system

Abstract

The system according to the embodiment aims to collect and distribute information based on the user's interests and manage discussions. [Solution] A system according to an embodiment includes an analysis unit, a collection unit, a distribution unit, and a management unit. The analysis unit analyzes user interests. The collection unit collects information based on the results of the analysis by the analysis unit. The distribution unit distributes the information collected by the collection unit. The management unit manages discussions based on the information distributed by the distribution unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently collect and distribute information based on user interests, nor manage discussions, and there is room for improvement.

[0005] The system according to the embodiment aims to collect and distribute information based on the user's interests and manage discussions. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a collection unit, a distribution unit, and a management unit. The analysis unit analyzes user interests. The collection unit collects information based on the results of the analysis by the analysis unit. The distribution unit distributes the information collected by the collection unit. The management unit manages discussions based on the information distributed by the distribution unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and distribute information based on user interests and manage discussions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention is a system for automatically collecting and distributing information that may be of interest to users in community management. This system analyzes users' interests and automatically collects related information based on the analysis. The collected information is then distributed to users, allowing them to hold discussions on the information. The system also has a function that encourages active participation by allowing users to start discussions themselves and invite others to join. For example, AI identifies users' interests based on their past behavioral history, posted content, browsing history, etc., and collects the latest information related to a particular topic from users who frequently post on that topic. The collected information is then distributed to users, who can view the information and post comments and opinions on it. For example, when the latest technology news is distributed, users can express their opinions on the news. Furthermore, users can start their own discussions, starting discussions on topics of interest to them, and other users can participate in the discussions. For example, users can post questions or opinions about a particular technology, and other users can respond or comment on them. This mechanism allows users to efficiently collect information that matches their interests and engage in discussions on it. Furthermore, encouraging active participation will help revitalize the entire community. For example, by allowing users to express their opinions, they will deepen their interactions with other users and increase the sense of community unity. This allows the AI ​​system to collect and distribute information based on users' interests and manage discussions.

[0029] A community management system according to an embodiment includes an analysis unit, a collection unit, a distribution unit, and a management unit. The analysis unit analyzes user interests. The analysis unit identifies user interests based on, for example, the user's past behavioral history, posted content, browsing history, etc. For example, the analysis unit collects the latest information related to a specific topic for a user who frequently posts on that topic. The collection unit collects information based on the results of the analysis by the analysis unit. For example, the collection unit automatically collects related news articles and academic papers based on the interests identified by the analysis unit. For example, the collection unit collects the latest news articles and social media posts on the Internet. The distribution unit distributes the information collected by the collection unit. For example, the distribution unit notifies users of the collected information so that the users can view the information. For example, the distribution unit can distribute the information using email notifications or push notifications. The management unit manages discussions based on the information distributed by the distribution unit. For example, the management unit allows users to post comments and opinions on the distributed information. For example, the management unit provides a forum or chat function to enable users to hold discussions. This allows the community management system according to the embodiment to collect and distribute information based on the interests of users and manage discussions.

[0030] The community management system includes a recording unit that records a user's behavioral history. The recording unit records the user's behavioral history. The recording unit records, for example, the user's click history, browsing history, purchase history, etc. For example, the recording unit records the links the user clicked on a website and the history of pages the user viewed. The recording unit can also record the history of products the user purchased at an online store. By recording the user's behavioral history, the recording unit can improve the accuracy of analysis. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's behavioral history data into a generation AI and cause the generation AI to analyze the behavioral history.

[0031] The community management system includes a notification unit that notifies users of discussions. The notification unit notifies users of discussions. The notification unit notifies users of discussions by, for example, email notification, push notification, alert, or other methods. For example, the notification unit notifies users by email when a new discussion has started. The notification unit can also send a push notification to the user's smartphone to notify them of the start of a new discussion. Furthermore, the notification unit can display an alert on a website to notify users of the start of a discussion. In this way, the notification unit can promote user participation by notifying users of the discussion. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input discussion start information to a generation AI and cause the generation AI to generate notification content.

[0032] The community management system includes a recommendation unit that recommends information to a user. The recommendation unit recommends appropriate information to the user. The recommendation unit recommends information based on, for example, the user's past behavior, interests, attributes, etc. For example, the recommendation unit recommends the latest information related to a topic that the user has previously viewed. The recommendation unit can also recommend new topics or related information based on the user's interests. Furthermore, the recommendation unit can also recommend appropriate information based on the user's attribute information (age, gender, occupation, etc.). In this way, the recommendation unit can improve user satisfaction by recommending appropriate information to the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's behavior history data into a generation AI and cause the generation AI to generate recommended content.

[0033] The analysis unit can analyze the user's past behavioral history and track changes in interests. For example, the analysis unit can identify topics that the user frequently viewed in the past and provide the latest information related to those topics. The analysis unit can also analyze the user's past posts, track changes in interests, and provide related information. Furthermore, the analysis unit can track changes in interests and suggest new related topics based on the user's past discussion participation history. In this way, by tracking changes in the user's interests, more relevant information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's behavioral history data into the generation AI and cause the generation AI to track changes in interests.

[0034] The analysis unit can analyze the context of the user's posted content and identify deeper interests. The analysis unit can, for example, analyze keywords in the user's posted content and identify related deep interests. The analysis unit can also analyze the context of the user's posted content and identify latent interests. Furthermore, the analysis unit can analyze the emotions in the user's posted content and identify the strength of the interest. In this way, latent interests can be identified by analyzing the context of the user's posted content. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's posted content data into a generation AI and have the generation AI perform context analysis.

