system

Abstract

An object of a system according to an embodiment is to propose a topic for smoothly advancing communication with a friend on an SNS.SOLUTION: A system includes an SNS account registration unit, a data collection unit, an interest analysis unit, and a topic suggestion unit. The SNS account registration unit registers an SNS account of a user. The data collection unit collects a history of posts and messages of the friend based on the SNS account registered by the SNS account registration unit. The interest and concern analyzing unit analyzes the data collected by the data collecting unit and specifies the interest and concern of the friend. The topic suggestion unit suggests a topic that the friend is likely to enjoy, a continuation of a previous conversation, or the like on the basis of the interest specified by the interest analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find appropriate topics to smoothly communicate with friends on social networking sites.

[0005] The system according to the embodiment aims to suggest topics that will facilitate smooth communication with friends on an SNS. [Means for solving the problem]

[0006] The system according to the embodiment includes an SNS account registration unit, a data collection unit, an interest analysis unit, and a topic suggestion unit. The SNS account registration unit registers the user's SNS account. The data collection unit collects the history of friends' posts and messages based on the SNS account registered by the SNS account registration unit. The interest analysis unit analyzes the data collected by the data collection unit and identifies the friends' interests. The topic suggestion unit suggests topics that the friends are likely to enjoy or continuations of previous conversations based on the interests identified by the interest analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest topics that will facilitate smooth communication with friends on the SNS. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 application or web service according to an embodiment of the present invention is a system in which, by registering a social networking service account, a history of posts and messages from friends is collected, and a generation AI analyzes the interests of friends, suggesting topics that friends might enjoy, continuations of previous conversations, etc. This allows the application or web service to smoothly continue conversations with friends and promote deeper communication.

[0029] An application or web service according to an embodiment includes an SNS account registration unit, a data collection unit, an interest analysis unit, and a topic suggestion unit. The SNS account registration unit registers a user's SNS account. For example, a user can register accounts for Facebook, Twitter, Instagram, and the like. The data collection unit collects a history of friends' posts and messages based on the SNS accounts registered by the SNS account registration unit. For example, the data collection unit collects text messages, image posts, video messages, and the like from friends. The interest analysis unit analyzes the data collected by the data collection unit to identify the friends' interests. For example, the generation AI analyzes the topics frequently posted by friends and the topics discussed in past messages. The topic suggestion unit suggests topics that the friends might enjoy or continuations of previous conversations based on the interests identified by the interest analysis unit. For example, the topic suggestion unit makes suggestions such as, "Why don't we talk about recent sports news?" or "How about suggesting a next travel destination as a continuation of the story about your last trip?" This allows the application or web service according to an embodiment to smoothly continue conversations with friends and promote deeper communication. For example, users can continue conversations with friends without interruption by being suggested suitable topics to continue conversations with friends. Also, by suggesting topics based on friends' interests, users can deepen their relationships with friends.

[0030] The data collection unit can also collect the history of friends' "likes" and comments. For example, when a user registers a social media account, the data collection unit requests permission to collect the history of friends' "likes" and comments. This allows for a detailed analysis of which posts friends are interested in. For example, if a friend frequently "likes" posts in a particular genre, the data collection unit can suggest topics related to that genre. This allows for a more detailed analysis of friends' interests.

[0031] The interest analysis unit analyzes data in real time and can respond immediately if a friend's interests change. For example, the interest analysis unit analyzes data collected by the generative AI in real time and builds a system that responds immediately if a friend's interests change. For example, if a friend starts a new hobby, it will suggest topics related to that hobby. This allows the system to respond immediately to changes in a friend's interests.

[0032] The SNS account registration unit has a function that allows a user to register multiple SNS accounts at once, making it possible to integrate and analyze data from different SNSs. For example, the SNS account registration unit adds a function that allows a user to register multiple SNS accounts at once, and builds a system that integrates and analyzes data from different SNSs. For example, Facebook, Twitter, and Instagram accounts can be registered at once, and the data can be integrated and analyzed. This makes it possible to integrate and analyze data from different SNSs.

