System

Abstract

The system according to the embodiment aims to propose friends and business partners matching the user's values and goals.SOLUTION: A system according to an embodiment includes a collection unit and a suggestion unit. The collection unit collects information on a hobby, an activity range, a personality, and a preference of the user. The proposal unit proposes a friend having a matching value based on the information collected by the collection unit. The collection unit collects information on skills, experiences, and knowledge of each industry of the user. The proposal unit proposes a business partner having a common work goal based on the information collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult for users to find trustworthy friends and business partners, and there is room for improvement in terms of psychological safety and career development.

[0005] The system according to the embodiment aims to suggest friends and business partners that match the user's values ​​and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit and a suggestion unit. The collection unit collects information on the user's hobbies, range of activities, personality, and preferences. The suggestion unit suggests friends who share similar values ​​based on the information collected by the collection unit. The collection unit collects information on the user's skills, experience, and knowledge of various industries. The suggestion unit suggests business partners who share common work goals based on the information collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest friends and business partners that match the user's values ​​and goals. [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 friend and business partner suggestion system according to an embodiment of the present invention collects information about a user's hobbies, range of activities, personality, and preferences to suggest friends who share similar values, and further collects information about the user's skills, experience, and knowledge of various industries to suggest business partners who share similar work goals. The friend and business partner suggestion system collects information about a user's hobbies, range of activities, personality, and preferences to suggest friends who share similar values ​​and compatibility. It also collects information about a user's skills, experience, and knowledge of various industries to suggest business partners who share similar work goals and visions. For example, the friend and business partner suggestion system collects information about a user's hobbies, range of activities, personality, and preferences. For example, if a user likes sports, frequently engages in outdoor activities, and has a sociable personality, this information is collected. Next, the friend and business partner suggestion system uses the collected information to suggest friends who share similar values ​​and compatibility. For example, it suggests friends who like the same sports and enjoy outdoor activities. Furthermore, the friend and business partner suggestion system collects information about a user's skills, experience, and knowledge of various industries. For example, if a user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, this information is collected. Next, the friend and business partner suggestion system uses the collected information to suggest business partners who share common work goals and visions. For example, it suggests business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing. This allows the friend and business partner suggestion system to maintain users' psychological safety, facilitate career development, and improve life satisfaction and work motivation. This allows the friend and business partner suggestion system to maintain users' psychological safety, facilitate career development, and improve life satisfaction and work motivation. For example, by finding friends who share similar values ​​and chemistry, users can have friends they can truly trust and maintain their psychological safety.In addition, by finding business partners who share the same work goals and vision, users can easily build their careers and increase their motivation at work.

[0029] A friend and business partner suggestion system according to an embodiment includes a collection unit and a suggestion unit. The collection unit collects information about a user's hobbies, range of activities, personality, and preferences. The collection unit collects detailed information, such as the user's hobbies, activities, personality, and preferences. For example, if the user likes sports, often engages in outdoor activities, and has a sociable personality, the collection unit collects such information. The collection unit also collects information about the user's skills, experience, and knowledge of various industries. The collection unit collects detailed information, such as the user's skills, experience, and industry expertise. For example, if the user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, the collection unit collects such information. The suggestion unit suggests friends who share similar values ​​based on the information collected by the collection unit. For example, the suggestion unit suggests friends who like the same sports and enjoy outdoor activities. The suggestion unit also suggests business partners who share common work goals based on the information collected by the collection unit. The suggestion unit may suggest business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing, for example. This allows the friend and business partner suggestion system according to the embodiment to suggest friends and business partners who share similar values ​​and chemistry with the user based on the user's hobbies and skills.

[0030] The collection unit analyzes the user's past behavioral history and selects an appropriate information collection method. For example, the collection unit prioritizes the use of information collection means that the user has frequently used in the past. The collection unit can also analyze the user's behavioral patterns and select the most efficient information collection method. The collection unit can also select the optimal information collection method for a specific time period from the user's past behavioral history. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and have the generation AI select the optimal information collection method.

[0031] When collecting information, the collection unit filters the information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. The collection unit can also collect highly relevant information based on the user's current areas of interest. This makes it possible to collect highly relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform filtering.

[0032] When collecting information, the collection unit selects an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input voice data to a generation AI and have the generation AI perform voice recognition.

