system

Abstract

The system according to this embodiment aims to suggest Japanese food-related products to overseas consumers that are in line with their religion, food culture, and trends. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a clustering unit, and a prediction unit. The data collection unit collects the user's profile information, behavioral history, and purchase history. The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. The clustering unit performs clustering based on the data analyzed by the analysis unit. The prediction unit performs prediction and analysis based on the data clustered by the clustering unit and suggests the most suitable Japanese food-related products.

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 conventional technology, it has not been fully carried out to appropriately suggest Japanese food-related products that conform to religions, food cultures, and trends for overseas consumers, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest Japanese food-related products that conform to religions, food cultures, and trends for overseas consumers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a clustering unit, and a prediction unit. The data collection unit collects user profile information, behavioral history, and purchase history. The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. The clustering unit performs clustering based on the data analyzed by the analysis unit. The prediction unit performs prediction and analysis based on the data clustered by the clustering unit and suggests the most suitable Japanese food-related products. [Effects of the Invention]

[0007] The system according to this embodiment can suggest Japanese food-related products to overseas consumers that are in line with their religion, food culture, and trends. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 Japanese food matching system according to an embodiment of the present invention is a web platform for increasing opportunities for overseas consumers to enjoy Japanese food at home. This Japanese food matching system provides a service that suggests Japanese food-related products and their manufacturers to overseas buyers, tailored to their religion, food culture, and trends. The platform utilizes generative AI to suggest optimal Japanese food suppliers, manufacturers, menus, recipes, and ingredients in multiple languages ​​based on the user's profile information, behavioral history, and purchase history. This will increase the export value of agricultural, forestry, and fishery products and food, and stimulate domestic production. First, the user accesses the platform and enters their profile information, behavioral history, and purchase history. This information is analyzed by the generative AI, and Japanese food-related products and their manufacturers tailored to the user's religion, food culture, and trends are suggested. For example, a user with dietary restrictions based on a specific religion will be suggested Japanese food products suitable for those restrictions. Products based on specific regions and trends are also suggested. Next, the generative AI performs clustering based on the entered data and classifies the users into several groups. This system groups users with common attributes together and suggests the most suitable Japanese food products for each group. For example, a group of users living in a specific region will be suggested Japanese food products popular in that region. Furthermore, the generating AI performs predictions and analyses based on clustered data to suggest the most suitable Japanese food products for each user. For example, it can predict and suggest products that a user might be interested in based on their past purchase history and behavioral history. This platform will increase opportunities for overseas consumers to enjoy Japanese food at home, promoting the consumption of Japanese food in homes. It will also increase the export value of agricultural, forestry, and fishery products and food, stimulating domestic production. For example, users with dietary restrictions based on a specific religion will be suggested Japanese food products that are suitable for those restrictions. Products based on specific regions and trends will also be suggested. In this way, the Japanese food matching system can increase opportunities for overseas consumers to enjoy Japanese food at home, increase the export value of agricultural, forestry, and fishery products and food, and stimulate domestic production.

[0029] The Japanese food matching system according to this embodiment comprises a data collection unit, an analysis unit, a clustering unit, and a prediction unit. The data collection unit collects user profile information, behavioral history, and purchase history. For example, the data collection unit prompts the user to enter profile information when they access the platform. Profile information includes age, gender, occupation, hobbies, etc. The data collection unit can also track the user's behavioral history and collect website browsing history and app usage history. Furthermore, the data collection unit records the user's purchase history and collects information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. For example, the analysis unit uses generative AI to analyze the collected data. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. The analysis unit can consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. Furthermore, the analysis unit can suggest the most suitable Japanese food-related products based on the user's food culture and trends. The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. The clustering unit performs clustering based on common user attributes, for example, using generative AI. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. The clustering unit can classify users by region and suggest Japanese food products that are popular in that region. The prediction unit performs prediction and analysis based on the data clustered by the clustering unit, suggesting the most suitable Japanese food-related products. The prediction unit performs predictions based on past purchase history and behavioral history, for example, using generative AI. The prediction unit can predict and suggest products that the user is likely to be interested in. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's lifestyle and areas of interest. As a result, the Japanese food matching system according to this embodiment can suggest the most suitable Japanese food-related products based on the user's profile information, behavioral history, and purchase history.This will increase opportunities for users to enjoy Japanese food at home, boost exports of agricultural, forestry, and fishery products and food, and stimulate domestic production.

[0030] The data collection unit collects user profile information, behavioral history, and purchase history. For example, when a user accesses the platform, the unit prompts them to enter profile information. This profile information includes age, gender, occupation, and hobbies. The data collection unit can also track user behavioral history and collect website browsing history and app usage history. Specifically, it collects detailed data such as which pages the user viewed, which links they clicked, and how long they spent on each page. Furthermore, the data collection unit records user purchase history, collecting information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. This allows for a detailed understanding of user preferences and purchasing patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and clustering units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit ensures security by anonymizing and encrypting data to protect user privacy. This allows users to use the platform with peace of mind.

