system

Abstract

An object of the system according to the embodiment is to take appropriate measures based on an attribute of a party to a match-up and grasp a common topic and an NG word in advance.SOLUTION: A system includes an attribute input unit, a personalization unit, a trend analysis unit, and a notification unit. The attribute input unit inputs an attribute of the opponent. A personalizing part personalizes the opposite party based on the attribute of the opposite party inputted by the attribute input part, and analyzes the tendency of the ideal picture. The trend analysis unit analyzes a trend of a favorite fashion or topic of the latest partner and an NG word from a public directory. The notification unit shares the information analyzed by the trend analysis unit in advance.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 come up with appropriate countermeasures based on the attributes of the people attending the group blind date, and it was difficult to identify common topics and taboo words in advance.

[0005] The system according to the embodiment aims to take appropriate measures based on the attributes of the people you meet at a group date, and to identify common topics and taboo words in advance. [Means for solving the problem]

[0006] The system according to the embodiment includes an attribute input unit, a personalization unit, a trend analysis unit, and a notification unit. The attribute input unit inputs the attributes of the other person. The personalization unit personalizes the other person based on the attributes of the other person input by the attribute input unit and analyzes trends in ideal images. The trend analysis unit analyzes the latest favorite fashions or topical trends of the other person and taboo words from popular guidebooks. The notification unit shares the information analyzed by the trend analysis unit in advance. [Effects of the Invention]

[0007] The system according to the embodiment can take appropriate measures based on the attributes of the people you meet at a group date, and can identify common topics and taboo words in advance. [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) The group dating support system according to an embodiment of the present invention is a system that personalizes the other party by inputting the other party's attributes and analyzes the ideal image trends. Furthermore, it supports the success of group dating by sharing in advance the latest favorite fashions, trending topics, and taboo words from popular guidebooks. This allows the group dating support system to personalize based on the other party's attributes and analyze the ideal image trends, making it easier to gain empathy and improving the success rate.

[0029] A group dating support system according to an embodiment includes an attribute input unit, a personalization unit, a trend analysis unit, and a notification unit. The attribute input unit inputs the attributes of the other party. For example, information such as age, gender, hobbies, and occupation can be input. The personalization unit personalizes the other party based on the attributes of the other party input by the attribute input unit and analyzes the ideal image trend. For example, if the other party likes sports, it analyzes that the other party tends to like sports-related topics. The trend analysis unit analyzes the other party's latest favorite fashions, topic trends, and taboo words from popular guidebooks. For example, if the other party is interested in the latest fashions, information on this is provided. In addition, by informing the other party in advance of topics and words that the other party dislikes (taboo words), conversation failures can be prevented. The notification unit shares the information analyzed by the trend analysis unit in advance. For example, if the other party likes traveling, travel-related topics can be provided to gain sympathy. As a result, the group dating support system according to an embodiment personalizes the other party based on the attributes of the other party and analyzes the ideal image trend, making it easier to gain sympathy and improving the success rate.

[0030] The personalization unit analyzes the other party's past social media posts to provide more detailed personalization. For example, the personalization unit inputs the other party's social media account, and the generation AI analyzes the content of past posts. For example, it extracts the content the other party posts frequently and topics of interest, and reflects this in personalization. The personalization unit also analyzes images and videos posted on social media to gain a detailed understanding of the other party's hobbies and lifestyle. For example, it can identify the other party's preferences from photos of travel destinations and food. The personalization unit also analyzes comments and replies on social media to understand what topics the other party responds positively to. For example, it can personalize based on reactions to specific events or occurrences. This makes it possible to provide more detailed personalization by analyzing the other party's past social media posts.

