. This allows optimal image analysis to be performed by taking into account information about the surrounding environment.

[0065] The analysis unit can analyze the emotion of the consumer when taking a picture of the package and customize the method of providing information based on that emotion. The analysis unit, for example, uses an emotion estimation function to analyze the emotion of the consumer when taking a picture of the package and customize the method of providing information based on that emotion. For example, the emotion estimation function can be used to analyze the facial expression of the consumer when taking a picture of the package and estimate the emotion. The analysis unit can also analyze the emotion of the consumer when taking a picture of the package and customize the method of providing information based on that emotion. For example, the emotion estimation function can be used to analyze the facial expression of the consumer when taking a picture of the package and estimate the emotion. This makes it possible to customize the method of providing information based on the consumer's emotion.

[0066] The provision unit allows the generation AI to provide additional information based on the content of a question verbally asked by a consumer. For example, when photographing a product package, the provision unit also uses voice input, and the generation AI provides additional information based on the content of the question verbally asked by the consumer. For example, when photographing a product package, a function is added that allows the consumer to input a question by voice. For example, the question is, "What are the ingredients in this product?" The provision unit also allows the generation AI to provide additional information based on the content of a question verbally asked by the consumer. For example, the generation AI provides additional information based on the content of a question verbally asked by the consumer. This makes it possible to provide additional information based on the content of a question verbally asked by the consumer.

[0067] The providing unit can scan the barcode or QR code of a product to obtain additional digital information. For example, the providing unit scans the barcode or QR code of a product at the same time as photographing the product package to obtain the additional digital information. For example, a function for automatically scanning the barcode or QR code when photographing the product package is added. For example, scanning automatically starts when the camera detects a barcode. The providing unit also scans the barcode or QR code of a product to obtain the additional digital information. For example, the providing unit scans the barcode or QR code of a product to obtain the additional digital information. This makes it possible to scan the barcode or QR code of a product to obtain the additional digital information.

[0068] The analysis unit can analyze the emotions of consumers when they take pictures of packages in real time, and provide feedback to draw out positive emotions. The analysis unit can, for example, use an emotion estimation function to analyze the emotions of consumers when they take pictures of packages in real time, and provide feedback to draw out positive emotions. For example, the emotion estimation function can be used to analyze facial expressions of consumers when they take pictures of packages and estimate emotions. The analysis unit can also analyze the emotions of consumers when they take pictures of packages in real time, and provide feedback to draw out positive emotions. For example, the emotion estimation function can be used to analyze facial expressions of consumers when they take pictures of packages and estimate emotions. This makes it possible to analyze the emotions of consumers in real time, and provide feedback to draw out positive emotions.

[0069] When analyzing ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added that automatically refers to past research data and academic papers. For example, the latest research results on a specific ingredient can be displayed. Furthermore, when analyzing ingredient information, the analysis unit can refer to past research data and academic papers to provide information that is based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added that automatically refers to past research data and academic papers. This makes it possible to provide information that is based on scientific evidence when analyzing ingredient information.

[0070] The analysis unit can customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice. The analysis unit, for example, customizes the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provides personalized advice. For example, a function is added to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle. For example, ingredient information is provided based on allergy information and dietary restrictions. The analysis unit can also customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice. For example, a function is added to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle. This makes it possible to customize the analysis results of the ingredient information based on the consumer's health condition and lifestyle and provide personalized advice.

[0071] The analysis unit can analyze the emotions felt by consumers when they receive the ingredient information and provide information to alleviate negative emotions. The analysis unit can, for example, use an emotion estimation function to analyze the emotions felt by consumers when they receive the ingredient information and provide information to alleviate negative emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive the ingredient information. For example, if the consumer is feeling anxious, that emotion can be detected. The analysis unit can also analyze the emotions felt by consumers when they receive the ingredient information and provide information to alleviate negative emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive the ingredient information. This can provide information to alleviate negative emotions felt by consumers when they receive the ingredient information.

[0072] The analysis unit can visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. The analysis unit can, for example, visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of the ingredient information as graphs or charts. For example, the effects and risks of ingredients can be visually displayed. The analysis unit can also visualize and provide the analysis results of the ingredient information to make it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of the ingredient information as graphs or charts. This allows the analysis results of the ingredient information to be visualized and provided to make it easier for consumers to understand intuitively.

[0073] The analysis unit can automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. The analysis unit can, for example, automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. For example, a function is added to automatically translate the analysis results of the ingredient information into different languages. For example, translation into multiple languages ​​such as English, French, and Chinese. The analysis unit can also automatically translate the analysis results of the ingredient information into different languages ​​to accommodate international consumers. For example, a function is added to automatically translate the analysis results of the ingredient information into different languages. This allows the analysis results of the ingredient information to be automatically translated into different languages ​​to accommodate international consumers.