[0031] The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are tailored to the user's religion, food culture, and trends. The analysis unit analyzes the collected data using, for example, generative AI. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. Specifically, it identifies the user's preferred flavors, ingredients, and cooking methods based on the user's profile information, behavioral history, and purchase history. The analysis unit can also consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. For example, it can identify halal-certified products or vegetarian Japanese food products. Furthermore, the analysis unit can suggest the most suitable Japanese food-related products based on the user's food culture and trends. For example, it can identify Japanese food popular in a particular region or seasonal trend products. In addition, the analysis unit can predict and suggest products that the user may be interested in in the future based on their past purchase and behavioral history. As a result, the analysis unit can provide personalized suggestions tailored to the user's preferences and needs, improving user satisfaction.

[0032] The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. For example, the clustering unit uses generative AI to perform clustering based on common user attributes. Specifically, it groups users with high similarity based on data such as age, gender, occupation, hobbies, behavioral history, and purchase history. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. For example, it uses algorithms such as K-means or hierarchical clustering to cluster users. The clustering unit can classify users by region and suggest popular Japanese food products in those regions. For example, since users living in urban areas and those living in rural areas have different preferences for Japanese food and trends, it suggests products appropriate for each region. Furthermore, the clustering unit can perform more detailed clustering based on users' lifestyles and areas of interest. This allows the clustering unit to respond to diverse user needs and provide more personalized suggestions.

[0033] The prediction unit performs predictions and analyses based on data clustered by the clustering unit, and suggests the most suitable Japanese food-related products. For example, the prediction unit uses generative AI to make predictions based on past purchase history and behavioral history. Specifically, it analyzes trends in products the user has purchased in the past and patterns of products they have viewed, and predicts products they are likely to be interested in in the future. The prediction unit can predict and suggest products that the user is likely to be interested in. For example, it can suggest products that are suited to specific seasons or events, or products that are suitable for the user's lifestyle. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's living situation and areas of interest. For example, it can suggest low-calorie or organic Japanese food products to health-conscious users, and easy-to-prepare products to busy users. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, allowing it to respond to the latest situation. As a result, the prediction unit can always provide highly accurate suggestions based on the latest information, improving user satisfaction.

[0034] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the devices and apps the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the most efficient data collection timing. The data collection unit can also select the optimal data collection method (email, app notification, etc.) based on the user's past data collection history. This enables efficient data collection by selecting the optimal data collection method based on past behavior history. Some or all of the above processes 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 behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0035] The data collection unit can filter profile information, behavioral history, and purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect only relevant data based on topics the user is currently interested in. The data collection unit can also filter and collect appropriate data according to the user's lifestyle (e.g., traveling, working). The data collection unit can also collect only relevant purchase history based on the user's current purchasing behavior. This allows for the collection of highly relevant information by filtering data based on the user's lifestyle 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 the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0036] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information, behavioral history, and purchase history. For example, if a user is in a specific region, the data collection unit will prioritize the collection of behavioral history related to that region. The data collection unit can also prioritize the collection of relevant purchase history based on the user's current location. The data collection unit can also collect optimal profile information based on the user's geographical location. This allows for the priority collection of highly relevant 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 geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0037] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information, behavioral history, and purchase history. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant behavioral history. The data collection unit can also collect relevant purchase history based on a user's social media follow and like history. The data collection unit can also analyze the user's social media activity time and determine the optimal timing for data collection. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above-described processes 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 social media data into a generating AI and have the generating AI collect relevant information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of moderate importance. By adjusting the level of detail of the analysis based on the importance of the information, efficient analysis becomes possible. 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 importance data of the collected information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a personal characteristics analysis algorithm to profile information. The analysis unit can also apply a behavioral pattern analysis algorithm to behavioral history. The analysis unit can also apply a purchase behavior analysis algorithm to purchase history. By applying analysis algorithms according to the category of information, highly accurate analysis becomes possible. 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 information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. The analysis unit may also prioritize the analysis of information collected during a specific period. This enables efficient analysis by determining the priority of analysis based on when the information was collected. 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 information collection time data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information with high relevance. The analysis unit may also postpone the analysis of information with low relevance. The analysis unit may also prioritize the analysis of information related to a specific theme. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0042] The clustering unit can improve the accuracy of clustering by considering the interrelationships of information during clustering. For example, the clustering unit analyzes the interrelationships of information and classifies highly related information into the same cluster. The clustering unit can also adjust the clustering algorithm by considering the interrelationships of information. The clustering unit can also improve the accuracy of clustering based on the interrelationships of information. As a result, the accuracy of clustering is improved by considering the interrelationships of information. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without AI. For example, the clustering unit can input data on the interrelationships of information into a generating AI and have the generating AI perform the clustering accuracy improvement.

