System

Abstract

An object of a system according to an embodiment is to support a user to efficiently make daily decisions.SOLUTION: A system includes an information collection part, a decision notification part, and a decision support part. The information collecting unit collects information such as a user's schedule, weather, and news. The decision notification unit notifies a matter to be decided on the day on the basis of the information collected by the information collection unit. The decision support unit supports the user to make a decision on the basis of the matters notified by the decision notification unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that busy working people and people raising children can become tired of making so many decisions every day.

[0005] The system according to the embodiment aims to support users in making efficient daily decisions. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a decision notification unit, and a decision support unit. The information collection unit collects information such as the user's schedule, weather, and news. The decision notification unit notifies the user of decisions to be made that day based on the information collected by the information collection unit. The decision support unit supports the user in making decisions based on the matters notified by the decision notification unit. [Effects of the Invention]

[0007] The system according to the embodiment can assist users in making efficient daily decisions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The decision support system according to the embodiment of the present invention collects information such as a user's schedule, weather, and news, notifies the user of the decisions to be made that day, and provides support for decision-making. In this way, the decision support system can support the user in making daily decisions efficiently.

[0029] A decision support system according to an embodiment includes an information collection unit, a decision notification unit, and a decision support unit. The information collection unit collects information such as a user's schedule, weather, and news. For example, the information collection unit acquires schedule information from a user's calendar app. The information collection unit can also acquire weather information from a weather forecast service. The information collection unit can also collect the latest news from a news site. The decision notification unit notifies the user of decisions to be made that day based on the information collected by the information collection unit. For example, if the user's schedule includes an important meeting, the decision notification unit notifies the user of preparations to be made for the meeting. If the weather is bad, the decision notification unit can suggest items to bring when going out. The decision notification unit can also send these notifications to the user's smartphone or PC. The decision support unit supports the user in making decisions based on the information notified by the decision notification unit. For example, if the user is unsure what clothes to wear, the decision support unit can suggest the most appropriate outfit based on weather information and schedule information. Furthermore, when the user is unsure which news to prioritize, the decision support unit allows the generation AI to select and suggest news based on the user's interests and concerns. This allows the decision support system according to the embodiment to support the user in making efficient daily decisions.

[0030] The information collection unit can acquire schedule information from a user's calendar app and weather information from a weather forecast service. The information collection unit acquires schedule information from, for example, a user's calendar app. For example, schedule information can be acquired from Google Calendar or Outlook Calendar. The information collection unit also acquires weather information from a weather forecast service. For example, weather information can be acquired from data from the Japan Meteorological Agency or private weather forecast services. This allows for efficient collection of the user's schedule and weather information.

[0031] If an important meeting is included in the schedule, the decision notification unit can notify the user of the preparations that should be made for the meeting. For example, if an important meeting is included in the schedule, the decision notification unit notifies the user of the preparations that should be made for the meeting. For example, the decision notification unit can notify the user to prepare materials for the meeting or to practice a presentation. The decision notification unit can also notify the user to reconfirm the location and time of the meeting. This allows the user to make appropriate preparations for an important meeting.

[0032] When a user is unsure what clothes to wear, the decision support unit can suggest the most suitable clothes based on weather information and schedule information. For example, when a user is unsure what clothes to wear, the decision support unit can suggest the most suitable clothes based on weather information and schedule information. For example, if it is raining, the decision support unit can suggest bringing rain gear. Also, if an important meeting is included in the schedule, the decision support unit can suggest formal clothes. The decision support unit can also suggest clothes that the user prefers based on the user's past clothing history. This allows the user to select appropriate clothes.

[0033] The information collection unit can analyze the user's past behavioral history and collect predicted behavior in advance based on future schedules and weather. For example, the generation AI analyzes the user's past behavioral history and collects predicted behavior in advance based on future schedules and weather. For example, based on past schedule data, the information collection unit predicts behavioral patterns for specific days of the week and weather conditions and collects necessary information in advance. The information collection unit also analyzes the user's past behavioral history and learns behavioral patterns for specific events and weather conditions. For example, for a user who often takes an umbrella when going out on rainy days, the information collection unit notifies the user to take an umbrella based on the weather forecast. The information collection unit also predicts behavior according to future schedules and weather based on the user's past behavioral history and collects necessary information in advance. For example, if past data indicates a tendency to participate in a specific event, the generation AI collects information related to that event in advance. This allows the user's behavior to be predicted and necessary information to be collected in advance.

