system

Abstract

The system according to the embodiment aims to acquire relevant information based on the user's requests and propose an appropriate schedule. [Solution] A system according to an embodiment includes an analysis unit, an acquisition unit, and a proposal unit. The analysis unit analyzes a user's requests. The acquisition unit acquires related information based on the requests analyzed by the analysis unit. The proposal unit proposes a schedule based on the information acquired by the acquisition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that they have not adequately acquired relevant information based on the user's requests and proposed appropriate schedules.

[0005] The system according to the embodiment aims to acquire relevant information based on the user's requests and propose an appropriate schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an acquisition unit, and a proposal unit. The analysis unit analyzes a user's request. The acquisition unit acquires related information based on the request analyzed by the analysis unit. The proposal unit proposes a schedule based on the information acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can acquire relevant information based on the user's requests and propose an appropriate schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A scheduling support system according to an embodiment of the present invention uses AI to search and analyze user requests and, based on the results, supports scheduling by reflecting related information (e.g., weather, travel time, etc.). In the scheduling support system, a user inputs a request, and AI searches and analyzes the request and collects related information. For example, if a user inputs "I would like to schedule a meeting tomorrow afternoon," AI searches weather forecasts and traffic information to propose an optimal schedule. This allows users to plan schedules efficiently. For example, a user inputs a request such as "I would like to schedule a meeting tomorrow afternoon." This request is input into AI. Next, AI searches and analyzes the input request. AI collects and analyzes information related to the request. For example, it searches weather forecasts and traffic information to collect information related to the request. Based on the collected information, AI proposes an optimal schedule. For example, if the weather forecast predicts rain, AI may suggest an indoor conference room or a schedule that takes travel time into account based on traffic information. This mechanism allows users to plan schedules efficiently. For example, by planning a schedule that takes weather and traffic conditions into account, travel time can be reduced, allowing for more efficient meetings. This allows the scheduling support system to analyze the user's requests, obtain related information, and propose an optimal schedule.

[0029] A scheduling support system according to an embodiment includes an analysis unit, an acquisition unit, and a proposal unit. The analysis unit analyzes a user's request. The user's request may be in text format, audio format, or a specific category, but is not limited to these examples. The analysis unit analyzes the user's request using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the request using semantic analysis. The acquisition unit acquires related information based on the request analyzed by the analysis unit. The related information includes, for example, weather forecasts, traffic information, and news, but is not limited to these examples. The acquisition unit acquires the latest information using, for example, a weather forecast API or a traffic information API. For example, the acquisition unit acquires a weather forecast for a specified date and time using the weather forecast API and acquires traffic conditions for a specified route using the traffic information API. The proposal unit proposes a schedule based on the information acquired by the acquisition unit. The schedule may be in a format such as a calendar or a task list, but is not limited to these examples. For example, if the acquired weather forecast predicts rain, the suggestion unit suggests an indoor conference room. The suggestion unit also suggests a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit suggests a schedule that avoids time periods when traffic congestion is expected. In this way, the scheduling support system according to the embodiment can analyze the user's requests, acquire related information, and suggest an optimal schedule.

[0030] The analysis unit can analyze the user's request using natural language processing. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the request using semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of the sentence using grammatical analysis and identify sentence elements such as the subject, predicate, and object. The analysis unit can also understand the meaning of the request and identify the intention of the request using semantic analysis. This allows the use of natural language processing to more accurately analyze the user's request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's request into a generation AI, which then analyzes the request.

[0031] The acquisition unit can acquire weather forecasts or traffic information from an API. Examples of APIs include a weather forecast API and a traffic information API. The acquisition unit, for example, acquires a weather forecast for a specified date and time using a weather forecast API. For example, the acquisition unit inputs a specified date, time, and location to the weather forecast API and acquires the corresponding weather forecast. The acquisition unit can also acquire traffic conditions for a specified route using a traffic information API. For example, the acquisition unit can input a specified departure point and destination to the traffic information API and acquire the corresponding traffic conditions. By acquiring weather forecasts and traffic information from the API, a schedule can be proposed based on the latest information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data acquired from the weather forecast API or traffic information API to the generation AI, which then analyzes the data.

[0032] The suggestion unit can propose an appropriate schedule based on the acquired information. An appropriate schedule includes, for example, prioritization based on the user's requests and optimization of time periods. For example, if the acquired weather forecast predicts rain, the suggestion unit can propose an indoor conference room. For example, if the weather forecast predicts rain, the suggestion unit can prevent the user from getting wet by suggesting an indoor conference room. The suggestion unit can also propose a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit can optimize the user's travel time by proposing a schedule that avoids time periods when traffic congestion is expected. This allows the suggestion unit to provide a schedule that meets the user's requests by proposing an optimal schedule based on the acquired information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the acquired information into a generation AI, which can then propose an optimal schedule.

[0033] The suggestion unit can suggest an indoor conference room when the weather forecast is rainy. The indoor conference room includes conditions such as location, facilities, and available hours. For example, the suggestion unit can suggest an indoor conference room when the weather forecast is rainy. For example, when the weather forecast is rainy, the suggestion unit can suggest an indoor conference room to prevent the user from getting wet. The suggestion unit can also suggest an optimal conference room by taking into consideration the location and facilities of the indoor conference room. For example, the suggestion unit can suggest an indoor conference room close to the user's current location. The suggestion unit can also suggest an optimal conference room by taking into consideration the conference room's facilities (e.g., a projector, a whiteboard, etc.). By doing so, by suggesting an indoor conference room when the weather forecast is rainy, user convenience can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input weather forecast data into the generation AI, which can then suggest an optimal conference room.

