System

Abstract

An object of a system according to an embodiment is to propose an optimal plan on the basis of a current situation of a user.SOLUTION: A system according to an embodiment includes a question reception unit, a situation grasping unit, a plan generation unit, and a proposal unit. The inquiry receiving unit receives an inquiry from a user. The situation grasping unit grasps the current situation based on the question received by the question receiving unit. The plan generation unit generates an optimal plan based on the situation grasped by the situation grasping unit. The proposal unit proposes the plan generated by the plan generation unit to the user.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 techniques have had the problem of making it difficult to find optimal activities for sudden schedule changes or spare time.

[0005] The system according to the embodiment aims to propose an optimal plan based on the user's current situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a question receiving unit, a situation understanding unit, a plan generating unit, and a proposal unit. The question receiving unit receives a question from a user. The situation understanding unit understands the current situation based on the question received by the question receiving unit. The plan generating unit generates an optimal plan based on the situation understood by the situation understanding unit. The proposal unit proposes the plan generated by the plan generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal plan based on the user's current situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The time-killing plan suggestion tool according to an embodiment of the present invention is a system in which a generation AI suggests an optimal time-killing plan when a user is unsure of what to do in their spare time. As a result, the time-killing plan suggestion tool can provide the user with the optimal plan for their spare time.

[0029] A time-killing plan suggestion tool according to an embodiment includes a question receiving unit, a situation understanding unit, a plan generation unit, and a suggestion unit. The question receiving unit receives a question from a user. For example, if the user asks, "I want to do something, but I don't know what to do," the question receiving unit receives the question. The user can also receive specific questions, such as, "Tell me what I can do in the next hour." The situation understanding unit understands the current situation based on the question received by the question receiving unit. For example, the situation understanding unit identifies the user's current location using GPS information from the user's smartphone and obtains weather forecast data to check the weather. It also checks whether the user is alone or with multiple people. The plan generation unit generates an optimal plan based on the situation understood by the situation understanding unit. For example, if the user asks, "I want to relax at a cafe," the plan generation unit checks the business hours and congestion status of cafes near the user's current location and suggests the most suitable cafe. Also, if the user asks, "I want to see a movie," the plan generation unit checks the screening schedules of nearby movie theaters and suggests the most suitable movie. The suggestion unit suggests the plan generated by the plan generation unit to the user. For example, the suggestion unit may suggest, "There's a cafe open five minutes' walk from your current location. Why not spend some time relaxing there?". It may also make a specific suggestion such as, "There's a movie starting in 15 minutes at a nearby movie theater. Tickets are still available for purchase." In this way, the time-killing plan suggestion tool according to the embodiment can suggest an optimal time-killing plan based on the user's questions.

[0030] The query receiver can analyze the user's past query history and make personalized suggestions. For example, if the user has a query history of "I want to relax at a cafe," the generation AI will use that history to suggest cafes that suit the user's preferences. For example, it will refer to reviews and ratings of cafes visited in the past. If the user has a query history of "I want to see a movie," the generation AI will use that history to suggest movies in the user's favorite genre. For example, it will refer to the genres and ratings of movies seen in the past. If the user has a query history of "I want to go shopping," the generation AI will use that history to suggest shopping spots that the user might be interested in. For example, it will refer to information about shopping malls and stores visited in the past. This makes it possible to make personalized suggestions based on the user's past history.

[0031] The question receiver can interactively elicit detailed information from the user's questions and make more specific suggestions. For example, when a user asks, "I want to do something fun," the generator AI will interactively ask, "How much time do you have?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you have an hour, how about enjoying coffee at a nearby cafe?" When a user asks, "I want to do something relaxing," the generator AI will interactively ask, "Do you want to spend time alone or with someone?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you're alone, how about relaxing at a nearby spa?" When a user asks, "I want to try something new," the generator AI will interactively ask, "What genre are you interested in?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you're interested in cooking, how about taking a cooking class nearby?" This allows the generator AI to elicit detailed information from the user's questions and make specific suggestions.

[0032] The question receiver receives the user's question via voice input, which the generation AI analyzes using voice recognition technology. For example, when a user voice-inputs, "I want to do something fun," the generation AI analyzes the question using voice recognition technology and suggests an appropriate activity. For example, it might suggest, "How about going for a jog in a nearby park?". Similarly, when a user voice-inputs, "I want to do something relaxing," the generation AI analyzes the question using voice recognition technology and suggests an appropriate relaxation activity. For example, it might suggest, "How about relaxing at a nearby spa?". Similarly, when a user voice-inputs, "I want to try something new," the generation AI analyzes the question using voice recognition technology and suggests an appropriate activity that will provide a new experience. For example, it might suggest, "How about joining a nearby art workshop?". This makes it possible to make suggestions based on the user's voice input.

[0033] The question receiver receives the user's question in the form of an image or video, and the generation AI analyzes the visual information and makes suggestions. For example, when a user sends a question via an image or video such as "I want to do something fun," the generation AI analyzes the visual information and suggests an appropriate activity. For example, if a user sends a photo of a park, the generation AI may suggest, "How about going jogging in that park?". Similarly, when a user sends a question via an image or video such as "I want to do something relaxing," the generation AI analyzes the visual information and suggests an appropriate relaxation activity. For example, if a user sends a photo of a spa, the generation AI may suggest, "How about relaxing at that spa?". Similarly, when a user sends a question via an image or video such as "I want to try something new," the generation AI analyzes the visual information and suggests an activity that provides an appropriate new experience. For example, if a user sends a video of an art workshop, the generation AI may suggest, "How about participating in that workshop?" This makes it possible to make suggestions based on the user's visual information.

