System

Abstract

An object of a system according to an embodiment is to provide information personalized on the basis of an action history or hobbies and preferences of a user in real time.SOLUTION: A system includes a collection unit, a learning unit, a generation unit, and a provision unit. The collection unit collects an action history or hobbies and preferences of a user. The learning unit learns the data collected by the collection unit. The generation unit generates personalized information on the basis of the data learned by the learning unit. The provision unit provides the information generated by the generation unit to the user in real time.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 not adequately provided personalized information based on a user's behavioral history and hobbies and preferences in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized information based on the user's behavioral history and hobbies and preferences in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a generation unit, and a provision unit. The collection unit collects a user's behavioral history or hobbies and preferences. The learning unit learns the data collected by the collection unit. The generation unit generates personalized information based on the data learned by the learning unit. The provision unit provides the information generated by the generation unit to the user in real time. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized information based on the user's behavior history and hobbies and preferences 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) A navigation system according to an embodiment of the present invention learns a user's behavioral history and hobbies and preferences and provides real-time navigation based on the information. The navigation system collects the user's behavioral history and hobbies and preferences, and uses a generation AI to learn from the data to generate personalized information for the user and provide the information in real time. For example, when a user visits a tourist destination, the navigation system provides information on tourist spots and restaurants of interest based on the user's past behavioral history and hobbies and preferences. Furthermore, when a user is shopping, the navigation system provides related product and sale information based on the user's past purchase history and preferences. This allows the user to obtain information that is truly interesting and necessary to them in real time. This allows the navigation system to improve the user's experience and provide a more convenient and comfortable life. For example, this information includes information on tourist spots and restaurants at tourist destinations, and information on related products and sales while shopping.

[0029] A navigation system according to an embodiment includes a collection unit, a learning unit, a generation unit, and a provision unit. The collection unit collects a user's behavioral history or hobbies and preferences. For example, the collection unit can collect data such as places visited by the user, services used, and products purchased by the user. The collection unit can also collect data on places visited by the user using GPS data or location information services. The collection unit can also collect usage histories of online services and in-store services. The learning unit learns the data collected by the collection unit. For example, the learning unit can analyze a user's behavioral patterns and hobbies and preferences using a machine learning algorithm. The learning unit can also learn a user's hobbies and preferences using a clustering or classification algorithm. The generation unit generates personalized information based on the data learned by the learning unit. For example, the generation unit can generate information based on the user's interests or customized content. The generation unit can also generate information optimized for the user using a recommendation system. The provision unit provides the information generated by the generation unit to the user in real time. For example, the provision unit can provide the generated information to the user through AR glasses. The providing unit can also provide the generated information in the form of text, image, audio, etc. This enables the navigation system according to the embodiment to provide personalized information based on the user's behavior history and hobbies and preferences in real time.

[0030] The collection unit can collect data on places visited by the user, services used, and products purchased by the user. For example, the collection unit can collect data on places visited by the user using GPS data or location information services. The collection unit can also collect usage histories of online services and in-store services. For example, the collection unit can collect data on places visited by the user as GPS data and analyze the data using location information services. The collection unit can also collect a history of online services used by the user and analyze the types and frequency of services used. Furthermore, the collection unit can collect data on products purchased by the user and analyze the purchase history. For example, the collection unit can collect online purchase history and in-store purchase history and analyze the types and prices of purchased products. This enables the collection unit to collect data based on the user's behavioral history and hobbies and preferences.

[0031] The learning unit can analyze the collected data and learn the user's behavioral patterns and hobbies and preferences. The learning unit can analyze the collected data using, for example, a machine learning algorithm. For example, the learning unit can identify places that the user frequently visits and services that the user uses using frequency analysis. The learning unit can also analyze the user's behavioral patterns using time series analysis. Furthermore, the learning unit can learn the user's hobbies and preferences using clustering or classification algorithms. For example, the learning unit can group the user's hobbies and preferences using clustering and learn the characteristics of users belonging to a specific group. The learning unit can also classify the user's hobbies and preferences using a classification algorithm and identify users with specific hobbies and preferences. This enables the learning unit to analyze and learn the user's behavioral patterns and hobbies and preferences.

[0032] The generation unit can generate information suitable for the user based on the learned data. The generation unit can generate, for example, information based on the user's interests or customized content. For example, the generation unit can generate information optimal for the user using a recommendation system. The generation unit can also generate information based on the user's behavioral patterns and hobbies and preferences using a generation AI. For example, the generation unit can generate information about places frequently visited by the user and provide information about tourist attractions and restaurants related to those places. The generation unit can also generate related product and sale information based on the user's purchase history. For example, if the user likes to purchase clothes from a particular brand, the generation unit can generate information about new products and sales from that brand. This enables the generation unit to generate information optimal for the user.

[0033] The providing unit can provide the generated information to the user in real time. For example, the providing unit can provide the generated information to the user through AR glasses. For example, the providing unit can provide the generated information in the form of text, images, audio, or the like. The providing unit can also update the generated information in real time to provide the user with the latest information. For example, when the user visits a tourist spot, the providing unit can provide information on tourist spots and restaurants customized based on the user's past behavior history and hobbies and preferences in real time. Furthermore, when the user is shopping, the providing unit can provide information on related products and sales based on the user's past purchase history and preferences in real time. This enables the providing unit to provide the generated information in real time.

[0034] The providing unit can provide information through the AR glasses. The providing unit can, for example, provide the generated information to the user through the AR glasses. For example, the providing unit can use the AR glasses to display information by overlaying it on the user's field of view. The providing unit can also provide customized information based on the user's location information through the AR glasses. For example, when the user visits a tourist spot, the providing unit can display information about tourist spots and restaurants through the AR glasses. The providing unit can also display related product and sale information through the AR glasses when the user is shopping. This enables the providing unit to provide information through the AR glasses.

