system

Abstract

The system according to this embodiment aims to generate and efficiently provide original content based on user interests. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an acquisition unit. The collection unit collects the user's behavior history and interests. The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. The generation unit generates original content based on the analysis results obtained by the analysis unit. The provision unit provides the content generated by the generation unit to the user. The acquisition unit acquires asset content from each SoftBank Group company.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult for a user to find interesting content from a large amount of information, and efficient information provision is required.

[0005] The system according to an embodiment aims to generate and efficiently provide original content based on the interests of a user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an acquisition unit. The collection unit collects the user's behavioral history and interests. The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. The generation unit generates original content based on the analysis results obtained by the analysis unit. The provision unit provides the content generated by the generation unit to the user. The acquisition unit acquires asset content from each SoftBank Group company. [Effects of the Invention]

[0007] The system according to this embodiment can generate and efficiently provide original content based on user interests. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The personalized content generation system according to an embodiment of the present invention is a system in which AI performs personalized analysis of a user's behavioral history and interests, and automatically generates original content that is optimal for the user. This personalized content generation system performs personalized analysis of a user's behavioral history and interests, and automatically generates original content that is optimal for the user based on the analysis results. This original content is simply organized based on the user's interests and concerns, and can be easily grasped without having to search for information. Furthermore, since this system is generated based on the asset content of each SoftBank Group company, it is expected to have the effect of driving traffic to the content of each SoftBank Group company and creating synergistic effects. This can be aimed at increasing the number of users and improving sales of each company's content. With this system, users can save the trouble of searching for information and easily obtain information that is optimal for them, thus making daily information gathering more efficient. In addition, it is mutually beneficial for each SoftBank Group company as it leads to increased content usage and sales. Thus, the personalized content generation system can automatically generate and provide optimal original content based on the user's behavioral history and interests.

[0029] The personalized content generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an acquisition unit. The collection unit collects the user's behavior history and interests. For example, the collection unit can collect data such as websites and apps that the user frequently visits, videos that are watched, and activities on social media. The collection unit can also use AI to automatically collect the user's behavior history and interests. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit can analyze the user's search keywords and clicked links to identify the user's interests. The analysis unit can also use AI to analyze the collected data and identify the user's interests. The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, the generation unit can generate original content such as articles, videos, and images based on the user's interests. The generation unit can also use AI to automatically generate original content based on the analysis results. The provision unit provides the content generated by the generation unit to the user. For example, the provision unit can provide the generated original content to the user through a website or app. The provisioning unit can also provide the generated content to the user using AI. The acquisition unit acquires asset content from each SoftBank Group company. The acquisition unit can acquire data from partner companies and information from public APIs, for example. The acquisition unit can also automatically acquire asset content from each SoftBank Group company using AI. As a result, the personalized content generation system according to the embodiment can automatically generate and provide optimal original content based on the user's behavior history and interests.

[0030] The data collection unit collects user behavior history and interests. For example, it can collect data such as websites and apps that users frequently visit, videos they watch, and their activity on social media. Specifically, it collects detailed information such as website browsing history, app usage frequency, genres and durations of videos watched, and social media posts, likes, and shares. This data may be collected directly from the user's device or through cloud services. The data collection unit can also use AI to automatically collect user behavior history and interests. The AI ​​can learn user behavior patterns and predict behavior at specific times and in specific situations. For example, if a user tends to watch videos of a specific genre at a specific time, the AI ​​will prioritize collecting content related to that time. Furthermore, to protect user privacy, the data collection unit anonymizes and encrypts data to securely collect and manage it. This allows the data collection unit to efficiently collect diverse user behavior data and improve the overall personalization accuracy of the system.

[0031] The analysis department analyzes data collected by the data collection department to identify user interests. For example, the analysis department can identify user interests by analyzing user search keywords and clicked links. Specifically, it analyzes in detail the frequency and relevance of search keywords, the content of clicked links, and the time spent on them. The analysis department can also use AI to analyze collected data and identify user interests. The AI ​​uses natural language processing technology to analyze user search keywords and social media posts to extract user concerns and trends. It can also use machine learning algorithms to learn user behavior patterns and predict potential interests. For example, it can analyze patterns of keywords users frequently search for and links they click to identify interests in specific genres or themes. Furthermore, the analysis department can perform more accurate interest identification by comparing past data and the behavior data of other users. This allows the analysis department to accurately identify diverse user interests and improve the overall personalization accuracy of the system.

[0032] The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, the generation unit can generate original content such as articles, videos, and images based on the user's interests. Specifically, it can automatically generate articles on themes that the user is interested in, or edit and generate related videos and images. The generation unit can also use AI to automatically generate original content based on the analysis results. The AI ​​uses natural language generation technology to create articles and blog posts tailored to the user's interests. It can also use image generation technology to generate customized images based on the user's interests. Furthermore, it can use video generation technology to create short videos and slideshows tailored to the user's interests. For example, if the user is interested in travel, it can generate articles and videos that include recommended spots and tourist information for travel destinations. In this way, the generation unit can efficiently generate original content optimized for the user's interests, improving the overall personalization accuracy of the system.

