System

Abstract

To provide a system which efficiently collects schedule information of a user, and proposes and adjusts the latest plan.SOLUTION: A system includes a collection part, a processing part, and a proposal part. The collection part collects schedule information of a user. The processing part determines or adjusts the latest plan of the user, based on the schedule information collected by the collection part. The proposal part makes a proposal on the latest plan determined or the latest plan adjusted by the processing part to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had problems in that the cost of collecting and utilizing information is high, and users' schedule management is not performed efficiently.

[0005] The system according to the embodiment aims to efficiently collect schedule information of a user and propose and adjust upcoming plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a processing unit, and a suggestion unit. The collection unit collects schedule information of a user. The processing unit determines or adjusts an upcoming plan for the user based on the schedule information collected by the collection unit. The suggestion unit suggests to the user the upcoming plan or the adjusted upcoming plan determined by the processing unit. Effect of the Invention

[0007] The system according to the embodiment can efficiently collect schedule information of a user and propose and adjust upcoming plans. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that uses a generation AI to collect a user's schedule information, and proposes and adjusts an upcoming plan. This system collects a user's schedule information, determines or adjusts an upcoming plan, and proposes it to the user. For example, if a user inputs "meeting" into a calendar app, the system can collect that information and propose appropriate break times and travel times before and after the meeting. Also, if a user writes "business trip" in an email, the system can adjust the business trip schedule based on that information and propose the optimal means of transportation and accommodation. Furthermore, if a user posts "travel" on SNS, the system can collect that information and propose a travel plan. This system allows users to reduce the cost of collecting and utilizing information and efficiently manage their schedules. Also, by collecting and using future data rather than predictions based on past data, more accurate plans can be proposed. This allows users to live happier lives. This allows the system to efficiently collect a user's schedule information and propose and adjust an upcoming plan.

[0029] The system according to the embodiment includes a collection unit, a processing unit, and a suggestion unit. The collection unit collects schedule information of a user. The schedule information of the user includes, for example, a meeting schedule, a personal schedule, a task deadline, and the like, but is not limited to such examples. The collection unit can collect schedule information from, for example, a calendar app, email, SNS, and the like. The collection unit can also use AI to collect future data in which the user's intent is strong. The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information and future data to optimize the user's immediate plan. The suggestion unit proposes the immediate plan determined or adjusted by the processing unit to the user. The suggestion unit can use AI to estimate the user's emotions and adjust the way the proposal is expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. In addition, when the user is relaxed, the suggestion unit can also make a suggestion including detailed information. This allows the system to efficiently collect the user's schedule information and propose / adjust immediate plans.

[0030] The collection unit collects the user's schedule information. The user's schedule information includes, but is not limited to, for example, meeting schedules, personal schedules, task deadlines, and the like. The collection unit can collect schedule information from, for example, a calendar app, email, SNS, and the like. Specifically, the collection unit obtains meeting and event information entered by the user from the calendar app, and analyzes received invitations and reminders from email to extract schedules. From SNS, information on events and group activities that the user plans to participate in can be collected. The collection unit can also use AI to collect future data in which the user's intent is strong. For example, natural language processing technology is used to analyze the user's intention from email and SNS messages and extract important schedules. Furthermore, the collection unit can learn the user's past behavioral patterns and history and predict future schedules. For example, the collection unit analyzes patterns of meetings and events that the user has frequently participated in in the past and predicts the next schedule. In addition, the collection unit can synchronize data between the user's devices to provide consistent schedule information. This allows the user to always check the latest schedule information even when using multiple devices. The Collection Department places importance on security and privacy, and has implemented encryption technology and access control to safely handle user data, thereby minimizing the risk of users' personal information being leaked to third parties.

[0031] The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information with future data to optimize the user's immediate plan. Specifically, the AI ​​uses a machine learning algorithm to learn the user's past behavior patterns and priorities and generate an optimal plan. For example, the AI ​​analyzes the priorities of meetings and tasks that the user considered important in the past and reflects them in the immediate plan. The processing unit can also adjust the plan taking into account the user's current situation and environment. For example, if the user is on the move, the processing unit adjusts the plan taking into account the travel time and proposes a reasonable schedule. Furthermore, the processing unit can change or adjust the plan in real time. For example, if the time of a meeting is changed or a new task is added, the processing unit immediately updates the plan and notifies the user. The processing unit can also propose an optimal solution when multiple schedules overlap or when a time-sensitive schedule occurs. For example, the processing unit reschedules less important schedules or reevaluates the priorities of tasks to create an efficient schedule. This allows the processing unit to efficiently manage the user's schedule and provide an optimal plan while reducing stress.