[0033] The data collection unit also collects information on groups and events that friends participate in, allowing for a wider range of interest analysis. For example, the data collection unit collects information on groups and events that friends participate in, building a system that analyzes wider range of interests. For example, it collects information on hobby groups and events that friends participate in, and suggests topics related to those hobbies. This allows for a wider range of interest analysis of friends.

[0034] The interest analysis unit also takes into account a friend's past purchasing history and browsing history, enabling more accurate analysis. For example, the interest analysis unit will build a system that takes into account a friend's past purchasing history when the generation AI analyzes a friend's interests. For example, it will suggest topics related to products that a friend has purchased in the past. This allows for more accurate analysis of a friend's interests.

[0035] The interest analysis unit can analyze changes in interests by taking into account the time of day and frequency of friends' posts and messages. For example, the interest analysis unit constructs a system in which the generation AI takes into account the time of day and frequency of friends' posts and messages to analyze changes in interests. For example, it suggests topics related to content that friends frequently post during specific time periods. This makes it possible to analyze changes in friends' interests.

[0036] The interest analysis unit can also take into account the geographic location information of friends and analyze region-specific interests. For example, the interest analysis unit constructs a system in which the generation AI takes into account the geographic location information of friends and analyzes region-specific interests. For example, it suggests topics related to events and news in the area where the friend lives. This makes it possible to analyze region-specific interests.

[0037] The interest analysis unit also takes into account demographic information such as the age and gender of friends, enabling more personalized analysis. For example, the interest analysis unit will build a system in which the generation AI takes into account demographic information such as the age and gender of friends to perform more personalized analysis. For example, it will suggest topics that are suited to the age group of friends. This will enable more personalized analysis.

[0038] The topic suggestion unit can make more specific suggestions, including quoting friends' past posts and messages. For example, the topic suggestion unit builds a system that includes quotes from friends' past posts and messages in the topics suggested by the generation AI. For example, it can quote content that a friend has previously posted and suggest a topic as a continuation of that. This makes it possible to make more specific suggestions.

[0039] The topic suggestion unit can make timely suggestions, including the latest news and trending information related to a friend's interests. For example, the topic suggestion unit builds a system that includes the latest news and trending information related to a friend's interests in the topics suggested by the generation AI. For example, it suggests the latest news related to topics that a friend is interested in. This makes it possible to make timely suggestions.

[0040] The topic suggestion unit can make visually appealing suggestions, including images and videos related to a friend's interests. For example, the topic suggestion unit builds a system that includes images and videos related to a friend's interests in topics suggested by the generative AI. For example, it suggests images and videos related to topics that a friend is interested in. This makes it possible to make visually appealing suggestions.

[0041] The topic suggestion unit can make interactive suggestions, including quizzes and surveys related to the friend's interests. For example, the topic suggestion unit builds a system that includes quizzes and surveys related to the friend's interests in topics suggested by the generation AI. For example, it suggests quizzes related to topics that the friend is interested in. This makes interactive suggestions possible.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The SNS account registration unit may also have a function that allows users to register an account anonymously. For example, if a user values ​​privacy, they can register an SNS account anonymously and collect their friends' posts and message history. This allows users to continue communicating smoothly with their friends while protecting their privacy. Also, by using an anonymous account, users can deepen their relationships with friends without disclosing their real names or personal information. Furthermore, by using an anonymous account, users can centrally manage their activities on different SNS platforms.

[0044] The data collection unit can prioritize collecting posts and messages from friends that contain specific keywords. For example, if a friend frequently mentions a specific event or topic, it will prioritize collecting posts and messages that contain that keyword. This allows the user to obtain information about specific topics that their friends are interested in. Also, by collecting posts and messages that contain specific keywords, the user can quickly understand changes in their friends' interests. Furthermore, by using the keyword collection function, the user can suggest new topics related to their friends' interests.