[0033] The delivery unit provides users with content generated by the generation unit. For example, the delivery unit can provide the generated original content to users through websites or apps. Specifically, it can display the generated content on the homepage of a website or the home screen of an app that the user accesses. The delivery unit can also use AI to provide users with generated content. The AI ​​learns the user's browsing history and behavior patterns and provides content at the optimal time. For example, if a user frequently uses the app during a specific time period, the AI ​​will display content according to that time. The delivery unit can also collect user feedback and continuously improve the accuracy and relevance of the content it provides. For example, it can analyze user ratings and comments on the content and reflect them in the next content generation. This allows the delivery unit to provide users with the most suitable content and improve the overall personalization accuracy of the system.

[0034] The acquisition unit acquires asset content from various SoftBank Group companies. For example, it can acquire data from partner companies and information from public APIs. Specifically, it collects product and service information provided by partner companies, as well as news articles and weather information obtainable through public APIs. The acquisition unit can also automatically acquire asset content from various SoftBank Group companies using AI. The AI ​​periodically checks public APIs and automatically acquires new data and updates. Furthermore, it streamlines data integration with partner companies, enabling the rapid acquisition of necessary information. For example, it can acquire new product launch information and campaign information in real time and reflect it in the content provided to users. In addition, the acquisition unit evaluates the quality of the acquired data and selects and uses only highly reliable information. This allows the acquisition unit to efficiently acquire diverse asset content from various SoftBank Group companies and improve the overall personalization accuracy of the system.

[0035] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize data collection from websites that the user has frequently accessed in the past. It can also analyze the usage patterns of apps the user frequently uses and determine the optimal collection timing. Furthermore, the data collection unit can analyze the genres of videos the user has watched and collect relevant data. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. If the user is on holiday, the data collection unit can also collect data related to hobbies and leisure activities. Furthermore, if the user is participating in a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can collect news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can collect tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the data collection unit can collect local news and weather information. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI collect highly relevant data.

[0038] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. The data collection unit can also analyze the content of posts from accounts that the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activities of groups and communities that the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0039] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during data analysis. For example, the analysis unit can prioritize the analysis of topics the user has shown interest in in the past. Furthermore, the analysis unit can select the optimal analysis method based on the user's past behavior patterns. In addition, the analysis unit can adjust its analysis algorithm by referring to the results of data analysis previously performed by the user. This allows for the optimization of the analysis algorithm by referencing the user's past behavior patterns, resulting in more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input user behavior pattern data into a generating AI and have the generating AI optimize the analysis algorithm.

[0040] The analysis unit can track changes in user interests in real time during data analysis and reflect them in the analysis results. For example, the analysis unit can analyze in real time topics that users have recently become interested in. Furthermore, if user interests change, the analysis unit can immediately reflect those changes in the analysis results. In addition, if a user participates in a specific event, the analysis unit can analyze data related to that event in real time. This allows for more appropriate analysis results by tracking changes in user interests in real time. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user interest data into a generating AI and have the generating AI perform real-time analysis.

[0041] The analysis unit can customize the analysis results by taking into account the user's geographical location during data analysis. For example, if the user is in a specific region, the analysis unit can prioritize analyzing data related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information about their travel destination. Additionally, if the user is at home, the analysis unit can prioritize analyzing local news and event information. This allows for the provision of more relevant analysis results by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's location data into a generating AI and have the generating AI perform the customization of the analysis results.

[0042] The analysis unit can supplement its analysis results by referencing the user's social media activity during data analysis. For example, if a user frequently posts about a particular topic, the analysis unit can prioritize analyzing data related to that topic. The analysis unit can also reflect the content of posts from accounts the user follows in its analysis results. Furthermore, the analysis unit can reflect the activities of groups and communities the user participates in. This allows for more comprehensive analysis results by referencing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0043] The generation unit can generate optimal content by referring to the user's past interests during content creation. For example, the generation unit can generate relevant content based on topics the user has shown interest in in the past. It can also generate optimal content by referring to the user's past behavioral history. Furthermore, it can generate relevant content based on the genres of videos the user has watched in the past. This allows for the generation of more relevant content by referring to the user's past interests. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input user interest data into a generation AI and have the generation AI generate optimal content.

[0044] The generation unit can customize content based on the user's current lifestyle when generating content. For example, if the user is at work, the generation unit can generate work-related content. It can also generate content related to hobbies and entertainment if the user is on holiday. Furthermore, if the user is participating in a specific event, the generation unit can generate content related to that event. This allows for the provision of more appropriate content by customizing it based on the user's lifestyle. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user lifestyle data into a generation AI and have the generation AI perform content customization.