[0034] The situation understanding unit can analyze the user's current activity history and make suggestions based on past behavioral patterns. For example, the situation understanding unit analyzes the location information history of the user's smartphone and makes suggestions based on places visited in the past and how often. For example, it can suggest cafes that the user frequently visits. It can also analyze the user's past activity history and suggest similar activities. For example, if the user has a history of visiting a movie theater in the past, it can suggest screening schedules for nearby movie theaters. It can also analyze the user's past behavioral patterns and make suggestions based on the time of day and day of the week. For example, if the user often shops on weekends, it can suggest nearby shopping malls. This makes it possible to make suggestions based on the user's past behavioral patterns.

[0035] The situation understanding unit can analyze the user's social media posts to understand their current interests and concerns and make suggestions. The situation understanding unit, for example, analyzes the content of the user's social media posts to understand their current interests and concerns. For example, if the user has recently posted photos of cafes, the unit will suggest nearby cafes. The situation understanding unit also analyzes the user's social media hashtags to understand topics of interest. For example, if the user frequently uses the hashtag "#movies," the unit will suggest movie screening schedules at nearby movie theaters. The situation understanding unit also analyzes the user's social media follow list to make suggestions based on accounts of interest. For example, if the user follows many restaurant accounts, the unit will suggest nearby restaurants. This makes it possible to make suggestions based on the user's current interests and concerns.

[0036] The situation assessment unit can analyze the user's health condition (e.g., number of steps and heart rate) and make suggestions based on the user's physical condition. For example, the situation assessment unit analyzes step count data obtained from the user's smartwatch and suggests activities based on the user's physical condition. For example, if the number of steps is low, it suggests light exercise. It can also analyze the user's heart rate data and suggest relaxation activities if relaxation is needed. For example, if the heart rate is high, it suggests a nearby spa. It can also analyze the user's sleep data and suggest relaxing activities if fatigue is building up. For example, if the user's sleep time is short, it suggests taking a break at a nearby cafe. This makes it possible to make suggestions based on the user's health condition.

[0037] The situation assessment unit can acquire the user's current situation from a wearable device such as a smartwatch or fitness tracker. For example, the situation assessment unit acquires heart rate data from the user's smartwatch and suggests a relaxation activity if relaxation is needed. For example, if the heart rate is high, it suggests a nearby spa. The situation assessment unit also acquires step count data from the user's fitness tracker and suggests an active activity if exercise is needed. For example, if the user's step count is low, it suggests jogging in a nearby park. The situation assessment unit also acquires sleep data from the user's smartwatch and suggests a relaxing activity if fatigue is building up. For example, if the user's sleep time is short, it suggests taking a break at a nearby cafe. This makes it possible to make suggestions based on data from wearable devices.

[0038] The situation recognition unit can acquire the user's current situation from smart home devices (e.g., temperature sensors and lighting). For example, the situation recognition unit acquires room temperature data from the user's smart home devices and suggests activities that will allow them to stay at a comfortable room temperature. For example, if the room temperature is high, it will suggest a cool cafe. It also acquires lighting data from the user's smart home devices and suggests activities in a relaxing environment. For example, if the lighting is dim, it will suggest a relaxing spa. It also acquires audio data from the user's smart home devices and suggests activities in a quiet environment. For example, if the volume is high, it will suggest a quiet library. This makes it possible to make suggestions based on data from smart home devices.

[0039] The plan generation unit can analyze the user's past feedback and improve the accuracy of suggestions. For example, the plan generation unit analyzes feedback the user has provided on previously proposed plans and proposes a new plan based on similar feedback. For example, the plan generation unit re-proposes a cafe that the user has previously rated highly. The plan generation unit also analyzes the user's past feedback and develops an algorithm to improve the accuracy of suggestions. For example, it excludes activities that the user has previously rated poorly. The plan generation unit also builds a personalized model to improve the accuracy of suggestions based on the user's feedback data. For example, it makes suggestions that reflect the user's preferences and ratings. This makes it possible to improve the accuracy of suggestions based on the user's past feedback.

[0040] The plan generation unit can generate a relaxation plan according to the user's current mood and physical condition. For example, the plan generation unit analyzes the user's current mood and suggests a relaxation plan if relaxation is needed. For example, if the user is feeling stressed, it suggests a nearby spa. It also analyzes the user's physical condition data and suggests a relaxation plan if relaxation is needed. For example, if the user's heart rate is high, it suggests a cafe where they can relax. It also develops an algorithm for generating relaxation plans according to the user's mood and physical condition. For example, it suggests optimal relaxation activities based on the user's emotional state and health data. This makes it possible to generate relaxation plans based on the user's mood and physical condition.

[0041] The plan generation unit can suggest activities that can be done in a short time based on the user's current situation. For example, when the user asks, "Tell me what I can do in an hour," the generation AI in the plan generation unit suggests activities that can be done in a short time based on the current situation. For example, it may suggest enjoying a coffee at a nearby cafe. In addition, an algorithm may be developed to analyze the user's current situation and suggest activities that can be done in a short time. For example, it may suggest the most suitable activity based on the user's location information and time. In addition, a system may be built that suggests activities that can be done in a short time based on the user's current situation. For example, it may suggest that the user take a walk in a nearby park. This makes it possible to suggest activities that can be done in a short time based on the user's current situation.