[0035] The providing unit can provide information customized based on the user's past behavioral history and hobbies and preferences. The providing unit can provide information customized based on the user's past behavioral history and hobbies and preferences, for example. For example, the providing unit can provide information about places the user frequently visits. The providing unit can also provide related product and sale information based on the user's purchase history. For example, if the user likes to purchase clothes from a particular brand, the providing unit can provide new product and sale information from that brand. Furthermore, the providing unit can provide information about events and activities of interest to the user based on the user's hobbies and preferences. For example, the providing unit can provide information about related concerts and live events based on the user's favorite music genre. This enables the providing unit to provide customized information based on the user's past behavioral history and hobbies and preferences.

[0036] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. For example, the collection unit can analyze the frequency of places the user has visited in the past and prioritize collecting data on important places. The collection unit can also analyze the user's past behavioral patterns and concentrate data collection in a specific time period. The collection unit can also analyze the types of services the user has used in the past and collect related data. For example, the collection unit can analyze data on places the user has visited in the past and prioritize collecting data on places the user has used frequently. The collection unit can also analyze the user's behavioral patterns and concentrate data collection in a specific time period. The collection unit can also analyze the types of services the user has used in the past and collect related data. This enables the collection unit to select an optimal data collection method based on the user's past behavioral history.

[0037] When collecting data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is sightseeing, the collection unit can prioritize collecting data about tourist spots. Furthermore, when the user is shopping, the collection unit can also collect data about related stores and products. Furthermore, when the user is participating in an event, the collection unit can also collect information related to the event. For example, when the user is sightseeing, the collection unit can prioritize collecting data about tourist spots. Furthermore, when the user is shopping, the collection unit can also collect data about related stores and products. Furthermore, when the user is participating in an event, the collection unit can also collect information related to the event. This enables the collection unit to filter data collection based on the user's current activity status and areas of interest.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user is using voice input, the collection unit can prioritize collecting voice data. Also, when the user is using text input, the collection unit can also collect text data. Furthermore, when the user is uploading an image, the collection unit can collect and analyze image data. For example, when the user is using voice input, the collection unit can prioritize collecting voice data. Also, when the user is using text input, the collection unit can also collect text data. Furthermore, when the user is uploading an image, the collection unit can collect and analyze image data. This enables the collection unit to select the optimal collection means depending on the user's input method.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also collect information about the user's destination. Furthermore, when the user is participating in a specific event, the collection unit can also collect data related to the event. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also collect information about the user's destination. Furthermore, when the user is participating in a specific event, the collection unit can also collect data related to the event. This enables the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0040] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables the collection unit to collect related data based on the user's social media activities.

[0041] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the data collection method based on feedback provided by the user in the past. The collection unit can also change the type of data to be collected by reflecting the user's feedback. Furthermore, the collection unit can adjust the frequency of data collection based on the user's feedback. For example, the collection unit can adjust the data collection method based on feedback provided by the user in the past. The collection unit can also change the type of data to be collected by reflecting the user's feedback. Furthermore, the collection unit can adjust the frequency of data collection based on the user's feedback. This enables the collection unit to customize the collection method based on the user's past feedback.

[0042] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, adjust parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and identify areas in which the learning algorithm needs to be improved. For example, the learning unit can adjust parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and identify areas in which the learning algorithm needs to be improved. This makes it possible for the learning unit to optimize the learning algorithm by referring to past learning data.

[0043] During learning, the learning unit can analyze the user's behavioral patterns and adjust the update frequency of the learning data. The learning unit can, for example, determine the update frequency of the learning data based on the user's behavioral patterns. The learning unit can also analyze the user's behavioral patterns and increase the update frequency of important data. Furthermore, the learning unit can adjust the update timing of the learning data by referring to the user's behavioral patterns. For example, the learning unit can determine the update frequency of the learning data based on the user's behavioral patterns. The learning unit can also analyze the user's behavioral patterns and increase the update frequency of important data. Furthermore, the learning unit can adjust the update timing of the learning data by referring to the user's behavioral patterns. This enables the learning unit to adjust the update frequency of the learning data based on the user's behavioral patterns.

[0044] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information collected from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources and reflect the information in the learning algorithm. Furthermore, the learning unit can increase the diversity of the learning data based on the information from different data sources. For example, the learning unit can integrate information collected from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources and reflect the information in the learning algorithm. Furthermore, the learning unit can increase the diversity of the learning data based on the information from different data sources. This enables the learning unit to enrich the learning data by integrating information from different data sources.

[0045] During learning, the learning unit can weight the learning data based on the time the data was collected. For example, the learning unit can perform learning by assigning a high weight to recently collected data. The learning unit can also perform learning by assigning a low weight to older data. Furthermore, the learning unit can adjust the weighting for data collected during a specific period. For example, the learning unit can perform learning by assigning a high weight to recently collected data. The learning unit can also perform learning by assigning a low weight to older data. Furthermore, the learning unit can adjust the weighting for data collected during a specific period. This enables the learning unit to weight the learning data based on the time the data was collected.

[0046] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. For example, the learning unit can adjust parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of learning by reflecting user feedback. Furthermore, the learning unit can identify areas for improvement in the learning algorithm by referring to user feedback. For example, the learning unit can adjust parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of learning by reflecting user feedback. Furthermore, the learning unit can identify areas for improvement in the learning algorithm by referring to user feedback. This enables the learning unit to adjust the learning algorithm based on user feedback.

[0047] The learning unit can optimize the learning data during learning, taking into account the user's lifestyle rhythm. The learning unit can, for example, adjust the update timing of the learning data based on the user's lifestyle rhythm. The learning unit can also select the type of learning data taking into account the user's lifestyle rhythm. Furthermore, the learning unit can determine the priority of the learning data by referring to the user's lifestyle rhythm. For example, the learning unit can adjust the update timing of the learning data based on the user's lifestyle rhythm. The learning unit can also select the type of learning data by referring to the user's lifestyle rhythm. Furthermore, the learning unit can determine the priority of the learning data by referring to the user's lifestyle rhythm. This enables the learning unit to optimize the learning data based on the user's lifestyle rhythm.