[0074] The analysis unit can analyze the emotions of consumers when they receive the ingredient information in real time and provide feedback to elicit positive emotions. The analysis unit, for example, uses an emotion estimation function to analyze the emotions of consumers when they receive the ingredient information in real time and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of consumers when they receive the ingredient information in real time. For example, the emotion is estimated by analyzing the consumer's facial expressions and voice. The analysis unit can also analyze the emotions of consumers when they receive the ingredient information in real time and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions of consumers when they receive the ingredient information in real time. This makes it possible to analyze the emotions of consumers when they receive the ingredient information in real time and provide feedback to elicit positive emotions.

[0075] When analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, a function to evaluate the reliability of the poster is added. For example, reliability is evaluated based on the poster's past review history and rating score. Furthermore, when analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially. For example, when the generation AI analyzes review information, a function to evaluate the reliability of the poster is added. As a result, when analyzing review information, the analysis unit can evaluate the reliability and expertise of the poster and provide highly reliable information preferentially.

[0076] The analysis unit can customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. The analysis unit can, for example, customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. For example, a function is added to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history. For example, word-of-mouth information related to products purchased in the past is preferentially displayed. The analysis unit can also customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice. For example, a function is added to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history. This makes it possible to customize the analysis results of the word-of-mouth information based on the consumer's past purchase history and preferences, and provide personalized advice.

[0077] The analysis unit can analyze the emotions felt by consumers when they receive word-of-mouth information and provide information to elicit positive emotions. The analysis unit can, for example, use an emotion estimation function to analyze the emotions felt by consumers when they receive word-of-mouth information and provide information to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive word-of-mouth information. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive word-of-mouth information and provide information to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive word-of-mouth information. This can analyze the emotions felt by consumers when they receive word-of-mouth information and provide information to elicit positive emotions.

[0078] The analysis unit can visualize and provide the word-of-mouth information to make it easier for consumers to understand intuitively. The analysis unit, for example, visualizes and provides the word-of-mouth information to make it easier for consumers to understand intuitively. For example, a function for visualizing the word-of-mouth information as a graph or chart can be added. For example, the distribution and trends of reviews can be visually displayed. The analysis unit can also visualize and provide the word-of-mouth information to make it easier for consumers to understand intuitively. For example, a function for visualizing the word-of-mouth information as a graph or chart can be added. This allows the word-of-mouth information to be visualized and provided to make it easier for consumers to understand intuitively.

[0079] The analysis unit can automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. The analysis unit can, for example, automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. For example, a function for automatically translating word-of-mouth information into different languages ​​is added. For example, translation into multiple languages ​​such as English, French, and Chinese is performed. The analysis unit can also automatically translate the word-of-mouth information into different languages ​​to accommodate international consumers. For example, a function for automatically translating word-of-mouth information into different languages ​​is added. This allows word-of-mouth information to be automatically translated into different languages ​​to accommodate international consumers.

[0080] The analysis unit can analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions. The analysis unit, for example, uses an emotion estimation function to analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information in real time. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information in real time. This makes it possible to analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions.

[0081] The analysis unit can analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions. The analysis unit, for example, uses an emotion estimation function to analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information in real time. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information and provide feedback to elicit positive emotions. For example, the emotion estimation function is used to analyze the emotions that consumers feel when they receive word-of-mouth information in real time. This makes it possible to analyze the emotions that consumers feel when they receive word-of-mouth information in real time, and provide feedback to elicit positive emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] When analyzing the ingredient information listed on product packaging, the analysis unit can provide personalized advice by taking into account the consumer's past purchase history and preferences. For example, the analysis unit can compare the ingredient information of a current product with the ingredient information contained in products the consumer has previously purchased, providing useful information to the consumer. The analysis results of the ingredient information can also be customized based on the consumer's preferences, and information that is likely to interest the consumer can be displayed preferentially. Furthermore, the analysis unit can emphasize ingredient information related to allergies or dietary restrictions based on the consumer's past purchase history.

[0084] If a consumer is allergic to a particular ingredient, the information provider can provide information that highlights whether that ingredient is included. For example, if the consumer registers their allergy information in the app in advance, the information provider can analyze the product's ingredient information and display a warning if the product contains an ingredient related to the allergy. Furthermore, if the product does not contain any ingredients related to the allergy, the information provider can clearly state this to provide peace of mind. Furthermore, it is also possible to suggest alternative products based on the allergy information.

[0085] The analysis unit can analyze the emotions felt by consumers when they receive the ingredient information and provide information to alleviate negative emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive the ingredient information and detect any anxiety or concern they may feel. If the consumer feels anxious, the analysis unit can provide additional information to alleviate the emotion or a message that provides reassurance. This can alleviate negative emotions felt by consumers when they receive the ingredient information.

[0086] When analyzing a product's ingredient information, the provision unit can refer to past research data and academic papers to provide information based on scientific evidence. For example, when the generation AI analyzes ingredient information, a function can be added to automatically refer to past research data and academic papers. This makes it possible to display the latest research results and scientific evidence for specific ingredients and provide consumers with highly reliable information. Furthermore, providing the results of ingredient information analysis based on scientific evidence can deepen consumer understanding.

