System

Abstract

An object of the system according to the embodiment is to optimize content and services provided by a generation AI based on attribute information and interest information of a user.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects attribute information or interest information of a user. The generation unit generates an instruction for the generation AI based on the information collected by the collection unit. The providing unit provides the content or the service provided by the generated AI to the user based on the instruction generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that the content and services provided by generative AI do not adequately correspond to the user's attributes and interests.

[0005] The system according to the embodiment aims to optimize the content and services provided by the generation AI based on the user's attribute information and interest information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects user attribute information or interest information. The generation unit generates instructions for the generation AI based on the information collected by the collection unit. The provision unit provides the user with content or services provided by the generation AI based on the instructions generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the content and services provided by the generation AI based on the user's attribute information and interest information. [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 system according to an embodiment of the present invention is an API that, when an external service uses a generation AI to provide functions for users, adds user attribute and interest information to instructions to the AI. This API collects user attribute information (e.g., age, gender, region) and interest information (e.g., hobbies, interests), generates instructions for the generation AI, and provides the user with content and services provided by the generation AI. This allows the system to provide personalized services based on the user's attributes and interests. For example, if a user is interested in movies, the generation AI recommends movies based on the user's interests. This provides personalized services based on the user's attributes and interests.

[0029] An information provision system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects user attribute information or interest information. For example, the collection unit can collect information when a user logs in to a service. The collection unit can also collect user attribute information (such as age, gender, and region) and interest information (such as hobbies and interests). The generation unit analyzes the collected information and generates specific instructions for the generation AI. For example, the generation unit can generate instructions for recommending movies based on the user's interests. The generation unit can also generate instructions for the generation AI based on the collected information. The provision unit provides content and services generated by the generation AI to the user. For example, the provision unit can provide movie recommendations generated by the generation AI to the user. The provision unit can also provide content and services generated by the generation AI to the user. This allows the information provision system according to an embodiment to provide personalized services based on the user's attributes and interests.

[0030] The collection unit can collect information when a user logs in to a service. The collection unit, for example, collects information when a user logs in to a service. For example, the collection unit can collect information at the first login. The collection unit can also collect information at each login. This allows information to be collected automatically when a user uses a service.

[0031] The generation unit can analyze the collected information and generate specific instructions for the generation AI. The generation unit can, for example, analyze the collected information and generate specific instructions for the generation AI. For example, the generation unit can analyze the collected information using data mining technology. The generation unit can also analyze the collected information using statistical analysis technology. This makes it possible to generate specific instructions based on the collected information.

[0032] The providing unit can provide the content and services generated by the generation AI to the user. The providing unit, for example, provides the content and services generated by the generation AI to the user. For example, the providing unit can provide the content in real time. The providing unit can also provide the content by batch processing. This allows the content and services generated by the generation AI to be provided to the user.

[0033] The collection unit can collect user attribute information and interest information. The collection unit, for example, collects user attribute information (age, gender, region, etc.). For example, the collection unit can collect attribute information when a user logs in to a service. The collection unit can also collect user interest information (hobbies, concerns, etc.). For example, the collection unit can collect interest information when a user uses a service. In this way, the user attribute information and interest information can be collected.

[0034] The generation unit can generate instructions to recommend movies based on the user's interests. The generation unit generates, for example, instructions to recommend movies based on the user's interests. For example, the generation unit can analyze the user's interests in movie genres and generate instructions to recommend movies based on the interests. The generation unit can also generate instructions to recommend movies based on the user's favorite actors. In this way, instructions to recommend movies based on the user's interests can be generated.

[0035] The collection unit can analyze the user's past behavioral history and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit can prioritize and select collection methods (surveys, clicks, etc.) that the user has frequently used in the past. The collection unit can also select and apply the most efficient collection method from the user's past behavioral history. Furthermore, the collection unit can analyze collection methods that the user has preferred in the past and suggest the optimal method. This makes it possible to select the optimal collection method based on the user's past behavioral history.