[0031] The personalization unit can automatically generate a playlist of music or movies that the other party likes based on attribute information and provide it as a topic of conversation. For example, the personalization unit uses a generation AI to create a music playlist that the other party is likely to like based on attribute information such as the other party's age and hobbies. For example, it can provide a playlist that includes the other party's favorite genres and artists. The personalization unit also analyzes movie preferences and automatically generates a movie list that the other party is likely to be interested in. For example, it can create a list based on the other party's favorite movie genres and directors. The personalization unit also provides music and movie playlists as conversation topics, making it easier to find common topics with the other party. For example, it can suggest topics about the songs and movies included in the playlist. In this way, providing a playlist of the other party's favorite music or movies makes it easier to find common topics.

[0032] The trend analysis unit can analyze the latest fashion magazines or blogs and suggest fashion items that suit the other person's preferences. For example, the generation AI in the trend analysis unit analyzes the latest fashion magazines and suggests fashion items that suit the other person's preferences. For example, it lists items based on the other person's favorite brands and styles. The trend analysis unit also analyzes fashion blogs and suggests trend items that the other person might be interested in. For example, it makes suggestions based on the latest trends and popular items. The trend analysis unit also analyzes the other person's past fashion choices and identifies items that the other person likes. For example, it makes suggestions based on items the other person has purchased or styles they have worn in the past. This provides topics for conversation by suggesting fashion items that suit the other person's preferences.

[0033] The trend analysis unit analyzes the latest news or trends and can provide topics that the other party is likely to be interested in in real time. For example, the generation AI in the trend analysis unit analyzes the latest news sites and social media and provides topics that the other party is likely to be interested in in real time. For example, suggestions are made based on the accounts and topics that the other party follows. The trend analysis unit also analyzes trending information and provides topics that the other party is likely to be interested in. For example, it suggests topics related to the latest technology and entertainment. The trend analysis unit also analyzes the other party's past search history and browsing history and provides news and trends that the other party is likely to be interested in. For example, it makes suggestions based on keywords that the other party frequently searches for. This allows the conversation to proceed smoothly by providing topics that the other party is likely to be interested in in real time.

[0034] The notification unit allows the generation AI to analyze traffic information and suggest the optimal means of transportation or route for the return trip. For example, the generation AI analyzes traffic information in real time and suggests the optimal means of transportation for the return trip. For example, it provides the optimal route from the nearest station or bus stop. The notification unit also analyzes traffic congestion and delay information and suggests the optimal return route. For example, it provides an alternative route to avoid congestion. The notification unit also suggests options for transportation, allowing the user to select the optimal means. For example, it provides options such as taxi, train, and bus. This allows the user to return home smoothly by suggesting the optimal means of transportation and route for the return trip.

[0035] The notification unit allows the generation AI to analyze information about nearby restaurants or cafes and suggest the best place for the after-party. For example, the notification unit may list restaurants and cafes that match the user's preferences. The notification unit may also analyze restaurant and cafe review sites to suggest highly rated places. For example, the notification unit may make suggestions based on places the user has visited in the past or highly rated places. The notification unit may also take into account information such as transportation accessibility and business hours when suggesting a place for the after-party. For example, the notification unit may suggest a place that is close to the nearest station or has long business hours. This allows the best place for the after-party to be suggested, making the group date run more smoothly.

[0036] The notification unit allows the generation AI to analyze information about nearby events and suggest date plans after the group date. For example, the notification unit allows the generation AI to analyze information about nearby events and suggest date plans after the group date. For example, it may suggest concerts or exhibitions being held nearby. The notification unit also automatically generates date plans based on event information. For example, it creates plans that include events such as movie screenings and live performances. The notification unit also creates date plans by suggesting events that match the other person's hobbies and interests. For example, it may suggest plans based on events in the other person's favorite genre. In this way, the notification unit supports the user's dates by suggesting date plans after the group date.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The matchmaking support system may further include a voice analysis unit that analyzes the user's voice tone. The voice analysis unit, for example, analyzes the user's voice tone and speaking pattern to determine whether the user is nervous or relaxed. For example, if the user's voice tone becomes higher or the speaking speed becomes faster, it may determine that the user is nervous. The voice analysis unit may also provide appropriate advice based on the user's voice tone. For example, it may suggest taking deep breaths to relax or advise the user to slow down their speaking speed. In this way, by analyzing the user's voice tone, more appropriate support can be provided.

