system

Abstract

The system according to this embodiment aims to provide information on the ingredients and risks of a product quickly and comprehensively. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit photographs the product packaging and collects information. The analysis unit analyzes the information collected by the collection unit and analyzes the effects and risks of the ingredients. The provision unit provides the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and comprehensively obtain information on the components and risks of products.

[0005] The system according to the embodiment aims to quickly and comprehensively provide information on the components and risks of products.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit takes pictures of product packages and collects information. The analysis unit analyzes the information collected by the collection unit and analyzes the effects and risks of the components. The provision unit provides the analysis results obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can provide information about the ingredients and risks of a product quickly and comprehensively. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The product information provision system according to an embodiment of the present invention is a system that photographs product packaging, collects, analyzes, and provides information. This system allows users to instantly obtain detailed ingredient analysis and a comprehensive product evaluation simply by photographing product packaging. For example, the product information provision system works by having the user photograph product packaging. At this time, the system uses advanced optical character recognition (OCR) technology to read the information on the packaging. For example, when a cosmetic product packaging is photographed, the effects of each ingredient on the skin and their suitability for different skin types are displayed. In the case of food products, information is provided not only on nutritional value but also on the safety and sustainability of additives. Next, the system comprehensively analyzes the effects, potential risks, and allergy information of each ingredient by cross-referencing it with a vast database on the internet. For example, regarding cosmetic ingredients, the system analyzes the latest scientific research and consumer reviews to provide reliable information that is updated in real time. This allows consumers to understand the true value of products and make choices that align with their needs and values. This service goes beyond being a mere information provision tool; it functions as a comprehensive product advisor that enhances consumer health awareness and supports smarter purchasing decisions. For example, when a cosmetic product packaging is photographed, the effects of each ingredient on the skin and their suitability for different skin types are displayed. In the case of food products, information is provided not only on nutritional value but also on the safety and sustainability of additives. This system allows consumers to easily access detailed information and reviews that cannot be obtained from the limited information on product packaging alone. For example, regarding cosmetic ingredients, the system analyzes the latest scientific research and consumer reviews, providing reliable information that is updated in real time. This allows consumers to understand the true value of products and make choices that align with their needs and values. In this way, the product information system can raise consumer health awareness and support smarter purchasing decisions.

[0029] The product information provision system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit photographs the product packaging and collects information. For example, the collection unit photographs the product packaging with a camera and saves it as image data. The collection unit can also read barcodes and QR codes (registered trademarks). For example, the collection unit scans a barcode printed on the product packaging to obtain product information. Furthermore, the collection unit can read text information written on the product packaging using OCR technology. For example, the collection unit obtains the product's ingredient list and usage instructions as text data. The analysis unit analyzes the information collected by the collection unit and analyzes the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. Furthermore, the analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit evaluates the possibility that a particular ingredient may cause allergies. Furthermore, the analysis unit can analyze the interactions of ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit evaluates the effects of specific components when combined with other components. The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides the analysis results to the user as a text report, for example. The provision unit can also visually display the analysis results as graphs or charts. For example, the provision unit displays the effects and risks of components as graphs. Furthermore, the provision unit can provide the analysis results to the user as alert notifications. For example, the provision unit displays an alert if a specific component may cause an allergy. This allows the product information provision system according to the embodiment to provide consumers with detailed information about products. Some or all of the above-described processes in the collection unit, analysis unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data obtained by photographing the product packaging into a generation AI and have the generation AI generate text data from the image data. The analysis unit can input the collected component information into a generation AI and have the generation AI evaluate the effects and risks of the components.The service provider can provide users with the analysis results obtained by the generating AI.