[0034] The information collection unit can analyze emotions and interests from a user's social media account and collect related news and event information. For example, the generation AI analyzes the user's social media account and extracts emotions and interests from the content of posts and reactions. For example, if a user has a positive reaction to a particular topic, it collects news and event information related to that topic. The information collection unit also analyzes emotions and interests from the user's social media account and collects related news and event information. For example, if a user frequently uses a particular hashtag, it collects information related to that hashtag. The information collection unit also analyzes the user's social media account and collects related news and event information based on emotions and interests. For example, if a user has an interest in a particular event, it collects the latest information related to that event. This makes it possible to collect related information based on the user's emotions and interests.

[0035] The information collection unit can collect information from the user's home IoT devices and make suggestions based on the living environment. For example, the information collection unit uses a generation AI to collect information from the user's home IoT devices and make suggestions based on the living environment. For example, it can suggest necessary ingredients based on refrigerator inventory information. The information collection unit also collects information from the user's home IoT devices and makes suggestions based on the living environment. For example, it can suggest a comfortable living environment based on the air conditioner's set temperature and the brightness of the lights. The information collection unit also uses a generation AI to collect information from the home IoT devices and make suggestions based on the living environment. For example, it can analyze voice commands from a smart speaker and make suggestions based on the user's needs. This makes it possible to make appropriate suggestions based on the user's living environment.

[0036] The information collection unit can collect the user's health data and provide decision-making support based on their health condition. For example, the generation AI in the information collection unit collects the user's health data and provides decision-making support based on their health condition. For example, it suggests exercise or rest based on their heart rate and sleep patterns. The information collection unit also collects the user's health data and provides decision-making support based on their health condition. For example, it suggests healthy lifestyle habits based on their daily activity level and dietary habits. The information collection unit also collects the user's health data and provides decision-making support based on their health condition. For example, it analyzes their stress level and suggests relaxation methods. This allows appropriate decision-making support to be provided based on the user's health condition.

[0037] The decision notification unit can analyze the user's current location information and notify the user of decisions based on the location. For example, the generation AI analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is in a specific location, the decision notification unit notifies the user of information and suggestions related to that location. The decision notification unit also analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is out and about, the decision notification unit provides information about nearby restaurants and cafes. The decision notification unit also analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is traveling, the decision notification unit notifies the user of tourist attractions and transportation information. This makes it possible to notify the user of appropriate decisions based on their current location information.

[0038] The decision notification unit can work in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, the generation AI of the decision notification unit works in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, important decisions are communicated to the user by voice through a smart speaker. The decision notification unit can also work in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, the voice assistant notifies the user of decisions while the user is driving a car. The decision notification unit can also work in conjunction with the generation AI of the voice assistant to notify the user of decisions by voice. For example, important information is communicated by voice while the user is doing housework. This allows decisions to be communicated by voice through the voice assistant.

[0039] The decision notification unit can work in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, the generation AI works in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, the optimal route to the destination is suggested through a navigation system. The decision notification unit also works in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, it notifies the user of points to be careful of while driving based on traffic conditions and weather information. The decision notification unit also works in conjunction with the in-vehicle system to notify the user of decisions that need to be made while driving. For example, it suggests the nearest gas station based on the amount of gas remaining. This allows the user to be appropriately notified of decisions that need to be made while driving.

[0040] The decision support unit can analyze the user's past decision results and suggest options with a high success rate. For example, the decision support unit uses a generation AI to analyze the user's past decision results and suggest options with a high success rate. For example, it presents the optimal option in a similar situation based on options that have been successful in the past. The decision support unit also analyzes the user's past decision results and suggest options with a high success rate. For example, it suggests the optimal action under specific conditions based on past data. The decision support unit also uses a generation AI to analyze the user's past decision results and suggest options with a high success rate. For example, it presents options that are suitable for the current situation based on past successful cases. This makes it possible to suggest options with a high success rate based on the user's past decision results.

[0041] The decision support unit can collect opinions of the user's friends and family and provide support from multiple perspectives. In the decision support unit, for example, the generation AI collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on the opinions of friends and family, it suggests the best option for the user. In addition, the decision support unit collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on the opinions of family, it supports the user in making the best decision. In addition, the decision support unit collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on advice from friends, it suggests the best action for the user. In this way, it is possible to collect opinions of the user's friends and family and provide support from multiple perspectives.