[0036] The collection unit can filter attribute information and interest information based on the user's current activity status and areas of interest when collecting the attribute information and interest information. For example, the collection unit can filter attribute information and interest information based on the user's current activity status and areas of interest when collecting the attribute information and interest information. For example, the collection unit can prioritize collecting related attribute information and interest information based on content currently being viewed by the user. Furthermore, when the user is performing a specific activity, the collection unit can filter and collect information related to the activity. Furthermore, the collection unit can exclude unnecessary information and collect only necessary information based on the user's current areas of interest. This allows information to be filtered based on the user's current activity status and areas of interest.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting attribute information and interest information. For example, when collecting attribute information and interest information, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uploads images, the collection unit can collect information using image analysis technology. This makes it possible to select the optimal collection means depending on the user's input method.

[0038] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting attribute information and interest information. For example, when collecting attribute information and interest information, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information around the user's home. This makes it possible to prioritize collecting highly relevant information based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activities when collecting the attribute information and interest information, and collect related information. For example, the collection unit can analyze the user's social media activities when collecting the attribute information and interest information, and collect related information. For example, the collection unit can collect related attribute information and interest information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected based on the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting attribute information and interest information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting attribute information and interest information. For example, the collection unit can customize the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. Furthermore, the collection unit can improve the collection method by reflecting the user's feedback. This allows the collection method to be customized based on the user's past feedback.

[0041] The generation unit can adjust the level of detail of the instruction based on the importance of the user's attribute information and interest information when generating the instruction. For example, the generation unit can adjust the level of detail of the instruction based on the importance of the user's attribute information and interest information when generating the instruction. For example, the generation unit can generate detailed instructions when the user's attribute information is important. The generation unit can also generate specific instructions when the user's interest information is important. Furthermore, the generation unit can generate balanced instructions when both the user's attribute information and interest information are important. This allows the level of detail of the instruction to be adjusted based on the importance of the user's attribute information and interest information.

[0042] The generation unit can apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information when generating an instruction. For example, the generation unit can apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information when generating an instruction. For example, if the user's attribute information is related to age, the generation unit can apply an instruction generation algorithm according to age. Furthermore, if the user's interest information is related to hobbies, the generation unit can also apply an instruction generation algorithm according to hobbies. Furthermore, if the user's interest information is related to both the user's attribute information and interest information, the generation unit can also apply a composite instruction generation algorithm. This makes it possible to apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information.

[0043] The generation unit can improve the accuracy of instructions when generating instructions by referring to the user's past instruction results. For example, the generation unit can analyze the instruction results received by the user in the past to generate highly accurate instructions. The generation unit can also select an optimal instruction generation method from the user's past instruction results. Furthermore, the generation unit can improve the instruction generation algorithm based on the user's past instruction results. This makes it possible to improve the accuracy of instructions by referring to the user's past instruction results.

[0044] When generating instructions, the generation unit can determine the priority of instructions based on the user's attribute information and the time of submission of interest information. For example, when generating instructions, the generation unit determines the priority of instructions based on the user's attribute information and the time of submission of interest information. For example, the generation unit can generate instructions preferentially based on information recently submitted by the user. The generation unit can also determine the priority of instructions based on information previously submitted by the user. Furthermore, the generation unit can generate optimal instructions depending on the time of submission of information by the user. This makes it possible to determine the priority of instructions based on the user's attribute information and the time of submission of interest information.

[0045] The generation unit can adjust the order of instructions based on the relevance of the user's attribute information and interest information when generating instructions. For example, the generation unit can adjust the order of instructions based on the relevance of the user's attribute information and interest information when generating instructions. For example, when the user's attribute information is relevant, the generation unit can prioritize generating instructions based on the attribute information. Furthermore, when the user's interest information is relevant, the generation unit can also prioritize generating instructions based on the interest information. Furthermore, when both the user's attribute information and interest information are relevant, the generation unit can also generate instructions in a balanced order. This allows the order of instructions to be adjusted based on the relevance of the user's attribute information and interest information.