[0035] The analysis unit can analyze region-specific interests based on the user's geographical location information. For example, the analysis unit analyzes local event information based on the user's current location. The analysis unit can also analyze region-specific news based on the user's geographical location information. Furthermore, the analysis unit can analyze information about popular spots in the region, taking into account the user's geographical location information. This makes it possible to analyze region-specific interests by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into the generation AI and cause the generation AI to analyze region-specific interests.

[0036] The analysis unit can analyze the user's social media activity and identify related interests. For example, the analysis unit can analyze the user's social media posts to identify related interests. The analysis unit can also analyze the user's social media followers and accounts they follow to identify related interests. Furthermore, the analysis unit can analyze the user's social media like and share history to identify related interests. In this way, related interests can be identified by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI identify the interests.

[0037] The collection unit can evaluate the reliability of information at the time of collection and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of the information source and prioritizes collecting highly reliable information. The collection unit can also evaluate the content of the information and prioritize collecting highly reliable information. Furthermore, the collection unit can evaluate the reliability of the information provider and prioritize collecting highly reliable information. In this way, highly reliable information can be provided by evaluating the reliability of the information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input reliability data of the information source to the generation AI and cause the generation AI to evaluate the reliability.

[0038] When collecting, the collection unit can prioritize collecting the latest information by taking into account the freshness of the information. The collection unit, for example, evaluates the publication date and time of the information and prioritizes collecting the latest information. The collection unit can also evaluate the update frequency of the information and prioritize collecting the latest information. Furthermore, the collection unit can evaluate the update history of the information provider and prioritize collecting the latest information. This makes it possible to provide the latest information by taking into account the freshness of the information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input publication date and time data of the information to the generation AI and have the generation AI evaluate the freshness.

[0039] The collection unit can collect region-specific information by taking into account the user's geographical location information. For example, the collection unit collects local event information based on the user's current location. The collection unit can also collect region-specific news based on the user's geographical location information. Furthermore, the collection unit can collect information about popular spots in the region by taking into account the user's geographical location information. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect region-specific information.

[0040] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related information. The collection unit can also analyze the user's social media followers and the accounts they follow and collect related information. Furthermore, the collection unit can analyze the user's social media like and share history and collect related information. In this way, by analyzing the user's social media activities, related information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect information.

[0041] The distribution unit can adjust the level of detail of the distribution based on the importance of the information during distribution. For example, the distribution unit distributes information of high importance in detail. The distribution unit can also distribute information of low importance in a concise manner. Furthermore, the distribution unit can adjust the level of detail of the distribution content according to the importance. In this way, by adjusting the level of detail of the distribution based on the importance of the information, more appropriate information can be provided. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the distribution.

[0042] The distribution unit can apply different distribution algorithms depending on the category of information during distribution. For example, the distribution unit distributes information in the news category with emphasis on timeliness. The distribution unit can also distribute information in the entertainment category with emphasis on visual elements. Furthermore, the distribution unit can distribute information in the technology category with emphasis on detailed explanations. In this way, by applying different distribution algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information category data into the generation AI and cause the generation AI to apply the distribution algorithm.

[0043] The distribution unit can determine the priority of distribution based on the time of submission of information at the time of distribution. For example, the distribution unit prioritizes the distribution of the latest information. The distribution unit can also postpone information that was submitted earlier. Furthermore, the distribution unit can determine the priority of distribution based on the time of submission. In this way, by determining the priority of distribution based on the time of submission of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of distribution.

[0044] The distribution unit can adjust the order of distribution based on the relevance of the information during distribution. For example, the distribution unit prioritizes the distribution of information that is most relevant to the user's interests. The distribution unit can also postpone information with low relevance. Furthermore, the distribution unit can adjust the order of distribution based on the relevance of the information. In this way, by adjusting the order of distribution based on the relevance of the information, more appropriate information can be provided. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of distribution.

[0045] When managing discussions, the management unit can select the optimal management method by referring to past discussion histories. The management unit, for example, analyzes past discussion histories and selects the optimal management method. The management unit can also select a management method by referring to successful examples of past discussions. Furthermore, the management unit can improve the management method by referring to unsuccessful examples of past discussions. In this way, the optimal management method can be selected by referring to past discussion histories. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input past discussion history data into a generation AI and have the generation AI select the optimal management method.

[0046] The management unit can manage discussions while taking into consideration user attribute information. The management unit selects an appropriate management method, for example, depending on the user's age group. The management unit can also select an appropriate management method depending on the user's interests. Furthermore, the management unit can select an appropriate management method depending on the user's activity history. This allows for more appropriate discussion management by taking into consideration the user's attribute information. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input user attribute information data into the generation AI and have the generation AI select a management method.

[0047] The management unit can manage discussions while taking into account the user's geographical location information. For example, the management unit can manage region-specific discussions based on the user's current location. The management unit can also manage discussions about local events based on the user's geographical location information. Furthermore, the management unit can manage discussions about local news by taking into account the user's geographical location information. In this way, region-specific discussions can be managed by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit can be performed using AI, for example, or without using AI. For example, the management unit can input the user's geographical location data into the generation AI and have the generation AI manage the discussions.

[0048] When managing a discussion, the management unit can improve the accuracy of management by referring to related external information. For example, the management unit can supplement the content of the discussion by referring to external news sources. The management unit can also improve the quality of the discussion by referring to the opinions of external experts. Furthermore, the management unit can enrich the information of the discussion by referring to an external database. In this way, by referring to related external information, the accuracy of discussion management is improved. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input external information data into a generation AI and cause the generation AI to improve the accuracy of management.

[0049] The recording unit can evaluate the reliability of data when recording and preferentially record highly reliable data. The recording unit, for example, evaluates the reliability of a data provider and preferentially records highly reliable data. The recording unit can also evaluate the content of the data and preferentially record highly reliable data. Furthermore, the recording unit can evaluate the origin of the data and preferentially record highly reliable data. In this way, highly reliable data can be recorded by evaluating the reliability of the data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input reliability data of the data to a generation AI and have the generation AI perform a reliability evaluation.