[0045] The interest analysis unit analyzes the language and slang used in friends' posts and messages to make more personalized suggestions. For example, if a friend frequently uses a particular slang or dialect, the system will take those linguistic characteristics into account when suggesting topics. This allows users to communicate with friends more naturally. Language analysis also allows users to gain a deeper understanding of their friends' cultural backgrounds and interests. Furthermore, by using the language analysis function, users can make suggestions that are tailored to their friends' language styles.

[0046] The SNS account registration unit has a function that allows users to register multiple SNS accounts at once, enabling data from different SNSs to be integrated and analyzed. For example, Facebook, Twitter, and Instagram accounts can be registered at once, and data can be integrated and analyzed. This allows data from different SNSs to be integrated and analyzed. In addition, by registering multiple SNS accounts at once, users can centrally manage their friends' activities on different platforms. Furthermore, by integrating data from different SNSs, users can gain a more comprehensive understanding of their friends' interests.

[0047] The data collection unit also collects information on groups and events that friends participate in, allowing for a wider range of interest analysis. For example, it collects information on hobby groups and events that friends participate in and suggests topics related to those hobbies. This allows for a wider range of interest analysis of friends. Furthermore, by collecting information on groups and events, users can quickly grasp new interests and concerns of their friends. Furthermore, by collecting information on groups and events, users can more easily find common topics to discuss with their friends.

[0048] The interest analysis unit can perform a more accurate analysis by taking into account the friend's past purchase history and browsing history. For example, it can suggest topics related to products that the friend has previously purchased. This allows for a more accurate analysis of the friend's interests. Also, by taking into account the purchase history and browsing history, the user can understand the friend's specific interests and concerns. Furthermore, by taking into account the purchase history and browsing history, the user can make suggestions that are tailored to the friend's interests.

[0049] The interest analysis unit can analyze changes in a friend's interests by taking into account the time of day and frequency of a friend's posts and messages. For example, it can suggest topics related to content that a friend frequently posts during a specific time period. This allows the user to analyze changes in a friend's interests. Also, by taking into account the time of day and frequency, a user can understand the rhythm of a friend's life and activity patterns. Furthermore, by taking into account the time of day and frequency, a user can make timely suggestions that match the interests of a friend.

[0050] The interest analysis unit can also consider the geographical location information of friends to analyze region-specific interests. For example, it can suggest topics related to events and news in the area where the friend lives. This allows region-specific interests to be analyzed. Furthermore, by considering the geographical location information, the user can suggest specific topics related to the friend's region. Furthermore, by considering the geographical location information, the user can more easily find topics related to the common region with the friend.

[0051] The interest analysis unit can also take into account demographic information such as the friend's age and gender to perform more personalized analysis. For example, it can suggest topics that match the friend's age group. This allows for more personalized analysis. Also, by taking into account demographic information, the user can understand the specific interests and concerns of the friend. Furthermore, by taking into account demographic information, the user can make suggestions that match the friend's interests.

[0052] The topic suggestion unit can make more specific suggestions by including quotes from friends' past posts and messages. For example, we will build a system in which the topics suggested by the generation AI include quotes from friends' past posts and messages. For example, the system can quote content previously posted by a friend and suggest a topic as a continuation of that. This allows for more specific suggestions. Furthermore, by including quotes from past posts and messages, users can continue conversations with friends more naturally. Furthermore, by including quotes from past posts and messages, users can make suggestions that are tailored to their friends' interests.

[0053] The topic suggestion unit can make timely suggestions, including the latest news and trending information related to a friend's interests. For example, we will build a system in which the topics suggested by the generation AI include the latest news and trending information related to a friend's interests. For example, we can suggest the latest news related to topics that a friend is interested in. This makes it possible to make timely suggestions. Furthermore, by including the latest news and trending information, a user can make conversations with friends more interesting. Furthermore, by including the latest news and trending information, a user can make suggestions that are tailored to the interests of their friends.

