System

Abstract

An object of a system according to an embodiment is to estimate an emotion of a partner and provide appropriate advice on the basis of the estimated emotion.SOLUTION: A system according to an embodiment includes an emotion estimation unit, an information acquisition unit, and an advice providing unit. The emotion estimation unit estimates an emotion of the other party using the camera and the microphone. The information acquisition unit acquires information around the current location on the basis of the emotion estimated by the emotion estimation unit. The advice providing unit provides advice on the basis of the information acquired by the information acquisition unit and the past conversation log.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 technology has had the problem of making it difficult to accurately grasp the emotions of others during dates or sales calls and provide appropriate advice based on that.

[0005] The system according to the embodiment aims to estimate the emotions of the other party and provide appropriate advice based on the estimation. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion estimation unit, an information acquisition unit, and an advice providing unit. The emotion estimation unit estimates the emotion of the other party using a camera and a microphone. The information acquisition unit acquires information about the area around the current location based on the emotion estimated by the emotion estimation unit. The advice providing unit provides advice based on the information acquired by the information acquisition unit and past conversation logs. [Effects of the Invention]

[0007] The system according to the embodiment can estimate the emotions of the other person and provide appropriate advice based on the estimation. [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) The dating and sales assistance system according to an embodiment of the present invention is a system that assists in dating and sales using emotion estimation AI based on image and voice recognition and a large-scale language model, which enables the dating and sales assistance system to respond appropriately according to the other party's emotions and situation.

[0029] A dating and sales assistance system according to an embodiment includes an emotion estimation unit, an information acquisition unit, and an advice provision unit. The emotion estimation unit estimates the emotion of the other party using a camera and a microphone. For example, the emotion estimation unit analyzes the other party's facial expression and detects changes in expression, such as smiling or furrowing of the brow. The emotion estimation unit can also detect changes in emotion by analyzing changes in the tone and pitch of the other party's voice. The emotion estimation unit can also detect changes in emotion by analyzing the other party's eye movements and blinking frequency. The information acquisition unit acquires information about the surrounding area of ​​the current location based on the emotion estimated by the emotion estimation unit. For example, the information acquisition unit estimates the current location using a Visual Positioning System (VPS) and acquires information about surrounding objects and buildings. The information acquisition unit can also identify the current location using GPS data and collect surrounding information from the Internet. The information acquisition unit can also analyze past conversation logs to understand the other party's interests. The advice provision unit provides advice based on the information acquired by the information acquisition unit and the past conversation logs. For example, if the other party is talking about a movie they like, the advice providing unit may suggest that they talk about a movie they recently saw. Furthermore, if there is a cafe nearby, the advice providing unit may suggest that they take a break at the cafe. Furthermore, if the other party is feeling anxious, the advice providing unit may suggest that they talk about calming topics to help them relax. This allows the dating and sales assistance system according to the embodiment to respond appropriately according to the other party's emotions and situation.

[0030] The information acquisition unit also acquires environmental data such as the ambient temperature and humidity, and can suggest appropriate actions to the user. The information acquisition unit, for example, acquires the ambient temperature and humidity using a sensor, and suggests appropriate actions to the user. For example, if the temperature is high, it provides advice to take a break in a cool place. The information acquisition unit also analyzes the ambient environmental data and suggests appropriate actions to the user. For example, if the humidity is high, it provides advice to stay hydrated. The information acquisition unit also acquires ambient weather data and suggests appropriate actions to the user. For example, if it looks like it's going to rain, it provides advice to take an umbrella. This makes it possible to suggest appropriate actions that take environmental data into consideration.

[0031] The information acquisition unit can analyze the movements and behavior patterns of people in the vicinity and predict congestion levels. For example, the information acquisition unit analyzes the movements and behavior patterns of people in the vicinity using a camera to predict congestion levels. For example, it provides advice to avoid crowded places. The information acquisition unit also analyzes the behavior patterns of people in the vicinity and displays the congestion level in real time. For example, it displays crowded places in red and provides advice to avoid them. The information acquisition unit also analyzes the movements and behavior patterns of people in the vicinity and develops an algorithm to predict congestion levels. For example, it predicts crowded times based on past data and provides advice to avoid them. This makes it possible to predict congestion levels and suggest appropriate actions to take.

[0032] The information acquisition unit can analyze surrounding audio data and detect specific events or activities. For example, the information acquisition unit acquires surrounding audio data using a microphone and analyzes specific events or activities. For example, it detects where music is playing and notifies the user that a live event is being held. The information acquisition unit also analyzes surrounding audio data and develops an algorithm to detect specific activities. For example, it detects people cheering and notifies the user that a sporting event is being held. The information acquisition unit also analyzes surrounding audio data and displays specific events or activities in real time. For example, it displays the location of a music festival on a map. This makes it possible to detect specific events or activities and provide appropriate information.

[0033] When analyzing past conversation logs, the advice providing unit can provide more appropriate advice by taking into account the context and tone of the conversation. For example, when analyzing past conversation logs, the advice providing unit understands the context of the conversation and provides appropriate advice. For example, it suggests topics based on hobbies and interests that the other person has previously talked about. Furthermore, when analyzing past conversation logs, the advice providing unit considers the tone of the conversation and provides appropriate advice. For example, if the other person has previously talked about an enjoyable topic, it suggests a similarly enjoyable topic. Furthermore, when analyzing past conversation logs, the advice providing unit analyzes a combination of the context and tone of the conversation and provides more appropriate advice. For example, it suggests topics by taking into account what the other person has previously said and the current situation. In this way, more appropriate advice can be provided by taking into account the context and tone of the conversation.