[0050] When recording, the recording unit can prioritize recording the latest data, taking into account the freshness of the data. The recording unit, for example, evaluates the publication date and time of the data and prioritizes recording the latest data. The recording unit can also evaluate the update frequency of the data and prioritize recording the latest data. Furthermore, the recording unit can evaluate the update history of the data provider and prioritize recording the latest data. In this way, the latest data can be recorded by taking into account the freshness of the data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the publication date and time data of the data to the generation AI and have the generation AI evaluate the freshness.

[0051] The recording unit can determine the priority of recording based on the time of data submission when recording. For example, the recording unit prioritizes recording the most recent data. The recording unit can also postpone data that was submitted earlier. Furthermore, the recording unit can determine the priority of recording based on the time of submission. In this way, by determining the priority of recording based on the time of data submission, more appropriate data can be recorded. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of recording.

[0052] The notification unit can adjust the level of detail of the notification based on the importance of the information when notifying. For example, the notification unit notifies information of high importance in detail. The notification unit can also notify information of low importance in a concise manner. Furthermore, the notification unit can adjust the level of detail of the notification content according to the importance. As a result, by adjusting the level of detail of the notification based on the importance of the information, more appropriate notification can be provided. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0053] The notification unit can apply different notification algorithms depending on the category of information when making a notification. For example, the notification unit may notify information in the news category by emphasizing timeliness. The notification unit may also notify information in the entertainment category by emphasizing visual elements. Furthermore, the notification unit may notify information in the technology category by emphasizing detailed explanations. In this way, by applying different notification algorithms depending on the category of information, more appropriate notifications can be made. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input information category data into a generation AI and cause the generation AI to apply the notification algorithm.

[0054] At the time of notification, the notification unit can determine the priority of notifications based on the time of submission of information. For example, the notification unit prioritizes notifying the latest information. The notification unit can also postpone notifying information that has been submitted earlier. Furthermore, the notification unit can also determine the priority of notifications based on the time of submission. In this way, by determining the priority of notifications based on the time of submission of information, more appropriate notifications can be made. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information submission time data into the generation AI and have the generation AI determine the priority of notifications.

[0055] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the information when making a recommendation. For example, the recommendation unit recommends information with high importance in detail. The recommendation unit can also recommend information with low importance in a concise manner. Furthermore, the recommendation unit can adjust the level of detail of the recommendation content according to the importance. In this way, by adjusting the level of detail of the recommendation based on the importance of the information, more appropriate recommendations can be made. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0056] The recommendation unit can apply different recommendation algorithms depending on the category of information when making a recommendation. For example, the recommendation unit can recommend information in the news category by emphasizing timeliness. The recommendation unit can also recommend information in the entertainment category by emphasizing visual elements. Furthermore, the recommendation unit can recommend information in the technology category by emphasizing detailed explanations. In this way, by applying different recommendation algorithms depending on the category of information, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0057] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of submission of the information. For example, the recommendation unit prioritizes recommending the most recent information. The recommendation unit can also postpone information that was submitted earlier. Furthermore, the recommendation unit can determine the priority of the recommendation based on the time of submission. In this way, by determining the priority of the recommendation based on the time of submission of the information, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the time of submission of the information to the generation AI and have the generation AI determine the priority of the recommendation.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can also take into account the user's social media activity when analyzing the user's interests. For example, it can analyze the content the user frequently shares and the accounts the user follows to identify interest trends. It can also analyze the user's comment and like history on social media to identify more detailed interests. It can also analyze the activities of online communities and groups in which the user participates and collect related information. By taking the user's social media activity into account, the analysis unit can perform more accurate interest analysis.

[0060] The recording unit can also take the user's geographical location information into account when recording the user's behavioral history. For example, it can record the history of places the user has visited and events the user has participated in. Also, if the user is frequently active in a particular area, it can prioritize recording information related to that area. Furthermore, it can analyze the user's movement patterns and track changes in interests. By taking the user's geographical location information into account, the recording unit can record a more detailed behavioral history.

[0061] The notification unit can also take into account the user's past notification history when notifying a discussion. For example, it can analyze what notifications the user has responded to in the past and prioritize the use of notification methods that have generated good responses. It can also increase the effectiveness of notifications by avoiding notification methods that the user has ignored in the past. Furthermore, it can also determine the optimal notification timing based on the user's notification history. In this way, the notification unit can provide more effective notifications by taking into account the user's past notification history.

[0062] The recommendation unit can also take the user's purchasing history into consideration when recommending information to the user. For example, the recommendation unit can analyze the user's history of past purchases and recommend related products and services. It can also prioritize the recommendation of products in categories that the user frequently purchases. Furthermore, it can recommend new products and services that the user is likely to be interested in based on the user's purchasing history. This allows the recommendation unit to recommend more appropriate information by taking the user's purchasing history into consideration.

[0063] The analysis unit can also take into account the user's device usage history when analyzing the user's past behavioral history. For example, it can identify which device the user frequently uses to view information and provide information optimized for that device. It can also analyze which device the user uses at a specific time period and provide information at the optimal time. Furthermore, it can track changes in the user's interests based on the user's device usage history. This allows the analysis unit to provide more relevant information by taking into account the user's device usage history.

[0064] The analysis unit can also take into account the user's language usage patterns when analyzing the context of the user's posted content. For example, it can identify keywords and phrases frequently used by the user and provide related information based on those keywords and phrases. It can also analyze the context of the user's posted content to identify potential interests. Furthermore, it can identify the strength of the user's interests based on the user's language usage patterns. This allows the analysis unit to perform more detailed interest analysis by taking the user's language usage patterns into account.