[0048] The generation unit can adjust the level of detail of the information to be generated based on the user's behavioral patterns when generating information. The generation unit can, for example, generate detailed information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral patterns. The generation unit can also adjust the level of detail of the information by referring to the user's behavioral patterns. For example, the generation unit can generate detailed information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral patterns. The generation unit can also adjust the level of detail of the information by referring to the user's behavioral patterns. This makes it possible for the generation unit to adjust the level of detail of the information to be generated based on the user's behavioral patterns.

[0049] The generation unit can apply different generation algorithms depending on the user's field of interest when generating information. For example, the generation unit can apply a specific generation algorithm to generate information related to a field in which the user is interested. The generation unit can also select an optimal generation algorithm based on the user's field of interest. Furthermore, the generation unit can adjust parameters of the generation algorithm depending on the user's field of interest. For example, the generation unit can apply a specific generation algorithm to generate information related to a field in which the user is interested. The generation unit can also select an optimal generation algorithm based on the user's field of interest. Furthermore, the generation unit can adjust parameters of the generation algorithm depending on the user's field of interest. This makes it possible for the generation unit to apply different generation algorithms depending on the user's field of interest.

[0050] When generating information, the generation unit can improve the accuracy of generation by referring to the user's past behavioral history. The generation unit can improve the accuracy of the information to be generated, for example, based on the user's past behavioral history. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past behavioral history. Furthermore, the generation unit can analyze the user's past behavioral history and optimize the content of the information to be generated. For example, the generation unit can improve the accuracy of the information to be generated based on the user's past behavioral history. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past behavioral history. Furthermore, the generation unit can analyze the user's past behavioral history and optimize the content of the information to be generated. This enables the generation unit to improve the accuracy of generation based on the user's past behavioral history.

[0051] When generating information, the generation unit can determine the priority of the information to be generated based on the user's behavioral history. For example, the generation unit can prioritize generating information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral history. Furthermore, the generation unit can adjust the priority of the information to be generated by referring to the user's behavioral history. For example, the generation unit can prioritize generating information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral history. Furthermore, the generation unit can adjust the priority of the information to be generated by referring to the user's behavioral history. This enables the generation unit to determine the priority of the information to be generated based on the user's behavioral history.

[0052] When generating information, the generation unit can adjust the order of information to be generated based on the relevance of the user. For example, the generation unit can prioritize generating information in which the user is interested. The generation unit can also adjust the order of information to be generated based on the relevance of the user. Furthermore, the generation unit can optimize the order of information to be generated by referring to the user's past behavior history. For example, the generation unit can prioritize generating information in which the user is interested. The generation unit can also adjust the order of information to be generated based on the relevance of the user. Furthermore, the generation unit can optimize the order of information to be generated by referring to the user's past behavior history. This enables the generation unit to adjust the order of information to be generated based on the relevance of the user.

[0053] When generating information, the generation unit can adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise. For example, if the user has technical knowledge, the generation unit can generate information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate information in simple language. Furthermore, the generation unit can adjust the use of technical terms in the information to be generated based on the user's level of expertise. For example, if the user has technical knowledge, the generation unit can generate information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate information in simple language. Furthermore, the generation unit can adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise. This allows the generation unit to adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise.

[0054] When providing information, the providing unit can select the optimal providing method by referring to the user's past behavioral history. For example, the providing unit can select the optimal method based on the providing method used by the user in the past. The providing unit can also adjust the timing of information provision by referring to the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and select the optimal information providing method. For example, the providing unit can select the optimal method based on the providing method used by the user in the past. The providing unit can also adjust the timing of information provision by referring to the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and select the optimal information providing method. This enables the providing unit to select the optimal providing method based on the user's past behavioral history.

[0055] When providing information, the providing unit can customize the content to be provided based on the user's current activity status. For example, when the user is sightseeing, the providing unit can provide information about tourist spots. Furthermore, when the user is shopping, the providing unit can provide information about related stores and products. Furthermore, when the user is participating in an event, the providing unit can provide information related to the event. For example, when the user is sightseeing, the providing unit can provide information about tourist spots. Furthermore, when the user is shopping, the providing unit can provide information about related stores and products. Furthermore, when the user is participating in an event, the providing unit can provide information related to the event. This enables the providing unit to customize the content to be provided based on the user's current activity status.

[0056] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit can adjust the information providing method based on user feedback. The providing unit can also change the type of information to be provided by reflecting user feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to user feedback. For example, the providing unit can adjust the information providing method based on user feedback. The providing unit can also change the type of information to be provided by reflecting user feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to user feedback. This enables the providing unit to improve the information providing method based on user feedback.

[0057] When providing information, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can provide information related to the area. Furthermore, when the user is traveling, the providing unit can also provide information about the user's destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. For example, when the user is in a specific area, the providing unit can provide information related to the area. Furthermore, when the user is traveling, the providing unit can also provide information about the user's destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. This enables the providing unit to select the optimal providing method based on the user's geographical location information.

[0058] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This enables the providing unit to customize the provided content based on the user's social media activity.

[0059] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The providing unit can, for example, adjust the information providing method based on the user's past feedback. The providing unit can also change the type of information to be provided by reflecting the user's past feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to the user's past feedback. For example, the providing unit can adjust the information providing method based on the user's past feedback. The providing unit can also change the type of information to be provided by reflecting the user's past feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to the user's past feedback. This enables the providing unit to customize the information providing method based on the user's past feedback.

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

[0061] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and number of steps, and evaluates the user's health condition based on this data. For example, the health management unit may provide a notification urging the user to take a break if the user has been walking for a long time. The health management unit may also provide appropriate exercise and dietary advice based on the user's health condition. Furthermore, the health management unit may periodically analyze the user's health data and detect health risks early. This enables the navigation system to support the user's health management and promote a healthier lifestyle.

[0062] The navigation system may further include a schedule management unit that manages the user's schedule. The schedule management unit collects the user's calendar and schedule information and optimizes the user's schedule based on this data. For example, the schedule management unit may suggest an optimal travel route based on the user's schedule. The schedule management unit may also set reminders based on the user's schedule to notify the user so that they do not forget important appointments. Furthermore, the schedule management unit may suggest appropriate break times and relaxation activities based on the user's schedule. This allows the navigation system to support the user's schedule management and provide more efficient time management.