[0034] When analyzing past conversation logs, the advice providing unit can learn the interests and concerns of the other party and accumulate information that will be useful in future conversations. The advice providing unit, for example, analyzes past conversation logs to learn the interests and concerns of the other party. For example, it accumulates information about the other party's favorite movies and music and uses this information for future conversations. The advice providing unit also analyzes past conversation logs to save the other party's interests in a database. For example, it accumulates information about travel destinations and hobbies that the other party has talked about and uses this information in the next conversation. The advice providing unit also analyzes past conversation logs to develop an algorithm that learns the other party's interests and concerns. For example, it identifies topics that the other party frequently talks about and accumulates information that will be useful in future conversations. In this way, the advice providing unit can learn the other party's interests and accumulate information that will be useful in future conversations.

[0035] The advice providing unit applies the past conversation log analysis system to customer support and can provide optimal support based on the customer's past inquiry history. The advice providing unit, for example, applies the past conversation log analysis system to customer support and provides optimal support based on the customer's past inquiry history. For example, it provides an appropriate answer based on the content of the past inquiry. Furthermore, in customer support, the advice providing unit analyzes past conversation logs and provides information that is useful for solving the customer's problem. For example, it proposes a solution based on the past inquiry history. Furthermore, in customer support, the advice providing unit analyzes past conversation logs and provides support that meets the customer's needs. For example, it proposes a service that meets the customer's request based on the content of the past inquiry. This makes it possible to provide optimal support based on the customer's past inquiry history.

[0036] The advice providing unit applies the past conversation log analysis system to the field of education and can provide optimal study advice based on a student's past learning history. The advice providing unit, for example, applies the past conversation log analysis system to the field of education and provides optimal study advice based on a student's past learning history. For example, it suggests an appropriate learning method based on past learning content. Furthermore, in the field of education, the advice providing unit analyzes past conversation logs and provides optimal study advice based on a student's learning history. For example, it suggests a study plan based on past grades and learning content. Furthermore, in the field of education, the advice providing unit analyzes past conversation logs and develops an algorithm that provides optimal study advice based on a student's learning history. For example, it evaluates learning progress based on past learning content and provides appropriate advice. This makes it possible to provide optimal study advice based on a student's past learning history.

[0037] The advice providing unit applies the past conversation log analysis system to the medical field and can provide optimal treatment advice based on a patient's past medical history. The advice providing unit, for example, applies the past conversation log analysis system to the medical field and provides optimal treatment advice based on a patient's past medical history. For example, it proposes an appropriate treatment method based on past medical details. Furthermore, in the medical field, the advice providing unit analyzes past conversation logs and provides optimal treatment advice based on a patient's medical history. For example, it proposes a treatment plan based on past medical details. Furthermore, in the medical field, the advice providing unit analyzes past conversation logs and develops an algorithm that provides optimal treatment advice based on a patient's medical history. For example, it evaluates the progress of treatment based on past medical details and provides appropriate advice. This makes it possible to provide optimal treatment advice based on a patient's past medical history.

[0038] The advice providing unit can track the user's gaze and display advice in front of the user's gaze when displaying advice using AR. For example, the advice providing unit uses AR glasses to track the user's gaze and display advice in front of the user's gaze. For example, it displays information related to the location where the user is looking. The advice providing unit also develops an algorithm that tracks the user's gaze and displays advice in front of the user's gaze. For example, it displays advice related to an object the user is looking at. The advice providing unit also builds a system that uses AR glasses to track the user's gaze and display advice in front of the user's gaze. For example, it displays information related to the location where the user is looking in real time. This makes it possible to provide information more intuitively by tracking the user's gaze and displaying advice in front of the user's gaze.

[0039] The advice providing unit can recognize a user's gestures and display advice corresponding to the gestures when displaying advice using AR. For example, the advice providing unit uses AR glasses to recognize a user's gestures and display advice corresponding to the gestures. For example, when a user raises their hand, relevant information is displayed. The advice providing unit also develops an algorithm that recognizes a user's gestures and displays advice corresponding to the gestures. For example, information related to the location where the user points is displayed. The advice providing unit also builds a system that uses AR glasses to recognize a user's gestures and display advice corresponding to the gestures. For example, when a user waves their hand, relevant information is displayed in real time. This enables more intuitive information to be provided by recognizing a user's gestures and displaying advice corresponding to the gestures.

[0040] The advice providing unit applies the AR advice display system to a driving assistance system and can provide driving advice to a driver in real time while driving. The advice providing unit, for example, applies the AR advice display system to a driving assistance system and provides driving advice to a driver in real time while driving. For example, it provides advice to avoid obstacles ahead. The advice providing unit also provides appropriate driving advice to the driver using AR advice display in the driving assistance system. For example, it provides advice on the timing of lane changes. The advice providing unit also develops an algorithm for providing appropriate driving advice to the driver using AR advice display in the driving assistance system. For example, it analyzes traffic conditions ahead and provides appropriate driving advice. In this way, safe driving is supported by providing driving advice to the driver in real time while driving.

[0041] The advice providing unit applies the AR advice display system to sports training and can provide training advice to athletes in real time during training. The advice providing unit, for example, applies the AR advice display system to sports training and provides training advice to athletes in real time during training. For example, advice is provided to correct form or adjust training intensity. The advice providing unit also provides appropriate training advice to athletes using AR advice display during sports training. For example, training progress is displayed in real time. The advice providing unit also develops an algorithm that provides appropriate training advice to athletes using AR advice display during sports training. For example, the algorithm analyzes training data and provides appropriate advice. This provides training advice in real time to athletes in training, supporting effective training.