[0065] The analysis unit can also take into account local culture and customs when analyzing region-specific interests based on the user's geographic location information. For example, the analysis unit can provide information related to events and festivals popular in a particular region. The analysis unit can also analyze and provide information related to local food culture and tourist spots. Furthermore, the analysis unit can analyze and provide information related to local history and traditions. This allows the analysis unit to provide more relevant information by taking into account local culture and customs.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The analysis unit analyzes the user's interests. The analysis unit identifies the user's interests based on, for example, the user's past behavioral history, posted content, browsing history, etc. For example, for a user who posts frequently on a specific topic, the analysis unit collects the latest information related to that topic. Step 2: The collection unit collects information based on the results of the analysis by the analysis unit. For example, the collection unit automatically collects relevant news articles and academic papers based on the interests identified by the analysis unit. For example, the collection unit collects the latest news articles and social media posts on the Internet. Step 3: The distribution unit distributes the information collected by the collection unit. For example, the distribution unit notifies the user of the collected information so that the user can view the information. For example, the distribution unit can distribute the information using email notifications or push notifications. Step 4: The management unit manages discussions based on the information distributed by the distribution unit. For example, the management unit enables users to post comments and opinions on the distributed information. For example, the management unit provides a forum or chat function to enable users to hold discussions.

[0068] (Example 2) An AI system according to an embodiment of the present invention is a system for automatically collecting and distributing information that may be of interest to users in community management. This system analyzes users' interests and automatically collects related information based on the analysis. The collected information is then distributed to users, allowing them to hold discussions on the information. The system also has a function that encourages active participation by allowing users to start discussions themselves and invite others to join. For example, AI identifies users' interests based on their past behavioral history, posted content, browsing history, etc., and collects the latest information related to a particular topic from users who frequently post on that topic. The collected information is then distributed to users, who can view the information and post comments and opinions on it. For example, when the latest technology news is distributed, users can express their opinions on the news. Furthermore, users can start their own discussions, starting discussions on topics of interest to them, and other users can participate in the discussions. For example, users can post questions or opinions about a particular technology, and other users can respond or comment on them. This mechanism allows users to efficiently collect information that matches their interests and engage in discussions on it. Furthermore, encouraging active participation will help revitalize the entire community. For example, by allowing users to express their opinions, they will deepen their interactions with other users and increase the sense of community unity. This allows the AI ​​system to collect and distribute information based on users' interests and manage discussions.

[0069] A community management system according to an embodiment includes an analysis unit, a collection unit, a distribution unit, and a management unit. The analysis unit analyzes user interests. The analysis unit identifies user interests based on, for example, the user's past behavioral history, posted content, browsing history, etc. For example, the analysis unit collects the latest information related to a specific topic for a user who frequently posts on that topic. The collection unit collects information based on the results of the analysis by the analysis unit. For example, the collection unit automatically collects related news articles and academic papers based on the interests identified by the analysis unit. For example, the collection unit collects the latest news articles and social media posts on the Internet. The distribution unit distributes the information collected by the collection unit. For example, the distribution unit notifies users of the collected information so that the users can view the information. For example, the distribution unit can distribute the information using email notifications or push notifications. The management unit manages discussions based on the information distributed by the distribution unit. For example, the management unit allows users to post comments and opinions on the distributed information. For example, the management unit provides a forum or chat function to enable users to hold discussions. This allows the community management system according to the embodiment to collect and distribute information based on the interests of users and manage discussions.

[0070] The community management system includes a recording unit that records a user's behavioral history. The recording unit records the user's behavioral history. The recording unit records, for example, the user's click history, browsing history, purchase history, etc. For example, the recording unit records the links the user clicked on a website and the history of pages the user viewed. The recording unit can also record the history of products the user purchased at an online store. By recording the user's behavioral history, the recording unit can improve the accuracy of analysis. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's behavioral history data into a generation AI and cause the generation AI to analyze the behavioral history.

[0071] The community management system includes a notification unit that notifies users of discussions. The notification unit notifies users of discussions. The notification unit notifies users of discussions by, for example, email notification, push notification, alert, or other methods. For example, the notification unit notifies users by email when a new discussion has started. The notification unit can also send a push notification to the user's smartphone to notify them of the start of a new discussion. Furthermore, the notification unit can display an alert on a website to notify users of the start of a discussion. In this way, the notification unit can promote user participation by notifying users of the discussion. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input discussion start information to a generation AI and cause the generation AI to generate notification content.

[0072] The community management system includes a recommendation unit that recommends information to a user. The recommendation unit recommends appropriate information to the user. The recommendation unit recommends information based on, for example, the user's past behavior, interests, attributes, etc. For example, the recommendation unit recommends the latest information related to a topic that the user has previously viewed. The recommendation unit can also recommend new topics or related information based on the user's interests. Furthermore, the recommendation unit can also recommend appropriate information based on the user's attribute information (age, gender, occupation, etc.). In this way, the recommendation unit can improve user satisfaction by recommending appropriate information to the user. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's behavior history data into a generation AI and cause the generation AI to generate recommended content.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit can perform a detailed analysis and provide more relevant information. Alternatively, if the user is relaxed, the analysis unit can perform a concise analysis and provide only basic information. Furthermore, if the user is stressed, the analysis unit can increase the accuracy of the analysis and prioritize the most relevant information. This allows for more appropriate information to be provided by adjusting the accuracy of the analysis based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0074] The analysis unit can analyze the user's past behavioral history and track changes in interests. For example, the analysis unit can identify topics that the user frequently viewed in the past and provide the latest information related to those topics. The analysis unit can also analyze the user's past posts, track changes in interests, and provide related information. Furthermore, the analysis unit can track changes in interests and suggest new related topics based on the user's past discussion participation history. In this way, by tracking changes in the user's interests, more relevant information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's behavioral history data into the generation AI and cause the generation AI to track changes in interests.