[0063] The navigation system may further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit may collect the user's driving data and analyze the driving style based on this data. For example, the driving analysis unit may analyze the user's acceleration and braking frequency and provide safe driving advice. The driving analysis unit may also make suggestions to improve fuel efficiency based on the user's driving patterns. Furthermore, the driving analysis unit may provide a training program to support the improvement of driving skills based on the user's driving data. This enables the navigation system to analyze the user's driving style and support safe and efficient driving.

[0064] The navigation system may further include a diet management unit that manages the user's diet history. The diet management unit can collect the user's diet history and make dietary suggestions based on this data. For example, the diet management unit can suggest nutritionally balanced meals based on the user's past diet history. The diet management unit can also provide a meal plan tailored to the user's health condition. Furthermore, the diet management unit can analyze the user's diet history and suggest areas for improving the user's diet. This allows the navigation system to support the user's dietary management and provide a healthier diet.

[0065] The navigation system may further include an exercise management unit that manages the user's exercise history. The exercise management unit can collect the user's exercise history and make exercise suggestions based on this data. For example, the exercise management unit can suggest an appropriate exercise plan based on the user's past exercise history. The exercise management unit can also provide exercise advice tailored to the user's health condition. Furthermore, the exercise management unit can analyze the user's exercise history and suggest areas for improvement in exercise. This enables the navigation system to support the user's exercise management and provide a healthier lifestyle.

[0066] The navigation system may further include a sleep management unit that manages the user's sleep history. The sleep management unit can collect the user's sleep history and make sleep suggestions based on this data. For example, the sleep management unit can suggest an appropriate sleep schedule based on the user's past sleep history. The sleep management unit can also provide sleep advice tailored to the user's health condition. Furthermore, the sleep management unit can analyze the user's sleep history and suggest areas for improving sleep. This allows the navigation system to support the user's sleep management and provide a healthier lifestyle.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects the user's behavioral history or hobbies and preferences. For example, the collection unit can collect data such as the places the user visited, the services used, and the products purchased. The collection unit can also collect data on the places the user visited using GPS data or location information services. Furthermore, the collection unit can also collect the usage history of online services and in-store services. Step 2: The learning unit learns the data collected by the collection unit. For example, the learning unit can use a machine learning algorithm to analyze the user's behavioral patterns and hobbies and preferences. The learning unit can also use a clustering or classification algorithm to learn the user's hobbies and preferences. Step 3: The generator generates personalized information based on the data learned by the learning unit. For example, the generator can generate information based on the user's interests or customized content. The generator can also generate information optimal for the user using a recommendation system. Step 4: The providing unit provides the information generated by the generating unit to the user in real time. For example, the providing unit may provide the generated information to the user through AR glasses. The providing unit may also provide the generated information in the form of text, images, audio, etc.

[0069] (Example 2) A navigation system according to an embodiment of the present invention learns a user's behavioral history and hobbies and preferences and provides real-time navigation based on the information. The navigation system collects the user's behavioral history and hobbies and preferences, and uses a generation AI to learn from the data to generate personalized information for the user and provide the information in real time. For example, when a user visits a tourist destination, the navigation system provides information on tourist spots and restaurants of interest based on the user's past behavioral history and hobbies and preferences. Furthermore, when a user is shopping, the navigation system provides related product and sale information based on the user's past purchase history and preferences. This allows the user to obtain information that is truly interesting and necessary to them in real time. This allows the navigation system to improve the user's experience and provide a more convenient and comfortable life. For example, this information includes information on tourist spots and restaurants at tourist destinations, and information on related products and sales while shopping.

[0070] A navigation system according to an embodiment includes a collection unit, a learning unit, a generation unit, and a provision unit. The collection unit collects a user's behavioral history or hobbies and preferences. For example, the collection unit can collect data such as places visited by the user, services used, and products purchased by the user. The collection unit can also collect data on places visited by the user using GPS data or location information services. The collection unit can also collect usage histories of online services and in-store services. The learning unit learns the data collected by the collection unit. For example, the learning unit can analyze a user's behavioral patterns and hobbies and preferences using a machine learning algorithm. The learning unit can also learn a user's hobbies and preferences using a clustering or classification algorithm. The generation unit generates personalized information based on the data learned by the learning unit. For example, the generation unit can generate information based on the user's interests or customized content. The generation unit can also generate information optimized for the user using a recommendation system. The provision unit provides the information generated by the generation unit to the user in real time. For example, the provision unit can provide the generated information to the user through AR glasses. The providing unit can also provide the generated information in the form of text, image, audio, etc. This enables the navigation system according to the embodiment to provide personalized information based on the user's behavior history and hobbies and preferences in real time.

[0071] The collection unit can collect data on places visited by the user, services used, and products purchased by the user. For example, the collection unit can collect data on places visited by the user using GPS data or location information services. The collection unit can also collect usage histories of online services and in-store services. For example, the collection unit can collect data on places visited by the user as GPS data and analyze the data using location information services. The collection unit can also collect a history of online services used by the user and analyze the types and frequency of services used. Furthermore, the collection unit can collect data on products purchased by the user and analyze the purchase history. For example, the collection unit can collect online purchase history and in-store purchase history and analyze the types and prices of purchased products. This enables the collection unit to collect data based on the user's behavioral history and hobbies and preferences.

[0072] The learning unit can analyze the collected data and learn the user's behavioral patterns and hobbies and preferences. The learning unit can analyze the collected data using, for example, a machine learning algorithm. For example, the learning unit can identify places that the user frequently visits and services that the user uses using frequency analysis. The learning unit can also analyze the user's behavioral patterns using time series analysis. Furthermore, the learning unit can learn the user's hobbies and preferences using clustering or classification algorithms. For example, the learning unit can group the user's hobbies and preferences using clustering and learn the characteristics of users belonging to a specific group. The learning unit can also classify the user's hobbies and preferences using a classification algorithm and identify users with specific hobbies and preferences. This enables the learning unit to analyze and learn the user's behavioral patterns and hobbies and preferences.