[0042] The advice providing unit applies the AR advice display system to a cooking assistance system and can provide cooking advice to a user who is cooking in real time. The advice providing unit, for example, applies the AR advice display system to a cooking assistance system and provides cooking advice to a user who is cooking in real time. For example, it displays cooking procedures and amounts of seasonings. The advice providing unit also uses AR advice display in the cooking assistance system to provide appropriate cooking advice to the user. For example, it displays how to cut ingredients and cooking time. The advice providing unit also develops an algorithm for the cooking assistance system that uses AR advice display to provide appropriate cooking advice to the user. For example, it analyzes recipe data and provides appropriate advice. In this way, cooking advice can be provided in real time to the user who is cooking, thereby providing effective cooking assistance.

[0043] The advice providing unit can analyze the movement of the user's pupils using the AR contact lenses and display information according to changes in the pupils. For example, the advice providing unit analyzes the movement of the user's pupils using the AR contact lenses and displays information according to changes in the pupils. For example, if the pupils dilate, it displays information that the user is interested in. The advice providing unit also develops an algorithm that analyzes the movement of the user's pupils and displays information according to changes in the pupils. For example, if the pupils constrict, it displays information to help the user relax. The advice providing unit also builds a system that analyzes the movement of the user's pupils using the AR contact lenses and displays information according to changes in the pupils. For example, it displays appropriate information in real time according to the movement of the pupils. This makes it possible to provide more intuitive information by analyzing the movement of the user's pupils and displaying information according to changes in the pupils.

[0044] The advice providing unit can analyze the user's blinking pattern using the AR contact lenses and display information corresponding to the blinks. For example, the advice providing unit analyzes the user's blinking pattern using the AR contact lenses and displays information corresponding to the blinks. For example, if the user blinks frequently, information to encourage relaxation is displayed. The advice providing unit also analyzes the user's blinking pattern and develops an algorithm to display information corresponding to the blinks. For example, if the blink intervals are short, information to encourage concentration is displayed. The advice providing unit also builds a system in which the AR contact lenses analyze the user's blinking pattern and display information corresponding to the blinks. For example, appropriate information is displayed in real time according to the blinking pattern. This allows for more intuitive information to be provided by analyzing the user's blinking pattern and displaying information corresponding to the blinks.

[0045] The advice providing unit can automatically recognize an object that comes into the user's field of view using the AR contact lenses and display information related to the object. For example, the advice providing unit automatically recognizes an object that comes into the user's field of view using the AR contact lenses and displays information related to the object. For example, the advice providing unit displays the history and features of a building that comes into view. The advice providing unit also develops an algorithm that automatically recognizes an object that comes into the user's field of view and displays information related to the object. For example, it displays information about a product that comes into view. The advice providing unit also builds a system that automatically recognizes an object that comes into the user's field of view using the AR contact lenses and displays information related to the object. For example, it displays a profile of a person that comes into view. This enables more intuitive information provision by automatically recognizing an object that comes into the user's field of view and displaying information related to the object.

[0046] The advice providing unit can apply the AR contact lenses to the medical field and provide surgical guides to surgeons in real time during surgery. The advice providing unit, for example, applies the AR contact lenses to the medical field and provides surgical guides to surgeons in real time during surgery. For example, it displays surgical procedures and important points. The advice providing unit also uses the AR contact lenses in the medical field to provide appropriate surgical guides to surgeons in the medical field. For example, it displays points to note during surgery and next steps. The advice providing unit also develops an algorithm in the medical field that uses the AR contact lenses to provide appropriate surgical guides to surgeons in the medical field. For example, it analyzes surgical data and provides appropriate guides. In this way, surgical guides are provided to surgeons in real time during surgery, thereby improving the accuracy and safety of the surgery.

[0047] The advice providing unit can apply the AR contact lenses in the field of education to provide real-time teaching support information to teachers during classes. The advice providing unit, for example, applies the AR contact lenses in the field of education to provide real-time teaching support information to teachers during classes. For example, it displays the progress of the class and students' reactions. The advice providing unit also uses the AR contact lenses in the field of education to provide appropriate teaching support information to teachers during classes. For example, it displays the content of the class and the next steps. The advice providing unit also develops an algorithm in the field of education that uses the AR contact lenses to provide appropriate teaching support information to teachers during classes. For example, it analyzes class data and provides appropriate support information. This provides real-time teaching support information to teachers during classes, thereby improving the quality of lessons.

[0048] The advice providing unit can apply the AR contact lenses to the entertainment field to provide real-time event information to audiences during a live event. For example, the advice providing unit applies the AR contact lenses to the entertainment field to provide real-time event information to audiences during a live event. For example, it displays performer profiles and set lists. The advice providing unit also uses the AR contact lenses to provide appropriate event information to audiences during a live event in the entertainment field. For example, it displays information about the next performance and the venue. The advice providing unit also develops an algorithm to provide appropriate event information to audiences during a live event using the AR contact lenses in the entertainment field. For example, it analyzes event data and provides appropriate information. This provides real-time event information to audiences during a live event, thereby increasing their enjoyment of the event.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The dating and sales assistance system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may monitor the user's health condition by acquiring the user's heart rate and blood pressure using a sensor. The health monitoring unit may also monitor the user's health condition by acquiring the user's body temperature and oxygen saturation using a sensor. Furthermore, the health monitoring unit may analyze the user's sleep patterns and monitor the user's health condition. This allows the user's health condition to be monitored and appropriate measures to be taken.

[0051] The dating and sales assistance system may further include a nutritional evaluation unit that records the user's dietary content and evaluates the nutritional balance. For example, the nutritional evaluation unit may record the content of the user's meals and evaluate the nutritional balance. The nutritional evaluation unit may also analyze the calories and nutrients in the user's meals and evaluate the nutritional balance. Furthermore, the nutritional evaluation unit may analyze the frequency and amount of the user's meals and evaluate the nutritional balance. This makes it possible to evaluate the user's nutritional balance and provide appropriate advice.

