system

Abstract

The system according to this embodiment aims to improve the convenience for foreign tourists when using online shopping in Japan. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, a voice assistant unit, and a delivery unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit. The provision unit recommends products based on the analysis results obtained by the analysis unit. The voice assistant unit searches for products recommended by the provision unit using voice commands. The delivery unit delivers the products searched by the voice assistant unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, when foreign tourists use e-commerce in Japan, there are problems such as language barriers and difficulties in selecting products, resulting in low convenience.

[0005] The system according to the embodiment aims to improve the convenience when foreign tourists use e-commerce in Japan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a provision unit, a voice assistant unit, and a delivery unit. The data collection unit collects user data. The analysis unit analyzes the data collected by the data collection unit. The provision unit recommends products based on the analysis results obtained by the analysis unit. The voice assistant unit searches for the products recommended by the provision unit using voice commands. The delivery unit delivers the products searched by the voice assistant unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve the convenience for foreign tourists when using online shopping in Japan. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied 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 support application according to an embodiment of the present invention is a support application that utilizes generative AI to solve the problems that foreign tourists face when using online shopping in Japan. This support application is equipped with functions such as a multilingual chatbot, personalized product recommendations, a voice assistant, delivery schedule optimization support, automatic summarization of reviews, security and authentication, and UI / UX optimization. For example, the support application provides a multilingual chatbot, enabling users to use the application without feeling a language barrier. The chatbot, equipped with a real-time translation function, translates content entered in the user's native language into Japanese and, conversely, translates Japanese explanations into the user's native language. The chatbot is also available 24 hours a day and responds to questions about delivery status, product details, return procedures, etc., in multiple languages. Next, the support application provides a personalized product recommendation function, recommending highly relevant products based on the user's purchase history and browsing history. It analyzes behavioral data within the application and displays recommended products. Furthermore, the support application provides a voice assistant function, enabling users to search for desired products and categories by voice. The conversational assistant supports each step from product selection to purchase and delivery procedures through voice dialogue. The service also provides a delivery schedule optimization support function, where AI automatically suggests the optimal delivery date and time based on the user's length of stay. It tracks the delivery status in real time and notifies the user of the latest information. It also features an automatic review summarization function, where AI analyzes numerous reviews, extracts key points, and displays an easy-to-understand summary. Reviews in other languages ​​are also automatically translated into the user's native language. Security and authentication functions are also provided, using AI facial recognition technology to verify the user's identity when receiving packages at hotels. If abnormal activity is detected, the user is immediately notified. Finally, it provides a UI / UX optimization function, where AI optimizes the design and layout of the user interface to improve usability. As a result, it makes it easier for foreign tourists to use online shopping in Japan, resolving issues such as language barriers, payment methods, delivery problems, difficulty in choosing products, and lack of customer support. Consequently, a comfortable shopping experience is provided, contributing to the revitalization of the local economy.This will allow the support app to address the challenges foreign tourists face when using online shopping in Japan, providing a more comfortable shopping experience.

[0029] The support application according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a voice assistant unit, and a delivery unit. The collection unit collects user data. User data includes, but is not limited to, purchase history, browsing history, and location information. For example, the collection unit collects the user's purchase history. The collection unit can collect a list of products the user has purchased in the past and the date and time of purchase. The collection unit can also collect the user's browsing history. The collection unit can collect a list of products the user has viewed and the date and time of viewing. Furthermore, the collection unit can also collect the user's location information. The collection unit can collect GPS data and address information. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can analyze past purchase history and browsing history to determine the user's preferences. For example, the analysis unit can analyze patterns of products the user has purchased in the past to determine the user's preferences. The analysis unit can also analyze the user's browsing history to determine the user's interests and preferences. Furthermore, the analysis unit can analyze survey results and determine user preferences. The supply unit recommends products based on the analysis results obtained by the analysis unit. For example, the supply unit recommends highly relevant products based on user preferences. The supply unit can also recommend products based on the user's past purchase and browsing history. For example, the supply unit recommends related products based on the categories of products the user has purchased in the past. The supply unit can also recommend products based on the user's interests and preferences. Furthermore, the supply unit can also recommend products based on the user's survey results. The voice assistant unit searches for desired products and categories by voice. For example, the voice assistant unit performs voice searches using voice recognition technology and natural language processing technology. The voice assistant unit can analyze what the user inputs by voice and search for related products and categories. For example, if the user inputs "I'm looking for a smartphone" by voice, the voice assistant unit will search for products related to smartphones.Furthermore, the voice assistant unit can search for camera-related products if the user voice-inputs "I'm looking for a camera." Additionally, if the user voice-inputs "I'm looking for travel supplies," the voice assistant unit can search for travel supplies-related products. The delivery unit delivers the products searched for by the voice assistant unit. The delivery unit can, for example, suggest the optimal delivery date and time based on the user's length of stay. The delivery unit can automatically suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery unit considers the user's length of stay in Japan and suggests a delivery date and time so that the product arrives during their stay. The delivery unit can also track the delivery status in real time and notify the user of the latest information. The delivery unit can integrate with the delivery company's system to obtain real-time delivery status information and notify the user. For example, the delivery unit can notify the user of the product's shipping status and estimated delivery date and time. This enables the support application according to the embodiment to efficiently collect, analyze, recommend products, perform voice searches, and deliver products based on user data. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's purchase history into the AI ​​and have the AI ​​perform the collection of purchase history. The analysis unit can input the data collected by the collection unit into the AI ​​and have the AI ​​perform the data analysis. The provision unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform product recommendations. The voice assistant unit can input the user's voice input into the AI ​​and have the AI ​​perform voice searches. The delivery unit can input the products searched by the voice assistant unit into the AI ​​and have the AI ​​perform delivery date and time suggestions.

[0030] The data collection unit collects user data. User data includes, but is not limited to, purchase history, browsing history, and location information. For example, the data collection unit can collect a user's purchase history. The data collection unit can collect a list of products the user has previously purchased and the date and time of purchase. The data collection unit can also collect a user's browsing history. The data collection unit can collect a list of products the user has viewed and the date and time of viewing. Furthermore, the data collection unit can also collect the user's location information. The data collection unit can collect GPS data and address information. This allows the data collection unit to understand the user's behavior and preferences in detail. For example, the data collection unit can collect details of products the user has purchased at a specific store and browsing history on online shopping sites to clarify the user's purchasing patterns. The data collection unit can also collect the user's location information in real time to understand where the user is located. This allows for analysis of what products the user is interested in in a particular area. Furthermore, the data collection unit can collect sensor data from the user's device to understand the user's environment and behavior in detail. For example, the accelerometer and gyroscope sensors in a smartphone can be used to collect data on the user's movement patterns and activity levels. This allows the data collection unit to gain a detailed understanding of the user's lifestyle and behavioral patterns, and provide more accurate data.

