system

Abstract

The system according to the embodiment aims to propose an optimal action plan for a user to deal with spare time or sudden changes in plans. [Solution] A system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an execution unit. The reception unit inputs a user's desired action. The acquisition unit acquires a current situation based on the information input by the reception unit. The proposal unit proposes an appropriate plan based on the information acquired by the acquisition unit. The execution unit executes the plan proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to find an optimal action plan to deal with spare time or sudden schedule changes.

[0005] The system according to the embodiment aims to propose an optimal action plan for a user to deal with spare time or sudden changes in plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a proposal unit, and an execution unit. The reception unit inputs a user's desired action. The acquisition unit acquires a current situation based on the information input by the reception unit. The proposal unit proposes an appropriate plan based on the information acquired by the acquisition unit. The execution unit executes the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal action plan for a user to deal with spare time or sudden changes in plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A time-killing plan suggestion system according to an embodiment of the present invention is a system that suggests optimal time-killing plans when a user is unsure of what to do in their spare time. When the system asks the user what they want to do, it considers the user's current situation (location, time, number of people, etc.) and suggests the optimal plan. The system obtains the user's current location, weather, the business status and congestion status of surrounding stores, etc. in real time and provides the user with the optimal plan. For example, when a user inputs a specific request such as "I want to relax at a cafe" or "I want to watch a movie," this information is input into the system. Next, the system obtains the user's current situation. For example, it collects information such as the user's current location, the current time, the number of accompanying people, and the weather. This allows it to suggest the optimal plan based on the user's situation. Based on the collected information, the system obtains the business status and congestion status of surrounding stores in real time. For example, it checks the business status and congestion status of cafes and suggests cafes with vacancies. It also checks movie theater screening schedules and congestion status and suggests the optimal movie. Finally, the system suggests the optimal time-killing plan. For example, it suggests specific plans such as "relaxing at a nearby cafe" or "watching a movie at a movie theater." In this way, users can make effective use of their spare time. This system allows users to execute the optimal plan without having to think about what to do in their spare time. For example, even if there is a sudden change in the weather or a change in plans, the system will suggest the optimal plan in real time, so users can spend their spare time with peace of mind. In this way, the time-killing plan suggestion system can effectively utilize the user's spare time and provide the optimal plan.

[0029] A time-killing plan proposal system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an execution unit. The reception unit inputs a user's desired activity. For example, the user can input specific requests such as "I want to relax at a cafe" or "I want to watch a movie." The acquisition unit acquires a current situation based on the information input by the reception unit. For example, the acquisition unit collects information such as the user's current location, the current time, the number of accompanying persons, and the weather. For example, the acquisition unit identifies the user's current location using a GPS and acquires the current time from a clock in the system. The acquisition unit can also acquire weather information from the Internet and collect the number of accompanying persons based on input from the user. The proposal unit proposes an optimal plan based on the information acquired by the acquisition unit. For example, the proposal unit obtains the business status and congestion status of surrounding stores in real time based on the collected information and proposes an optimal plan. For example, the proposal unit checks the business status and congestion status of cafes via the Internet and proposes an available cafe. The proposal unit can also check movie theater screening schedules and congestion status to propose an optimal movie. The execution unit supports the execution of the proposed plan. For example, the execution unit can make a reservation at the suggested cafe. The execution unit can also purchase tickets for the suggested movie. In this way, the time-killing plan proposal system according to the embodiment can propose and implement an optimal time-killing plan based on what the user wants to do.

[0030] The acquisition unit can collect information on the user's current location, time, number of people, and weather. The acquisition unit, for example, identifies the user's current location using GPS. For example, the acquisition unit acquires the current location using the GPS function of the user's smartphone. The acquisition unit can also acquire the current time from a clock in the system. For example, the acquisition unit periodically updates the clock in the system to acquire the accurate current time. The acquisition unit can also collect the number of accompanying people based on input from the user. For example, the acquisition unit stores the number of accompanying people entered by the user in the system and provides it to the suggestion unit. The acquisition unit can also acquire weather information from the Internet. For example, the acquisition unit acquires current weather information from a weather forecast site and reflects it in the system. This allows the acquisition unit to collect information according to the user's current situation.

[0031] The suggestion unit can obtain the business status and congestion status of surrounding stores in real time based on the collected information and propose an appropriate plan. The suggestion unit, for example, checks the business status and congestion status of cafes via the Internet. For example, the suggestion unit obtains information from cafe official websites or services that provide congestion status and proposes cafes that are open. The suggestion unit can also check movie theater screening schedules and congestion status. For example, the suggestion unit obtains information from movie theater official websites or ticket sales sites and proposes the most suitable movie. Furthermore, the suggestion unit can propose plans based on the user's preferences and budget based on the collected information. For example, the suggestion unit selects and proposes the most suitable plan based on the user's desired activities and budget input. In this way, the suggestion unit can obtain the business status and congestion status of surrounding stores in real time and propose the most suitable plan to the user.

[0032] The execution unit can support the execution of the proposed plan. For example, the execution unit makes a reservation at a proposed cafe. For example, the execution unit accesses the cafe's reservation system and completes the reservation by entering the user's name, number of people, and reservation time. The execution unit can also purchase tickets for the proposed movie. For example, the execution unit accesses a movie theater's ticket sales site, selects the showtime and seats, and purchases tickets. Furthermore, the execution unit can provide the user with information necessary for the execution of the proposed plan. For example, the execution unit notifies the user of information such as the address and business hours of the cafe, the location and showtimes of the movie theater, etc. In this way, the execution unit supports the execution of the proposed plan and enables the user to execute the plan smoothly.

[0033] The suggestion unit can suggest an optimal time-killing plan based on what the user wants to do. For example, if the user inputs "I want to relax at a cafe," the suggestion unit checks the business hours and congestion status of nearby cafes and suggests an available cafe. For example, the suggestion unit acquires information from cafe official websites or a service that provides congestion status and selects an optimal cafe. Furthermore, if the user inputs "I want to see a movie," the suggestion unit checks movie theater screening schedules and congestion status and suggests an optimal movie. For example, the suggestion unit acquires information from movie theater official websites or ticket sales sites and selects an optimal movie based on the movies currently showing and screening times. Furthermore, the suggestion unit can suggest plans based on the user's preferences and budget. For example, the suggestion unit selects and suggests an optimal plan based on the user's desired activities and budget. In this way, the suggestion unit can suggest an optimal plan based on the user's desired activities and budget, thereby improving user satisfaction.