[0045] The generation unit can generate optimal content by considering the user's geographical location information during content generation. For example, if the user is in a specific region, the generation unit can generate news and event information related to that region. Furthermore, if the user is traveling, the generation unit can generate tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the generation unit can generate local news and weather information. This allows for the provision of more relevant content by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's location data into a generation AI and have the generation AI generate optimal content.

[0046] The generation unit can analyze a user's social media activity and generate relevant content when generating content. For example, if a user frequently posts about a particular topic, the generation unit can generate content related to that topic. The generation unit can also generate relevant content based on the posts of accounts the user follows. Furthermore, the generation unit can generate relevant content based on the activities of groups and communities the user participates in. This allows for the provision of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI generate relevant content.

[0047] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when delivering content. For example, the delivery unit can prioritize providing content formats (videos, articles, etc.) that the user has frequently viewed in the past. It can also prioritize providing content genres that the user has previously given high ratings to. Furthermore, the delivery unit can select the optimal delivery method based on the content formats that the user has previously watched for extended periods. This makes it possible to provide more appropriate content by referring to the user's past browsing history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user browsing history data into a generating AI and have the generating AI select the optimal delivery method.

[0048] The content delivery unit can customize the delivery method based on the user's current lifestyle when providing content. For example, if the user is at work, the delivery unit can provide content that can be viewed in a short time. If the user is on holiday, the delivery unit can also provide content that can be viewed for a longer period of time. Furthermore, if the user is participating in a specific event, the delivery unit can provide content related to that event. By customizing the delivery method based on the user's lifestyle, it becomes possible to provide more appropriate content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0049] The content delivery unit can select the optimal delivery method by considering the user's geographical location when providing content. For example, if the user is in a specific region, the delivery unit can provide news and event information related to that region. Furthermore, if the user is traveling, the delivery unit can provide tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the delivery unit can provide local news and weather information. This allows for more appropriate content delivery by considering the user's geographical location. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's location data into a generating AI and have the generating AI select the optimal delivery method.

[0050] The content provider can analyze a user's social media activity and provide relevant content when delivering content. For example, if a user frequently posts about a particular topic, the provider can provide content related to that topic. The provider can also provide relevant content based on the posts of accounts the user follows. Furthermore, the provider can provide relevant content based on the activities of groups and communities the user participates in. This allows for the provision of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the content provider may be performed using AI or not. For example, the content provider can input the user's social media data into a generating AI and have the generating AI deliver relevant content.

[0051] The acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring asset content. For example, the acquisition unit can prioritize acquiring asset content that the user has frequently accessed in the past. The acquisition unit can also analyze the usage patterns of apps that the user frequently uses and determine the optimal acquisition timing. Furthermore, the acquisition unit can analyze the genre of videos that the user has watched and acquire related asset content. This makes it possible to acquire more appropriate asset content by referring to the user's past behavior history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user behavior history data into a generating AI and have the generating AI select the optimal acquisition method.

[0052] The acquisition unit can prioritize the acquisition of highly relevant content by considering the user's geographical location when acquiring asset content. For example, if the user is in a specific region, the acquisition unit can prioritize the acquisition of asset content related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of tourist information and restaurant information for the travel destination. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of local news and weather information. This allows for the acquisition of more relevant asset content by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into a generating AI and have the generating AI acquire highly relevant content.

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

[0054] The data collection unit can collect not only user behavior history and interests, but also user health data. For example, it can collect data such as heart rate, steps, and sleep patterns from the user's smartwatch or fitness tracker. It can also collect user meal and exercise records to gain a comprehensive understanding of their health status. Furthermore, the data collection unit can monitor the user's stress level and fatigue level in real time and provide optimal content based on health data. This makes it possible to provide personalized content that takes the user's health status into consideration.

[0055] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, it can prioritize data collection from websites that the user has frequently accessed in the past. It can also analyze the usage patterns of apps that the user frequently uses and determine the optimal collection timing. Furthermore, it can analyze the genres of videos the user has watched and collect relevant data. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history.

[0056] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if the user is at work, the unit can prioritize collecting work-related data. If the user is on holiday, the unit can also collect data related to hobbies and leisure activities. Furthermore, if the user is participating in a specific event, the unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and areas of interest.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the unit can collect news and event information related to that region. Furthermore, if the user is traveling, the unit can collect tourist information and restaurant information for their destination. Additionally, if the user is at home, the unit can collect local news and weather information. This allows the system to prioritize the collection of highly relevant data by considering the user's geographical location.

[0058] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the unit can collect data related to that topic. The unit can also analyze the content of posts from accounts the user follows and collect relevant data. Furthermore, the unit can analyze the activities of groups and communities the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity.

[0059] The analysis unit can optimize its analysis algorithm by referencing the user's past behavior patterns during data analysis. For example, it can prioritize analyzing topics that the user has shown interest in in the past. Furthermore, the analysis unit can select the optimal analysis method based on the user's past behavior patterns. In addition, the analysis unit can adjust its analysis algorithm by referring to the results of data analysis the user has used in the past. This allows for the optimization of the analysis algorithm by referencing the user's past behavior patterns, resulting in more accurate analysis results.