[0031] The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the data using methods such as statistical analysis and machine learning algorithms. To determine user preferences, the analysis department can analyze past purchase and browsing history. For example, it can analyze patterns of products a user has purchased in the past to determine their preferences. It can also analyze a user's browsing history to determine their interests and preferences. Furthermore, it can analyze survey results to determine user preferences. Specifically, the analysis department uses machine learning algorithms to extract features from a user's purchase and browsing history and model their preferences. For example, it can use clustering algorithms to classify users into groups with similar preferences and analyze the characteristics of each group. It can also use collaborative filtering to compare data from other users and predict the behavior of users with similar preferences. Furthermore, it can use natural language processing techniques to analyze user survey results and review comments to extract user emotions and opinions. This allows the analysis department to accurately determine user preferences and interests and build a foundation for providing personalized services.

[0032] The service department recommends products based on the analysis results obtained by the analysis department. For example, the service department recommends highly relevant products based on the user's preferences. The service department can also recommend products based on the user's past purchase and browsing history. For example, the service department recommends related products based on the categories of products the user has purchased in the past. The service department can also recommend products based on the user's interests and preferences. Furthermore, the service department can recommend products based on the results of user surveys. Specifically, the service department selects the most suitable products for the user using the user preference model provided by the analysis department. For example, it can use a recommendation engine to recommend products that match the user's preferences in real time. Furthermore, the service department can recommend products that are appropriate to the user's current situation and environment. For example, if the user is in a specific region, it can recommend popular products and services in that region. In addition, the service department can predict products that the user may be interested in in the future based on the user's past behavioral data and recommend them proactively. This allows the service department to always provide the user with the most suitable products and improve user satisfaction.

[0033] The voice assistant unit searches for desired products and categories using voice commands. The voice assistant unit performs voice searches using technologies such as speech recognition and natural language processing. It analyzes the user's voice input and searches for related products and categories. For example, if the user says "I'm looking for a smartphone," the voice assistant unit will search for smartphone-related products. Similarly, if the user says "I'm looking for a camera," it can search for camera-related products. Furthermore, if the user says "I'm looking for travel goods," it can search for travel goods-related products. Specifically, the voice assistant unit uses a speech recognition engine to convert the user's voice into text and natural language processing technology to analyze the meaning of the text. For example, if the user says "I want a new smartphone," the voice assistant unit extracts the keyword "smartphone" and searches for related products. The voice assistant unit can also provide more accurate search results by considering the user's past search and purchase history. Additionally, the voice assistant unit can read out detailed product information and reviews in response to the user's voice commands. This allows the voice assistant to provide users with an intuitive and easy-to-use interface, improving the efficiency of product searches.

[0034] The delivery department delivers products searched by the voice assistant department. The delivery department can, for example, suggest the optimal delivery date and time based on the user's length of stay. The delivery department can automatically suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery department will consider the length of the user's stay in Japan and suggest a delivery date and time so that the product arrives during their stay. The delivery department can also track the delivery status in real time and notify the user of the latest information. The delivery department can integrate with the delivery company's system to obtain delivery status in real time and notify the user. For example, the delivery department will notify the user of the product's shipping status and estimated delivery date and time. Specifically, the delivery department calculates the optimal delivery date and time considering the user's accommodation information and schedule. For example, if the user is staying at a specific hotel, the delivery department will adjust the delivery date and time to ensure that the product is received, taking into account the hotel's check-in and check-out times. The delivery department can also use the delivery company's API to obtain delivery status in real time and notify the user. For example, it will notify the user when the product has been shipped and will immediately notify them if the estimated delivery date and time changes. Furthermore, the delivery department can easily handle changes to delivery dates and times, as well as redelivery requests, according to the user's wishes. This allows the delivery department to provide users with a flexible and convenient delivery service, ensuring smooth receipt of goods.

[0035] The data collection unit can collect the user's purchase history and browsing history. For example, the data collection unit can collect a list of products the user has purchased in the past and the date and time of purchase. The data collection unit can also collect a list of products the user has viewed and the date and time of viewing. By collecting the user's purchase history and browsing history, the data collection unit can provide more personalized services. 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 purchase history into AI and have AI perform the collection of the purchase history.

[0036] The analysis unit can analyze the data collected by the collection unit to determine user preferences. For example, the analysis unit can analyze past purchase history and browsing history to determine user preferences. By determining user preferences, the analysis unit can recommend more appropriate products. 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 data collected by the collection unit into AI and have AI perform the data analysis.

[0037] The supply unit can recommend highly relevant products based on the analysis results obtained by the analysis unit. For example, the supply unit recommends highly relevant products based on the user's preferences. By recommending highly relevant products, the supply unit improves user satisfaction. Some or all of the above processing in the supply unit may be performed using AI, or not using AI. For example, the supply unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform product recommendations.

[0038] The voice assistant unit allows users to search for desired products or categories by voice. The voice assistant unit performs voice searches using, for example, speech recognition technology and natural language processing technology. The voice assistant unit improves user convenience by allowing users to search for products and categories by voice. Some or all of the above-described processes in the voice assistant unit may be performed using, for example, AI, or not using AI. For example, the voice assistant unit can input the user's voice into the AI ​​and have the AI ​​perform a voice search.

[0039] The delivery department can suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery department can automatically suggest the optimal delivery date and time based on the user's length of stay. By suggesting the optimal delivery date and time based on the user's length of stay, the delivery department improves delivery efficiency. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's length of stay into the AI ​​and have the AI ​​suggest the optimal delivery date and time.

[0040] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on relevant products based on the categories of products the user has purchased in the past. The data collection unit can analyze the user's purchase frequency and focus on collecting data on frequently purchased products. The data collection unit can prioritize collecting data on specific brands or stores from the user's purchase history. This allows the optimal data collection method to be selected by analyzing the user's past purchase 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 purchase history into AI and have the AI ​​select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related product data. If the user is interested in health, the data collection unit can prioritize collecting health-related product data. If the user is participating in a specific event, the data collection unit can prioritize collecting product data related to that event. This allows for the collection of highly relevant data 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 data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is staying in a specific area, the data collection unit can prioritize the collection of data on stores and services in that area. If the user is in a tourist destination, the data collection unit can prioritize the collection of data on products and services related to that tourist destination. If the user is on the move, the data collection unit can prioritize the collection of data related to the destination area. In this way, highly relevant data can be collected by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to products and services that the user has shared on social media. The data collection unit can collect data related to brands and influencers that the user follows. The data collection unit can collect data related to topics that the user has shown interest in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have AI perform the collection of relevant data.

[0044] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns when analyzing data. For example, the analysis unit can adjust its analysis algorithm based on patterns of products the user has purchased in the past. The analysis unit can refer to the user's past browsing history and prioritize the analysis of highly relevant data. The analysis unit can predict future behavior from the user's past behavior patterns and optimize its analysis algorithm. In this way, the analysis algorithm can be optimized by referring to the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into AI and have the AI ​​perform the optimization of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis based on user attribute information during data analysis. For example, the analysis unit can adjust its analysis algorithm based on the user's age and gender. The analysis unit can prioritize the analysis of highly relevant data based on the user's interests. The analysis unit can improve the accuracy of its analysis based on the user's purchase history and browsing history. By improving the accuracy of the analysis based on user attribute information, it can provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI and have the AI ​​perform the analysis accuracy improvement.