[0073] The generation unit can generate information suitable for the user based on the learned data. The generation unit can generate, for example, information based on the user's interests or customized content. For example, the generation unit can generate information optimal for the user using a recommendation system. The generation unit can also generate information based on the user's behavioral patterns and hobbies and preferences using a generation AI. For example, the generation unit can generate information about places frequently visited by the user and provide information about tourist attractions and restaurants related to those places. The generation unit can also generate related product and sale information based on the user's purchase history. For example, if the user likes to purchase clothes from a particular brand, the generation unit can generate information about new products and sales from that brand. This enables the generation unit to generate information optimal for the user.

[0074] The providing unit can provide the generated information to the user in real time. For example, the providing unit can provide the generated information to the user through AR glasses. For example, the providing unit can provide the generated information in the form of text, images, audio, or the like. The providing unit can also update the generated information in real time to provide the user with the latest information. For example, when the user visits a tourist spot, the providing unit can provide information on tourist spots and restaurants customized based on the user's past behavior history and hobbies and preferences in real time. Furthermore, when the user is shopping, the providing unit can provide information on related products and sales based on the user's past purchase history and preferences in real time. This enables the providing unit to provide the generated information in real time.

[0075] The providing unit can provide information through the AR glasses. The providing unit can, for example, provide the generated information to the user through the AR glasses. For example, the providing unit can use the AR glasses to display information by overlaying it on the user's field of view. The providing unit can also provide customized information based on the user's location information through the AR glasses. For example, when the user visits a tourist spot, the providing unit can display information about tourist spots and restaurants through the AR glasses. The providing unit can also display related product and sale information through the AR glasses when the user is shopping. This enables the providing unit to provide information through the AR glasses.

[0076] The providing unit can provide information customized based on the user's past behavioral history and hobbies and preferences. The providing unit can provide information customized based on the user's past behavioral history and hobbies and preferences, for example. For example, the providing unit can provide information about places the user frequently visits. The providing unit can also provide related product and sale information based on the user's purchase history. For example, if the user likes to purchase clothes from a particular brand, the providing unit can provide new product and sale information from that brand. Furthermore, the providing unit can provide information about events and activities of interest to the user based on the user's hobbies and preferences. For example, the providing unit can provide information about related concerts and live events based on the user's favorite music genre. This enables the providing unit to provide customized information based on the user's past behavioral history and hobbies and preferences.

[0077] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on changes in facial expressions and adjust the timing of data collection. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of data collection. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of data collection. This enables the collection unit to adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The collection unit can analyze the user's past behavioral history and select an appropriate data collection method. For example, the collection unit can analyze the frequency of places the user has visited in the past and prioritize collecting data on important places. The collection unit can also analyze the user's past behavioral patterns and concentrate data collection in a specific time period. The collection unit can also analyze the types of services the user has used in the past and collect related data. For example, the collection unit can analyze data on places the user has visited in the past and prioritize collecting data on places the user has used frequently. The collection unit can also analyze the user's behavioral patterns and concentrate data collection in a specific time period. The collection unit can also analyze the types of services the user has used in the past and collect related data. This enables the collection unit to select an optimal data collection method based on the user's past behavioral history.

[0079] When collecting data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, when the user is sightseeing, the collection unit can prioritize collecting data about tourist spots. Furthermore, when the user is shopping, the collection unit can also collect data about related stores and products. Furthermore, when the user is participating in an event, the collection unit can also collect information related to the event. For example, when the user is sightseeing, the collection unit can prioritize collecting data about tourist spots. Furthermore, when the user is shopping, the collection unit can also collect data about related stores and products. Furthermore, when the user is participating in an event, the collection unit can also collect information related to the event. This enables the collection unit to filter data collection based on the user's current activity status and areas of interest.

[0080] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, when the user is using voice input, the collection unit can prioritize collecting voice data. Also, when the user is using text input, the collection unit can also collect text data. Furthermore, when the user is uploading an image, the collection unit can collect and analyze image data. For example, when the user is using voice input, the collection unit can prioritize collecting voice data. Also, when the user is using text input, the collection unit can also collect text data. Furthermore, when the user is uploading an image, the collection unit can collect and analyze image data. This enables the collection unit to select the optimal collection means depending on the user's input method.

[0081] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on changes in facial expressions and determine the priority of data to be collected. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of data to be collected. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on heart rate fluctuations and determine the priority of data to be collected. This enables the collection unit to determine the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also collect information about the user's destination. Furthermore, when the user is participating in a specific event, the collection unit can also collect data related to the event. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also collect information about the user's destination. Furthermore, when the user is participating in a specific event, the collection unit can also collect data related to the event. This enables the collection unit to prioritize collecting highly relevant data based on the user's geographical location information.

[0083] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit can collect data on places where the user has checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This enables the collection unit to collect related data based on the user's social media activities.

[0084] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can adjust the data collection method based on feedback provided by the user in the past. The collection unit can also change the type of data to be collected by reflecting the user's feedback. Furthermore, the collection unit can adjust the frequency of data collection based on the user's feedback. For example, the collection unit can adjust the data collection method based on feedback provided by the user in the past. The collection unit can also change the type of data to be collected by reflecting the user's feedback. Furthermore, the collection unit can adjust the frequency of data collection based on the user's feedback. This enables the collection unit to customize the collection method based on the user's past feedback.

[0085] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in facial expressions and select training data. The learning unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice, calculate an emotion score, and select training data. Furthermore, the learning unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on heart rate fluctuations and select training data. This enables the learning unit to select training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, adjust parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and identify areas in which the learning algorithm needs to be improved. For example, the learning unit can adjust parameters of the learning algorithm based on past learning data. The learning unit can also improve the accuracy of learning by referring to past learning data. Furthermore, the learning unit can analyze past learning data and identify areas in which the learning algorithm needs to be improved. This makes it possible for the learning unit to optimize the learning algorithm by referring to past learning data.

