System

Abstract

An object of the system according to the embodiment is to personalize an optimal object for a user in real time.SOLUTION: A system includes a user data collection unit, an analysis unit, and a generation unit. The user data collection unit collects behavior data and preference data of a user. The analysis unit analyzes the data collected by the user data collection unit. The generation unit generates an object optimal for the user on the basis of the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide personalized services to an unspecified number of users, and have involved high learning and financial costs.

[0005] The system according to the embodiment aims to personalize the most suitable object for the user in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a user data collection unit, an analysis unit, and a generation unit. The user data collection unit collects user behavior data and preference data. The analysis unit analyzes the data collected by the user data collection unit. The generation unit generates an object optimal for the user based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can personalize the most suitable object for the user in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personalized service system according to the embodiment of the present invention uses a generative AI to provide personalized services to individual users even in a service shared by many users, thereby reducing learning costs and monetary costs and improving the user experience.

[0029] The personalized service system according to the embodiment includes a user data collection unit, an analysis unit, and a generation unit. The user data collection unit collects user behavior data and preference data. For example, the user data collection unit collects information such as products viewed by the user in the past, purchase history, and website browsing history. The user data collection unit can also collect user behavior data in real time. For example, the user data collection unit collects information such as the page the user is currently viewing and the links the user has clicked. The analysis unit analyzes the data collected by the user data collection unit. For example, the analysis unit can analyze the user's preferences using data mining technology. The analysis unit can also analyze the user's behavior patterns using a machine learning algorithm. For example, the analysis unit can predict the user's future behavior based on the user's past behavior data. The generation unit generates an object that is optimal for the user based on the data analyzed by the analysis unit. For example, the generation unit can generate furniture with a design that the user prefers or products that meet the user's needs. The generation unit can also generate objects that reflect the user's preferences and needs in real time. For example, the generation unit can display products that match the user's preferences while the user is browsing a website. This allows the personalized service system according to the embodiment to generate personalized objects in real time based on the user's behavior data and preference data.

[0030] The user data collection unit can collect the user's biometric data and analyze it with the generation AI. For example, the user data collection unit can use a wearable device to monitor the user's heart rate and electrodermal activity in real time and analyze the data with the generation AI. This can identify the situations in which the user is feeling stressed. The user data collection unit can also measure the user's heart rate and electrodermal activity while the user is performing a specific activity and analyze the stress level and relaxation level based on the data. For example, it can compare data from when the user is exercising and when the user is relaxing. The user data collection unit can also collect the user's biometric data while the user is sleeping and analyze it with the generation AI to evaluate the quality of sleep and relaxation level. For example, it can identify times when the user is in deep sleep or when stress is low. In this way, the user's stress level and relaxation level can be determined by collecting the user's biometric data and analyzing it with the generation AI.

[0031] The user data collection unit analyzes the user's social media activity and can grasp topics and trends in real time. The user data collection unit, for example, analyzes the accounts the user follows on social media and the posts the user has liked to identify topics of interest. For example, it grasps the level of interest in specific brands or products. The user data collection unit also analyzes articles and videos the user has shared on social media to grasp trends in real time. For example, it identifies trending topics and popular products. The user data collection unit also analyzes hashtags the user uses on social media to identify themes and trends of interest. For example, it grasps the level of interest in specific events or campaigns. In this way, the user's social media activity can be analyzed and topics and trends of interest can be grasped in real time.

[0032] The user data collection unit can analyze voice commands and conversation content using a voice assistant or smart speaker. The user data collection unit, for example, collects voice commands given by a user to a voice assistant and analyzes the content of those commands. For example, it identifies information and services that the user frequently requests. The user data collection unit also analyzes the content of conversations the user has through a smart speaker to understand topics of interest and needs. For example, it identifies products and services that the user often talks about. The user data collection unit also uses a voice assistant to collect a history of questions and requests made by the user and analyzes preferences and needs based on that data. For example, it identifies information and services that the user frequently asks about. In this way, it is possible to understand the user's preferences and needs by analyzing the user's voice commands and conversation content using a voice assistant or smart speaker.

[0033] The user data collection unit can collect location information and analyze movement patterns. The user data collection unit, for example, uses the GPS function of a smartphone to collect user location information in real time and analyze places visited and movement patterns. For example, it identifies stores and areas frequently visited by the user. The user data collection unit also analyzes the means of transportation and movement routes used by the user to understand their lifestyle. For example, it identifies their commute route and how they spend their weekends. The user data collection unit also collects location information of events and activities in which the user participates and analyzes themes of interest and lifestyle. For example, it identifies how often they attend sporting events and concerts. In this way, by collecting the user's location information and analyzing places visited and movement patterns, it is possible to understand the user's lifestyle.

[0034] The generation unit can predict potential needs based on purchase history and preference data and generate objects that correspond to them. For example, the generation unit analyzes the user's past purchase history and predicts products that the user has not yet purchased but may be interested in. For example, it suggests products in the same category. The generation unit also predicts potential needs based on the user's preference data and generates customized products that correspond to them. For example, it suggests products that incorporate designs and functions that the user prefers. The generation unit also analyzes user behavior data and develops algorithms that predict potential needs. For example, it makes new suggestions based on products and services that the user frequently views. In this way, it is possible to predict potential needs based on the user's past purchase history and preference data and generate objects that correspond to them.

[0035] The generation unit can generate special objects tailored to life events. For example, the generation unit generates a gift with a special design for the user's birthday. For example, it proposes a customized gift tailored to the user's tastes and hobbies. The generation unit also generates special objects tailored to the user's anniversaries. For example, it proposes customized jewelry or artwork for a wedding anniversary. The generation unit also generates interior and decorative items with a special design tailored to the user's life events. For example, it proposes customized furniture and decorative items for a housewarming gift. In this way, it is possible to generate special objects tailored to the user's life events.