[0034] The acquisition unit can acquire the user's current situation in real time. For example, the acquisition unit acquires the user's current location in real time using a GPS. For example, the acquisition unit periodically updates the current location using the GPS function of the user's smartphone and acquires it in real time. The acquisition unit can also acquire the current time in real time from a clock in the system. For example, the acquisition unit periodically updates the clock in the system and acquires the accurate current time in real time. The acquisition unit can also collect the number of accompanying persons in real time based on an input from the user. For example, the acquisition unit stores the number of accompanying persons entered by the user in the system and updates it in real time. The acquisition unit can also acquire weather information from the Internet in real time. For example, the acquisition unit periodically acquires current weather information from a weather forecast site and reflects it in the system. As a result, the acquisition unit can acquire the user's current situation in real time and propose a plan based on the latest information.

[0035] The reception unit can analyze the user's past input history and suggest an optimal input method. The reception unit, for example, automatically displays as candidates things to do that the user has frequently input in the past. For example, the reception unit searches a database for the user's past input history and displays as candidates the things to do that have been frequently input. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit analyzes the user's past input history and preferentially displays the most used input method. Furthermore, the reception unit can predict and suggest things to do that will be used in a specific time period from the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and predicts and suggests things to do that have been frequently input in a specific time period. In this way, the reception unit can provide the user with an optimal input method by analyzing the past input history.

[0036] The reception unit can automatically complete the input of things to do based on the user's current activity status. For example, when the user is traveling, the reception unit automatically completes things to do related to traveling. For example, the reception unit detects that the user is traveling using an acceleration sensor or GPS information of the user's smartphone, and displays things to do related to traveling as candidates. Furthermore, when the user is taking a break, the reception unit can automatically complete things to do that allow the user to relax. For example, the reception unit detects the state of the user's smartphone (screen is off, charging, etc.), determines that the user is taking a break, and displays things to do that allow the user to relax as candidates. Furthermore, when the user is working, the reception unit can automatically complete things to do that can be done in between work. For example, the reception unit analyzes the user's calendar information and device usage status, determines that the user is working, and displays things to do that can be done in a short amount of time as candidates. In this way, the reception unit can automatically complete the input based on the user's current activity status, thereby reducing the burden on the user when entering information.

[0037] The reception unit can present input candidates for things to do by taking into account the user's geographical location information. For example, if the user is near their current location, the reception unit presents nearby things to do as candidates. For example, the reception unit identifies the user's current location using GPS information from the user's smartphone and displays nearby things to do as candidates. Furthermore, if the user is in a specific area, the reception unit can present things to do related to that area as candidates. For example, the reception unit displays things to do related to the area as candidates based on the user's current location information. Furthermore, if the user is traveling, the reception unit can present things to do at the user's travel destination as candidates. For example, the reception unit displays things to do that can be done at the user's travel destination as candidates based on the user's travel destination information. In this way, the reception unit can present input candidates suitable for the user by taking into account the geographical location information.

[0038] The reception unit can analyze the user's social media activity and suggest related to-dos. The reception unit can, for example, suggest to-dos related to places the user has shared on social media. For example, the reception unit can analyze the user's social media account and display to-dos related to the shared places as candidates. The reception unit can also suggest to-dos based on posts from accounts the user follows. For example, the reception unit can analyze the content of posts from accounts the user follows and display related to-dos as candidates. The reception unit can also suggest to-dos related to events the user plans to attend. For example, the reception unit can analyze the user's calendar information and social media event information and display related to-dos as candidates. In this way, the reception unit can suggest to-dos related to the user by analyzing social media activity.

[0039] The acquisition unit can analyze the user's past behavioral history and select the optimal acquisition method. The acquisition unit, for example, acquires the current situation based on places the user has visited in the past. For example, the acquisition unit searches a database for the user's past behavioral history and acquires the current situation based on the places the user has visited in the past. The acquisition unit can also analyze the user's past behavioral patterns and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past behavioral patterns using pattern recognition technology and select the optimal acquisition method. Furthermore, the acquisition unit can acquire the current situation by referring to the user's past movement history. For example, the acquisition unit can analyze the user's past movement history in chronological order and acquire the current situation. In this way, the acquisition unit can select the optimal acquisition method by analyzing the past behavioral history.

[0040] When acquiring the current situation, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is interested in health, the acquisition unit prioritizes acquiring health-related information. For example, the acquisition unit prioritizes acquiring health-related information based on the user's past behavioral history and survey results. Furthermore, if the user is interested in hobbies, the acquisition unit can prioritize acquiring hobby-related information. For example, the acquisition unit prioritizes acquiring hobby-related information based on the user's past selections and behavioral history. Furthermore, if the user is interested in work, the acquisition unit can prioritize acquiring work-related information. For example, the acquisition unit prioritizes acquiring work-related information based on the user's schedule and behavioral history. In this way, the acquisition unit can acquire information suitable for the user by filtering based on the living situation and areas of interest.

[0041] When acquiring the current situation, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is near the current location, the acquisition unit prioritizes acquiring nearby information. For example, the acquisition unit identifies the current location using GPS information of the user's smartphone and prioritizes acquiring nearby information. Furthermore, when the user is in a specific area, the acquisition unit can prioritize acquiring information related to that area. For example, the acquisition unit prioritizes acquiring information related to the area based on the user's current location information. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring information about the user's travel destination. For example, the acquisition unit prioritizes acquiring information related to the location based on the user's travel destination information. In this way, the acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information.

[0042] When acquiring the current situation, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires information related to places shared by the user on social media. For example, the acquisition unit analyzes the user's social media account and acquires information related to the shared places. The acquisition unit can also acquire information based on posts from accounts followed by the user. For example, the acquisition unit analyzes the content of posts from accounts followed by the user and acquires related information. Furthermore, the acquisition unit can acquire information related to events the user plans to attend. For example, the acquisition unit analyzes the user's calendar information and social media event information and acquires related information. In this way, the acquisition unit can acquire related information by analyzing social media activity.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. For example, for a plan with high importance, the suggestion unit makes a proposal including detailed information. For example, the suggestion unit identifies a plan with high importance based on information input by the user and past behavior history, and displays a proposal including detailed information. Furthermore, the suggestion unit can make a concise proposal for a plan with low importance. For example, the suggestion unit identifies a plan with low importance and displays a concise proposal. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a plan with medium importance. In this way, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan and make an optimal proposal for the user.

[0044] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. For example, in the case of a leisure-related plan, the proposal unit applies a relaxing proposal algorithm. For example, the proposal unit identifies a leisure-related plan based on the user's input information and past behavioral history, and applies a relaxing proposal algorithm. Furthermore, the proposal unit can apply an efficient proposal algorithm in the case of a business-related plan. For example, the proposal unit identifies a business-related plan and applies an efficient proposal algorithm. Furthermore, the proposal unit can apply a health-conscious proposal algorithm in the case of a health-related plan. For example, the proposal unit identifies a health-related plan and applies a health-conscious proposal algorithm. In this way, the proposal unit can apply different proposal algorithms depending on the category of the plan and make optimal proposals for the user.

