System

Abstract

An object of a system according to an embodiment is to enable a user to automatically grasp a travel time to a scheduled place.SOLUTION: A system according to an embodiment includes a reception unit, an acquisition unit, a calculation unit, and a proposal unit. The reception unit inputs a scheduled place in the schedule of the user. The acquisition unit acquires information on a current location or a home of the user based on the information input by the reception unit. The calculation unit calculates a travel time to the scheduled place based on the information acquired by the acquisition unit. The proposal unit proposes the travel time calculated by the calculation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology requires users to manually search for travel times to their planned locations, which is a time-consuming process.

[0005] The system according to the embodiment aims to enable a user to automatically know the travel time to a planned location. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, a calculation unit, and a suggestion unit. The reception unit inputs the planned location into the user's schedule. The acquisition unit acquires information about the user's current location or home based on the information input by the reception unit. The calculation unit calculates the travel time to the planned location based on the information acquired by the acquisition unit. The suggestion unit suggests the travel time calculated by the calculation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can automatically allow a user to know the travel time to a planned location. [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) An AI system according to an embodiment of the present invention automatically calculates and suggests to the user the travel time to a scheduled location based on information such as the location, home, and current location entered in the user's schedule. The AI ​​system automatically calculates and suggests the travel time to the scheduled location based on the user's schedule, and the AI ​​acquires information about the user's current location and home and calculates the travel time to the scheduled location. The calculated travel time is then suggested to the user. For example, the AI ​​system allows the user to enter the scheduled location into the schedule. The user can select either their home or their current location as the starting point. The AI ​​system then analyzes the input information and acquires information about the current location and home. The AI ​​system identifies the starting point based on the user's smartphone location information or the registered home address. The AI ​​system then calculates the travel time from the starting point to the scheduled location. The AI ​​system calculates the optimal route based on map data and traffic information, and calculates the travel time based on that route. Finally, the calculated travel time is suggested to the user. This allows the user to easily adjust their schedule and plan their schedule efficiently. This allows the AI ​​system to automatically calculate and suggest travel times based on the user's schedule. For example, a businessperson can create a schedule that takes travel time into account in order to efficiently handle multiple meetings. In addition, travelers can make plans that take travel time into account so that they can tour tourist spots efficiently.

[0029] The AI ​​system according to the embodiment includes a reception unit, an acquisition unit, a calculation unit, and a suggestion unit. The reception unit inputs the location of the planned event into a user's schedule. The user's schedule may include, but is not limited to, events in a calendar app, handwritten notes, or emails. For example, when the user inputs the location of the planned event into the schedule, the reception unit allows the user to select their home or current location as the departure point. The acquisition unit acquires information about the user's current location or home based on the information input by the reception unit. The acquisition unit identifies the departure point based on, for example, location information from the user's smartphone or the user's registered home address. For example, when the user selects the current location, the acquisition unit acquires GPS information from the smartphone and identifies the current location. The calculation unit calculates the travel time to the planned event based on the information acquired by the acquisition unit. For example, the calculation unit calculates an optimal route based on map data and traffic information, and calculates the travel time based on the route. For example, the calculation unit calculates an optimal route based on map data and calculates the travel time based on the route. The suggestion unit suggests the travel time calculated by the calculation unit to the user. The suggestion unit suggests the calculated travel time to the user in the form of, for example, a notification, an alert, or a reminder. As a result, the AI ​​system according to the embodiment can automatically calculate and suggest travel times based on the user's schedule. For example, a business person can create a schedule that takes travel time into account in order to efficiently handle multiple meetings. Also, a traveler can create a plan that takes travel time into account in order to efficiently tour tourist spots.

[0030] The reception unit analyzes the user's past schedule history and suggests an appropriate input method. For example, the reception unit automatically displays as candidates locations of plans that the user has frequently input in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest locations planned to be used during a specific time period from the user's past schedule history. In this way, the optimal input method can be suggested by analyzing the past schedule history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past schedule data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When inputting the planned location, the reception unit filters the results based on the user's current task or area of ​​interest. For example, the reception unit preferentially displays locations related to the user's current task. The reception unit can also suggest related locations as candidates based on the user's area of ​​interest. The reception unit can also filter out unnecessary candidate locations based on the user's current task or area of ​​interest. This allows related locations to be preferentially displayed based on the user's current task or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's task data and area of ​​interest data to a generation AI and cause the generation AI to filter related locations.