[0075] The analysis unit can analyze the context of the user's posted content and identify deeper interests. The analysis unit can, for example, analyze keywords in the user's posted content and identify related deep interests. The analysis unit can also analyze the context of the user's posted content and identify latent interests. Furthermore, the analysis unit can analyze the emotions in the user's posted content and identify the strength of the interest. In this way, latent interests can be identified by analyzing the context of the user's posted content. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's posted content data into a generation AI and have the generation AI perform context analysis.

[0076] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize providing detailed analysis results. Furthermore, if the user is relaxed, the analysis unit can prioritize providing basic analysis results. Furthermore, if the user is stressed, the analysis unit can prioritize providing the most relevant analysis results. This allows for more appropriate information to be provided by prioritizing the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI prioritize the analysis results.

[0077] The analysis unit can analyze region-specific interests based on the user's geographical location information. For example, the analysis unit analyzes local event information based on the user's current location. The analysis unit can also analyze region-specific news based on the user's geographical location information. Furthermore, the analysis unit can analyze information about popular spots in the region, taking into account the user's geographical location information. This makes it possible to analyze region-specific interests by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into the generation AI and cause the generation AI to analyze region-specific interests.

[0078] The analysis unit can analyze the user's social media activity and identify related interests. For example, the analysis unit can analyze the user's social media posts to identify related interests. The analysis unit can also analyze the user's social media followers and accounts they follow to identify related interests. Furthermore, the analysis unit can analyze the user's social media like and share history to identify related interests. In this way, related interests can be identified by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's social media data into a generation AI and have the generation AI identify the interests.

[0079] The collection unit can estimate the user's emotions and adjust the type of information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit can prioritize collecting detailed information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting basic information. Furthermore, when the user is stressed, the collection unit can prioritize collecting the most relevant information. By adjusting the type of information to be collected based on the user's emotions, more appropriate information can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of information to be collected.

[0080] The collection unit can evaluate the reliability of information at the time of collection and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of the information source and prioritizes collecting highly reliable information. The collection unit can also evaluate the content of the information and prioritize collecting highly reliable information. Furthermore, the collection unit can evaluate the reliability of the information provider and prioritize collecting highly reliable information. In this way, highly reliable information can be provided by evaluating the reliability of the information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input reliability data of the information source to the generation AI and cause the generation AI to evaluate the reliability.

[0081] When collecting, the collection unit can prioritize collecting the latest information by taking into account the freshness of the information. The collection unit, for example, evaluates the publication date and time of the information and prioritizes collecting the latest information. The collection unit can also evaluate the update frequency of the information and prioritize collecting the latest information. Furthermore, the collection unit can evaluate the update history of the information provider and prioritize collecting the latest information. This makes it possible to provide the latest information by taking into account the freshness of the information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input publication date and time data of the information to the generation AI and have the generation AI evaluate the freshness.

[0082] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit can prioritize collecting detailed information. Furthermore, when the user is relaxed, the collection unit can prioritize collecting basic information. Furthermore, when the user is stressed, the collection unit can prioritize collecting the most relevant information. This allows for more appropriate information to be provided by determining the priority of information to be collected based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information to be collected.

[0083] The collection unit can collect region-specific information by taking into account the user's geographical location information. For example, the collection unit collects local event information based on the user's current location. The collection unit can also collect region-specific news based on the user's geographical location information. Furthermore, the collection unit can collect information about popular spots in the region by taking into account the user's geographical location information. This makes it possible to provide region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location data into the generation AI and cause the generation AI to collect region-specific information.

[0084] The collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the content of the user's social media posts and collect related information. The collection unit can also analyze the user's social media followers and the accounts they follow and collect related information. Furthermore, the collection unit can analyze the user's social media like and share history and collect related information. In this way, by analyzing the user's social media activities, related information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and have the generation AI collect information.

[0085] The delivery unit can estimate the user's emotions and adjust the way the content is expressed based on the estimated user's emotions. For example, if the user is excited, the delivery unit uses a visually stimulating expression. Furthermore, if the user is relaxed, the delivery unit can use a calm expression. Furthermore, if the user is stressed, the delivery unit can use a simple, highly visible expression. This allows for more appropriate information to be provided by adjusting the way the content is expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the content is expressed.

[0086] The distribution unit can adjust the level of detail of the distribution based on the importance of the information during distribution. For example, the distribution unit distributes information of high importance in detail. The distribution unit can also distribute information of low importance in a concise manner. Furthermore, the distribution unit can adjust the level of detail of the distribution content according to the importance. In this way, by adjusting the level of detail of the distribution based on the importance of the information, more appropriate information can be provided. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the distribution.

[0087] The distribution unit can apply different distribution algorithms depending on the category of information during distribution. For example, the distribution unit distributes information in the news category with emphasis on timeliness. The distribution unit can also distribute information in the entertainment category with emphasis on visual elements. Furthermore, the distribution unit can distribute information in the technology category with emphasis on detailed explanations. In this way, by applying different distribution algorithms depending on the category of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information category data into the generation AI and cause the generation AI to apply the distribution algorithm.

[0088] The delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated user emotions. For example, if the user is excited, the delivery unit can deliver information immediately. Furthermore, if the user is relaxed, the delivery unit can deliver information at an appropriate timing. Furthermore, if the user is stressed, the delivery unit can deliver information at an optimal timing. This allows for adjusting the timing of delivery based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of delivery.