[0030] The data collection unit photographs product packaging and collects information. For example, the data collection unit photographs product packaging with a camera and saves it as image data. Specifically, the data collection unit uses a high-resolution camera to photograph the entire product packaging and acquire image data. This image data includes the product's appearance, design, and label information. The data collection unit can also read barcodes and QR codes (registered trademarks). For example, the data collection unit scans barcodes printed on product packaging to acquire product information. Barcode scanners are used to quickly and accurately acquire detailed information about the product. Furthermore, the data collection unit can read text information written on product packaging using OCR technology. For example, the data collection unit acquires the product's ingredient list and usage instructions as text data. OCR technology is used to recognize characters in an image and convert them into digital text. This allows the data collection unit to efficiently collect detailed product information and store it in a database. The data collection unit centrally manages this data and makes it accessible to the analysis unit and the provision unit. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, when a new product is launched on the market, the data collection unit can quickly collect data and update the entire system. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information collected by the data collection unit to analyze the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. Specifically, the analysis unit compares the collected ingredient information with a database to evaluate the scientific effects and efficacy of each ingredient. For example, it evaluates the impact of specific vitamins or minerals on health. The analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit evaluates the possibility that a particular ingredient may cause allergies. By analyzing consumer reviews and past cases, it is possible to perform risk assessments for specific ingredients. Furthermore, the analysis unit can analyze the interactions of ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit evaluates the effects when a particular ingredient is combined with other ingredients. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate the effects and risks of the ingredients. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past consumer reviews, it can predict consumer reactions to specific ingredients and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The service provider provides the analysis results obtained by the analysis provider. For example, the service provider provides the analysis results to the user as a text report. Specifically, the service provider generates a detailed text report of the analysis results and provides it to the user. This report includes detailed information on the effects and risks of the ingredients. The service provider can also visually display the analysis results as graphs and charts. For example, the service provider displays the effects and risks of the ingredients as a graph. Visual displays are effective in making the information easier for users to understand intuitively. Furthermore, the service provider can provide the analysis results to the user as alert notifications. For example, the service provider displays an alert if a particular ingredient may cause an allergy. Alert notifications help users quickly grasp important information and take appropriate action. By providing this information in various formats, the service provider makes it easy for users to obtain detailed product information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, based on user feedback, the content and display format of the report can be reviewed to provide more user-friendly information. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide information to users and obtain detailed product information.

[0033] The data collection unit can photograph product packaging and collect information. For example, the data collection unit can photograph product packaging with a camera and save it as image data. The data collection unit can also read barcodes and QR codes. For example, the data collection unit can scan a barcode printed on product packaging to obtain product information. Furthermore, the data collection unit can read text information written on product packaging using OCR technology. For example, the data collection unit can obtain the product's ingredient list and usage instructions as text data. This allows for obtaining detailed ingredient information by photographing product packaging and collecting information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data obtained by photographing product packaging into a generating AI and have the generating AI generate text data from the image data.

[0034] The analysis unit can analyze the collected information and assess the effects and risks of the ingredients. For example, the analysis unit can compare the collected ingredient information with scientific research data to evaluate the effects of the ingredients. The analysis unit can also assess the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit can assess the likelihood that a particular ingredient will cause allergies. Furthermore, the analysis unit can analyze the interactions between ingredients and assess the effects and risks when multiple ingredients are combined. For example, the analysis unit can assess the effects when a particular ingredient is combined with other ingredients. In this way, the effects and risks of ingredients can be understood by analyzing the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected ingredient information into a generating AI and have the generating AI perform the evaluation of the effects and risks of the ingredients.

[0035] The service provider can provide the user with the analysis results. For example, the service provider can provide the user with the analysis results as a text report. The service provider can also visually display the analysis results as graphs or charts. For example, the service provider can display the effects and risks of ingredients as graphs. Furthermore, the service provider can provide the user with the analysis results as alert notifications. For example, the service provider can display an alert if a particular ingredient may cause an allergy. By providing the user with the analysis results, the user can obtain detailed information about the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the user with analysis results obtained by a generating AI.

[0036] The analysis unit can analyze the effects, risks, and allergy information of ingredients by cross-referencing them with databases on the internet. For example, the analysis unit can evaluate the effects of ingredients by cross-referencing collected ingredient information with online scientific paper databases. The analysis unit can also evaluate the risks of ingredients by cross-referencing them with product databases. For example, the analysis unit can evaluate the likelihood that a particular ingredient will cause an allergy. Furthermore, the analysis unit can evaluate allergy information of ingredients based on medical data and consumer reports. For example, the analysis unit can evaluate the likelihood that a particular ingredient will cause an allergy. This allows for a comprehensive analysis of the effects, risks, and allergy information of ingredients by cross-referencing them with databases on the internet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected ingredient information into a generating AI and have the generating AI perform an evaluation of the effects and risks of the ingredients.

[0037] The service provider can analyze the latest scientific research and consumer reviews and provide information that is updated in real time. For example, the service provider can analyze the latest scientific research and evaluate the effects and risks of ingredients. The service provider can also analyze consumer reviews and evaluate ingredients. For example, the service provider can evaluate ingredients based on data from online review sites. Furthermore, the service provider can provide information that is updated in real time. For example, the service provider can acquire data regularly and provide the latest information. This allows the service provider to provide highly reliable information in real time by analyzing the latest scientific research and consumer reviews. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide users with analysis results obtained by generative AI.

[0038] The data collection unit can analyze the user's past shooting history and select the optimal shooting method. For example, the data collection unit can suggest the optimal shooting method based on the shooting angles and lighting conditions the user has used in the past. The data collection unit can also analyze the types of products the user has photographed in the past and suggest the optimal shooting method for similar products. Furthermore, the data collection unit can select the shooting method with the highest success rate from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing past shooting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0039] The data collection unit can filter product packaging images based on the user's current purchase history and areas of interest. For example, the data collection unit can prioritize photographing product packaging that the user is highly interested in based on their purchase history. The data collection unit can also filter and photograph relevant product packaging based on the user's areas of interest. Furthermore, the data collection unit can combine the user's purchase history and areas of interest to photograph the most relevant product packaging. This allows for the collection of highly relevant information by filtering based on the user's purchase history and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform the filtering.