[0054] The topic suggestion unit can make visually appealing suggestions, including images and videos related to a friend's interests. For example, we will build a system in which the topics suggested by the generative AI include images and videos related to a friend's interests. For example, we will suggest images and videos related to topics that a friend is interested in. This will enable visually appealing suggestions. In addition, by including images and videos, users can enrich their conversations with friends. Furthermore, by incorporating visual elements, users will be more likely to attract their friends' interest.

[0055] The topic suggestion unit can make interactive suggestions, including quizzes and surveys related to a friend's interests. For example, we will build a system in which the topics suggested by the generation AI include quizzes and surveys related to a friend's interests. For example, we can suggest quizzes related to topics that a friend is interested in. This makes interactive suggestions possible. In addition, by including quizzes and surveys, users can have more lively conversations with their friends. Furthermore, by incorporating interactive elements, users can more easily attract their friends' interests.

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

[0057] Step 1: The SNS account registration unit registers the user's SNS account. For example, the user can register accounts for Facebook, Twitter, Instagram, etc. Step 2: The data collection unit collects the friend's posting and message history based on the SNS account registered by the SNS account registration unit, such as the friend's text messages, image postings, video messages, etc. Step 3: The interest analysis unit analyzes the data collected by the data collection unit to identify the friends' interests. For example, the generation AI analyzes the topics that friends frequently post about and the topics that have been discussed in past messages. Step 4: The topic suggestion module suggests topics that your friend might enjoy or continuations of previous conversations based on the interests identified by the interest analysis module. For example, it suggests things like, "Why don't we talk about the latest sports news?" or "How about suggesting your next travel destination as a continuation of your previous trip story?"

[0058] (Example 2) An application or web service according to an embodiment of the present invention is a system in which, by registering a social networking service account, a history of posts and messages from friends is collected, and a generation AI analyzes the interests of friends, suggesting topics that friends might enjoy, continuations of previous conversations, etc. This allows the application or web service to smoothly continue conversations with friends and promote deeper communication.

[0059] An application or web service according to an embodiment includes an SNS account registration unit, a data collection unit, an interest analysis unit, and a topic suggestion unit. The SNS account registration unit registers a user's SNS account. For example, a user can register accounts for Facebook, Twitter, Instagram, and the like. The data collection unit collects a history of friends' posts and messages based on the SNS accounts registered by the SNS account registration unit. For example, the data collection unit collects text messages, image posts, video messages, and the like from friends. The interest analysis unit analyzes the data collected by the data collection unit to identify the friends' interests. For example, the generation AI analyzes the topics frequently posted by friends and the topics discussed in past messages. The topic suggestion unit suggests topics that the friends might enjoy or continuations of previous conversations based on the interests identified by the interest analysis unit. For example, the topic suggestion unit makes suggestions such as, "Why don't we talk about recent sports news?" or "How about suggesting a next travel destination as a continuation of the story about your last trip?" This allows the application or web service according to an embodiment to smoothly continue conversations with friends and promote deeper communication. For example, users can continue conversations with friends without interruption by being suggested suitable topics to continue conversations with friends. Also, by suggesting topics based on friends' interests, users can deepen their relationships with friends.

[0060] The data collection unit can also collect the history of friends' "likes" and comments. For example, when a user registers a social media account, the data collection unit requests permission to collect the history of friends' "likes" and comments. This allows for a detailed analysis of which posts friends are interested in. For example, if a friend frequently "likes" posts in a particular genre, the data collection unit can suggest topics related to that genre. This allows for a more detailed analysis of friends' interests.

[0061] The interest analysis unit analyzes data in real time and can respond immediately if a friend's interests change. For example, the interest analysis unit analyzes data collected by the generative AI in real time and builds a system that responds immediately if a friend's interests change. For example, if a friend starts a new hobby, it will suggest topics related to that hobby. This allows the system to respond immediately to changes in a friend's interests.

[0062] The data collection unit can use the emotion estimation function to analyze the emotions in friends' posts and messages, and preferentially collect posts and messages with positive emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotions in friends' posts and messages, and builds a system that preferentially collects posts and messages with positive emotions. For example, posts in which friends express joy or happiness are preferentially collected. This makes it possible to preferentially collect posts and messages with positive emotions.

