system

Abstract

The system according to the embodiment aims to provide a method for users of a matching app to communicate effectively. [Solution] A system according to an embodiment includes an analysis unit, a proposal unit, and a recommendation unit. The analysis unit analyzes the other party's profile or past conversation history. The proposal unit proposes topics or ways to proceed with the conversation based on the analysis results obtained by the analysis unit. The recommendation unit provides compatibility or a recommendation rate based on the topics or ways to proceed with the conversation proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, users of matching apps had no idea how to communicate effectively, making it difficult to match people.

[0005] The system according to the embodiment aims to provide a method for users of a matching app to communicate effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a recommendation unit. The analysis unit analyzes the other party's profile or past conversation history. The proposal unit proposes topics or ways of proceeding with the conversation based on the analysis results obtained by the analysis unit. The recommendation unit provides compatibility or a recommendation rate based on the topics or ways of proceeding with the conversation proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a method for users of a matching app to communicate effectively. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching support system according to an embodiment of the present invention utilizes a generation AI to provide advice and hints to help users communicate effectively with other parties. In this matching support system, the generation AI analyzes the profile and past conversation history of a person the user is interested in and suggests topics to discuss and ways to advance the conversation based on the analysis results. Furthermore, the generation AI provides compatibility and a recommendation rate based on the content of the conversation. This allows users to communicate effectively and improves the success rate of matching. For example, the generation AI analyzes the profile and past conversation history of a person the user is interested in. For example, it extracts the other party's hobbies and interests, keywords from past conversations, and analysis results of the other party's profile photo to understand the other party's personality and preferences. Next, the generation AI suggests topics to discuss and ways to advance the conversation based on the analysis results. For example, if the other party likes movies, it suggests bringing up a movie they recently saw. If the other party likes traveling, it suggests asking about their next travel destination. Furthermore, the generation AI provides compatibility and a recommendation rate based on the content of the conversation. For example, if the conversation reveals many common hobbies and interests, it determines that the two parties are compatible and provides a high recommendation rate. On the other hand, if the conversation is not very exciting, it will determine that compatibility is low and provide a low recommendation rate. This mechanism allows users to communicate effectively and improves the success rate of matching. For example, even if a user has trouble conversing with a partner, the generation AI can provide appropriate advice, allowing the conversation to proceed smoothly. In addition, by taking into account compatibility with the partner and the recommendation rate, better matching can be achieved. This allows the matching support system to enable users to communicate effectively with partners and improve the success rate of matching.

[0029] A matching support system according to an embodiment includes an analysis unit, a suggestion unit, and a recommendation unit. The analysis unit analyzes the other party's profile or past conversation history. The other party's profile includes, but is not limited to, information such as age, gender, hobbies, and occupation. The past conversation history includes, but is not limited to, information such as text messages and voice call records. The analysis unit analyzes the other party's profile and past conversation history using, for example, natural language processing technology or a machine learning algorithm. The suggestion unit suggests topics or conversation progressions based on the analysis results obtained by the analysis unit. The suggestion unit suggests topics such as movies, travel, sports, and music. The suggestion unit can use a generative AI to suggest appropriate topics based on the other party's hobbies and interests. For example, if the other party likes movies, the suggestion unit suggests bringing up a topic about a movie they've recently seen. If the other party likes traveling, the suggestion unit suggests asking about their next travel destination. The recommendation unit provides compatibility or a recommendation rate based on the topics or conversation progressions suggested by the suggestion unit. The recommendation unit provides compatibility and a recommendation rate based on, for example, the number of common hobbies and interests, the level of conversation excitement, and past matching success rates. The recommendation unit can use a generation AI to analyze the content of the conversation and calculate compatibility and a recommendation rate. For example, if there are many common hobbies and interests in the conversation, it determines that the compatibility is good and provides a high recommendation rate. On the other hand, if the conversation is not very exciting, it determines that the compatibility is low and provides a low recommendation rate. As a result, the matching support system according to the embodiment can enable users to communicate effectively with others and improve the success rate of matching.