[0046] The analysis unit can perform data analysis while considering the geographical distribution of users. For example, if a user is staying in a particular region, the analysis unit will prioritize the analysis of data from that region. Based on the geographical distribution of users, the analysis unit can analyze regional trends. The analysis unit can analyze highly relevant data by considering the movement patterns of users. This allows for the analysis of regional trends by considering the geographical distribution of users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into AI and have the AI ​​perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during data analysis. For example, the analysis unit can optimize its analysis algorithms by referring to relevant academic papers. The analysis unit can improve the accuracy of its analysis by referring to industry best practices. The analysis unit can increase the reliability of its analysis results by referring to other research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature into AI and have AI perform the analysis accuracy improvement.

[0048] The recommendation unit can adjust the level of detail in product recommendations based on the importance of the product. For example, for expensive products, the recommendation unit can provide recommendations that include detailed descriptions and reviews. For everyday products, the recommendation unit can provide recommendations that include concise descriptions. For new products, the recommendation unit can provide recommendations that highlight their features. By adjusting the level of detail in recommendations based on the importance of the product, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input product importance data into AI and have the AI ​​perform the recommendation detail adjustment.

[0049] The recommendation unit can apply different recommendation algorithms depending on the product category when recommending products. For example, in the case of fashion products, the recommendation unit can make recommendations based on trend information. In the case of electronic devices, the recommendation unit can make recommendations based on technical features. In the case of food products, the recommendation unit can make recommendations based on user preferences. By applying different recommendation algorithms depending on the product category, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product category data into AI and have the AI ​​perform the application of the recommendation algorithm.

[0050] The recommendation unit can determine the priority of product recommendations based on the product submission date. For example, the recommendation unit can prioritize new products. It can also prioritize seasonal or limited-time products. It can prioritize products on sale or discounted products. By determining the priority of recommendations based on the product submission date, more appropriate product recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product submission date data into AI and have the AI ​​perform the recommendation priority determination.

[0051] The recommendation unit can adjust the order of recommendations based on product relevance when recommending products. For example, the recommendation unit can prioritize recommending highly relevant products based on the user's past purchase history. The recommendation unit can prioritize recommending highly relevant products based on the user's browsing history. The recommendation unit can prioritize recommending highly relevant products based on the user's interests. By adjusting the order of recommendations based on product relevance, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product relevance data into AI and have the AI ​​perform the recommendation order adjustment.

[0052] The voice assistant unit can provide the most appropriate response during voice searches by referring to the user's past search history. For example, the voice assistant unit can provide relevant responses based on products or categories the user has searched for in the past. The voice assistant unit can prioritize responses using frequently searched keywords from the user's past search history. The voice assistant unit can analyze the user's past search patterns and provide the most relevant responses. In this way, the voice assistant unit can provide the most appropriate response by referring to the user's past search history. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's past search history data into AI and have the AI ​​execute the most appropriate response.

[0053] The voice assistant unit can improve the accuracy of its responses during voice searches based on the user's attribute information. For example, the voice assistant unit can provide the most appropriate response based on the user's age and gender. The voice assistant unit can provide highly relevant responses based on the user's interests and preferences. The voice assistant unit can improve the accuracy of its responses based on the user's purchase and browsing history. By improving the accuracy of responses based on the user's attribute information, more accurate responses become possible. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's attribute information into AI and have the AI ​​perform the task of improving the accuracy of its responses.

[0054] The voice assistant unit can provide the most appropriate response during voice searches by taking into account the user's geographical location. For example, if the user is staying in a specific area, the voice assistant unit can provide responses related to shops and services in that area. If the user is in a tourist destination, the voice assistant unit can prioritize providing information related to that tourist destination. If the user is on the move, the voice assistant unit can provide information related to the destination area. In this way, the voice assistant unit can provide the most appropriate response by taking into account the user's geographical location. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's geographical location information into the AI ​​and have the AI ​​execute the most appropriate response.

[0055] The voice assistant unit can improve the accuracy of its responses by referring to relevant literature during voice searches. For example, the voice assistant unit can refer to relevant academic papers to improve the accuracy of its responses. The voice assistant unit can refer to industry best practices to provide optimal responses. The voice assistant unit can refer to other research results to increase the reliability of its responses. This allows for improved response accuracy by referring to relevant literature. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input relevant literature into AI and have the AI ​​perform the task of improving response accuracy.

[0056] The delivery department can provide optimal suggestions by referring to the user's past delivery history when proposing delivery dates and times. For example, the delivery department can provide optimal suggestions based on the delivery dates and times the user has used in the past. The delivery department can prioritize suggesting frequently used dates and times from the user's past delivery history. The delivery department can analyze the user's past delivery patterns and provide the most efficient suggestions. In this way, it can provide optimal suggestions by referring to the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's past delivery history data into AI and have the AI ​​execute optimal suggestions.

[0057] The delivery department can improve the accuracy of its delivery date and time suggestions based on user attribute information. For example, the delivery department can provide optimal suggestions based on the user's age and gender. The delivery department can provide highly relevant suggestions based on the user's interests and preferences. The delivery department can improve the accuracy of its suggestions based on the user's purchase and browsing history. By improving the accuracy of suggestions based on user attribute information, more accurate suggestions become possible. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input user attribute information into AI and have the AI ​​perform the task of improving the accuracy of suggestions.

[0058] The delivery department can provide optimal suggestions when proposing delivery dates and times, taking into account the user's geographical location. For example, if the user is staying in a particular area, the delivery department can prioritize delivery options for that area. If the user is in a tourist destination, the delivery department can provide delivery options related to that tourist destination. If the user is on the move, the delivery department can provide delivery options related to the destination area. In this way, the delivery department can provide optimal suggestions by taking into account the user's geographical location. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's geographical location information into AI and have the AI ​​make optimal suggestions.

[0059] The delivery department can improve the accuracy of its delivery date and time suggestions by referring to relevant literature. For example, the delivery department can improve the accuracy of its suggestions by referring to relevant academic papers. The delivery department can provide optimal suggestions by referring to industry best practices. The delivery department can increase the reliability of its suggestions by referring to other research results. In this way, the accuracy of suggestions can be improved by referring to relevant literature. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input relevant literature into AI and have the AI ​​perform the improvement of the accuracy of its suggestions.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The support app can have the functionality to learn user preferences and predict future purchasing behavior based on the user's purchase and browsing history. For example, the data collection unit can analyze patterns of products the user has purchased in the past and predict products the user is likely to purchase next. The analysis unit can analyze the user's browsing history and identify new products that the user may be interested in. The delivery unit can then provide personalized product recommendations to the user based on these predictions. This is expected to improve the user's purchasing experience and increase the frequency of app usage.