[0036] The generation unit can generate objects that match the preferences of pets and family members. The generation unit generates customized products for pets based on the preferences of the user's pet, for example. For example, toys and beds incorporating designs and materials that pets like are suggested. The generation unit also generates customized products that the whole family can enjoy based on the preferences of the user's family members. For example, games and activities that match the hobbies and interests of the family members are suggested. The generation unit also analyzes preference data of the user's pets and family members to generate customized products that match special events and anniversaries. For example, a special gift for a pet's birthday is suggested. In this way, objects that match the preferences of the user's pets and family members can be generated.

[0037] The generation unit can generate customizable objects based on hobbies and interests. For example, the generation unit generates customizable furniture and interior decor based on the user's hobbies and interests. For example, furniture incorporating designs and functions preferred by the user is suggested. The generation unit also generates customizable artwork tailored to the user's hobbies and interests. For example, paintings and sculptures incorporating the user's favorite themes and styles are suggested. The generation unit also generates customizable accessories and jewelry based on the user's hobbies and interests. For example, accessories incorporating designs and materials preferred by the user are suggested. In this way, customizable objects based on the user's hobbies and interests can be generated.

[0038] The generation unit analyzes real-time behavioral data and can instantly provide information and products that the user currently needs. For example, the generation unit analyzes keywords that the user searches for on a website in real time and instantly displays related information and products. For example, if the user searches for "travel," travel-related products and services are suggested. The generation unit also analyzes the operations the user performs on the app in real time and provides information and products that the user currently needs. For example, if the user searches for a cooking recipe, related ingredients and cooking utensils are suggested. The generation unit also analyzes the behavior the user performs in a store in real time and suggests products that the user currently needs. For example, if the user stays in a particular section for a long time, products in that section are introduced. In this way, the generation unit can analyze the user's real-time behavioral data and instantly provide information and products that the user currently needs.

[0039] The generation unit can personalize and suggest nearby stores and services based on real-time location information. The generation unit, for example, uses the GPS function of the user's smartphone to obtain location information in real time and suggest nearby stores and services. For example, when the user is in a shopping mall, it provides information about stores in the mall. The generation unit also analyzes location information in real time when the user is in a specific area and suggests services related to that area. For example, when the user is in a tourist spot, it introduces tourist spots and restaurants. The generation unit also analyzes location information while the user is moving in real time and suggests stores and services along the user's route. For example, it introduces cafes and convenience stores that the user can stop by on their commute. This makes it possible to personalize and suggest nearby stores and services based on the user's real-time location information.

[0040] The generation unit can analyze voice commands and provide personalization through voice interaction. For example, the generation unit analyzes voice commands given by a user to a voice assistant in real time and provides personalized information and products. For example, if a user says, "Tell me about nearby restaurants," the generation unit suggests restaurants that match their preferences. The generation unit also analyzes the content of conversations the user has through a smart speaker in real time and provides personalized services. For example, if a user says, "Play some relaxing music," the generation unit plays music that matches their preferences. The generation unit also analyzes operations the user performs using voice commands in the car in real time and provides personalized information and services. For example, if a user says, "Tell me where the next gas station is," the generation unit provides information about the nearest gas station. This makes it possible to analyze a user's voice commands and provide personalization through voice interaction.

[0041] The generation unit analyzes real-time social media activity and can instantly suggest topics and events that the user is interested in. For example, the generation unit analyzes accounts that the user follows on social media and posts that the user has liked in real time to suggest topics and events that the user is interested in. For example, the generation unit provides live performance information for artists that the user follows. The generation unit also analyzes articles and videos that the user has shared on social media in real time to suggest related topics and events. For example, the generation unit provides screening information for movies that the user has shared. The generation unit also analyzes hashtags that the user uses on social media in real time to suggest topics and events that the user is interested in. For example, the generation unit provides event information related to hashtags used by the user. In this way, the generation unit can analyze the user's real-time social media activity and instantly suggest topics and events that the user is interested in.

[0042] The generation unit can share behavioral data on the cloud and integrate multiple user data to distribute learning costs. The generation unit, for example, shares user behavioral data on the cloud and integrates multiple user data to develop a learning algorithm. For example, it integrates data of users with the same interests to build a learning model. The generation unit also develops an efficient learning algorithm based on the user data shared on the cloud. For example, it combines data from different users to optimize the learning model. The generation unit also integrates multiple user data on the cloud to build a system that distributes learning costs. For example, it collects user data in real time and analyzes it on the cloud. This makes it possible to share user behavioral data on the cloud and integrate multiple user data to distribute learning costs.

[0043] The generation unit automatically generates optimal advertisements and promotions based on the preference data, thereby reducing monetary costs. The generation unit, for example, automatically generates personalized advertisements based on the user's preference data. For example, it displays advertisements for products and services that interest the user. The generation unit also analyzes the user's preference data and automatically generates optimal promotions. For example, it suggests events and campaigns that the user is interested in. The generation unit also develops algorithms that maximize the effectiveness of advertisements and promotions based on the user's preference data. For example, it displays advertisements at times when the user is most likely to respond. This makes it possible to automatically generate optimal advertisements and promotions based on the user's preference data and reduce monetary costs.