[0039] The group dating support system can further include a body temperature analysis unit that monitors the user's body temperature. The body temperature analysis unit, for example, measures the user's body temperature with a sensor, and the generation AI analyzes the data. For example, if the user's body temperature is rising, it determines that the user is nervous. The body temperature analysis unit can also provide appropriate advice based on the user's body temperature. For example, if the body temperature is rising, it can provide advice on how to relax. The body temperature analysis unit can also monitor changes in the user's body temperature in real time and provide advice at the appropriate time. In this way, by monitoring the user's body temperature, more appropriate support can be provided.

[0040] The group dating support system can further include a gait analysis unit that analyzes the user's walking pattern. The gait analysis unit, for example, measures the user's walking pattern with a sensor, and the generation AI analyzes the data. For example, if the user's walking becomes faster or more irregular, it can determine that the user is nervous. The gait analysis unit can also provide appropriate advice based on the user's walking pattern. For example, if the user's walking becomes faster, it can provide advice on how to relax. The gait analysis unit can also monitor changes in the user's walking pattern in real time and provide advice at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's walking pattern.

[0041] The group dating support system can further include a food analysis unit that analyzes the user's food preferences. The food analysis unit, for example, analyzes the user's past dining history, and the generation AI identifies the user's preferences based on that data. For example, it lists the user's favorite dishes and restaurants. The food analysis unit can also suggest appropriate restaurants and cafes based on the user's food preferences. For example, it can suggest restaurants that serve the user's favorite dishes. The food analysis unit can also monitor changes in the user's food preferences in real time and provide suggestions at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's food preferences.

[0042] The group dating support system can further include a movement analysis unit that analyzes the user's movement patterns. The movement analysis unit, for example, measures the user's movement patterns with a sensor, and the generation AI analyzes the data. For example, if the user is not getting enough exercise, it determines that the user is in poor health. The movement analysis unit can also provide appropriate advice based on the user's movement patterns. For example, if the user is not getting enough exercise, it can suggest moderate exercise. The movement analysis unit can also monitor changes in the user's movement patterns in real time and provide advice at the appropriate time. This makes it possible to provide more appropriate support by analyzing the user's movement patterns.

[0043] The group dating support system can further include a hobby analysis unit that analyzes the user's hobbies. The hobby analysis unit, for example, analyzes the user's past behavioral history, and the generation AI identifies the user's hobbies based on that data. For example, it lists the user's favorite sports and activities. The hobby analysis unit can also suggest appropriate activities and events based on the user's hobbies. For example, it can suggest the user's favorite sports events and activities. The hobby analysis unit can also monitor changes in the user's hobbies in real time and provide suggestions at the appropriate time. This makes it possible to provide more appropriate support by analyzing the user's hobbies.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The attribute input unit inputs the attributes of the other party, such as age, sex, hobbies, and occupation. Step 2: The personalization unit personalizes the other person based on the other person's attributes input by the attribute input unit and analyzes the ideal image tendency. For example, if the other person likes sports, it analyzes that they tend to like topics related to sports. Step 3: The trend analysis section analyzes the other person's latest favorite fashions, topic trends, and taboo words from popular guidebooks. For example, if the other person is interested in the latest fashion, the section will provide that information. It also helps prevent conversation failures by letting the other person know in advance which topics and words (taboo words) they dislike. Step 4: The notification unit shares the information analyzed by the trend analysis unit in advance. For example, if the other person likes traveling, you can provide travel-related topics to gain empathy.