[0062] The support app can have a function that automatically notifies users of events and campaigns that might interest them, based on their purchase and browsing history. For example, the data collection unit collects data on events and campaigns that the user has participated in in the past. The analysis unit can analyze this data to identify new events and campaigns that might interest the user. The delivery unit can notify the user of these events and campaigns and encourage their participation. This enables the provision of information based on the user's interests, improving user engagement.

[0063] A support app can have a function that automatically suggests new products and services that a user might be interested in, based on their purchase and browsing history. For example, the data collection unit collects data on products the user has purchased in the past. The analysis unit analyzes this data and can identify new products and services that the user might be interested in. The delivery unit can then suggest these new products and services to the user and encourage them to purchase them. This enables product suggestions based on the user's interests, thereby increasing the user's willingness to buy.

[0064] Support apps can have a function that automatically provides content that users are likely to be interested in, based on their purchase and browsing history. For example, the data collection unit collects data on products that users have previously viewed. The analysis unit can analyze this data to identify articles and videos that users are likely to be interested in. The delivery unit can provide this content to users and encourage them to view it. This enables content delivery based on user interests and improves user engagement.

[0065] The support app can have a function that automatically provides coupons and discount information that might interest the user, based on their purchase and browsing history. For example, the data collection unit collects data on products the user has purchased in the past. The analysis unit analyzes this data and can identify coupons and discount information that might interest the user. The provision unit can provide this coupon and discount information to the user and encourage their use. This makes it possible to provide coupons and discount information based on the user's interests, thereby increasing the user's willingness to purchase.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user data. User data includes purchase history, browsing history, and location information. For example, the data collection unit collects the user's purchase history, including a list of previously purchased items and the date and time of purchase. The data collection unit also collects the user's browsing history, including a list of viewed items and the date and time of viewing. Furthermore, the data collection unit collects the user's location information, including GPS data and address information. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department uses statistical analysis and machine learning algorithms to analyze the data and determine user preferences. For example, the analysis department analyzes past purchase and browsing history to determine user preferences, interests, and concerns. It can also analyze survey results to determine user preferences. Step 3: The supply department recommends products based on the analysis results obtained by the analysis department. The supply department recommends highly relevant products based on the user's preferences, as well as products based on past purchase history, browsing history, and survey results. Step 4: The voice assistant unit searches for the product or category desired by the user using voice. The voice assistant unit performs voice searches using speech recognition technology and natural language processing technology, analyzing what the user inputs by voice to find relevant products and categories. Step 5: The delivery department delivers the items searched for by the voice assistant department. The delivery department suggests the optimal delivery date and time based on the user's length of stay, tracks the delivery status in real time, and notifies the user of the latest information.

[0068] (Example of form 2) The support application according to an embodiment of the present invention is a support application that utilizes generative AI to solve the problems that foreign tourists face when using online shopping in Japan. This support application is equipped with functions such as a multilingual chatbot, personalized product recommendations, a voice assistant, delivery schedule optimization support, automatic summarization of reviews, security and authentication, and UI / UX optimization. For example, the support application provides a multilingual chatbot, enabling users to use the application without feeling a language barrier. The chatbot, equipped with a real-time translation function, translates content entered in the user's native language into Japanese and, conversely, translates Japanese explanations into the user's native language. The chatbot is also available 24 hours a day and responds to questions about delivery status, product details, return procedures, etc., in multiple languages. Next, the support application provides a personalized product recommendation function, recommending highly relevant products based on the user's purchase history and browsing history. It analyzes behavioral data within the application and displays recommended products. Furthermore, the support application provides a voice assistant function, enabling users to search for desired products and categories by voice. The conversational assistant supports each step from product selection to purchase and delivery procedures through voice dialogue. The service also provides a delivery schedule optimization support function, where AI automatically suggests the optimal delivery date and time based on the user's length of stay. It tracks the delivery status in real time and notifies the user of the latest information. It also features an automatic review summarization function, where AI analyzes numerous reviews, extracts key points, and displays an easy-to-understand summary. Reviews in other languages ​​are also automatically translated into the user's native language. Security and authentication functions are also provided, using AI facial recognition technology to verify the user's identity when receiving packages at hotels. If abnormal activity is detected, the user is immediately notified. Finally, it provides a UI / UX optimization function, where AI optimizes the design and layout of the user interface to improve usability. As a result, it makes it easier for foreign tourists to use online shopping in Japan, resolving issues such as language barriers, payment methods, delivery problems, difficulty in choosing products, and lack of customer support. Consequently, a comfortable shopping experience is provided, contributing to the revitalization of the local economy.This will allow the support app to address the challenges foreign tourists face when using online shopping in Japan, providing a more comfortable shopping experience.

[0069] The support application according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a voice assistant unit, and a delivery unit. The collection unit collects user data. User data includes, but is not limited to, purchase history, browsing history, and location information. For example, the collection unit collects the user's purchase history. The collection unit can collect a list of products the user has purchased in the past and the date and time of purchase. The collection unit can also collect the user's browsing history. The collection unit can collect a list of products the user has viewed and the date and time of viewing. Furthermore, the collection unit can also collect the user's location information. The collection unit can collect GPS data and address information. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can analyze past purchase history and browsing history to determine the user's preferences. For example, the analysis unit can analyze patterns of products the user has purchased in the past to determine the user's preferences. The analysis unit can also analyze the user's browsing history to determine the user's interests and preferences. Furthermore, the analysis unit can analyze survey results and determine user preferences. The supply unit recommends products based on the analysis results obtained by the analysis unit. For example, the supply unit recommends highly relevant products based on user preferences. The supply unit can also recommend products based on the user's past purchase and browsing history. For example, the supply unit recommends related products based on the categories of products the user has purchased in the past. The supply unit can also recommend products based on the user's interests and preferences. Furthermore, the supply unit can also recommend products based on the user's survey results. The voice assistant unit searches for desired products and categories by voice. For example, the voice assistant unit performs voice searches using voice recognition technology and natural language processing technology. The voice assistant unit can analyze what the user inputs by voice and search for related products and categories. For example, if the user inputs "I'm looking for a smartphone" by voice, the voice assistant unit will search for products related to smartphones.Furthermore, the voice assistant unit can search for camera-related products if the user voice-inputs "I'm looking for a camera." Additionally, if the user voice-inputs "I'm looking for travel supplies," the voice assistant unit can search for travel supplies-related products. The delivery unit delivers the products searched for by the voice assistant unit. The delivery unit can, for example, suggest the optimal delivery date and time based on the user's length of stay. The delivery unit can automatically suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery unit considers the user's length of stay in Japan and suggests a delivery date and time so that the product arrives during their stay. The delivery unit can also track the delivery status in real time and notify the user of the latest information. The delivery unit can integrate with the delivery company's system to obtain real-time delivery status information and notify the user. For example, the delivery unit can notify the user of the product's shipping status and estimated delivery date and time. This enables the support application according to the embodiment to efficiently collect, analyze, recommend products, perform voice searches, and deliver products based on user data. Some or all of the above-described processes in the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's purchase history into the AI ​​and have the AI ​​perform the collection of purchase history. The analysis unit can input the data collected by the collection unit into the AI ​​and have the AI ​​perform the data analysis. The provision unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform product recommendations. The voice assistant unit can input the user's voice input into the AI ​​and have the AI ​​perform voice searches. The delivery unit can input the products searched by the voice assistant unit into the AI ​​and have the AI ​​perform delivery date and time suggestions.