[0040] The data collection unit can prioritize photographing highly relevant products by considering the user's geographical location when photographing product packaging. For example, if the user is in a specific region, the data collection unit will prioritize photographing the packaging of products popular in that region. The data collection unit can also prioritize photographing the packaging of products sold in nearby stores based on the user's location. Furthermore, the data collection unit can combine the user's location with their past purchase history to photograph highly relevant product packaging. This allows for the collection of highly relevant product information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant products.

[0041] The data collection unit can analyze a user's social media activity when photographing product packaging and photograph related products. For example, the data collection unit can prioritize photographing products mentioned by the user on social media. It can also photograph products that the user's social media followers are interested in. Furthermore, the data collection unit can identify and photograph trending products from the user's social media activity. In this way, information on related products can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select related products.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed ingredient analysis for expensive products. It can also perform a simplified ingredient analysis for products used daily. Furthermore, if a product contains a specific ingredient, the analysis unit can focus on that ingredient. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of cosmetics, the analysis unit can apply an analysis algorithm that emphasizes the impact on the skin. In the case of food products, the analysis unit can also apply an analysis algorithm that emphasizes nutritional value and safety. Furthermore, in the case of pharmaceuticals, the analysis unit can apply an analysis algorithm that emphasizes efficacy and side effects. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit will prioritize the analysis of new products or seasonal products. The analysis unit can also perform analysis quickly if the user is in a hurry. Furthermore, if the user needs the information by a specific deadline, the analysis unit can perform the analysis according to that deadline. This allows for faster information provision by prioritizing analysis based on the product submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product submission date data into a generating AI and have the generating AI determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit may prioritize the analysis of products of the same brand. It can also prioritize the analysis of products related to the user's areas of interest. Furthermore, the analysis unit may prioritize the analysis of highly relevant products based on the user's purchase history. By adjusting the order of analysis based on the relevance of products, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The service provider can analyze the user's past purchasing behavior and select the optimal service delivery method at the time of delivery. For example, the service provider can prioritize providing information on products that the user has previously preferred to purchase. The service provider can also select the optimal information delivery method based on the user's past purchasing behavior. Furthermore, the service provider can analyze the user's past purchasing behavior and provide information on related products. This makes it possible to provide optimal information by analyzing the user's past purchasing behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past purchasing behavior data into a generating AI and have the generating AI select the optimal service delivery method.

[0047] The information delivery unit can customize the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the information delivery unit can provide concise information. Alternatively, if the user is relaxed, the information delivery unit can provide detailed information. Furthermore, the information delivery unit can select the most appropriate means of delivery according to the user's living situation. This makes it possible to provide optimal information according to the user's living situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0048] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide information on popular products in that region. The service provider can also provide information on products available at nearby stores based on the user's location information. Furthermore, the service provider can combine the user's location information with their past purchase history to provide information on highly relevant products. This allows for the provision of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's location information data into a generating AI and have the generating AI select the optimal service delivery method.

[0049] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can prioritize providing information on products mentioned by the user on social media. It can also provide information on products that the user's social media followers are interested in. Furthermore, the service provider can provide information on trending products based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose a means of delivery.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can analyze the user's past shooting history and select the optimal shooting method. For example, the data collection unit can suggest the optimal shooting method based on the shooting angles and lighting conditions the user has used in the past. The data collection unit can also analyze the types of products the user has photographed in the past and suggest the optimal shooting method for similar products. Furthermore, the data collection unit can select the shooting method with the highest success rate from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing past shooting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0052] The data collection unit can filter product packaging images based on the user's current purchase history and areas of interest. For example, the data collection unit can prioritize photographing product packaging that the user is highly interested in based on their purchase history. The data collection unit can also filter and photograph relevant product packaging based on the user's areas of interest. Furthermore, the data collection unit can combine the user's purchase history and areas of interest to photograph the most relevant product packaging. This allows for the collection of highly relevant information by filtering based on the user's purchase history and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform the filtering.

[0053] The data collection unit can prioritize photographing highly relevant products by considering the user's geographical location when photographing product packaging. For example, if the user is in a specific region, the data collection unit will prioritize photographing the packaging of products popular in that region. The data collection unit can also prioritize photographing the packaging of products sold in nearby stores based on the user's location. Furthermore, the data collection unit can combine the user's location with their past purchase history to photograph highly relevant product packaging. This allows for the collection of highly relevant product information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant products.