[0042] The decision support unit can analyze the user's purchasing history and suggest shopping lists and purchase timings. In the decision support unit, for example, the generation AI analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it lists products that are purchased regularly and notifies the user of the optimal purchase timing. The decision support unit also analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it lists necessary products based on past purchase data and suggests the best time to purchase them. In the decision support unit, the generation AI analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it notifies the user when a specific product goes on sale and encourages the user to purchase it. This makes it possible to suggest shopping lists and purchase timings based on the user's purchasing history.

[0043] The decision support unit can analyze the user's travel history and suggest the next travel destination or activity. In the decision support unit, for example, the generation AI analyzes the user's travel history and suggests the next travel destination or activity. For example, it suggests a new travel destination based on places visited in the past and preferred activities. The decision support unit also analyzes the user's travel history and suggests the next travel destination or activity. For example, it suggests the optimal travel plan for the user based on past travel data. In the decision support unit, the generation AI analyzes the user's travel history and suggests the next travel destination or activity. For example, it lists travel destinations and activities that match the user's preferences. This makes it possible to suggest the next travel destination or activity based on the user's travel history.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The information collection unit can analyze a user's past purchasing history and predict future purchasing behavior. For example, it can create a list of products that are purchased regularly and notify the user of the next purchase timing. It can also predict when a specific product will go on sale and notify the user. Furthermore, it can analyze the user's purchasing patterns and suggest new products and services. This allows for efficient support of the user's purchasing behavior.

[0046] The information collection unit can collect information from the user's home IoT devices and make suggestions based on the user's living environment. For example, it can suggest necessary ingredients based on refrigerator inventory information. It can also suggest a comfortable living environment based on the air conditioner's temperature setting and lighting brightness. It can also analyze voice commands from a smart speaker and make suggestions based on the user's needs. This allows it to make appropriate suggestions based on the user's living environment.

[0047] The information collection unit can collect the user's health data and provide decision support based on the user's health condition. For example, it can suggest exercise or rest based on the user's heart rate and sleep patterns. It can also suggest healthy lifestyle habits based on the user's daily activity level and dietary habits. It can also analyze the user's stress level and suggest relaxation methods. This allows the system to provide appropriate decision support based on the user's health condition.

[0048] The information collection unit can analyze the user's travel history and suggest the user's next travel destination or activity. For example, it can suggest new travel destinations based on places visited in the past and preferred activities. It can also suggest the user's optimal travel plan based on past travel data. It can also list travel destinations and activities that match the user's preferences. This makes it possible to suggest the user's next travel destination or activity based on the user's travel history.

[0049] The decision support unit can collect opinions from the user's friends and family and provide support from multiple perspectives. For example, it can suggest the best option to the user based on the opinions of friends and family. It can also support the user in making the best decision based on the opinions of family members. Furthermore, it can suggest the best action for the user based on advice from friends. This makes it possible to collect opinions from the user's friends and family and provide support from multiple perspectives.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information collection unit collects information such as the user's schedule, weather, news, etc. For example, the information collection unit obtains schedule information from the user's calendar app, weather information from a weather forecast service, and the latest news from a news site. Step 2: The decision notification unit notifies the user of the decisions they need to make that day based on the information collected by the information collection unit. For example, if the user's schedule includes an important meeting, the decision notification unit notifies them of the preparations they need to make for that meeting, and if the weather is bad, it suggests items they should take when going out. These notifications are also sent to the user's smartphone or computer. Step 3: The decision support unit helps the user make decisions based on the information notified by the decision notification unit. For example, if the user is unsure what clothes to wear, the unit will suggest the most suitable outfit based on weather and schedule information. If the user is unsure which news to read first, the generation AI will select and suggest news based on the user's interests.

[0052] (Example 2) The decision support system according to the embodiment of the present invention collects information such as a user's schedule, weather, and news, notifies the user of the decisions to be made that day, and provides support for decision-making. In this way, the decision support system can support the user in making daily decisions efficiently.