[0030] The analysis unit can analyze the analysis results of the other party's hobbies, interests, keywords from past conversations, and profile picture. For example, the analysis unit analyzes the other party's hobbies. For example, if the other party likes sports, it extracts keywords related to sports. The analysis unit can also analyze the other party's interests. For example, if the other party is interested in science, it extracts keywords related to science. The analysis unit can also analyze keywords from past conversations. For example, it extracts words that appear frequently or are highly important in past conversations. The analysis unit can also analyze the analysis results of the other party's profile picture. For example, it can analyze the other party's profile picture using facial recognition technology or an image analysis algorithm. This makes it possible to understand the other party's personality and preferences. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the other party's profile picture into the generation AI and have the generation AI perform image analysis.

[0031] The suggestion unit can suggest topics such as movies, travel, sports, and music. For example, if the other party likes movies, the suggestion unit can suggest topics related to movies. For example, it can suggest bringing up a topic about a movie that the other party has recently seen. Furthermore, if the other party likes travel, the suggestion unit can also suggest topics related to travel. For example, it can suggest asking about the next travel destination the other party would like to go to. Furthermore, if the other party likes sports, the suggestion unit can also suggest topics related to sports. For example, it can suggest bringing up a topic about recent game results. Furthermore, if the other party likes music, the suggestion unit can also suggest topics related to music. For example, it can suggest bringing up a topic about music that the other party has recently listened to. This makes it possible to suggest ways to proceed with a conversation with the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input a prompt to the generation AI to suggest a topic based on the other party's hobbies and interests, and cause the generation AI to suggest a topic.

[0032] The recommendation unit can provide compatibility or a recommendation rate based on the number of common hobbies or interests, the level of conversation enthusiasm, and past matching success rates. The recommendation unit can provide compatibility based on, for example, the number of common hobbies or interests. For example, if there are many common hobbies or interests, it determines that compatibility is good and provides a high recommendation rate. The recommendation unit can also provide compatibility based on the level of conversation enthusiasm. For example, if the conversation is lively, it determines that compatibility is good and provides a high recommendation rate. The recommendation unit can also provide compatibility based on past matching success rates. For example, if the past matching success rate is high, it determines that compatibility is good and provides a high recommendation rate. In this way, compatibility and a recommendation rate can be provided. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recommendation unit can input the content of the conversation into the generation AI and have the generation AI calculate the compatibility and recommendation rate.

[0033] The analysis unit can analyze the user's past matching history and select an appropriate analysis method. For example, the analysis unit can analyze the user's past successful matching patterns and prioritize analyzing profiles of partners with similar patterns. The analysis unit can also analyze the causes of the user's past unsuccessful matches and select an analysis method to avoid similar problems. The analysis unit can also prioritize analyzing partners with specific hobbies or interests from the user's past matching history. This allows the optimal analysis method to be selected based on the past matching history. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past matching history into the generation AI and have the generation AI select an analysis method.

[0034] The analysis unit can perform filtering based on the user's current interests and lifestyle. For example, the analysis unit filters the other party's profile based on topics that the user is currently interested in. The analysis unit can also prioritize analysis of highly relevant information based on the user's current lifestyle (e.g., how busy the user is at work or changes in hobbies). The analysis unit can also filter the other party's profile based on events and activities that the user has recently participated in. This allows filtering to be performed based on the user's current interests and lifestyle. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's current interests and lifestyle into the generation AI and have the generation AI perform filtering.