[0034] The suggestion unit can propose a schedule that takes travel time into consideration based on traffic information. Travel time includes, for example, transportation mode, distance, traffic conditions, etc. The suggestion unit can propose a schedule that takes travel time into consideration based on traffic information, for example. For example, the suggestion unit can optimize the user's travel time by proposing a schedule that avoids time periods when traffic congestion is expected. The suggestion unit can also propose an optimal travel time by taking into consideration transportation mode (e.g., car, train, bus, etc.). For example, if travel by car is optimal, the suggestion unit can propose a schedule that takes car travel time into consideration. The suggestion unit can also propose an optimal travel time by taking into consideration the operating conditions of trains and buses. In this way, the user's travel time can be optimized by proposing a schedule that takes travel time into consideration based on traffic information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input traffic information data to the generation AI, which can then propose an optimal travel time.

[0035] When analyzing requests, the analysis unit can improve the accuracy of the analysis by referring to the user's past request history. The analysis unit, for example, improves the analysis accuracy by referring to the user's past request history. The past request history includes, for example, past schedules, past request contents, etc. The analysis unit, for example, prioritizes analyzing similar requests based on requests previously input by the user. The analysis unit can also prioritize analyzing frequently occurring requests from the user's past request history. For example, the analysis unit analyzes the user's past request history, finds specific patterns, and improves the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the user's past request history. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past request history data into the generation AI, which then analyzes the requests.

[0036] The analysis unit can analyze requests based on the user's current situation when analyzing the requests. The analysis unit, for example, analyzes the requests taking into account the user's current situation. The current situation includes, for example, location information, calendar information, and device information. The analysis unit, for example, analyzes the requests based on the user's current location information, taking into account nearby facilities. The analysis unit can also analyze the requests by referring to the user's calendar information so as to avoid conflicts with existing plans. For example, the analysis unit can propose a schedule based on the user's calendar information so as not to conflict with existing plans. The analysis unit can also analyze the requests taking into account the user's current situation (e.g., weather and traffic conditions). For example, the analysis unit proposes an optimal schedule based on the current weather and traffic conditions. This allows for more appropriate analysis by taking the user's current situation into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's current situation data into the generation AI, which can then analyze the requests.

[0037] When analyzing requests, the analysis unit can analyze the user's social media activity and extract related requests. The analysis unit, for example, analyzes the user's social media activity to extract requests. Social media activity includes, for example, the content of posts, the number of likes, and comments. The analysis unit, for example, analyzes the user's social media posts and extracts related requests. The analysis unit can also analyze the user's social media activity patterns to analyze requests. For example, the analysis unit can analyze requests by referring to the activities of the user's friends on social media. In this way, related requests can be extracted by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media data into a generation AI, which then analyzes the requests.

[0038] When analyzing a request, the analysis unit can select the optimal analysis method by taking into account the user's device information. The analysis unit, for example, analyzes the request by taking into account the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the analysis unit uses an analysis method optimized for mobile devices. Furthermore, if the user is using a tablet, the analysis unit can also use an analysis method optimized for large screens. For example, if the user is using a desktop computer, the analysis unit can also use an advanced analysis method. This makes it possible to select the optimal analysis method by taking into account the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's device information into the generation AI, which can then select the optimal analysis method.

[0039] When acquiring information, the acquisition unit can select the optimal acquisition method by referring to the user's past acquisition history. The acquisition unit, for example, acquires information by referring to the user's past acquisition history. The past acquisition history includes, for example, the type, frequency, and usage status of information acquired in the past. The acquisition unit, for example, preferentially acquires similar information based on information acquired by the user in the past. The acquisition unit can also preferentially acquire frequently acquired information from the user's past acquisition history. For example, the acquisition unit analyzes the user's past acquisition history, finds a specific pattern, and selects the optimal acquisition method. In this way, the optimal acquisition method can be selected by referring to the user's past acquisition history. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past acquisition history data into the generation AI, which then acquires the information.

[0040] The acquisition unit may acquire information while taking into consideration the user's current situation. The acquisition unit may acquire information while taking into consideration the user's current situation, for example. The current situation may include, for example, location information, calendar information, and device information. The acquisition unit may, for example, prioritize acquiring information about nearby facilities based on the user's current location information. The acquisition unit may also refer to the user's calendar information and prioritize acquiring information related to an existing schedule. For example, the acquisition unit may acquire information related to an existing schedule based on the user's calendar information. The acquisition unit may also acquire information while taking into consideration the user's current situation (e.g., weather or traffic conditions). For example, the acquisition unit may acquire optimal information based on the current weather or traffic conditions. This allows more appropriate information to be acquired by taking the user's current situation into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit may input the user's current situation data into the generation AI, which may then acquire the information.

[0041] When acquiring information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, analyzes the user's social media activity to acquire information. Social media activity includes, for example, the content of posts, the number of likes, and comments. The acquisition unit, for example, analyzes the user's social media posts to acquire related information. The acquisition unit can also analyze the user's social media activity patterns to acquire related information. For example, the acquisition unit can acquire 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 activity. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's social media data into the generation AI, which then acquires the information.

[0042] When acquiring information, the acquisition unit can select the optimal acquisition method by taking into account the user's device information. The acquisition unit acquires information by taking into account, for example, the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the acquisition unit uses an acquisition method optimized for mobile devices. Furthermore, if the user is using a tablet, the acquisition unit can also use an acquisition method optimized for large screens. For example, if the user is using a desktop computer, the acquisition unit can also use an advanced acquisition method. This makes it possible to select the optimal acquisition method by taking into account the user's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's device information into a generation AI, which can select the optimal acquisition method.