[0053] A decision support system according to an embodiment includes an information collection unit, a decision notification unit, and a decision support unit. The information collection unit collects information such as a user's schedule, weather, and news. For example, the information collection unit acquires schedule information from a user's calendar app. The information collection unit can also acquire weather information from a weather forecast service. The information collection unit can also collect the latest news from a news site. The decision notification unit notifies the user of decisions to be made that day based on the information collected by the information collection unit. For example, if the user's schedule includes an important meeting, the decision notification unit notifies the user of preparations to be made for the meeting. If the weather is bad, the decision notification unit can suggest items to bring when going out. The decision notification unit can also send these notifications to the user's smartphone or PC. The decision support unit supports the user in making decisions based on the information notified by the decision notification unit. For example, if the user is unsure what clothes to wear, the decision support unit can suggest the most appropriate outfit based on weather information and schedule information. Furthermore, when the user is unsure which news to prioritize, the decision support unit allows the generation AI to select and suggest news based on the user's interests and concerns. This allows the decision support system according to the embodiment to support the user in making efficient daily decisions.

[0054] The information collection unit can acquire schedule information from a user's calendar app and weather information from a weather forecast service. The information collection unit acquires schedule information from, for example, a user's calendar app. For example, schedule information can be acquired from Google Calendar or Outlook Calendar. The information collection unit also acquires weather information from a weather forecast service. For example, weather information can be acquired from data from the Japan Meteorological Agency or private weather forecast services. This allows for efficient collection of the user's schedule and weather information.

[0055] If an important meeting is included in the schedule, the decision notification unit can notify the user of the preparations that should be made for the meeting. For example, if an important meeting is included in the schedule, the decision notification unit notifies the user of the preparations that should be made for the meeting. For example, the decision notification unit can notify the user to prepare materials for the meeting or to practice a presentation. The decision notification unit can also notify the user to reconfirm the location and time of the meeting. This allows the user to make appropriate preparations for an important meeting.

[0056] When a user is unsure what clothes to wear, the decision support unit can suggest the most suitable clothes based on weather information and schedule information. For example, when a user is unsure what clothes to wear, the decision support unit can suggest the most suitable clothes based on weather information and schedule information. For example, if it is raining, the decision support unit can suggest bringing rain gear. Also, if an important meeting is included in the schedule, the decision support unit can suggest formal clothes. The decision support unit can also suggest clothes that the user prefers based on the user's past clothing history. This allows the user to select appropriate clothes.

[0057] The information collection unit can analyze the user's past behavioral history and collect predicted behavior in advance based on future schedules and weather. For example, the generation AI analyzes the user's past behavioral history and collects predicted behavior in advance based on future schedules and weather. For example, based on past schedule data, the information collection unit predicts behavioral patterns for specific days of the week and weather conditions and collects necessary information in advance. The information collection unit also analyzes the user's past behavioral history and learns behavioral patterns for specific events and weather conditions. For example, for a user who often takes an umbrella when going out on rainy days, the information collection unit notifies the user to take an umbrella based on the weather forecast. The information collection unit also predicts behavior according to future schedules and weather based on the user's past behavioral history and collects necessary information in advance. For example, if past data indicates a tendency to participate in a specific event, the generation AI collects information related to that event in advance. This allows the user's behavior to be predicted and necessary information to be collected in advance.

[0058] The information collection unit can analyze emotions and interests from a user's social media account and collect related news and event information. For example, the generation AI analyzes the user's social media account and extracts emotions and interests from the content of posts and reactions. For example, if a user has a positive reaction to a particular topic, it collects news and event information related to that topic. The information collection unit also analyzes emotions and interests from the user's social media account and collects related news and event information. For example, if a user frequently uses a particular hashtag, it collects information related to that hashtag. The information collection unit also analyzes the user's social media account and collects related news and event information based on emotions and interests. For example, if a user has an interest in a particular event, it collects the latest information related to that event. This makes it possible to collect related information based on the user's emotions and interests.

[0059] The information collection unit can use the emotion estimation function to analyze the user's emotional state in real time and collect information that matches the mood of the day. For example, the information collection unit uses the emotion estimation function to analyze the user's facial expressions and voice and estimate the emotional state in real time. For example, if the user is feeling stressed, it collects information related to relaxation. The information collection unit also analyzes the user's emotional state in real time and collects information that matches the mood of the day. For example, if the user is expressing positive emotions, it collects information related to entertainment and hobbies. The information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and collects information that matches the mood of the day. For example, if the user is tired, it collects information that is useful for refreshing. This makes it possible to collect appropriate information based on the user's emotional state.