[0045] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. For example, the proposal unit preferentially proposes plans that are submitted early. For example, the proposal unit identifies plans that are submitted early based on information input by the user and past behavioral history, and preferentially proposes the plans. The proposal unit can also postpone plans that are submitted late. For example, the proposal unit identifies plans that are submitted late and postpones them. Furthermore, the proposal unit can gradually adjust the priority of the proposal depending on the time of submission. For example, the proposal unit sets a moderate priority for plans that are submitted at a medium time. In this way, the proposal unit can determine the priority of the proposal based on the time of submission of the plan, and make the optimal proposal for the user.

[0046] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the plans. For example, the proposal unit preferentially proposes highly relevant plans. For example, the proposal unit identifies highly relevant plans based on the user's input information and past behavior history, and preferentially proposes them. The proposal unit can also postpone less relevant plans. For example, the proposal unit identifies less relevant plans and postpones them. Furthermore, the proposal unit can gradually adjust the order of proposals according to the relevance. For example, the proposal unit sets an appropriate order for plans with medium relevance. This allows the proposal unit to adjust the order of proposals based on the relevance of the plans, and make optimal proposals for the user.

[0047] At the time of execution, the execution unit can analyze the user's past behavior history and select the optimal execution method. The execution unit selects the optimal execution method, for example, based on execution methods used by the user in the past. For example, the execution unit searches a database for the user's past behavior history and selects the optimal execution method based on the execution methods used in the past. The execution unit can also select an efficient execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior patterns and selects an efficient execution method. Furthermore, the execution unit can select the optimal execution method by referring to the user's past movement history. For example, the execution unit analyzes the user's past movement history in chronological order and selects the optimal execution method. In this way, the execution unit can select the optimal execution method by analyzing the past behavior history.

[0048] At the time of execution, the execution unit can customize the execution means based on the user's current living situation. For example, if the user is interested in health, the execution unit provides health-conscious execution means. For example, the execution unit provides health-related execution means based on the user's past behavioral history and survey results. Furthermore, if the user is interested in hobbies, the execution unit can provide hobby-related execution means. For example, the execution unit provides hobby-related execution means based on the user's past selections and behavioral history. Furthermore, if the user is interested in work, the execution unit can provide work-related execution means. For example, the execution unit provides work-related execution means based on the user's schedule and behavioral history. In this way, the execution unit can customize the execution means based on the current living situation and provide execution means suitable for the user.

[0049] At the time of execution, the execution unit can select the optimal execution method by taking into account the user's geographical location information. For example, if the user is near a current location, the execution unit provides nearby execution methods. For example, the execution unit identifies the current location using GPS information from the user's smartphone and displays nearby execution methods. Furthermore, if the user is in a specific area, the execution unit can provide execution methods related to that area. For example, the execution unit displays execution methods related to the area based on the user's current location information. Furthermore, if the user is traveling, the execution unit can provide execution methods for the user's travel destination. For example, the execution unit displays the optimal execution method for that location based on the user's travel destination information. In this way, the execution unit can select the optimal execution method by taking into account the geographical location information.

[0050] During execution, the execution unit can analyze the user's social media activity and suggest an action means. For example, the execution unit can suggest an action means related to a location shared by the user on social media. For example, the execution unit can analyze the user's social media account and display an action means related to the shared location. The execution unit can also suggest an action means based on posts from accounts the user follows. For example, the execution unit can analyze the content of posts from accounts the user follows and display related action means. Furthermore, the execution unit can suggest an action means related to an event the user plans to attend. For example, the execution unit can analyze the user's calendar information and social media event information and display related action means. In this way, the execution unit can suggest related action means by analyzing social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can analyze the user's past behavioral history and acquire optimal information based on the current situation. For example, it prioritizes acquiring information related to the current situation based on places the user has visited and services the user has used in the past. Specifically, the acquisition unit searches a database for the user's past behavioral history and acquires the current situation based on information about cafes and movie theaters the user has visited in the past. The acquisition unit can also analyze the user's past behavioral patterns and acquire optimal information. For example, the acquisition unit can analyze the user's past behavioral patterns using pattern recognition technology to acquire information related to the current situation. Furthermore, the current situation can be acquired by referring to the user's past movement history. For example, the acquisition unit can analyze the user's past movement history in chronological order to acquire information related to the current situation. In this way, the acquisition unit can acquire optimal information based on the current situation by analyzing the past behavioral history.

[0053] The acquisition unit can automatically complete the input of things to do based on the user's current activity status. For example, if the user is on the move, it automatically completes things to do related to travel. Specifically, the acquisition unit detects that the user is on the move using an acceleration sensor or GPS information of the user's smartphone, and displays things to do related to travel as candidates. Furthermore, if the user is on a break, it can automatically complete things to do that allow the user to relax. For example, the acquisition unit detects the state of the user's smartphone (e.g., the screen is off, the device is charging), determines that the user is on a break, and displays things to do that allow the user to relax as candidates. Furthermore, if the user is at work, it can automatically complete things to do in between work. For example, the acquisition unit analyzes the user's calendar information and device usage status, determines that the user is at work, and displays things to do that can be done in a short amount of time as candidates. In this way, the acquisition unit automatically completes the input based on the user's current activity status, thereby reducing the burden on the user.

[0054] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates things to do that the user has frequently input in the past. Specifically, the reception unit searches a database for the user's past input history and displays as candidates the things to do that have been frequently input. It can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit analyzes the user's past input history and preferentially displays the most used input method. It can also predict and suggest things to do that will be used in a specific time period from the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and predicts and suggests things to do that have been frequently input in a specific time period. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history.

[0055] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan. For example, for a highly important plan, the suggestion unit makes a proposal including detailed information. Specifically, the suggestion unit identifies a highly important plan based on the user's input information and past behavior history, and displays a proposal including detailed information. Furthermore, for a low-importance plan, the suggestion unit can make a concise proposal. For example, the suggestion unit identifies a low-importance plan and displays a concise proposal. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a plan with medium importance. In this way, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan and make an optimal proposal for the user.

[0056] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. For example, in the case of a leisure-related plan, a relaxing proposal algorithm is applied. Specifically, the proposal unit identifies a leisure-related plan based on the user's input information and past behavioral history, and applies a relaxing proposal algorithm. In addition, in the case of a business-related plan, an efficient proposal algorithm can be applied. For example, the proposal unit identifies a business-related plan and applies an efficient proposal algorithm. Furthermore, in the case of a health-related plan, a health-conscious proposal algorithm can be applied. For example, the proposal unit identifies a health-related plan and applies a health-conscious proposal algorithm. In this way, the proposal unit can apply different proposal algorithms depending on the category of the plan and make optimal proposals for the user.