[0032] The suggestion unit proposes to the user the immediate plan determined or adjusted by the processing unit. The suggestion unit can use AI to estimate the user's emotions and adjust the way in which the suggestion is expressed based on the estimated user's emotions. Specifically, the AI ​​analyzes the user's past reactions and behavioral data to estimate the current emotional state. For example, the AI ​​learns situations in which the user felt stressed or relaxed in the past and predicts the current emotional state. If the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. For example, by emphasizing only important plans and omitting unnecessary information, the user can quickly understand. In addition, the suggestion unit can also make a suggestion including detailed information if the user is relaxed. For example, the suggestion unit provides background information of the plan and details of related tasks so that the user can grasp the overall situation. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggested content. For example, the suggestion unit records how the user responded to the suggestion and reflects it in the next suggestion. In addition, the suggestion unit can provide suggestions to the user using multiple communication means. For example, the suggestion unit conveys the suggestion to the user in the most optimal way through smartphone notifications, emails, voice assistants, etc. This enables the suggestion unit to make flexible suggestions according to the user's emotional state and situation, thereby improving the user's satisfaction.

[0033] The collection unit can collect schedule information from a calendar app, email, and SNS. The collection unit can collect meeting schedules from a calendar app, for example. For example, the collection unit automatically acquires meeting schedules entered by the user in the calendar app. The collection unit can also collect schedule information from email. For example, the collection unit analyzes meeting invitations received by the user via email and collects them as schedule information. The collection unit can also collect schedule information from SNS. For example, the collection unit collects information about "travel" posted by the user on the SNS and imports it as schedule information. This allows the collection unit to collect schedule information from a variety of information sources.

[0034] The collection unit can collect future data that has a strong user intent. For example, when a user inputs an important meeting schedule, the collection unit can collect the information. For example, when a user inputs "important meeting" in a calendar app, the collection unit collects the information preferentially. In addition, when a user makes a travel plan, the collection unit can also collect the information. For example, when a user posts "travel" on an SNS, the collection unit collects the information and imports it as schedule information. This allows the collection unit to collect future data based on the user intent. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The processing unit can determine or adjust a plan based on the collected schedule information and future data. The processing unit can, for example, suggest appropriate break times and travel times before and after a meeting based on the collected meeting schedule information and future data. For example, the processing unit combines a meeting schedule entered by the user in a calendar application with future data in which the user's intent is strong to suggest appropriate break times and travel times before and after a meeting. The processing unit can also adjust a business trip schedule based on the collected business trip schedule information and future data. For example, the processing unit combines a business trip schedule received by the user by email with future data in which the user's intent is strong to suggest optimal transportation and accommodation. This allows the processing unit to optimize a plan based on the collected schedule information and future data.