[0035] The analysis unit can prioritize analyzing highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can prioritize analyzing profiles of people related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing profiles of people nearby based on the user's current location. Furthermore, if the user is attending a specific event, the analysis unit can prioritize analyzing profiles of people related to that event. This allows highly relevant information to be prioritized analyzed based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0036] The analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit can analyze the profile of other users based on the hobbies and interests the user has shared on social media. The analysis unit can also analyze the profile of related users based on the accounts the user follows on social media. The analysis unit can also analyze the profile of other users based on the groups and events the user participates in on social media. This makes it possible to analyze related information based on the user's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and have the generation AI analyze the related information.

[0037] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the other person. For example, if the other person is an important person, the suggestion unit causes the generation AI to make a detailed and polite proposal. Furthermore, if the other person is an ordinary person, the suggestion unit can also cause the generation AI to make a basic proposal. Furthermore, if the other person is not very important, the suggestion unit can also cause the generation AI to make a concise proposal. In this way, the level of detail of the proposal can be adjusted based on the importance of the other person. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the importance of the other person to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0038] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the other party. For example, if the other party likes movies, the suggestion unit can have the generation AI suggest topics related to movies. Also, if the other party likes travel, the suggestion unit can have the generation AI suggest topics related to travel. Also, if the other party likes sports, the suggestion unit can have the generation AI suggest topics related to sports. This makes it possible to apply different suggestion algorithms depending on the category of the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the other party's category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0039] When making a proposal, the suggestion unit can determine the priority of the proposal based on when the other party's profile was updated. For example, if the other party's profile has been recently updated, the suggestion unit causes the generation AI to preferentially suggest that information. Furthermore, if the other party's profile has not been updated for a long period of time, the suggestion unit can also cause the generation AI to make a basic proposal. Furthermore, if the other party's profile is frequently updated, the suggestion unit can also cause the generation AI to make a proposal based on the latest information. This makes it possible to determine the priority of the proposal based on when the other party's profile was updated. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input when the other party's profile was updated into the generation AI and cause the generation AI to determine the priority of the proposals.

[0040] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the other party. For example, if the other party has a common hobby with the user, the suggestion unit causes the generation AI to preferentially suggest that topic. Furthermore, if the other party has a common friend with the user, the suggestion unit can also cause the generation AI to preferentially suggest that topic. Furthermore, if the other party lives in the same area as the user, the suggestion unit can also cause the generation AI to preferentially suggest that topic. This makes it possible to adjust the order of suggestions based on the relevance of the other party. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the relevance of the other party to the generation AI and cause the generation AI to adjust the order of suggestions.

[0041] When making a recommendation, the recommendation unit can analyze the user's past matching success rate and select the optimal recommendation method. For example, the recommendation unit can analyze the user's past successful matching patterns and recommend partners with similar patterns. The recommendation unit can also analyze the causes of the user's past failed matches and select a recommendation method to avoid similar problems. The recommendation unit can also recommend partners with specific hobbies or interests based on the user's past matching success rate. This makes it possible to select the optimal recommendation method based on the past matching success rate. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit can input the user's past matching success rate into the generation AI and have the generation AI select the recommendation method.

[0042] The recommendation unit can customize the recommendation means based on the user's current living situation when making a recommendation. For example, if the user is currently busy, the generation AI can make effective recommendations in a short amount of time. Furthermore, if the user is currently relaxing, the recommendation unit can make detailed and rich recommendations. Furthermore, if the user is currently participating in a specific event, the recommendation unit can recommend people related to that event. This makes it possible to customize the recommendation means based on the user's current living situation. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, the generation AI. For example, the recommendation unit can input the user's current living situation into the generation AI and have the generation AI customize the recommendation means.

[0043] When making a recommendation, the recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the recommendation unit can recommend people related to that area. Furthermore, if the user is traveling, the recommendation unit can recommend people who are nearby based on the user's current location. Furthermore, if the user is attending a specific event, the recommendation unit can recommend people related to that event. This makes it possible to select the optimal recommendation method based on the user's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal recommendation method.