[0052] The dating and sales assistance system may further include an exercise evaluation unit that records the user's amount of exercise and evaluates their exercise habits. For example, the exercise evaluation unit may record the user's number of steps and distance traveled and evaluate the amount of exercise. The exercise evaluation unit may also analyze the type and intensity of the user's exercise and evaluate their exercise habits. Furthermore, the exercise evaluation unit may analyze the frequency and duration of the user's exercise and evaluate their exercise habits. This makes it possible to evaluate the user's exercise habits and provide appropriate advice.

[0053] The dating and sales assistance system may further include a sleep evaluation unit that records the user's sleep state and evaluates the quality of the sleep. For example, the sleep evaluation unit may record the user's sleep time and sleep cycle and evaluate the quality of the sleep. The sleep evaluation unit may also analyze the user's heart rate and breathing pattern to evaluate the quality of the sleep. Furthermore, the sleep evaluation unit may analyze the user's body movements and frequency of turning over in sleep to evaluate the quality of the sleep. This makes it possible to evaluate the user's sleep quality and provide appropriate advice.

[0054] The dating and sales assistance system may further include a hobby learning unit that learns the user's hobbies and interests and provides personalized advice. For example, the hobby learning unit may analyze the user's past actions and conversation logs to learn the user's hobbies and interests. The hobby learning unit may also analyze the user's internet search history and social media posts to learn the user's hobbies and interests. The hobby learning unit may also analyze the user's purchase history and events attended to learn the user's hobbies and interests. This makes it possible to learn the user's hobbies and interests and provide personalized advice.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The emotion estimation unit uses a camera and microphone to estimate the other person's emotions. For example, the emotion estimation unit analyzes the other person's facial expressions and detects changes in expression such as smiling or furrowing of the brow. It can also analyze changes in the tone and pitch of the other person's voice to detect changes in emotion. It can also analyze the other person's eye movements and blinking frequency to detect changes in emotion. Step 2: The information acquisition unit acquires information about the area around the current location based on the emotion estimated by the emotion estimation unit. For example, the information acquisition unit estimates the current location using a Visual Positioning System (VPS) and acquires information about surrounding objects and buildings. It can also identify the current location using GPS data and collect information about the surrounding area from the Internet. It can also analyze past conversation logs to understand the other person's interests. Step 3: The advice providing unit provides advice based on the information acquired by the information acquiring unit and past conversation logs. For example, if the other person is talking about a movie they like, the advice providing unit can suggest that they talk about a movie they recently saw. If there is a cafe nearby, the advice providing unit can also suggest that they take a break there. Furthermore, if the other person is feeling anxious, the advice providing unit can suggest that they talk about calming topics to help them relax.

[0057] (Example 2) The dating and sales assistance system according to an embodiment of the present invention is a system that assists in dating and sales using emotion estimation AI based on image and voice recognition and a large-scale language model, which enables the dating and sales assistance system to respond appropriately according to the other party's emotions and situation.

[0058] A dating and sales assistance system according to an embodiment includes an emotion estimation unit, an information acquisition unit, and an advice provision unit. The emotion estimation unit estimates the emotion of the other party using a camera and a microphone. For example, the emotion estimation unit analyzes the other party's facial expression and detects changes in expression, such as smiling or furrowing of the brow. The emotion estimation unit can also detect changes in emotion by analyzing changes in the tone and pitch of the other party's voice. The emotion estimation unit can also detect changes in emotion by analyzing the other party's eye movements and blinking frequency. The information acquisition unit acquires information about the surrounding area of ​​the current location based on the emotion estimated by the emotion estimation unit. For example, the information acquisition unit estimates the current location using a Visual Positioning System (VPS) and acquires information about surrounding objects and buildings. The information acquisition unit can also identify the current location using GPS data and collect surrounding information from the Internet. The information acquisition unit can also analyze past conversation logs to understand the other party's interests. The advice provision unit provides advice based on the information acquired by the information acquisition unit and the past conversation logs. For example, if the other party is talking about a movie they like, the advice providing unit may suggest that they talk about a movie they recently saw. Furthermore, if there is a cafe nearby, the advice providing unit may suggest that they take a break at the cafe. Furthermore, if the other party is feeling anxious, the advice providing unit may suggest that they talk about calming topics to help them relax. This allows the dating and sales assistance system according to the embodiment to respond appropriately according to the other party's emotions and situation.

[0059] The emotion estimation unit analyzes subtle changes in the other person's facial expression and tone of voice in real time, and can instantly provide feedback on changes in emotion. For example, the emotion estimation unit analyzes subtle movements of the other person's facial muscles to detect changes in expression, such as smiling or furrowing of the brow, in real time. This allows the other person's emotional changes to be instantly grasped and fed back to the user. The emotion estimation unit also analyzes changes in the tone and pitch of the other person's voice to detect changes in emotion in real time. For example, a higher voice indicates excitement, and a lower voice indicates calm. The emotion estimation unit also analyzes the other person's eye movements and blinking frequency to detect changes in emotion in real time. For example, frequent blinking indicates nervousness, and averting one's eyes indicates anxiety. This allows the other person's emotional changes to be instantly grasped and appropriate responses to be made.

[0060] The emotion estimation unit can learn the other party's past emotional history and compare it with their current emotion to detect abnormal changes. For example, the emotion estimation unit stores the other party's past emotional history in a database and compares it with their current emotion to detect abnormal changes. For example, it issues an alert if a normally calm person suddenly shows anger. The emotion estimation unit also learns the other party's past emotional patterns and compares it with their current emotion to detect abnormal changes. For example, it issues an alert if a person who was happy in the same situation in the past is feeling anxious this time. The emotion estimation unit also analyzes the other party's past emotional data and compares it with their current emotion to detect abnormal changes. For example, it alerts the user if a person who previously laughed about the same topic now has a blank expression. This makes it possible to detect abnormal changes in the other party's emotions and respond appropriately.