[0032] When inputting the planned location, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the planned location using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also identify the planned location using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0033] When inputting the planned location, the reception unit prioritizes inputting highly relevant locations in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location. The reception unit can also prioritize displaying locations close to the user's home. The reception unit can also prioritize displaying highly relevant locations based on the user's past movement history. In this way, highly relevant locations can be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant locations.

[0034] When the planned location is input, the reception unit analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, related places can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related places.

[0035] The reception unit customizes the input method by reflecting the user's past feedback when inputting the planned location. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input method.

[0036] The acquisition unit analyzes the user's past location information history and selects an appropriate acquisition method. The acquisition unit selects the optimal acquisition method based on, for example, places the user has frequently visited in the past. The acquisition unit can also select an acquisition method that avoids congestion based on the user's past location information history. The acquisition unit can also analyze the user's past location information history and select the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the past location information history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's location information history data to a generation AI and cause the generation AI to select the optimal acquisition method.

[0037] When acquiring information about the current location or home, the acquisition unit filters the information based on the user's current living situation or areas of interest. The acquisition unit, for example, prioritizes acquiring highly relevant information based on the user's current living situation. The acquisition unit can also filter relevant information based on the user's areas of interest. The acquisition unit can also filter unnecessary information based on the user's current living situation or areas of interest. This allows relevant information to be acquired preferentially based on the user's current living situation or areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's living situation data or area of ​​interest data to the generation AI and cause the generation AI to filter the relevant information.

[0038] When acquiring information about the current location or home, the acquisition unit selects an appropriate acquisition means according to the user's input method. For example, if the user selects voice input, the acquisition unit acquires information about the current location or home using voice recognition technology. Furthermore, if the user selects text input, the acquisition unit can also support keyboard input. Furthermore, if the user selects image input, the acquisition unit can also identify information about the current location or home using image recognition technology. This improves information acquisition efficiency by selecting the optimal acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0039] When acquiring information about the current location or home, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information close to the user's current location. The acquisition unit can also prioritize acquiring information close to the user's home. The acquisition unit can also prioritize acquiring highly relevant information based on the user's past movement history. This makes it possible to prioritize acquiring highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant information.

[0040] The acquisition unit analyzes the user's social media activities and collects related information when acquiring information about the current location or home. The acquisition unit, for example, acquires information about places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related information. The acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, related information can be acquired by analyzing the user's social media activities. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0041] The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring information about the current location or home. For example, the acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The acquisition unit can also customize the acquisition interface based on the user's past feedback. The acquisition unit can also optimize the acquisition procedure by reflecting the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0042] When calculating travel time, the calculation unit selects an appropriate route by referring to the user's past travel history. For example, the calculation unit selects an optimal route based on routes the user has used in the past. The calculation unit can also select a route that avoids congestion based on the user's past travel history. The calculation unit can also analyze the user's past travel history and select the most efficient route. In this way, the optimal route can be selected by referring to the past travel history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's travel history data into a generation AI and have the generation AI select an optimal route.

[0043] The calculation unit takes into account the user's current traffic conditions and weather information when calculating travel time. The calculation unit, for example, calculates the optimal route based on real-time traffic congestion information. The calculation unit can also calculate the optimal route taking into account the real-time operation status of public transportation. The calculation unit can also calculate the optimal route based on real-time weather information. This allows for more accurate calculation of travel time by taking into account the current traffic conditions and weather information. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input real-time traffic information and weather information into the generation AI and cause the generation AI to calculate the optimal route.

[0044] The calculation unit improves the calculation algorithm by reflecting user feedback when calculating travel time. The calculation unit improves the calculation algorithm, for example, based on feedback provided by the user in the past. The calculation unit can also improve calculation accuracy by reflecting user feedback. The calculation unit can also adjust parameters of the calculation algorithm based on user feedback. In this way, the calculation algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input user feedback data into the generation AI and cause the generation AI to improve the calculation algorithm.

[0045] The calculation unit selects the optimal route by taking into account the user's geographical location information when calculating travel time. For example, the calculation unit prioritizes calculating routes that are closest to the user's current location. The calculation unit can also prioritize calculating routes that are close to the user's home. The calculation unit can also prioritize calculating highly relevant routes based on the user's past travel history. This allows the optimal route to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal route.