[0070] The data collection unit collects user data. User data includes, but is not limited to, purchase history, browsing history, and location information. For example, the data collection unit can collect a user's purchase history. The data collection unit can collect a list of products the user has previously purchased and the date and time of purchase. The data collection unit can also collect a user's browsing history. The data collection unit can collect a list of products the user has viewed and the date and time of viewing. Furthermore, the data collection unit can also collect the user's location information. The data collection unit can collect GPS data and address information. This allows the data collection unit to understand the user's behavior and preferences in detail. For example, the data collection unit can collect details of products the user has purchased at a specific store and browsing history on online shopping sites to clarify the user's purchasing patterns. The data collection unit can also collect the user's location information in real time to understand where the user is located. This allows for analysis of what products the user is interested in in a particular area. Furthermore, the data collection unit can collect sensor data from the user's device to understand the user's environment and behavior in detail. For example, the accelerometer and gyroscope sensors in a smartphone can be used to collect data on the user's movement patterns and activity levels. This allows the data collection unit to gain a detailed understanding of the user's lifestyle and behavioral patterns, and provide more accurate data.

[0071] The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the data using methods such as statistical analysis and machine learning algorithms. To determine user preferences, the analysis department can analyze past purchase and browsing history. For example, it can analyze patterns of products a user has purchased in the past to determine their preferences. It can also analyze a user's browsing history to determine their interests and preferences. Furthermore, it can analyze survey results to determine user preferences. Specifically, the analysis department uses machine learning algorithms to extract features from a user's purchase and browsing history and model their preferences. For example, it can use clustering algorithms to classify users into groups with similar preferences and analyze the characteristics of each group. It can also use collaborative filtering to compare data from other users and predict the behavior of users with similar preferences. Furthermore, it can use natural language processing techniques to analyze user survey results and review comments to extract user emotions and opinions. This allows the analysis department to accurately determine user preferences and interests and build a foundation for providing personalized services.

[0072] The service department recommends products based on the analysis results obtained by the analysis department. For example, the service department recommends highly relevant products based on the user's preferences. The service department can also recommend products based on the user's past purchase and browsing history. For example, the service department recommends related products based on the categories of products the user has purchased in the past. The service department can also recommend products based on the user's interests and preferences. Furthermore, the service department can recommend products based on the results of user surveys. Specifically, the service department selects the most suitable products for the user using the user preference model provided by the analysis department. For example, it can use a recommendation engine to recommend products that match the user's preferences in real time. Furthermore, the service department can recommend products that are appropriate to the user's current situation and environment. For example, if the user is in a specific region, it can recommend popular products and services in that region. In addition, the service department can predict products that the user may be interested in in the future based on the user's past behavioral data and recommend them proactively. This allows the service department to always provide the user with the most suitable products and improve user satisfaction.

[0073] The voice assistant unit searches for desired products and categories using voice commands. The voice assistant unit performs voice searches using technologies such as speech recognition and natural language processing. It analyzes the user's voice input and searches for related products and categories. For example, if the user says "I'm looking for a smartphone," the voice assistant unit will search for smartphone-related products. Similarly, if the user says "I'm looking for a camera," it can search for camera-related products. Furthermore, if the user says "I'm looking for travel goods," it can search for travel goods-related products. Specifically, the voice assistant unit uses a speech recognition engine to convert the user's voice into text and natural language processing technology to analyze the meaning of the text. For example, if the user says "I want a new smartphone," the voice assistant unit extracts the keyword "smartphone" and searches for related products. The voice assistant unit can also provide more accurate search results by considering the user's past search and purchase history. Additionally, the voice assistant unit can read out detailed product information and reviews in response to the user's voice commands. This allows the voice assistant to provide users with an intuitive and easy-to-use interface, improving the efficiency of product searches.

[0074] The delivery department delivers products searched by the voice assistant department. The delivery department can, for example, suggest the optimal delivery date and time based on the user's length of stay. The delivery department can automatically suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery department will consider the length of the user's stay in Japan and suggest a delivery date and time so that the product arrives during their stay. The delivery department can also track the delivery status in real time and notify the user of the latest information. The delivery department can integrate with the delivery company's system to obtain delivery status in real time and notify the user. For example, the delivery department will notify the user of the product's shipping status and estimated delivery date and time. Specifically, the delivery department calculates the optimal delivery date and time considering the user's accommodation information and schedule. For example, if the user is staying at a specific hotel, the delivery department will adjust the delivery date and time to ensure that the product is received, taking into account the hotel's check-in and check-out times. The delivery department can also use the delivery company's API to obtain delivery status in real time and notify the user. For example, it will notify the user when the product has been shipped and will immediately notify them if the estimated delivery date and time changes. Furthermore, the delivery department can easily handle changes to delivery dates and times, as well as redelivery requests, according to the user's wishes. This allows the delivery department to provide users with a flexible and convenient delivery service, ensuring smooth receipt of goods.

[0075] The data collection unit can collect the user's purchase history and browsing history. For example, the data collection unit can collect a list of products the user has purchased in the past and the date and time of purchase. The data collection unit can also collect a list of products the user has viewed and the date and time of viewing. By collecting the user's purchase history and browsing history, the data collection unit can provide more personalized services. 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 purchase history into AI and have AI perform the collection of the purchase history.

[0076] The analysis unit can analyze the data collected by the collection unit to determine user preferences. For example, the analysis unit can analyze past purchase history and browsing history to determine user preferences. By determining user preferences, the analysis unit can recommend more appropriate products. 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 data collected by the collection unit into AI and have AI perform the data analysis.

[0077] The supply unit can recommend highly relevant products based on the analysis results obtained by the analysis unit. For example, the supply unit recommends highly relevant products based on the user's preferences. By recommending highly relevant products, the supply unit improves user satisfaction. Some or all of the above processing in the supply unit may be performed using AI, or not using AI. For example, the supply unit can input the analysis results obtained by the analysis unit into the AI ​​and have the AI ​​perform product recommendations.

[0078] The voice assistant unit allows users to search for desired products or categories by voice. The voice assistant unit performs voice searches using, for example, speech recognition technology and natural language processing technology. The voice assistant unit improves user convenience by allowing users to search for products and categories by voice. Some or all of the above-described processes in the voice assistant unit may be performed using, for example, AI, or not using AI. For example, the voice assistant unit can input the user's voice into the AI ​​and have the AI ​​perform a voice search.