[0043] The clustering unit can perform clustering while considering the attribute information of the information provider. For example, the clustering unit performs clustering based on the attribute information of the information provider (age, gender, region, etc.). The clustering unit can also adjust the clustering algorithm while considering the attribute information of the information provider. The clustering unit can also improve the accuracy of clustering based on the attribute information of the information provider. As a result, the accuracy of clustering is improved by considering the attribute information of the information provider. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without using AI. For example, the clustering unit can input the attribute information data of the information provider into a generating AI and have the generating AI perform the clustering.

[0044] The clustering unit can perform clustering while considering the geographical distribution of information. For example, the clustering unit can perform clustering by region based on the geographical distribution of information. The clustering unit can also adjust the clustering algorithm while considering the geographical distribution of information. The clustering unit can also improve the accuracy of clustering based on the geographical distribution of information. This makes it possible to perform region-specific clustering by considering the geographical distribution of information. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without using AI. For example, the clustering unit can input geographical distribution data of information into a generating AI and have the generating AI perform the clustering.

[0045] The clustering unit can improve the accuracy of clustering by referring to relevant literature during the clustering process. For example, the clustering unit can adjust the clustering criteria by referring to relevant literature. The clustering unit can also improve the clustering algorithm based on the relevant literature. The clustering unit can improve the accuracy of clustering by referring to relevant literature. As a result, the accuracy of clustering is improved by referring to relevant literature. Some or all of the above processes in the clustering unit may be performed using AI, for example, or without AI. For example, the clustering unit can input relevant literature data into a generating AI and have the generating AI perform the clustering.

[0046] The prediction unit can analyze past behavioral history to select the optimal prediction method during prediction. For example, the prediction unit can select the most suitable prediction algorithm based on the user's past behavioral history. The prediction unit can also analyze the user's past behavioral patterns to determine the optimal prediction method. The prediction unit can also select an algorithm to improve prediction accuracy by referring to the user's past behavioral history. This enables highly accurate predictions by selecting the optimal prediction method based on past behavioral history. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal prediction method.

[0047] The prediction unit can customize its prediction methods based on the user's current living situation. For example, if the user is traveling, the prediction unit can predict the best Japanese food-related products for their travel destination. If the user is working, the prediction unit can also predict Japanese food-related products that can be enjoyed during work breaks. If the user is at home, the prediction unit can also predict Japanese food-related products that can be easily prepared at home. By providing prediction methods tailored to the user's living situation, more appropriate prediction results can be provided. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user living situation data into a generating AI and have the generating AI perform the customization of the prediction methods.

[0048] The prediction unit can select the optimal prediction method by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit will predict popular Japanese food-related products in that region. The prediction unit can also select the optimal prediction method based on the user's current location. The prediction unit can also select the optimal prediction algorithm based on the user's geographical location information. This allows for the provision of prediction results appropriate to the region by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal prediction method.

[0049] The prediction unit can analyze the user's social media activity and propose prediction methods during the prediction process. For example, the prediction unit can analyze the content of the user's social media posts and predict related Japanese food products. The prediction unit can also propose the optimal prediction method based on the user's social media follow and like history. The prediction unit can also analyze the user's social media activity time and determine the optimal prediction timing. In this way, by analyzing social media activity, it can provide relevant prediction methods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of prediction methods.

[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 Japanese food matching system can provide users with information on regional Japanese food events and festivals based on their geographical location. For example, if a user is in a specific region, it can provide information on Japanese food events held in that region. If a user is traveling, it can also provide information on Japanese food festivals and events in their travel destination. Furthermore, it can provide information on Japanese food-related workshops and cooking classes in the user's area of ​​residence. This allows users to have opportunities to participate in regional Japanese food events.

[0052] The Japanese food matching system can analyze a user's social media activity and suggest relevant Japanese food products. For example, if a user frequently posts about Japanese food on social media, the system can suggest relevant products based on the content of those posts. It can also suggest products that the user might be interested in based on the Japanese food accounts they follow and the posts they like. Furthermore, it can analyze the user's social media activity time to determine the optimal timing for suggestions. This enables optimal suggestions based on social media activity.

[0053] The Japanese food matching system can offer regular subscription services based on a user's past purchase history. For example, if a user regularly purchases a specific Japanese food product, the system can suggest a subscription service that delivers that product regularly. It can also offer a subscription box combining new products based on the user's past purchases. Furthermore, it can analyze the user's purchase history and offer subscription services that include seasonal recommendations. This allows users to enjoy Japanese food regularly.

[0054] The Japanese food matching system can provide personalized Japanese food gift suggestions based on the user's profile information. For example, it can suggest special Japanese food gifts to coincide with the user's birthday or anniversary. It can also suggest relevant Japanese food gifts based on the user's hobbies and interests. Furthermore, it can provide personalized gift suggestions based on information about the user's family and friends. This allows users to choose the perfect Japanese food gift for a special occasion.