[0046] The generation unit may adjust the use of technical terms in the instructions according to the user's level of expertise when generating the instructions. For example, the generation unit may adjust the use of technical terms in the instructions according to the user's level of expertise when generating the instructions. For example, if the user has technical knowledge, the generation unit may generate instructions that use a lot of technical terms. In addition, if the user is a beginner, the generation unit may generate instructions that are explained in simple terms. Furthermore, the generation unit may adjust the use of appropriate technical terms according to the user's level of expertise. This allows the use of technical terms in the instructions to be adjusted according to the user's level of expertise.

[0047] The providing unit can select the optimal providing method by analyzing the user's past usage history when providing content or a service. For example, when providing content or a service, the providing unit analyzes the user's past usage history and selects the optimal providing method. For example, the providing unit can preferentially select a providing method that the user has used preferentially in the past. The providing unit can also select the most efficient providing method from the user's past usage history. Furthermore, the providing unit can improve the providing method based on the user's past usage history. This makes it possible to select the optimal providing method based on the user's past usage history.

[0048] The providing unit can customize the provided content based on the user's current activity status when providing content or a service. For example, the providing unit customizes the provided content based on the user's current activity status when providing content or a service. For example, the providing unit can provide related content based on content currently being viewed by the user. Furthermore, when the user is performing a specific activity, the providing unit can also provide content related to that activity. Furthermore, the providing unit can provide optimal content based on the user's current activity status. This makes it possible to customize the provided content based on the user's current activity status.

[0049] The providing unit can improve the provision method by reflecting user feedback when providing content or a service. For example, the providing unit can improve the provision method by reflecting user feedback when providing content or a service. For example, the providing unit can improve the provision method based on feedback provided by the user on the provided content. The providing unit can also analyze the user feedback and select the optimal provision method. Furthermore, the providing unit can customize the provision method by reflecting user feedback. This makes it possible to improve the provision method based on user feedback.

[0050] The providing unit can select the optimal providing method in consideration of the user's geographical location information when providing content or a service. For example, the providing unit selects the optimal providing method in consideration of the user's geographical location information when providing content or a service. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the user's travel destination. Furthermore, when the user is at home, the providing unit can also provide information about the area around the user's home. This makes it possible to select the optimal providing method based on the user's geographical location information.

[0051] The providing unit can analyze the user's social media activity and suggest content to be provided when providing content or services. For example, the providing unit can analyze the user's social media activity and suggest content to be provided when providing content or services. For example, the providing unit can provide related content based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related content. Furthermore, the providing unit can provide related content by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested based on the user's social media activity.

[0052] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content or a service. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing content or a service. For example, the providing unit can customize the delivery method based on the user's past feedback. The providing unit can also select the optimal delivery method from the user's past feedback. Furthermore, the providing unit can improve the delivery method by reflecting the user's feedback. In this way, the delivery method can be customized based on the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The collection unit can also monitor the user's device usage and determine the optimal timing for collecting information. For example, the collection unit can identify time periods when the user frequently uses the device and collect information during those time periods. The collection unit can also collect information by avoiding time periods when the user is not using the device. Furthermore, when the user is using a specific application, the collection unit can prioritize collecting information related to that application. This makes it possible to determine the optimal timing for collecting information based on the user's device usage.

[0055] The generation unit can also analyze the user's past search history to improve the accuracy of the generated instructions. For example, the generation unit can generate related instructions based on keywords the user has searched for in the past. The generation unit can also identify topics of interest from the user's search history and generate instructions related to those topics. Furthermore, the generation unit can analyze the user's search history to generate instructions that match the content of past searches. This can improve the accuracy of the instructions based on the user's past search history.

[0056] The providing unit can also provide location-based content using the user's real-time location information. For example, when the user is in a specific area, the providing unit can provide event information related to that area. When the user is traveling, the providing unit can provide information on tourist spots and restaurants in the travel destination. Furthermore, when the user is at home, the providing unit can provide information on stores and services near the user's home. This allows content to be provided based on the user's real-time location information.

[0057] The collection unit can also monitor the user's social media activity and update the interest information in real time. For example, if the user posts about a new hobby on social media, the collection unit can collect that information and update the interest information. In addition, if the user frequently comments on a particular topic on social media, the collection unit can collect information related to that topic. Furthermore, the collection unit can collect related interest information based on the activities of the user's friends on social media. This allows the interest information to be updated in real time based on the user's social media activity.