[0042] The plan generation unit can suggest indoor activities based on the user's current situation. For example, when a user asks, "Tell me what I can do on a rainy day," the generation AI will suggest indoor activities based on the current situation. For example, it may suggest enjoying a movie at a nearby movie theater. In addition, an algorithm will be developed to analyze the user's current situation and suggest indoor activities. For example, it may suggest the most suitable activity based on the user's location information and weather data. In addition, a system will be built that suggests indoor activities based on the user's current situation. For example, it may suggest that the user read at a nearby cafe. This makes it possible to suggest indoor activities based on the user's current situation.

[0043] The suggestion unit can analyze the user's past selection history and make personalized suggestions. For example, the suggestion unit analyzes the history of plans the user has selected in the past and suggests similar plans. For example, it may re-suggest a cafe the user has visited in the past. The suggestion unit also analyzes the user's past selection history and develops an algorithm for making personalized suggestions. For example, it may suggest the optimal plan based on the user's preferences and ratings. The suggestion unit also builds a system that makes personalized suggestions based on the user's selection history data. For example, it may re-suggest an activity that the user has given a high rating to in the past. This makes it possible to make personalized suggestions based on the user's past selection history.

[0044] The suggestion unit can analyze the user's current schedule and make suggestions that fit the schedule. For example, the suggestion unit obtains schedule data from the user's calendar app and suggests plans that fit the available time. For example, if the user has one hour of free time, it will suggest a nearby cafe. In addition, it develops an algorithm to analyze the user's schedule data and make suggestions that fit the schedule. For example, it will suggest the optimal activity to fit the user's plans. In addition, it builds a system that makes suggestions that fit the schedule based on the user's schedule data. For example, if the user has free time in the afternoon, it will suggest a nearby movie theater. This makes it possible to make suggestions based on the user's schedule.

[0045] The suggestion unit can propose multiple options based on the user's current situation, allowing the user to select one. The suggestion unit, for example, analyzes the user's current situation and proposes multiple activities. For example, when the user asks, "I want to do something fun," the suggestion unit presents options such as a nearby cafe, movie theater, or park. The system also develops an algorithm for proposing multiple options based on the user's current situation. For example, the system proposes multiple optimal activities based on the user's location information and time. The system also builds a system that proposes multiple activities so that the user can select an option. For example, the system allows the user to choose between "spending time at a cafe" or "watching a movie." This makes it possible to propose options from which the user can choose.

[0046] The suggestion unit can suggest plans that can be shared with friends and family based on the user's current situation. For example, when a user asks, "I want to spend time with my family," the suggestion unit's generation AI will suggest family activities based on the current situation. For example, it may suggest a picnic in a nearby park. The suggestion unit will also develop an algorithm to analyze the user's current situation and suggest plans that can be shared with friends and family. For example, it will suggest the most suitable activity based on the user's location information and number of people. The suggestion unit will also build a system that suggests plans that users can share with friends and family. For example, it will allow the user to choose between "spending time with friends" or "spending time with family." This will make it possible to suggest plans that users can share with their friends and family.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The query receiver can also make suggestions in response to the user's query, taking into account the user's current health condition. For example, when a user asks, "I want to do something relaxing," the generation AI analyzes the heart rate data obtained from the user's smartwatch, and if the heart rate is high, it will suggest activities suitable for relaxation. For example, it will suggest nearby spas or massage parlors. Also, when a user asks, "I want to do something active," the generation AI analyzes the user's step count data, and if the step count is low, it will suggest light exercise. For example, it will suggest jogging or cycling in a nearby park. This makes it possible to make suggestions based on the user's health condition.

[0049] The query receiver can also make suggestions in response to the user's query, taking into account the user's current schedule. For example, when a user asks, "I want to do something fun," the generation AI retrieves schedule data from the user's calendar app and suggests activities that fit with available time. For example, if the user has an hour of free time, it will suggest enjoying a coffee at a nearby cafe. Similarly, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's schedule data and suggests activities that fit with the time of day when relaxation is needed. For example, if the user has free time in the afternoon, it will suggest a nearby spa. This makes it possible to make suggestions based on the user's schedule.

[0050] The query receiver can also make suggestions to users that take into account the current weather conditions. For example, when a user asks, "I want to do something fun," the generation AI retrieves weather forecast data and suggests activities that correspond to the weather. For example, on a sunny day, it will suggest a picnic in a nearby park, and on a rainy day, it will suggest enjoying a movie at a nearby cinema. Also, when a user asks, "I want to do something relaxing," the generation AI analyzes weather data and suggests activities that are suitable for relaxation. For example, on a cold day, it will suggest relaxing at a nearby spa. This makes it possible to make suggestions based on the user's weather.

[0051] The question receiver can also make suggestions in response to a user's questions, taking into account the user's current interests and concerns. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's social media posts and suggests activities based on their current interests and concerns. For example, if the user has recently posted photos of cafes, the generation AI will suggest nearby cafes. Also, when a user asks, "I want to do something relaxing," the generation AI will analyze the user's social media hashtags and suggest activities suitable for relaxation. For example, if the user frequently uses "#relax," the generation AI will suggest a nearby spa. This makes it possible to make suggestions based on the user's interests and concerns.