[0060] The analytics department can track changes in user interests in real time during data analysis and reflect these changes in the analysis results. For example, the analytics department can analyze in real time when a user begins to take an interest in a new topic. Furthermore, if a user's interests change, the analytics department can immediately reflect that change in the analysis results. In addition, if a user participates in a specific event, the analytics department can analyze data related to that event in real time. This allows for more accurate analysis results by tracking changes in user interests in real time.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects user behavior history and interests. For example, it can collect data such as websites and apps that users frequently visit, videos they watch, and their activity on social media. The data collection unit can also use AI to automatically collect user behavior history and interests. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify user interests. For example, it can analyze user search keywords and clicked links to identify user interests. The analysis unit can also use AI to analyze the collected data and identify user interests. Step 3: The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, it can generate original content such as articles, videos, and images based on the user's interests and preferences. The generation unit can also use AI to automatically generate original content based on the analysis results. Step 4: The delivery unit provides the content generated by the generation unit to the user. For example, the generated original content can be provided to the user through a website or app. The delivery unit can also use AI to provide the generated content to the user. Step 5: The acquisition unit acquires asset content from each SoftBank Group company. For example, it can acquire data from partner companies and information from public APIs. The acquisition unit can also use AI to automatically acquire asset content from each SoftBank Group company.

[0063] (Example of form 2) The personalized content generation system according to an embodiment of the present invention is a system in which AI performs personalized analysis of a user's behavioral history and interests, and automatically generates original content that is optimal for the user. This personalized content generation system performs personalized analysis of a user's behavioral history and interests, and automatically generates original content that is optimal for the user based on the analysis results. This original content is simply organized based on the user's interests and concerns, and can be easily grasped without having to search for information. Furthermore, since this system is generated based on the asset content of each SoftBank Group company, it is expected to have the effect of driving traffic to the content of each SoftBank Group company and creating synergistic effects. This can be aimed at increasing the number of users and improving sales of each company's content. With this system, users can save the trouble of searching for information and easily obtain information that is optimal for them, thus making daily information gathering more efficient. In addition, it is mutually beneficial for each SoftBank Group company as it leads to increased content usage and sales. Thus, the personalized content generation system can automatically generate and provide optimal original content based on the user's behavioral history and interests.

[0064] The personalized content generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an acquisition unit. The collection unit collects the user's behavior history and interests. For example, the collection unit can collect data such as websites and apps that the user frequently visits, videos that are watched, and activities on social media. The collection unit can also use AI to automatically collect the user's behavior history and interests. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit can analyze the user's search keywords and clicked links to identify the user's interests. The analysis unit can also use AI to analyze the collected data and identify the user's interests. The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, the generation unit can generate original content such as articles, videos, and images based on the user's interests. The generation unit can also use AI to automatically generate original content based on the analysis results. The provision unit provides the content generated by the generation unit to the user. For example, the provision unit can provide the generated original content to the user through a website or app. The provisioning unit can also provide the generated content to the user using AI. The acquisition unit acquires asset content from each SoftBank Group company. The acquisition unit can acquire data from partner companies and information from public APIs, for example. The acquisition unit can also automatically acquire asset content from each SoftBank Group company using AI. As a result, the personalized content generation system according to the embodiment can automatically generate and provide optimal original content based on the user's behavior history and interests.

[0065] The data collection unit collects user behavior history and interests. For example, it can collect data such as websites and apps that users frequently visit, videos they watch, and their activity on social media. Specifically, it collects detailed information such as website browsing history, app usage frequency, genres and durations of videos watched, and social media posts, likes, and shares. This data may be collected directly from the user's device or through cloud services. The data collection unit can also use AI to automatically collect user behavior history and interests. The AI ​​can learn user behavior patterns and predict behavior at specific times and in specific situations. For example, if a user tends to watch videos of a specific genre at a specific time, the AI ​​will prioritize collecting content related to that time. Furthermore, to protect user privacy, the data collection unit anonymizes and encrypts data to securely collect and manage it. This allows the data collection unit to efficiently collect diverse user behavior data and improve the overall personalization accuracy of the system.

[0066] The analysis department analyzes data collected by the data collection department to identify user interests. For example, the analysis department can identify user interests by analyzing user search keywords and clicked links. Specifically, it analyzes in detail the frequency and relevance of search keywords, the content of clicked links, and the time spent on them. The analysis department can also use AI to analyze collected data and identify user interests. The AI ​​uses natural language processing technology to analyze user search keywords and social media posts to extract user concerns and trends. It can also use machine learning algorithms to learn user behavior patterns and predict potential interests. For example, it can analyze patterns of keywords users frequently search for and links they click to identify interests in specific genres or themes. Furthermore, the analysis department can perform more accurate interest identification by comparing past data and the behavior data of other users. This allows the analysis department to accurately identify diverse user interests and improve the overall personalization accuracy of the system.