[0057] At the time of execution, the execution unit can analyze the user's past behavior history and select the optimal execution method. For example, the execution unit selects the optimal execution method based on the execution methods used by the user in the past. Specifically, the execution unit searches the user's past behavior history from a database and selects the optimal execution method based on the execution methods used in the past. In addition, the execution unit can select an efficient execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior patterns and selects an efficient execution method. Furthermore, the execution unit can select the optimal execution method by referring to the user's past movement history. For example, the execution unit analyzes the user's past movement history in chronological order and selects the optimal execution method. In this way, the execution unit can select the optimal execution method by analyzing the past behavior history.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit inputs the user's desired action. For example, the user can input specific requests such as "I want to relax at a cafe" or "I want to watch a movie." Step 2: The acquisition unit acquires the current situation based on the information input by the reception unit. For example, it collects information such as the user's current location, the current time, the number of accompanying persons, and the weather. For example, the acquisition unit identifies the user's current location using a GPS and acquires the current time from a clock in the system. The acquisition unit can also acquire weather information from the Internet and collect the number of accompanying persons based on input from the user. Step 3: The suggestion unit proposes the optimal plan based on the information acquired by the acquisition unit. For example, the suggestion unit acquires the business status and congestion status of surrounding stores in real time based on the collected information and proposes the optimal plan. For example, the suggestion unit checks the business status and congestion status of cafes via the Internet and proposes cafes that are open. The suggestion unit can also check the screening schedule and congestion status of movie theaters and propose the optimal movie to watch. Step 4: The execution unit supports the execution of the proposed plan. For example, the execution unit can make a reservation at a proposed cafe. The execution unit can also purchase tickets for a proposed movie.

[0060] (Example 2) A time-killing plan suggestion system according to an embodiment of the present invention is a system that suggests optimal time-killing plans when a user is unsure of what to do in their spare time. When the system asks the user what they want to do, it considers the user's current situation (location, time, number of people, etc.) and suggests the optimal plan. The system obtains the user's current location, weather, the business status and congestion status of surrounding stores, etc. in real time and provides the user with the optimal plan. For example, when a user inputs a specific request such as "I want to relax at a cafe" or "I want to watch a movie," this information is input into the system. Next, the system obtains the user's current situation. For example, it collects information such as the user's current location, the current time, the number of accompanying people, and the weather. This allows it to suggest the optimal plan based on the user's situation. Based on the collected information, the system obtains the business status and congestion status of surrounding stores in real time. For example, it checks the business status and congestion status of cafes and suggests cafes with vacancies. It also checks movie theater screening schedules and congestion status and suggests the optimal movie. Finally, the system suggests the optimal time-killing plan. For example, it suggests specific plans such as "relaxing at a nearby cafe" or "watching a movie at a movie theater." In this way, users can make effective use of their spare time. This system allows users to execute the optimal plan without having to think about what to do in their spare time. For example, even if there is a sudden change in the weather or a change in plans, the system will suggest the optimal plan in real time, so users can spend their spare time with peace of mind. In this way, the time-killing plan suggestion system can effectively utilize the user's spare time and provide the optimal plan.

[0061] A time-killing plan proposal system according to an embodiment includes a reception unit, an acquisition unit, a proposal unit, and an execution unit. The reception unit inputs a user's desired activity. For example, the user can input specific requests such as "I want to relax at a cafe" or "I want to watch a movie." The acquisition unit acquires a current situation based on the information input by the reception unit. For example, the acquisition unit collects information such as the user's current location, the current time, the number of accompanying persons, and the weather. For example, the acquisition unit identifies the user's current location using a GPS and acquires the current time from a clock in the system. The acquisition unit can also acquire weather information from the Internet and collect the number of accompanying persons based on input from the user. The proposal unit proposes an optimal plan based on the information acquired by the acquisition unit. For example, the proposal unit obtains the business status and congestion status of surrounding stores in real time based on the collected information and proposes an optimal plan. For example, the proposal unit checks the business status and congestion status of cafes via the Internet and proposes an available cafe. The proposal unit can also check movie theater screening schedules and congestion status to propose an optimal movie. The execution unit supports the execution of the proposed plan. For example, the execution unit can make a reservation at the suggested cafe. The execution unit can also purchase tickets for the suggested movie. In this way, the time-killing plan proposal system according to the embodiment can propose and implement an optimal time-killing plan based on what the user wants to do.

[0062] The acquisition unit can collect information on the user's current location, time, number of people, and weather. The acquisition unit, for example, identifies the user's current location using GPS. For example, the acquisition unit acquires the current location using the GPS function of the user's smartphone. The acquisition unit can also acquire the current time from a clock in the system. For example, the acquisition unit periodically updates the clock in the system to acquire the accurate current time. The acquisition unit can also collect the number of accompanying people based on input from the user. For example, the acquisition unit stores the number of accompanying people entered by the user in the system and provides it to the suggestion unit. The acquisition unit can also acquire weather information from the Internet. For example, the acquisition unit acquires current weather information from a weather forecast site and reflects it in the system. This allows the acquisition unit to collect information according to the user's current situation.

[0063] The suggestion unit can obtain the business status and congestion status of surrounding stores in real time based on the collected information and propose an appropriate plan. The suggestion unit, for example, checks the business status and congestion status of cafes via the Internet. For example, the suggestion unit obtains information from cafe official websites or services that provide congestion status and proposes cafes that are open. The suggestion unit can also check movie theater screening schedules and congestion status. For example, the suggestion unit obtains information from movie theater official websites or ticket sales sites and proposes the most suitable movie. Furthermore, the suggestion unit can propose plans based on the user's preferences and budget based on the collected information. For example, the suggestion unit selects and proposes the most suitable plan based on the user's desired activities and budget input. In this way, the suggestion unit can obtain the business status and congestion status of surrounding stores in real time and propose the most suitable plan to the user.

[0064] The execution unit can support the execution of the proposed plan. For example, the execution unit makes a reservation at a proposed cafe. For example, the execution unit accesses the cafe's reservation system and completes the reservation by entering the user's name, number of people, and reservation time. The execution unit can also purchase tickets for the proposed movie. For example, the execution unit accesses a movie theater's ticket sales site, selects the showtime and seats, and purchases tickets. Furthermore, the execution unit can provide the user with information necessary for the execution of the proposed plan. For example, the execution unit notifies the user of information such as the address and business hours of the cafe, the location and showtimes of the movie theater, etc. In this way, the execution unit supports the execution of the proposed plan and enables the user to execute the plan smoothly.