[0033] When collecting information, the collection unit prioritizes collecting highly relevant information taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also collect information about nearby interesting places based on the user's geographical location. The collection unit can also prioritize collecting area-specific information based on the user's location information. This makes it possible to provide highly relevant information by collecting information taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting information, the collection unit analyzes the user's social media activities and collects related information. For example, the collection unit collects information related to topics in which the user has shown interest on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0035] The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially uses information collection methods that the user has previously rated highly. The collection unit can also improve the collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0036] When collecting information, the collection unit selects the optimal collection means by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit selects an information collection means optimized for mobile devices. Furthermore, if the user is using a tablet, the collection unit can select an information collection means optimized for large screens. Furthermore, if the user is using a desktop, the collection unit can select a means for collecting detailed information. In this way, the optimal collection means can be selected by taking into account the user's device information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's device information into the generation AI and cause the generation AI to select the optimal collection means.

[0037] The collection unit customizes information based on the user's occupation and lifestyle when collecting information. For example, if the user is a businessman, the collection unit may prioritize collecting business-related information. Furthermore, if the user is a student, the collection unit may prioritize collecting information related to academics. Furthermore, the collection unit may collect highly relevant information based on the user's lifestyle. In this way, highly relevant information can be provided by customizing information based on the user's occupation and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's occupation and lifestyle data into the generation AI and cause the generation AI to customize the information.

[0038] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the information when making a suggestion. For example, the suggestion unit makes a detailed suggestion for information with a high level of importance. The suggestion unit can also make a concise suggestion for information with a low level of importance. The suggestion unit can also adjust the level of detail of the suggestion according to the user's level of interest. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0039] When making a suggestion, the suggestion unit applies different suggestion algorithms depending on the category of information. For example, for information related to hobbies, the suggestion unit makes suggestions based on the user's past behavioral history. For business-related information, the suggestion unit can also make suggestions based on the user's skills and experience. For information related to daily life, the suggestion unit can also make suggestions based on the user's lifestyle. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0040] When making a suggestion, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions by referring to suggestions that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also improve the suggestion algorithm based on user feedback. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0041] At the time of proposal, the proposal unit determines the priority of the proposal based on the time when the information was submitted. For example, the proposal unit prioritizes the most recent information. The proposal unit can also prioritize the proposal of information in which the user has shown interest in the past. The proposal unit can also lower the priority of the proposal for information that was submitted a long time ago. In this way, by determining the priority of the proposal based on the time when the information was submitted, the most recent information can be prioritized. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information submission time data to the generation AI and cause the generation AI to determine the priority of the proposal.

[0042] The suggestion unit adjusts the order of suggestions based on the relevance of information when making suggestions. For example, the suggestion unit prioritizes suggesting information related to the user's current interests. The suggestion unit can also prioritize suggesting highly relevant information based on the user's past behavioral history. The suggestion unit can also prioritize suggesting highly relevant information based on user feedback. This allows for optimal suggestions for the user by adjusting the order of suggestions based on the relevance of information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0043] When making a proposal, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the proposal using technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also make the proposal in simple language. Furthermore, the suggestion unit can also adjust the content of the proposal according to the user's level of expertise. This allows for a more understandable proposal by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0044] When making a suggestion, the suggestion unit provides optimal suggestions by taking into account the user's geographical location information. For example, the suggestion unit suggests nearby events or activities based on the user's current location. The suggestion unit can also suggest area-specific information based on the user's geographical location information. The suggestion unit can also provide optimal suggestions by taking into account the user's location information. This enables more relevant suggestions by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's location information data into the generation AI and cause the generation AI to provide optimal suggestions.

[0045] When making a suggestion, the suggestion unit analyzes the user's social media activity and makes a relevant suggestion. For example, the suggestion unit makes suggestions related to topics in which the user has shown interest on social media. The suggestion unit can also analyze the content of the user's social media posts and make relevant suggestions. The suggestion unit can also make relevant suggestions by referring to the activities of the user's friends on social media. This makes it possible to make highly relevant suggestions by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to execute relevant suggestions.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The collection unit can also analyze the user's musical preferences and collect information based on the user's musical preferences. For example, if the user likes rock music, information about rock concerts can be collected. If the user likes classical music, information about classical concerts can be collected. Furthermore, if the user likes jazz music, information about jazz festivals can be collected. This makes it possible to collect information based on the user's musical preferences.