[0052] The query receiver can also make suggestions in response to a user's query, taking into account the user's current activity history. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's smartphone location history and makes suggestions based on past visited places and frequency. For example, it can suggest cafes the user frequently visits. Also, when a user asks, "I want to do something relaxing," the generation AI can analyze the user's past activity history and suggest similar activities. For example, if the user has a history of visiting a movie theater in the past, it can suggest the screening schedule of a nearby movie theater. This makes it possible to make suggestions based on the user's past behavioral patterns.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The question receiver receives a question from the user. For example, the user may ask, "I want to do something, but I don't know what to do," or a specific question such as, "Tell me what I can do in the next hour." Step 2: The situation assessment unit assesses the current situation based on the query received by the query reception unit. For example, it may use the GPS information from the user's smartphone to determine the user's current location, obtain weather forecast data, and check the weather. It may also check whether the user is alone or with other people. Step 3: The plan generation unit generates an optimal plan based on the situation grasped by the situation grasp unit. For example, if the user says, "I want to relax at a cafe," the plan generation unit checks the business status and congestion status of cafes around the current location and suggests the most suitable cafe. Similarly, if the user says, "I want to see a movie," the plan generation unit checks the screening schedules of nearby movie theaters and suggests the most suitable movie. Step 4: The suggestion unit proposes the plan generated by the plan generation unit to the user. For example, it may suggest, "There's a cafe open five minutes' walk from your current location. Why not spend some time relaxing there?" It may also make specific suggestions, such as, "There's a movie starting in 15 minutes at a nearby movie theater. Tickets are still available for purchase."

[0055] (Example 2) The time-killing plan suggestion tool according to an embodiment of the present invention is a system in which a generation AI suggests an optimal time-killing plan when a user is unsure of what to do in their spare time. As a result, the time-killing plan suggestion tool can provide the user with the optimal plan for their spare time.

[0056] A time-killing plan suggestion tool according to an embodiment includes a question receiving unit, a situation understanding unit, a plan generation unit, and a suggestion unit. The question receiving unit receives a question from a user. For example, if the user asks, "I want to do something, but I don't know what to do," the question receiving unit receives the question. The user can also receive specific questions, such as, "Tell me what I can do in the next hour." The situation understanding unit understands the current situation based on the question received by the question receiving unit. For example, the situation understanding unit identifies the user's current location using GPS information from the user's smartphone and obtains weather forecast data to check the weather. It also checks whether the user is alone or with multiple people. The plan generation unit generates an optimal plan based on the situation understood by the situation understanding unit. For example, if the user asks, "I want to relax at a cafe," the plan generation unit checks the business hours and congestion status of cafes near the user's current location and suggests the most suitable cafe. Also, if the user asks, "I want to see a movie," the plan generation unit checks the screening schedules of nearby movie theaters and suggests the most suitable movie. The suggestion unit suggests the plan generated by the plan generation unit to the user. For example, the suggestion unit may suggest, "There's a cafe open five minutes' walk from your current location. Why not spend some time relaxing there?". It may also make a specific suggestion such as, "There's a movie starting in 15 minutes at a nearby movie theater. Tickets are still available for purchase." In this way, the time-killing plan suggestion tool according to the embodiment can suggest an optimal time-killing plan based on the user's questions.

[0057] The question receiver performs sentiment analysis on the user's questions and can make suggestions based on the user's emotional state. For example, when a user asks, "I want to do something fun," the generator AI performs sentiment analysis. If the user is feeling positive, it will suggest active activities. For example, it will suggest jogging or cycling in a nearby park. Similarly, when a user asks, "I want to do something relaxing," the generator AI performs sentiment analysis. If the user is feeling stressed, it will suggest activities suitable for relaxation. For example, it will suggest nearby spas or massage parlors. Similarly, when a user asks, "I want to try something new," the generator AI performs sentiment analysis. If the user is feeling curious, it will suggest activities that provide new experiences. For example, it will suggest nearby cooking classes or art workshops. This makes it possible to make suggestions based on the user's emotional state.

[0058] The query receiver can analyze the user's past query history and make personalized suggestions. For example, if the user has a query history of "I want to relax at a cafe," the generation AI will use that history to suggest cafes that suit the user's preferences. For example, it will refer to reviews and ratings of cafes visited in the past. If the user has a query history of "I want to see a movie," the generation AI will use that history to suggest movies in the user's favorite genre. For example, it will refer to the genres and ratings of movies seen in the past. If the user has a query history of "I want to go shopping," the generation AI will use that history to suggest shopping spots that the user might be interested in. For example, it will refer to information about shopping malls and stores visited in the past. This makes it possible to make personalized suggestions based on the user's past history.

[0059] The question receiver can interactively elicit detailed information from the user's questions and make more specific suggestions. For example, when a user asks, "I want to do something fun," the generator AI will interactively ask, "How much time do you have?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you have an hour, how about enjoying coffee at a nearby cafe?" When a user asks, "I want to do something relaxing," the generator AI will interactively ask, "Do you want to spend time alone or with someone?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you're alone, how about relaxing at a nearby spa?" When a user asks, "I want to try something new," the generator AI will interactively ask, "What genre are you interested in?" and suggest specific activities based on the user's answer. For example, it might suggest, "If you're interested in cooking, how about taking a cooking class nearby?" This allows the generator AI to elicit detailed information from the user's questions and make specific suggestions.