[0061] The emotion estimation unit takes into account surrounding environmental sounds and background noise when estimating the other party's emotions, enabling more accurate emotion analysis. The emotion estimation unit, for example, analyzes not only the other party's tone of voice and facial expression, but also the surrounding environmental sounds and background noise to estimate emotions. For example, because voices are difficult to hear in noisy environments, emphasis is placed on facial expression analysis. The emotion estimation unit also analyzes surrounding environmental sounds to identify factors that affect the other party's emotions. For example, the effect of music playing in the background on emotions is taken into account and reflected in the analysis results. The emotion estimation unit also filters background noise to improve the accuracy of analyzing the other party's voice and facial expressions. For example, it removes ambient noise and accurately analyzes the other party's tone and pitch. This enables more accurate emotion analysis by taking into account surrounding environmental sounds and background noise.

[0062] The information acquisition unit also acquires environmental data such as the ambient temperature and humidity, and can suggest appropriate actions to the user. The information acquisition unit, for example, acquires the ambient temperature and humidity using a sensor, and suggests appropriate actions to the user. For example, if the temperature is high, it provides advice to take a break in a cool place. The information acquisition unit also analyzes the ambient environmental data and suggests appropriate actions to the user. For example, if the humidity is high, it provides advice to stay hydrated. The information acquisition unit also acquires ambient weather data and suggests appropriate actions to the user. For example, if it looks like it's going to rain, it provides advice to take an umbrella. This makes it possible to suggest appropriate actions that take environmental data into consideration.

[0063] The information acquisition unit can analyze the movements and behavior patterns of people in the vicinity and predict congestion levels. For example, the information acquisition unit analyzes the movements and behavior patterns of people in the vicinity using a camera to predict congestion levels. For example, it provides advice to avoid crowded places. The information acquisition unit also analyzes the behavior patterns of people in the vicinity and displays the congestion level in real time. For example, it displays crowded places in red and provides advice to avoid them. The information acquisition unit also analyzes the movements and behavior patterns of people in the vicinity and develops an algorithm to predict congestion levels. For example, it predicts crowded times based on past data and provides advice to avoid them. This makes it possible to predict congestion levels and suggest appropriate actions to take.

[0064] The information acquisition unit can analyze surrounding audio data and detect specific events or activities. For example, the information acquisition unit acquires surrounding audio data using a microphone and analyzes specific events or activities. For example, it detects where music is playing and notifies the user that a live event is being held. The information acquisition unit also analyzes surrounding audio data and develops an algorithm to detect specific activities. For example, it detects people cheering and notifies the user that a sporting event is being held. The information acquisition unit also analyzes surrounding audio data and displays specific events or activities in real time. For example, it displays the location of a music festival on a map. This makes it possible to detect specific events or activities and provide appropriate information.

[0065] When analyzing past conversation logs, the advice providing unit can provide more appropriate advice by taking into account the context and tone of the conversation. For example, when analyzing past conversation logs, the advice providing unit understands the context of the conversation and provides appropriate advice. For example, it suggests topics based on hobbies and interests that the other person has previously talked about. Furthermore, when analyzing past conversation logs, the advice providing unit considers the tone of the conversation and provides appropriate advice. For example, if the other person has previously talked about an enjoyable topic, it suggests a similarly enjoyable topic. Furthermore, when analyzing past conversation logs, the advice providing unit analyzes a combination of the context and tone of the conversation and provides more appropriate advice. For example, it suggests topics by taking into account what the other person has previously said and the current situation. In this way, more appropriate advice can be provided by taking into account the context and tone of the conversation.

[0066] When analyzing past conversation logs, the advice providing unit can learn the interests and concerns of the other party and accumulate information that will be useful in future conversations. The advice providing unit, for example, analyzes past conversation logs to learn the interests and concerns of the other party. For example, it accumulates information about the other party's favorite movies and music and uses this information for future conversations. The advice providing unit also analyzes past conversation logs to save the other party's interests in a database. For example, it accumulates information about travel destinations and hobbies that the other party has talked about and uses this information in the next conversation. The advice providing unit also analyzes past conversation logs to develop an algorithm that learns the other party's interests and concerns. For example, it identifies topics that the other party frequently talks about and accumulates information that will be useful in future conversations. In this way, the advice providing unit can learn the other party's interests and accumulate information that will be useful in future conversations.

[0067] When analyzing past conversation logs, the advice providing unit can have the emotion estimation AI track changes in the other person's emotions and analyze emotional trends. For example, the advice providing unit analyzes past conversation logs, and the emotion estimation AI tracks changes in the other person's emotions. For example, the emotion estimation AI records changes in emotions according to what the other person says and analyzes trends. The advice providing unit also analyzes past conversation logs, and the emotion estimation AI analyzes trends in the other person's emotions. For example, it identifies how the other person expresses emotions on specific topics and uses this information for future conversations. The advice providing unit also analyzes past conversation logs, and develops an algorithm for the emotion estimation AI to track changes in the other person's emotions. For example, it analyzes changes in emotions in real time according to what the other person says and analyzes trends. This makes it possible to track changes in the other person's emotions and analyze trends in emotions, thereby enabling more appropriate advice to be provided.

[0068] The advice providing unit applies the past conversation log analysis system to customer support and can provide optimal support based on the customer's past inquiry history. The advice providing unit, for example, applies the past conversation log analysis system to customer support and provides optimal support based on the customer's past inquiry history. For example, it provides an appropriate answer based on the content of the past inquiry. Furthermore, in customer support, the advice providing unit analyzes past conversation logs and provides information that is useful for solving the customer's problem. For example, it proposes a solution based on the past inquiry history. Furthermore, in customer support, the advice providing unit analyzes past conversation logs and provides support that meets the customer's needs. For example, it proposes a service that meets the customer's request based on the content of the past inquiry. This makes it possible to provide optimal support based on the customer's past inquiry history.