[0046] (Example 2) The group dating support system according to an embodiment of the present invention is a system that personalizes the other party by inputting the other party's attributes and analyzes the ideal image trends. Furthermore, it supports the success of group dating by sharing in advance the latest favorite fashions, trending topics, and taboo words from popular guidebooks. This allows the group dating support system to personalize based on the other party's attributes and analyze the ideal image trends, making it easier to gain empathy and improving the success rate.

[0047] A group dating support system according to an embodiment includes an attribute input unit, a personalization unit, a trend analysis unit, and a notification unit. The attribute input unit inputs the attributes of the other party. For example, information such as age, gender, hobbies, and occupation can be input. The personalization unit personalizes the other party based on the attributes of the other party input by the attribute input unit and analyzes the ideal image trend. For example, if the other party likes sports, it analyzes that the other party tends to like sports-related topics. The trend analysis unit analyzes the other party's latest favorite fashions, topic trends, and taboo words from popular guidebooks. For example, if the other party is interested in the latest fashions, information on this is provided. In addition, by informing the other party in advance of topics and words that the other party dislikes (taboo words), conversation failures can be prevented. The notification unit shares the information analyzed by the trend analysis unit in advance. For example, if the other party likes traveling, travel-related topics can be provided to gain sympathy. As a result, the group dating support system according to an embodiment personalizes the other party based on the attributes of the other party and analyzes the ideal image trend, making it easier to gain sympathy and improving the success rate.

[0048] The personalization unit analyzes the other party's past social media posts to provide more detailed personalization. For example, the personalization unit inputs the other party's social media account, and the generation AI analyzes the content of past posts. For example, it extracts the content the other party posts frequently and topics of interest, and reflects this in personalization. The personalization unit also analyzes images and videos posted on social media to gain a detailed understanding of the other party's hobbies and lifestyle. For example, it can identify the other party's preferences from photos of travel destinations and food. The personalization unit also analyzes comments and replies on social media to understand what topics the other party responds positively to. For example, it can personalize based on reactions to specific events or occurrences. This makes it possible to provide more detailed personalization by analyzing the other party's past social media posts.

[0049] The personalization unit can automatically generate a playlist of music or movies that the other party likes based on attribute information and provide it as a topic of conversation. For example, the personalization unit uses a generation AI to create a music playlist that the other party is likely to like based on attribute information such as the other party's age and hobbies. For example, it can provide a playlist that includes the other party's favorite genres and artists. The personalization unit also analyzes movie preferences and automatically generates a movie list that the other party is likely to be interested in. For example, it can create a list based on the other party's favorite movie genres and directors. The personalization unit also provides music and movie playlists as conversation topics, making it easier to find common topics with the other party. For example, it can suggest topics about the songs and movies included in the playlist. In this way, providing a playlist of the other party's favorite music or movies makes it easier to find common topics.

[0050] The personalization unit can use the emotion estimation function to analyze the emotional state estimated from the attribute information and suggest topics that match that emotion. For example, the personalization unit estimates emotions based on the other person's attribute information and suggests topics that the other person likes when they are relaxed. For example, it can provide relaxing topics related to hobbies and interests. The personalization unit also identifies topics that the other person should avoid when they are feeling stressed based on the emotion estimation results. For example, it can suggest avoiding topics related to work or study. The personalization unit also suggests topics that will evoke positive emotions in the other person based on the emotion estimation data. For example, it can provide topics related to past successes or happy memories. In this way, suggesting topics that match the other person's emotional state makes it easier to gain empathy.

[0051] The trend analysis unit can analyze the latest fashion magazines or blogs and suggest fashion items that suit the other person's preferences. For example, the generation AI in the trend analysis unit analyzes the latest fashion magazines and suggests fashion items that suit the other person's preferences. For example, it lists items based on the other person's favorite brands and styles. The trend analysis unit also analyzes fashion blogs and suggests trend items that the other person might be interested in. For example, it makes suggestions based on the latest trends and popular items. The trend analysis unit also analyzes the other person's past fashion choices and identifies items that the other person likes. For example, it makes suggestions based on items the other person has purchased or styles they have worn in the past. This provides topics for conversation by suggesting fashion items that suit the other person's preferences.