[0063] The SNS account registration unit has a function that allows a user to register multiple SNS accounts at once, making it possible to integrate and analyze data from different SNSs. For example, the SNS account registration unit adds a function that allows a user to register multiple SNS accounts at once, and builds a system that integrates and analyzes data from different SNSs. For example, Facebook, Twitter, and Instagram accounts can be registered at once, and the data can be integrated and analyzed. This makes it possible to integrate and analyze data from different SNSs.

[0064] The data collection unit also collects information on groups and events that friends participate in, allowing for a wider range of interest analysis. For example, the data collection unit collects information on groups and events that friends participate in, building a system that analyzes wider range of interests. For example, it collects information on hobby groups and events that friends participate in, and suggests topics related to those hobbies. This allows for a wider range of interest analysis of friends.

[0065] The data collection unit can use the emotion estimation function to analyze the emotions in friends' posts and messages in real time and filter out posts and messages with negative emotions. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotions in friends' posts and messages in real time and filters out posts and messages with negative emotions. For example, it filters out posts expressing anger or sadness. This makes it possible to filter out posts and messages with negative emotions.

[0066] The interest analysis unit also takes into account a friend's past purchasing history and browsing history, enabling more accurate analysis. For example, the interest analysis unit will build a system that takes into account a friend's past purchasing history when the generation AI analyzes a friend's interests. For example, it will suggest topics related to products that a friend has purchased in the past. This allows for more accurate analysis of a friend's interests.

[0067] The interest analysis unit can analyze changes in interests by taking into account the time of day and frequency of friends' posts and messages. For example, the interest analysis unit constructs a system in which the generation AI takes into account the time of day and frequency of friends' posts and messages to analyze changes in interests. For example, it suggests topics related to content that friends frequently post during specific time periods. This makes it possible to analyze changes in friends' interests.

[0068] The interest analysis unit can use the emotion estimation function to analyze the emotions in friends' posts and messages, and prioritize analysis of interests associated with positive emotions. The interest analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions in friends' posts and messages, and prioritizes analysis of interests associated with positive emotions. For example, it suggests topics related to posts in which friends express joy or fun. This makes it possible to prioritize analysis of interests associated with positive emotions.

[0069] The interest analysis unit can also take into account the geographic location information of friends and analyze region-specific interests. For example, the interest analysis unit constructs a system in which the generation AI takes into account the geographic location information of friends and analyzes region-specific interests. For example, it suggests topics related to events and news in the area where the friend lives. This makes it possible to analyze region-specific interests.

[0070] The interest analysis unit also takes into account demographic information such as the age and gender of friends, enabling more personalized analysis. For example, the interest analysis unit will build a system in which the generation AI takes into account demographic information such as the age and gender of friends to perform more personalized analysis. For example, it will suggest topics that are suited to the age group of friends. This will enable more personalized analysis.

[0071] The interest analysis unit can use the emotion estimation function to analyze the emotions in friends' posts and messages in real time and filter out interests associated with negative emotions. The interest analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotions in friends' posts and messages in real time and filters out interests associated with negative emotions. For example, it filters out topics that friends find stressful. This makes it possible to filter out interests associated with negative emotions.

[0072] The topic suggestion unit can make more specific suggestions, including quoting friends' past posts and messages. For example, the topic suggestion unit builds a system that includes quotes from friends' past posts and messages in the topics suggested by the generation AI. For example, it can quote content that a friend has previously posted and suggest a topic as a continuation of that. This makes it possible to make more specific suggestions.

[0073] The topic suggestion unit can make timely suggestions, including the latest news and trending information related to a friend's interests. For example, the topic suggestion unit builds a system that includes the latest news and trending information related to a friend's interests in the topics suggested by the generation AI. For example, it suggests the latest news related to topics that a friend is interested in. This makes it possible to make timely suggestions.