[0067] The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, the generation unit can generate original content such as articles, videos, and images based on the user's interests. Specifically, it can automatically generate articles on themes that the user is interested in, or edit and generate related videos and images. The generation unit can also use AI to automatically generate original content based on the analysis results. The AI ​​uses natural language generation technology to create articles and blog posts tailored to the user's interests. It can also use image generation technology to generate customized images based on the user's interests. Furthermore, it can use video generation technology to create short videos and slideshows tailored to the user's interests. For example, if the user is interested in travel, it can generate articles and videos that include recommended spots and tourist information for travel destinations. In this way, the generation unit can efficiently generate original content optimized for the user's interests, improving the overall personalization accuracy of the system.

[0068] The delivery unit provides users with content generated by the generation unit. For example, the delivery unit can provide the generated original content to users through websites or apps. Specifically, it can display the generated content on the homepage of a website or the home screen of an app that the user accesses. The delivery unit can also use AI to provide users with generated content. The AI ​​learns the user's browsing history and behavior patterns and provides content at the optimal time. For example, if a user frequently uses the app during a specific time period, the AI ​​will display content according to that time. The delivery unit can also collect user feedback and continuously improve the accuracy and relevance of the content it provides. For example, it can analyze user ratings and comments on the content and reflect them in the next content generation. This allows the delivery unit to provide users with the most suitable content and improve the overall personalization accuracy of the system.

[0069] The acquisition unit acquires asset content from various SoftBank Group companies. For example, it can acquire data from partner companies and information from public APIs. Specifically, it collects product and service information provided by partner companies, as well as news articles and weather information obtainable through public APIs. The acquisition unit can also automatically acquire asset content from various SoftBank Group companies using AI. The AI ​​periodically checks public APIs and automatically acquires new data and updates. Furthermore, it streamlines data integration with partner companies, enabling the rapid acquisition of necessary information. For example, it can acquire new product launch information and campaign information in real time and reflect it in the content provided to users. In addition, the acquisition unit evaluates the quality of the acquired data and selects and uses only highly reliable information. This allows the acquisition unit to efficiently acquire diverse asset content from various SoftBank Group companies and improve the overall personalization accuracy of the system.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce data collection and collect data when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect data in real time and reflect it immediately. Additionally, if the user is tired, the data collection unit can temporarily stop data collection and resume it after the user has rested. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize data collection from websites that the user has frequently accessed in the past. It can also analyze the usage patterns of apps the user frequently uses and determine the optimal collection timing. Furthermore, the data collection unit can analyze the genres of videos the user has watched and collect relevant data. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0072] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, if the user is at work, the data collection unit can prioritize collecting work-related data. If the user is on holiday, the data collection unit can also collect data related to hobbies and leisure activities. Furthermore, if the user is participating in a specific event, the data collection unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's lifestyle data into a generating AI and have the generating AI perform the filtering.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit can prioritize collecting entertainment-related data. If the user is stressed, the data collection unit can prioritize collecting data related to relaxation and stress relief. Furthermore, if the user is focused, the data collection unit can prioritize collecting data related to learning and work. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can collect news and event information related to that region. Furthermore, if the user is traveling, the data collection unit can collect tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the data collection unit can collect local news and weather information. This allows for the priority collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's location data into a generating AI and have the generating AI collect highly relevant data.

[0075] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. The data collection unit can also analyze the content of posts from accounts that the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activities of groups and communities that the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI perform the collection of relevant data.

[0076] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed data analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise and to-the-point data analysis. Furthermore, if the user is excited, the analysis unit can provide visually appealing data analysis results. By adjusting the data analysis method according to the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The analysis unit can optimize its analysis algorithm by referring to the user's past behavior patterns during data analysis. For example, the analysis unit can prioritize the analysis of topics the user has shown interest in in the past. Furthermore, the analysis unit can select the optimal analysis method based on the user's past behavior patterns. In addition, the analysis unit can adjust its analysis algorithm by referring to the results of data analysis previously performed by the user. This allows for the optimization of the analysis algorithm by referencing the user's past behavior patterns, resulting in more accurate analysis results. Some or all of the above processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input user behavior pattern data into a generating AI and have the generating AI optimize the analysis algorithm.

[0078] The analysis unit can track changes in user interests in real time during data analysis and reflect them in the analysis results. For example, the analysis unit can analyze in real time topics that users have recently become interested in. Furthermore, if user interests change, the analysis unit can immediately reflect those changes in the analysis results. In addition, if a user participates in a specific event, the analysis unit can analyze data related to that event in real time. This allows for more appropriate analysis results by tracking changes in user interests in real time. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user interest data into a generating AI and have the generating AI perform real-time analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more visually appealing display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can customize the analysis results by taking into account the user's geographical location during data analysis. For example, if the user is in a specific region, the analysis unit can prioritize analyzing data related to that region. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing information about their travel destination. Additionally, if the user is at home, the analysis unit can prioritize analyzing local news and event information. This allows for the provision of more relevant analysis results by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's location data into a generating AI and have the generating AI perform the customization of the analysis results.