[0052] The trend analysis unit analyzes the latest news or trends and can provide topics that the other party is likely to be interested in in real time. For example, the generation AI in the trend analysis unit analyzes the latest news sites and social media and provides topics that the other party is likely to be interested in in real time. For example, suggestions are made based on the accounts and topics that the other party follows. The trend analysis unit also analyzes trending information and provides topics that the other party is likely to be interested in. For example, it suggests topics related to the latest technology and entertainment. The trend analysis unit also analyzes the other party's past search history and browsing history and provides news and trends that the other party is likely to be interested in. For example, it makes suggestions based on keywords that the other party frequently searches for. This allows the conversation to proceed smoothly by providing topics that the other party is likely to be interested in in real time.

[0053] The trend analysis unit can use the emotion estimation function to analyze topics or words that the other party dislikes and provide alerts to avoid them during the conversation. For example, the trend analysis unit uses the emotion estimation function to identify topics or words that the other party dislikes and provides alerts to avoid them during the conversation. For example, it suggests avoiding topics to which the other party has had a negative reaction in the past. The trend analysis unit also performs emotion estimation in real time during the conversation and displays an alert when a topic that the other party dislikes is mentioned. For example, it analyzes the other party's facial expressions and voice to detect a negative reaction. The trend analysis unit also lists topics or words that the other party may dislike in advance based on the other party's attribute information and suggests avoiding them during the conversation. For example, it avoids topics related to the other party's past experiences or trauma. In this way, avoiding topics and words that the other party dislikes prevents the conversation from failing.

[0054] The notification unit allows the generation AI to analyze traffic information and suggest the optimal means of transportation or route for the return trip. For example, the generation AI analyzes traffic information in real time and suggests the optimal means of transportation for the return trip. For example, it provides the optimal route from the nearest station or bus stop. The notification unit also analyzes traffic congestion and delay information and suggests the optimal return route. For example, it provides an alternative route to avoid congestion. The notification unit also suggests options for transportation, allowing the user to select the optimal means. For example, it provides options such as taxi, train, and bus. This allows the user to return home smoothly by suggesting the optimal means of transportation and route for the return trip.

[0055] The notification unit allows the generation AI to analyze information about nearby restaurants or cafes and suggest the best place for the after-party. For example, the notification unit may list restaurants and cafes that match the user's preferences. The notification unit may also analyze restaurant and cafe review sites to suggest highly rated places. For example, the notification unit may make suggestions based on places the user has visited in the past or highly rated places. The notification unit may also take into account information such as transportation accessibility and business hours when suggesting a place for the after-party. For example, the notification unit may suggest a place that is close to the nearest station or has long business hours. This allows the best place for the after-party to be suggested, making the group date run more smoothly.

[0056] The notification unit can use the emotion estimation function to analyze the user's emotional state and send cheering messages at appropriate times. The notification unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and send cheering messages at appropriate times. For example, it sends a message that helps the user relax when the user is feeling nervous. The notification unit also sends an encouraging message when the user is feeling stressed based on the emotion estimation result. For example, it sends a positive message when the user is feeling anxious. The notification unit also sends a message that makes the user feel positive emotions based on the emotion estimation data. For example, it sends a message that reminds the user of a successful experience. In this way, the notification unit supports the user by sending cheering messages according to the user's emotional state.

[0057] The notification unit allows the generation AI to analyze information about nearby events and suggest date plans after the group date. For example, the notification unit allows the generation AI to analyze information about nearby events and suggest date plans after the group date. For example, it may suggest concerts or exhibitions being held nearby. The notification unit also automatically generates date plans based on event information. For example, it creates plans that include events such as movie screenings and live performances. The notification unit also creates date plans by suggesting events that match the other person's hobbies and interests. For example, it may suggest plans based on events in the other person's favorite genre. In this way, the notification unit supports the user's dates by suggesting date plans after the group date.