[0065] The suggestion unit can suggest an optimal time-killing plan based on what the user wants to do. For example, if the user inputs "I want to relax at a cafe," the suggestion unit checks the business hours and congestion status of nearby cafes and suggests an available cafe. For example, the suggestion unit acquires information from cafe official websites or a service that provides congestion status and selects an optimal cafe. Furthermore, if the user inputs "I want to see a movie," the suggestion unit checks movie theater screening schedules and congestion status and suggests an optimal movie. For example, the suggestion unit acquires information from movie theater official websites or ticket sales sites and selects an optimal movie based on the movies currently showing and screening times. Furthermore, the suggestion unit can suggest plans based on the user's preferences and budget. For example, the suggestion unit selects and suggests an optimal plan based on the user's desired activities and budget. In this way, the suggestion unit can suggest an optimal plan based on the user's desired activities and budget, thereby improving user satisfaction.

[0066] The acquisition unit can acquire the user's current situation in real time. For example, the acquisition unit acquires the user's current location in real time using a GPS. For example, the acquisition unit periodically updates the current location using the GPS function of the user's smartphone and acquires it in real time. The acquisition unit can also acquire the current time in real time from a clock in the system. For example, the acquisition unit periodically updates the clock in the system and acquires the accurate current time in real time. The acquisition unit can also collect the number of accompanying persons in real time based on an input from the user. For example, the acquisition unit stores the number of accompanying persons entered by the user in the system and updates it in real time. The acquisition unit can also acquire weather information from the Internet in real time. For example, the acquisition unit periodically acquires current weather information from a weather forecast site and reflects it in the system. As a result, the acquisition unit can acquire the user's current situation in real time and propose a plan based on the latest information.

[0067] The reception unit can estimate the user's emotions and adjust the input method for what the user wants to do based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, the reception unit detects that the user is feeling stressed using facial expression recognition technology and displays a simple interface. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit detects that the user is relaxed using voice analysis technology and displays detailed input options. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input so that the user can quickly input what they want to do. For example, the reception unit detects that the user is in a hurry using behavior analysis technology and displays voice input with priority. In this way, the reception unit can provide an input method that corresponds to the user's emotions, improving user convenience.

[0068] The reception unit can analyze the user's past input history and suggest an optimal input method. The reception unit, for example, automatically displays as candidates things to do that the user has frequently input in the past. For example, the reception unit searches a database for the user's past input history and displays as candidates the things to do that have been frequently input. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit analyzes the user's past input history and preferentially displays the most used input method. Furthermore, the reception unit can predict and suggest things to do that will be used in a specific time period from the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and predicts and suggests things to do that have been frequently input in a specific time period. In this way, the reception unit can provide the user with an optimal input method by analyzing the past input history.

[0069] The reception unit can automatically complete the input of things to do based on the user's current activity status. For example, when the user is traveling, the reception unit automatically completes things to do related to traveling. For example, the reception unit detects that the user is traveling using an acceleration sensor or GPS information of the user's smartphone, and displays things to do related to traveling as candidates. Furthermore, when the user is taking a break, the reception unit can automatically complete things to do that allow the user to relax. For example, the reception unit detects the state of the user's smartphone (screen is off, charging, etc.), determines that the user is taking a break, and displays things to do that allow the user to relax as candidates. Furthermore, when the user is working, the reception unit can automatically complete things to do that can be done in between work. For example, the reception unit analyzes the user's calendar information and device usage status, determines that the user is working, and displays things to do that can be done in a short amount of time as candidates. In this way, the reception unit can automatically complete the input based on the user's current activity status, thereby reducing the burden on the user when entering information.

[0070] The reception unit can estimate the user's emotions and prioritize the inputted to-dos based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes suggesting relaxing to-dos. For example, the reception unit can detect that the user is feeling stressed using facial expression recognition technology and prioritize displaying relaxing to-dos. Furthermore, if the user is relaxed, the reception unit can prioritize suggesting active to-dos. For example, the reception unit can detect that the user is relaxing using user voice analysis technology and prioritize displaying active to-dos. Furthermore, if the user is in a hurry, the reception unit can prioritize suggesting to-dos that can be completed in a short amount of time. For example, the reception unit can detect that the user is in a hurry using user behavior analysis technology and prioritize displaying to-dos that can be completed in a short amount of time. In this way, the reception unit can prioritize suggesting to-dos based on the user's emotions and propose a more appropriate plan.

[0071] The reception unit can present input candidates for things to do by taking into account the user's geographical location information. For example, if the user is near their current location, the reception unit presents nearby things to do as candidates. For example, the reception unit identifies the current location using GPS information from the user's smartphone and displays nearby things to do as candidates. Furthermore, if the user is in a specific area, the reception unit can present things to do related to that area as candidates. For example, the reception unit displays things to do related to the area as candidates based on the user's current location information. Furthermore, if the user is traveling, the reception unit can present things to do at the user's travel destination as candidates. For example, the reception unit displays things to do that can be done at the user's travel destination as candidates based on the user's travel destination information. In this way, the reception unit can present input candidates suitable for the user by taking into account the geographical location information.

[0072] The reception unit can analyze the user's social media activity and suggest related to-dos. The reception unit can, for example, suggest to-dos related to places the user has shared on social media. For example, the reception unit can analyze the user's social media account and display to-dos related to the shared places as candidates. The reception unit can also suggest to-dos based on posts from accounts the user follows. For example, the reception unit can analyze the content of posts from accounts the user follows and display related to-dos as candidates. The reception unit can also suggest to-dos related to events the user plans to attend. For example, the reception unit can analyze the user's calendar information and social media event information and display related to-dos as candidates. In this way, the reception unit can suggest to-dos related to the user by analyzing social media activity.

[0073] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the current situation based on the estimated user emotions. For example, if the user is relaxed, the acquisition unit periodically acquires the situation. For example, the acquisition unit detects that the user is relaxed using a user's facial expression recognition technology and periodically acquires the situation. Furthermore, if the user is in a hurry, the acquisition unit can frequently acquire the situation. For example, the acquisition unit detects that the user is in a hurry using a user's behavior analysis technology and frequently acquires the situation. Furthermore, if the user is feeling stressed, the acquisition unit can reduce the frequency of acquiring the situation. For example, the acquisition unit detects that the user is feeling stressed using a user's voice analysis technology and reduce the frequency of acquiring the situation. This allows the acquisition unit to adjust the acquisition timing based on the user's emotions and acquire more appropriate information.