[0060] The information collection unit can collect information from the user's home IoT devices and make suggestions based on the living environment. For example, the information collection unit uses a generation AI to collect information from the user's home IoT devices and make suggestions based on the living environment. For example, it can suggest necessary ingredients based on refrigerator inventory information. The information collection unit also collects information from the user's home IoT devices and makes suggestions based on the living environment. For example, it can suggest a comfortable living environment based on the air conditioner's set temperature and the brightness of the lights. The information collection unit also uses a generation AI to collect information from the home IoT devices and make suggestions based on the living environment. For example, it can analyze voice commands from a smart speaker and make suggestions based on the user's needs. This makes it possible to make appropriate suggestions based on the user's living environment.

[0061] The information collection unit can collect the user's health data and provide decision-making support based on their health condition. For example, the generation AI in the information collection unit collects the user's health data and provides decision-making support based on their health condition. For example, it suggests exercise or rest based on their heart rate and sleep patterns. The information collection unit also collects the user's health data and provides decision-making support based on their health condition. For example, it suggests healthy lifestyle habits based on their daily activity level and dietary habits. The information collection unit also collects the user's health data and provides decision-making support based on their health condition. For example, it analyzes their stress level and suggests relaxation methods. This allows appropriate decision-making support to be provided based on the user's health condition.

[0062] The information collection unit can use the emotion estimation function to analyze the emotional states of the user's family and colleagues and collect information for facilitating communication. For example, the information collection unit uses the emotion estimation function to analyze the emotional states of the user's family and colleagues and collect information for facilitating communication. For example, based on the emotional states of the family, the information collection unit suggests an appropriate communication method. The information collection unit also analyzes the emotional states of the user's family and colleagues and collects information for facilitating communication. For example, based on the emotional states of the colleagues, the information collection unit suggests appropriate conversation timing. The information collection unit also uses the emotion estimation function to analyze the emotional states of the user's family and colleagues and collect information for facilitating communication. For example, based on the emotional states of the family, the information collection unit provides advice on how to show empathy. This makes it possible to collect information for facilitating communication with the user's family and colleagues.

[0063] The decision notification unit can analyze the user's current location information and notify the user of decisions based on the location. For example, the generation AI analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is in a specific location, the decision notification unit notifies the user of information and suggestions related to that location. The decision notification unit also analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is out and about, the decision notification unit provides information about nearby restaurants and cafes. The decision notification unit also analyzes the user's current location information and notifies the user of decisions based on the location. For example, if the user is traveling, the decision notification unit notifies the user of tourist attractions and transportation information. This makes it possible to notify the user of appropriate decisions based on their current location information.

[0064] The decision notification unit can use the emotion estimation function to provide notifications at optimal timing depending on the user's emotional state. The decision notification unit, for example, uses the emotion estimation function to provide notifications at optimal timing depending on the user's emotional state. For example, important notifications are provided when the user is relaxed. The decision notification unit also analyzes the user's emotional state in real time and provides notifications at optimal timing. For example, notifications are refrained when the user is feeling stressed. The decision notification unit also uses the emotion estimation function to provide notifications at optimal timing depending on the user's emotional state. For example, notifications are delayed when the user is concentrating. This allows notifications to be provided at optimal timing depending on the user's emotional state.

[0065] The decision notification unit can work in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, the generation AI of the decision notification unit works in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, important decisions are communicated to the user by voice through a smart speaker. The decision notification unit can also work in conjunction with the user's voice assistant to notify the user of decisions by voice. For example, the voice assistant notifies the user of decisions while the user is driving a car. The decision notification unit can also work in conjunction with the generation AI of the voice assistant to notify the user of decisions by voice. For example, important information is communicated by voice while the user is doing housework. This allows decisions to be communicated by voice through the voice assistant.

[0066] The decision notification unit can work in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, the generation AI works in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, the optimal route to the destination is suggested through a navigation system. The decision notification unit also works in conjunction with the user's in-vehicle system to notify the user of decisions that need to be made while driving. For example, it notifies the user of points to be careful of while driving based on traffic conditions and weather information. The decision notification unit also works in conjunction with the in-vehicle system to notify the user of decisions that need to be made while driving. For example, it suggests the nearest gas station based on the amount of gas remaining. This allows the user to be appropriately notified of decisions that need to be made while driving.

[0067] The decision notification unit uses the emotion estimation function to customize the notification content according to the user's emotional state, thereby reducing stress. The decision notification unit, for example, uses the emotion estimation function to customize the notification content according to the user's emotional state. For example, if the user is feeling stressed, the decision notification unit makes suggestions to help the user relax. The decision notification unit also analyzes the user's emotional state in real time and customizes the notification content. For example, if the user is tired, the decision notification unit makes a notification encouraging the user to rest. The decision notification unit also uses the emotion estimation function to customize the notification content according to the user's emotional state, thereby reducing stress. For example, if the user is feeling anxious, the decision notification unit provides information that gives the user a sense of security. In this way, the notification content can be customized according to the user's emotional state, thereby reducing stress.