[0043] When making a proposal, the suggestion unit can make an optimal proposal by referring to the user's past schedule history. The suggestion unit, for example, makes a proposal by referring to the user's past schedule history. The past schedule history includes, for example, past plans, execution results, and feedback. The suggestion unit, for example, makes similar proposals based on schedules set by the user in the past. The suggestion unit can also prioritize frequently set schedules from the user's past schedule history. For example, the suggestion unit analyzes the user's past schedule history, finds specific patterns, and makes an optimal proposal. In this way, optimal proposals can be made by referring to the user's past schedule history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past schedule history data into the generation AI, which then makes the proposal.

[0044] The suggestion unit can make a suggestion taking into account the user's current situation when making a suggestion. The suggestion unit makes a suggestion, for example, taking into account the user's current situation. The current situation includes, for example, location information, calendar information, device information, etc. The suggestion unit can make a suggestion, for example, based on the user's current location information, taking into account nearby facilities. The suggestion unit can also make a suggestion by referring to the user's calendar information so as not to overlap with existing plans. For example, the suggestion unit can propose a schedule based on the user's calendar information so as not to overlap with existing plans. The suggestion unit can also make a suggestion taking into account the user's current situation (e.g., weather or traffic conditions). For example, the suggestion unit proposes an optimal schedule based on the current weather or traffic conditions. This allows for more appropriate suggestions to be made by taking the user's current situation into consideration. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current situation data into the generation AI, which can then make the suggestion.

[0045] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a relevant suggestion. The suggestion unit, for example, analyzes the user's social media activity and makes the suggestion. Social media activity includes, for example, the content of posts, the number of likes, and comments. The suggestion unit, for example, analyzes the user's social media posts and makes a relevant suggestion. The suggestion unit can also analyze the user's social media activity patterns and make a relevant suggestion. For example, the suggestion unit can make a suggestion based on the activity 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, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's social media data into the generation AI, which then makes the suggestion.

[0046] When making a proposal, the suggestion unit can select the optimal proposal method by taking into account the user's device information. The suggestion unit makes a proposal by taking into account, for example, the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the suggestion unit uses a proposal method optimized for mobile devices. Furthermore, if the user is using a tablet, the suggestion unit can also use a proposal method optimized for large screens. For example, if the user is using a desktop computer, the suggestion unit can also use an advanced proposal method. This makes it possible to select the optimal proposal method by taking into account the user's device information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's device information into the generation AI, which can then select the optimal proposal method.

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

[0048] When analyzing a user's requests, the analysis unit can learn the user's past behavioral patterns and predict the requests. For example, if the user has previously engaged in a specific activity on a specific day of the week or during a specific time period, the analysis unit can learn that pattern and predict a similar request before it is entered. The analysis unit can also automatically adjust the priority of requests based on the user's past behavioral data. For example, by prioritizing analysis of activities that the user frequently engaged in in the past, requests can be responded to more quickly. Furthermore, the analysis unit can predict future requests based on the user's behavioral patterns and obtain related information in advance. This makes it possible to create a situation where the necessary information is already prepared before the user enters a request.

[0049] The acquisition unit can acquire information based on the user's current health condition. For example, if the user is using a wearable device, the acquisition unit can acquire data such as heart rate and stress level from the device to understand the user's health condition. The acquisition unit can prioritize acquisition of information for proposing an appropriate schedule based on the user's health condition. For example, if the user shows a high stress level, the acquisition unit prioritizes acquisition of information about activities and places where the user can relax. Furthermore, if the user is not getting enough exercise, the acquisition unit can also acquire information about places and times of day that are suitable for exercise. This makes it possible to provide more appropriate information based on the user's health condition.

[0050] The analysis unit can take into account feedback from the user's social network when analyzing the user's requests. For example, it can analyze comments and reactions from friends and followers to requests shared by the user on social media and reflect that feedback in the analysis of the requests. The analysis unit can also adjust the priority of requests based on feedback from social networks. For example, by prioritizing analysis of requests supported by many friends, it can propose schedules that are beneficial to more people. The analysis unit can also complement the content of the requests based on feedback from social networks. For example, it can propose a more detailed schedule by reflecting additional information suggested by friends in the requests.

[0051] The analysis unit can take into account the user's current activity status when analyzing the user's request. For example, if the user is currently exercising, the analysis unit can prioritize analyzing requests related to that activity. Also, if the user is at work, the analysis unit can prioritize analyzing requests related to work. Furthermore, the analysis unit can supplement the content of the request based on the user's current activity status. For example, if the user is exercising, the analysis unit can add information related to exercise to the request. Also, if the user is at work, the analysis unit can add information related to work to the request. In this way, by taking the user's current activity status into consideration, more appropriate analysis of the request can be performed.

[0052] The suggestion unit can learn from the user's past feedback and improve the accuracy of suggestions. For example, the suggestion unit can analyze feedback on suggestions made by the user in the past and adjust the suggestion algorithm based on that feedback. The suggestion unit can also improve the content and format of suggestions based on the user's past feedback. For example, by preferentially using a suggestion format that the user preferred in the past, user satisfaction can be improved. The suggestion unit can also adjust the timing of suggestions based on the user's past feedback. For example, if the user was more likely to accept suggestions during a specific time period in the past, the suggestion acceptance rate can be improved by making suggestions during that time period. In this way, the user's past feedback can be utilized to make more accurate suggestions.