[0087] During learning, the learning unit can analyze the user's behavioral patterns and adjust the update frequency of the learning data. The learning unit can, for example, determine the update frequency of the learning data based on the user's behavioral patterns. The learning unit can also analyze the user's behavioral patterns and increase the update frequency of important data. Furthermore, the learning unit can adjust the update timing of the learning data by referring to the user's behavioral patterns. For example, the learning unit can determine the update frequency of the learning data based on the user's behavioral patterns. The learning unit can also analyze the user's behavioral patterns and increase the update frequency of important data. Furthermore, the learning unit can adjust the update timing of the learning data by referring to the user's behavioral patterns. This enables the learning unit to adjust the update frequency of the learning data based on the user's behavioral patterns.

[0088] During learning, the learning unit can integrate information from different data sources to enrich the learning data. For example, the learning unit can integrate information collected from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources and reflect the information in the learning algorithm. Furthermore, the learning unit can increase the diversity of the learning data based on the information from different data sources. For example, the learning unit can integrate information collected from different data sources to enrich the learning data. The learning unit can also analyze information from different data sources and reflect the information in the learning algorithm. Furthermore, the learning unit can increase the diversity of the learning data based on the information from different data sources. This enables the learning unit to enrich the learning data by integrating information from different data sources.

[0089] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on changes in facial expressions and adjust the frequency of learning. The learning unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the frequency of learning. Furthermore, the learning unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the learning unit can calculate an emotion score based on heart rate fluctuations and adjust the frequency of learning. This enables the learning unit to adjust the frequency of learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] During learning, the learning unit can weight the learning data based on the time the data was collected. For example, the learning unit can perform learning by assigning a high weight to recently collected data. The learning unit can also perform learning by assigning a low weight to older data. Furthermore, the learning unit can adjust the weighting for data collected during a specific period. For example, the learning unit can perform learning by assigning a high weight to recently collected data. The learning unit can also perform learning by assigning a low weight to older data. Furthermore, the learning unit can adjust the weighting for data collected during a specific period. This enables the learning unit to weight the learning data based on the time the data was collected.

[0091] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. For example, the learning unit can adjust parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of learning by reflecting user feedback. Furthermore, the learning unit can identify areas for improvement in the learning algorithm by referring to user feedback. For example, the learning unit can adjust parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of learning by reflecting user feedback. Furthermore, the learning unit can identify areas for improvement in the learning algorithm by referring to user feedback. This enables the learning unit to adjust the learning algorithm based on user feedback.

[0092] The learning unit can optimize the learning data during learning, taking into account the user's lifestyle rhythm. The learning unit can, for example, adjust the update timing of the learning data based on the user's lifestyle rhythm. The learning unit can also select the type of learning data taking into account the user's lifestyle rhythm. Furthermore, the learning unit can determine the priority of the learning data by referring to the user's lifestyle rhythm. For example, the learning unit can adjust the update timing of the learning data based on the user's lifestyle rhythm. The learning unit can also select the type of learning data by referring to the user's lifestyle rhythm. Furthermore, the learning unit can determine the priority of the learning data by referring to the user's lifestyle rhythm. This enables the learning unit to optimize the learning data based on the user's lifestyle rhythm.

[0093] The generation unit can estimate the user's emotion and adjust the expression method of the generated information based on the estimated user's emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression and adjust the expression method of the generated information. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the expression method of the generated information. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the expression method of the generated information. This enables the generation unit to adjust the expression method of the generated information based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The generation unit can adjust the level of detail of the information to be generated based on the user's behavioral patterns when generating information. The generation unit can, for example, generate detailed information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral patterns. The generation unit can also adjust the level of detail of the information by referring to the user's behavioral patterns. For example, the generation unit can generate detailed information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral patterns. The generation unit can also adjust the level of detail of the information by referring to the user's behavioral patterns. This makes it possible for the generation unit to adjust the level of detail of the information to be generated based on the user's behavioral patterns.

[0095] The generation unit can apply different generation algorithms depending on the user's field of interest when generating information. For example, the generation unit can apply a specific generation algorithm to generate information related to a field in which the user is interested. The generation unit can also select an optimal generation algorithm based on the user's field of interest. Furthermore, the generation unit can adjust parameters of the generation algorithm depending on the user's field of interest. For example, the generation unit can apply a specific generation algorithm to generate information related to a field in which the user is interested. The generation unit can also select an optimal generation algorithm based on the user's field of interest. Furthermore, the generation unit can adjust parameters of the generation algorithm depending on the user's field of interest. This makes it possible for the generation unit to apply different generation algorithms depending on the user's field of interest.

[0096] When generating information, the generation unit can improve the accuracy of generation by referring to the user's past behavioral history. The generation unit can improve the accuracy of the information to be generated, for example, based on the user's past behavioral history. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past behavioral history. Furthermore, the generation unit can analyze the user's past behavioral history and optimize the content of the information to be generated. For example, the generation unit can improve the accuracy of the information to be generated based on the user's past behavioral history. The generation unit can also adjust parameters of the generation algorithm by referring to the user's past behavioral history. Furthermore, the generation unit can analyze the user's past behavioral history and optimize the content of the information to be generated. This enables the generation unit to improve the accuracy of generation based on the user's past behavioral history.

[0097] The generation unit can estimate the user's emotion and adjust the length of the generated information based on the estimated user emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on changes in facial expression and adjust the length of the generated information. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the length of the generated information. Furthermore, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on fluctuations in heart rate and adjust the length of the generated information. This allows the generation unit to adjust the length of the generated information based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0098] When generating information, the generation unit can determine the priority of the information to be generated based on the user's behavioral history. For example, the generation unit can prioritize generating information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral history. Furthermore, the generation unit can adjust the priority of the information to be generated by referring to the user's behavioral history. For example, the generation unit can prioritize generating information about places frequently visited by the user. The generation unit can also prioritize generating important information based on the user's behavioral history. Furthermore, the generation unit can adjust the priority of the information to be generated by referring to the user's behavioral history. This enables the generation unit to determine the priority of the information to be generated based on the user's behavioral history.