[0046] The calculation unit analyzes the user's social media activity when calculating the travel time and presents relevant routes. For example, the calculation unit proposes routes based on locations where the user has checked in on social media. The calculation unit can also analyze the content of the user's social media posts to propose relevant routes. The calculation unit can also propose relevant routes based on the activities of the user's friends on social media. In this way, relevant routes can be proposed by analyzing the user's social media activity. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's social media data into a generation AI and cause the generation AI to propose relevant routes.

[0047] When calculating travel time, the calculation unit makes appropriate suggestions by reflecting the user's past feedback. For example, the calculation unit preferentially suggests calculation methods that the user has previously preferred. The calculation unit can also customize the calculation interface based on the user's past feedback. The calculation unit can also optimize the calculation procedure by reflecting the user's past feedback. In this way, the calculation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input user feedback data into a generation AI and have the generation AI customize the calculation method.

[0048] When proposing travel time, the suggestion unit makes an optimal suggestion by referring to the user's past schedule history. The suggestion unit makes the optimal suggestion, for example, based on the travel time used by the user in the past. The suggestion unit can also make suggestions to avoid congestion based on the user's past schedule history. The suggestion unit can also analyze the user's past schedule history and make the most efficient suggestion. In this way, optimal suggestions can be made by referring to the past schedule history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's schedule history data into a generation AI and cause the generation AI to execute an optimal suggestion.

[0049] When proposing a travel time, the suggestion unit customizes the suggestion content based on the user's current task and area of ​​interest. For example, the suggestion unit prioritizes suggesting travel times related to the user's current task. The suggestion unit can also suggest relevant travel times based on the user's area of ​​interest. The suggestion unit can also filter unnecessary suggestions based on the user's current task and area of ​​interest. This makes it possible to suggest relevant travel times based on the user's current task and area of ​​interest. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's task data and area of ​​interest data into the generation AI and cause the generation AI to customize the suggestion content.

[0050] The suggestion unit improves the suggestion method by reflecting user feedback when suggesting travel times. The suggestion unit improves the suggestion method based on, for example, feedback previously provided by the user. The suggestion unit can also improve the accuracy of suggestions by reflecting user feedback. The suggestion unit can also adjust parameters of the suggestion method based on user feedback. In this way, the suggestion method can be improved by reflecting user feedback. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input user feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0051] When proposing a travel time, the suggestion unit makes an appropriate suggestion based on the user's geographical location information. For example, the suggestion unit prioritizes suggesting travel times that are close to the user's current location. The suggestion unit can also prioritize suggesting travel times that are close to the user's home. The suggestion unit can also prioritize suggesting highly relevant travel times based on the user's past travel history. This allows for optimal suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's location information data into the generation AI and cause the generation AI to make an optimal suggestion.

[0052] When suggesting travel time, the suggestion unit analyzes the user's social media activity and presents relevant suggestions. The suggestion unit makes suggestions based on, for example, locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and make relevant suggestions. The suggestion unit can also make relevant suggestions by referring to the activities of the user's friends on social media. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to execute relevant suggestions.

[0053] When proposing a travel time, the suggestion unit customizes the suggestion method by reflecting the user's past feedback. For example, the suggestion unit preferentially suggests suggestion methods that the user has previously preferred. The suggestion unit can also customize the suggestion interface based on the user's past feedback. The suggestion unit can also optimize the suggestion procedure by reflecting the user's past feedback. In this way, the suggestion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data into a generation AI and cause the generation AI to customize the suggestion method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can also analyze the user's past travel history and acquire information about travel destinations. For example, the acquisition unit can suggest the user's next travel destination based on information about tourist spots and hotels that the user has visited in the past. The acquisition unit can also suggest the optimal travel time to avoid crowds based on the user's travel history. This makes it possible to make more comfortable travel plans by utilizing the user's past travel history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI.

[0056] The calculation unit can also calculate travel time taking into account the user's energy consumption. For example, if the user travels by foot, the calculation unit can calculate the calories burned and suggest a healthy route. The calculation unit can also suggest the optimal travel method taking into account the energy consumption when using a bicycle or public transportation. This makes it possible to calculate travel time that supports the user's health management. The calculation of energy consumption is based on, for example, the user's weight and travel distance.

[0057] The suggestion unit can also customize the content of the suggestions based on the user's past feedback. For example, it can prioritize suggestion methods that the user has previously preferred. It can also optimize the content of the suggestions based on the user's feedback. This makes it possible to make suggestions that suit the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI.