[0053] The analysis unit can take the user's cultural background into consideration when analyzing the user's requests. For example, if the user belongs to a particular culture or religion, it can prioritize analysis of requests related to that culture or religion. The analysis unit can also complement the content of the request based on the user's cultural background. For example, if the user plans to attend a particular cultural event, it can add information related to that event to the request. The analysis unit can also adjust the priority of the request based on the user's cultural background. For example, if the user prioritizes a particular religious ceremony, it can prioritize analysis of that request and suggest a more appropriate schedule. In this way, it is possible to perform a more appropriate analysis of the request by taking the user's cultural background into consideration.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The analysis unit analyzes the user's request. The user's request may be in text format, audio format, or a specific category. The analysis unit analyzes the user's request using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the user's request into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the request. Step 2: The acquisition unit acquires related information based on the request analyzed by the analysis unit. The related information includes weather forecasts, traffic information, news, etc. The acquisition unit acquires the latest information using a weather forecast API and a traffic information API. For example, the acquisition unit acquires a weather forecast for a specified date and time using the weather forecast API, and acquires traffic conditions for a specified route using the traffic information API. Step 3: The suggestion unit proposes a schedule based on the information acquired by the acquisition unit. The schedule may be in a calendar format, a task list format, or the like. If the acquired weather forecast predicts rain, the suggestion unit proposes an indoor conference room. The suggestion unit also proposes a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit proposes a schedule that avoids times when traffic congestion is expected.

[0056] (Example 2) A scheduling support system according to an embodiment of the present invention uses AI to search and analyze user requests and, based on the results, supports scheduling by reflecting related information (e.g., weather, travel time, etc.). In the scheduling support system, a user inputs a request, and AI searches and analyzes the request and collects related information. For example, if a user inputs "I would like to schedule a meeting tomorrow afternoon," AI searches weather forecasts and traffic information to propose an optimal schedule. This allows users to plan schedules efficiently. For example, a user inputs a request such as "I would like to schedule a meeting tomorrow afternoon." This request is input into AI. Next, AI searches and analyzes the input request. AI collects and analyzes information related to the request. For example, it searches weather forecasts and traffic information to collect information related to the request. Based on the collected information, AI proposes an optimal schedule. For example, if the weather forecast predicts rain, AI may suggest an indoor conference room or a schedule that takes travel time into account based on traffic information. This mechanism allows users to plan schedules efficiently. For example, by planning a schedule that takes weather and traffic conditions into account, travel time can be reduced, allowing for more efficient meetings. This allows the scheduling support system to analyze the user's requests, obtain related information, and propose an optimal schedule.

[0057] A scheduling support system according to an embodiment includes an analysis unit, an acquisition unit, and a proposal unit. The analysis unit analyzes a user's request. The user's request may be in text format, audio format, or a specific category, but is not limited to these examples. The analysis unit analyzes the user's request using, for example, natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis, analyzes the sentence structure using grammatical analysis, and understands the meaning of the request using semantic analysis. The acquisition unit acquires related information based on the request analyzed by the analysis unit. The related information includes, for example, weather forecasts, traffic information, and news, but is not limited to these examples. The acquisition unit acquires the latest information using, for example, a weather forecast API or a traffic information API. For example, the acquisition unit acquires a weather forecast for a specified date and time using the weather forecast API and acquires traffic conditions for a specified route using the traffic information API. The proposal unit proposes a schedule based on the information acquired by the acquisition unit. The schedule may be in a format such as a calendar or a task list, but is not limited to these examples. For example, if the acquired weather forecast predicts rain, the suggestion unit suggests an indoor conference room. The suggestion unit also suggests a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit suggests a schedule that avoids time periods when traffic congestion is expected. In this way, the scheduling support system according to the embodiment can analyze the user's requests, acquire related information, and suggest an optimal schedule.

[0058] The analysis unit can analyze the user's request using natural language processing. Natural language processing includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis, analyzes the structure of the sentence using grammatical analysis, and understands the meaning of the request using semantic analysis. For example, the analysis unit breaks the user's request down into words using morphological analysis and identifies the part of speech of each word. The analysis unit can also analyze the structure of the sentence using grammatical analysis and identify sentence elements such as the subject, predicate, and object. The analysis unit can also understand the meaning of the request and identify the intention of the request using semantic analysis. This allows the use of natural language processing to more accurately analyze the user's request. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's request into a generation AI, which then analyzes the request.

[0059] The acquisition unit can acquire weather forecasts or traffic information from an API. Examples of APIs include a weather forecast API and a traffic information API. The acquisition unit, for example, acquires a weather forecast for a specified date and time using a weather forecast API. For example, the acquisition unit inputs a specified date, time, and location to the weather forecast API and acquires the corresponding weather forecast. The acquisition unit can also acquire traffic conditions for a specified route using a traffic information API. For example, the acquisition unit can input a specified departure point and destination to the traffic information API and acquire the corresponding traffic conditions. By acquiring weather forecasts and traffic information from the API, a schedule can be proposed based on the latest information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input data acquired from the weather forecast API or traffic information API to the generation AI, which then analyzes the data.

[0060] The suggestion unit can propose an appropriate schedule based on the acquired information. An appropriate schedule includes, for example, prioritization based on the user's requests and optimization of time periods. For example, if the acquired weather forecast predicts rain, the suggestion unit can propose an indoor conference room. For example, if the weather forecast predicts rain, the suggestion unit can prevent the user from getting wet by suggesting an indoor conference room. The suggestion unit can also propose a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit can optimize the user's travel time by proposing a schedule that avoids time periods when traffic congestion is expected. This allows the suggestion unit to provide a schedule that meets the user's requests by proposing an optimal schedule based on the acquired information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the acquired information into a generation AI, which can then propose an optimal schedule.