[0054] The data collection unit can analyze a user's social media activity when photographing product packaging and photograph related products. For example, the data collection unit can prioritize photographing products mentioned by the user on social media. It can also photograph products that the user's social media followers are interested in. Furthermore, the data collection unit can identify and photograph trending products from the user's social media activity. In this way, information on related products can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select related products.

[0055] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed ingredient analysis for expensive products. It can also perform a simplified ingredient analysis for products used daily. Furthermore, if a product contains a specific ingredient, the analysis unit can focus on that ingredient. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0056] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of cosmetics, the analysis unit can apply an analysis algorithm that emphasizes the impact on the skin. In the case of food products, the analysis unit can also apply an analysis algorithm that emphasizes nutritional value and safety. Furthermore, in the case of pharmaceuticals, the analysis unit can apply an analysis algorithm that emphasizes efficacy and side effects. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit photographs the product packaging and collects information. For example, the data collection unit photographs the product packaging with a camera and saves it as image data. The data collection unit can also read barcodes and QR codes. For example, the data collection unit scans the barcode printed on the product packaging to obtain product information. Furthermore, the data collection unit can read text information written on the product packaging using OCR technology. For example, the data collection unit obtains the product's ingredient list and usage instructions as text data. Step 2: The analysis unit analyzes the information collected by the data collection unit to analyze the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. The analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit can assess the likelihood that a particular ingredient will cause allergies. Furthermore, the analysis unit can analyze the interactions between ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit can evaluate the effects when a particular ingredient is combined with other ingredients. Step 3: The service provider provides the analysis results obtained by the analysis unit. The service provider can, for example, provide the analysis results to the user as a text report. The service provider can also visually display the analysis results as graphs or charts. For example, the service provider can display the effects and risks of ingredients as graphs. Furthermore, the service provider can provide the analysis results to the user as alert notifications. For example, the service provider can display an alert if a particular ingredient may cause an allergy.

[0059] (Example of form 2) The product information provision system according to an embodiment of the present invention is a system that photographs product packaging, collects, analyzes, and provides information. This system allows users to instantly obtain detailed ingredient analysis and a comprehensive product evaluation simply by photographing product packaging. For example, the product information provision system works by having the user photograph product packaging. At this time, the system uses advanced optical character recognition (OCR) technology to read the information on the packaging. For example, when a cosmetic product packaging is photographed, the effects of each ingredient on the skin and their suitability for different skin types are displayed. In the case of food products, information is provided not only on nutritional value but also on the safety and sustainability of additives. Next, the system comprehensively analyzes the effects, potential risks, and allergy information of each ingredient by cross-referencing it with a vast database on the internet. For example, regarding cosmetic ingredients, the system analyzes the latest scientific research and consumer reviews to provide reliable information that is updated in real time. This allows consumers to understand the true value of products and make choices that align with their needs and values. This service goes beyond being a mere information provision tool; it functions as a comprehensive product advisor that enhances consumer health awareness and supports smarter purchasing decisions. For example, when a cosmetic product packaging is photographed, the effects of each ingredient on the skin and their suitability for different skin types are displayed. In the case of food products, information is provided not only on nutritional value but also on the safety and sustainability of additives. This system allows consumers to easily access detailed information and reviews that cannot be obtained from the limited information on product packaging alone. For example, regarding cosmetic ingredients, the system analyzes the latest scientific research and consumer reviews, providing reliable information that is updated in real time. This allows consumers to understand the true value of products and make choices that align with their needs and values. In this way, the product information system can raise consumer health awareness and support smarter purchasing decisions.

[0060] The product information provision system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit photographs the product packaging and collects information. For example, the collection unit photographs the product packaging with a camera and saves it as image data. The collection unit can also read barcodes and QR codes. For example, the collection unit scans a barcode printed on the product packaging to obtain product information. Furthermore, the collection unit can read text information written on the product packaging using OCR technology. For example, the collection unit obtains the product's ingredient list and usage instructions as text data. The analysis unit analyzes the information collected by the collection unit and analyzes the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. The analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit evaluates the possibility that a particular ingredient may cause allergies. Furthermore, the analysis unit can analyze the interactions of ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit evaluates the effects of specific components when combined with other components. The provision unit provides the analysis results obtained by the analysis unit. The provision unit provides the analysis results to the user as a text report, for example. The provision unit can also visually display the analysis results as graphs or charts. For example, the provision unit displays the effects and risks of components as graphs. Furthermore, the provision unit can provide the analysis results to the user as alert notifications. For example, the provision unit displays an alert if a specific component may cause an allergy. This allows the product information provision system according to the embodiment to provide consumers with detailed information about products. Some or all of the above-described processes in the collection unit, analysis unit, and provision unit may be performed using AI, for example, or without AI. For example, the collection unit can input image data obtained by photographing the product packaging into a generation AI and have the generation AI generate text data from the image data. The analysis unit can input the collected component information into a generation AI and have the generation AI evaluate the effects and risks of the components.The service provider can provide users with the analysis results obtained by the generating AI.