[0058] The calculation unit can also optimize the travel time calculation method by referencing the user's past travel history. For example, the calculation unit can select the optimal route based on routes the user has used in the past. It can also select a route that avoids congestion based on the user's past travel history. This makes it possible to utilize the user's past travel history to calculate travel times more efficiently. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI.

[0059] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, it can acquire information about places where the user has checked in on social media. It can also analyze the content of the user's social media posts and acquire related information. This makes it possible to acquire related information by utilizing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the location of the appointment into the user's schedule. The user's schedule includes appointments in a calendar app, handwritten notes, emails, etc. When the user inputs the location of the appointment into the schedule, they can select their home or current location as the departure point. Step 2: The acquisition unit acquires information about the user's current location or home based on the information input by the reception unit. The acquisition unit identifies the starting point based on the location information of the user's smartphone or the registered home address. For example, if the user selects the current location, the acquisition unit acquires the smartphone's GPS information and identifies the current location. Step 3: The calculation unit calculates the travel time to the scheduled location based on the information acquired by the acquisition unit. The calculation unit calculates the optimal route based on map data and traffic information, and calculates the travel time based on that route. Step 4: The suggestion unit suggests the travel time calculated by the calculation unit to the user. The suggestion unit suggests the calculated travel time to the user in the form of a notification, an alert, a reminder, or the like.

[0062] (Example 2) An AI system according to an embodiment of the present invention automatically calculates and suggests to the user the travel time to a scheduled location based on information such as the location, home, and current location entered in the user's schedule. The AI ​​system automatically calculates and suggests the travel time to the scheduled location based on the user's schedule, and the AI ​​acquires information about the user's current location and home and calculates the travel time to the scheduled location. The calculated travel time is then suggested to the user. For example, the AI ​​system allows the user to enter the scheduled location into the schedule. The user can select either their home or their current location as the starting point. The AI ​​system then analyzes the input information and acquires information about the current location and home. The AI ​​system identifies the starting point based on the user's smartphone location information or the registered home address. The AI ​​system then calculates the travel time from the starting point to the scheduled location. The AI ​​system calculates the optimal route based on map data and traffic information, and calculates the travel time based on that route. Finally, the calculated travel time is suggested to the user. This allows the user to easily adjust their schedule and plan their schedule efficiently. This allows the AI ​​system to automatically calculate and suggest travel times based on the user's schedule. For example, a businessperson can create a schedule that takes travel time into account in order to efficiently handle multiple meetings. In addition, travelers can make plans that take travel time into account so that they can tour tourist spots efficiently.

[0063] The AI ​​system according to the embodiment includes a reception unit, an acquisition unit, a calculation unit, and a suggestion unit. The reception unit inputs the location of the planned event into a user's schedule. The user's schedule may include, but is not limited to, events in a calendar app, handwritten notes, or emails. For example, when the user inputs the location of the planned event into the schedule, the reception unit allows the user to select their home or current location as the departure point. The acquisition unit acquires information about the user's current location or home based on the information input by the reception unit. The acquisition unit identifies the departure point based on, for example, location information from the user's smartphone or the user's registered home address. For example, when the user selects the current location, the acquisition unit acquires GPS information from the smartphone and identifies the current location. The calculation unit calculates the travel time to the planned event based on the information acquired by the acquisition unit. For example, the calculation unit calculates an optimal route based on map data and traffic information, and calculates the travel time based on the route. For example, the calculation unit calculates an optimal route based on map data and calculates the travel time based on the route. The suggestion unit suggests the travel time calculated by the calculation unit to the user. The suggestion unit suggests the calculated travel time to the user in the form of, for example, a notification, an alert, or a reminder. As a result, the AI ​​system according to the embodiment can automatically calculate and suggest travel times based on the user's schedule. For example, a business person can create a schedule that takes travel time into account in order to efficiently handle multiple meetings. Also, a traveler can create a plan that takes travel time into account in order to efficiently tour tourist spots.

[0064] The reception unit estimates the user's emotions and changes the input method for the planned location 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. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable the user to quickly input the planned location. This allows for more appropriate input by adjusting the input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0065] The reception unit analyzes the user's past schedule history and suggests an appropriate input method. For example, the reception unit automatically displays as candidates locations of plans that the user has frequently input in the past. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest locations planned to be used during a specific time period from the user's past schedule history. In this way, the optimal input method can be suggested by analyzing the past schedule history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past schedule data into a generation AI and have the generation AI suggest the optimal input method.