[0058] The collection unit can also use sensor information from the user's device to grasp the user's activity status in real time and determine the optimal timing for collecting information. For example, the collection unit can use an acceleration sensor in the user's smartphone to detect whether the user is walking or sitting and collect information according to that status. The collection unit can also use a heart rate sensor in the user's smartwatch to grasp the user's stress level and collect information when stress is low. Furthermore, the collection unit can use location information from the user's device to collect information related to when the user is in a specific location. This makes it possible to determine the optimal timing for collecting information based on the sensor information from the user's device.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects user attribute information or interest information. For example, the collection unit can collect information when a user logs in to the service. The collection unit can also collect user attribute information (age, gender, region, etc.) and interest information (hobbies, interests, etc.). Step 2: The generator analyzes the collected information and generates specific instructions for the generator AI. For example, the generator can generate instructions for recommending movies based on the user's interests. The generator can also generate instructions for the generator AI based on the collected information. Step 3: The providing unit provides the content and services generated by the generation AI to the user. For example, the providing unit can provide the user with movie recommendations generated by the generation AI. The providing unit can also provide the user with content and services generated by the generation AI.

[0061] (Example 2) A system according to an embodiment of the present invention is an API that, when an external service uses a generation AI to provide functions for users, adds user attribute and interest information to instructions to the AI. This API collects user attribute information (e.g., age, gender, region) and interest information (e.g., hobbies, interests), generates instructions for the generation AI, and provides the user with content and services provided by the generation AI. This allows the system to provide personalized services based on the user's attributes and interests. For example, if a user is interested in movies, the generation AI recommends movies based on the user's interests. This provides personalized services based on the user's attributes and interests.

[0062] An information provision system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects user attribute information or interest information. For example, the collection unit can collect information when a user logs in to a service. The collection unit can also collect user attribute information (such as age, gender, and region) and interest information (such as hobbies and interests). The generation unit analyzes the collected information and generates specific instructions for the generation AI. For example, the generation unit can generate instructions for recommending movies based on the user's interests. The generation unit can also generate instructions for the generation AI based on the collected information. The provision unit provides content and services generated by the generation AI to the user. For example, the provision unit can provide movie recommendations generated by the generation AI to the user. The provision unit can also provide content and services generated by the generation AI to the user. This allows the information provision system according to an embodiment to provide personalized services based on the user's attributes and interests.

[0063] The collection unit can collect information when a user logs in to a service. The collection unit, for example, collects information when a user logs in to a service. For example, the collection unit can collect information at the first login. The collection unit can also collect information at each login. This allows information to be collected automatically when a user uses a service.

[0064] The generation unit can analyze the collected information and generate specific instructions for the generation AI. The generation unit can, for example, analyze the collected information and generate specific instructions for the generation AI. For example, the generation unit can analyze the collected information using data mining technology. The generation unit can also analyze the collected information using statistical analysis technology. This makes it possible to generate specific instructions based on the collected information.

[0065] The providing unit can provide the content and services generated by the generation AI to the user. The providing unit, for example, provides the content and services generated by the generation AI to the user. For example, the providing unit can provide the content in real time. The providing unit can also provide the content by batch processing. This allows the content and services generated by the generation AI to be provided to the user.

[0066] The collection unit can collect user attribute information and interest information. The collection unit, for example, collects user attribute information (age, gender, region, etc.). For example, the collection unit can collect attribute information when a user logs in to a service. The collection unit can also collect user interest information (hobbies, concerns, etc.). For example, the collection unit can collect interest information when a user uses a service. In this way, the user attribute information and interest information can be collected.

[0067] The generation unit can generate instructions to recommend movies based on the user's interests. The generation unit generates, for example, instructions to recommend movies based on the user's interests. For example, the generation unit can analyze the user's interests in movie genres and generate instructions to recommend movies based on the interests. The generation unit can also generate instructions to recommend movies based on the user's favorite actors. In this way, instructions to recommend movies based on the user's interests can be generated.