[0048] The collection unit can also analyze the user's food preferences and collect information based on the food preferences. For example, if the user is a vegetarian, information on vegetarian restaurants can be collected. If the user likes spicy food, information on restaurants that serve spicy food can be collected. Furthermore, if the user likes sweet food, information on restaurants that specialize in desserts can be collected. This makes it possible to collect information based on the user's food preferences.

[0049] The collection unit can also analyze the user's travel history and collect information based on the user's travel preferences. For example, if the user likes beach resorts, information about beach resorts can be collected. If the user likes mountainous areas, information about mountainous areas can be collected. Furthermore, if the user likes urban tourism, information about urban tourism can be collected. This makes it possible to collect information based on the user's travel preferences.

[0050] The collection unit can also analyze the user's reading history and collect information based on the user's reading preferences. For example, if the user likes mystery novels, it can collect information on new mystery novel releases. If the user likes history books, it can also collect information on history books. Furthermore, if the user likes science books, it can also collect information on science books. This makes it possible to collect information based on the user's reading preferences.

[0051] The collection unit can also analyze the user's exercise history and collect information based on the user's exercise preferences. For example, if the user likes running, information about running events can be collected. If the user likes yoga, information about yoga classes can be collected. Furthermore, if the user likes training at the gym, information about gyms can be collected. This makes it possible to collect information based on the user's exercise preferences.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The collection unit collects information about the user's hobbies, range of activities, personality, and preferences. For example, if the user likes sports, often engages in outdoor activities, and has a sociable personality, this information is collected. The collection unit also collects information about the user's skills, experience, and knowledge of various industries. For example, if the user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, this information is collected. Step 2: The suggestion unit suggests friends who share similar values ​​based on the information collected by the collection unit. For example, it suggests friends who like the same sports and enjoy outdoor activities. The suggestion unit also suggests business partners who share common work goals based on the information collected by the collection unit. For example, it suggests business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing.

[0054] (Example 2) A friend and business partner suggestion system according to an embodiment of the present invention collects information about a user's hobbies, range of activities, personality, and preferences to suggest friends who share similar values, and further collects information about the user's skills, experience, and knowledge of various industries to suggest business partners who share similar work goals. The friend and business partner suggestion system collects information about a user's hobbies, range of activities, personality, and preferences to suggest friends who share similar values ​​and compatibility. It also collects information about a user's skills, experience, and knowledge of various industries to suggest business partners who share similar work goals and visions. For example, the friend and business partner suggestion system collects information about a user's hobbies, range of activities, personality, and preferences. For example, if a user likes sports, frequently engages in outdoor activities, and has a sociable personality, this information is collected. Next, the friend and business partner suggestion system uses the collected information to suggest friends who share similar values ​​and compatibility. For example, it suggests friends who like the same sports and enjoy outdoor activities. Furthermore, the friend and business partner suggestion system collects information about a user's skills, experience, and knowledge of various industries. For example, if a user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, this information is collected. Next, the friend and business partner suggestion system uses the collected information to suggest business partners who share common work goals and visions. For example, it suggests business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing. This allows the friend and business partner suggestion system to maintain users' psychological safety, facilitate career development, and improve life satisfaction and work motivation. This allows the friend and business partner suggestion system to maintain users' psychological safety, facilitate career development, and improve life satisfaction and work motivation. For example, by finding friends who share similar values ​​and chemistry, users can have friends they can truly trust and maintain their psychological safety.In addition, by finding business partners who share the same work goals and vision, users can easily build their careers and increase their motivation at work.

[0055] A friend and business partner suggestion system according to an embodiment includes a collection unit and a suggestion unit. The collection unit collects information about a user's hobbies, range of activities, personality, and preferences. The collection unit collects detailed information, such as the user's hobbies, activities, personality, and preferences. For example, if the user likes sports, often engages in outdoor activities, and has a sociable personality, the collection unit collects such information. The collection unit also collects information about the user's skills, experience, and knowledge of various industries. The collection unit collects detailed information, such as the user's skills, experience, and industry expertise. For example, if the user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, the collection unit collects such information. The suggestion unit suggests friends who share similar values ​​based on the information collected by the collection unit. For example, the suggestion unit suggests friends who like the same sports and enjoy outdoor activities. The suggestion unit also suggests business partners who share common work goals based on the information collected by the collection unit. The suggestion unit may suggest business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing, for example. This allows the friend and business partner suggestion system according to the embodiment to suggest friends and business partners who share similar values ​​and chemistry with the user based on the user's hobbies and skills.