[0079] The delivery department can suggest the optimal delivery date and time based on the user's length of stay. For example, the delivery department can automatically suggest the optimal delivery date and time based on the user's length of stay. By suggesting the optimal delivery date and time based on the user's length of stay, the delivery department improves delivery efficiency. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's length of stay into the AI ​​and have the AI ​​suggest the optimal delivery date and time.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can quickly collect the necessary data to save the user's time. This reduces the user's burden by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 user emotion data into an AI and have the AI ​​perform emotion estimation.

[0081] The data collection unit can analyze the user's past purchase history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data on relevant products based on the categories of products the user has purchased in the past. The data collection unit can analyze the user's purchase frequency and focus on collecting data on frequently purchased products. The data collection unit can prioritize collecting data on specific brands or stores from the user's purchase history. This allows the optimal data collection method to be selected by analyzing the user's past purchase 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 purchase history into AI and have the AI ​​select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is traveling, the data collection unit can prioritize collecting travel-related product data. If the user is interested in health, the data collection unit can prioritize collecting health-related product data. If the user is participating in a specific event, the data collection unit can prioritize collecting product data related to that event. This allows for the collection of highly relevant data 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 data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit may prioritize collecting entertainment-related data. If the user is tired, the data collection unit may prioritize collecting relaxation-related data. If the user is anxious, the data collection unit may prioritize collecting data that provides a sense of security. This allows for the collection of more appropriate data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is staying in a specific area, the data collection unit can prioritize the collection of data on stores and services in that area. If the user is in a tourist destination, the data collection unit can prioritize the collection of data on products and services related to that tourist destination. If the user is on the move, the data collection unit can prioritize the collection of data related to the destination area. In this way, highly relevant data can be collected by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0085] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data. For example, the data collection unit can collect data related to products and services that the user has shared on social media. The data collection unit can collect data related to brands and influencers that the user follows. The data collection unit can collect data related to topics that the user has shown interest in on social media. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity data into AI and have AI perform the collection of relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide concise results. If the user is excited, the analysis unit can provide visually appealing analysis results. This allows for more appropriate analysis results by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, 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 not using AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0087] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns when analyzing data. For example, the analysis unit can adjust its analysis algorithm based on patterns of products the user has purchased in the past. The analysis unit can refer to the user's past browsing history and prioritize the analysis of highly relevant data. The analysis unit can predict future behavior from the user's past behavior patterns and optimize its analysis algorithm. In this way, the analysis algorithm can be optimized by referring to the user's past behavior patterns. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior pattern data into AI and have the AI ​​perform the optimization of the analysis algorithm.

[0088] The analysis unit can improve the accuracy of its analysis based on user attribute information during data analysis. For example, the analysis unit can adjust its analysis algorithm based on the user's age and gender. The analysis unit can prioritize the analysis of highly relevant data based on the user's interests. The analysis unit can improve the accuracy of its analysis based on the user's purchase history and browsing history. By improving the accuracy of the analysis based on user attribute information, it can provide more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information into AI and have the AI ​​perform the analysis accuracy improvement.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. 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 not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0090] The analysis unit can perform data analysis while considering the geographical distribution of users. For example, if a user is staying in a particular region, the analysis unit will prioritize the analysis of data from that region. Based on the geographical distribution of users, the analysis unit can analyze regional trends. The analysis unit can analyze highly relevant data by considering the movement patterns of users. This allows for the analysis of regional trends by considering the geographical distribution of users. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user geographical distribution data into AI and have the AI ​​perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during data analysis. For example, the analysis unit can optimize its analysis algorithms by referring to relevant academic papers. The analysis unit can improve the accuracy of its analysis by referring to industry best practices. The analysis unit can increase the reliability of its analysis results by referring to other research results. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input relevant literature into AI and have AI perform the analysis accuracy improvement.

[0092] The service provider can estimate the user's emotions and adjust the way product recommendations are presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide recommendations that include detailed product descriptions. If the user is in a hurry, the service provider can provide recommendations that include concise product descriptions. If the user is excited, the service provider can provide visually appealing product recommendations. By adjusting the way product recommendations are presented based on the user's emotions, more appropriate product recommendations become possible. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0093] The recommendation unit can adjust the level of detail in product recommendations based on the importance of the product. For example, for expensive products, the recommendation unit can provide recommendations that include detailed descriptions and reviews. For everyday products, the recommendation unit can provide recommendations that include concise descriptions. For new products, the recommendation unit can provide recommendations that highlight their features. By adjusting the level of detail in recommendations based on the importance of the product, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not using AI. For example, the recommendation unit can input product importance data into AI and have the AI ​​perform the recommendation detail adjustment.

[0094] The recommendation unit can apply different recommendation algorithms depending on the product category when recommending products. For example, in the case of fashion products, the recommendation unit can make recommendations based on trend information. In the case of electronic devices, the recommendation unit can make recommendations based on technical features. In the case of food products, the recommendation unit can make recommendations based on user preferences. By applying different recommendation algorithms depending on the product category, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product category data into AI and have the AI ​​perform the application of the recommendation algorithm.

[0095] The service provider can estimate the user's emotions and prioritize recommended products based on those emotions. For example, if the user is excited, the service provider may prioritize recommending entertainment-related products. If the user is tired, the service provider may prioritize recommending relaxation-related products. If the user is feeling anxious, the service provider may prioritize recommending products that provide a sense of security. By prioritizing recommended products based on the user's emotions, more appropriate product recommendations become possible. Emotion estimation is achieved using an emotion estimation function, for example, with 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 service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0096] The recommendation unit can determine the priority of product recommendations based on the product submission date. For example, the recommendation unit can prioritize new products. It can also prioritize seasonal or limited-time products. It can prioritize products on sale or discounted products. By determining the priority of recommendations based on the product submission date, more appropriate product recommendations can be made. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or not. For example, the recommendation unit can input product submission date data into AI and have the AI ​​perform the recommendation priority determination.

[0097] The recommendation unit can adjust the order of recommendations based on product relevance when recommending products. For example, the recommendation unit can prioritize recommending highly relevant products based on the user's past purchase history. The recommendation unit can prioritize recommending highly relevant products based on the user's browsing history. The recommendation unit can prioritize recommending highly relevant products based on the user's interests. By adjusting the order of recommendations based on product relevance, more appropriate product recommendations become possible. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input product relevance data into AI and have the AI ​​perform the recommendation order adjustment.

[0098] The voice assistant unit can estimate the user's emotions and adjust the voice search response method based on the estimated user emotions. For example, if the user is nervous, the voice assistant unit can respond in a calm voice. If the user is relaxed, the voice assistant unit can respond in a cheerful voice. If the user is in a hurry, the voice assistant unit can provide a quick and concise response. This allows for more appropriate responses by adjusting the voice search response method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 voice assistant unit may be performed using AI, or not using AI. For example, the voice assistant unit can input user emotion data into AI and have the AI ​​perform emotion estimation.

[0099] The voice assistant unit can provide the most appropriate response during voice searches by referring to the user's past search history. For example, the voice assistant unit can provide relevant responses based on products or categories the user has searched for in the past. The voice assistant unit can prioritize responses using frequently searched keywords from the user's past search history. The voice assistant unit can analyze the user's past search patterns and provide the most relevant responses. In this way, the voice assistant unit can provide the most appropriate response by referring to the user's past search history. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's past search history data into AI and have the AI ​​execute the most appropriate response.