[0044] The generation unit can anonymize data and share it while protecting privacy. The generation unit, for example, builds a system that anonymizes user data and shares it on the cloud while protecting privacy. For example, the generation unit collects and analyzes data in a form that does not identify individuals. The generation unit also develops an efficient learning algorithm based on the anonymized user data. For example, the generation unit optimizes a learning model using the anonymized data. The generation unit also develops a protocol for anonymizing user data and sharing it while protecting privacy. For example, the generation unit builds a system that automates the anonymization and sharing of data. This makes it possible to anonymize user data and share it while protecting privacy.

[0045] The generation unit manages data in a distributed network, thereby reducing learning costs while ensuring data security. The generation unit, for example, manages user data in a distributed network and develops learning algorithms while ensuring security. For example, blockchain technology is used to prevent data tampering. The generation unit also uses a distributed network to build a system that safely shares user data and reduces learning costs. For example, data is managed and analyzed simultaneously in a distributed manner. The generation unit also develops efficient learning algorithms based on user data managed in the distributed network. For example, the generation unit optimizes learning models while performing distributed processing of data. This allows user data to be managed in a distributed network and learning costs to be reduced while ensuring data security.

[0046] The generation unit analyzes the behavioral data and can quickly provide the information and products that the user desires. The generation unit, for example, analyzes the user's behavioral data and builds a system that quickly provides the information that the user desires. For example, related information is instantly displayed based on keywords searched by the user. The generation unit also quickly suggests products that the user is interested in based on the user's behavioral data. For example, items related to products viewed by the user are displayed. The generation unit also analyzes the user's behavioral data and develops a system that provides the information and products that the user desires in real time. For example, related products are suggested when the user is viewing a specific category. This makes it possible to analyze the user's behavioral data and quickly provide the information and products that the user desires.

[0047] The generation unit can personalize and provide content and services that interest the user based on the preference data. The generation unit, for example, builds a system that provides personalized content based on the user's preference data. For example, it displays articles and videos that interest the user. The generation unit also analyzes the user's preference data and provides personalized services that interest the user. For example, it suggests restaurants and events that the user likes. The generation unit also develops a system that provides personalized products based on the user's preference data. For example, it suggests products that incorporate designs and functions that the user prefers. This makes it possible to provide personalized content and services that interest the user based on the user's preference data.

[0048] The generation unit can analyze voice commands and provide services through voice interaction. For example, the generation unit analyzes a user's voice commands and provides personalized services through voice interaction. For example, when a user says, "Tell me about nearby cafes," the generation unit suggests cafes that match the user's preferences. The generation unit also provides personalized information through voice interaction based on the user's voice commands. For example, when a user asks, "What's the weather like today?", the generation unit provides weather information based on the user's location information. The generation unit also analyzes a user's voice commands and provides personalized entertainment content through voice interaction. For example, when a user says, "Play some relaxing music," the generation unit plays music that matches the user's preferences. In this way, the generation unit can analyze a user's voice commands and provide services through voice interaction.

[0049] The generation unit can provide services according to the places the user visits and their movement patterns based on the location information. The generation unit, for example, builds a system that provides services according to the places the user visits based on the user's location information. For example, if the user is in a shopping mall, it provides store information within the mall. The generation unit also analyzes the user's movement patterns and provides services along the user's movement route. For example, it suggests cafes and convenience stores that the user can stop by on their way to work. The generation unit also develops a system that provides services related to the places the user visits based on the user's location information. For example, if the user is in a tourist spot, it introduces tourist spots and restaurants. This makes it possible to provide services according to the places the user visits and their movement patterns based on the user's location information.

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

[0051] The user data collection unit collects the user's health data, and the analysis unit can evaluate the user's health condition. For example, if the user uses a wearable device, the analysis unit can collect heart rate, step count, and sleep data from the device. If the user uses a food recording app, the data can be collected to evaluate nutritional balance. Furthermore, it is possible to collect the results of the user's regular health checkups and analyze changes in the user's long-term health condition. This makes it possible to provide individual health advice and recommended lifestyle habits based on the user's health data.

[0052] The user data collection unit can collect user purchasing data and have the analysis unit analyze purchasing patterns. For example, data on products purchased in the past by the user can be collected to identify frequently purchased products and brands. It is also possible to analyze products purchased by the user during specific seasons or events to understand seasonal purchasing patterns. Furthermore, it is also possible to collect products viewed by the user when shopping online and products added to the cart to identify products that the user is most interested in purchasing. This makes it possible to provide individual purchasing advice and recommended products based on the user's purchasing data.

[0053] The user data collection unit collects data on the user's hobbies and interests, and the analysis unit can suggest events and activities that match the hobbies. For example, if the user is interested in music, nearby concerts and live events can be suggested. If the user is interested in sports, information on local sporting events and matches can be provided. Furthermore, if the user is interested in art and culture, information on art galleries and museums can be suggested. This makes it possible to provide events and activities based on the user's hobbies and interests, enriching the user's lifestyle.

[0054] The user data collection unit collects the user's travel data, and the analysis unit can customize the travel plan. For example, it collects data on the destinations and accommodations the user has visited in the past to identify the user's preferred travel style. It can also analyze data on the activities and tourist spots the user has visited during their trip to suggest travel plans for the next time. It can also collect photos taken by the user and reviews posted by the user during their trip to evaluate their satisfaction with the trip. This makes it possible to provide individual travel plans and recommended travel destinations based on the user's travel data.