[0081] The analysis unit can supplement its analysis results by referencing the user's social media activity during data analysis. For example, if a user frequently posts about a particular topic, the analysis unit can prioritize analyzing data related to that topic. The analysis unit can also reflect the content of posts from accounts the user follows in its analysis results. Furthermore, the analysis unit can reflect the activities of groups and communities the user participates in. This allows for more comprehensive analysis results by referencing the user's social media activity. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's social media data into a generating AI and have the generating AI perform the supplementation of the analysis results.

[0082] The generation unit can estimate the user's emotions and adjust the content generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate concise and to-the-point content. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. In this way, by adjusting the content generation method according to the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] The generation unit can generate optimal content by referring to the user's past interests during content creation. For example, the generation unit can generate relevant content based on topics the user has shown interest in in the past. It can also generate optimal content by referring to the user's past behavioral history. Furthermore, it can generate relevant content based on the genres of videos the user has watched in the past. This allows for the generation of more relevant content by referring to the user's past interests. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input user interest data into a generation AI and have the generation AI generate optimal content.

[0084] The generation unit can customize content based on the user's current lifestyle when generating content. For example, if the user is at work, the generation unit can generate work-related content. It can also generate content related to hobbies and entertainment if the user is on holiday. Furthermore, if the user is participating in a specific event, the generation unit can generate content related to that event. This allows for the provision of more appropriate content by customizing it based on the user's lifestyle. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user lifestyle data into a generation AI and have the generation AI perform content customization.

[0085] The generation unit can estimate the user's emotions and determine the priority of content to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit can prioritize generating entertainment-related content. If the user is stressed, the generation unit can prioritize generating content related to relaxation and stress relief. Furthermore, if the user is focused, the generation unit can prioritize generating content related to learning and work. This allows for the provision of more appropriate content by prioritizing content 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] The generation unit can generate optimal content by considering the user's geographical location information during content generation. For example, if the user is in a specific region, the generation unit can generate news and event information related to that region. Furthermore, if the user is traveling, the generation unit can generate tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the generation unit can generate local news and weather information. This allows for the provision of more relevant content by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not. For example, the generation unit can input the user's location data into a generation AI and have the generation AI generate optimal content.

[0087] The generation unit can analyze a user's social media activity and generate relevant content when generating content. For example, if a user frequently posts about a particular topic, the generation unit can generate content related to that topic. The generation unit can also generate relevant content based on the posts of accounts the user follows. Furthermore, the generation unit can generate relevant content based on the activities of groups and communities the user participates in. This allows for the provision of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI generate relevant content.

[0088] The content delivery unit can estimate the user's emotions and adjust the content delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit can deliver content at a leisurely pace. If the user is in a hurry, the delivery unit can deliver concise and to-the-point content. Furthermore, if the user is excited, the delivery unit can deliver content with visually stimulating effects. By adjusting the content delivery method according to the user's emotions, more appropriate content delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The content delivery unit can select the optimal delivery method by referring to the user's past browsing history when delivering content. For example, the delivery unit can prioritize providing content formats (videos, articles, etc.) that the user has frequently viewed in the past. It can also prioritize providing content genres that the user has previously given high ratings to. Furthermore, the delivery unit can select the optimal delivery method based on the content formats that the user has previously watched for extended periods. This makes it possible to provide more appropriate content by referring to the user's past browsing history. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user browsing history data into a generating AI and have the generating AI select the optimal delivery method.

[0090] The content delivery unit can customize the delivery method based on the user's current lifestyle when providing content. For example, if the user is at work, the delivery unit can provide content that can be viewed in a short time. If the user is on holiday, the delivery unit can also provide content that can be viewed for a longer period of time. Furthermore, if the user is participating in a specific event, the delivery unit can provide content related to that event. By customizing the delivery method based on the user's lifestyle, it becomes possible to provide more appropriate content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of the delivery method.

[0091] The service provider can estimate the user's emotions and prioritize the content to be provided based on those emotions. For example, if the user is relaxed, the service provider can prioritize entertainment-related content. If the user is stressed, the service provider can prioritize content related to relaxation and stress relief. Furthermore, if the user is focused, the service provider can prioritize content related to learning and work. This allows for more appropriate content delivery by prioritizing content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The content delivery unit can select the optimal delivery method by considering the user's geographical location when providing content. For example, if the user is in a specific region, the delivery unit can provide news and event information related to that region. Furthermore, if the user is traveling, the delivery unit can provide tourist information and restaurant information for their travel destination. Additionally, if the user is at home, the delivery unit can provide local news and weather information. This allows for more appropriate content delivery by considering the user's geographical location. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's location data into a generating AI and have the generating AI select the optimal delivery method.