[0099] When generating information, the generation unit can adjust the order of information to be generated based on the relevance of the user. For example, the generation unit can prioritize generating information in which the user is interested. The generation unit can also adjust the order of information to be generated based on the relevance of the user. Furthermore, the generation unit can optimize the order of information to be generated by referring to the user's past behavior history. For example, the generation unit can prioritize generating information in which the user is interested. The generation unit can also adjust the order of information to be generated based on the relevance of the user. Furthermore, the generation unit can optimize the order of information to be generated by referring to the user's past behavior history. This enables the generation unit to adjust the order of information to be generated based on the relevance of the user.

[0100] When generating information, the generation unit can adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise. For example, if the user has technical knowledge, the generation unit can generate information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate information in simple language. Furthermore, the generation unit can adjust the use of technical terms in the information to be generated based on the user's level of expertise. For example, if the user has technical knowledge, the generation unit can generate information that uses a lot of technical terms. Also, if the user does not have technical knowledge, the generation unit can generate information in simple language. Furthermore, the generation unit can adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise. This allows the generation unit to adjust the use of technical terms in the information to be generated in accordance with the user's level of expertise.

[0101] The providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user emotions. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expressions and adjust the method of providing information. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the method of providing information. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on fluctuations in heart rate and adjust the method of providing information. This enables the providing unit to adjust the method of providing information based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0102] When providing information, the providing unit can select the optimal providing method by referring to the user's past behavioral history. For example, the providing unit can select the optimal method based on the providing method used by the user in the past. The providing unit can also adjust the timing of information provision by referring to the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and select the optimal information providing method. For example, the providing unit can select the optimal method based on the providing method used by the user in the past. The providing unit can also adjust the timing of information provision by referring to the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and select the optimal information providing method. This enables the providing unit to select the optimal providing method based on the user's past behavioral history.

[0103] When providing information, the providing unit can customize the content to be provided based on the user's current activity status. For example, when the user is sightseeing, the providing unit can provide information about tourist spots. Furthermore, when the user is shopping, the providing unit can provide information about related stores and products. Furthermore, when the user is participating in an event, the providing unit can provide information related to the event. For example, when the user is sightseeing, the providing unit can provide information about tourist spots. Furthermore, when the user is shopping, the providing unit can provide information about related stores and products. Furthermore, when the user is participating in an event, the providing unit can provide information related to the event. This enables the providing unit to customize the content to be provided based on the user's current activity status.

[0104] The providing unit can improve the information providing method by reflecting user feedback when providing information. For example, the providing unit can adjust the information providing method based on user feedback. The providing unit can also change the type of information to be provided by reflecting user feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to user feedback. For example, the providing unit can adjust the information providing method based on user feedback. The providing unit can also change the type of information to be provided by reflecting user feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to user feedback. This enables the providing unit to improve the information providing method based on user feedback.

[0105] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on changes in facial expression and determine the priority of information provision. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice, calculate an emotion score, and determine the priority of information provision. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit can calculate an emotion score based on heart rate fluctuations and determine the priority of information provision. This enables the providing unit to determine the priority of information provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0106] When providing information, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can provide information related to the area. Furthermore, when the user is traveling, the providing unit can also provide information about the user's destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. For example, when the user is in a specific area, the providing unit can provide information related to the area. Furthermore, when the user is traveling, the providing unit can also provide information about the user's destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. This enables the providing unit to select the optimal providing method based on the user's geographical location information.

[0107] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's posts on social media and provide related information. Furthermore, the providing unit can provide related information by referring to the activities of the user's friends on social media. This enables the providing unit to customize the provided content based on the user's social media activity.

[0108] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. The providing unit can, for example, adjust the information providing method based on the user's past feedback. The providing unit can also change the type of information to be provided by reflecting the user's past feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to the user's past feedback. For example, the providing unit can adjust the information providing method based on the user's past feedback. The providing unit can also change the type of information to be provided by reflecting the user's past feedback. Furthermore, the providing unit can adjust the frequency of information provision by referring to the user's past feedback. This enables the providing unit to customize the information providing method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect a user's behavioral history and hobbies and preferences using the camera 42 and microphone 38B of the smart device 14. For example, the learning unit can be realized by the specific processing unit 290 of the data processing device 12 and learns the collected data. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and generates personalized information based on the learned data. For example, the provision unit can provide information to the user in real time via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect a user's behavioral history and hobbies and preferences using the camera 42 and microphone 238 of the smart glasses 214. For example, the learning unit can be realized by the specific processing unit 290 of the data processing device 12 and learns the collected data. For example, the generation unit can be realized by the specific processing unit 290 of the data processing device 12 and generates personalized information based on the learned data. For example, the provision unit can provide information to the user in real time through the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, learning unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect the user's behavioral history and hobbies and preferences using the camera 42 and microphone 238 of the headset type terminal 314. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected data. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized information based on the learned data. For example, the provision unit can provide information to the user in real time via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, learning unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect the user's behavioral history and hobbies and preferences using the camera 42 and microphone 238 of the robot 414. For example, the learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the collected data. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized information based on the learned data. For example, the provision unit can provide information to the user in real time through the control unit 46A of the robot 414.

[0109] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0110] The navigation system may further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate, blood pressure, and number of steps, and evaluates the user's health condition based on this data. For example, the health management unit may provide a notification urging the user to take a break if the user has been walking for a long time. The health management unit may also provide appropriate exercise and dietary advice based on the user's health condition. Furthermore, the health management unit may periodically analyze the user's health data and detect health risks early. This enables the navigation system to support the user's health management and promote a healthier lifestyle.

[0111] The navigation system may further include a schedule management unit that manages the user's schedule. The schedule management unit collects the user's calendar and schedule information and optimizes the user's schedule based on this data. For example, the schedule management unit may suggest an optimal travel route based on the user's schedule. The schedule management unit may also set reminders based on the user's schedule to notify the user so that they do not forget important appointments. Furthermore, the schedule management unit may suggest appropriate break times and relaxation activities based on the user's schedule. This allows the navigation system to support the user's schedule management and provide more efficient time management.