[0055] The Japanese food matching system can provide information on Japanese restaurants in a specific region based on the user's geographical location. For example, if a user is in a particular area, it can provide information on popular Japanese restaurants in that area. If the user is traveling, it can also provide recommendations for Japanese restaurants in their travel destination. Furthermore, it can provide information on newly opened Japanese restaurants in the user's area. This makes it easy for users to find Japanese restaurants in their local area.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects user profile information, browsing history, and purchase history. For example, when a user accesses the platform, the data collection unit prompts them to enter profile information. Profile information includes age, gender, occupation, hobbies, etc. The data collection unit can also track user browsing history and collect website browsing history and app usage history. Furthermore, the data collection unit records the user's purchase history, collecting information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests Japanese food-related products and their manufacturers that are tailored to the user's religion, food culture, and trends. The analysis unit analyzes the collected data, for example, using generative AI. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. The analysis unit can consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. The analysis unit can also suggest the most suitable Japanese food-related products based on the user's food culture and trends. Step 3: The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. The clustering unit performs clustering based on common attributes of users, for example, using generative AI. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. The clustering unit can classify users by region and suggest popular Japanese food products in that region. Step 4: The prediction unit performs prediction and analysis based on the data clustered by the clustering unit and suggests the most suitable Japanese food-related products. The prediction unit, for example, uses generative AI to make predictions based on past purchase history and behavioral history. The prediction unit can predict and suggest products that the user is likely to be interested in. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's lifestyle and areas of interest.

[0058] (Example of form 2) The Japanese food matching system according to an embodiment of the present invention is a web platform for increasing opportunities for overseas consumers to enjoy Japanese food at home. This Japanese food matching system provides a service that suggests Japanese food-related products and their manufacturers to overseas buyers, tailored to their religion, food culture, and trends. The platform utilizes generative AI to suggest optimal Japanese food suppliers, manufacturers, menus, recipes, and ingredients in multiple languages ​​based on the user's profile information, behavioral history, and purchase history. This will increase the export value of agricultural, forestry, and fishery products and food, and stimulate domestic production. First, the user accesses the platform and enters their profile information, behavioral history, and purchase history. This information is analyzed by the generative AI, and Japanese food-related products and their manufacturers tailored to the user's religion, food culture, and trends are suggested. For example, a user with dietary restrictions based on a specific religion will be suggested Japanese food products suitable for those restrictions. Products based on specific regions and trends are also suggested. Next, the generative AI performs clustering based on the entered data and classifies the users into several groups. This system groups users with common attributes together and suggests the most suitable Japanese food products for each group. For example, a group of users living in a specific region will be suggested Japanese food products popular in that region. Furthermore, the generating AI performs predictions and analyses based on clustered data to suggest the most suitable Japanese food products for each user. For example, it can predict and suggest products that a user might be interested in based on their past purchase history and behavioral history. This platform will increase opportunities for overseas consumers to enjoy Japanese food at home, promoting the consumption of Japanese food in homes. It will also increase the export value of agricultural, forestry, and fishery products and food, stimulating domestic production. For example, users with dietary restrictions based on a specific religion will be suggested Japanese food products that are suitable for those restrictions. Products based on specific regions and trends will also be suggested. In this way, the Japanese food matching system can increase opportunities for overseas consumers to enjoy Japanese food at home, increase the export value of agricultural, forestry, and fishery products and food, and stimulate domestic production.

[0059] The Japanese food matching system according to this embodiment comprises a data collection unit, an analysis unit, a clustering unit, and a prediction unit. The data collection unit collects user profile information, behavioral history, and purchase history. For example, the data collection unit prompts the user to enter profile information when they access the platform. Profile information includes age, gender, occupation, hobbies, etc. The data collection unit can also track the user's behavioral history and collect website browsing history and app usage history. Furthermore, the data collection unit records the user's purchase history and collects information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. For example, the analysis unit uses generative AI to analyze the collected data. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. The analysis unit can consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. Furthermore, the analysis unit can suggest the most suitable Japanese food-related products based on the user's food culture and trends. The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. The clustering unit performs clustering based on common user attributes, for example, using generative AI. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. The clustering unit can classify users by region and suggest Japanese food products that are popular in that region. The prediction unit performs prediction and analysis based on the data clustered by the clustering unit, suggesting the most suitable Japanese food-related products. The prediction unit performs predictions based on past purchase history and behavioral history, for example, using generative AI. The prediction unit can predict and suggest products that the user is likely to be interested in. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's lifestyle and areas of interest. As a result, the Japanese food matching system according to this embodiment can suggest the most suitable Japanese food-related products based on the user's profile information, behavioral history, and purchase history.This will increase opportunities for users to enjoy Japanese food at home, boost exports of agricultural, forestry, and fishery products and food, and stimulate domestic production.

[0060] The data collection unit collects user profile information, behavioral history, and purchase history. For example, when a user accesses the platform, the unit prompts them to enter profile information. This profile information includes age, gender, occupation, and hobbies. The data collection unit can also track user behavioral history and collect website browsing history and app usage history. Specifically, it collects detailed data such as which pages the user viewed, which links they clicked, and how long they spent on each page. Furthermore, the data collection unit records user purchase history, collecting information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. This allows for a detailed understanding of user preferences and purchasing patterns. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and clustering units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. Furthermore, the data collection unit ensures security by anonymizing and encrypting data to protect user privacy. This allows users to use the platform with peace of mind.