[0044] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a recommendation method. The recommendation unit can recommend related parties based on, for example, hobbies and interests shared by the user on social media. The recommendation unit can also recommend related parties based on accounts the user follows on social media. The recommendation unit can also recommend related parties based on groups and events the user participates in on social media. This makes it possible to suggest optimal recommendation methods based on the user's social media activity. Some or all of the above-mentioned processing in the recommendation unit can be performed using, or without, a generation AI. For example, the recommendation unit can input the user's social media activity into the generation AI and cause the generation AI to suggest recommendation methods.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0047] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0048] The recommendation unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the recommendation unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the recommendation unit can suggest topics that will further enhance that emotion. Furthermore, the recommendation unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0049] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0050] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The analysis unit analyzes the other party's profile or past conversation history. The other party's profile includes information such as age, gender, hobbies, and occupation, while the past conversation history includes text messages and voice call records. The analysis unit analyzes this data using natural language processing technology and machine learning algorithms. Step 2: The suggestion unit suggests topics or ways to proceed with the conversation based on the analysis results obtained by the analysis unit. Using a generative AI, the suggestion unit suggests appropriate topics based on the other person's hobbies and interests. For example, if the other person is a movie lover, it will suggest talking about movies they've recently seen, and if they like traveling, it will suggest asking about the next travel destination they'd like to go on. Step 3: The recommendation unit provides compatibility or a recommendation rate based on the topics or conversation progression suggested by the suggestion unit. The recommendation unit uses a generative AI to analyze the content of the conversation and calculate compatibility and a recommendation rate based on the number of common hobbies and interests, the level of conversation enthusiasm, and past matching success rates. For example, if there are many common hobbies and interests, a high recommendation rate is provided, and if the conversation is not exciting, a low recommendation rate is provided.

[0053] (Example 2) A matching support system according to an embodiment of the present invention utilizes a generation AI to provide advice and hints to help users communicate effectively with other parties. In this matching support system, the generation AI analyzes the profile and past conversation history of a person the user is interested in and suggests topics to discuss and ways to advance the conversation based on the analysis results. Furthermore, the generation AI provides compatibility and a recommendation rate based on the content of the conversation. This allows users to communicate effectively and improves the success rate of matching. For example, the generation AI analyzes the profile and past conversation history of a person the user is interested in. For example, it extracts the other party's hobbies and interests, keywords from past conversations, and analysis results of the other party's profile photo to understand the other party's personality and preferences. Next, the generation AI suggests topics to discuss and ways to advance the conversation based on the analysis results. For example, if the other party likes movies, it suggests bringing up a movie they recently saw. If the other party likes traveling, it suggests asking about their next travel destination. Furthermore, the generation AI provides compatibility and a recommendation rate based on the content of the conversation. For example, if the conversation reveals many common hobbies and interests, it determines that the two parties are compatible and provides a high recommendation rate. On the other hand, if the conversation is not very exciting, it will determine that compatibility is low and provide a low recommendation rate. This mechanism allows users to communicate effectively and improves the success rate of matching. For example, even if a user has trouble conversing with a partner, the generation AI can provide appropriate advice, allowing the conversation to proceed smoothly. In addition, by taking into account compatibility with the partner and the recommendation rate, better matching can be achieved. This allows the matching support system to enable users to communicate effectively with partners and improve the success rate of matching.