[0093] The content provider can analyze a user's social media activity and provide relevant content when delivering content. For example, if a user frequently posts about a particular topic, the provider can provide content related to that topic. The provider can also provide relevant content based on the posts of accounts the user follows. Furthermore, the provider can provide relevant content based on the activities of groups and communities the user participates in. This allows for the provision of more relevant content by analyzing the user's social media activity. Some or all of the above processing in the content provider may be performed using AI or not. For example, the content provider can input the user's social media data into a generating AI and have the generating AI deliver relevant content.

[0094] The acquisition unit can estimate the user's emotions and adjust the timing of asset content acquisition based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can acquire asset content at a leisurely pace. If the user is in a hurry, the acquisition unit can acquire asset content quickly. Furthermore, if the user is excited, the acquisition unit can acquire asset content with visually stimulating effects. By adjusting the timing of asset content acquisition according to the user's emotions, more appropriate content acquisition becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The acquisition unit can select the optimal acquisition method by referring to the user's past behavior history when acquiring asset content. For example, the acquisition unit can prioritize acquiring asset content that the user has frequently accessed in the past. The acquisition unit can also analyze the usage patterns of apps that the user frequently uses and determine the optimal acquisition timing. Furthermore, the acquisition unit can analyze the genre of videos that the user has watched and acquire related asset content. This makes it possible to acquire more appropriate asset content by referring to the user's past behavior history. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user behavior history data into a generating AI and have the generating AI select the optimal acquisition method.

[0096] The acquisition unit can estimate the user's emotions and determine the priority of asset content to acquire based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit can prioritize acquiring entertainment-related asset content. If the user is stressed, the acquisition unit can also prioritize acquiring asset content related to relaxation and stress relief. Furthermore, if the user is focused, the acquisition unit can also prioritize acquiring asset content related to learning and work. This allows for more appropriate content acquisition by prioritizing asset content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The acquisition unit can prioritize the acquisition of highly relevant content by considering the user's geographical location when acquiring asset content. For example, if the user is in a specific region, the acquisition unit can prioritize the acquisition of asset content related to that region. Furthermore, if the user is traveling, the acquisition unit can prioritize the acquisition of tourist information and restaurant information for the travel destination. Additionally, if the user is at home, the acquisition unit can prioritize the acquisition of local news and weather information. This allows for the acquisition of more relevant asset content by considering the user's geographical location. Some or all of the above processing in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into a generating AI and have the generating AI acquire highly relevant content.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can collect not only user behavior history and interests, but also user health data. For example, it can collect data such as heart rate, steps, and sleep patterns from the user's smartwatch or fitness tracker. It can also collect user meal and exercise records to gain a comprehensive understanding of their health status. Furthermore, the data collection unit can monitor the user's stress level and fatigue level in real time and provide optimal content based on health data. This makes it possible to provide personalized content that takes the user's health status into consideration.

[0100] The data collection unit can estimate the user's emotions and adjust the frequency of data collection based on those estimates. For example, if the user is stressed, the unit can reduce the frequency of data collection, and if the user is relaxed, it can increase the frequency. Furthermore, if the user is excited, the unit can collect data in real time and reflect it immediately. Additionally, if the user is tired, the unit can temporarily stop data collection and resume it after the user has rested. This allows for more appropriate data collection by adjusting the frequency of data collection according to the user's emotions.

[0101] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, it can prioritize data collection from websites that the user has frequently accessed in the past. It can also analyze the usage patterns of apps that the user frequently uses and determine the optimal collection timing. Furthermore, it can analyze the genres of videos the user has watched and collect relevant data. This enables efficient data collection by selecting the optimal data collection method based on the user's past behavior history.

[0102] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, if the user is at work, the unit can prioritize collecting work-related data. If the user is on holiday, the unit can also collect data related to hobbies and leisure activities. Furthermore, if the user is participating in a specific event, the unit can collect data related to that event. This allows for the collection of more relevant data by filtering it based on the user's lifestyle and areas of interest.

[0103] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is relaxed, the unit can prioritize collecting entertainment-related data. If the user is stressed, the unit can prioritize collecting data related to relaxation and stress relief. Furthermore, if the user is focused, the unit can prioritize collecting data related to learning and work. This allows for more appropriate data collection by prioritizing data according to the user's emotions.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the unit can collect news and event information related to that region. Furthermore, if the user is traveling, the unit can collect tourist information and restaurant information for their destination. Additionally, if the user is at home, the unit can collect local news and weather information. This allows the system to prioritize the collection of highly relevant data by considering the user's geographical location.

[0105] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user frequently posts about a particular topic, the unit can collect data related to that topic. The unit can also analyze the content of posts from accounts the user follows and collect relevant data. Furthermore, the unit can analyze the activities of groups and communities the user participates in and collect relevant data. This allows for the efficient collection of relevant data by analyzing a user's social media activity.

[0106] The analytics unit can estimate the user's emotions and adjust the data analysis method based on those estimated emotions. For example, if the user is relaxed, the analytics unit can perform a detailed data analysis to provide deeper insights. If the user is in a hurry, the analytics unit can perform a concise and to-the-point data analysis. Furthermore, if the user is excited, the analytics unit can provide visually appealing data analysis results. By adjusting the data analysis method according to the user's emotions, more appropriate analysis results can be obtained.