[0074] The topic suggestion unit can use the emotion estimation function to suggest topics that match the friend's emotions and make suggestions that elicit positive emotions. The topic suggestion unit, for example, uses the emotion estimation function to build a system that suggests topics that match the friend's emotions. For example, it suggests topics that make the friend feel joyful or happy. This makes it possible to make suggestions that elicit positive emotions.

[0075] The topic suggestion unit can make visually appealing suggestions, including images and videos related to a friend's interests. For example, the topic suggestion unit builds a system that includes images and videos related to a friend's interests in topics suggested by the generative AI. For example, it suggests images and videos related to topics that a friend is interested in. This makes it possible to make visually appealing suggestions.

[0076] The topic suggestion unit can make interactive suggestions, including quizzes and surveys related to the friend's interests. For example, the topic suggestion unit builds a system that includes quizzes and surveys related to the friend's interests in topics suggested by the generation AI. For example, it suggests quizzes related to topics that the friend is interested in. This makes interactive suggestions possible.

[0077] The topic suggestion unit can use the emotion estimation function to suggest topics that match the friend's emotions in real time and filter out topics that have negative emotions. The topic suggestion unit, for example, uses the emotion estimation function to build a system that suggests topics that match the friend's emotions in real time. For example, it suggests topics that make the friend feel happy or entertained in real time. This makes it possible to filter out topics that have negative emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The SNS account registration unit may also have a function that allows users to register an account anonymously. For example, if a user values ​​privacy, they can register an SNS account anonymously and collect their friends' posts and message history. This allows users to continue communicating smoothly with their friends while protecting their privacy. Also, by using an anonymous account, users can deepen their relationships with friends without disclosing their real names or personal information. Furthermore, by using an anonymous account, users can centrally manage their activities on different SNS platforms.

[0080] The data collection unit can prioritize collecting posts and messages from friends that contain specific keywords. For example, if a friend frequently mentions a specific event or topic, it will prioritize collecting posts and messages that contain that keyword. This allows the user to obtain information about specific topics that their friends are interested in. Also, by collecting posts and messages that contain specific keywords, the user can quickly understand changes in their friends' interests. Furthermore, by using the keyword collection function, the user can suggest new topics related to their friends' interests.

[0081] The interest analysis unit analyzes the language and slang used in friends' posts and messages to make more personalized suggestions. For example, if a friend frequently uses a particular slang or dialect, the system will take those linguistic characteristics into account when suggesting topics. This allows users to communicate with friends more naturally. Language analysis also allows users to gain a deeper understanding of their friends' cultural backgrounds and interests. Furthermore, by using the language analysis function, users can make suggestions that are tailored to their friends' language styles.

[0082] The data collection unit uses the emotion estimation function to analyze the emotions in friends' posts and messages, and can prioritize collecting posts and messages with positive emotions. For example, it can prioritize collecting posts in which friends express joy or happiness. This allows posts and messages with positive emotions to be prioritized. Furthermore, by collecting posts with positive emotions, the user can make conversations with friends more enjoyable. Furthermore, by collecting posts with positive emotions, the user can suggest topics that will lift their friends' spirits.

[0083] The SNS account registration unit has a function that allows users to register multiple SNS accounts at once, enabling data from different SNSs to be integrated and analyzed. For example, Facebook, Twitter, and Instagram accounts can be registered at once, and data can be integrated and analyzed. This allows data from different SNSs to be integrated and analyzed. In addition, by registering multiple SNS accounts at once, users can centrally manage their friends' activities on different platforms. Furthermore, by integrating data from different SNSs, users can gain a more comprehensive understanding of their friends' interests.

[0084] The data collection unit also collects information on groups and events that friends participate in, allowing for a wider range of interest analysis. For example, it collects information on hobby groups and events that friends participate in and suggests topics related to those hobbies. This allows for a wider range of interest analysis of friends. Furthermore, by collecting information on groups and events, users can quickly grasp new interests and concerns of their friends. Furthermore, by collecting information on groups and events, users can more easily find common topics to discuss with their friends.