[0054] A matching support system according to an embodiment includes an analysis unit, a suggestion unit, and a recommendation unit. The analysis unit analyzes the other party's profile or past conversation history. The other party's profile includes, but is not limited to, information such as age, gender, hobbies, and occupation. The past conversation history includes, but is not limited to, information such as text messages and voice call records. The analysis unit analyzes the other party's profile and past conversation history using, for example, natural language processing technology or a machine learning algorithm. The suggestion unit suggests topics or conversation progressions based on the analysis results obtained by the analysis unit. The suggestion unit suggests topics such as movies, travel, sports, and music. The suggestion unit can use a generative AI to suggest appropriate topics based on the other party's hobbies and interests. For example, if the other party likes movies, the suggestion unit suggests bringing up a topic about a movie they've recently seen. If the other party likes traveling, the suggestion unit suggests asking about their next travel destination. The recommendation unit provides compatibility or a recommendation rate based on the topics or conversation progressions suggested by the suggestion unit. The recommendation unit provides compatibility and a recommendation rate based on, for example, the number of common hobbies and interests, the level of conversation excitement, and past matching success rates. The recommendation unit can use a generation AI to analyze the content of the conversation and calculate compatibility and a recommendation rate. For example, if there are many common hobbies and interests in the conversation, it determines that the compatibility is good and provides a high recommendation rate. On the other hand, if the conversation is not very exciting, it determines that the compatibility is low and provides a low recommendation rate. As a result, the matching support system according to the embodiment can enable users to communicate effectively with others and improve the success rate of matching.

[0055] The analysis unit can analyze the analysis results of the other party's hobbies, interests, keywords from past conversations, and profile picture. For example, the analysis unit analyzes the other party's hobbies. For example, if the other party likes sports, it extracts keywords related to sports. The analysis unit can also analyze the other party's interests. For example, if the other party is interested in science, it extracts keywords related to science. The analysis unit can also analyze keywords from past conversations. For example, it extracts words that appear frequently or are highly important in past conversations. The analysis unit can also analyze the analysis results of the other party's profile picture. For example, it can analyze the other party's profile picture using facial recognition technology or an image analysis algorithm. This makes it possible to understand the other party's personality and preferences. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the other party's profile picture into the generation AI and have the generation AI perform image analysis.

[0056] The suggestion unit can suggest topics such as movies, travel, sports, and music. For example, if the other party likes movies, the suggestion unit can suggest topics related to movies. For example, it can suggest bringing up a topic about a movie that the other party has recently seen. Furthermore, if the other party likes travel, the suggestion unit can also suggest topics related to travel. For example, it can suggest asking about the next travel destination the other party would like to go to. Furthermore, if the other party likes sports, the suggestion unit can also suggest topics related to sports. For example, it can suggest bringing up a topic about recent game results. Furthermore, if the other party likes music, the suggestion unit can also suggest topics related to music. For example, it can suggest bringing up a topic about music that the other party has recently listened to. This makes it possible to suggest ways to proceed with a conversation with the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input a prompt to the generation AI to suggest a topic based on the other party's hobbies and interests, and cause the generation AI to suggest a topic.

[0057] The recommendation unit can provide compatibility or a recommendation rate based on the number of common hobbies or interests, the level of conversation enthusiasm, and past matching success rates. The recommendation unit can provide compatibility based on, for example, the number of common hobbies or interests. For example, if there are many common hobbies or interests, it determines that compatibility is good and provides a high recommendation rate. The recommendation unit can also provide compatibility based on the level of conversation enthusiasm. For example, if the conversation is lively, it determines that compatibility is good and provides a high recommendation rate. The recommendation unit can also provide compatibility based on past matching success rates. For example, if the past matching success rate is high, it determines that compatibility is good and provides a high recommendation rate. In this way, compatibility and a recommendation rate can be provided. Some or all of the above-mentioned processing in the recommendation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the recommendation unit can input the content of the conversation into the generation AI and have the generation AI calculate the compatibility and recommendation rate.

[0058] The analysis unit can estimate the user's emotions and adjust the depth of analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can cause the generation AI to perform a simple keyword analysis and provide only basic information. Alternatively, if the user is relaxed, the analysis unit can cause the generation AI to perform a detailed profile analysis and provide deep insights. Alternatively, if the user is excited, the analysis unit can cause the generation AI to perform a quick analysis and immediately provide a topic. This allows the depth of analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the depth of analysis.