[0061] The analysis unit analyzes the information collected by the data collection unit and suggests Japanese food-related products and their manufacturers that are tailored to the user's religion, food culture, and trends. The analysis unit analyzes the collected data using, for example, generative AI. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. Specifically, it identifies the user's preferred flavors, ingredients, and cooking methods based on the user's profile information, behavioral history, and purchase history. The analysis unit can also consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. For example, it can identify halal-certified products or vegetarian Japanese food products. Furthermore, the analysis unit can suggest the most suitable Japanese food-related products based on the user's food culture and trends. For example, it can identify Japanese food popular in a particular region or seasonal trend products. In addition, the analysis unit can predict and suggest products that the user may be interested in in the future based on their past purchase and behavioral history. As a result, the analysis unit can provide personalized suggestions tailored to the user's preferences and needs, improving user satisfaction.

[0062] The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. For example, the clustering unit uses generative AI to perform clustering based on common user attributes. Specifically, it groups users with high similarity based on data such as age, gender, occupation, hobbies, behavioral history, and purchase history. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. For example, it uses algorithms such as K-means or hierarchical clustering to cluster users. The clustering unit can classify users by region and suggest popular Japanese food products in those regions. For example, since users living in urban areas and those living in rural areas have different preferences for Japanese food and trends, it suggests products appropriate for each region. Furthermore, the clustering unit can perform more detailed clustering based on users' lifestyles and areas of interest. This allows the clustering unit to respond to diverse user needs and provide more personalized suggestions.

[0063] The prediction unit performs predictions and analyses based on data clustered by the clustering unit, and suggests the most suitable Japanese food-related products. For example, the prediction unit uses generative AI to make predictions based on past purchase history and behavioral history. Specifically, it analyzes trends in products the user has purchased in the past and patterns of products they have viewed, and predicts products they are likely to be interested in in the future. The prediction unit can predict and suggest products that the user is likely to be interested in. For example, it can suggest products that are suited to specific seasons or events, or products that are suitable for the user's lifestyle. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's living situation and areas of interest. For example, it can suggest low-calorie or organic Japanese food products to health-conscious users, and easy-to-prepare products to busy users. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, allowing it to respond to the latest situation. As a result, the prediction unit can always provide highly accurate suggestions based on the latest information, improving user satisfaction.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of data collection for profile information, behavioral history, and purchase history based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. If the user is excited, the data collection unit can also prioritize real-time data acquisition by performing an urgent collection. If the user is tired, the data collection unit can adjust the collection timing and collect data after the user has rested. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions. 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 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 facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0065] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can select the optimal data collection method based on the devices and apps the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and determine the most efficient data collection timing. The data collection unit can also select the optimal data collection method (email, app notification, etc.) based on the user's past data collection history. This enables efficient data collection by selecting the optimal data collection method based on past behavior history. Some or all of the above processes 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 behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0066] The data collection unit can filter profile information, behavioral history, and purchase history based on the user's current lifestyle and areas of interest. For example, the data collection unit can collect only relevant data based on topics the user is currently interested in. The data collection unit can also filter and collect appropriate data according to the user's lifestyle (e.g., traveling, working). The data collection unit can also collect only relevant purchase history based on the user's current purchasing behavior. This allows for the collection of highly relevant information by filtering data based on the user's lifestyle 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 the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0067] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed profile information. If the user is in a hurry, the data collection unit may also prioritize collecting behavioral history and purchase history. If the user is excited, the data collection unit may also prioritize collecting real-time behavioral history. This allows for the priority collection of important information by prioritizing information according to 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 emotion estimation.

[0068] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting profile information, behavioral history, and purchase history. For example, if a user is in a specific region, the data collection unit will prioritize the collection of behavioral history related to that region. The data collection unit can also prioritize the collection of relevant purchase history based on the user's current location. The data collection unit can also collect optimal profile information based on the user's geographical location. This allows for the priority collection of highly relevant 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 geographical location data into a generating AI and have the generating AI perform the collection of highly relevant information.

[0069] The data collection unit can analyze a user's social media activity and collect relevant information when collecting profile information, behavioral history, and purchase history. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant behavioral history. The data collection unit can also collect relevant purchase history based on a user's social media follow and like history. The data collection unit can also analyze the user's social media activity time and determine the optimal timing for data collection. This allows for the efficient collection of relevant information by analyzing social media activity. Some or all of the above-described processes 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 social media data into a generating AI and have the generating AI collect relevant information.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually appealing analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 a generative AI and have the generative AI perform emotion estimation.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a concise analysis on information of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on information of moderate importance. By adjusting the level of detail of the analysis based on the importance of the information, efficient analysis becomes possible. 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 importance data of the collected information into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a personal characteristics analysis algorithm to profile information. The analysis unit can also apply a behavioral pattern analysis algorithm to behavioral history. The analysis unit can also apply a purchase behavior analysis algorithm to purchase history. By applying analysis algorithms according to the category of information, highly accurate analysis becomes possible. 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 information category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is excited, the analysis unit can also provide visually appealing analysis results. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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 a generative AI and have the generative AI perform emotion estimation.