[0061] The data collection unit photographs product packaging and collects information. For example, the data collection unit photographs product packaging with a camera and saves it as image data. Specifically, the data collection unit uses a high-resolution camera to photograph the entire product packaging and acquire image data. This image data includes the product's appearance, design, and label information. The data collection unit can also read barcodes and QR codes. For example, the data collection unit scans barcodes printed on product packaging to acquire product information. Barcode scanners are used to quickly and accurately acquire detailed information about the product. Furthermore, the data collection unit can read text information written on product packaging using OCR technology. For example, the data collection unit acquires the product's ingredient list and usage instructions as text data. OCR technology is used to recognize characters in images and convert them into digital text. This allows the data collection unit to efficiently collect detailed product information and store it in a database. The data collection unit centrally manages this data and makes it accessible to the analysis and provisioning units. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, when a new product is launched on the market, the data collection unit can quickly collect data and update the entire system. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0062] The analysis unit analyzes the information collected by the data collection unit to analyze the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. Specifically, the analysis unit compares the collected ingredient information with a database to evaluate the scientific effects and efficacy of each ingredient. For example, it evaluates the impact of specific vitamins or minerals on health. The analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit evaluates the possibility that a particular ingredient may cause allergies. By analyzing consumer reviews and past cases, it is possible to perform risk assessments for specific ingredients. Furthermore, the analysis unit can analyze the interactions of ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit evaluates the effects when a particular ingredient is combined with other ingredients. This allows the analysis unit to quickly and accurately analyze the collected data and comprehensively evaluate the effects and risks of the ingredients. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past consumer reviews, it can predict consumer reactions to specific ingredients and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and safety of the system.

[0063] The service provider provides the analysis results obtained by the analysis provider. For example, the service provider provides the analysis results to the user as a text report. Specifically, the service provider generates a detailed text report of the analysis results and provides it to the user. This report includes detailed information on the effects and risks of the ingredients. The service provider can also visually display the analysis results as graphs and charts. For example, the service provider displays the effects and risks of the ingredients as a graph. Visual displays are effective in making the information easier for users to understand intuitively. Furthermore, the service provider can provide the analysis results to the user as alert notifications. For example, the service provider displays an alert if a particular ingredient may cause an allergy. Alert notifications help users quickly grasp important information and take appropriate action. By providing this information in various formats, the service provider makes it easy for users to obtain detailed product information. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the information provided. For example, based on user feedback, the content and display format of the report can be reviewed to provide more user-friendly information. The service provider can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to quickly and reliably provide information to users and obtain detailed product information.

[0064] The data collection unit can photograph product packaging and collect information. For example, the data collection unit can photograph product packaging with a camera and save it as image data. The data collection unit can also read barcodes and QR codes. For example, the data collection unit can scan a barcode printed on product packaging to obtain product information. Furthermore, the data collection unit can read text information written on product packaging using OCR technology. For example, the data collection unit can obtain the product's ingredient list and usage instructions as text data. This allows for obtaining detailed ingredient information by photographing product packaging and collecting information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input image data obtained by photographing product packaging into a generating AI and have the generating AI generate text data from the image data.

[0065] The analysis unit can analyze the collected information and assess the effects and risks of the ingredients. For example, the analysis unit can compare the collected ingredient information with scientific research data to evaluate the effects of the ingredients. The analysis unit can also assess the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit can assess the likelihood that a particular ingredient will cause allergies. Furthermore, the analysis unit can analyze the interactions between ingredients and assess the effects and risks when multiple ingredients are combined. For example, the analysis unit can assess the effects when a particular ingredient is combined with other ingredients. In this way, the effects and risks of ingredients can be understood by analyzing the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected ingredient information into a generating AI and have the generating AI perform the evaluation of the effects and risks of the ingredients.

[0066] The service provider can provide the user with the analysis results. For example, the service provider can provide the user with the analysis results as a text report. The service provider can also visually display the analysis results as graphs or charts. For example, the service provider can display the effects and risks of ingredients as graphs. Furthermore, the service provider can provide the user with the analysis results as alert notifications. For example, the service provider can display an alert if a particular ingredient may cause an allergy. By providing the user with the analysis results, the user can obtain detailed information about the product. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the user with analysis results obtained by a generating AI.