[0066] When inputting the planned location, the reception unit filters the results based on the user's current task or area of ​​interest. For example, the reception unit preferentially displays locations related to the user's current task. The reception unit can also suggest related locations as candidates based on the user's area of ​​interest. The reception unit can also filter out unnecessary candidate locations based on the user's current task or area of ​​interest. This allows related locations to be preferentially displayed based on the user's current task or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's task data and area of ​​interest data to a generation AI and cause the generation AI to filter related locations.

[0067] When inputting the planned location, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the planned location using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also identify the planned location using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0068] The reception unit estimates the user's emotions and prioritizes the locations of planned events to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes displaying the locations of important events. Furthermore, if the user is relaxed, the reception unit can also display all planned locations evenly. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the nearest planned location. This allows important events to be input preferentially by prioritizing the locations of planned events to be input based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0069] When inputting the planned location, the reception unit prioritizes inputting highly relevant locations in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying locations close to the user's current location. The reception unit can also prioritize displaying locations close to the user's home. The reception unit can also prioritize displaying highly relevant locations based on the user's past movement history. In this way, highly relevant locations can be input preferentially by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant locations.

[0070] When the planned location is input, the reception unit analyzes the user's social media activity and suggests related places. For example, the reception unit suggests places where the user has checked in on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related places. The reception unit can also suggest related places by referring to the activity of the user's friends on social media. In this way, related places can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related places.

[0071] The reception unit customizes the input method by reflecting the user's past feedback when inputting the planned location. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's past feedback. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input method.

[0072] The acquisition unit estimates the user's emotions and changes the timing of acquiring information about the current location or home based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit quickly acquires information about the current location or home. Furthermore, when the user is relaxed, the acquisition unit can also acquire information about the current location or home at an appropriate timing. Furthermore, when the user is in a hurry, the acquisition unit can also instantly acquire information about the current location or home. By adjusting the timing of information acquisition according to the user's emotions, information can be acquired at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI. For example, the acquisition unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The acquisition unit analyzes the user's past location information history and selects an appropriate acquisition method. The acquisition unit selects the optimal acquisition method based on, for example, places the user has frequently visited in the past. The acquisition unit can also select an acquisition method that avoids congestion based on the user's past location information history. The acquisition unit can also analyze the user's past location information history and select the most efficient acquisition method. In this way, the optimal acquisition method can be selected by analyzing the past location information history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's location information history data to a generation AI and cause the generation AI to select the optimal acquisition method.

[0074] When acquiring information about the current location or home, the acquisition unit filters the information based on the user's current living situation or areas of interest. The acquisition unit, for example, prioritizes acquiring highly relevant information based on the user's current living situation. The acquisition unit can also filter relevant information based on the user's areas of interest. The acquisition unit can also filter unnecessary information based on the user's current living situation or areas of interest. This allows relevant information to be acquired preferentially based on the user's current living situation or areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's living situation data or area of ​​interest data to the generation AI and cause the generation AI to filter the relevant information.

[0075] When acquiring information about the current location or home, the acquisition unit selects an appropriate acquisition means according to the user's input method. For example, if the user selects voice input, the acquisition unit acquires information about the current location or home using voice recognition technology. Furthermore, if the user selects text input, the acquisition unit can also support keyboard input. Furthermore, if the user selects image input, the acquisition unit can also identify information about the current location or home using image recognition technology. This improves information acquisition efficiency by selecting the optimal acquisition means according to the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0076] The acquisition unit estimates the user's emotions and determines 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 important information. Furthermore, when the user is relaxed, the acquisition unit can also acquire all information equally. Furthermore, when the user is in a hurry, the acquisition unit can also prioritize acquiring the most relevant information. By determining the priority of information according to the user's emotions, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] When acquiring information about the current location or home, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring information close to the user's current location. The acquisition unit can also prioritize acquiring information close to the user's home. The acquisition unit can also prioritize acquiring highly relevant information based on the user's past movement history. This makes it possible to prioritize acquiring highly relevant information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant information.