[0074] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. The analysis unit may also postpone the analysis of older information. The analysis unit may also prioritize the analysis of information collected during a specific period. This enables efficient analysis by determining the priority of analysis based on when the information was collected. 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 information collection time data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information with high relevance. The analysis unit may also postpone the analysis of information with low relevance. The analysis unit may also prioritize the analysis of information related to a specific theme. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0076] The clustering unit can estimate the user's emotions and adjust the clustering criteria based on the estimated emotions. For example, if the user is relaxed, the clustering unit can apply detailed clustering criteria. If the user is in a hurry, the clustering unit can also apply concise clustering criteria. If the user is excited, the clustering unit can also apply visually appealing clustering criteria. This allows for more appropriate clustering by adjusting the clustering criteria according to 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 clustering unit may be performed using AI, or not using AI. For example, the clustering unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The clustering unit can improve the accuracy of clustering by considering the interrelationships of information during clustering. For example, the clustering unit analyzes the interrelationships of information and classifies highly related information into the same cluster. The clustering unit can also adjust the clustering algorithm by considering the interrelationships of information. The clustering unit can also improve the accuracy of clustering based on the interrelationships of information. As a result, the accuracy of clustering is improved by considering the interrelationships of information. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without AI. For example, the clustering unit can input data on the interrelationships of information into a generating AI and have the generating AI perform the clustering accuracy improvement.

[0078] The clustering unit can perform clustering while considering the attribute information of the information provider. For example, the clustering unit performs clustering based on the attribute information of the information provider (age, gender, region, etc.). The clustering unit can also adjust the clustering algorithm while considering the attribute information of the information provider. The clustering unit can also improve the accuracy of clustering based on the attribute information of the information provider. As a result, the accuracy of clustering is improved by considering the attribute information of the information provider. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without using AI. For example, the clustering unit can input the attribute information data of the information provider into a generating AI and have the generating AI perform the clustering.

[0079] The clustering unit can estimate the user's emotions and adjust the order in which the clustering results are displayed based on the estimated user emotions. For example, if the user is relaxed, the clustering unit can display detailed clustering results. If the user is in a hurry, the clustering unit can also display concise clustering results. If the user is excited, the clustering unit can also display visually appealing clustering results. By adjusting the display order of clustering results according to the user's emotions, more appropriate results can be displayed. 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 clustering unit may be performed using AI, for example, or without AI. For example, the clustering unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The clustering unit can perform clustering while considering the geographical distribution of information. For example, the clustering unit can perform clustering by region based on the geographical distribution of information. The clustering unit can also adjust the clustering algorithm while considering the geographical distribution of information. The clustering unit can also improve the accuracy of clustering based on the geographical distribution of information. This makes it possible to perform region-specific clustering by considering the geographical distribution of information. Some or all of the above processing in the clustering unit may be performed using AI, for example, or without using AI. For example, the clustering unit can input geographical distribution data of information into a generating AI and have the generating AI perform the clustering.

[0081] The clustering unit can improve the accuracy of clustering by referring to relevant literature during the clustering process. For example, the clustering unit can adjust the clustering criteria by referring to relevant literature. The clustering unit can also improve the clustering algorithm based on the relevant literature. The clustering unit can improve the accuracy of clustering by referring to relevant literature. As a result, the accuracy of clustering is improved by referring to relevant literature. Some or all of the above processes in the clustering unit may be performed using AI, for example, or without AI. For example, the clustering unit can input relevant literature data into a generating AI and have the generating AI perform the clustering.

[0082] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is relaxed, the prediction unit can provide a detailed prediction result. If the user is in a hurry, the prediction unit can also provide a concise prediction result. If the user is excited, the prediction unit can also provide a visually appealing prediction result. By adjusting the prediction method according to the user's emotions, more appropriate prediction results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0083] The prediction unit can analyze past behavioral history to select the optimal prediction method during prediction. For example, the prediction unit can select the most suitable prediction algorithm based on the user's past behavioral history. The prediction unit can also analyze the user's past behavioral patterns to determine the optimal prediction method. The prediction unit can also select an algorithm to improve prediction accuracy by referring to the user's past behavioral history. This enables highly accurate predictions by selecting the optimal prediction method based on past behavioral history. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal prediction method.