[0087] The analysis unit can analyze the emotions consumers have when they receive ingredient information in real time and provide feedback that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions consumers have when they receive ingredient information in real time and provide information that will cause the consumers to have positive emotions. In addition, by analyzing the facial expressions and voices of consumers when they receive ingredient information and estimating their emotions, it is possible to provide feedback that is beneficial to the consumers. This makes it possible to analyze the emotions consumers have when they receive ingredient information in real time and elicit positive emotions.

[0088] The providing unit can visualize and provide ingredient information about products, making it easier for consumers to understand intuitively. For example, a function can be added to visualize the analysis results of ingredient information as graphs or charts. This allows the effects and risks of ingredients to be visually displayed, making it easier for consumers to understand intuitively. Furthermore, visualizing the analysis results of ingredient information makes it easier for consumers to quickly grasp the information. Furthermore, using visualized information, it is possible to provide information to consumers more effectively.

[0089] The analysis unit can analyze the emotions felt by consumers when they receive word-of-mouth information and provide information that will elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive word-of-mouth information and provide information that will cause the consumers to have positive emotions. In addition, by analyzing the facial expressions and voices of consumers when they receive word-of-mouth information and estimating their emotions, it is possible to provide feedback that is beneficial to the consumers. This makes it possible to analyze the emotions felt by consumers when they receive word-of-mouth information and elicit positive emotions.

[0090] The providing unit can automatically translate ingredient information of products into different languages ​​to accommodate international consumers. For example, a function for automatically translating the analysis results of ingredient information into different languages ​​can be added. This allows translation into multiple languages ​​such as English, French, and Chinese to accommodate international consumers. Furthermore, providing ingredient information translated into different languages ​​can be used as a reference for international consumers when selecting products. Furthermore, using information translated into different languages, more effective information provision can be made to international consumers.

[0091] The analysis unit can analyze the emotions felt by consumers when they receive the ingredient information and provide information to alleviate negative emotions. For example, the emotion estimation function can be used to analyze the emotions felt by consumers when they receive the ingredient information and detect any anxiety or concern they may feel. If the consumer feels anxious, the analysis unit can provide additional information to alleviate the emotion or a message that provides reassurance. This can alleviate negative emotions felt by consumers when they receive the ingredient information.

[0092] When analyzing the ingredient information of a product, the providing unit can customize it based on the consumer's health condition and lifestyle and provide individualized advice. For example, a function is added to customize the analysis results of ingredient information based on the consumer's health condition and lifestyle. This makes it possible to provide ingredient information based on allergy information and dietary restrictions. Furthermore, by customizing the analysis results of ingredient information based on the consumer's health condition and lifestyle and providing individualized advice, it is possible to provide more useful information to consumers. Furthermore, by customizing the analysis results of ingredient information based on the consumer's health condition and lifestyle, it is possible to provide more effective information to consumers.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The camera unit takes a picture of the product packaging. For example, the product packaging can be photographed using a smartphone or camera. The camera unit can also have a function to automatically adjust the image resolution and focus. For example, the camera unit can automatically adjust the brightness when taking a picture in a dark place. Step 2: The analysis unit analyzes the image captured by the photography unit. For example, the generation AI recognizes the letters and figures on the packaging and extracts them as text data. The generation AI also analyzes the effects, efficacy, and risks of the product based on its ingredient information. Furthermore, the generation AI can collect and analyze word-of-mouth information on the Internet. For example, it collects reviews and ratings posted by product users, analyzes their content, and summarizes it. Step 3: The provision unit provides the ingredient information, effects, efficacy, and risks analyzed by the analysis unit to the consumer. For example, this can be displayed as a text message via a smartphone app. The information can also be provided audibly using a voice assistant. This allows consumers to receive information in a way that suits them best.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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. [Explanation of symbols]

[0162] 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 photography department that takes photos of product packaging, an analysis unit that analyzes the image captured by the imaging unit; and a providing unit that provides consumers with information on ingredients, effects, efficacy, and risks analyzed by the analyzing unit. A system characterized by:

2. The analysis unit Recognize the characters and figures written on the package and extract them as text data 2. The system of claim 1.

3. The providing unit Displaying information as a text message through a smartphone app 2. The system of claim 1.

4. The analysis unit When analyzing the ingredient information, we refer to past research data and academic papers to provide information based on scientific evidence.

2. The system of claim 1.

5. The analysis unit When analyzing reviews, the reliability and expertise of the poster is evaluated, and the information with high reliability is given priority.

2. The system of claim 1.

6. The analysis unit The emotion of the consumer when photographing the package is analyzed, and the method of providing information is customized based on the emotion.

2. The system of claim 1.

7. The analysis unit Analyzing the emotions felt by the consumer when receiving the ingredient information and providing information to reduce the negative emotions.

2. The system of claim 1.

8. The analysis unit Analyze the emotions felt by the consumer when receiving word-of-mouth information and provide information to elicit positive emotions.

2. The system of claim 1.

Images