[0059] The analysis unit can analyze the user's past matching history and select an appropriate analysis method. For example, the analysis unit can analyze the user's past successful matching patterns and prioritize analyzing profiles of partners with similar patterns. The analysis unit can also analyze the causes of the user's past unsuccessful matches and select an analysis method to avoid similar problems. The analysis unit can also prioritize analyzing partners with specific hobbies or interests from the user's past matching history. This allows the optimal analysis method to be selected based on the past matching history. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's past matching history into the generation AI and have the generation AI select an analysis method.

[0060] The analysis unit can perform filtering based on the user's current interests and lifestyle. For example, the analysis unit filters the other party's profile based on topics that the user is currently interested in. The analysis unit can also prioritize analysis of highly relevant information based on the user's current lifestyle (e.g., how busy the user is at work or changes in hobbies). The analysis unit can also filter the other party's profile based on events and activities that the user has recently participated in. This allows filtering to be performed based on the user's current interests and lifestyle. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's current interests and lifestyle into the generation AI and have the generation AI perform filtering.

[0061] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can cause the generation AI to prioritize simple and easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can cause the generation AI to prioritize detailed and in-depth analysis results. Furthermore, if the user is excited, the analysis unit can cause the generation AI to prioritize analysis results that are immediately useful. This allows the prioritization of analysis results to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0062] The analysis unit can prioritize analyzing highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can prioritize analyzing profiles of people related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing profiles of people nearby based on the user's current location. Furthermore, if the user is attending a specific event, the analysis unit can prioritize analyzing profiles of people related to that event. This allows highly relevant information to be prioritized analyzed based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI and cause the generation AI to analyze highly relevant information.

[0063] The analysis unit can analyze the user's social media activity and analyze related information. For example, the analysis unit can analyze the profile of other users based on the hobbies and interests the user has shared on social media. The analysis unit can also analyze the profile of related users based on the accounts the user follows on social media. The analysis unit can also analyze the profile of other users based on the groups and events the user participates in on social media. This makes it possible to analyze related information based on the user's social media activity. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity into the generation AI and have the generation AI analyze the related information.

[0064] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can cause the generation AI to make simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can cause the generation AI to make detailed and expressive suggestions. Furthermore, if the user is excited, the suggestion unit can cause the generation AI to make quick and intuitive suggestions. This allows the way the suggestions are expressed to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0065] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the other person. For example, if the other person is an important person, the suggestion unit causes the generation AI to make a detailed and polite proposal. Furthermore, if the other person is an ordinary person, the suggestion unit can also cause the generation AI to make a basic proposal. Furthermore, if the other person is not very important, the suggestion unit can also cause the generation AI to make a concise proposal. In this way, the level of detail of the proposal can be adjusted based on the importance of the other person. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the importance of the other person to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0066] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the other party. For example, if the other party likes movies, the suggestion unit can have the generation AI suggest topics related to movies. Also, if the other party likes travel, the suggestion unit can have the generation AI suggest topics related to travel. Also, if the other party likes sports, the suggestion unit can have the generation AI suggest topics related to sports. This makes it possible to apply different suggestion algorithms depending on the category of the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the other party's category into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0067] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can cause the generation AI to make short, concise suggestions. If the user is relaxed, the suggestion unit can cause the generation AI to make detailed, longer suggestions. If the user is excited, the suggestion unit can cause the generation AI to make quick, concise suggestions. This allows the length of the suggestions to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0068] When making a proposal, the suggestion unit can determine the priority of the proposal based on when the other party's profile was updated. For example, if the other party's profile has been recently updated, the suggestion unit causes the generation AI to preferentially suggest that information. Furthermore, if the other party's profile has not been updated for a long period of time, the suggestion unit can also cause the generation AI to make a basic proposal. Furthermore, if the other party's profile is frequently updated, the suggestion unit can also cause the generation AI to make a proposal based on the latest information. This makes it possible to determine the priority of the proposal based on when the other party's profile was updated. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input when the other party's profile was updated into the generation AI and cause the generation AI to determine the priority of the proposals.