[0084] The prediction unit can customize its prediction methods based on the user's current living situation. For example, if the user is traveling, the prediction unit can predict the best Japanese food-related products for their travel destination. If the user is working, the prediction unit can also predict Japanese food-related products that can be enjoyed during work breaks. If the user is at home, the prediction unit can also predict Japanese food-related products that can be easily prepared at home. By providing prediction methods tailored to the user's living situation, more appropriate prediction results can be provided. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user living situation data into a generating AI and have the generating AI perform the customization of the prediction methods.

[0085] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated emotions. For example, if the user is relaxed, the prediction unit may prioritize providing detailed prediction results. If the user is in a hurry, the prediction unit may also prioritize providing concise prediction results. If the user is excited, the prediction unit may also prioritize providing visually appealing prediction results. This allows for the prioritization of important prediction results by determining the priority of predictions according to 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 prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0086] The prediction unit can select the optimal prediction method by considering the user's geographical location information during prediction. For example, if the user is in a specific region, the prediction unit will predict popular Japanese food-related products in that region. The prediction unit can also select the optimal prediction method based on the user's current location. The prediction unit can also select the optimal prediction algorithm based on the user's geographical location information. This allows for the provision of prediction results appropriate to the region by considering the user's geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal prediction method.

[0087] The prediction unit can analyze the user's social media activity and propose prediction methods during the prediction process. For example, the prediction unit can analyze the content of the user's social media posts and predict related Japanese food products. The prediction unit can also propose the optimal prediction method based on the user's social media follow and like history. The prediction unit can also analyze the user's social media activity time and determine the optimal prediction timing. In this way, by analyzing social media activity, it can provide relevant prediction methods. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of prediction methods.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The Japanese food matching system can estimate the user's emotions and customize its suggestions based on those emotions. For example, if a user is feeling stressed, it can suggest relaxing Japanese food menus and recipes. If a user is excited, it can suggest exciting new products and trending Japanese foods. Furthermore, if a user is sad, it can suggest comforting, home-style Japanese food. This allows the system to provide the most suitable Japanese food-related products tailored to the user's emotions.

[0090] The Japanese food matching system can provide users with information on regional Japanese food events and festivals based on their geographical location. For example, if a user is in a specific region, it can provide information on Japanese food events held in that region. If a user is traveling, it can also provide information on Japanese food festivals and events in their travel destination. Furthermore, it can provide information on Japanese food-related workshops and cooking classes in the user's area of ​​residence. This allows users to have opportunities to participate in regional Japanese food events.

[0091] The Japanese food matching system can analyze a user's social media activity and suggest relevant Japanese food products. For example, if a user frequently posts about Japanese food on social media, the system can suggest relevant products based on the content of those posts. It can also suggest products that the user might be interested in based on the Japanese food accounts they follow and the posts they like. Furthermore, it can analyze the user's social media activity time to determine the optimal timing for suggestions. This enables optimal suggestions based on social media activity.

[0092] The Japanese food matching system can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, it can provide detailed descriptions and recipes. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is excited, it can provide suggestions using visually appealing images and videos. This enables the system to provide optimal suggestions tailored to the user's emotions.

[0093] The Japanese food matching system can offer regular subscription services based on a user's past purchase history. For example, if a user regularly purchases a specific Japanese food product, the system can suggest a subscription service that delivers that product regularly. It can also offer a subscription box combining new products based on the user's past purchases. Furthermore, it can analyze the user's purchase history and offer subscription services that include seasonal recommendations. This allows users to enjoy Japanese food regularly.

[0094] The Japanese food matching system can estimate the user's emotions and adjust the difficulty of recipes based on those emotions. For example, if the user is relaxed, it can provide a detailed recipe that can be prepared over a longer period of time. If the user is in a hurry, it can provide a simple recipe that can be prepared quickly. Furthermore, if the user is excited, it can provide a challenging recipe. This allows the system to provide the optimal recipe tailored to the user's emotions.

[0095] The Japanese food matching system can provide personalized Japanese food gift suggestions based on the user's profile information. For example, it can suggest special Japanese food gifts to coincide with the user's birthday or anniversary. It can also suggest relevant Japanese food gifts based on the user's hobbies and interests. Furthermore, it can provide personalized gift suggestions based on information about the user's family and friends. This allows users to choose the perfect Japanese food gift for a special occasion.

[0096] The Japanese food matching system can estimate the user's emotions and suggest cooking classes based on those emotions. For example, if the user is relaxed, it can suggest a cooking class that proceeds at a leisurely pace. If the user is in a hurry, it can suggest a cooking class that can be completed in a short time. Furthermore, if the user is excited, it can suggest a cooking class with exciting content. In this way, it can provide the optimal cooking class tailored to the user's emotions.

[0097] The Japanese food matching system can provide information on Japanese restaurants in a specific region based on the user's geographical location. For example, if a user is in a particular area, it can provide information on popular Japanese restaurants in that area. If the user is traveling, it can also provide recommendations for Japanese restaurants in their travel destination. Furthermore, it can provide information on newly opened Japanese restaurants in the user's area. This makes it easy for users to find Japanese restaurants in their local area.