[0100] The voice assistant unit can improve the accuracy of its responses during voice searches based on the user's attribute information. For example, the voice assistant unit can provide the most appropriate response based on the user's age and gender. The voice assistant unit can provide highly relevant responses based on the user's interests and preferences. The voice assistant unit can improve the accuracy of its responses based on the user's purchase and browsing history. By improving the accuracy of responses based on the user's attribute information, more accurate responses become possible. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's attribute information into AI and have the AI ​​perform the task of improving the accuracy of its responses.

[0101] The voice assistant unit can estimate the user's emotions and determine the priority of voice searches based on the estimated emotions. For example, if the user is excited, the voice assistant unit can prioritize entertainment-related search results. If the user is tired, the voice assistant unit can prioritize relaxation-related search results. If the user is feeling anxious, the voice assistant unit can prioritize reassuring search results. By prioritizing voice searches based on the user's emotions, more appropriate search 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 voice assistant unit may be performed using AI, for example, or not using AI. For example, the voice assistant unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0102] The voice assistant unit can provide the most appropriate response during voice searches by taking into account the user's geographical location. For example, if the user is staying in a specific area, the voice assistant unit can provide responses related to shops and services in that area. If the user is in a tourist destination, the voice assistant unit can prioritize providing information related to that tourist destination. If the user is on the move, the voice assistant unit can provide information related to the destination area. In this way, the voice assistant unit can provide the most appropriate response by taking into account the user's geographical location. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input the user's geographical location information into the AI ​​and have the AI ​​execute the most appropriate response.

[0103] The voice assistant unit can improve the accuracy of its responses by referring to relevant literature during voice searches. For example, the voice assistant unit can refer to relevant academic papers to improve the accuracy of its responses. The voice assistant unit can refer to industry best practices to provide optimal responses. The voice assistant unit can refer to other research results to increase the reliability of its responses. This allows for improved response accuracy by referring to relevant literature. Some or all of the above processing in the voice assistant unit may be performed using AI, for example, or without AI. For example, the voice assistant unit can input relevant literature into AI and have the AI ​​perform the task of improving response accuracy.

[0104] The delivery unit can estimate the user's emotions and adjust the delivery date and time suggestions based on the estimated emotions. For example, if the user is relaxed, the delivery unit can offer detailed delivery date and time options. If the user is in a hurry, the delivery unit can prioritize offering expedited delivery options. If the user is excited, the delivery unit can offer visually appealing delivery options. This allows for the provision of more appropriate delivery options by adjusting the delivery date and time suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0105] The delivery department can provide optimal suggestions by referring to the user's past delivery history when proposing delivery dates and times. For example, the delivery department can provide optimal suggestions based on the delivery dates and times the user has used in the past. The delivery department can prioritize suggesting frequently used dates and times from the user's past delivery history. The delivery department can analyze the user's past delivery patterns and provide the most efficient suggestions. In this way, it can provide optimal suggestions by referring to the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's past delivery history data into AI and have the AI ​​execute optimal suggestions.

[0106] The delivery department can improve the accuracy of its delivery date and time suggestions based on user attribute information. For example, the delivery department can provide optimal suggestions based on the user's age and gender. The delivery department can provide highly relevant suggestions based on the user's interests and preferences. The delivery department can improve the accuracy of its suggestions based on the user's purchase and browsing history. By improving the accuracy of suggestions based on user attribute information, more accurate suggestions become possible. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input user attribute information into AI and have the AI ​​perform the task of improving the accuracy of suggestions.

[0107] The delivery department can estimate the user's emotions and prioritize delivery dates and times based on those estimated emotions. For example, if the user is excited, the delivery department can prioritize providing expedited delivery options. If the user is tired, the delivery department can prioritize delivering relaxation-related products. If the user is feeling anxious, the delivery department can prioritize providing reassuring delivery options. This allows for the provision of more appropriate delivery options by prioritizing delivery dates and times based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery department may be performed using AI or not. For example, the delivery department can input user emotion data into an AI and have the AI ​​perform emotion estimation.

[0108] The delivery department can provide optimal suggestions when proposing delivery dates and times, taking into account the user's geographical location. For example, if the user is staying in a particular area, the delivery department can prioritize delivery options for that area. If the user is in a tourist destination, the delivery department can provide delivery options related to that tourist destination. If the user is on the move, the delivery department can provide delivery options related to the destination area. In this way, the delivery department can provide optimal suggestions by taking into account the user's geographical location. Some or all of the above processing in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input the user's geographical location information into AI and have the AI ​​make optimal suggestions.

[0109] The delivery department can improve the accuracy of its delivery date and time suggestions by referring to relevant literature. For example, the delivery department can improve the accuracy of its suggestions by referring to relevant academic papers. The delivery department can provide optimal suggestions by referring to industry best practices. The delivery department can increase the reliability of its suggestions by referring to other research results. In this way, the accuracy of suggestions can be improved by referring to relevant literature. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input relevant literature into AI and have the AI ​​perform the improvement of the accuracy of its suggestions.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The support app can have the functionality to learn user preferences and predict future purchasing behavior based on the user's purchase and browsing history. For example, the data collection unit can analyze patterns of products the user has purchased in the past and predict products the user is likely to purchase next. The analysis unit can analyze the user's browsing history and identify new products that the user may be interested in. The delivery unit can then provide personalized product recommendations to the user based on these predictions. This is expected to improve the user's purchasing experience and increase the frequency of app usage.

[0112] Support apps can have the ability to estimate a user's emotions and dynamically change the interface design based on those emotions. For example, the data collection unit can estimate emotions from the user's facial expressions and voice, and if the user is stressed, it can change the interface to a simple and calming design. The analysis unit can analyze the user's emotional data and change the design to a colorful and cheerful one if the user is relaxed. The delivery unit can adjust the interface layout and color scheme according to the user's emotions. This provides an optimal interface tailored to the user's emotions, improving user satisfaction.

[0113] The support app can have a function that automatically notifies users of events and campaigns that might interest them, based on their purchase and browsing history. For example, the data collection unit collects data on events and campaigns that the user has participated in in the past. The analysis unit can analyze this data to identify new events and campaigns that might interest the user. The delivery unit can notify the user of these events and campaigns and encourage their participation. This enables the provision of information based on the user's interests, improving user engagement.

[0114] Support apps can have features that estimate a user's emotions and adjust customer support responses based on those emotions. For example, the data collection unit can estimate emotions from the user's voice or text, and if the user is angry, it can prompt customer support for a quick response. The analysis unit can analyze the user's emotion data and, if the user is feeling anxious, can recommend a polite and reassuring response. The delivery unit can adjust customer support responses according to the user's emotions. This ensures that optimal customer support is provided in accordance with the user's emotions, improving user satisfaction.