[0068] The decision support unit can analyze the user's past decision results and suggest options with a high success rate. For example, the decision support unit uses a generation AI to analyze the user's past decision results and suggest options with a high success rate. For example, it presents the optimal option in a similar situation based on options that have been successful in the past. The decision support unit also analyzes the user's past decision results and suggest options with a high success rate. For example, it suggests the optimal action under specific conditions based on past data. The decision support unit also uses a generation AI to analyze the user's past decision results and suggest options with a high success rate. For example, it presents options that are suitable for the current situation based on past successful cases. This makes it possible to suggest options with a high success rate based on the user's past decision results.

[0069] The decision support unit can collect opinions of the user's friends and family and provide support from multiple perspectives. In the decision support unit, for example, the generation AI collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on the opinions of friends and family, it suggests the best option for the user. In addition, the decision support unit collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on the opinions of family, it supports the user in making the best decision. In addition, the decision support unit collects opinions of the user's friends and family and provides support from multiple perspectives. For example, based on advice from friends, it suggests the best action for the user. In this way, it is possible to collect opinions of the user's friends and family and provide support from multiple perspectives.

[0070] The decision support unit can use the emotion estimation function to suggest optimal options based on the user's emotional state. For example, the decision support unit uses the emotion estimation function to suggest optimal options based on the user's emotional state. For example, when the user is relaxed, it suggests options that will refresh the user. The decision support unit also analyzes the user's emotional state in real time and suggests optimal options. For example, when the user is feeling stressed, it suggests options that will help reduce stress. The decision support unit also uses the emotion estimation function to suggest optimal options based on the user's emotional state. For example, when the user is expressing positive emotions, it suggests challenging options. In this way, it is possible to suggest optimal options based on the user's emotional state.

[0071] The decision support unit can analyze the user's purchasing history and suggest shopping lists and purchase timings. In the decision support unit, for example, the generation AI analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it lists products that are purchased regularly and notifies the user of the optimal purchase timing. The decision support unit also analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it lists necessary products based on past purchase data and suggests the best time to purchase them. In the decision support unit, the generation AI analyzes the user's purchasing history and suggests shopping lists and purchase timings. For example, it notifies the user when a specific product goes on sale and encourages the user to purchase it. This makes it possible to suggest shopping lists and purchase timings based on the user's purchasing history.

[0072] The decision support unit can analyze the user's travel history and suggest the next travel destination or activity. In the decision support unit, for example, the generation AI analyzes the user's travel history and suggests the next travel destination or activity. For example, it suggests a new travel destination based on places visited in the past and preferred activities. The decision support unit also analyzes the user's travel history and suggests the next travel destination or activity. For example, it suggests the optimal travel plan for the user based on past travel data. In the decision support unit, the generation AI analyzes the user's travel history and suggests the next travel destination or activity. For example, it lists travel destinations and activities that match the user's preferences. This makes it possible to suggest the next travel destination or activity based on the user's travel history.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The decision support unit can estimate the user's emotional state and suggest an environment that helps the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxation music or a meditation app. If the user is tired, it can also suggest short periods of exercise or stretching to refresh the user. Furthermore, if the user is showing positive emotions, it can suggest activities related to hobbies and interests. This makes it possible to provide the optimal environment according to the user's emotional state.

[0075] The information collection unit can analyze a user's past purchasing history and predict future purchasing behavior. For example, it can create a list of products that are purchased regularly and notify the user of the next purchase timing. It can also predict when a specific product will go on sale and notify the user. Furthermore, it can analyze the user's purchasing patterns and suggest new products and services. This allows for efficient support of the user's purchasing behavior.

[0076] The decision notification unit can estimate the user's emotional state and adjust the content and timing of notifications based on the estimated emotion. For example, sending important notifications when the user is relaxed can reduce stress. Also, delaying notifications when the user is concentrating can prevent interruptions to work. Furthermore, when the user is expressing positive emotions, challenging tasks can be suggested. This allows optimal notifications to be provided according to the user's emotional state.