[0061] The suggestion unit can suggest an indoor conference room when the weather forecast is rainy. The indoor conference room includes conditions such as location, facilities, and available hours. For example, the suggestion unit can suggest an indoor conference room when the weather forecast is rainy. For example, when the weather forecast is rainy, the suggestion unit can suggest an indoor conference room to prevent the user from getting wet. The suggestion unit can also suggest an optimal conference room by taking into consideration the location and facilities of the indoor conference room. For example, the suggestion unit can suggest an indoor conference room close to the user's current location. The suggestion unit can also suggest an optimal conference room by taking into consideration the conference room's facilities (e.g., a projector, a whiteboard, etc.). By doing so, by suggesting an indoor conference room when the weather forecast is rainy, user convenience can be improved. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input weather forecast data into the generation AI, which can then suggest an optimal conference room.

[0062] The suggestion unit can propose a schedule that takes travel time into consideration based on traffic information. Travel time includes, for example, transportation mode, distance, traffic conditions, etc. The suggestion unit can propose a schedule that takes travel time into consideration based on traffic information, for example. For example, the suggestion unit can optimize the user's travel time by proposing a schedule that avoids time periods when traffic congestion is expected. The suggestion unit can also propose an optimal travel time by taking into consideration transportation mode (e.g., car, train, bus, etc.). For example, if travel by car is optimal, the suggestion unit can propose a schedule that takes car travel time into consideration. The suggestion unit can also propose an optimal travel time by taking into consideration the operating conditions of trains and buses. In this way, the user's travel time can be optimized by proposing a schedule that takes travel time into consideration based on traffic information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input traffic information data to a generation AI, which can then propose an optimal travel time.

[0063] The analysis unit can estimate the user's emotions and adjust the priority of requests based on the estimated user emotions. The analysis unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also analyze the user's text input and estimate the emotions using text analysis technology. For example, the analysis unit can analyze the content and writing style of the user's text input to estimate the emotions. The analysis unit adjusts the priority of requests based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize suggesting a schedule that allows the user to relax. Also, if the user is in a hurry, the analysis unit can prioritize suggesting requests that can be responded to quickly. In this way, by adjusting the priority of requests based on the user's emotions, a more appropriate schedule can be proposed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may then estimate the emotion.

[0064] When analyzing requests, the analysis unit can improve the accuracy of the analysis by referring to the user's past request history. The analysis unit, for example, improves the analysis accuracy by referring to the user's past request history. The past request history includes, for example, past schedules, past request contents, etc. The analysis unit, for example, prioritizes analyzing similar requests based on requests previously input by the user. The analysis unit can also prioritize analyzing frequently occurring requests from the user's past request history. For example, the analysis unit analyzes the user's past request history, finds specific patterns, and improves the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the user's past request history. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's past request history data into the generation AI, which then analyzes the requests.

[0065] The analysis unit can analyze requests based on the user's current situation when analyzing the requests. The analysis unit, for example, analyzes the requests taking into account the user's current situation. The current situation includes, for example, location information, calendar information, and device information. The analysis unit, for example, analyzes the requests based on the user's current location information, taking into account nearby facilities. The analysis unit can also analyze the requests by referring to the user's calendar information so as to avoid conflicts with existing plans. For example, the analysis unit can propose a schedule based on the user's calendar information so as not to conflict with existing plans. The analysis unit can also analyze the requests taking into account the user's current situation (e.g., weather and traffic conditions). For example, the analysis unit proposes an optimal schedule based on the current weather and traffic conditions. This allows for more appropriate analysis by taking the user's current situation into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's current situation data into the generation AI, which can then analyze the requests.

[0066] The analysis unit can estimate the user's emotions and adjust the method for analyzing requests based on the estimated user emotions. The analysis unit uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit can also analyze the user's text input and estimate the emotions using text analysis technology. For example, the analysis unit can analyze the content and writing style of the user's text input to estimate the emotions. The analysis unit adjusts the method for analyzing requests based on the estimated emotions. For example, if the user is nervous, the analysis unit can analyze the requests using a simple analysis method. On the other hand, if the user is relaxed, the analysis unit can analyze the requests using a detailed analysis method. This allows for more appropriate analysis by adjusting the method for analyzing requests based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input user emotion data into the generation AI, which may then estimate the emotion.

[0067] When analyzing requests, the analysis unit can analyze the user's social media activity and extract related requests. The analysis unit, for example, analyzes the user's social media activity to extract requests. Social media activity includes, for example, the content of posts, the number of likes, and comments. The analysis unit, for example, analyzes the user's social media posts and extracts related requests. The analysis unit can also analyze the user's social media activity patterns to analyze requests. For example, the analysis unit can analyze requests by referring to the activities of the user's friends on social media. In this way, related requests can be extracted by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's social media data into a generation AI, which then analyzes the requests.