[0089] The distribution unit can determine the priority of distribution based on the time of submission of information at the time of distribution. For example, the distribution unit prioritizes the distribution of the latest information. The distribution unit can also postpone information that was submitted earlier. Furthermore, the distribution unit can determine the priority of distribution based on the time of submission. In this way, by determining the priority of distribution based on the time of submission of information, more appropriate information can be provided. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data on the time of submission of information to the generation AI and have the generation AI determine the priority of distribution.

[0090] The distribution unit can adjust the order of distribution based on the relevance of the information during distribution. For example, the distribution unit prioritizes the distribution of information that is most relevant to the user's interests. The distribution unit can also postpone information with low relevance. Furthermore, the distribution unit can adjust the order of distribution based on the relevance of the information. In this way, by adjusting the order of distribution based on the relevance of the information, more appropriate information can be provided. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of distribution.

[0091] The management unit can estimate the user's emotions and adjust the discussion management method based on the estimated user's emotions. For example, if the user is excited, the management unit can adopt a management method that promotes lively discussion. Furthermore, if the user is relaxed, the management unit can adopt a management method that promotes calm discussion. Furthermore, if the user is stressed, the management unit can adopt a management method that smooths the progress of the discussion. By adjusting the discussion management method based on the user's emotions, more appropriate discussions can be promoted. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the discussion management method.

[0092] When managing discussions, the management unit can select the optimal management method by referring to past discussion histories. The management unit, for example, analyzes past discussion histories and selects the optimal management method. The management unit can also select a management method by referring to successful examples of past discussions. Furthermore, the management unit can improve the management method by referring to unsuccessful examples of past discussions. In this way, the optimal management method can be selected by referring to past discussion histories. Some or all of the above-mentioned processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input past discussion history data into a generation AI and have the generation AI select the optimal management method.

[0093] The management unit can manage discussions while taking into consideration user attribute information. The management unit selects an appropriate management method, for example, depending on the user's age group. The management unit can also select an appropriate management method depending on the user's interests. Furthermore, the management unit can select an appropriate management method depending on the user's activity history. This allows for more appropriate discussion management by taking into consideration the user's attribute information. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input user attribute information data into the generation AI and have the generation AI select a management method.

[0094] The management unit can estimate the user's emotions and prioritize discussions based on the estimated user emotions. For example, if the user is excited, the management unit can prioritize active discussions. Furthermore, if the user is relaxed, the management unit can prioritize calm discussions. Furthermore, if the user is stressed, the management unit can prioritize smooth discussions. This allows for more appropriate discussions by prioritizing discussions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the discussions.

[0095] The management unit can manage discussions while taking into account the user's geographical location information. For example, the management unit can manage region-specific discussions based on the user's current location. The management unit can also manage discussions about local events based on the user's geographical location information. Furthermore, the management unit can manage discussions about local news by taking into account the user's geographical location information. In this way, region-specific discussions can be managed by taking into account the user's geographical location information. Some or all of the above-described processing in the management unit can be performed using AI, for example, or without using AI. For example, the management unit can input the user's geographical location data into the generation AI and have the generation AI manage the discussions.

[0096] When managing a discussion, the management unit can improve the accuracy of management by referring to related external information. For example, the management unit can supplement the content of the discussion by referring to external news sources. The management unit can also improve the quality of the discussion by referring to the opinions of external experts. Furthermore, the management unit can enrich the information of the discussion by referring to an external database. In this way, by referring to related external information, the accuracy of discussion management is improved. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input external information data into a generation AI and cause the generation AI to improve the accuracy of management.

[0097] The recording unit can estimate the user's emotions and adjust the type of data to be recorded based on the estimated user emotions. For example, when the user is excited, the recording unit records detailed data. Furthermore, when the user is relaxed, the recording unit can also record basic data. Furthermore, when the user is stressed, the recording unit can also record the most relevant data. By adjusting the type of data to be recorded based on the user's emotions, more appropriate data can be recorded. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI adjust the type of data to be recorded.

[0098] The recording unit can evaluate the reliability of data when recording and preferentially record highly reliable data. The recording unit, for example, evaluates the reliability of a data provider and preferentially records highly reliable data. The recording unit can also evaluate the content of the data and preferentially record highly reliable data. Furthermore, the recording unit can evaluate the origin of the data and preferentially record highly reliable data. In this way, highly reliable data can be recorded by evaluating the reliability of the data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input reliability data of the data to a generation AI and have the generation AI perform a reliability evaluation.

[0099] When recording, the recording unit can prioritize recording the latest data, taking into account the freshness of the data. The recording unit, for example, evaluates the publication date and time of the data and prioritizes recording the latest data. The recording unit can also evaluate the update frequency of the data and prioritize recording the latest data. Furthermore, the recording unit can evaluate the update history of the data provider and prioritize recording the latest data. In this way, the latest data can be recorded by taking into account the freshness of the data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the publication date and time data of the data to the generation AI and have the generation AI evaluate the freshness.

[0100] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated user's emotions. For example, the recording unit records data frequently when the user is excited. Furthermore, the recording unit can also record data at an appropriate frequency when the user is relaxed. Furthermore, the recording unit can also record data at an optimal frequency when the user is stressed. By adjusting the recording frequency based on the user's emotions, data can be recorded at a more appropriate frequency. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI adjust the recording frequency.

[0101] The recording unit can determine the priority of recording based on the time of data submission when recording. For example, the recording unit prioritizes recording the most recent data. The recording unit can also postpone data that was submitted earlier. Furthermore, the recording unit can determine the priority of recording based on the time of submission. In this way, by determining the priority of recording based on the time of data submission, more appropriate data can be recorded. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input data on the time of data submission to the generation AI and have the generation AI determine the priority of recording.