[0069] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the other party. For example, if the other party has a common hobby with the user, the suggestion unit causes the generation AI to preferentially suggest that topic. Furthermore, if the other party has a common friend with the user, the suggestion unit can also cause the generation AI to preferentially suggest that topic. Furthermore, if the other party lives in the same area as the user, the suggestion unit can also cause the generation AI to preferentially suggest that topic. This makes it possible to adjust the order of suggestions based on the relevance of the other party. Some or all of the above-described processing in the suggestion unit may be performed using, or without, the generation AI. For example, the suggestion unit can input the relevance of the other party to the generation AI and cause the generation AI to adjust the order of suggestions.

[0070] The recommendation unit can estimate the user's emotions and adjust the recommendation method based on the estimated user emotions. For example, if the user is nervous, the generation AI can make simple and easy-to-understand recommendations. Furthermore, if the user is relaxed, the recommendation unit can make detailed and expressive recommendations. Furthermore, if the user is excited, the recommendation unit can make quick and intuitive recommendations. This allows the recommendation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI adjust the recommendation method.

[0071] When making a recommendation, the recommendation unit can analyze the user's past matching success rate and select the optimal recommendation method. For example, the recommendation unit can analyze the user's past successful matching patterns and recommend partners with similar patterns. The recommendation unit can also analyze the causes of the user's past failed matches and select a recommendation method to avoid similar problems. The recommendation unit can also recommend partners with specific hobbies or interests based on the user's past matching success rate. This makes it possible to select the optimal recommendation method based on the past matching success rate. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit can input the user's past matching success rate into the generation AI and have the generation AI select the recommendation method.

[0072] The recommendation unit can customize the recommendation means based on the user's current living situation when making a recommendation. For example, if the user is currently busy, the generation AI can make effective recommendations in a short amount of time. Furthermore, if the user is currently relaxing, the recommendation unit can make detailed and rich recommendations. Furthermore, if the user is currently participating in a specific event, the recommendation unit can recommend people related to that event. This makes it possible to customize the recommendation means based on the user's current living situation. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, the generation AI. For example, the recommendation unit can input the user's current living situation into the generation AI and have the generation AI customize the recommendation means.

[0073] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user emotions. For example, if the user is nervous, the generation AI can prioritize simple and easy-to-understand recommendations. Furthermore, if the user is relaxed, the recommendation unit can prioritize detailed and expressive recommendations. Furthermore, if the user is excited, the recommendation unit can prioritize quick and intuitive recommendations. This allows the recommendation priority to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recommendation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the recommendation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of recommendations.

[0074] When making a recommendation, the recommendation unit can select the optimal recommendation method by taking into account the user's geographical location information. For example, if the user lives in a specific area, the recommendation unit can recommend people related to that area. Furthermore, if the user is traveling, the recommendation unit can recommend people who are nearby based on the user's current location. Furthermore, if the user is attending a specific event, the recommendation unit can recommend people related to that event. This makes it possible to select the optimal recommendation method based on the user's geographical location information. Some or all of the above-described processing in the recommendation unit may be performed using, or without, a generation AI. For example, the recommendation unit can input the user's geographical location information into the generation AI and cause the generation AI to select the optimal recommendation method.