[0068] When analyzing a request, the analysis unit can select the optimal analysis method by taking into account the user's device information. The analysis unit, for example, analyzes the request by taking into account the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the analysis unit uses an analysis method optimized for mobile devices. Furthermore, if the user is using a tablet, the analysis unit can also use an analysis method optimized for large screens. For example, if the user is using a desktop computer, the analysis unit can also use an advanced analysis method. This makes it possible to select the optimal analysis method by taking into account the user's device information. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's device information into the generation AI, which can then select the optimal analysis method.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. The acquisition unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the acquisition unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the acquisition unit can analyze the tone and speed of the user's voice to estimate the emotions. The acquisition unit can also analyze the user's text input and estimate the emotions using text analysis technology. For example, the acquisition unit can analyze the content and writing style of the user's text input to estimate the emotions. Based on the estimated emotions, the acquisition unit determines the priority of information to be acquired. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring information that will help the user relax. Also, if the user is in a hurry, the acquisition unit can prioritize acquiring information that will allow a quick response. In this way, by determining the priority of information based on the user's emotions, more appropriate information can be acquired. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit inputs user emotion data into the generation AI, which then estimates the emotion.

[0070] When acquiring information, the acquisition unit can select the optimal acquisition method by referring to the user's past acquisition history. The acquisition unit, for example, acquires information by referring to the user's past acquisition history. The past acquisition history includes, for example, the type, frequency, and usage status of information acquired in the past. The acquisition unit, for example, preferentially acquires similar information based on information acquired by the user in the past. The acquisition unit can also preferentially acquire frequently acquired information from the user's past acquisition history. For example, the acquisition unit analyzes the user's past acquisition history, finds a specific pattern, and selects the optimal acquisition method. In this way, the optimal acquisition method can be selected by referring to the user's past acquisition history. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's past acquisition history data into the generation AI, which then acquires the information.

[0071] The acquisition unit may acquire information while taking into consideration the user's current situation. The acquisition unit may acquire information while taking into consideration the user's current situation, for example. The current situation may include, for example, location information, calendar information, and device information. The acquisition unit may, for example, prioritize acquiring information about nearby facilities based on the user's current location information. The acquisition unit may also refer to the user's calendar information and prioritize acquiring information related to an existing schedule. For example, the acquisition unit may acquire information related to an existing schedule based on the user's calendar information. The acquisition unit may also acquire information while taking into consideration the user's current situation (e.g., weather or traffic conditions). For example, the acquisition unit may acquire optimal information based on the current weather or traffic conditions. This allows more appropriate information to be acquired by taking the user's current situation into consideration. Some or all of the above-described processing in the acquisition unit may be performed using, or without, a generation AI. For example, the acquisition unit may input the user's current situation data into the generation AI, which may then acquire the information.

[0072] The acquisition unit can estimate the user's emotion and adjust the type of information to be acquired based on the estimated user's emotion. The acquisition unit, for example, uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotion. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit can analyze the tone and speed of the user's voice to estimate the emotion. The acquisition unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, the acquisition unit can analyze the content and writing style of the user's text input to estimate the emotion. The acquisition unit adjusts the type of information to be acquired based on the estimated emotion. For example, if the user is nervous, the acquisition unit can prioritize acquiring information that will help them relax. Also, if the user is relaxed, the acquisition unit can prioritize acquiring detailed information. As a result, by adjusting the type of information based on the user's emotion, more appropriate information can be acquired. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the acquisition unit may be performed using, or without, the generation AI. For example, the acquisition unit inputs user emotion data into the generation AI, which then estimates the emotion.

[0073] When acquiring information, the acquisition unit can analyze the user's social media activity and acquire related information. The acquisition unit, for example, analyzes the user's social media activity to acquire information. Social media activity includes, for example, the content of posts, the number of likes, and comments. The acquisition unit, for example, analyzes the user's social media posts to acquire related information. The acquisition unit can also analyze the user's social media activity patterns to acquire related information. For example, the acquisition unit can acquire 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 activity. Some or all of the above-described processing in the acquisition unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's social media data into the generation AI, which then acquires the information.

[0074] When acquiring information, the acquisition unit can select the optimal acquisition method by taking into account the user's device information. The acquisition unit acquires information by taking into account, for example, the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the acquisition unit uses an acquisition method optimized for mobile devices. Furthermore, if the user is using a tablet, the acquisition unit can also use an acquisition method optimized for large screens. For example, if the user is using a desktop computer, the acquisition unit can also use an advanced acquisition method. This makes it possible to select the optimal acquisition method by taking into account the user's device information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input the user's device information into a generation AI, which can select the optimal acquisition method.

[0075] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotion. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate the emotion. The suggestion unit can also analyze the user's text input and estimate the emotion using text analysis technology. For example, the suggestion unit can analyze the content and writing style of the user's text input to estimate the emotion. The suggestion unit adjusts the way the suggestion is presented based on the estimated emotion. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion. If the user is relaxed, the suggestion unit can provide a suggestion that includes detailed information. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way the suggestion is presented based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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, or without, the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then estimate the emotion.

[0076] When making a proposal, the suggestion unit can make an optimal proposal by referring to the user's past schedule history. The suggestion unit, for example, makes a proposal by referring to the user's past schedule history. The past schedule history includes, for example, past plans, execution results, and feedback. The suggestion unit, for example, makes similar proposals based on schedules set by the user in the past. The suggestion unit can also prioritize frequently set schedules from the user's past schedule history. For example, the suggestion unit analyzes the user's past schedule history, finds specific patterns, and makes an optimal proposal. In this way, optimal proposals can be made by referring to the user's past schedule history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past schedule history data into the generation AI, which then makes the proposal.