[0055] The user data collection unit collects the user's entertainment data, and the analysis unit can customize the entertainment plan. For example, data on movies and TV dramas the user has watched in the past can be collected to identify the user's preferred genre. The unit can also analyze data on songs the user has played on a music streaming service to suggest the next song they should listen to. Furthermore, data on the games the user has played can be collected to identify the user's preferred game genre. This makes it possible to provide an individual entertainment plan and recommended content based on the user's entertainment data.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user data collection unit collects user behavior data and preference data. For example, it collects information such as the products the user has viewed in the past, their purchase history, and their website browsing history. The user data collection unit can also collect user behavior data in real time. For example, it collects information such as the page the user is currently viewing and the links the user has clicked. Step 2: The analysis unit analyzes the data collected by the user data collection unit. For example, it uses data mining technology to analyze user preferences. The analysis unit can also analyze user behavior patterns using machine learning algorithms. For example, it can predict future behavior based on the user's past behavior data. Step 3: The generation unit generates an object that is optimal for the user based on the data analyzed by the analysis unit. For example, it generates furniture with a design that the user likes or products that meet the user's needs. The generation unit can also generate objects that reflect the user's tastes and needs in real time. For example, when the user is browsing a website, it displays products that match the user's preferences.

[0058] (Example 2) The personalized service system according to the embodiment of the present invention uses a generative AI to provide personalized services to individual users even in a service shared by many users, thereby reducing learning costs and monetary costs and improving the user experience.

[0059] The personalized service system according to the embodiment includes a user data collection unit, an analysis unit, and a generation unit. The user data collection unit collects user behavior data and preference data. For example, the user data collection unit collects information such as products viewed by the user in the past, purchase history, and website browsing history. The user data collection unit can also collect user behavior data in real time. For example, the user data collection unit collects information such as the page the user is currently viewing and the links the user has clicked. The analysis unit analyzes the data collected by the user data collection unit. For example, the analysis unit can analyze the user's preferences using data mining technology. The analysis unit can also analyze the user's behavior patterns using a machine learning algorithm. For example, the analysis unit can predict the user's future behavior based on the user's past behavior data. The generation unit generates an object that is optimal for the user based on the data analyzed by the analysis unit. For example, the generation unit can generate furniture with a design that the user prefers or products that meet the user's needs. The generation unit can also generate objects that reflect the user's preferences and needs in real time. For example, the generation unit can display products that match the user's preferences while the user is browsing a website. This allows the personalized service system according to the embodiment to generate personalized objects in real time based on the user's behavior data and preference data.

[0060] The user data collection unit can collect user emotional data and analyze emotional fluctuations. For example, when a user browses a website, the user data collection unit uses a camera or microphone to analyze the user's facial expressions and tone of voice in real time to collect emotional data. This allows the user to understand which content the user has positive emotions about. Furthermore, when a user purchases a product, the user data collection unit collects emotional data before and after the purchase and analyzes the emotional fluctuations leading up to the purchase decision. For example, if a user felt anxious before purchasing but was satisfied after the purchase, the user's preferences can be analyzed based on that data. Furthermore, the user data collection unit analyzes the content and comments posted by the user on social media to collect emotional data. For example, the unit identifies topics with many posts expressing positive emotions and suggests products and services related to those topics. In this way, by collecting user emotional data and analyzing emotional fluctuations, the user's preferences can be more precisely understood.

[0061] The user data collection unit can collect the user's biometric data and analyze it with the generation AI. For example, the user data collection unit can use a wearable device to monitor the user's heart rate and electrodermal activity in real time and analyze the data with the generation AI. This can identify the situations in which the user is feeling stressed. The user data collection unit can also measure the user's heart rate and electrodermal activity while the user is performing a specific activity and analyze the stress level and relaxation level based on the data. For example, it can compare data from when the user is exercising and when the user is relaxing. The user data collection unit can also collect the user's biometric data while the user is sleeping and analyze it with the generation AI to evaluate the quality of sleep and relaxation level. For example, it can identify times when the user is in deep sleep or when stress is low. In this way, the user's stress level and relaxation level can be determined by collecting the user's biometric data and analyzing it with the generation AI.

[0062] The user data collection unit analyzes the user's social media activity and can grasp topics and trends in real time. The user data collection unit, for example, analyzes the accounts the user follows on social media and the posts the user has liked to identify topics of interest. For example, it grasps the level of interest in specific brands or products. The user data collection unit also analyzes articles and videos the user has shared on social media to grasp trends in real time. For example, it identifies trending topics and popular products. The user data collection unit also analyzes hashtags the user uses on social media to identify themes and trends of interest. For example, it grasps the level of interest in specific events or campaigns. In this way, the user's social media activity can be analyzed and topics and trends of interest can be grasped in real time.

[0063] The user data collection unit can analyze voice commands and conversation content using a voice assistant or smart speaker. The user data collection unit, for example, collects voice commands given by a user to a voice assistant and analyzes the content of those commands. For example, it identifies information and services that the user frequently requests. The user data collection unit also analyzes the content of conversations the user has through a smart speaker to understand topics of interest and needs. For example, it identifies products and services that the user often talks about. The user data collection unit also uses a voice assistant to collect a history of questions and requests made by the user and analyzes preferences and needs based on that data. For example, it identifies information and services that the user frequently asks about. In this way, it is possible to understand the user's preferences and needs by analyzing the user's voice commands and conversation content using a voice assistant or smart speaker.

[0064] The user data collection unit can collect location information and analyze movement patterns. The user data collection unit, for example, uses the GPS function of a smartphone to collect user location information in real time and analyze places visited and movement patterns. For example, it identifies stores and areas frequently visited by the user. The user data collection unit also analyzes the means of transportation and movement routes used by the user to understand their lifestyle. For example, it identifies their commute route and how they spend their weekends. The user data collection unit also collects location information of events and activities in which the user participates and analyzes themes of interest and lifestyle. For example, it identifies how often they attend sporting events and concerts. In this way, by collecting the user's location information and analyzing places visited and movement patterns, it is possible to understand the user's lifestyle.