[0060] The question receiver receives the user's question via voice input, which the generation AI analyzes using voice recognition technology. For example, when a user voice-inputs, "I want to do something fun," the generation AI analyzes the question using voice recognition technology and suggests an appropriate activity. For example, it might suggest, "How about going for a jog in a nearby park?". Similarly, when a user voice-inputs, "I want to do something relaxing," the generation AI analyzes the question using voice recognition technology and suggests an appropriate relaxation activity. For example, it might suggest, "How about relaxing at a nearby spa?". Similarly, when a user voice-inputs, "I want to try something new," the generation AI analyzes the question using voice recognition technology and suggests an appropriate activity that will provide a new experience. For example, it might suggest, "How about joining a nearby art workshop?". This makes it possible to make suggestions based on the user's voice input.

[0061] The question receiver receives the user's question in the form of an image or video, and the generation AI analyzes the visual information and makes suggestions. For example, when a user sends a question via an image or video such as "I want to do something fun," the generation AI analyzes the visual information and suggests an appropriate activity. For example, if a user sends a photo of a park, the generation AI may suggest, "How about going jogging in that park?". Similarly, when a user sends a question via an image or video such as "I want to do something relaxing," the generation AI analyzes the visual information and suggests an appropriate relaxation activity. For example, if a user sends a photo of a spa, the generation AI may suggest, "How about relaxing at that spa?". Similarly, when a user sends a question via an image or video such as "I want to try something new," the generation AI analyzes the visual information and suggests an activity that provides an appropriate new experience. For example, if a user sends a video of an art workshop, the generation AI may suggest, "How about participating in that workshop?" This makes it possible to make suggestions based on the user's visual information.

[0062] The question receiver uses its emotion estimation function to analyze the user's facial expression and tone of voice when asking a question and make suggestions based on their emotions. For example, when a user asks, "I want to do something fun," the generator AI analyzes the user's facial expression and tone of voice. If the user has positive emotions, it suggests an active activity. For example, it might suggest, "How about jogging in a nearby park?" When a user asks, "I want to do something relaxing," the generator AI analyzes the user's facial expression and tone of voice. If the user is feeling stressed, it might suggest an activity suitable for relaxation. For example, it might suggest, "How about relaxing at a nearby spa?" When a user asks, "I want to try something new," the generator AI analyzes the user's facial expression and tone of voice. If the user is curious, it might suggest an activity that provides a new experience. For example, it might suggest, "How about joining a nearby art workshop?" This makes it possible to make suggestions based on the user's emotions.

[0063] The situation understanding unit can analyze the user's current activity history and make suggestions based on past behavioral patterns. For example, the situation understanding unit analyzes the location information history of the user's smartphone and makes suggestions based on places visited in the past and how often. For example, it can suggest cafes that the user frequently visits. It can also analyze the user's past activity history and suggest similar activities. For example, if the user has a history of visiting a movie theater in the past, it can suggest screening schedules for nearby movie theaters. It can also analyze the user's past behavioral patterns and make suggestions based on the time of day and day of the week. For example, if the user often shops on weekends, it can suggest nearby shopping malls. This makes it possible to make suggestions based on the user's past behavioral patterns.

[0064] The situation understanding unit can analyze the user's social media posts to understand their current interests and concerns and make suggestions. The situation understanding unit, for example, analyzes the content of the user's social media posts to understand their current interests and concerns. For example, if the user has recently posted photos of cafes, the unit will suggest nearby cafes. The situation understanding unit also analyzes the user's social media hashtags to understand topics of interest. For example, if the user frequently uses the hashtag "#movies," the unit will suggest movie screening schedules at nearby movie theaters. The situation understanding unit also analyzes the user's social media follow list to make suggestions based on accounts of interest. For example, if the user follows many restaurant accounts, the unit will suggest nearby restaurants. This makes it possible to make suggestions based on the user's current interests and concerns.

[0065] The situation assessment unit can analyze the user's health condition (e.g., number of steps and heart rate) and make suggestions based on the user's physical condition. For example, the situation assessment unit analyzes step count data obtained from the user's smartwatch and suggests activities based on the user's physical condition. For example, if the number of steps is low, it suggests light exercise. It can also analyze the user's heart rate data and suggest relaxation activities if relaxation is needed. For example, if the heart rate is high, it suggests a nearby spa. It can also analyze the user's sleep data and suggest relaxing activities if fatigue is building up. For example, if the user's sleep time is short, it suggests taking a break at a nearby cafe. This makes it possible to make suggestions based on the user's health condition.

[0066] The situation assessment unit can acquire the user's current situation from a wearable device such as a smartwatch or fitness tracker. For example, the situation assessment unit acquires heart rate data from the user's smartwatch and suggests a relaxation activity if relaxation is needed. For example, if the heart rate is high, it suggests a nearby spa. The situation assessment unit also acquires step count data from the user's fitness tracker and suggests an active activity if exercise is needed. For example, if the user's step count is low, it suggests jogging in a nearby park. The situation assessment unit also acquires sleep data from the user's smartwatch and suggests a relaxing activity if fatigue is building up. For example, if the user's sleep time is short, it suggests taking a break at a nearby cafe. This makes it possible to make suggestions based on data from wearable devices.