[0056] The collection unit estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit may collect information during a relaxing time. Furthermore, if the user is excited, the collection unit may collect information after the user has calmed down. Furthermore, if the user is tired, the collection unit may collect information after the user has rested. This allows for more appropriate information collection by adjusting the timing of information collection 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 may 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 collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0057] The collection unit analyzes the user's past behavioral history and selects an appropriate information collection method. For example, the collection unit prioritizes the use of information collection means that the user has frequently used in the past. The collection unit can also analyze the user's behavioral patterns and select the most efficient information collection method. The collection unit can also select the optimal information collection method for a specific time period from the user's past behavioral history. This enables efficient information collection by selecting the optimal information collection method based on the user's past behavioral history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and have the generation AI select the optimal information collection method.

[0058] When collecting information, the collection unit filters the information based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting information related to areas in which the user is currently interested. The collection unit can also filter appropriate information according to the user's living situation. The collection unit can also collect highly relevant information based on the user's current areas of interest. This makes it possible to collect highly relevant information by filtering information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's area of ​​interest data to a generation AI and have the generation AI perform filtering.

[0059] When collecting information, the collection unit selects an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, if the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, if the user uses image input, the collection unit can also collect information using image recognition technology. This enables efficient information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input voice data to a generation AI and have the generation AI perform voice recognition.

[0060] The collection unit estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting information that will help the user relax. Furthermore, if the user is excited, the collection unit can also prioritize collecting information that will help the user calm down. Furthermore, if the user is tired, the collection unit can also prioritize collecting information related to rest. This enables more appropriate information collection by determining the priority of information based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] When collecting information, the collection unit prioritizes collecting highly relevant information taking into account the user's geographical location information. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also collect information about nearby interesting places based on the user's geographical location. The collection unit can also prioritize collecting area-specific information based on the user's location information. This makes it possible to provide highly relevant information by collecting information taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's location information data into the generation AI and cause the generation AI to collect highly relevant information.

[0062] When collecting information, the collection unit analyzes the user's social media activities and collects related information. For example, the collection unit collects information related to topics in which the user has shown interest on social media. The collection unit can also analyze the content of the user's social media posts and collect related information. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related information.

[0063] The collection unit customizes the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit preferentially uses information collection methods that the user has previously rated highly. The collection unit can also improve the collection method based on the user's past feedback. The collection unit can also adjust the type of information to be collected by reflecting the user's feedback. In this way, the collection method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0064] When collecting information, the collection unit selects the optimal collection means by taking into account the user's device information. For example, if the user is using a smartphone, the collection unit selects an information collection means optimized for mobile devices. Furthermore, if the user is using a tablet, the collection unit can select an information collection means optimized for large screens. Furthermore, if the user is using a desktop, the collection unit can select a means for collecting detailed information. In this way, the optimal collection means can be selected by taking into account the user's device information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's device information into the generation AI and cause the generation AI to select the optimal collection means.

[0065] The collection unit customizes information based on the user's occupation and lifestyle when collecting information. For example, if the user is a businessman, the collection unit may prioritize collecting business-related information. Furthermore, if the user is a student, the collection unit may prioritize collecting information related to academics. Furthermore, the collection unit may collect highly relevant information based on the user's lifestyle. In this way, highly relevant information can be provided by customizing information based on the user's occupation and lifestyle. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's occupation and lifestyle data into the generation AI and cause the generation AI to customize the information.

[0066] The suggestion unit estimates the user's emotions and adjusts the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit may make suggestions using soft expressions. If the user is nervous, the suggestion unit may make suggestions using simple and clear expressions. If the user is excited, the suggestion unit may make suggestions using energetic expressions. This allows for more appropriate suggestions by adjusting the way the suggestions are expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0067] The suggestion unit adjusts the level of detail of the suggestion based on the importance of the information when making a suggestion. For example, the suggestion unit makes a detailed suggestion for information with a high level of importance. The suggestion unit can also make a concise suggestion for information with a low level of importance. The suggestion unit can also adjust the level of detail of the suggestion according to the user's level of interest. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0068] When making a suggestion, the suggestion unit applies different suggestion algorithms depending on the category of information. For example, for information related to hobbies, the suggestion unit makes suggestions based on the user's past behavioral history. For business-related information, the suggestion unit can also make suggestions based on the user's skills and experience. For information related to daily life, the suggestion unit can also make suggestions based on the user's lifestyle. This enables more appropriate suggestions to be made by applying different suggestion algorithms depending on the category of information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information category data into the generation AI and cause the generation AI to apply the suggestion algorithm.