[0069] The advice providing unit applies the past conversation log analysis system to the field of education and can provide optimal study advice based on a student's past learning history. The advice providing unit, for example, applies the past conversation log analysis system to the field of education and provides optimal study advice based on a student's past learning history. For example, it suggests an appropriate learning method based on past learning content. Furthermore, in the field of education, the advice providing unit analyzes past conversation logs and provides optimal study advice based on a student's learning history. For example, it suggests a study plan based on past grades and learning content. Furthermore, in the field of education, the advice providing unit analyzes past conversation logs and develops an algorithm that provides optimal study advice based on a student's learning history. For example, it evaluates learning progress based on past learning content and provides appropriate advice. This makes it possible to provide optimal study advice based on a student's past learning history.

[0070] The advice providing unit applies the past conversation log analysis system to the medical field and can provide optimal treatment advice based on a patient's past medical history. The advice providing unit, for example, applies the past conversation log analysis system to the medical field and provides optimal treatment advice based on a patient's past medical history. For example, it proposes an appropriate treatment method based on past medical details. Furthermore, in the medical field, the advice providing unit analyzes past conversation logs and provides optimal treatment advice based on a patient's medical history. For example, it proposes a treatment plan based on past medical details. Furthermore, in the medical field, the advice providing unit analyzes past conversation logs and develops an algorithm that provides optimal treatment advice based on a patient's medical history. For example, it evaluates the progress of treatment based on past medical details and provides appropriate advice. This makes it possible to provide optimal treatment advice based on a patient's past medical history.

[0071] The advice providing unit can track the user's gaze and display advice in front of the user's gaze when displaying advice using AR. For example, the advice providing unit uses AR glasses to track the user's gaze and display advice in front of the user's gaze. For example, it displays information related to the location where the user is looking. The advice providing unit also develops an algorithm that tracks the user's gaze and displays advice in front of the user's gaze. For example, it displays advice related to an object the user is looking at. The advice providing unit also builds a system that uses AR glasses to track the user's gaze and display advice in front of the user's gaze. For example, it displays information related to the location where the user is looking in real time. This makes it possible to provide information more intuitively by tracking the user's gaze and displaying advice in front of the user's gaze.

[0072] The advice providing unit can recognize a user's gestures and display advice corresponding to the gestures when displaying advice using AR. For example, the advice providing unit uses AR glasses to recognize a user's gestures and display advice corresponding to the gestures. For example, when a user raises their hand, relevant information is displayed. The advice providing unit also develops an algorithm that recognizes a user's gestures and displays advice corresponding to the gestures. For example, information related to the location where the user points is displayed. The advice providing unit also builds a system that uses AR glasses to recognize a user's gestures and display advice corresponding to the gestures. For example, when a user waves their hand, relevant information is displayed in real time. This enables more intuitive information to be provided by recognizing a user's gestures and displaying advice corresponding to the gestures.

[0073] The advice providing unit can analyze the user's emotional state and display advice according to the emotion when displaying advice using AR. For example, the advice providing unit uses AR glasses to analyze the user's emotional state and display advice according to the emotion. For example, if the user is nervous, the advice providing unit provides advice to relax. The advice providing unit also develops an algorithm that analyzes the user's emotional state and displays advice according to the emotion. For example, if the user is happy, the advice providing unit displays relevant information. The advice providing unit also builds a system in which the AR glasses analyze the user's emotional state and display advice according to the emotion. For example, if the user is feeling anxious, the advice providing unit provides advice to relax. This makes it possible to provide more appropriate information by analyzing the user's emotional state and displaying advice according to the emotion.

[0074] The advice providing unit applies the AR advice display system to a driving assistance system and can provide driving advice to a driver in real time while driving. The advice providing unit, for example, applies the AR advice display system to a driving assistance system and provides driving advice to a driver in real time while driving. For example, it provides advice to avoid obstacles ahead. The advice providing unit also provides appropriate driving advice to the driver using AR advice display in the driving assistance system. For example, it provides advice on the timing of lane changes. The advice providing unit also develops an algorithm for providing appropriate driving advice to the driver using AR advice display in the driving assistance system. For example, it analyzes traffic conditions ahead and provides appropriate driving advice. In this way, safe driving is supported by providing driving advice to the driver in real time while driving.

[0075] The advice providing unit applies the AR advice display system to sports training and can provide training advice to athletes in real time during training. The advice providing unit, for example, applies the AR advice display system to sports training and provides training advice to athletes in real time during training. For example, advice is provided to correct form or adjust training intensity. The advice providing unit also provides appropriate training advice to athletes using AR advice display during sports training. For example, training progress is displayed in real time. The advice providing unit also develops an algorithm that provides appropriate training advice to athletes using AR advice display during sports training. For example, the algorithm analyzes training data and provides appropriate advice. This provides training advice in real time to athletes in training, supporting effective training.

[0076] The advice providing unit applies the AR advice display system to a cooking assistance system and can provide cooking advice to a user who is cooking in real time. The advice providing unit, for example, applies the AR advice display system to a cooking assistance system and provides cooking advice to a user who is cooking in real time. For example, it displays cooking procedures and amounts of seasonings. The advice providing unit also uses AR advice display in the cooking assistance system to provide appropriate cooking advice to the user. For example, it displays how to cut ingredients and cooking time. The advice providing unit also develops an algorithm for the cooking assistance system that uses AR advice display to provide appropriate cooking advice to the user. For example, it analyzes recipe data and provides appropriate advice. In this way, cooking advice can be provided in real time to the user who is cooking, thereby providing effective cooking assistance.