[0085] The data collection unit uses the emotion estimation function to analyze the emotions in friends' posts and messages in real time and can filter out posts and messages with negative emotions. For example, it can filter out posts expressing anger or sadness. This allows it to filter out posts and messages with negative emotions. Furthermore, by filtering out posts with negative emotions, users can make their conversations with friends more positive. Furthermore, by filtering out posts with negative emotions, users can suggest appropriate topics to their friends without offending them.

[0086] The interest analysis unit can perform a more accurate analysis by taking into account the friend's past purchase history and browsing history. For example, it can suggest topics related to products that the friend has previously purchased. This allows for a more accurate analysis of the friend's interests. Also, by taking into account the purchase history and browsing history, the user can understand the friend's specific interests and concerns. Furthermore, by taking into account the purchase history and browsing history, the user can make suggestions that are tailored to the friend's interests.

[0087] The interest analysis unit can analyze changes in a friend's interests by taking into account the time of day and frequency of a friend's posts and messages. For example, it can suggest topics related to content that a friend frequently posts during a specific time period. This allows the user to analyze changes in a friend's interests. Also, by taking into account the time of day and frequency, a user can understand the rhythm of a friend's life and activity patterns. Furthermore, by taking into account the time of day and frequency, a user can make timely suggestions that match the interests of a friend.

[0088] The interest analysis unit uses the emotion estimation function to analyze the emotions in friends' posts and messages, and can prioritize analyzing interests associated with positive emotions. For example, it can suggest topics related to posts in which friends express joy or enjoyment. This allows for prioritized analysis of interests associated with positive emotions. Furthermore, by prioritizing the analysis of interests associated with positive emotions, the user can make conversations with friends more enjoyable. Furthermore, by prioritizing the analysis of interests associated with positive emotions, the user can suggest topics that will lift their friends' spirits.

[0089] The interest analysis unit can also consider the geographical location information of friends to analyze region-specific interests. For example, it can suggest topics related to events and news in the area where the friend lives. This allows region-specific interests to be analyzed. Furthermore, by considering the geographical location information, the user can suggest specific topics related to the friend's region. Furthermore, by considering the geographical location information, the user can more easily find topics related to the common region with the friend.

[0090] The interest analysis unit can also take into account demographic information such as the friend's age and gender to perform more personalized analysis. For example, it can suggest topics that match the friend's age group. This allows for more personalized analysis. Also, by taking into account demographic information, the user can understand the specific interests and concerns of the friend. Furthermore, by taking into account demographic information, the user can make suggestions that match the friend's interests.

[0091] The interest analysis unit uses the emotion estimation function to analyze the emotions in friends' posts and messages in real time and can filter out interests associated with negative emotions. For example, it can filter out topics that friends feel stressed about. This allows it to filter out interests associated with negative emotions. Furthermore, by filtering out interests associated with negative emotions, users can make their conversations with friends more positive. Furthermore, by filtering out interests associated with negative emotions, users can suggest appropriate topics without offending their friends.

[0092] The topic suggestion unit can make more specific suggestions by including quotes from friends' past posts and messages. For example, we will build a system in which the topics suggested by the generation AI include quotes from friends' past posts and messages. For example, the system can quote content previously posted by a friend and suggest a topic as a continuation of that. This allows for more specific suggestions. Furthermore, by including quotes from past posts and messages, users can continue conversations with friends more naturally. Furthermore, by including quotes from past posts and messages, users can make suggestions that are tailored to their friends' interests.

[0093] The topic suggestion unit can make timely suggestions, including the latest news and trending information related to a friend's interests. For example, we will build a system in which the topics suggested by the generation AI include the latest news and trending information related to a friend's interests. For example, we can suggest the latest news related to topics that a friend is interested in. This makes it possible to make timely suggestions. Furthermore, by including the latest news and trending information, a user can make conversations with friends more interesting. Furthermore, by including the latest news and trending information, a user can make suggestions that are tailored to the interests of their friends.