[0067] The situation recognition unit can acquire the user's current situation from smart home devices (e.g., temperature sensors and lighting). For example, the situation recognition unit acquires room temperature data from the user's smart home devices and suggests activities that will allow them to stay at a comfortable room temperature. For example, if the room temperature is high, it will suggest a cool cafe. It also acquires lighting data from the user's smart home devices and suggests activities in a relaxing environment. For example, if the lighting is dim, it will suggest a relaxing spa. It also acquires audio data from the user's smart home devices and suggests activities in a quiet environment. For example, if the volume is high, it will suggest a quiet library. This makes it possible to make suggestions based on data from smart home devices.

[0068] The situation understanding unit can use the emotion estimation function to understand the user's current emotional state and make suggestions based on the emotion. For example, the situation understanding unit analyzes the user's facial expression and suggests a relaxation activity if the user is feeling stressed. For example, if the user looks tired, it suggests a nearby spa. It also analyzes the user's tone of voice and suggests an active activity if the user has positive emotions. For example, if the user sounds happy, it suggests jogging in a nearby park. It also analyzes the user's emotional state in real time and suggests an activity based on the emotion. For example, if the user is excited, it suggests an activity that will provide a new experience. This makes it possible to make suggestions based on the user's emotional state.

[0069] The plan generation unit can analyze the user's past feedback and improve the accuracy of suggestions. For example, the plan generation unit analyzes feedback the user has provided on previously proposed plans and proposes a new plan based on similar feedback. For example, the plan generation unit re-proposes a cafe that the user has previously rated highly. The plan generation unit also analyzes the user's past feedback and develops an algorithm to improve the accuracy of suggestions. For example, it excludes activities that the user has previously rated poorly. The plan generation unit also builds a personalized model to improve the accuracy of suggestions based on the user's feedback data. For example, it makes suggestions that reflect the user's preferences and ratings. This makes it possible to improve the accuracy of suggestions based on the user's past feedback.

[0070] The plan generation unit can generate a relaxation plan according to the user's current mood and physical condition. For example, the plan generation unit analyzes the user's current mood and suggests a relaxation plan if relaxation is needed. For example, if the user is feeling stressed, it suggests a nearby spa. It also analyzes the user's physical condition data and suggests a relaxation plan if relaxation is needed. For example, if the user's heart rate is high, it suggests a cafe where they can relax. It also develops an algorithm for generating relaxation plans according to the user's mood and physical condition. For example, it suggests optimal relaxation activities based on the user's emotional state and health data. This makes it possible to generate relaxation plans based on the user's mood and physical condition.

[0071] The plan generation unit can analyze the user's current emotional state and generate an entertainment plan based on the emotion. For example, the plan generation unit analyzes the user's emotional state and suggests an entertainment plan if the user has positive emotions. For example, if the user looks happy, it suggests a nearby movie theater. The plan generation unit also analyzes the user's emotional state in real time and generates an entertainment plan based on the emotion. For example, if the user is excited, it suggests an activity that will provide a new experience. The plan generation unit also develops an algorithm for generating an entertainment plan based on the user's emotional data. For example, it suggests an optimal entertainment activity based on the user's emotional score. This makes it possible to generate an entertainment plan based on the user's emotional state.

[0072] The plan generation unit can suggest activities that can be done in a short time based on the user's current situation. For example, when the user asks, "Tell me what I can do in an hour," the generation AI in the plan generation unit suggests activities that can be done in a short time based on the current situation. For example, it may suggest enjoying a coffee at a nearby cafe. In addition, an algorithm may be developed to analyze the user's current situation and suggest activities that can be done in a short time. For example, it may suggest the most suitable activity based on the user's location information and time. In addition, a system may be built that suggests activities that can be done in a short time based on the user's current situation. For example, it may suggest that the user take a walk in a nearby park. This makes it possible to suggest activities that can be done in a short time based on the user's current situation.

[0073] The plan generation unit can suggest indoor activities based on the user's current situation. For example, when a user asks, "Tell me what I can do on a rainy day," the generation AI will suggest indoor activities based on the current situation. For example, it may suggest enjoying a movie at a nearby movie theater. In addition, an algorithm will be developed to analyze the user's current situation and suggest indoor activities. For example, it may suggest the most suitable activity based on the user's location information and weather data. In addition, a system will be built that suggests indoor activities based on the user's current situation. For example, it may suggest that the user read at a nearby cafe. This makes it possible to suggest indoor activities based on the user's current situation.

[0074] The plan generation unit can use the emotion estimation function to suggest activities based on the user's emotions. For example, the plan generation unit analyzes the user's emotional state and suggests active activities if the user is feeling positive. For example, if the user looks happy, it suggests jogging in a nearby park. It also analyzes the user's emotional state in real time and suggests activities based on the emotion. For example, if the user is feeling stressed, it suggests a relaxation activity. It also develops an algorithm for suggesting activities based on the user's emotion data. For example, it suggests the optimal activity based on the user's emotion score. This makes it possible to suggest activities based on the user's emotions.

[0075] The suggestion unit can analyze the user's past selection history and make personalized suggestions. For example, the suggestion unit analyzes the history of plans the user has selected in the past and suggests similar plans. For example, it may re-suggest a cafe the user has visited in the past. The suggestion unit also analyzes the user's past selection history and develops an algorithm for making personalized suggestions. For example, it may suggest the optimal plan based on the user's preferences and ratings. The suggestion unit also builds a system that makes personalized suggestions based on the user's selection history data. For example, it may re-suggest an activity that the user has given a high rating to in the past. This makes it possible to make personalized suggestions based on the user's past selection history.