[0077] The advice providing unit can analyze the movement of the user's pupils using the AR contact lenses and display information according to changes in the pupils. For example, the advice providing unit analyzes the movement of the user's pupils using the AR contact lenses and displays information according to changes in the pupils. For example, if the pupils dilate, it displays information that the user is interested in. The advice providing unit also develops an algorithm that analyzes the movement of the user's pupils and displays information according to changes in the pupils. For example, if the pupils constrict, it displays information to help the user relax. The advice providing unit also builds a system that analyzes the movement of the user's pupils using the AR contact lenses and displays information according to changes in the pupils. For example, it displays appropriate information in real time according to the movement of the pupils. This makes it possible to provide more intuitive information by analyzing the movement of the user's pupils and displaying information according to changes in the pupils.

[0078] The advice providing unit can analyze the user's blinking pattern using the AR contact lenses and display information corresponding to the blinks. For example, the advice providing unit analyzes the user's blinking pattern using the AR contact lenses and displays information corresponding to the blinks. For example, if the user blinks frequently, information to encourage relaxation is displayed. The advice providing unit also analyzes the user's blinking pattern and develops an algorithm to display information corresponding to the blinks. For example, if the blink intervals are short, information to encourage concentration is displayed. The advice providing unit also builds a system in which the AR contact lenses analyze the user's blinking pattern and display information corresponding to the blinks. For example, appropriate information is displayed in real time according to the blinking pattern. This allows for more intuitive information to be provided by analyzing the user's blinking pattern and displaying information corresponding to the blinks.

[0079] The advice providing unit can automatically recognize an object that comes into the user's field of view using the AR contact lenses and display information related to the object. For example, the advice providing unit automatically recognizes an object that comes into the user's field of view using the AR contact lenses and displays information related to the object. For example, the advice providing unit displays the history and features of a building that comes into view. The advice providing unit also develops an algorithm that automatically recognizes an object that comes into the user's field of view and displays information related to the object. For example, it displays information about a product that comes into view. The advice providing unit also builds a system that automatically recognizes an object that comes into the user's field of view using the AR contact lenses and displays information related to the object. For example, it displays a profile of a person that comes into view. This enables more intuitive information provision by automatically recognizing an object that comes into the user's field of view and displaying information related to the object.

[0080] The advice providing unit can apply the AR contact lenses to the medical field and provide surgical guides to surgeons in real time during surgery. The advice providing unit, for example, applies the AR contact lenses to the medical field and provides surgical guides to surgeons in real time during surgery. For example, it displays surgical procedures and important points. The advice providing unit also uses the AR contact lenses in the medical field to provide appropriate surgical guides to surgeons in the medical field. For example, it displays points to note during surgery and next steps. The advice providing unit also develops an algorithm in the medical field that uses the AR contact lenses to provide appropriate surgical guides to surgeons in the medical field. For example, it analyzes surgical data and provides appropriate guides. In this way, surgical guides are provided to surgeons in real time during surgery, thereby improving the accuracy and safety of the surgery.

[0081] The advice providing unit can apply the AR contact lenses in the field of education to provide real-time teaching support information to teachers during classes. The advice providing unit, for example, applies the AR contact lenses in the field of education to provide real-time teaching support information to teachers during classes. For example, it displays the progress of the class and students' reactions. The advice providing unit also uses the AR contact lenses in the field of education to provide appropriate teaching support information to teachers during classes. For example, it displays the content of the class and the next steps. The advice providing unit also develops an algorithm in the field of education that uses the AR contact lenses to provide appropriate teaching support information to teachers during classes. For example, it analyzes class data and provides appropriate support information. This provides real-time teaching support information to teachers during classes, thereby improving the quality of lessons.

[0082] The advice providing unit can apply the AR contact lenses to the entertainment field to provide real-time event information to audiences during a live event. For example, the advice providing unit applies the AR contact lenses to the entertainment field to provide real-time event information to audiences during a live event. For example, it displays performer profiles and set lists. The advice providing unit also uses the AR contact lenses to provide appropriate event information to audiences during a live event in the entertainment field. For example, it displays information about the next performance and the venue. The advice providing unit also develops an algorithm to provide appropriate event information to audiences during a live event using the AR contact lenses in the entertainment field. For example, it analyzes event data and provides appropriate information. This provides real-time event information to audiences during a live event, thereby increasing their enjoyment of the event.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The dating and sales assistance system may further include a stress assessment unit that estimates the user's emotions and assesses the user's stress level based on the estimated emotions. For example, the stress assessment unit may acquire the user's heart rate and galvanic skin response using a sensor to assess the stress level. The stress assessment unit may also analyze the user's breathing pattern to assess the stress level. Furthermore, the stress assessment unit may analyze changes in the tone and pitch of the user's voice to assess the stress level. This allows the user's stress level to be assessed and appropriate measures to be taken.

[0085] The dating and sales assistance system may further include a concentration evaluation unit that estimates the user's emotions and evaluates the user's concentration based on the estimated emotions. For example, the concentration evaluation unit may analyze the user's eye movements and blink frequency to evaluate the user's concentration. The concentration evaluation unit may also acquire the user's brain waves using a sensor to evaluate the user's concentration. Furthermore, the concentration evaluation unit may analyze the user's posture and movements to evaluate the user's concentration. This allows the user's concentration to be evaluated and appropriate measures to be taken.

[0086] The dating and sales assistance system may further include a happiness evaluation unit that estimates the user's emotions and evaluates the user's happiness level based on the estimated emotions. For example, the happiness evaluation unit may analyze the frequency and duration of the user's smiles to evaluate the user's happiness level. The happiness evaluation unit may also analyze changes in the tone and pitch of the user's voice to evaluate the user's happiness level. Furthermore, the happiness evaluation unit may acquire the user's body temperature and heart rate using a sensor to evaluate the user's happiness level. This allows the user's happiness level to be evaluated and appropriate measures to be taken.