[0068] The collection unit can estimate a user's emotions and adjust the timing of collecting attribute information and interest information based on the estimated user emotions. The collection unit, for example, estimates a user's emotions and adjusts the timing of collecting attribute information and interest information based on the estimated user emotions. For example, if a user is relaxed, the collection unit can collect attribute information and interest information while the user is using a service. Furthermore, if a user is stressed, the collection unit can collect attribute information and interest information after the user has finished using a service. Furthermore, if a user is in a hurry, the collection unit can quickly collect minimal information and collect more detailed information later. This allows the timing of collecting information to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The collection unit can analyze the user's past behavioral history and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit can prioritize and select collection methods (surveys, clicks, etc.) that the user has frequently used in the past. The collection unit can also select and apply the most efficient collection method from the user's past behavioral history. Furthermore, the collection unit can analyze collection methods that the user has preferred in the past and suggest the optimal method. This makes it possible to select the optimal collection method based on the user's past behavioral history.

[0070] The collection unit can filter attribute information and interest information based on the user's current activity status and areas of interest when collecting the attribute information and interest information. For example, the collection unit can filter attribute information and interest information based on the user's current activity status and areas of interest when collecting the attribute information and interest information. For example, the collection unit can prioritize collecting related attribute information and interest information based on content currently being viewed by the user. Furthermore, when the user is performing a specific activity, the collection unit can filter and collect information related to the activity. Furthermore, the collection unit can exclude unnecessary information and collect only necessary information based on the user's current areas of interest. This allows information to be filtered based on the user's current activity status and areas of interest.

[0071] The collection unit can select the optimal collection means depending on the user's input method when collecting attribute information and interest information. For example, when collecting attribute information and interest information, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Also, if the user uses text input, the collection unit can collect information using text analysis technology. Furthermore, if the user uploads images, the collection unit can collect information using image analysis technology. This makes it possible to select the optimal collection means depending on the user's input method.

[0072] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, the collection unit can prioritize collecting interest information when the user is excited. The collection unit can also prioritize collecting attribute information when the user is relaxed. Furthermore, the collection unit can prioritize collecting minimal information when the user is stressed. This makes it possible to determine the priority of information to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The collection unit can prioritize collecting highly relevant information in consideration of the user's geographical location information when collecting attribute information and interest information. For example, when collecting attribute information and interest information, the collection unit prioritizes collecting highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting information related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting information around the user's home. This makes it possible to prioritize collecting highly relevant information based on the user's geographical location information.

[0074] The collection unit can analyze the user's social media activities when collecting the attribute information and interest information, and collect related information. For example, the collection unit can analyze the user's social media activities when collecting the attribute information and interest information, and collect related information. For example, the collection unit can collect related attribute information and interest information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related information. Furthermore, the collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be collected based on the user's social media activities.

[0075] The collection unit can customize the collection method by reflecting the user's past feedback when collecting attribute information and interest information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting attribute information and interest information. For example, the collection unit can customize the collection method based on feedback provided by the user in the past. The collection unit can also select the optimal collection means based on the user's past feedback. Furthermore, the collection unit can improve the collection method by reflecting the user's feedback. This allows the collection method to be customized based on the user's past feedback.

[0076] The generation unit can estimate the user's emotion and adjust the way in which instructions are expressed based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the way in which instructions are expressed based on the estimated user's emotion. For example, the generation unit can generate instructions using soft expressions when the user is relaxed. The generation unit can also generate instructions using concise and clear expressions when the user is in a hurry. Furthermore, the generation unit can generate instructions using energetic expressions when the user is excited. This makes it possible to adjust the way in which instructions are expressed depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The generation unit can adjust the level of detail of the instruction based on the importance of the user's attribute information and interest information when generating the instruction. For example, the generation unit can adjust the level of detail of the instruction based on the importance of the user's attribute information and interest information when generating the instruction. For example, the generation unit can generate detailed instructions when the user's attribute information is important. The generation unit can also generate specific instructions when the user's interest information is important. Furthermore, the generation unit can generate balanced instructions when both the user's attribute information and interest information are important. This allows the level of detail of the instruction to be adjusted based on the importance of the user's attribute information and interest information.