[0078] The acquisition unit analyzes the user's social media activities and collects related information when acquiring information about the current location or home. The acquisition unit, for example, acquires information about places where the user has checked in on social media. The acquisition unit can also analyze the content of the user's social media posts to acquire related information. The acquisition unit can also acquire related information by referring to the activities of the user's friends on social media. In this way, related information can be acquired by analyzing the user's social media activities. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0079] The acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring information about the current location or home. For example, the acquisition unit preferentially suggests an acquisition method that the user has previously preferred. The acquisition unit can also customize the acquisition interface based on the user's past feedback. The acquisition unit can also optimize the acquisition procedure by reflecting the user's past feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0080] The calculation unit estimates the user's emotions and changes the travel time calculation method based on the estimated user emotions. For example, if the user is feeling stressed, the calculation unit may prioritize the shortest route. Furthermore, if the user is relaxed, the calculation unit may also prioritize the scenic route. Furthermore, if the user is in a hurry, the calculation unit may also prioritize the fastest route. This allows for a more appropriate travel time to be calculated by adjusting the travel time calculation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] When calculating travel time, the calculation unit selects an appropriate route by referring to the user's past travel history. For example, the calculation unit selects an optimal route based on routes the user has used in the past. The calculation unit can also select a route that avoids congestion based on the user's past travel history. The calculation unit can also analyze the user's past travel history and select the most efficient route. In this way, the optimal route can be selected by referring to the past travel history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's travel history data into a generation AI and have the generation AI select an optimal route.

[0082] The calculation unit takes into account the user's current traffic conditions and weather information when calculating travel time. The calculation unit, for example, calculates the optimal route based on real-time traffic congestion information. The calculation unit can also calculate the optimal route taking into account the real-time operation status of public transportation. The calculation unit can also calculate the optimal route based on real-time weather information. This allows for more accurate calculation of travel time by taking into account the current traffic conditions and weather information. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input real-time traffic information and weather information into the generation AI and cause the generation AI to calculate the optimal route.

[0083] The calculation unit improves the calculation algorithm by reflecting user feedback when calculating travel time. The calculation unit improves the calculation algorithm, for example, based on feedback provided by the user in the past. The calculation unit can also improve calculation accuracy by reflecting user feedback. The calculation unit can also adjust parameters of the calculation algorithm based on user feedback. In this way, the calculation algorithm can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input user feedback data into the generation AI and cause the generation AI to improve the calculation algorithm.

[0084] The calculation unit estimates the user's emotions and prioritizes the travel times to be calculated based on the estimated user emotions. For example, if the user is feeling stressed, the calculation unit prioritizes important travel times. Furthermore, if the user is relaxed, the calculation unit can also calculate all travel times equally. Furthermore, if the user is in a hurry, the calculation unit can prioritize the most relevant travel times. This allows important travel times to be prioritized by determining the priorities of travel times according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The calculation unit selects the optimal route by taking into account the user's geographical location information when calculating travel time. For example, the calculation unit prioritizes calculating routes that are closest to the user's current location. The calculation unit can also prioritize calculating routes that are close to the user's home. The calculation unit can also prioritize calculating highly relevant routes based on the user's past travel history. This allows the optimal route to be selected by taking the user's geographical location information into consideration. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal route.

[0086] The calculation unit analyzes the user's social media activity when calculating the travel time and presents relevant routes. For example, the calculation unit proposes routes based on locations where the user has checked in on social media. The calculation unit can also analyze the content of the user's social media posts to propose relevant routes. The calculation unit can also propose relevant routes based on the activities of the user's friends on social media. In this way, relevant routes can be proposed by analyzing the user's social media activity. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's social media data into a generation AI and cause the generation AI to propose relevant routes.

[0087] When calculating travel time, the calculation unit makes appropriate suggestions by reflecting the user's past feedback. For example, the calculation unit preferentially suggests calculation methods that the user has previously preferred. The calculation unit can also customize the calculation interface based on the user's past feedback. The calculation unit can also optimize the calculation procedure by reflecting the user's past feedback. In this way, the calculation method can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input user feedback data into a generation AI and have the generation AI customize the calculation method.

[0088] The suggestion unit estimates the user's emotions and changes the travel time suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit provides a simple and highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide a suggestion method that focuses on the main points. This enables more appropriate suggestions by adjusting the suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0089] When proposing travel time, the suggestion unit makes an optimal suggestion by referring to the user's past schedule history. The suggestion unit makes the optimal suggestion, for example, based on the travel time used by the user in the past. The suggestion unit can also make suggestions to avoid congestion based on the user's past schedule history. The suggestion unit can also analyze the user's past schedule history and make the most efficient suggestion. In this way, optimal suggestions can be made by referring to the past schedule history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's schedule history data into a generation AI and cause the generation AI to execute an optimal suggestion.