[0087] The dating and sales assistance system may further include a fatigue evaluation unit that estimates the user's emotions and evaluates the user's fatigue level based on the estimated emotions. For example, the fatigue evaluation unit may analyze the user's eye movements and blink frequency to evaluate the user's fatigue level. The fatigue evaluation unit may also acquire the user's heart rate and skin electrical response using a sensor to evaluate the user's fatigue level. Furthermore, the fatigue evaluation unit may analyze the user's posture and movements to evaluate the user's fatigue level. This allows the user's fatigue level to be evaluated and appropriate measures to be taken.

[0088] The dating and sales assistance system may further include a motivation evaluation unit that estimates the user's emotions and evaluates the user's motivation based on the estimated emotions. For example, the motivation evaluation unit may analyze changes in the user's tone and pitch to evaluate the user's motivation. The motivation evaluation unit may also analyze the user's facial expressions and posture to evaluate the user's motivation. Furthermore, the motivation evaluation unit may analyze the user's behavioral patterns to evaluate the user's motivation. This allows the user's motivation to be evaluated and appropriate responses to be taken.

[0089] The dating and sales assistance system may further include a health monitoring unit that monitors the user's health condition. For example, the health monitoring unit may monitor the user's health condition by acquiring the user's heart rate and blood pressure using a sensor. The health monitoring unit may also monitor the user's health condition by acquiring the user's body temperature and oxygen saturation using a sensor. Furthermore, the health monitoring unit may analyze the user's sleep patterns and monitor the user's health condition. This allows the user's health condition to be monitored and appropriate measures to be taken.

[0090] The dating and sales assistance system may further include a nutritional evaluation unit that records the user's dietary content and evaluates the nutritional balance. For example, the nutritional evaluation unit may record the content of the user's meals and evaluate the nutritional balance. The nutritional evaluation unit may also analyze the calories and nutrients in the user's meals and evaluate the nutritional balance. Furthermore, the nutritional evaluation unit may analyze the frequency and amount of the user's meals and evaluate the nutritional balance. This makes it possible to evaluate the user's nutritional balance and provide appropriate advice.

[0091] The dating and sales assistance system may further include an exercise evaluation unit that records the user's amount of exercise and evaluates their exercise habits. For example, the exercise evaluation unit may record the user's number of steps and distance traveled and evaluate the amount of exercise. The exercise evaluation unit may also analyze the type and intensity of the user's exercise and evaluate their exercise habits. Furthermore, the exercise evaluation unit may analyze the frequency and duration of the user's exercise and evaluate their exercise habits. This makes it possible to evaluate the user's exercise habits and provide appropriate advice.

[0092] The dating and sales assistance system may further include a sleep evaluation unit that records the user's sleep state and evaluates the quality of the sleep. For example, the sleep evaluation unit may record the user's sleep time and sleep cycle and evaluate the quality of the sleep. The sleep evaluation unit may also analyze the user's heart rate and breathing pattern to evaluate the quality of the sleep. Furthermore, the sleep evaluation unit may analyze the user's body movements and frequency of turning over in sleep to evaluate the quality of the sleep. This makes it possible to evaluate the user's sleep quality and provide appropriate advice.

[0093] The dating and sales assistance system may further include a hobby learning unit that learns the user's hobbies and interests and provides personalized advice. For example, the hobby learning unit may analyze the user's past actions and conversation logs to learn the user's hobbies and interests. The hobby learning unit may also analyze the user's internet search history and social media posts to learn the user's hobbies and interests. The hobby learning unit may also analyze the user's purchase history and events attended to learn the user's hobbies and interests. This makes it possible to learn the user's hobbies and interests and provide personalized advice.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The emotion estimation unit uses a camera and microphone to estimate the other person's emotions. For example, the emotion estimation unit analyzes the other person's facial expressions and detects changes in expression such as smiling or furrowing of the brow. It can also analyze changes in the tone and pitch of the other person's voice to detect changes in emotion. It can also analyze the other person's eye movements and blinking frequency to detect changes in emotion. Step 2: The information acquisition unit acquires information about the area around the current location based on the emotion estimated by the emotion estimation unit. For example, the information acquisition unit estimates the current location using a Visual Positioning System (VPS) and acquires information about surrounding objects and buildings. It can also identify the current location using GPS data and collect information about the surrounding area from the Internet. It can also analyze past conversation logs to understand the other person's interests. Step 3: The advice providing unit provides advice based on the information acquired by the information acquiring unit and past conversation logs. For example, if the other person is talking about a movie they like, the advice providing unit can suggest that they talk about a movie they recently saw. If there is a cafe nearby, the advice providing unit can also suggest that they take a break there. Furthermore, if the other person is feeling anxious, the advice providing unit can suggest that they talk about calming topics to help them relax.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 AI 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 AI 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0132] The 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.

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

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 AI 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion estimation unit that estimates the emotion of the other party using a camera and a microphone; an information acquisition unit that acquires information about the surrounding area of ​​the current location based on the emotion estimated by the emotion estimation unit; an advice providing unit that provides advice based on the information acquired by the information acquiring unit and a past conversation log. A system characterized by:

2. The emotion estimation unit The system analyzes the subtle changes in the other person's facial expressions and tone of voice in real time and instantly provides feedback on the changes in their emotions.

2. The system of claim 1.

3. The emotion estimation unit Learn the person's past emotional history and compare it with their current emotional state to detect any abnormal changes 2. The system of claim 1.

4. The emotion estimation unit When estimating the other person's emotions, the system takes into account surrounding environmental sounds and background noise to perform more accurate emotion analysis.

2. The system of claim 1.

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