[0067] The analysis unit can analyze the effects, risks, and allergy information of ingredients by cross-referencing them with databases on the internet. For example, the analysis unit can evaluate the effects of ingredients by cross-referencing collected ingredient information with online scientific paper databases. The analysis unit can also evaluate the risks of ingredients by cross-referencing them with product databases. For example, the analysis unit can evaluate the likelihood that a particular ingredient will cause an allergy. Furthermore, the analysis unit can evaluate allergy information of ingredients based on medical data and consumer reports. For example, the analysis unit can evaluate the likelihood that a particular ingredient will cause an allergy. This allows for a comprehensive analysis of the effects, risks, and allergy information of ingredients by cross-referencing them with databases on the internet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected ingredient information into a generating AI and have the generating AI perform an evaluation of the effects and risks of the ingredients.

[0068] The service provider can analyze the latest scientific research and consumer reviews and provide information that is updated in real time. For example, the service provider can analyze the latest scientific research and evaluate the effects and risks of ingredients. The service provider can also analyze consumer reviews and evaluate ingredients. For example, the service provider can evaluate ingredients based on data from online review sites. Furthermore, the service provider can provide information that is updated in real time. For example, the service provider can acquire data regularly and provide the latest information. This allows the service provider to provide highly reliable information in real time by analyzing the latest scientific research and consumer reviews. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can provide users with analysis results obtained by generative AI.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of product packaging photography based on the estimated emotions. For example, if the user is excited, the data collection unit can speed up the photography timing to quickly collect information. Conversely, if the user is relaxed, the data collection unit can delay the photography timing to collect more detailed information. Furthermore, if the user is stressed, the data collection unit can adjust the photography timing to reduce the user's burden. This allows for more appropriate information collection by adjusting the photography timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0070] The data collection unit can analyze the user's past shooting history and select the optimal shooting method. For example, the data collection unit can suggest the optimal shooting method based on the shooting angles and lighting conditions the user has used in the past. The data collection unit can also analyze the types of products the user has photographed in the past and suggest the optimal shooting method for similar products. Furthermore, the data collection unit can select the shooting method with the highest success rate from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing past shooting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0071] The data collection unit can filter product packaging images based on the user's current purchase history and areas of interest. For example, the data collection unit can prioritize photographing product packaging that the user is highly interested in based on their purchase history. The data collection unit can also filter and photograph relevant product packaging based on the user's areas of interest. Furthermore, the data collection unit can combine the user's purchase history and areas of interest to photograph the most relevant product packaging. This allows for the collection of highly relevant information by filtering based on the user's purchase history and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform the filtering.

[0072] The data collection unit can estimate the user's emotions and determine the priority of products to photograph based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize photographing products that are of interest. If the user is relaxed, the data collection unit may also prioritize photographing products that require detailed information. Furthermore, if the user is stressed, the data collection unit may increase the priority of products that are easy to photograph. This allows for more appropriate information collection by prioritizing products based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] The data collection unit can prioritize photographing highly relevant products by considering the user's geographical location when photographing product packaging. For example, if the user is in a specific region, the data collection unit will prioritize photographing the packaging of products popular in that region. The data collection unit can also prioritize photographing the packaging of products sold in nearby stores based on the user's location. Furthermore, the data collection unit can combine the user's location with their past purchase history to photograph highly relevant product packaging. This allows for the collection of highly relevant product information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant products.

[0074] The data collection unit can analyze a user's social media activity when photographing product packaging and photograph related products. For example, the data collection unit can prioritize photographing products mentioned by the user on social media. It can also photograph products that the user's social media followers are interested in. Furthermore, the data collection unit can identify and photograph trending products from the user's social media activity. In this way, information on related products can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select related products.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis method for the effects and risks of the components based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed component analysis. If the user is in a hurry, the analysis unit can also perform a concise component analysis. Furthermore, if the user is excited, the analysis unit can perform a visually easy-to-understand component analysis. By adjusting the analysis method based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed ingredient analysis for expensive products. It can also perform a simplified ingredient analysis for products used daily. Furthermore, if a product contains a specific ingredient, the analysis unit can focus on that ingredient. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of cosmetics, the analysis unit can apply an analysis algorithm that emphasizes the impact on the skin. In the case of food products, the analysis unit can also apply an analysis algorithm that emphasizes nutritional value and safety. Furthermore, in the case of pharmaceuticals, the analysis unit can apply an analysis algorithm that emphasizes efficacy and side effects. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The analysis unit can determine the priority of analysis based on the product submission date during the analysis process. For example, the analysis unit will prioritize the analysis of new products or seasonal products. The analysis unit can also perform analysis quickly if the user is in a hurry. Furthermore, if the user needs the information by a specific deadline, the analysis unit can perform the analysis according to that deadline. This allows for faster information provision by prioritizing analysis based on the product submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product submission date data into a generating AI and have the generating AI determine the analysis priority.