[0074] The acquisition unit can analyze the user's past behavioral history and select the optimal acquisition method. The acquisition unit, for example, acquires the current situation based on places the user has visited in the past. For example, the acquisition unit searches a database for the user's past behavioral history and acquires the current situation based on the places the user has visited in the past. The acquisition unit can also analyze the user's past behavioral patterns and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past behavioral patterns using pattern recognition technology and select the optimal acquisition method. Furthermore, the acquisition unit can acquire the current situation by referring to the user's past movement history. For example, the acquisition unit can analyze the user's past movement history in chronological order and acquire the current situation. In this way, the acquisition unit can select the optimal acquisition method by analyzing the past behavioral history.

[0075] When acquiring the current situation, the acquisition unit can perform filtering based on the user's current living situation and areas of interest. For example, if the user is interested in health, the acquisition unit prioritizes acquiring health-related information. For example, the acquisition unit prioritizes acquiring health-related information based on the user's past behavioral history and survey results. Furthermore, if the user is interested in hobbies, the acquisition unit can prioritize acquiring hobby-related information. For example, the acquisition unit prioritizes acquiring hobby-related information based on the user's past selections and behavioral history. Furthermore, if the user is interested in work, the acquisition unit can prioritize acquiring work-related information. For example, the acquisition unit prioritizes acquiring work-related information based on the user's schedule and behavioral history. In this way, the acquisition unit can acquire information suitable for the user by filtering based on the living situation and areas of interest.

[0076] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes acquiring information that helps the user relax. For example, the acquisition unit detects that the user is feeling stressed using a facial expression recognition technology, and prioritizes acquiring information that helps the user relax. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring active information. For example, the acquisition unit detects that the user is relaxed using a voice analysis technology, and prioritizes acquiring active information. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired in a short time. For example, the acquisition unit detects that the user is in a hurry using a behavior analysis technology, and prioritizes acquiring information that can be acquired in a short time. In this way, the acquisition unit can prioritize information based on the user's emotions and acquire more appropriate information.

[0077] When acquiring the current situation, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is near the current location, the acquisition unit prioritizes acquiring nearby information. For example, the acquisition unit identifies the current location using GPS information of the user's smartphone and prioritizes acquiring nearby information. Furthermore, when the user is in a specific area, the acquisition unit can prioritize acquiring information related to that area. For example, the acquisition unit prioritizes acquiring information related to the area based on the user's current location information. Furthermore, when the user is traveling, the acquisition unit can prioritize acquiring information about the user's travel destination. For example, the acquisition unit prioritizes acquiring information related to the location based on the user's travel destination information. In this way, the acquisition unit can prioritize acquiring highly relevant information by taking into account the geographical location information.

[0078] When acquiring the current situation, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, acquires information related to places shared by the user on social media. For example, the acquisition unit analyzes the user's social media account and acquires information related to the shared places. The acquisition unit can also acquire information based on posts from accounts followed by the user. For example, the acquisition unit analyzes the content of posts from accounts followed by the user and acquires related information. Furthermore, the acquisition unit can acquire information related to events the user plans to attend. For example, the acquisition unit analyzes the user's calendar information and social media event information and acquires related information. In this way, the acquisition unit can acquire related information by analyzing social media activity.

[0079] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit makes a simple and highly visible suggestion. For example, the suggestion unit detects that the user is nervous using facial expression recognition technology and displays a simple and highly visible suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a suggestion including detailed information. For example, the suggestion unit detects that the user is relaxed using user voice analysis technology and displays a suggestion including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. For example, the suggestion unit detects that the user is in a hurry using user behavior analysis technology and displays a suggestion that focuses on the main points. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions and make more appropriate suggestions.

[0080] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. For example, for a plan with high importance, the suggestion unit makes a proposal including detailed information. For example, the suggestion unit identifies a plan with high importance based on information input by the user and past behavior history, and displays a proposal including detailed information. Furthermore, the suggestion unit can make a concise proposal for a plan with low importance. For example, the suggestion unit identifies a plan with low importance and displays a concise proposal. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a plan with medium importance. In this way, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan and make an optimal proposal for the user.

[0081] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. For example, in the case of a leisure-related plan, the proposal unit applies a relaxing proposal algorithm. For example, the proposal unit identifies a leisure-related plan based on the user's input information and past behavioral history, and applies a relaxing proposal algorithm. Furthermore, the proposal unit can apply an efficient proposal algorithm in the case of a business-related plan. For example, the proposal unit identifies a business-related plan and applies an efficient proposal algorithm. Furthermore, the proposal unit can apply a health-conscious proposal algorithm in the case of a health-related plan. For example, the proposal unit identifies a health-related plan and applies a health-conscious proposal algorithm. In this way, the proposal unit can apply different proposal algorithms depending on the category of the plan and make optimal proposals for the user.

[0082] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit makes short and to-the-point suggestions. For example, the suggestion unit detects that the user is in a hurry using user behavior analysis technology and displays short and to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can make longer suggestions including detailed explanations. For example, the suggestion unit detects that the user is relaxed using user voice analysis technology and displays longer suggestions including detailed explanations. Furthermore, if the user is excited, the suggestion unit can make visually stimulating suggestions. For example, the suggestion unit detects that the user is excited using user facial expression recognition technology and displays visually stimulating suggestions. This allows the suggestion unit to adjust the length of the suggestions based on the user's emotions and make more appropriate suggestions.

[0083] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the plan. For example, the proposal unit preferentially proposes plans that are submitted early. For example, the proposal unit identifies plans that are submitted early based on information input by the user and past behavioral history, and preferentially proposes the plans. The proposal unit can also postpone plans that are submitted late. For example, the proposal unit identifies plans that are submitted late and postpones them. Furthermore, the proposal unit can gradually adjust the priority of the proposal depending on the time of submission. For example, the proposal unit sets a moderate priority for plans that are submitted at a medium time. In this way, the proposal unit can determine the priority of the proposal based on the time of submission of the plan, and make the optimal proposal for the user.

[0084] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the plans. For example, the proposal unit preferentially proposes highly relevant plans. For example, the proposal unit identifies highly relevant plans based on the user's input information and past behavior history, and preferentially proposes them. The proposal unit can also postpone less relevant plans. For example, the proposal unit identifies less relevant plans and postpones them. Furthermore, the proposal unit can gradually adjust the order of proposals according to the relevance. For example, the proposal unit sets an appropriate order for plans with medium relevance. This allows the proposal unit to adjust the order of proposals based on the relevance of the plans, and make optimal proposals for the user.