[0069] When making a suggestion, the suggestion unit improves the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions by referring to suggestions that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results to improve the accuracy of the suggestion. The suggestion unit can also improve the suggestion algorithm based on user feedback. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0070] The suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user emotion. For example, if the user is in a hurry, the suggestion unit may provide short, concise suggestions. If the user is relaxed, the suggestion unit may also provide detailed suggestions. If the user is excited, the suggestion unit may also provide visually stimulating suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] At the time of proposal, the proposal unit determines the priority of the proposal based on the time when the information was submitted. For example, the proposal unit prioritizes the most recent information. The proposal unit can also prioritize the proposal of information in which the user has shown interest in the past. The proposal unit can also lower the priority of the proposal for information that was submitted a long time ago. In this way, by determining the priority of the proposal based on the time when the information was submitted, the most recent information can be prioritized. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input information submission time data to the generation AI and cause the generation AI to determine the priority of the proposal.

[0072] The suggestion unit adjusts the order of suggestions based on the relevance of information when making suggestions. For example, the suggestion unit prioritizes suggesting information related to the user's current interests. The suggestion unit can also prioritize suggesting highly relevant information based on the user's past behavioral history. The suggestion unit can also prioritize suggesting highly relevant information based on user feedback. This allows for optimal suggestions for the user by adjusting the order of suggestions based on the relevance of information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input information relevance data to a generation AI and cause the generation AI to adjust the order of suggestions.

[0073] When making a proposal, the suggestion unit adjusts the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit makes the proposal using technical terminology. Furthermore, if the user does not have technical expertise, the suggestion unit can also make the proposal in simple language. Furthermore, the suggestion unit can also adjust the content of the proposal according to the user's level of expertise. This allows for a more understandable proposal by adjusting the use of technical terminology in the proposal according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit may input the user's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[0074] When making a suggestion, the suggestion unit provides optimal suggestions by taking into account the user's geographical location information. For example, the suggestion unit suggests nearby events or activities based on the user's current location. The suggestion unit can also suggest area-specific information based on the user's geographical location information. The suggestion unit can also provide optimal suggestions by taking into account the user's location information. This enables more relevant suggestions by taking into account the user's geographical location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's location information data into the generation AI and cause the generation AI to provide optimal suggestions.

[0075] When making a suggestion, the suggestion unit analyzes the user's social media activity and makes a relevant suggestion. For example, the suggestion unit makes suggestions related to topics in which the user has shown interest on social media. The suggestion unit can also analyze the content of the user's social media posts and make relevant suggestions. The suggestion unit can also make relevant suggestions by referring to the activities of the user's friends on social media. This makes it possible to make highly relevant suggestions by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to execute relevant suggestions. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information such as the user's hobbies, range of activities, personality, and preferences using the camera 42 and microphone 38B of the smart device 14, and processes this information by the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests friends and business partners who share similar values ​​based on the collected information. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion 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 collection unit and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information such as the user's hobbies, range of activities, personality, and preferences using the camera 42 and microphone 238 of the smart glasses 214, and processes this information using the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests friends and business partners who share similar values ​​based on the collected information. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion 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 collection unit and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects information such as the user's hobbies, range of activities, personality, and preferences using the camera 42 and microphone 238 of the headset-type terminal 314, and processes this information by the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests friends and business partners who share similar values ​​based on the collected information. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion 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 collection unit and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information such as the user's hobbies, range of activities, personality, and preferences using the camera 42 and microphone 238 of the robot 414, and processes this information by the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests friends and business partners who share similar values ​​based on the collected information. The collection unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and the suggestion 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 suggestion unit can also monitor the user's health condition and adjust the suggestions based on the health condition. For example, if the user is tired, it can suggest relaxing activities. If the user is healthy, it can also suggest active activities. Furthermore, if the user is ill, it can provide information useful for recovery. This makes it possible to make appropriate suggestions based on the user's health condition.

[0078] The collection unit can also analyze the user's musical preferences and collect information based on the user's musical preferences. For example, if the user likes rock music, information about rock concerts can be collected. If the user likes classical music, information about classical concerts can be collected. Furthermore, if the user likes jazz music, information about jazz festivals can be collected. This makes it possible to collect information based on the user's musical preferences.