[0080] The analysis unit can adjust the order of analysis based on the relevance of products during the analysis process. For example, the analysis unit may prioritize the analysis of products of the same brand. It can also prioritize the analysis of products related to the user's areas of interest. Furthermore, the analysis unit may prioritize the analysis of highly relevant products based on the user's purchase history. By adjusting the order of analysis based on the relevance of products, it becomes possible to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0081] The service provider can estimate the user's emotions and adjust the method of providing the analysis results based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the method of provision based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0082] The service provider can analyze the user's past purchasing behavior and select the optimal service delivery method at the time of delivery. For example, the service provider can prioritize providing information on products that the user has previously preferred to purchase. The service provider can also select the optimal information delivery method based on the user's past purchasing behavior. Furthermore, the service provider can analyze the user's past purchasing behavior and provide information on related products. This makes it possible to provide optimal information by analyzing the user's past purchasing behavior. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past purchasing behavior data into a generating AI and have the generating AI select the optimal service delivery method.

[0083] The information delivery unit can customize the means of delivery based on the user's current living situation at the time of delivery. For example, if the user is busy, the information delivery unit can provide concise information. Alternatively, if the user is relaxed, the information delivery unit can provide detailed information. Furthermore, the information delivery unit can select the most appropriate means of delivery according to the user's living situation. This makes it possible to provide optimal information according to the user's living situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of delivery.

[0084] The information provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is excited, the information provider will prioritize providing information that is of interest. If the user is relaxed, the information provider may also prioritize providing detailed information. Furthermore, if the user is stressed, the information provider may also prioritize providing concise information. This allows for more appropriate information to be provided by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0085] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific region, the service provider can provide information on popular products in that region. The service provider can also provide information on products available at nearby stores based on the user's location information. Furthermore, the service provider can combine the user's location information with their past purchase history to provide information on highly relevant products. This allows for the provision of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's location information data into a generating AI and have the generating AI select the optimal service delivery method.

[0086] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can prioritize providing information on products mentioned by the user on social media. It can also provide information on products that the user's social media followers are interested in. Furthermore, the service provider can provide information on trending products based on the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI propose a means of delivery.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection unit can estimate the user's emotions and adjust the timing of product packaging photography based on the estimated emotions. For example, if the user is excited, the data collection unit can speed up the photography timing to quickly collect information. Conversely, if the user is relaxed, the data collection unit can delay the photography timing to collect more detailed information. Furthermore, if the user is stressed, the data collection unit can adjust the photography timing to reduce the user's burden. This allows for more appropriate information collection by adjusting the photography timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0089] The data collection unit can analyze the user's past shooting history and select the optimal shooting method. For example, the data collection unit can suggest the optimal shooting method based on the shooting angles and lighting conditions the user has used in the past. The data collection unit can also analyze the types of products the user has photographed in the past and suggest the optimal shooting method for similar products. Furthermore, the data collection unit can select the shooting method with the highest success rate from the user's past shooting history. In this way, the optimal shooting method can be suggested by analyzing past shooting history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0090] The data collection unit can filter product packaging images based on the user's current purchase history and areas of interest. For example, the data collection unit can prioritize photographing product packaging that the user is highly interested in based on their purchase history. The data collection unit can also filter and photograph relevant product packaging based on the user's areas of interest. Furthermore, the data collection unit can combine the user's purchase history and areas of interest to photograph the most relevant product packaging. This allows for the collection of highly relevant information by filtering based on the user's purchase history and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform the filtering.

[0091] The data collection unit can estimate the user's emotions and determine the priority of products to photograph based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize photographing products that are of interest. If the user is relaxed, the data collection unit may also prioritize photographing products that require detailed information. Furthermore, if the user is stressed, the data collection unit may increase the priority of products that are easy to photograph. This allows for more appropriate information collection by prioritizing products based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0092] The data collection unit can prioritize photographing highly relevant products by considering the user's geographical location when photographing product packaging. For example, if the user is in a specific region, the data collection unit will prioritize photographing the packaging of products popular in that region. The data collection unit can also prioritize photographing the packaging of products sold in nearby stores based on the user's location. Furthermore, the data collection unit can combine the user's location with their past purchase history to photograph highly relevant product packaging. This allows for the collection of highly relevant product information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI select highly relevant products.

[0093] The data collection unit can analyze a user's social media activity when photographing product packaging and photograph related products. For example, the data collection unit can prioritize photographing products mentioned by the user on social media. It can also photograph products that the user's social media followers are interested in. Furthermore, the data collection unit can identify and photograph trending products from the user's social media activity. In this way, information on related products can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI select related products.