[0065] The user data collection unit can use the emotion estimation function to estimate emotions from text data and perform emotion-based data analysis. The user data collection unit, for example, analyzes text data entered by a user on a website or app and estimates emotions using the emotion estimation function. For example, it identifies products associated with positive emotions from the content of reviews and comments. The user data collection unit also analyzes text data posted by a user on social media and estimates emotions using the emotion estimation function. For example, it grasps fluctuations in emotions toward a specific topic. The user data collection unit also analyzes text data entered by a user in a conversation with a chatbot and estimates emotions using the emotion estimation function. For example, it identifies the problems and needs the user has from the emotion data. This makes it possible to use the emotion estimation function to estimate emotions from text data entered by a user and perform emotion-based data analysis.

[0066] The generation unit can generate objects with designs and colors that correspond to emotions based on the emotional data. For example, the generation unit generates furniture with designs and colors that evoke positive emotions based on the user's emotional data. For example, it incorporates colors and designs that have a relaxing effect. The generation unit also analyzes the user's emotional data and suggests interior designs that correspond to the emotions. For example, it generates color schemes and layouts that reduce stress. The generation unit also generates artworks and decorative items that correspond to the emotions based on the user's emotional data. For example, it suggests paintings and sculptures that evoke positive emotions. In this way, it is possible to generate objects with designs and colors that correspond to the emotions based on the user's emotional data.

[0067] The generation unit can predict potential needs based on purchase history and preference data and generate objects that correspond to them. For example, the generation unit analyzes the user's past purchase history and predicts products that the user has not yet purchased but may be interested in. For example, it suggests products in the same category. The generation unit also predicts potential needs based on the user's preference data and generates customized products that correspond to them. For example, it suggests products that incorporate designs and functions that the user prefers. The generation unit also analyzes user behavior data and develops algorithms that predict potential needs. For example, it makes new suggestions based on products and services that the user frequently views. In this way, it is possible to predict potential needs based on the user's past purchase history and preference data and generate objects that correspond to them.

[0068] The generation unit can generate special objects tailored to life events. For example, the generation unit generates a gift with a special design for the user's birthday. For example, it proposes a customized gift tailored to the user's tastes and hobbies. The generation unit also generates special objects tailored to the user's anniversaries. For example, it proposes customized jewelry or artwork for a wedding anniversary. The generation unit also generates interior and decorative items with a special design tailored to the user's life events. For example, it proposes customized furniture and decorative items for a housewarming gift. In this way, it is possible to generate special objects tailored to the user's life events.

[0069] The generation unit can generate objects that match the preferences of pets and family members. The generation unit generates customized products for pets based on the preferences of the user's pet, for example. For example, toys and beds incorporating designs and materials that pets like are suggested. The generation unit also generates customized products that the whole family can enjoy based on the preferences of the user's family members. For example, games and activities that match the hobbies and interests of the family members are suggested. The generation unit also analyzes preference data of the user's pets and family members to generate customized products that match special events and anniversaries. For example, a special gift for a pet's birthday is suggested. In this way, objects that match the preferences of the user's pets and family members can be generated.

[0070] The generation unit can generate customizable objects based on hobbies and interests. For example, the generation unit generates customizable furniture and interior decor based on the user's hobbies and interests. For example, furniture incorporating designs and functions preferred by the user is suggested. The generation unit also generates customizable artwork tailored to the user's hobbies and interests. For example, paintings and sculptures incorporating the user's favorite themes and styles are suggested. The generation unit also generates customizable accessories and jewelry based on the user's hobbies and interests. For example, accessories incorporating designs and materials preferred by the user are suggested. In this way, customizable objects based on the user's hobbies and interests can be generated.

[0071] The generation unit can use the emotion estimation function to suggest objects according to the user's emotions and generate objects that make the user's emotions positive. The generation unit can, for example, suggest objects that elicit positive emotions based on the user's emotion data. For example, it can generate furniture with a design and color that has a relaxing effect. The generation unit can also use the emotion estimation function to suggest artworks and decorative items according to the user's emotions. For example, it can generate paintings and sculptures that elicit positive emotions. The generation unit can also analyze the user's emotion data and suggest customized products to make the user emotions positive. For example, it can generate relaxation goods and aromas to reduce stress. In this way, the emotion estimation function can be used to suggest objects according to the user's emotions and generate objects that make the user emotions positive.

[0072] The generation unit can suggest services and products that correspond to the emotions based on the real-time emotional data. For example, when the user is browsing a website, the generation unit analyzes the emotional data in real time and suggests products that correspond to the emotions. For example, if the user is feeling stressed, products with a relaxing effect are displayed. Furthermore, when the user visits a store, the generation unit analyzes the emotional data in real time and suggests services that correspond to the emotions. For example, if the user is tired, a service that can refresh the user is provided. Furthermore, when the user is using an app, the generation unit analyzes the emotional data in real time and suggests content that corresponds to the emotions. For example, if the user is feeling positive, entertainment content is displayed. In this way, services and products that correspond to the emotions can be suggested based on the user's real-time emotional data.

[0073] The generation unit analyzes real-time behavioral data and can instantly provide information and products that the user currently needs. For example, the generation unit analyzes keywords that the user searches for on a website in real time and instantly displays related information and products. For example, if the user searches for "travel," travel-related products and services are suggested. The generation unit also analyzes the operations the user performs on the app in real time and provides information and products that the user currently needs. For example, if the user searches for a cooking recipe, related ingredients and cooking utensils are suggested. The generation unit also analyzes the behavior the user performs in a store in real time and suggests products that the user currently needs. For example, if the user stays in a particular section for a long time, products in that section are introduced. In this way, the generation unit can analyze the user's real-time behavioral data and instantly provide information and products that the user currently needs.