[0079] The suggestion unit can also estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion can be made during a time when the user can relax. If the user is excited, the suggestion can be made after the user has calmed down. Furthermore, if the user is tired, the suggestion can be made after the user has taken a rest. This makes it possible to make appropriate suggestions at appropriate times according to the user's emotions.

[0080] The collection unit can also analyze the user's food preferences and collect information based on the food preferences. For example, if the user is a vegetarian, information on vegetarian restaurants can be collected. If the user likes spicy food, information on restaurants that serve spicy food can be collected. Furthermore, if the user likes sweet food, information on restaurants that specialize in desserts can be collected. This makes it possible to collect information based on the user's food preferences.

[0081] The suggestion unit can also estimate the user's emotions and customize the content of the suggestion based on the estimated user's emotions. For example, if the user is sad, the suggestion unit can make a suggestion together with an encouraging message. If the user is happy, the suggestion unit can make a suggestion together with a congratulatory message. Furthermore, if the user is angry, the suggestion unit can make a suggestion together with advice to stay calm. This makes it possible to make appropriate suggestion content according to the user's emotions.

[0082] The collection unit can also analyze the user's travel history and collect information based on the user's travel preferences. For example, if the user likes beach resorts, information about beach resorts can be collected. If the user likes mountainous areas, information about mountainous areas can be collected. Furthermore, if the user likes urban tourism, information about urban tourism can be collected. This makes it possible to collect information based on the user's travel preferences.

[0083] The suggestion unit can also estimate the user's emotions and adjust the frequency of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions. Also, if the user is relaxed, the suggestion unit can increase the frequency of suggestions. Furthermore, if the user is excited, the suggestion unit can adjust the frequency of suggestions. This makes it possible to set an appropriate suggestion frequency according to the user's emotions.

[0084] The collection unit can also analyze the user's reading history and collect information based on the user's reading preferences. For example, if the user likes mystery novels, it can collect information on new mystery novel releases. If the user likes history books, it can also collect information on history books. Furthermore, if the user likes science books, it can also collect information on science books. This makes it possible to collect information based on the user's reading preferences.

[0085] The suggestion unit can also estimate the user's emotions and adjust the format of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can make suggestions that emphasize visuals. If the user is nervous, the suggestion unit can make suggestions that emphasize text. Furthermore, if the user is excited, the suggestion unit can make interactive suggestions. This makes it possible to make appropriate suggestions according to the user's emotions.

[0086] The collection unit can also analyze the user's exercise history and collect information based on the user's exercise preferences. For example, if the user likes running, information about running events can be collected. If the user likes yoga, information about yoga classes can be collected. Furthermore, if the user likes training at the gym, information about gyms can be collected. This makes it possible to collect information based on the user's exercise preferences.

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

[0088] Step 1: The collection unit collects information about the user's hobbies, range of activities, personality, and preferences. For example, if the user likes sports, often engages in outdoor activities, and has a sociable personality, this information is collected. The collection unit also collects information about the user's skills, experience, and knowledge of various industries. For example, if the user has marketing skills, experience in the IT industry, and is knowledgeable about digital marketing, this information is collected. Step 2: The suggestion unit suggests friends who share similar values ​​based on the information collected by the collection unit. For example, it suggests friends who like the same sports and enjoy outdoor activities. The suggestion unit also suggests business partners who share common work goals based on the information collected by the collection unit. For example, it suggests business partners who have the same marketing skills, experience in the IT industry, and an interest in digital marketing.

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

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

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

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

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

[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. a collection unit that collects information on the user's hobbies, range of activities, personality, and preferences; a suggestion unit that suggests friends who share similar values ​​based on the information collected by the collection unit; A collection department that collects information on users' skills, experience, and industry knowledge; a proposal unit that proposes business partners who share common business goals based on the information collected by the collection unit; Equipped with A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze users' past behavioral history and select the appropriate information collection method 2. The system of claim 1.

4. The collecting unit As information is collected, it is filtered based on the user's current life situation and interests.

2. The system of claim 1.

5. The collecting unit When collecting information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit When collecting information, prioritize collection of highly relevant information based on the user's geographic location information.

2. The system of claim 1.

8. The collecting unit When collecting information, we analyze your social media activity and collect relevant information.

2. The system of claim 1.

Images