[0076] The suggestion unit can analyze the user's current schedule and make suggestions that fit the schedule. For example, the suggestion unit obtains schedule data from the user's calendar app and suggests plans that fit the available time. For example, if the user has one hour of free time, it will suggest a nearby cafe. In addition, it develops an algorithm to analyze the user's schedule data and make suggestions that fit the schedule. For example, it will suggest the optimal activity to fit the user's plans. In addition, it builds a system that makes suggestions that fit the schedule based on the user's schedule data. For example, if the user has free time in the afternoon, it will suggest a nearby movie theater. This makes it possible to make suggestions based on the user's schedule.

[0077] The suggestion unit can analyze the user's current emotional state and make suggestions based on the emotion. For example, the suggestion unit analyzes the user's emotional state and suggests active activities if the user is feeling positive. For example, if the user looks happy, it suggests jogging in a nearby park. The suggestion unit also analyzes the user's emotional state in real time and makes suggestions based on the emotion. For example, if the user is feeling stressed, it suggests a relaxation activity. The suggestion unit also develops an algorithm for making suggestions based on the emotion based on the user's emotional data. For example, it suggests the optimal activity based on the user's emotional score. This makes it possible to make suggestions based on the user's emotional state.

[0078] The suggestion unit can propose multiple options based on the user's current situation, allowing the user to select one. The suggestion unit, for example, analyzes the user's current situation and proposes multiple activities. For example, when the user asks, "I want to do something fun," the suggestion unit presents options such as a nearby cafe, movie theater, or park. The system also develops an algorithm for proposing multiple options based on the user's current situation. For example, the system proposes multiple optimal activities based on the user's location information and time. The system also builds a system that proposes multiple activities so that the user can select an option. For example, the system allows the user to choose between "spending time at a cafe" or "watching a movie." This makes it possible to propose options from which the user can choose.

[0079] The suggestion unit can suggest plans that can be shared with friends and family based on the user's current situation. For example, when a user asks, "I want to spend time with my family," the suggestion unit's generation AI will suggest family activities based on the current situation. For example, it may suggest a picnic in a nearby park. The suggestion unit will also develop an algorithm to analyze the user's current situation and suggest plans that can be shared with friends and family. For example, it will suggest the most suitable activity based on the user's location information and number of people. The suggestion unit will also build a system that suggests plans that users can share with friends and family. For example, it will allow the user to choose between "spending time with friends" or "spending time with family." This will make it possible to suggest plans that users can share with their friends and family.

[0080] The suggestion unit uses the emotion estimation function to make suggestions based on the user's emotions and customize the content of the suggestions. For example, the suggestion unit analyzes the user's emotional state and suggests active activities if the user has positive emotions. For example, if the user looks happy, it suggests jogging in a nearby park. The suggestion unit also analyzes the user's emotional state in real time and makes suggestions based on emotions. For example, if the user is feeling stressed, it suggests a relaxation activity. The suggestion unit also develops an algorithm for making emotion-based suggestions based on the user's emotional data. For example, it suggests the optimal activity based on the user's emotion score. This makes it possible to make customized suggestions based on the user's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The query receiver can also make suggestions in response to the user's query, taking into account the user's current health condition. For example, when a user asks, "I want to do something relaxing," the generation AI analyzes the heart rate data obtained from the user's smartwatch, and if the heart rate is high, it will suggest activities suitable for relaxation. For example, it will suggest nearby spas or massage parlors. Also, when a user asks, "I want to do something active," the generation AI analyzes the user's step count data, and if the step count is low, it will suggest light exercise. For example, it will suggest jogging or cycling in a nearby park. This makes it possible to make suggestions based on the user's health condition.

[0083] The query receiver can also make suggestions in response to the user's query, taking into account the user's current schedule. For example, when a user asks, "I want to do something fun," the generation AI retrieves schedule data from the user's calendar app and suggests activities that fit with available time. For example, if the user has an hour of free time, it will suggest enjoying a coffee at a nearby cafe. Similarly, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's schedule data and suggests activities that fit with the time of day when relaxation is needed. For example, if the user has free time in the afternoon, it will suggest a nearby spa. This makes it possible to make suggestions based on the user's schedule.

[0084] The query receiver can also make suggestions to users that take into account the current weather conditions. For example, when a user asks, "I want to do something fun," the generation AI retrieves weather forecast data and suggests activities that correspond to the weather. For example, on a sunny day, it will suggest a picnic in a nearby park, and on a rainy day, it will suggest enjoying a movie at a nearby cinema. Also, when a user asks, "I want to do something relaxing," the generation AI analyzes weather data and suggests activities that are suitable for relaxation. For example, on a cold day, it will suggest relaxing at a nearby spa. This makes it possible to make suggestions based on the user's weather.

[0085] The question receiver can also make suggestions in response to a user's questions, taking into account the user's current interests and concerns. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's social media posts and suggests activities based on their current interests and concerns. For example, if the user has recently posted photos of cafes, the generation AI will suggest nearby cafes. Also, when a user asks, "I want to do something relaxing," the generation AI will analyze the user's social media hashtags and suggest activities suitable for relaxation. For example, if the user frequently uses "#relax," the generation AI will suggest a nearby spa. This makes it possible to make suggestions based on the user's interests and concerns.