[0058] The notification unit can use the emotion estimation function to analyze the user's emotional state and suggest relaxing music or activities. For example, the notification unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest relaxing music. For example, the notification unit provides a playlist that helps the user relax. The notification unit also suggests activities that help the user relax based on the emotion estimation results. For example, activities such as yoga or meditation are provided. The notification unit also suggests environments where the user can relax based on the emotion estimation data. For example, it lists relaxing places such as quiet cafes and parks. In this way, the system supports the user by suggesting relaxing music and activities according to the user's emotional state.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The matchmaking support system may further include a voice analysis unit that analyzes the user's voice tone. The voice analysis unit, for example, analyzes the user's voice tone and speaking pattern to determine whether the user is nervous or relaxed. For example, if the user's voice tone becomes higher or the speaking speed becomes faster, it may determine that the user is nervous. The voice analysis unit may also provide appropriate advice based on the user's voice tone. For example, it may suggest taking deep breaths to relax or advise the user to slow down their speaking speed. In this way, by analyzing the user's voice tone, more appropriate support can be provided.

[0061] The group dating support system may further include an expression analysis unit that analyzes the user's facial expression. The expression analysis unit, for example, captures the user's facial expression with a camera, and the generation AI analyzes the expression. For example, the expression analysis unit analyzes the user's facial expression, such as whether the user is smiling or frowning, to understand the user's emotional state. The expression analysis unit can also provide appropriate advice based on the user's facial expression. For example, if the user is not smiling, advice on how to relax is provided. The expression analysis unit can also monitor changes in the user's facial expression in real time and provide advice at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's facial expression.

[0062] The group dating support system can further include a body temperature analysis unit that monitors the user's body temperature. The body temperature analysis unit, for example, measures the user's body temperature with a sensor, and the generation AI analyzes the data. For example, if the user's body temperature is rising, it determines that the user is nervous. The body temperature analysis unit can also provide appropriate advice based on the user's body temperature. For example, if the body temperature is rising, it can provide advice on how to relax. The body temperature analysis unit can also monitor changes in the user's body temperature in real time and provide advice at the appropriate time. In this way, by monitoring the user's body temperature, more appropriate support can be provided.

[0063] The group dating support system can further include a heart rate analysis unit that monitors the user's heart rate. The heart rate analysis unit, for example, measures the user's heart rate with a sensor, and the generation AI analyzes the data. For example, if the user's heart rate is elevated, it determines that the user is nervous. The heart rate analysis unit can also provide appropriate advice based on the user's heart rate. For example, if the heart rate is elevated, it can provide advice on how to relax. The heart rate analysis unit can also monitor changes in the user's heart rate in real time and provide advice at the appropriate time. In this way, by monitoring the user's heart rate, more appropriate support can be provided.

[0064] The group dating support system can further include a gait analysis unit that analyzes the user's walking pattern. The gait analysis unit, for example, measures the user's walking pattern with a sensor, and the generation AI analyzes the data. For example, if the user's walking becomes faster or more irregular, it can determine that the user is nervous. The gait analysis unit can also provide appropriate advice based on the user's walking pattern. For example, if the user's walking becomes faster, it can provide advice on how to relax. The gait analysis unit can also monitor changes in the user's walking pattern in real time and provide advice at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's walking pattern.

[0065] The group dating support system can further include a food analysis unit that analyzes the user's food preferences. The food analysis unit, for example, analyzes the user's past dining history, and the generation AI identifies the user's preferences based on that data. For example, it lists the user's favorite dishes and restaurants. The food analysis unit can also suggest appropriate restaurants and cafes based on the user's food preferences. For example, it can suggest restaurants that serve the user's favorite dishes. The food analysis unit can also monitor changes in the user's food preferences in real time and provide suggestions at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's food preferences.