[0074] The generation unit can personalize and suggest nearby stores and services based on real-time location information. The generation unit, for example, uses the GPS function of the user's smartphone to obtain location information in real time and suggest nearby stores and services. For example, when the user is in a shopping mall, it provides information about stores in the mall. The generation unit also analyzes location information in real time when the user is in a specific area and suggests services related to that area. For example, when the user is in a tourist spot, it introduces tourist spots and restaurants. The generation unit also analyzes location information while the user is moving in real time and suggests stores and services along the user's route. For example, it introduces cafes and convenience stores that the user can stop by on their commute. This makes it possible to personalize and suggest nearby stores and services based on the user's real-time location information.

[0075] The generation unit can analyze voice commands and provide personalization through voice interaction. For example, the generation unit analyzes voice commands given by a user to a voice assistant in real time and provides personalized information and products. For example, if a user says, "Tell me about nearby restaurants," the generation unit suggests restaurants that match their preferences. The generation unit also analyzes the content of conversations the user has through a smart speaker in real time and provides personalized services. For example, if a user says, "Play some relaxing music," the generation unit plays music that matches their preferences. The generation unit also analyzes operations the user performs using voice commands in the car in real time and provides personalized information and services. For example, if a user says, "Tell me where the next gas station is," the generation unit provides information about the nearest gas station. This makes it possible to analyze a user's voice commands and provide personalization through voice interaction.

[0076] The generation unit analyzes real-time social media activity and can instantly suggest topics and events that the user is interested in. For example, the generation unit analyzes accounts that the user follows on social media and posts that the user has liked in real time to suggest topics and events that the user is interested in. For example, the generation unit provides live performance information for artists that the user follows. The generation unit also analyzes articles and videos that the user has shared on social media in real time to suggest related topics and events. For example, the generation unit provides screening information for movies that the user has shared. The generation unit also analyzes hashtags that the user uses on social media in real time to suggest topics and events that the user is interested in. For example, the generation unit provides event information related to hashtags used by the user. In this way, the generation unit can analyze the user's real-time social media activity and instantly suggest topics and events that the user is interested in.

[0077] The generation unit can use the emotion estimation function to suggest entertainment content that corresponds to the user's real-time emotions. For example, the generation unit analyzes the user's emotion data in real time and suggests a movie that corresponds to the emotion. For example, if the user feels like relaxing, a movie with a relaxing effect is displayed. The generation unit also analyzes the user's emotion data in real time and suggests music that corresponds to the emotion. For example, if the user has positive emotions, up-tempo music is played. The generation unit also analyzes the user's emotion data in real time and suggests games or activities that correspond to the emotion. For example, if the user is feeling stressed, a game with a relaxing effect is suggested. In this way, the emotion estimation function can be used to suggest entertainment content that corresponds to the user's real-time emotions.

[0078] The generation unit develops an efficient learning algorithm that corresponds to emotions based on the emotional data, thereby reducing learning costs. The generation unit, for example, develops a learning algorithm that elicits positive emotions based on the user's emotional data. For example, it designs an algorithm that allows the user to study efficiently when they are relaxed. The generation unit also analyzes the user's emotional data and proposes a learning schedule that corresponds to emotions. For example, it adjusts the schedule so that the user studies during times when they are most likely to concentrate. The generation unit also develops an algorithm that personalizes learning content based on the user's emotional data. For example, it organizes learning content around topics that interest the user. This makes it possible to develop an efficient learning algorithm that corresponds to emotions based on the user's emotional data, thereby reducing learning costs.

[0079] The generation unit can share behavioral data on the cloud and integrate multiple user data to distribute learning costs. The generation unit, for example, shares user behavioral data on the cloud and integrates multiple user data to develop a learning algorithm. For example, it integrates data of users with the same interests to build a learning model. The generation unit also develops an efficient learning algorithm based on the user data shared on the cloud. For example, it combines data from different users to optimize the learning model. The generation unit also integrates multiple user data on the cloud to build a system that distributes learning costs. For example, it collects user data in real time and analyzes it on the cloud. This makes it possible to share user behavioral data on the cloud and integrate multiple user data to distribute learning costs.

[0080] The generation unit automatically generates optimal advertisements and promotions based on the preference data, thereby reducing monetary costs. The generation unit, for example, automatically generates personalized advertisements based on the user's preference data. For example, it displays advertisements for products and services that interest the user. The generation unit also analyzes the user's preference data and automatically generates optimal promotions. For example, it suggests events and campaigns that the user is interested in. The generation unit also develops algorithms that maximize the effectiveness of advertisements and promotions based on the user's preference data. For example, it displays advertisements at times when the user is most likely to respond. This makes it possible to automatically generate optimal advertisements and promotions based on the user's preference data and reduce monetary costs.

[0081] The generation unit can anonymize data and share it while protecting privacy. The generation unit, for example, builds a system that anonymizes user data and shares it on the cloud while protecting privacy. For example, the generation unit collects and analyzes data in a form that does not identify individuals. The generation unit also develops an efficient learning algorithm based on the anonymized user data. For example, the generation unit optimizes a learning model using the anonymized data. The generation unit also develops a protocol for anonymizing user data and sharing it while protecting privacy. For example, the generation unit builds a system that automates the anonymization and sharing of data. This makes it possible to anonymize user data and share it while protecting privacy.