[0077] The suggestion unit can make a suggestion taking into account the user's current situation when making a suggestion. The suggestion unit makes a suggestion, for example, taking into account the user's current situation. The current situation includes, for example, location information, calendar information, device information, etc. The suggestion unit can make a suggestion, for example, based on the user's current location information, taking into account nearby facilities. The suggestion unit can also make a suggestion by referring to the user's calendar information so as not to overlap with existing plans. For example, the suggestion unit can propose a schedule based on the user's calendar information so as not to overlap with existing plans. The suggestion unit can also make a suggestion taking into account the user's current situation (e.g., weather or traffic conditions). For example, the suggestion unit proposes an optimal schedule based on the current weather or traffic conditions. This allows for more appropriate suggestions to be made by taking the user's current situation into consideration. Some or all of the above-described processing in the suggestion unit can be performed using, or without, a generation AI. For example, the suggestion unit can input the user's current situation data into the generation AI, which can then make the suggestion.

[0078] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The suggestion unit uses technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The suggestion unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate the emotions. The suggestion unit can also analyze the user's text input and estimate the emotions using text analysis technology. For example, the suggestion unit can analyze the content and writing style of the user's text input to estimate the emotions. The suggestion unit can prioritize suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions that will help them relax. Also, if the user is in a hurry, the suggestion unit can prioritize suggestions that can be responded to quickly. In this way, by prioritizing suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is realized using an emotion estimation function, for example, using 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, or without, the generation AI. For example, the suggestion unit may input user emotion data into the generation AI, which may then estimate the emotion.

[0079] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a relevant suggestion. The suggestion unit, for example, analyzes the user's social media activity and makes the suggestion. Social media activity includes, for example, the content of posts, the number of likes, and comments. The suggestion unit, for example, analyzes the user's social media posts and makes a relevant suggestion. The suggestion unit can also analyze the user's social media activity patterns and make a relevant suggestion. For example, the suggestion unit can make a suggestion based on the activity 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, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's social media data into the generation AI, which then makes the suggestion.

[0080] When making a proposal, the suggestion unit can select the optimal proposal method by taking into account the user's device information. The suggestion unit makes a proposal by taking into account, for example, the user's device information. The device information includes, for example, the device type, usage status, and setting information. For example, if the user is using a smartphone, the suggestion unit uses a proposal method optimized for mobile devices. Furthermore, if the user is using a tablet, the suggestion unit can also use a proposal method optimized for large screens. For example, if the user is using a desktop computer, the suggestion unit can also use an advanced proposal method. This makes it possible to select the optimal proposal method by taking into account the user's device information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's device information into the generation AI, which can then select the optimal proposal method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, acquisition unit, and proposal unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the user's requests using natural language processing technology. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information using a weather forecast API or a traffic information API. The proposal unit is realized, for example, by the control unit 46A of the smart device 14 and proposes an optimal schedule based on the acquired information. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, acquisition unit, and proposal unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes the user's request using natural language processing technology. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information using a weather forecast API or a traffic information API. The proposal unit is realized, for example, by the control unit 46A of the smart glasses 214 and proposes an optimal schedule based on the acquired information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, acquisition unit, and proposal unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 and analyzes the user's requests using natural language processing technology. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information using a weather forecast API or a traffic information API. The proposal unit is realized, for example, by the control unit 46A of the headset type terminal 314 and proposes an optimal schedule based on the acquired information. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, acquisition unit, and proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the user's request using natural language processing technology. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires related information using a weather forecast API or a traffic information API. The proposal unit is realized, for example, by the control unit 46A of the robot 414 and proposes an optimal schedule based on the acquired information.

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

[0082] When analyzing a user's requests, the analysis unit can learn the user's past behavioral patterns and predict the requests. For example, if the user has previously engaged in a specific activity on a specific day of the week or during a specific time period, the analysis unit can learn that pattern and predict a similar request before it is entered. The analysis unit can also automatically adjust the priority of requests based on the user's past behavioral data. For example, by prioritizing analysis of activities that the user frequently engaged in in the past, requests can be responded to more quickly. Furthermore, the analysis unit can predict future requests based on the user's behavioral patterns and obtain related information in advance. This makes it possible to create a situation where the necessary information is already prepared before the user enters a request.

[0083] The acquisition unit can acquire information based on the user's current health condition. For example, if the user is using a wearable device, the acquisition unit can acquire data such as heart rate and stress level from the device to understand the user's health condition. The acquisition unit can prioritize acquisition of information for proposing an appropriate schedule based on the user's health condition. For example, if the user shows a high stress level, the acquisition unit prioritizes acquisition of information about activities and places where the user can relax. Furthermore, if the user is not getting enough exercise, the acquisition unit can also acquire information about places and times of day that are suitable for exercise. This makes it possible to provide more appropriate information based on the user's health condition.

[0084] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make suggestions during times when the user can relax. Furthermore, if the user is concentrating, the suggestion unit can adjust the timing of suggestions so as not to disturb the user's concentration. Furthermore, the suggestion unit can adjust the content of suggestions based on the user's emotions. For example, if the user is tired, the suggestion unit can suggest a schedule for taking a rest. Furthermore, if the user is excited, the suggestion unit can suggest an activity to make use of that excitement. In this way, suggestions can be made with more appropriate timing and content based on the user's emotions.

[0085] The analysis unit can take into account feedback from the user's social network when analyzing the user's requests. For example, it can analyze comments and reactions from friends and followers to requests shared by the user on social media and reflect that feedback in the analysis of the requests. The analysis unit can also adjust the priority of requests based on feedback from social networks. For example, by prioritizing analysis of requests supported by many friends, it can propose schedules that are beneficial to more people. The analysis unit can also complement the content of the requests based on feedback from social networks. For example, it can propose a more detailed schedule by reflecting additional information suggested by friends in the requests.