[0112] The navigation system may further include a music recommendation unit that estimates the user's emotions and recommends music based on the estimated emotions. The music recommendation unit can estimate the user's emotions from their facial expressions and voice and recommend music that matches those emotions. For example, the music recommendation unit can recommend relaxing music when the user is feeling stressed. The music recommendation unit can also recommend music that will uplift the user's mood when the user is happy. Furthermore, the music recommendation unit can automatically generate a playlist based on the user's emotions and provide a music experience that matches the user's mood. This allows the navigation system to recommend music based on the user's emotions and provide a richer music experience.

[0113] The navigation system may further include a driving analysis unit that analyzes the user's driving style. The driving analysis unit may collect the user's driving data and analyze the driving style based on this data. For example, the driving analysis unit may analyze the user's acceleration and braking frequency and provide safe driving advice. The driving analysis unit may also make suggestions to improve fuel efficiency based on the user's driving patterns. Furthermore, the driving analysis unit may provide a training program to support the improvement of driving skills based on the user's driving data. This enables the navigation system to analyze the user's driving style and support safe and efficient driving.

[0114] The navigation system may further include a notification management unit that estimates the user's emotions and adjusts the priority of notifications based on the estimated emotions. The notification management unit can estimate the user's emotions from their facial expressions and voice, and adjust the priority of notifications based on the emotions. For example, the notification management unit can display only important notifications when the user is concentrating. The notification management unit can also postpone unimportant notifications when the user is relaxed. Furthermore, the notification management unit can adjust the content and timing of notifications based on the user's emotions, thereby reducing the user's stress. This allows the navigation system to manage notifications based on the user's emotions and provide a more comfortable user experience.

[0115] The navigation system may further include a diet management unit that manages the user's diet history. The diet management unit can collect the user's diet history and make dietary suggestions based on this data. For example, the diet management unit can suggest nutritionally balanced meals based on the user's past diet history. The diet management unit can also provide a meal plan tailored to the user's health condition. Furthermore, the diet management unit can analyze the user's diet history and suggest areas for improving the user's diet. This allows the navigation system to support the user's dietary management and provide a healthier diet.

[0116] The navigation system may further include a sightseeing suggestion unit that estimates the user's emotions and suggests sightseeing spots based on the estimated emotions. The sightseeing suggestion unit can estimate the user's emotions from their facial expressions and voice and suggest sightseeing spots that match the emotions. For example, the sightseeing suggestion unit can suggest quiet nature spots when the user wants to relax. The sightseeing suggestion unit can also suggest active attractions when the user is excited. Furthermore, the sightseeing suggestion unit can customize sightseeing routes based on the user's emotions and provide a sightseeing experience that matches the user's mood. This allows the navigation system to make sightseeing suggestions based on the user's emotions and provide a more satisfying sightseeing experience.

[0117] The navigation system may further include an exercise management unit that manages the user's exercise history. The exercise management unit can collect the user's exercise history and make exercise suggestions based on this data. For example, the exercise management unit can suggest an appropriate exercise plan based on the user's past exercise history. The exercise management unit can also provide exercise advice tailored to the user's health condition. Furthermore, the exercise management unit can analyze the user's exercise history and suggest areas for improvement in exercise. This enables the navigation system to support the user's exercise management and provide a healthier lifestyle.

[0118] The navigation system may further include an advertisement management unit that estimates the user's emotions and adjusts the display of advertisements based on the estimated emotions. The advertisement management unit can estimate the user's emotions from their facial expressions and voice and display advertisements that match those emotions. For example, the advertisement management unit can display advertisements for products with a relaxing effect when the user is relaxed. The advertisement management unit can also display advertisements for active products when the user is excited. Furthermore, the advertisement management unit can adjust the timing of advertisement display based on the user's emotions to provide an advertising experience that attracts the user's interest. This allows the navigation system to display advertisements based on the user's emotions and provide a more effective advertising experience.

[0119] The navigation system may further include a sleep management unit that manages the user's sleep history. The sleep management unit can collect the user's sleep history and make sleep suggestions based on this data. For example, the sleep management unit can suggest an appropriate sleep schedule based on the user's past sleep history. The sleep management unit can also provide sleep advice tailored to the user's health condition. Furthermore, the sleep management unit can analyze the user's sleep history and suggest areas for improving sleep. This allows the navigation system to support the user's sleep management and provide a healthier lifestyle.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects the user's behavioral history or hobbies and preferences. For example, the collection unit can collect data such as the places the user visited, the services used, and the products purchased. The collection unit can also collect data on the places the user visited using GPS data or location information services. Furthermore, the collection unit can also collect the usage history of online services and in-store services. Step 2: The learning unit learns the data collected by the collection unit. For example, the learning unit can use a machine learning algorithm to analyze the user's behavioral patterns and hobbies and preferences. The learning unit can also use a clustering or classification algorithm to learn the user's hobbies and preferences. Step 3: The generator generates personalized information based on the data learned by the learning unit. For example, the generator can generate information based on the user's interests or customized content. The generator can also generate information optimal for the user using a recommendation system. Step 4: The providing unit provides the information generated by the generating unit to the user in real time. For example, the providing unit may provide the generated information to the user through AR glasses. The providing unit may also provide the generated information in the form of text, images, audio, etc.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 7, a 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 collection unit that collects user behavior history or hobbies and preferences; a learning unit that learns the data collected by the collection unit; a generation unit that generates personalized information based on the data learned by the learning unit; a providing unit that provides the information generated by the generating unit to a user in real time; Equipped with A system characterized by:

2. The collecting unit Collecting information about the places you visit, the services you use, and the products you purchase The system of claim 1 .

3. The learning unit Analyze the collected data to learn user behavior patterns and preferences The system of claim 1 .

4. The generation unit Generate information appropriate for the user based on learned data The system of claim 1 .

5. The providing unit Providing generated information to users in real time The system of claim 1 .

6. The providing unit Providing information through AR glasses The system of claim 1 .

7. The providing unit Providing customized information based on the user's past behavioral history and preferences The system of claim 1 .

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions The system of claim 1 .

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