[0082] The generation unit manages data in a distributed network, thereby reducing learning costs while ensuring data security. The generation unit, for example, manages user data in a distributed network and develops learning algorithms while ensuring security. For example, blockchain technology is used to prevent data tampering. The generation unit also uses a distributed network to build a system that safely shares user data and reduces learning costs. For example, data is managed and analyzed simultaneously in a distributed manner. The generation unit also develops efficient learning algorithms based on user data managed in the distributed network. For example, the generation unit optimizes learning models while performing distributed processing of data. This allows user data to be managed in a distributed network and learning costs to be reduced while ensuring data security.

[0083] The generation unit uses the emotion estimation function to automatically generate advertisements and promotions according to emotions, thereby optimizing advertising costs. The generation unit automatically generates advertisements according to emotions, for example, based on user emotion data. For example, an advertisement is displayed when the user has positive emotions. The generation unit also uses the emotion estimation function to automatically generate promotions according to the user's emotions. For example, a product with a relaxing effect is suggested when the user is relaxed. The generation unit also develops an algorithm that maximizes the effectiveness of advertisements and promotions based on the user emotion data. For example, an advertisement is displayed at a time when the user is most likely to respond. In this way, the emotion estimation function can be used to automatically generate advertisements and promotions according to the user's emotions, thereby optimizing advertising costs.

[0084] The generation unit can provide an interface and design that corresponds to the emotion based on the emotion data, thereby improving the user experience. For example, the generation unit provides an interface design that elicits positive emotions based on the user's emotion data. For example, it incorporates colors and layouts that have a relaxing effect. The generation unit also analyzes the user's emotion data and provides a customized interface that corresponds to the emotion. For example, if the user is feeling stressed, it suggests a simple and intuitive design. The generation unit also provides an interactive design that corresponds to the emotion based on the user's emotion data. For example, it incorporates entertainment elements if the user is feeling positive. In this way, it is possible to provide an interface and design that corresponds to the emotion based on the user's emotion data, thereby improving the user experience.

[0085] The generation unit analyzes the behavioral data and can quickly provide the information and products that the user desires. The generation unit, for example, analyzes the user's behavioral data and builds a system that quickly provides the information that the user desires. For example, related information is instantly displayed based on keywords searched by the user. The generation unit also quickly suggests products that the user is interested in based on the user's behavioral data. For example, items related to products viewed by the user are displayed. The generation unit also analyzes the user's behavioral data and develops a system that provides the information and products that the user desires in real time. For example, related products are suggested when the user is viewing a specific category. This makes it possible to analyze the user's behavioral data and quickly provide the information and products that the user desires.

[0086] The generation unit can personalize and provide content and services that interest the user based on the preference data. The generation unit, for example, builds a system that provides personalized content based on the user's preference data. For example, it displays articles and videos that interest the user. The generation unit also analyzes the user's preference data and provides personalized services that interest the user. For example, it suggests restaurants and events that the user likes. The generation unit also develops a system that provides personalized products based on the user's preference data. For example, it suggests products that incorporate designs and functions that the user prefers. This makes it possible to provide personalized content and services that interest the user based on the user's preference data.

[0087] The generation unit can analyze voice commands and provide services through voice interaction. For example, the generation unit analyzes a user's voice commands and provides personalized services through voice interaction. For example, when a user says, "Tell me about nearby cafes," the generation unit suggests cafes that match the user's preferences. The generation unit also provides personalized information through voice interaction based on the user's voice commands. For example, when a user asks, "What's the weather like today?", the generation unit provides weather information based on the user's location information. The generation unit also analyzes a user's voice commands and provides personalized entertainment content through voice interaction. For example, when a user says, "Play some relaxing music," the generation unit plays music that matches the user's preferences. In this way, the generation unit can analyze a user's voice commands and provide services through voice interaction.

[0088] The generation unit can provide services according to the places the user visits and their movement patterns based on the location information. The generation unit, for example, builds a system that provides services according to the places the user visits based on the user's location information. For example, if the user is in a shopping mall, it provides store information within the mall. The generation unit also analyzes the user's movement patterns and provides services along the user's movement route. For example, it suggests cafes and convenience stores that the user can stop by on their way to work. The generation unit also develops a system that provides services related to the places the user visits based on the user's location information. For example, if the user is in a tourist spot, it introduces tourist spots and restaurants. This makes it possible to provide services according to the places the user visits and their movement patterns based on the user's location information.

[0089] The generation unit can use the emotion estimation function to suggest entertainment content according to the emotion, thereby improving the user experience. The generation unit, for example, suggests a movie according to the emotion based on the user's emotion data. For example, if the user feels like relaxing, it displays a movie that has a relaxing effect. The generation unit also suggests music according to the emotion based on the user's emotion data. For example, if the user has positive emotions, it plays up-tempo music. The generation unit also suggests games or activities according to the emotion based on the user's emotion data. For example, if the user is feeling stressed, it suggests a game that has a relaxing effect. In this way, the emotion estimation function can be used to suggest entertainment content according to the user's emotion, thereby improving the user experience.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The user data collection unit collects the user's health data, and the analysis unit can evaluate the user's health condition. For example, if the user uses a wearable device, the analysis unit can collect heart rate, step count, and sleep data from the device. If the user uses a food recording app, the data can be collected to evaluate nutritional balance. Furthermore, it is possible to collect the results of the user's regular health checkups and analyze changes in the user's long-term health condition. This makes it possible to provide individual health advice and recommended lifestyle habits based on the user's health data.

[0092] The user data collection unit can monitor the stress level in real time based on the user's emotional data, and the analysis unit can suggest stress reduction measures. For example, if the user is feeling stressed, it can provide relaxing music or a meditation guide. It can also identify specific situations that cause the user stress and provide advice on how to avoid those situations. It can also analyze the user's behavioral patterns when they feel stressed and suggest activities that are effective in reducing stress. In this way, it is possible to provide stress reduction measures based on the user's emotional data and support the user's mental health.