[0075] When making a recommendation, the recommendation unit can analyze the user's social media activity and suggest a recommendation method. The recommendation unit can recommend related parties based on, for example, hobbies and interests shared by the user on social media. The recommendation unit can also recommend related parties based on accounts the user follows on social media. The recommendation unit can also recommend related parties based on groups and events the user participates in on social media. This makes it possible to suggest optimal recommendation methods based on the user's social media activity. Some or all of the above-mentioned processing in the recommendation unit can be performed using, or without, a generation AI. For example, the recommendation unit can input the user's social media activity into the generation AI and cause the generation AI to suggest recommendation methods. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and recommendation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the other party's profile and past conversation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests topics and ways to proceed with the conversation based on the analysis results. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides compatibility and a recommendation rate. Some or all of the analysis unit, suggestion unit, and recommendation unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and recommendation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the other party's profile and past conversation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests topics and ways to proceed with the conversation based on the analysis results. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides compatibility and a recommendation rate. Some or all of the analysis unit, suggestion unit, and recommendation unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and recommendation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 and analyzes the other party's profile and past conversation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests topics and ways to proceed with the conversation based on the analysis results. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides compatibility and a recommendation rate. Some or all of the analysis unit, suggestion unit, and recommendation unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and recommendation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the other party's profile and past conversation history. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests topics and ways to proceed with the conversation based on the analysis results. The recommendation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides compatibility and a recommendation rate. Some or all of the analysis unit, suggestion unit, and recommendation unit may be realized, for example, by the control unit 46A of the robot 414.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0078] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0079] The recommendation unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the recommendation unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the recommendation unit can suggest topics that will further enhance that emotion. Furthermore, the recommendation unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0080] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0081] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0082] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0083] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0084] The recommendation unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the recommendation unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the recommendation unit can suggest topics that will further enhance that emotion. Furthermore, the recommendation unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0085] The analysis unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the analysis unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the analysis unit can suggest topics that will further enhance that emotion. Furthermore, the analysis unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0086] The suggestion unit can analyze not only the user's past conversation history but also the user's current psychological state in real time. For example, if the user is feeling stressed, the suggestion unit can suggest topics that will help relieve stress. Also, if the user is feeling happy, the suggestion unit can suggest topics that will further enhance that emotion. Furthermore, the suggestion unit can adjust the pace and tone of the conversation based on the user's psychological state. This allows the user to communicate more naturally and comfortably.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The analysis unit analyzes the other party's profile or past conversation history. The other party's profile includes information such as age, gender, hobbies, and occupation, while the past conversation history includes text messages and voice call records. The analysis unit analyzes this data using natural language processing technology and machine learning algorithms. Step 2: The suggestion unit suggests topics or ways to proceed with the conversation based on the analysis results obtained by the analysis unit. Using a generative AI, the suggestion unit suggests appropriate topics based on the other person's hobbies and interests. For example, if the other person is a movie lover, it will suggest talking about movies they've recently seen, and if they like traveling, it will suggest asking about the next travel destination they'd like to go on. Step 3: The recommendation unit provides compatibility or a recommendation rate based on the topics or conversation progression suggested by the suggestion unit. The recommendation unit uses a generative AI to analyze the content of the conversation and calculate compatibility and a recommendation rate based on the number of common hobbies and interests, the level of conversation enthusiasm, and past matching success rates. For example, if there are many common hobbies and interests, a high recommendation rate is provided, and if the conversation is not exciting, a low recommendation rate is provided.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0091] 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.

[0092] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0107] 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.

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

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 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.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

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

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the other party's profile or past conversation history; a suggestion unit that suggests a topic or a way to proceed with a conversation based on the analysis result obtained by the analysis unit; a recommendation unit that provides compatibility or a recommendation rate based on the topic or conversation progression suggested by the suggestion unit; Equipped with A system characterized by:

2. The analysis unit Analyze the other person's hobbies, interests, keywords from past conversations, and profile picture The system of claim 1 .

3. The proposal unit Suggest topics about movies, travel, sports, and music The system of claim 1 .

4. The recommendation unit Providing compatibility or recommendation rates based on the number of common hobbies or interests, conversational stamina, and past matching success rates The system of claim 1 .

5. The analysis unit Estimate user emotions and adjust analysis depth based on the estimated user emotions The system of claim 1 .

6. The analysis unit Analyze the user's past matching history and select the appropriate analysis method The system of claim 1 .

7. The analysis unit Filtering based on the user's current interests and life situation The system of claim 1 .

8. The analysis unit Estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The system of claim 1 .

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