[0078] The generation unit can apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information when generating an instruction. For example, the generation unit can apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information when generating an instruction. For example, if the user's attribute information is related to age, the generation unit can apply an instruction generation algorithm according to age. Furthermore, if the user's interest information is related to hobbies, the generation unit can also apply an instruction generation algorithm according to hobbies. Furthermore, if the user's interest information is related to both the user's attribute information and interest information, the generation unit can also apply a composite instruction generation algorithm. This makes it possible to apply different instruction generation algorithms depending on the categories of the user's attribute information and interest information.

[0079] The generation unit can improve the accuracy of instructions when generating instructions by referring to the user's past instruction results. For example, the generation unit can analyze the instruction results received by the user in the past to generate highly accurate instructions. The generation unit can also select an optimal instruction generation method from the user's past instruction results. Furthermore, the generation unit can improve the instruction generation algorithm based on the user's past instruction results. This makes it possible to improve the accuracy of instructions by referring to the user's past instruction results.

[0080] The generation unit can estimate the user's emotion and adjust the length of the instruction based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the instruction based on the estimated user's emotion. For example, if the user is in a hurry, the generation unit can generate short, to-the-point instructions. If the user is relaxed, the generation unit can also generate longer instructions including detailed explanations. Furthermore, if the user is excited, the generation unit can also generate instructions with visually stimulating effects. This allows the length of the instruction to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When generating instructions, the generation unit can determine the priority of instructions based on the user's attribute information and the time of submission of interest information. For example, when generating instructions, the generation unit determines the priority of instructions based on the user's attribute information and the time of submission of interest information. For example, the generation unit can generate instructions preferentially based on information recently submitted by the user. The generation unit can also determine the priority of instructions based on information previously submitted by the user. Furthermore, the generation unit can generate optimal instructions depending on the time of submission of information by the user. This makes it possible to determine the priority of instructions based on the user's attribute information and the time of submission of interest information.

[0082] The generation unit can adjust the order of instructions based on the relevance of the user's attribute information and interest information when generating instructions. For example, the generation unit can adjust the order of instructions based on the relevance of the user's attribute information and interest information when generating instructions. For example, when the user's attribute information is relevant, the generation unit can prioritize generating instructions based on the attribute information. Furthermore, when the user's interest information is relevant, the generation unit can also prioritize generating instructions based on the interest information. Furthermore, when both the user's attribute information and interest information are relevant, the generation unit can also generate instructions in a balanced order. This allows the order of instructions to be adjusted based on the relevance of the user's attribute information and interest information.

[0083] The generation unit may adjust the use of technical terms in the instructions according to the user's level of expertise when generating the instructions. For example, the generation unit may adjust the use of technical terms in the instructions according to the user's level of expertise when generating the instructions. For example, if the user has technical knowledge, the generation unit may generate instructions that use a lot of technical terms. In addition, if the user is a beginner, the generation unit may generate instructions that are explained in simple terms. Furthermore, the generation unit may adjust the use of appropriate technical terms according to the user's level of expertise. This allows the use of technical terms in the instructions to be adjusted according to the user's level of expertise.

[0084] The providing unit can estimate the user's emotions and adjust the method of providing content and services based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing content and services based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide content at a leisurely pace. Also, if the user is in a hurry, the providing unit can provide content quickly. Furthermore, if the user is excited, the providing unit can provide content with visually stimulating effects. This makes it possible to adjust the method of providing content and services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The providing unit can select the optimal providing method by analyzing the user's past usage history when providing content or a service. For example, when providing content or a service, the providing unit analyzes the user's past usage history and selects the optimal providing method. For example, the providing unit can preferentially select a providing method that the user has used preferentially in the past. The providing unit can also select the most efficient providing method from the user's past usage history. Furthermore, the providing unit can improve the providing method based on the user's past usage history. This makes it possible to select the optimal providing method based on the user's past usage history.