[0098] The Japanese food matching system can estimate the user's emotions and provide information about the history and culture of Japanese food based on those emotions. For example, if the user is relaxed, it can provide detailed information about the history and culture. If the user is in a hurry, it can provide concise information. Furthermore, if the user is excited, it can provide information using visually appealing images and videos. This enables the provision of optimal information tailored to the user's emotions.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects user profile information, browsing history, and purchase history. For example, when a user accesses the platform, the data collection unit prompts them to enter profile information. Profile information includes age, gender, occupation, hobbies, etc. The data collection unit can also track user browsing history and collect website browsing history and app usage history. Furthermore, the data collection unit records the user's purchase history, collecting information such as the type of product purchased, the date and time of purchase, and the frequency of purchase. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests Japanese food-related products and their manufacturers that are tailored to the user's religion, food culture, and trends. The analysis unit analyzes the collected data, for example, using generative AI. The generative AI uses data mining techniques and statistical analysis methods to identify the user's preferences and needs. The analysis unit can consider the user's dietary restrictions based on their religion and suggest Japanese food products that are suitable for those restrictions. The analysis unit can also suggest the most suitable Japanese food-related products based on the user's food culture and trends. Step 3: The clustering unit performs clustering based on the data analyzed by the analysis unit, classifying users into several groups. The clustering unit performs clustering based on common attributes of users, for example, using generative AI. The clustering unit adjusts the similarity scale and the algorithm used to achieve optimal clustering. The clustering unit can classify users by region and suggest popular Japanese food products in that region. Step 4: The prediction unit performs prediction and analysis based on the data clustered by the clustering unit and suggests the most suitable Japanese food-related products. The prediction unit, for example, uses generative AI to make predictions based on past purchase history and behavioral history. The prediction unit can predict and suggest products that the user is likely to be interested in. The prediction unit can also suggest the most suitable Japanese food-related products based on the user's lifestyle and areas of interest.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the data collection unit, analysis unit, clustering unit, and prediction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects user profile information, behavioral history, and purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable Japanese food-related products to the user. The clustering unit is implemented by the specific processing unit 290 of the data processing unit 12 and groups users based on the analyzed data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs prediction and analysis based on the clustered data to suggest the most suitable Japanese food-related products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the data collection unit, analysis unit, clustering unit, and prediction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's profile information, behavioral history, and purchase history. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable Japanese food-related products to the user. The clustering unit is implemented by the identification processing unit 290 of the data processing unit 12 and groups users based on the analyzed data. The prediction unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs prediction and analysis based on the clustered data to suggest the most suitable Japanese food-related products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the data collection unit, analysis unit, clustering unit, and prediction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects user profile information, behavioral history, and purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable Japanese food-related products to the user. The clustering unit is implemented by the specific processing unit 290 of the data processing unit 12 and groups users based on the analyzed data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs prediction and analysis based on the clustered data to suggest the most suitable Japanese food-related products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the data collection unit, analysis unit, clustering unit, and prediction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects user profile information, behavioral history, and purchase history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to suggest the most suitable Japanese food-related products to the user. The clustering unit is implemented by the specific processing unit 290 of the data processing unit 12 and groups users based on the analyzed data. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs prediction and analysis based on the clustered data to suggest the most suitable Japanese food-related products. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A data collection unit that collects user profile information, behavioral history, and purchase history, The analysis unit analyzes the information collected by the aforementioned collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. A clustering unit performs clustering based on the data analyzed by the aforementioned analysis unit, The system includes a prediction unit that performs prediction and analysis based on the data clustered by the aforementioned clustering unit and suggests the most suitable Japanese food-related products. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting profile information, behavioral history, and purchase history based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, we analyze the user's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The clustering unit, It estimates user sentiment and adjusts clustering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The clustering unit, When clustering, improve the accuracy of clustering by considering the interrelationships between pieces of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The clustering unit, During clustering, the attribute information of the information provider is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The clustering unit, It estimates user sentiment and adjusts the order in which clustering results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The clustering unit, When clustering, consider the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The clustering unit, During clustering, referencing relevant literature to improve clustering accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, past behavioral history is analyzed to select the optimal prediction method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, During the prediction process, the prediction method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, the optimal prediction method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When making predictions, we analyze users' social media activity and propose methods for making predictions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 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. A data collection unit that collects user profile information, behavioral history, and purchase history, The analysis unit analyzes the information collected by the aforementioned collection unit and suggests Japanese food-related products and their manufacturers that are in line with the user's religion, food culture, and trends. A clustering unit performs clustering based on the data analyzed by the aforementioned analysis unit, The system includes a prediction unit that performs prediction and analysis based on the data clustered by the aforementioned clustering unit and suggests the most suitable Japanese food-related products. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of collecting profile information, behavioral history, and purchase history based on the estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting profile information, behavioral history, and purchase history, we analyze the user's social media activity and collect relevant information. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system according to feature 1.

10. The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system according to feature 1.

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