[0077] The information collection unit can collect information from the user's home IoT devices and make suggestions based on the user's living environment. For example, it can suggest necessary ingredients based on refrigerator inventory information. It can also suggest a comfortable living environment based on the air conditioner's temperature setting and lighting brightness. It can also analyze voice commands from a smart speaker and make suggestions based on the user's needs. This allows it to make appropriate suggestions based on the user's living environment.

[0078] The decision support unit can estimate the user's emotional state and suggest an environment that helps the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxation music or a meditation app. If the user is tired, it can also suggest short periods of exercise or stretching to refresh the user. Furthermore, if the user is showing positive emotions, it can suggest activities related to hobbies and interests. This makes it possible to provide the optimal environment according to the user's emotional state.

[0079] The information collection unit can collect the user's health data and provide decision support based on the user's health condition. For example, it can suggest exercise or rest based on the user's heart rate and sleep patterns. It can also suggest healthy lifestyle habits based on the user's daily activity level and dietary habits. It can also analyze the user's stress level and suggest relaxation methods. This allows the system to provide appropriate decision support based on the user's health condition.

[0080] The decision notification unit can estimate the user's emotional state and adjust the content and timing of notifications based on the estimated emotion. For example, sending important notifications when the user is relaxed can reduce stress. Also, delaying notifications when the user is concentrating can prevent interruptions to work. Furthermore, when the user is expressing positive emotions, challenging tasks can be suggested. This allows optimal notifications to be provided according to the user's emotional state.

[0081] The information collection unit can analyze the user's travel history and suggest the user's next travel destination or activity. For example, it can suggest new travel destinations based on places visited in the past and preferred activities. It can also suggest the user's optimal travel plan based on past travel data. It can also list travel destinations and activities that match the user's preferences. This makes it possible to suggest the user's next travel destination or activity based on the user's travel history.

[0082] The decision support unit can collect opinions from the user's friends and family and provide support from multiple perspectives. For example, it can suggest the best option to the user based on the opinions of friends and family. It can also support the user in making the best decision based on the opinions of family members. Furthermore, it can suggest the best action for the user based on advice from friends. This makes it possible to collect opinions from the user's friends and family and provide support from multiple perspectives.

[0083] The decision support unit can estimate the user's emotional state and suggest an environment that helps the user relax based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxation music or a meditation app. If the user is tired, it can also suggest short periods of exercise or stretching to refresh the user. Furthermore, if the user is showing positive emotions, it can suggest activities related to hobbies and interests. This makes it possible to provide the optimal environment according to the user's emotional state.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The information collection unit collects information such as the user's schedule, weather, news, etc. For example, the information collection unit obtains schedule information from the user's calendar app, weather information from a weather forecast service, and the latest news from a news site. Step 2: The decision notification unit notifies the user of the decisions they need to make that day based on the information collected by the information collection unit. For example, if the user's schedule includes an important meeting, the decision notification unit notifies them of the preparations they need to make for that meeting, and if the weather is bad, it suggests items they should take when going out. These notifications are also sent to the user's smartphone or computer. Step 3: The decision support unit helps the user make decisions based on the information notified by the decision notification unit. For example, if the user is unsure what clothes to wear, the unit will suggest the most suitable outfit based on weather and schedule information. If the user is unsure which news to read first, the generation AI will select and suggest news based on the user's interests.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information collection unit that collects information such as a user's schedule, weather, and news; a decision notification unit that notifies the user of the decision to be made that day based on the information collected by the information collection unit; a decision support unit that supports the user in making a decision based on the matters notified by the decision notifying unit. A system characterized by:

2. The information collecting unit Get schedule information from the user's calendar app and weather information from a weather forecast service 2. The system of claim 1.

3. The decision notification unit If the schedule includes an important meeting, you will be notified of the preparations you need to make for that meeting.

2. The system of claim 1.

4. The decision support unit If the user is unsure what to wear, the app will suggest the best outfit based on weather and schedule information.

2. The system of claim 1.

5. The information collecting unit Analyzing the user's past behavior history and collecting predicted behavior in advance based on the future schedule and weather.

2. The system of claim 1.

6. The information collecting unit Analyzing the user's emotions and interests from the SNS account and collecting related news and event information 2. The system of claim 1.

7. The information collecting unit Analyze the user's emotional state in real time and collect the information tailored to the mood of the day.

2. The system of claim 1.

8. The information collecting unit Collects the information from the user's home IoT devices and makes suggestions based on the user's living environment.

2. The system of claim 1.

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