[0094] The topic suggestion unit can use the emotion estimation function to suggest topics that match the friend's emotions and make suggestions that elicit positive emotions. For example, a system can be built using the emotion estimation function to suggest topics that match the friend's emotions. For example, topics that make the friend feel joyful or happy can be suggested. This makes it possible to make suggestions that elicit positive emotions. Furthermore, by suggesting topics that match the friend's emotions, the user can make conversations with the friend more enjoyable. Furthermore, by suggesting topics that match the friend's emotions, the user can suggest topics that will lift the friend's spirits.

[0095] The topic suggestion unit can make visually appealing suggestions, including images and videos related to a friend's interests. For example, we will build a system in which the topics suggested by the generative AI include images and videos related to a friend's interests. For example, we will suggest images and videos related to topics that a friend is interested in. This will enable visually appealing suggestions. In addition, by including images and videos, users can enrich their conversations with friends. Furthermore, by incorporating visual elements, users will be more likely to attract their friends' interest.

[0096] The topic suggestion unit can make interactive suggestions, including quizzes and surveys related to a friend's interests. For example, we will build a system in which the topics suggested by the generation AI include quizzes and surveys related to a friend's interests. For example, we can suggest quizzes related to topics that a friend is interested in. This makes interactive suggestions possible. In addition, by including quizzes and surveys, users can have more lively conversations with their friends. Furthermore, by incorporating interactive elements, users can more easily attract their friends' interests.

[0097] The topic suggestion unit can use the emotion estimation function to suggest topics that match a friend's emotions in real time and filter out topics that have negative emotions. For example, a system can be built that uses the emotion estimation function to suggest topics that match a friend's emotions in real time. For example, topics that make a friend feel joyful or happy can be suggested in real time. This makes it possible to filter out topics that have negative emotions. Furthermore, by suggesting topics that match emotions in real time, a user can make conversations with friends more positive. Furthermore, by filtering out topics that have negative emotions, a user can suggest appropriate topics without offending a friend.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The SNS account registration unit registers the user's SNS account. For example, the user can register accounts for Facebook, Twitter, Instagram, etc. Step 2: The data collection unit collects the friend's posting and message history based on the SNS account registered by the SNS account registration unit, such as the friend's text messages, image postings, video messages, etc. Step 3: The interest analysis unit analyzes the data collected by the data collection unit to identify the friends' interests. For example, the generation AI analyzes the topics that friends frequently post about and the topics that have been discussed in past messages. Step 4: The topic suggestion module suggests topics that your friend might enjoy or continuations of previous conversations based on the interests identified by the interest analysis module. For example, it suggests things like, "Why don't we talk about the latest sports news?" or "How about suggesting your next travel destination as a continuation of your previous trip story?"

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 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 SNS account registration unit for registering a user's SNS account; a data collection unit that collects histories of posts and messages from friends based on the SNS accounts registered by the SNS account registration unit; an interest analysis unit that analyzes the data collected by the data collection unit and identifies the interests and concerns of friends; and a topic suggestion unit that suggests topics that friends may enjoy or continuations of previous conversations based on the interests and concerns identified by the interest analysis unit. A system characterized by:

2. The interest analysis unit Analyze the data in real time and respond immediately if the friend's interests change.

2. The system of claim 1.

3. The SNS account registration unit The system has a function that allows the user to register multiple SNS accounts at once, and integrates and analyzes the data from different SNS accounts.

2. The system of claim 1.

4. The interest analysis unit Analyze changes in interests by taking into account the time and frequency of posts and messages from the friends.

2. The system of claim 1.

5. The topic suggestion unit Make more specific suggestions, including quoting previous posts or messages from the friend.

2. The system of claim 1.

6. The data collection unit Analyzing the sentiment of the friend's posts and messages, and preferentially collecting posts and messages with positive sentiment.

2. The system of claim 1.

7. The interest analysis unit Analyze the sentiment of the friend's posts and messages, and prioritize interests with positive sentiment.

2. The system of claim 1.

8. The topic suggestion unit Suggest topics that match the friend's emotions and draw out positive emotions 2. The system of claim 1.

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