[0086] The providing unit can customize the provided content based on the user's current activity status when providing content or a service. For example, the providing unit customizes the provided content based on the user's current activity status when providing content or a service. For example, the providing unit can provide related content based on content currently being viewed by the user. Furthermore, when the user is performing a specific activity, the providing unit can also provide content related to that activity. Furthermore, the providing unit can provide optimal content based on the user's current activity status. This makes it possible to customize the provided content based on the user's current activity status.

[0087] The providing unit can improve the provision method by reflecting user feedback when providing content or a service. For example, the providing unit can improve the provision method by reflecting user feedback when providing content or a service. For example, the providing unit can improve the provision method based on feedback provided by the user on the provided content. The providing unit can also analyze the user feedback and select the optimal provision method. Furthermore, the providing unit can customize the provision method by reflecting user feedback. This makes it possible to improve the provision method based on user feedback.

[0088] The providing unit can estimate the user's emotions and determine the priority of content and services to be provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of content and services to be provided based on the estimated user emotions. For example, the providing unit can prioritize providing entertainment content when the user is relaxed. The providing unit can also quickly provide necessary information when the user is in a hurry. Furthermore, the providing unit can prioritize providing visually stimulating content when the user is excited. This makes it possible to determine the priority of content and services to be provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The providing unit can select the optimal providing method in consideration of the user's geographical location information when providing content or a service. For example, the providing unit selects the optimal providing method in consideration of the user's geographical location information when providing content or a service. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the user's travel destination. Furthermore, when the user is at home, the providing unit can also provide information about the area around the user's home. This makes it possible to select the optimal providing method based on the user's geographical location information.

[0090] The providing unit can analyze the user's social media activity and suggest content to be provided when providing content or services. For example, the providing unit can analyze the user's social media activity and suggest content to be provided when providing content or services. For example, the providing unit can provide related content based on information shared by the user on social media. The providing unit can also analyze the user's social media activity and provide related content. Furthermore, the providing unit can provide related content by referring to the activity of the user's friends on social media. In this way, content to be provided can be suggested based on the user's social media activity.

[0091] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content or a service. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing content or a service. For example, the providing unit can customize the delivery method based on the user's past feedback. The providing unit can also select the optimal delivery method from the user's past feedback. Furthermore, the providing unit can improve the delivery method by reflecting the user's feedback. In this way, the delivery method can be customized based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection 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 user attribute information and interest information via the control unit 46A of the smart device 14. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates specific instructions for the generation AI. The provision unit can provide content and services generated by the generation AI to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection 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 user attribute information and interest information using the microphone 238 of the smart glasses 214. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates specific instructions for the generation AI. The provision unit can provide content and services generated by the generation AI to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, and provision unit described above 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 user attribute information and interest information using the microphone 238 of the headset type terminal 314. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates specific instructions for the generation AI. The provision unit can provide the user with content and services generated by the generation AI via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, and provision unit described above 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 user attribute information and interest information using the microphone 238 of the robot 414. The generation unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates specific instructions for the generation AI. The provision unit can provide content and services generated by the generation AI to the user through the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The collection unit can also monitor the user's device usage and determine the optimal timing for collecting information. For example, the collection unit can identify time periods when the user frequently uses the device and collect information during those time periods. The collection unit can also collect information by avoiding time periods when the user is not using the device. Furthermore, when the user is using a specific application, the collection unit can prioritize collecting information related to that application. This makes it possible to determine the optimal timing for collecting information based on the user's device usage.

[0094] The generation unit can also analyze the user's past search history to improve the accuracy of the generated instructions. For example, the generation unit can generate related instructions based on keywords the user has searched for in the past. The generation unit can also identify topics of interest from the user's search history and generate instructions related to those topics. Furthermore, the generation unit can analyze the user's search history to generate instructions that match the content of past searches. This can improve the accuracy of the instructions based on the user's past search history.

[0095] The providing unit can also provide location-based content using the user's real-time location information. For example, when the user is in a specific area, the providing unit can provide event information related to that area. When the user is traveling, the providing unit can provide information on tourist spots and restaurants in the travel destination. Furthermore, when the user is at home, the providing unit can provide information on stores and services near the user's home. This allows content to be provided based on the user's real-time location information.