[0094] The analysis unit can estimate the user's emotions and adjust the analysis method for the effects and risks of the components based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed component analysis. If the user is in a hurry, the analysis unit can also perform a concise component analysis. Furthermore, if the user is excited, the analysis unit can perform a visually easy-to-understand component analysis. By adjusting the analysis method based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0095] The analysis unit can adjust the level of detail of the analysis based on the importance of the product during the analysis. For example, the analysis unit performs a detailed ingredient analysis for expensive products. It can also perform a simplified ingredient analysis for products used daily. Furthermore, if a product contains a specific ingredient, the analysis unit can focus on that ingredient. By adjusting the level of detail of the analysis based on the importance of the product, more appropriate analysis results can be obtained. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0096] The analysis unit can apply different analysis algorithms depending on the product category during analysis. For example, in the case of cosmetics, the analysis unit can apply an analysis algorithm that emphasizes the impact on the skin. In the case of food products, the analysis unit can also apply an analysis algorithm that emphasizes nutritional value and safety. Furthermore, in the case of pharmaceuticals, the analysis unit can apply an analysis algorithm that emphasizes efficacy and side effects. By applying different analysis algorithms depending on the product category, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input product category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection unit photographs the product packaging and collects information. For example, the data collection unit photographs the product packaging with a camera and saves it as image data. The data collection unit can also read barcodes and QR codes. For example, the data collection unit scans the barcode printed on the product packaging to obtain product information. Furthermore, the data collection unit can read text information written on the product packaging using OCR technology. For example, the data collection unit obtains the product's ingredient list and usage instructions as text data. Step 2: The analysis unit analyzes the information collected by the data collection unit to analyze the effects and risks of the ingredients. For example, the analysis unit compares the collected ingredient information with scientific research data to evaluate the effects of the ingredients. The analysis unit can also evaluate the risks of ingredients based on past cases and consumer reviews. For example, the analysis unit can assess the likelihood that a particular ingredient will cause allergies. Furthermore, the analysis unit can analyze the interactions between ingredients and evaluate the effects and risks when multiple ingredients are combined. For example, the analysis unit can evaluate the effects when a particular ingredient is combined with other ingredients. Step 3: The service provider provides the analysis results obtained by the analysis unit. The service provider can, for example, provide the analysis results to the user as a text report. The service provider can also visually display the analysis results as graphs or charts. For example, the service provider can display the effects and risks of ingredients as graphs. Furthermore, the service provider can provide the analysis results to the user as alert notifications. For example, the service provider can display an alert if a particular ingredient may cause an allergy.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0103] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 of the smart device 14 to photograph the product packaging and acquire image data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and evaluates the effects of the components by comparing the collected component information with scientific research data. The provision unit is implemented in the control unit 46A of the smart device 14, and provides the analysis results to the user as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, and data provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the smart glasses 214 to photograph the product packaging and acquire image data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the effects of the components by comparing the collected component information with scientific research data. The data provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the analysis results to the user as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 of the headset terminal 314 to photograph the product packaging and acquire image data. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and evaluates the effects of the components by comparing the collected component information with scientific research data. The provision unit is implemented in the control unit 46A of the headset terminal 314, and provides the analysis results to the user as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 of the robot 414 to photograph the product packaging and acquire image data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the effects of the components by comparing the collected component information with scientific research data. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the analysis results to the user as a text report or graph. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0161] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0162] 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.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0165] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The collection department photographs the product packaging and collects information, An analysis unit analyzes the information collected by the aforementioned collection unit and analyzes the effects and risks of the components, The system includes a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Photograph the product packaging and collect information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to determine the effects and risks of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the analysis results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze the effects, risks, and allergy information of the ingredients by cross-referencing them with online databases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We analyze the latest scientific research and consumer reviews to provide real-time updated information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of product packaging photography based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past shooting history and selects the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When photographing product packaging, filtering is performed based on the user's current purchase history and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of products to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When photographing product packaging, the system prioritizes photographing highly relevant products by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When photographing product packaging, analyze users' social media activity and photograph related products. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method for the effects and risks of the ingredients based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of product submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the products. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the method of providing analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, At the time of delivery, the optimal delivery method is selected by analyzing the user's past consumption behavior. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department photographs the product packaging and collects information, An analysis unit analyzes the information collected by the aforementioned collection unit and analyzes the effects and risks of the components, The system includes a providing unit that provides the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Photograph the product packaging and collect information. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to determine the effects and risks of the ingredients. The system according to feature 1.

4. The aforementioned supply unit is, Provide the analysis results to the user. The system according to feature 1.

5. The aforementioned analysis unit, We analyze the effects, risks, and allergy information of the ingredients by cross-referencing them with online databases. The system according to feature 1.

6. The aforementioned supply unit is, We analyze the latest scientific research and consumer reviews to provide real-time updated information. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of product packaging photography based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past shooting history and selects the optimal shooting method. The system according to feature 1.

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