[0102] The notification unit can estimate the user's emotions and adjust the way the notification content is expressed based on the estimated user's emotions. For example, if the user is excited, the notification unit can use a visually stimulating expression. Furthermore, if the user is relaxed, the notification unit can use a calm expression. Furthermore, if the user is stressed, the notification unit can use a simple, highly visible expression. This allows for more appropriate notification by adjusting the way the notification content is expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the notification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the notification content is expressed.

[0103] The notification unit can adjust the level of detail of the notification based on the importance of the information when notifying. For example, the notification unit notifies information of high importance in detail. The notification unit can also notify information of low importance in a concise manner. Furthermore, the notification unit can adjust the level of detail of the notification content according to the importance. As a result, by adjusting the level of detail of the notification based on the importance of the information, more appropriate notification can be provided. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information importance data to a generation AI and cause the generation AI to adjust the level of detail of the notification.

[0104] The notification unit can apply different notification algorithms depending on the category of information when making a notification. For example, the notification unit may notify information in the news category by emphasizing timeliness. The notification unit may also notify information in the entertainment category by emphasizing visual elements. Furthermore, the notification unit may notify information in the technology category by emphasizing detailed explanations. In this way, by applying different notification algorithms depending on the category of information, more appropriate notifications can be made. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input information category data into a generation AI and cause the generation AI to apply the notification algorithm.

[0105] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. For example, the notification unit can immediately notify the user when the user is excited. Furthermore, the notification unit can also notify the user at an appropriate time when the user is relaxed. Furthermore, the notification unit can also notify the user at an optimal time when the user is stressed. This allows for more appropriate notification by adjusting the timing of the notification based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of the notification.

[0106] At the time of notification, the notification unit can determine the priority of notifications based on the time of submission of information. For example, the notification unit prioritizes notifying the latest information. The notification unit can also postpone notifying information that has been submitted earlier. Furthermore, the notification unit can also determine the priority of notifications based on the time of submission. In this way, by determining the priority of notifications based on the time of submission of information, more appropriate notifications can be made. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input information submission time data into the generation AI and have the generation AI determine the priority of notifications.

[0107] The recommendation unit can estimate the user's emotions and adjust the presentation method of the recommendation content based on the estimated user's emotions. For example, if the user is excited, the recommendation unit can use a visually stimulating presentation method. Furthermore, if the user is relaxed, the recommendation unit can use a calm presentation method. Furthermore, if the user is stressed, the recommendation unit can use a simple, highly visible presentation method. This allows for more appropriate recommendations by adjusting the presentation method of the recommendation content based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the recommendation content.

[0108] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the information when making a recommendation. For example, the recommendation unit recommends information with high importance in detail. The recommendation unit can also recommend information with low importance in a concise manner. Furthermore, the recommendation unit can adjust the level of detail of the recommendation content according to the importance. In this way, by adjusting the level of detail of the recommendation based on the importance of the information, more appropriate recommendations can be made. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the recommendation.

[0109] The recommendation unit can apply different recommendation algorithms depending on the category of information when making a recommendation. For example, the recommendation unit can recommend information in the news category by emphasizing timeliness. The recommendation unit can also recommend information in the entertainment category by emphasizing visual elements. Furthermore, the recommendation unit can recommend information in the technology category by emphasizing detailed explanations. In this way, by applying different recommendation algorithms depending on the category of information, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input information category data into the generation AI and cause the generation AI to apply the recommendation algorithm.

[0110] The recommendation unit can estimate the user's emotions and adjust the timing of recommendations based on the estimated user emotions. For example, if the user is excited, the recommendation unit can make recommendations immediately. Furthermore, if the user is relaxed, the recommendation unit can also make recommendations at an appropriate timing. Furthermore, if the user is stressed, the recommendation unit can also make recommendations at an optimal timing. By adjusting the timing of recommendations based on the user's emotions, more appropriate recommendations can be made. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of recommendations.

[0111] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the time of submission of the information. For example, the recommendation unit prioritizes recommending the most recent information. The recommendation unit can also postpone information that was submitted earlier. Furthermore, the recommendation unit can determine the priority of the recommendation based on the time of submission. In this way, by determining the priority of the recommendation based on the time of submission of the information, more appropriate recommendations can be made. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the time of submission of the information to the generation AI and have the generation AI determine the priority of the recommendation. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described analysis unit, collection unit, distribution unit, management unit, recording unit, notification unit, and recommendation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the user's behavioral history and posted content. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information based on the analysis results. The distribution unit is realized by the control unit 46A of the smart device 14 and notifies the user of the collected information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages discussions. The recording unit is realized by the control unit 46A of the smart device 14 and records the user's behavioral history. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and notifies the user of the discussions. The recommendation unit is realized by the control unit 46A of the smart device 14 and recommends appropriate information to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, distribution unit, management unit, recording unit, notification unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the user's behavioral history and posted content. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information based on the analysis results. The distribution unit is realized by the control unit 46A of the smart glasses 214 and notifies the user of the collected information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages discussions. The recording unit is realized by the control unit 46A of the smart glasses 214 and records the user's behavioral history. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and notifies the user of the discussions. The recommendation unit is realized by the control unit 46A of the smart glasses 214 and recommends appropriate information to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, distribution unit, management unit, recording unit, notification unit, and recommendation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 and analyzes the user's behavioral history and posted content. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects related information based on the analysis results. The distribution unit is realized by the control unit 46A of the headset type terminal 314 and notifies the user of the collected information. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages discussions. The recording unit is realized by the control unit 46A of the headset type terminal 314 and records the user's behavioral history. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and notifies the user of the discussions. The recommendation unit is realized by, for example, the control unit 46A of the headset type terminal 314, and recommends appropriate information to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, collection unit, distribution unit, management unit, recording unit, notification unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the user's behavioral history and posted content. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects related information based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of the collected information. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and manages discussions. The recording unit is realized, for example, by the control unit 46A of the robot 414 and records the user's behavioral history. The notification unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and notifies the user of the discussions. The recommendation unit is realized, for example, by the control unit 46A of the robot 414 and recommends appropriate information to the user.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The analysis unit can also take into account the user's social media activity when analyzing the user's interests. For example, it can analyze the content the user frequently shares and the accounts the user follows to identify interest trends. It can also analyze the user's comment and like history on social media to identify more detailed interests. It can also analyze the activities of online communities and groups in which the user participates and collect related information. By taking the user's social media activity into account, the analysis unit can perform more accurate interest analysis.