[0085] The execution unit can estimate the user's emotions and adjust the execution method of the plan based on the estimated user emotions. For example, if the user is nervous, the execution unit provides a simple and highly visible execution method. For example, the execution unit detects that the user is nervous using facial expression recognition technology and displays a simple and highly visible execution method. Furthermore, if the user is relaxed, the execution unit can provide an execution method including detailed information. For example, the execution unit detects that the user is relaxed using user voice analysis technology and displays an execution method including detailed information. Furthermore, if the user is in a hurry, the execution unit can provide an execution method that focuses on the main points. For example, the execution unit detects that the user is in a hurry using user behavior analysis technology and displays an execution method that focuses on the main points. In this way, the execution unit can adjust the execution method of the plan based on the user's emotions and provide a more appropriate execution method.

[0086] At the time of execution, the execution unit can analyze the user's past behavior history and select the optimal execution method. The execution unit selects the optimal execution method, for example, based on execution methods used by the user in the past. For example, the execution unit searches a database for the user's past behavior history and selects the optimal execution method based on the execution methods used in the past. The execution unit can also select an efficient execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior patterns and selects an efficient execution method. Furthermore, the execution unit can select the optimal execution method by referring to the user's past movement history. For example, the execution unit analyzes the user's past movement history in chronological order and selects the optimal execution method. In this way, the execution unit can select the optimal execution method by analyzing the past behavior history.

[0087] At the time of execution, the execution unit can customize the execution means based on the user's current living situation. For example, if the user is interested in health, the execution unit provides health-conscious execution means. For example, the execution unit provides health-related execution means based on the user's past behavioral history and survey results. Furthermore, if the user is interested in hobbies, the execution unit can provide hobby-related execution means. For example, the execution unit provides hobby-related execution means based on the user's past selections and behavioral history. Furthermore, if the user is interested in work, the execution unit can provide work-related execution means. For example, the execution unit provides work-related execution means based on the user's schedule and behavioral history. In this way, the execution unit can customize the execution means based on the current living situation and provide execution means suitable for the user.

[0088] The execution unit can estimate the user's emotions and determine the priority of execution based on the estimated user emotions. For example, if the user is feeling stressed, the execution unit preferentially suggests activities that will help the user relax. For example, the execution unit can detect that the user is feeling stressed using facial expression recognition technology and preferentially display activities that will help the user relax. Furthermore, if the user is relaxed, the execution unit can preferentially suggest active activities. For example, the execution unit can detect that the user is relaxed using user voice analysis technology and preferentially display active activities. Furthermore, if the user is in a hurry, the execution unit can preferentially suggest activities that can be executed in a short time. For example, the execution unit can detect that the user is in a hurry using user behavior analysis technology and preferentially display activities that can be executed in a short time. This allows the execution unit to determine the priority of execution based on the user's emotions and perform more appropriate execution.

[0089] At the time of execution, the execution unit can select the optimal execution method by taking into account the user's geographical location information. For example, if the user is near a current location, the execution unit provides nearby execution methods. For example, the execution unit identifies the current location using GPS information from the user's smartphone and displays nearby execution methods. Furthermore, if the user is in a specific area, the execution unit can provide execution methods related to that area. For example, the execution unit displays execution methods related to the area based on the user's current location information. Furthermore, if the user is traveling, the execution unit can provide execution methods for the user's travel destination. For example, the execution unit displays the optimal execution method for that location based on the user's travel destination information. In this way, the execution unit can select the optimal execution method by taking into account the geographical location information.

[0090] During execution, the execution unit can analyze the user's social media activity and suggest an action means. For example, the execution unit can suggest an action means related to a location shared by the user on social media. For example, the execution unit can analyze the user's social media account and display an action means related to the shared location. The execution unit can also suggest an action means based on posts from accounts the user follows. For example, the execution unit can analyze the content of posts from accounts the user follows and display related action means. Furthermore, the execution unit can suggest an action means related to an event the user plans to attend. For example, the execution unit can analyze the user's calendar information and social media event information and display related action means. In this way, the execution unit can suggest related action means by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and execution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's desired action. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the user's current location, weather information, etc. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan. The execution unit is realized, for example, by the control unit 46A of the smart device 14 and supports the execution of the proposed plan. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and execution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's desired action. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the user's current location, weather information, etc. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan. The execution unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports the execution of the proposed plan. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and execution unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the user's desired action. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the user's current location, weather information, etc. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan. The execution unit is realized, for example, by the control unit 46A of the headset type terminal 314 and supports the execution of the proposed plan. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, proposal unit, and execution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's desired action. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects the user's current location, weather information, etc. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal plan. The execution unit is realized, for example, by the control unit 46A of the robot 414 and supports the execution of the proposed plan.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The suggestion unit can estimate the user's emotions and adjust the type of plan to be suggested based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting relaxing plans. Specifically, the suggestion unit can detect that the user is feeling stressed using facial expression recognition technology and suggest plans for relaxing cafes or spas. Furthermore, if the user is relaxed, the suggestion unit can suggest active plans. For example, the suggestion unit can detect that the user is feeling relaxed using user voice analysis technology and suggest plans for hiking or sports. Furthermore, if the user is in a hurry, the suggestion unit can suggest plans that can be enjoyed in a short amount of time. For example, the suggestion unit can detect that the user is in a hurry using user behavior analysis technology and suggest plans for cafes or movies that can be enjoyed in a short amount of time. In this way, the suggestion unit can adjust the type of plan based on the user's emotions and make more appropriate suggestions.

[0093] The acquisition unit can analyze the user's past behavioral history and acquire optimal information based on the current situation. For example, it prioritizes acquiring information related to the current situation based on places the user has visited and services the user has used in the past. Specifically, the acquisition unit searches a database for the user's past behavioral history and acquires the current situation based on information about cafes and movie theaters the user has visited in the past. The acquisition unit can also analyze the user's past behavioral patterns and acquire optimal information. For example, the acquisition unit can analyze the user's past behavioral patterns using pattern recognition technology to acquire information related to the current situation. Furthermore, the current situation can be acquired by referring to the user's past movement history. For example, the acquisition unit can analyze the user's past movement history in chronological order to acquire information related to the current situation. In this way, the acquisition unit can acquire optimal information based on the current situation by analyzing the past behavioral history.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit makes simple, highly visible suggestions. Specifically, the suggestion unit detects that the user is nervous using facial expression recognition technology and displays simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can make suggestions including detailed information. For example, the suggestion unit detects that the user is relaxed using user voice analysis technology and displays suggestions including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. For example, the suggestion unit detects that the user is in a hurry using user behavior analysis technology and displays suggestions that focus on the main points. In this way, the suggestion unit can adjust the way suggestions are expressed based on the user's emotions and make more appropriate suggestions.