[0090] When proposing a travel time, the suggestion unit customizes the suggestion content based on the user's current task and area of ​​interest. For example, the suggestion unit prioritizes suggesting travel times related to the user's current task. The suggestion unit can also suggest relevant travel times based on the user's area of ​​interest. The suggestion unit can also filter unnecessary suggestions based on the user's current task and area of ​​interest. This makes it possible to suggest relevant travel times based on the user's current task and area of ​​interest. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's task data and area of ​​interest data into the generation AI and cause the generation AI to customize the suggestion content.

[0091] The suggestion unit improves the suggestion method by reflecting user feedback when suggesting travel times. The suggestion unit improves the suggestion method based on, for example, feedback previously provided by the user. The suggestion unit can also improve the accuracy of suggestions by reflecting user feedback. The suggestion unit can also adjust parameters of the suggestion method based on user feedback. In this way, the suggestion method can be improved by reflecting user feedback. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input user feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0092] The suggestion unit estimates the user's emotions and prioritizes suggested travel times based on the estimated user emotions. For example, when the user is feeling stressed, the suggestion unit prioritizes important travel times. Furthermore, when the user is relaxed, the suggestion unit can equally suggest all travel times. Furthermore, when the user is in a hurry, the suggestion unit can prioritize suggesting the most relevant travel times. This allows important travel times to be prioritized by determining the priority of travel times according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] When proposing a travel time, the suggestion unit makes an appropriate suggestion based on the user's geographical location information. For example, the suggestion unit prioritizes suggesting travel times that are close to the user's current location. The suggestion unit can also prioritize suggesting travel times that are close to the user's home. The suggestion unit can also prioritize suggesting highly relevant travel times based on the user's past travel history. This allows for optimal suggestions to be made by taking the user's geographical location information into consideration. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's location information data into the generation AI and cause the generation AI to make an optimal suggestion.

[0094] When suggesting travel time, the suggestion unit analyzes the user's social media activity and presents relevant suggestions. The suggestion unit makes suggestions based on, for example, locations where the user has checked in on social media. The suggestion unit can also analyze the content of the user's social media posts and make relevant suggestions. The suggestion unit can also make relevant suggestions by referring to the activities of the user's friends on social media. In this way, relevant suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI and cause the generation AI to execute relevant suggestions.

[0095] When proposing a travel time, the suggestion unit customizes the suggestion method by reflecting the user's past feedback. For example, the suggestion unit preferentially suggests suggestion methods that the user has previously preferred. The suggestion unit can also customize the suggestion interface based on the user's past feedback. The suggestion unit can also optimize the suggestion procedure by reflecting the user's past feedback. In this way, the suggestion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input user feedback data into a generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, calculation unit, and suggestion 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 location of the appointment into the user's schedule. The acquisition unit acquires information about the user's current location and home using the camera 42 of the smart device 14 and location information. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates travel time based on map data and traffic information. The suggestion unit suggests the calculated travel time to the user via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, calculation unit, and suggestion 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 location of the appointment into the user's schedule. The acquisition unit acquires information about the user's current location and home using the camera 42 of the smart glasses 214 and location information. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates travel time based on map data and traffic information. The suggestion unit suggests the calculated travel time to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, calculation unit, and suggestion 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 location of the appointment into the user's schedule. The acquisition unit acquires information about the user's current location and home using the camera 42 of the headset type terminal 314 and location information. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates travel time based on map data and traffic information. The suggestion unit suggests the calculated travel time to the user via the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, calculation unit, and suggestion 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 planned location into the user's schedule. The acquisition unit acquires information about the user's current location and home using the camera 42 of the robot 414 and location information. The calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates travel time based on map data and traffic information. The suggestion unit suggests the calculated travel time to the user through the speaker 240 of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can also monitor the user's health condition and adjust the input method for the planned location based on the health condition. For example, if the user is tired, it can provide a simple input method, and if the user is energetic, it can provide detailed input options. The reception unit can also suggest appropriate rest locations based on the user's health data. This makes it possible to provide input methods and suggestions according to the user's health condition. Health condition monitoring is performed, for example, using sensors in a wearable device or smartphone.

[0098] The acquisition unit can also analyze the user's past travel history and acquire information about travel destinations. For example, the acquisition unit can suggest the user's next travel destination based on information about tourist spots and hotels that the user has visited in the past. The acquisition unit can also suggest the optimal travel time to avoid crowds based on the user's travel history. This makes it possible to make more comfortable travel plans by utilizing the user's past travel history. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI.