[0114] The recording unit can also take the user's geographical location information into account when recording the user's behavioral history. For example, it can record the history of places the user has visited and events the user has participated in. Also, if the user is frequently active in a particular area, it can prioritize recording information related to that area. Furthermore, it can analyze the user's movement patterns and track changes in interests. By taking the user's geographical location information into account, the recording unit can record a more detailed behavioral history.

[0115] The notification unit can also take into account the user's past notification history when notifying a discussion. For example, it can analyze what notifications the user has responded to in the past and prioritize the use of notification methods that have generated good responses. It can also increase the effectiveness of notifications by avoiding notification methods that the user has ignored in the past. Furthermore, it can also determine the optimal notification timing based on the user's notification history. In this way, the notification unit can provide more effective notifications by taking into account the user's past notification history.

[0116] The recommendation unit can also take the user's purchasing history into consideration when recommending information to the user. For example, the recommendation unit can analyze the user's history of past purchases and recommend related products and services. It can also prioritize the recommendation of products in categories that the user frequently purchases. Furthermore, it can recommend new products and services that the user is likely to be interested in based on the user's purchasing history. This allows the recommendation unit to recommend more appropriate information by taking the user's purchasing history into consideration.

[0117] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is excited, the analysis can be performed immediately and relevant information can be provided. Also, if the user is relaxed, the analysis can be performed at an appropriate timing. Furthermore, if the user is feeling stressed, the analysis can be performed at the optimal timing. In this way, by adjusting the timing of the analysis based on the user's emotions, more appropriate information can be provided.

[0118] The analysis unit can also take into account the user's device usage history when analyzing the user's past behavioral history. For example, it can identify which device the user frequently uses to view information and provide information optimized for that device. It can also analyze which device the user uses at a specific time period and provide information at the optimal time. Furthermore, it can track changes in the user's interests based on the user's device usage history. This allows the analysis unit to provide more relevant information by taking into account the user's device usage history.

[0119] The analysis unit can also take into account the user's language usage patterns when analyzing the context of the user's posted content. For example, it can identify keywords and phrases frequently used by the user and provide related information based on those keywords and phrases. It can also analyze the context of the user's posted content to identify potential interests. Furthermore, it can identify the strength of the user's interests based on the user's language usage patterns. This allows the analysis unit to perform more detailed interest analysis by taking the user's language usage patterns into account.

[0120] 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 excited, a visually stimulating display method can be used. If the user is relaxed, a calm display method can be used. Furthermore, if the user is stressed, a simple, highly visible display method can be used. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided.

[0121] The analysis unit can also take into account local culture and customs when analyzing region-specific interests based on the user's geographic location information. For example, the analysis unit can provide information related to events and festivals popular in a particular region. The analysis unit can also analyze and provide information related to local food culture and tourist spots. Furthermore, the analysis unit can analyze and provide information related to local history and traditions. This allows the analysis unit to provide more relevant information by taking into account local culture and customs.

[0122] The analysis unit may also take into account the emotions of the user's followers when analyzing the user's social media activities. For example, if the user's followers are excited, relevant information may be provided based on their emotions. Alternatively, if the user's followers are relaxed, appropriate information may be provided based on their emotions. Furthermore, if the user's followers are stressed, optimal information may be provided based on their emotions. In this way, the analysis unit can provide more appropriate information by taking into account the emotions of the user's followers.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The analysis unit analyzes the user's interests. The analysis unit identifies the user's interests based on, for example, the user's past behavioral history, posted content, browsing history, etc. For example, for a user who posts frequently on a specific topic, the analysis unit collects the latest information related to that topic. Step 2: The collection unit collects information based on the results of the analysis by the analysis unit. For example, the collection unit automatically collects relevant news articles and academic papers based on the interests identified by the analysis unit. For example, the collection unit collects the latest news articles and social media posts on the Internet. Step 3: The distribution unit distributes the information collected by the collection unit. For example, the distribution unit notifies the user of the collected information so that the user can view the information. For example, the distribution unit can distribute the information using email notifications or push notifications. Step 4: The management unit manages discussions based on the information distributed by the distribution unit. For example, the management unit enables users to post comments and opinions on the distributed information. For example, the management unit provides a forum or chat function to enable users to hold discussions.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the user's interests; a collection unit that collects information based on the results of the analysis by the analysis unit; a distribution unit that distributes the information collected by the collection unit; a management unit that manages discussions based on the information distributed by the distribution unit; A system characterized by:

2. Equipped with a recording unit that records the user's behavior history 2. The system of claim 1.

3. Equipped with a notification section that notifies users of discussions 2. The system of claim 1.

4. A recommendation unit is provided to recommend information to users.

2. The system of claim 1.

5. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

6. The analysis unit Analyze users' past behavior and track their changing interests 2. The system of claim 1.

7. The analysis unit Analyze the context of user posts to identify deeper interests 2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions.

2. The system of claim 1.

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