[0086] The query receiver can also make suggestions in response to a user's query, taking into account the user's current activity history. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's smartphone location history and makes suggestions based on past visited places and frequency. For example, it can suggest cafes the user frequently visits. Also, when a user asks, "I want to do something relaxing," the generation AI can analyze the user's past activity history and suggest similar activities. For example, if the user has a history of visiting a movie theater in the past, it can suggest the screening schedule of a nearby movie theater. This makes it possible to make suggestions based on the user's past behavioral patterns.

[0087] The question receiver can estimate the user's emotional state in response to the user's question and make suggestions based on their emotions. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling positive, it will suggest an active activity. For example, it will suggest jogging in a nearby park. Similarly, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling stressed, it will suggest an activity suitable for relaxation. For example, it will suggest relaxing at a nearby spa. This makes it possible to make suggestions based on the user's emotions.

[0088] The question receiver can estimate the user's emotional state in response to the user's question and suggest entertainment plans based on their emotions. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's facial expression and tone of voice, and if the user has positive emotions, it will suggest an entertainment plan. For example, it will suggest enjoying a movie at a nearby cinema. Similarly, when a user asks, "I want to try something new," the generation AI analyzes the user's facial expression and tone of voice, and if the user is in a curious state, it will suggest an activity that will provide a new experience. For example, it will suggest participating in a nearby art workshop. This makes it possible to suggest entertainment plans based on the user's emotions.

[0089] The question receiver can estimate the user's emotional state in response to the user's question and suggest a relaxation plan based on that emotion. For example, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling stressed, it will suggest a relaxation plan. For example, it will suggest relaxing at a nearby spa. Similarly, when a user asks, "I want to do something fun," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling positive, it will suggest a relaxing cafe. This makes it possible to suggest relaxation plans based on the user's emotions.

[0090] The query receiver can estimate the user's emotional state in response to the user's query and make personalized suggestions based on their emotions. For example, when a user asks, "I want to do something fun," the generation AI analyzes the user's facial expression and tone of voice, and if they are feeling positive, it will suggest activities that suit the user's preferences. For example, it may suggest a cafe that the user has previously rated highly. Similarly, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's facial expression and tone of voice, and if they are feeling stressed, it may suggest a relaxation activity that suits the user's preferences. For example, it may suggest a spa that the user has visited in the past. This makes it possible to make personalized suggestions based on the user's emotions.

[0091] The question receiver can estimate the user's emotional state in response to the user's question and suggest short activities based on their emotions. For example, when a user asks, "Tell me what I can do in an hour," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling positive, suggests an active activity that can be done in a short amount of time. For example, it might suggest jogging in a nearby park. Similarly, when a user asks, "I want to do something relaxing," the generation AI analyzes the user's facial expression and tone of voice, and if the user is feeling stressed, it might suggest a short relaxation activity. For example, it might suggest taking a break at a nearby cafe. This makes it possible to suggest short activities based on the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The question receiver receives a question from the user. For example, the user may ask, "I want to do something, but I don't know what to do," or a specific question such as, "Tell me what I can do in the next hour." Step 2: The situation assessment unit assesses the current situation based on the query received by the query reception unit. For example, it may use the GPS information from the user's smartphone to determine the user's current location, obtain weather forecast data, and check the weather. It may also check whether the user is alone or with other people. Step 3: The plan generation unit generates an optimal plan based on the situation grasped by the situation grasp unit. For example, if the user says, "I want to relax at a cafe," the plan generation unit checks the business status and congestion status of cafes around the current location and suggests the most suitable cafe. Similarly, if the user says, "I want to see a movie," the plan generation unit checks the screening schedules of nearby movie theaters and suggests the most suitable movie. Step 4: The suggestion unit proposes the plan generated by the plan generation unit to the user. For example, it may suggest, "There's a cafe open five minutes' walk from your current location. Why not spend some time relaxing there?" It may also make specific suggestions, such as, "There's a movie starting in 15 minutes at a nearby movie theater. Tickets are still available for purchase."

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

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

Claims

1. a query receiving unit that receives a query from a user; a situation grasping unit that grasps a current situation based on the question received by the question receiving unit; a plan generation unit that generates an optimal plan based on the situation grasped by the situation grasping unit; a proposal unit that proposes the plan generated by the plan generation unit to a user. A system characterized by:

2. The inquiry receiving unit Sentiment analysis is performed on the user's question, and suggestions are made according to the user's emotional state.

2. The system of claim 1.

3. The situation grasping unit Analyzing the user's health condition and making suggestions according to their physical condition 2. The system of claim 1.

4. The plan generation unit Analyzing the user's past feedback to improve the accuracy of suggestions 2. The system of claim 1.

5. The proposal unit Analyzing the user's past selection history and making personalized suggestions 2. The system of claim 1.

6. The inquiry receiving unit Analyze the user's facial expression and tone of voice when asking a question and make suggestions based on their emotions.

2. The system of claim 1.

7. The situation grasping unit Understanding the user's current emotional state and making emotionally based suggestions 2. The system of claim 1.

8. The plan generation unit Analyzing the user's current emotional state and generating an emotion-based entertainment plan.

2. The system of claim 1.

Images