[0096] The collection unit can also monitor the user's social media activity and update the interest information in real time. For example, if the user posts about a new hobby on social media, the collection unit can collect that information and update the interest information. In addition, if the user frequently comments on a particular topic on social media, the collection unit can collect information related to that topic. Furthermore, the collection unit can collect related interest information based on the activities of the user's friends on social media. This allows the interest information to be updated in real time based on the user's social media activity.

[0097] The generation unit can also estimate the user's emotion and adjust the tone of the instruction based on the estimated user's emotion. For example, the generation unit can generate instructions in a calm tone when the user is relaxed. The generation unit can also generate instructions in an encouraging tone when the user is stressed. Furthermore, the generation unit can generate instructions in an energetic tone when the user is excited. In this way, the tone of the instruction can be adjusted according to the user's emotion.

[0098] The providing unit can also estimate the user's emotions and adjust the content display method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can display content in a calm interface. If the user is in a hurry, the providing unit can highlight important information. Furthermore, if the user is excited, the providing unit can display content by adding visually stimulating effects. In this way, the content display method can be adjusted according to the user's emotions.

[0099] The collection unit can also estimate the user's emotions and determine the type of information to collect based on the estimated user emotions. For example, the collection unit can collect detailed attribute information when the user is relaxed. Alternatively, the collection unit can collect simple interest information when the user is stressed. Furthermore, the collection unit can collect entertainment-related information when the user is excited. This allows the type of information to be collected to be determined according to the user's emotions.

[0100] The generation unit may also estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. For example, if the user is relaxed, the generation unit may preferentially generate instructions related to long-term goals. Alternatively, if the user is in a hurry, the generation unit may preferentially generate instructions related to short-term goals. Furthermore, if the user is excited, the generation unit may preferentially generate instructions related to challenging tasks. In this way, the priority of instructions can be determined according to the user's emotions.

[0101] The providing unit can also estimate the user's emotions and determine the type of content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide relaxing music or videos. If the user is in a hurry, the providing unit can provide summary content to efficiently provide information. Furthermore, if the user is excited, the providing unit can provide highly entertaining content. In this way, the type of content to be provided can be determined according to the user's emotions.

[0102] The collection unit can also use sensor information from the user's device to grasp the user's activity status in real time and determine the optimal timing for collecting information. For example, the collection unit can use an acceleration sensor in the user's smartphone to detect whether the user is walking or sitting and collect information according to that status. The collection unit can also use a heart rate sensor in the user's smartwatch to grasp the user's stress level and collect information when stress is low. Furthermore, the collection unit can use location information from the user's device to collect information related to when the user is in a specific location. This makes it possible to determine the optimal timing for collecting information based on the sensor information from the user's device.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects user attribute information or interest information. For example, the collection unit can collect information when a user logs in to the service. The collection unit can also collect user attribute information (age, gender, region, etc.) and interest information (hobbies, interests, etc.). Step 2: The generator analyzes the collected information and generates specific instructions for the generator AI. For example, the generator can generate instructions for recommending movies based on the user's interests. The generator can also generate instructions for the generator AI based on the collected information. Step 3: The providing unit provides the content and services generated by the generation AI to the user. For example, the providing unit can provide the user with movie recommendations generated by the generation AI. The providing unit can also provide the user with content and services generated by the generation AI.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[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 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.

[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 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.

[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 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.

[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] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 attribute information or interest information; A generation unit that generates instructions for a generation AI based on the information collected by the collection unit; a providing unit that provides the user with content or services provided by the generation AI based on the instructions generated by the generation unit; A system characterized by:

2. The collecting unit Collect information when you log in to the service 2. The system of claim 1.

3. The generation unit Analyze the collected information and generate specific instructions for the generating AI.

2. The system of claim 1.

4. The providing unit Providing users with content and services generated by generative AI 2. The system of claim 1.

5. The collecting unit Collect user demographic information and interest information 2. The system of claim 1.

6. The generation unit Generate instructions to recommend movies based on user interests 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting attribute information and interest information based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past behavior history and select the optimal collection method 2. The system of claim 1.

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