[0093] The user data collection unit can collect user purchasing data and have the analysis unit analyze purchasing patterns. For example, data on products purchased in the past by the user can be collected to identify frequently purchased products and brands. It is also possible to analyze products purchased by the user during specific seasons or events to understand seasonal purchasing patterns. Furthermore, it is also possible to collect products viewed by the user when shopping online and products added to the cart to identify products that the user is most interested in purchasing. This makes it possible to provide individual purchasing advice and recommended products based on the user's purchasing data.

[0094] The user data collection unit can propose a fitness plan that corresponds to the user's emotions based on the user's emotional data. For example, if the user is feeling positive, it can propose energetic exercise. If the user is feeling stressed, it can also suggest yoga or stretching, which have a relaxing effect. Furthermore, it is possible to analyze the user's emotional data and customize a fitness plan according to emotional fluctuations. This makes it possible to provide a fitness plan that corresponds to the user's emotions based on the user's emotional data, supporting the user's health and well-being.

[0095] The user data collection unit collects data on the user's hobbies and interests, and the analysis unit can suggest events and activities that match the hobbies. For example, if the user is interested in music, nearby concerts and live events can be suggested. If the user is interested in sports, information on local sporting events and matches can be provided. Furthermore, if the user is interested in art and culture, information on art galleries and museums can be suggested. This makes it possible to provide events and activities based on the user's hobbies and interests, enriching the user's lifestyle.

[0096] The user data collection unit can propose a meal plan that corresponds to the user's emotions based on the user's emotional data. For example, if the user is feeling stressed, it can propose recipes using ingredients that have a relaxing effect. Also, if the user is feeling positive, it can provide an energetic meal plan. Furthermore, it is possible to analyze the user's emotional data and customize the meal plan according to emotional fluctuations. This makes it possible to provide a meal plan that corresponds to the user's emotions based on the user's emotional data, supporting the user's health and well-being.

[0097] The user data collection unit collects the user's travel data, and the analysis unit can customize the travel plan. For example, it collects data on the destinations and accommodations the user has visited in the past to identify the user's preferred travel style. It can also analyze data on the activities and tourist spots the user has visited during their trip to suggest travel plans for the next time. It can also collect photos taken by the user and reviews posted by the user during their trip to evaluate their satisfaction with the trip. This makes it possible to provide individual travel plans and recommended travel destinations based on the user's travel data.

[0098] The user data collection unit can propose a study plan that corresponds to the user's emotions based on the user's emotional data. For example, if the user is relaxed, it can propose a study plan to improve concentration. Also, if the user is feeling stressed, it can propose a study environment that has a relaxing effect. Furthermore, it is possible to analyze the user's emotional data and customize a study plan according to emotional fluctuations. This makes it possible to provide a study plan that corresponds to the user's emotions based on the user's emotional data, thereby improving study efficiency.

[0099] The user data collection unit collects the user's entertainment data, and the analysis unit can customize the entertainment plan. For example, data on movies and TV dramas the user has watched in the past can be collected to identify the user's preferred genre. The unit can also analyze data on songs the user has played on a music streaming service to suggest the next song they should listen to. Furthermore, data on the games the user has played can be collected to identify the user's preferred game genre. This makes it possible to provide an individual entertainment plan and recommended content based on the user's entertainment data.

[0100] The user data collection unit can propose a relaxation plan based on the user's emotional data, depending on the user's emotions. For example, if the user is feeling stressed, it can suggest relaxing aromatherapy or massage. If the user is feeling positive, it can also provide energetic activities. Furthermore, it is possible to analyze the user's emotional data and customize a relaxation plan based on emotional fluctuations. This makes it possible to provide a relaxation plan based on the user's emotional data, depending on the user's emotions, and support the user's mental health.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The user data collection unit collects user behavior data and preference data. For example, it collects information such as the products the user has viewed in the past, their purchase history, and their website browsing history. The user data collection unit can also collect user behavior data in real time. For example, it collects information such as the page the user is currently viewing and the links the user has clicked. Step 2: The analysis unit analyzes the data collected by the user data collection unit. For example, it uses data mining technology to analyze user preferences. The analysis unit can also analyze user behavior patterns using machine learning algorithms. For example, it can predict future behavior based on the user's past behavior data. Step 3: The generation unit generates an object that is optimal for the user based on the data analyzed by the analysis unit. For example, it generates furniture with a design that the user likes or products that meet the user's needs. The generation unit can also generate objects that reflect the user's tastes and needs in real time. For example, when the user is browsing a website, it displays products that match the user's preferences.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

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

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user data collection unit that collects user behavior data and preference data; an analysis unit that analyzes the data collected by the user data collection unit; a generation unit that generates an optimal object for the user based on the data analyzed by the analysis unit. A system characterized by:

2. The user data collection unit Collect user emotional data and analyze emotional fluctuations 2. The system of claim 1.

3. The user data collection unit Collect the user's biometric data and analyze it with the generative AI.

2. The system of claim 1.

4. The user data collection unit Analyze users' social media activity to understand topics and trends in real time 2. The system of claim 1.

5. The user data collection unit Utilizing voice assistants and smart speakers to analyze voice commands and conversations 2. The system of claim 1.

6. The user data collection unit Collecting user location information and analyzing movement patterns 2. The system of claim 1.

7. The user data collection unit Estimate emotions from text data and perform data analysis based on those emotions 2. The system of claim 1.

8. The generation unit Based on the emotion data, the object is generated with a design and color that corresponds to the emotion.

2. The system of claim 1.

Images