[0066] The group dating support system can further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit, for example, measures the user's sleep patterns with a sensor, and the generation AI analyzes the data. For example, if the user has not gotten enough sleep, it determines that the user is tired. The sleep analysis unit can also provide appropriate advice based on the user's sleep patterns. For example, if the user is tired, it can provide advice on how to relax. The sleep analysis unit can also monitor changes in the user's sleep patterns in real time and provide advice at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's sleep patterns.

[0067] The group dating support system can further include a movement analysis unit that analyzes the user's movement patterns. The movement analysis unit, for example, measures the user's movement patterns with a sensor, and the generation AI analyzes the data. For example, if the user is not getting enough exercise, it determines that the user is in poor health. The movement analysis unit can also provide appropriate advice based on the user's movement patterns. For example, if the user is not getting enough exercise, it can suggest moderate exercise. The movement analysis unit can also monitor changes in the user's movement patterns in real time and provide advice at the appropriate time. This makes it possible to provide more appropriate support by analyzing the user's movement patterns.

[0068] The matchmaking support system can further include a stress analysis unit that analyzes the user's stress level. The stress analysis unit, for example, measures the user's biometric data using a sensor, and the generation AI analyzes the data. For example, if the user's heart rate or body temperature is elevated, it determines that the user is feeling stressed. The stress analysis unit can also provide appropriate advice based on the user's stress level. For example, if the user is feeling stressed, it can provide advice on how to relax. The stress analysis unit can also monitor changes in the user's stress level in real time and provide advice at the appropriate time. This allows the system to provide more appropriate support by analyzing the user's stress level.

[0069] The group dating support system can further include a hobby analysis unit that analyzes the user's hobbies. The hobby analysis unit, for example, analyzes the user's past behavioral history, and the generation AI identifies the user's hobbies based on that data. For example, it lists the user's favorite sports and activities. The hobby analysis unit can also suggest appropriate activities and events based on the user's hobbies. For example, it can suggest the user's favorite sports events and activities. The hobby analysis unit can also monitor changes in the user's hobbies in real time and provide suggestions at the appropriate time. This makes it possible to provide more appropriate support by analyzing the user's hobbies.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The attribute input unit inputs the attributes of the other party, such as age, sex, hobbies, and occupation. Step 2: The personalization unit personalizes the other person based on the other person's attributes input by the attribute input unit and analyzes the ideal image tendency. For example, if the other person likes sports, it analyzes that they tend to like topics related to sports. Step 3: The trend analysis section analyzes the other person's latest favorite fashions, topic trends, and taboo words from popular guidebooks. For example, if the other person is interested in the latest fashion, the section will provide that information. It also helps prevent conversation failures by letting the other person know in advance which topics and words (taboo words) they dislike. Step 4: The notification unit shares the information analyzed by the trend analysis unit in advance. For example, if the other person likes traveling, you can provide travel-related topics to gain empathy.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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. Note that 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 attribute input unit for inputting the attributes of the other party; a personalization unit that personalizes the other person based on the attributes of the other person input by the attribute input unit and analyzes the tendency of the ideal image; A trend analysis department analyzes the latest fashion trends and taboo words of the other person from popular guidebooks, a notification unit that shares the information analyzed by the trend analysis unit in advance. A system characterized by:

2. The personalization unit Based on the user's attributes, a playlist of music or movies that matches the user's preferences is automatically generated and provided as a conversation topic.

2. The system of claim 1.

3. The trend analysis unit Analyze the latest fashion magazines or blogs and suggest fashion items that suit the recipient's tastes 2. The system of claim 1.

4. The notification unit Generative AI analyzes traffic information and suggests the best means of transportation or route for the return trip.

2. The system of claim 1.

5. The personalization unit Analyzing emotional states estimated from attribute information and suggesting topics that match those emotions 2. The system of claim 1.

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