[0095] The execution unit can estimate the user's emotions and adjust the execution method of the plan based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible execution method is provided. Specifically, the execution unit detects that the user is nervous using facial expression recognition technology and displays a simple and highly visible execution method. Furthermore, if the user is relaxed, an execution method including detailed information can be provided. For example, the execution unit detects that the user is relaxed using user voice analysis technology and displays an execution method including detailed information. Furthermore, if the user is in a hurry, an execution method that focuses on the main points can be provided. For example, the execution unit detects that the user is in a hurry using user behavior analysis technology and displays an execution method that focuses on the main points. In this way, the execution unit can adjust the execution method of the plan based on the user's emotions and provide a more appropriate execution method.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. Specifically, the suggestion unit can detect that the user is in a hurry using user behavior analysis technology and display short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. For example, the suggestion unit can detect that the user is relaxed using user voice analysis technology and display longer suggestions with detailed explanations. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, the suggestion unit can detect that the user is excited using user facial expression recognition technology and display visually stimulating suggestions. This allows the suggestion unit to adjust the length of suggestions based on the user's emotions and provide more appropriate suggestions.

[0097] The acquisition unit can automatically complete the input of things to do based on the user's current activity status. For example, if the user is on the move, it automatically completes things to do related to travel. Specifically, the acquisition unit detects that the user is on the move using an acceleration sensor or GPS information of the user's smartphone, and displays things to do related to travel as candidates. Furthermore, if the user is on a break, it can automatically complete things to do that allow the user to relax. For example, the acquisition unit detects the state of the user's smartphone (e.g., the screen is off, the device is charging), determines that the user is on a break, and displays things to do that allow the user to relax as candidates. Furthermore, if the user is at work, it can automatically complete things to do in between work. For example, the acquisition unit analyzes the user's calendar information and device usage status, determines that the user is at work, and displays things to do that can be done in a short amount of time as candidates. In this way, the acquisition unit automatically completes the input based on the user's current activity status, thereby reducing the burden on the user.

[0098] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display as candidates things to do that the user has frequently input in the past. Specifically, the reception unit searches a database for the user's past input history and displays as candidates the things to do that have been frequently input. It can also preferentially suggest input methods (such as voice and text) that the user has used in the past. For example, the reception unit analyzes the user's past input history and preferentially displays the most used input method. It can also predict and suggest things to do that will be used in a specific time period from the user's past input history. For example, the reception unit chronologically analyzes the user's past input history and predicts and suggests things to do that have been frequently input in a specific time period. In this way, the reception unit can provide the user with the optimal input method by analyzing the past input history.

[0099] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan. For example, for a highly important plan, the suggestion unit makes a proposal including detailed information. Specifically, the suggestion unit identifies a highly important plan based on the user's input information and past behavior history, and displays a proposal including detailed information. Furthermore, for a low-importance plan, the suggestion unit can make a concise proposal. For example, the suggestion unit identifies a low-importance plan and displays a concise proposal. Furthermore, the suggestion unit can gradually adjust the level of detail of the proposal depending on the importance. For example, the suggestion unit makes a proposal with an appropriate level of detail for a plan with medium importance. In this way, the suggestion unit can adjust the level of detail of the proposal based on the importance of the plan and make an optimal proposal for the user.

[0100] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the plan. For example, in the case of a leisure-related plan, a relaxing proposal algorithm is applied. Specifically, the proposal unit identifies a leisure-related plan based on the user's input information and past behavioral history, and applies a relaxing proposal algorithm. In addition, in the case of a business-related plan, an efficient proposal algorithm can be applied. For example, the proposal unit identifies a business-related plan and applies an efficient proposal algorithm. Furthermore, in the case of a health-related plan, a health-conscious proposal algorithm can be applied. For example, the proposal unit identifies a health-related plan and applies a health-conscious proposal algorithm. In this way, the proposal unit can apply different proposal algorithms depending on the category of the plan and make optimal proposals for the user.

[0101] At the time of execution, the execution unit can analyze the user's past behavior history and select the optimal execution method. For example, the execution unit selects the optimal execution method based on the execution methods used by the user in the past. Specifically, the execution unit searches the user's past behavior history from a database and selects the optimal execution method based on the execution methods used in the past. In addition, the execution unit can select an efficient execution method from the user's past behavior history. For example, the execution unit analyzes the user's past behavior patterns and selects an efficient execution method. Furthermore, the execution unit can select the optimal execution method by referring to the user's past movement history. For example, the execution unit analyzes the user's past movement history in chronological order and selects the optimal execution method. In this way, the execution unit can select the optimal execution method by analyzing the past behavior history.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The reception unit inputs the user's desired action. For example, the user can input specific requests such as "I want to relax at a cafe" or "I want to watch a movie." Step 2: The acquisition unit acquires the current situation based on the information input by the reception unit. For example, it collects information such as the user's current location, the current time, the number of accompanying persons, and the weather. For example, the acquisition unit identifies the user's current location using a GPS and acquires the current time from a clock in the system. The acquisition unit can also acquire weather information from the Internet and collect the number of accompanying persons based on input from the user. Step 3: The suggestion unit proposes the optimal plan based on the information acquired by the acquisition unit. For example, the suggestion unit acquires the business status and congestion status of surrounding stores in real time based on the collected information and proposes the optimal plan. For example, the suggestion unit checks the business status and congestion status of cafes via the Internet and proposes cafes that are open. The suggestion unit can also check the screening schedule and congestion status of movie theaters and propose the optimal movie to watch. Step 4: The execution unit supports the execution of the proposed plan. For example, the execution unit can make a reservation at a cafe that was proposed. The execution unit can also purchase tickets for a movie that was proposed.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a user's desired action; an acquisition unit that acquires a current situation based on the information input by the reception unit; a proposal unit that proposes an appropriate plan based on the information acquired by the acquisition unit; an execution unit that executes the plan proposed by the proposal unit A system characterized by:

2. The acquisition unit Collect information about the user's current location, time, number of people, and weather 2. The system of claim 1.

3. The proposal unit Based on the collected information, the system obtains information on the business status and congestion status of surrounding stores in real time and proposes an appropriate plan.

2. The system of claim 1.

4. The execution unit: Support the implementation of the proposed plan 2. The system of claim 1.

5. The proposal unit Propose the best time-killing plan based on what the user wants to do 2. The system of claim 1.

6. The acquisition unit Get the user's current status in real time 2. The system of claim 1.

7. The reception unit Inferring the user's emotions and adjusting the input method based on the inferred user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

9. The reception unit Auto-complete what you want to do based on your current activity 2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the inputted tasks based on the estimated user emotions.

2. The system of claim 1.

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