[0099] The calculation unit can also calculate travel time taking into account the user's energy consumption. For example, if the user travels by foot, the calculation unit can calculate the calories burned and suggest a healthy route. The calculation unit can also suggest the optimal travel method taking into account the energy consumption when using a bicycle or public transportation. This makes it possible to calculate travel time that supports the user's health management. The calculation of energy consumption is based on, for example, the user's weight and travel distance.

[0100] The suggestion unit can also estimate the user's emotions and personalize travel time suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest a relaxing route. Also, if the user is excited, it can suggest an active route. This makes it possible to suggest travel times that correspond to the user's emotions. Emotion estimation is performed, for example, using an emotion engine or generative AI.

[0101] The suggestion unit can also customize the content of the suggestions based on the user's past feedback. For example, it can prioritize suggestion methods that the user has previously preferred. It can also optimize the content of the suggestions based on the user's feedback. This makes it possible to make suggestions that suit the user's preferences. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI.

[0102] The acquisition unit can also estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. For example, if the user is feeling stressed, important information can be acquired first. Also, if the user is relaxed, all information can be acquired equally. This makes it possible to acquire information according to the user's emotions. Emotion estimation is performed using, for example, an emotion engine or a generation AI.

[0103] The calculation unit can also optimize the travel time calculation method by referencing the user's past travel history. For example, the calculation unit can select the optimal route based on routes the user has used in the past. It can also select a route that avoids congestion based on the user's past travel history. This makes it possible to utilize the user's past travel history to calculate travel times more efficiently. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, or may be performed without using AI.

[0104] The suggestion unit can also estimate the user's emotions and prioritize suggested travel times based on the estimated user emotions. For example, if the user is feeling stressed, important travel times can be suggested first. Also, if the user is relaxed, all travel times can be suggested equally. This makes it possible to suggest travel times according to the user's emotions. Emotions are estimated using, for example, an emotion engine or a generation AI.

[0105] The acquisition unit can also analyze the user's social media activity and acquire related information. For example, it can acquire information about places where the user has checked in on social media. It can also analyze the content of the user's social media posts and acquire related information. This makes it possible to acquire related information by utilizing the user's social media activity. Some or all of the above-mentioned processing in the acquisition unit may be performed using AI, or may be performed without using AI.

[0106] The suggestion unit can also estimate the user's emotions and change the suggestion method based on the estimated user emotions. For example, if the user is feeling stressed, it can provide a simple and highly visible suggestion method. Also, if the user is relaxed, it can provide a suggestion method that includes detailed information. This makes it possible to provide a suggestion method that corresponds to the user's emotions. Emotions are estimated using, for example, an emotion engine or a generation AI.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit inputs the location of the appointment into the user's schedule. The user's schedule includes appointments in a calendar app, handwritten notes, emails, etc. When the user inputs the location of the appointment into the schedule, they can select their home or current location as the departure point. Step 2: The acquisition unit acquires information about the user's current location or home based on the information input by the reception unit. The acquisition unit identifies the starting point based on the location information of the user's smartphone or the registered home address. For example, if the user selects the current location, the acquisition unit acquires the smartphone's GPS information and identifies the current location. Step 3: The calculation unit calculates the travel time to the scheduled location based on the information acquired by the acquisition unit. The calculation unit calculates the optimal route based on map data and traffic information, and calculates the travel time based on that route. Step 4: The suggestion unit suggests the travel time calculated by the calculation unit to the user. The suggestion unit suggests the calculated travel time to the user in the form of a notification, an alert, a reminder, or the like.

[0109] 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.

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

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

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

[0132] The 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.

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

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

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 the location of an appointment into a user's schedule; an acquisition unit that acquires information about the user's current location or home based on the information input by the reception unit; a calculation unit that calculates a travel time to a scheduled location based on the information acquired by the acquisition unit; a suggestion unit that suggests the travel time calculated by the calculation unit to a user. A system characterized by:

2. The reception unit Inferring user emotions and changing the method of entering the location of an appointment based on the estimated user emotions 2. The system of claim 1.

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

4. The reception unit Filtering based on the user's current task or area of ​​interest when entering an appointment location 2. The system of claim 1.

5. The reception unit When entering the location of an appointment, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the locations to be entered based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering an appointment location, prioritize the most relevant locations based on the user's geographic location 2. The system of claim 1.

8. The reception unit When entering an event location, analyze the user's social media activity and suggest related locations.

2. The system of claim 1.

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