[0086] The acquisition unit can estimate the user's emotions and evaluate the reliability of information to be acquired based on the estimated emotions. For example, if the user is feeling anxious, the acquisition unit can preferentially acquire information from highly reliable information sources. Also, if the user is relaxed, the acquisition unit can acquire information from a variety of information sources. Furthermore, the acquisition unit can adjust criteria for evaluating the reliability of information based on the user's emotions. For example, if the user is feeling stressed, the acquisition unit can evaluate the reliability of information using strict criteria. Also, if the user is relaxed, the acquisition unit can evaluate the reliability of information using flexible criteria. This makes it possible to provide more reliable information based on the user's emotions.

[0087] The suggestion unit can estimate the user's emotions and adjust the format of the suggestion based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make the suggestion in a simple, visually easy-to-understand format. On the other hand, if the user is relaxed, the suggestion unit can make the suggestion in a format including detailed information. Furthermore, the suggestion unit can adjust the tone of the suggestion based on the user's emotions. For example, if the user is nervous, the suggestion unit can make the suggestion in a calm, reassuring tone. On the other hand, if the user is excited, the suggestion unit can make the suggestion in a lively tone. In this way, the suggestion can be made in a more appropriate format and tone based on the user's emotions.

[0088] The analysis unit can take into account the user's current activity status when analyzing the user's request. For example, if the user is currently exercising, the analysis unit can prioritize analyzing requests related to that activity. Also, if the user is at work, the analysis unit can prioritize analyzing requests related to work. Furthermore, the analysis unit can supplement the content of the request based on the user's current activity status. For example, if the user is exercising, the analysis unit can add information related to exercise to the request. Also, if the user is at work, the analysis unit can add information related to work to the request. In this way, by taking the user's current activity status into consideration, more appropriate analysis of the request can be performed.

[0089] The acquisition unit can estimate the user's emotions and adjust the amount of information to be acquired based on the estimated emotions. For example, if the user is feeling stressed, the acquisition unit can acquire the minimum amount of information necessary. On the other hand, if the user is relaxed, the acquisition unit can acquire detailed information. Furthermore, the acquisition unit can adjust the frequency of information acquisition based on the user's emotions. For example, if the user is in a hurry, the acquisition unit can acquire information quickly. On the other hand, if the user has time to spare, the acquisition unit can acquire information slowly. This makes it possible to acquire information in a more appropriate amount and frequency based on the user's emotions.

[0090] The suggestion unit can learn from the user's past feedback and improve the accuracy of suggestions. For example, the suggestion unit can analyze feedback on suggestions made by the user in the past and adjust the suggestion algorithm based on that feedback. The suggestion unit can also improve the content and format of suggestions based on the user's past feedback. For example, by preferentially using a suggestion format that the user preferred in the past, user satisfaction can be improved. The suggestion unit can also adjust the timing of suggestions based on the user's past feedback. For example, if the user was more likely to accept suggestions during a specific time period in the past, the suggestion acceptance rate can be improved by making suggestions during that time period. In this way, the user's past feedback can be utilized to make more accurate suggestions.

[0091] The analysis unit can take the user's cultural background into consideration when analyzing the user's requests. For example, if the user belongs to a particular culture or religion, it can prioritize analysis of requests related to that culture or religion. The analysis unit can also complement the content of the request based on the user's cultural background. For example, if the user plans to attend a particular cultural event, it can add information related to that event to the request. The analysis unit can also adjust the priority of the request based on the user's cultural background. For example, if the user prioritizes a particular religious ceremony, it can prioritize analysis of that request and suggest a more appropriate schedule. In this way, it is possible to perform a more appropriate analysis of the request by taking the user's cultural background into consideration.

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

[0093] Step 1: The analysis unit analyzes the user's request. The user's request may be in text format, audio format, or a specific category. The analysis unit analyzes the user's request using natural language processing technology. Natural language processing technology includes morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit uses morphological analysis to break down the user's request into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the request. Step 2: The acquisition unit acquires related information based on the request analyzed by the analysis unit. The related information includes weather forecasts, traffic information, news, etc. The acquisition unit acquires the latest information using a weather forecast API and a traffic information API. For example, the acquisition unit acquires a weather forecast for a specified date and time using the weather forecast API, and acquires traffic conditions for a specified route using the traffic information API. Step 3: The suggestion unit proposes a schedule based on the information acquired by the acquisition unit. The schedule may be in a calendar format, a task list format, or the like. If the acquired weather forecast predicts rain, the suggestion unit proposes an indoor conference room. The suggestion unit also proposes a schedule that takes travel time into consideration based on the acquired traffic information. For example, the suggestion unit proposes a schedule that avoids times when traffic congestion is expected.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the 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.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes user requests; an acquisition unit that acquires related information based on the request analyzed by the analysis unit; a proposal unit that proposes a schedule based on the information acquired by the acquisition unit. A system characterized by:

2. The analysis unit Analyzing user requests using natural language processing The system of claim 1 .

3. The acquisition unit Get weather or traffic information from an API The system of claim 1 .

4. The proposal unit Propose an appropriate schedule based on the information obtained The system of claim 1 .

5. The proposal unit If rain is forecast, suggest an indoor meeting room The system of claim 1 .

6. The proposal unit Suggest a schedule based on travel time based on traffic information The system of claim 1 .

7. The analysis unit Estimate user emotions and adjust the priority of requests based on the estimated user emotions The system of claim 1 .

8. The analysis unit When analyzing requests, improve the accuracy of the analysis by referring to the user's past request history. The system of claim 1 .

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