[0107] The analysis unit can optimize its analysis algorithm by referencing the user's past behavior patterns during data analysis. For example, it can prioritize analyzing topics that the user has shown interest in in the past. Furthermore, the analysis unit can select the optimal analysis method based on the user's past behavior patterns. In addition, the analysis unit can adjust its analysis algorithm by referring to the results of data analysis the user has used in the past. This allows for the optimization of the analysis algorithm by referencing the user's past behavior patterns, resulting in more accurate analysis results.

[0108] The analytics department can track changes in user interests in real time during data analysis and reflect these changes in the analysis results. For example, the analytics department can analyze in real time when a user begins to take an interest in a new topic. Furthermore, if a user's interests change, the analytics department can immediately reflect that change in the analysis results. In addition, if a user participates in a specific event, the analytics department can analyze data related to that event in real time. This allows for more accurate analysis results by tracking changes in user interests in real time.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects user behavior history and interests. For example, it can collect data such as websites and apps that users frequently visit, videos they watch, and their activity on social media. The data collection unit can also use AI to automatically collect user behavior history and interests. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify user interests. For example, it can analyze user search keywords and clicked links to identify user interests. The analysis unit can also use AI to analyze the collected data and identify user interests. Step 3: The generation unit generates original content based on the analysis results obtained by the analysis unit. For example, it can generate original content such as articles, videos, and images based on the user's interests and preferences. The generation unit can also use AI to automatically generate original content based on the analysis results. Step 4: The delivery unit provides the content generated by the generation unit to the user. For example, the generated original content can be provided to the user through a website or app. The delivery unit can also use AI to provide the generated content to the user. Step 5: The acquisition unit acquires asset content from each SoftBank Group company. For example, it can acquire data from partner companies and information from public APIs. The acquisition unit can also use AI to automatically acquire asset content from each SoftBank Group company.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and acquisition unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior history and interests using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to identify the user's interests. The generation unit generates original content based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the content generated by the control unit 46A of the smart device 14 to the user. The acquisition unit acquires asset content from each SoftBank Group company using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and acquisition unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's behavior history and interests using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to identify the user's interests. The generation unit generates original content based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the content generated by the control unit 46A of the smart glasses 214 to the user. The acquisition unit acquires asset content from each SoftBank Group company using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and acquisition unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's behavioral history and interests using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to identify the user's interests. The generation unit generates original content based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the content generated by the control unit 46A of the headset terminal 314 to the user. The acquisition unit acquires asset content from various SoftBank Group companies using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and acquisition unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's behavior history and interests using the control unit 46A of the robot 414. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 to identify the user's interests. The generation unit generates original content based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit provides the content generated by the control unit 46A of the robot 414 to the user. The acquisition unit acquires asset content from various SoftBank Group companies using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects user behavior history and interests, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user interests, A generation unit that generates original content based on the analysis results obtained by the aforementioned analysis unit, A provisioning unit that provides the content generated by the generation unit to the user, It includes an acquisition unit that acquires asset content from each SoftBank Group company. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During data analysis, the analysis algorithm is optimized by referring to the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is During data analysis, changes in user interests are tracked in real time and reflected in the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During data analysis, the analysis results are customized by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During data analysis, the analysis results are supplemented by referencing users' social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates user sentiment and adjusts content generation methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating content, the system references the user's past interests to create the most relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating content, customize the content based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates user sentiment and determines the priority of content to generate based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating content, the system takes the user's geographical location into consideration to generate the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating content, the system analyzes users' social media activity to generate relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate user sentiment and adjust the content delivery method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing content, the system selects the optimal delivery method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing content, customize the delivery method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing content, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, we analyze users' social media activity and provide relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The acquisition unit is, It estimates the user's emotions and adjusts the timing of asset content retrieval based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The acquisition unit is, When retrieving asset content, the system selects the optimal retrieval method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The acquisition unit is, It estimates the user's emotions and determines the priority of asset content to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The acquisition unit is, When retrieving asset content, the system prioritizes retrieving highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior history and interests, An analysis unit analyzes the data collected by the aforementioned collection unit to identify user interests, A generation unit that generates original content based on the analysis results obtained by the aforementioned analysis unit, A provisioning unit that provides the content generated by the generation unit to the user, It includes an acquisition unit that acquires asset content from each SoftBank Group company. A system characterized by the following features.

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

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

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

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

6. The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system according to feature 1.

8. The aforementioned analysis unit is We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned analysis unit is During data analysis, the analysis algorithm is optimized by referring to the user's past behavior patterns. The system according to feature 1.

10. The aforementioned analysis unit is During data analysis, changes in user interests are tracked in real time and reflected in the analysis results. The system according to feature 1.

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