[0115] A support app can have a function that automatically suggests new products and services that a user might be interested in, based on their purchase and browsing history. For example, the data collection unit collects data on products the user has purchased in the past. The analysis unit analyzes this data and can identify new products and services that the user might be interested in. The delivery unit can then suggest these new products and services to the user and encourage them to purchase them. This enables product suggestions based on the user's interests, thereby increasing the user's willingness to buy.

[0116] The support app can have a function to estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, the data collection unit can estimate the user's emotions from their facial expressions and voice, and send a notification if the user is relaxed. The analysis unit can analyze the user's emotion data and delay notifications if the user is busy. The delivery unit can adjust the timing of notifications according to the user's emotions. This ensures that notifications are sent at the optimal time according to the user's emotions, reducing user stress.

[0117] Support apps can have a function that automatically provides content that users are likely to be interested in, based on their purchase and browsing history. For example, the data collection unit collects data on products that users have previously viewed. The analysis unit can analyze this data to identify articles and videos that users are likely to be interested in. The delivery unit can provide this content to users and encourage them to view it. This enables content delivery based on user interests and improves user engagement.

[0118] The support app can have a function to estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, the data collection unit can estimate the user's emotions from their facial expressions and voice, and display ads if the user is relaxed. The analysis unit can analyze the user's emotional data and refrain from displaying ads if the user is stressed. The delivery unit can adjust how ads are displayed according to the user's emotions. This enables optimal ad display tailored to the user's emotions, reducing user stress.

[0119] The support app can have a function that automatically provides coupons and discount information that might interest the user, based on their purchase and browsing history. For example, the data collection unit collects data on products the user has purchased in the past. The analysis unit analyzes this data and can identify coupons and discount information that might interest the user. The provision unit can provide this coupon and discount information to the user and encourage their use. This makes it possible to provide coupons and discount information based on the user's interests, thereby increasing the user's willingness to purchase.

[0120] Support apps can have the ability to estimate a user's emotions and guide them on how to use the app based on those emotions. For example, a data collection unit can estimate emotions from the user's facial expressions and voice, and provide detailed guidance if the user is confused. An analysis unit can analyze the user's emotional data and provide concise guidance if the user is relaxed. A delivery unit can guide the user on how to use the app according to their emotions. This provides the user with the most appropriate guidance based on their emotions, reducing their stress.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects user data. User data includes purchase history, browsing history, and location information. For example, the data collection unit collects the user's purchase history, including a list of previously purchased items and the date and time of purchase. The data collection unit also collects the user's browsing history, including a list of viewed items and the date and time of viewing. Furthermore, the data collection unit collects the user's location information, including GPS data and address information. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department uses statistical analysis and machine learning algorithms to analyze the data and determine user preferences. For example, the analysis department analyzes past purchase and browsing history to determine user preferences, interests, and concerns. It can also analyze survey results to determine user preferences. Step 3: The supply department recommends products based on the analysis results obtained by the analysis department. The supply department recommends highly relevant products based on the user's preferences, as well as products based on past purchase history, browsing history, and survey results. Step 4: The voice assistant unit searches for the product or category desired by the user using voice. The voice assistant unit performs voice searches using speech recognition technology and natural language processing technology, analyzing what the user inputs by voice to find relevant products and categories. Step 5: The delivery department delivers the items searched for by the voice assistant department. The delivery department suggests the optimal delivery date and time based on the user's length of stay, tracks the delivery status in real time, and notifies the user of the latest information.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects the user's purchase history and browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends products based on the analysis results. The voice assistant unit is implemented by the control unit 46A of the smart device 14 and performs voice searches using voice recognition technology. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal delivery date and time. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the user's purchase history and browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends products based on the analysis results. The voice assistant unit is implemented by the control unit 46A of the smart glasses 214 and performs voice searches using voice recognition technology. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal delivery date and time. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the user's purchase history and browsing history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends products based on the analysis results. The voice assistant unit is implemented by the control unit 46A of the headset terminal 314 and performs voice searches using voice recognition technology. The delivery unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal delivery date and time. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, voice assistant unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects the user's purchase history and browsing history. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends products based on the analysis results. The voice assistant unit is implemented by, for example, the control unit 46A of the robot 414 and performs voice searches using voice recognition technology. The delivery unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal delivery date and time. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A data collection unit that collects user data, An analysis unit analyzes the data collected by the aforementioned collection unit, A supply unit recommends products based on the analysis results obtained by the aforementioned analysis unit, A voice assistant unit that searches for products recommended by the aforementioned provision unit using voice commands, The system includes a delivery unit that delivers products searched by the voice assistant unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect users' purchase and browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The data collected by the aforementioned collection unit is analyzed to determine the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results obtained by the aforementioned analysis unit, highly relevant products are recommended. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice assistant unit is Users can search for desired products or categories using voice commands. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned delivery department, We suggest the optimal delivery date and time based on the user's length of stay. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing data, the analysis algorithm is optimized by referring to the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing data, improve the accuracy of the analysis based on user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing data, the analysis should take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing data, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the user's emotions and adjust the way product recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When recommending products, adjust the level of detail of the recommendation based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When recommending products, different recommendation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and prioritizes recommended products based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When recommending products, we prioritize recommendations based on when the products were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When recommending products, the order of recommendations is adjusted based on product relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned voice assistant unit is It estimates the user's emotions and adjusts the voice search response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned voice assistant unit is When using voice search, the system provides the most suitable response by referencing the user's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned voice assistant unit is Improve the accuracy of responses during voice search based on user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned voice assistant unit is It estimates the user's emotions and determines the priority of voice search based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned voice assistant unit is When performing a voice search, the system provides the most appropriate response by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned voice assistant unit is When performing a voice search, referencing related literature improves the accuracy of the response. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery date and time suggestion method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned delivery department, When suggesting delivery dates and times, we refer to the user's past delivery history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned delivery department, When suggesting delivery dates and times, improve the accuracy of suggestions based on user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned delivery department, The system estimates the user's emotions and prioritizes delivery dates and times based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned delivery department, When proposing delivery dates and times, we provide optimal suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned delivery department, When proposing delivery dates and times, we will improve the accuracy of the suggestions by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 data, An analysis unit analyzes the data collected by the aforementioned collection unit, A supply unit recommends products based on the analysis results obtained by the aforementioned analysis unit, A voice assistant unit that searches for products recommended by the aforementioned provision unit using voice commands, The system includes a delivery unit that delivers products searched by the voice assistant unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect users' purchase and browsing history. The system according to feature 1.

3. The aforementioned analysis unit is The data collected by the aforementioned collection unit is analyzed to determine the user's preferences. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results obtained by the aforementioned analysis unit, highly relevant products are recommended. The system according to feature 1.

5. The aforementioned voice assistant unit is Users can search for desired products or categories using voice commands. The system according to feature 1.

6. The aforementioned delivery department, We suggest the optimal delivery date and time based on the user's length of stay. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

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