[0036] The collection unit can analyze the user's past schedule history and select the optimal collection method. The collection unit can, for example, preferentially use a collection method (calendar, email, etc.) that the user has frequently used in the past. For example, if the user has frequently used a calendar app in the past, the collection unit preferentially uses that method. The collection unit can also collect from the user's past schedule history in a specific time period. For example, if the user has input a schedule in a specific time period in the past, the collection unit collects in that time period. Furthermore, the collection unit can analyze the user's past schedule history and select the most efficient collection method. For example, the collection unit selects the optimal collection method based on the user's past schedule history. As a result, the collection unit can efficiently collect information by selecting the optimal collection method based on the past schedule history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The collection unit can perform filtering based on the user's current activity status and field of interest when collecting schedule information. For example, when the user is currently in a meeting, the collection unit can collect only schedule information related to the meeting. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar application. In addition, when the user is traveling, the collection unit can also collect only schedule information related to the travel. For example, the collection unit preferentially collects information on "travel" posted by the user on SNS. Furthermore, the collection unit can also preferentially collect related schedule information based on the user's field of interest. For example, the collection unit collects related schedule information based on keywords searched by the user in the past and social media activities. This allows the collection unit to preferentially collect related information based on the user's current situation and interests. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0038] When collecting schedule information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit can preferentially collect schedule information related to the location. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar app. In addition, when the user is moving, the collection unit can also preferentially collect schedule information related to the destination. For example, the collection unit preferentially collects information on "travel" posted by the user on SNS. Furthermore, the collection unit can filter and collect highly relevant schedule information based on the user's current location. For example, the collection unit acquires the user's current location using GPS data or Wi-Fi location information, and collects highly relevant schedule information based on the information. This allows the collection unit to collect highly relevant information based on the user's current location. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0039] The collection unit can analyze the user's social media activity and collect related information when collecting the schedule information. For example, when a user posts "meeting" on an SNS, the collection unit can collect the information and preferentially collect schedule information related to the meeting. For example, when a user posts "travel" on an SNS, the collection unit collects the information and preferentially collects schedule information related to the travel. The collection unit can also analyze the user's social media activity and collect related schedule information by filtering. For example, when a user posts "business trip" on an SNS, the collection unit collects the information and preferentially collects schedule information related to the business trip. This allows the collection unit to collect related information based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0040] The processing unit can combine the collected schedule information and future data to optimize the user's immediate plan. The processing unit can, for example, propose an optimal plan based on the collected schedule information and future data. For example, the processing unit combines a meeting schedule entered by the user in a calendar application with future data in which the user's intent is strong to propose an optimal plan. The processing unit can also adjust the collected schedule information in consideration of future data. For example, the processing unit combines a business trip schedule received by the user by email with future data to propose optimal transportation means and accommodations. As a result, the processing unit can combine the collected schedule information and future data to optimize the user's immediate plan. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The processing unit can adjust the level of detail of the plan based on the importance of the schedule information. The processing unit can create a detailed plan based on schedule information with a high level of importance, for example. For example, the processing unit creates a detailed plan based on a schedule of an important meeting input by the user to a calendar app. The processing unit can also create a simplified plan based on schedule information with a low level of importance. For example, the processing unit creates a simplified plan based on a schedule of a general task received by the user by email. This allows the processing unit to adjust the level of detail of the plan according to the importance of the schedule information, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0042] The processing unit can determine the priority of the plan based on the submission time of the collected schedule information. The processing unit can, for example, preferentially process schedule information with a close submission deadline. For example, the processing unit preferentially processes a schedule of a task with a close submission deadline input by the user to a calendar application. The processing unit can also postpone schedule information with a distant submission deadline. For example, the processing unit postpones a schedule of a task with a distant submission deadline received by the user by email. This allows the processing unit to determine the priority of the plan based on the submission time, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The processing unit can adjust the order of the plans based on the relevance of the collected schedule information. The processing unit can, for example, preferentially process schedule information with high relevance. For example, the processing unit preferentially processes schedule information with high relevance input by the user to a calendar application. The processing unit can also postpone schedule information with low relevance. For example, the processing unit postpones schedule information with low relevance received by the user by email. This allows the processing unit to adjust the order of the plans based on the relevance of the schedule information, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed, for example, using AI, or may be performed without using AI.

[0044] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. The suggestion unit can make a detailed proposal based on a plan with high importance, for example. For example, the suggestion unit makes a detailed proposal based on a schedule of an important meeting input by the user to a calendar app. The suggestion unit can also make a simplified proposal based on a plan with low importance. For example, the suggestion unit makes a simplified proposal based on a schedule of a general task received by the user by email. This allows the suggestion unit to adjust the level of detail of the proposal according to the importance of the plan, thereby enabling efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The suggestion unit can apply different proposal algorithms depending on the category of the plan when making a proposal. For example, the suggestion unit can apply a proposal algorithm that emphasizes efficiency to a business-related plan. For example, the suggestion unit makes a proposal that emphasizes efficiency based on a business-related schedule input by the user to a calendar app. The suggestion unit can also apply a proposal algorithm that emphasizes fun to a leisure-related plan. For example, the suggestion unit makes a proposal that emphasizes fun based on a leisure-related schedule posted by the user on a SNS. Furthermore, the suggestion unit can apply a proposal algorithm that emphasizes health to a health-related plan. For example, the suggestion unit makes a proposal that emphasizes health based on a health-related schedule received by the user by email. This allows the suggestion unit to make a more appropriate proposal by applying different proposal algorithms depending on the category of the plan. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The suggestion unit can determine the priority of the proposal based on the submission time of the plan at the time of proposal. The suggestion unit can, for example, preferentially propose a plan with a near submission deadline. For example, the suggestion unit preferentially proposes a schedule of a task with a near submission deadline input by the user to a calendar app. The suggestion unit can also postpone a plan with a distant submission deadline. For example, the suggestion unit postpones a schedule of a task with a far submission deadline received by the user by email. This allows the suggestion unit to determine the priority of the proposal based on the submission time, thereby enabling efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The suggestion unit can adjust the order of proposals based on the relevance of the plans when making a proposal. The suggestion unit can, for example, preferentially propose highly relevant plans. For example, the suggestion unit preferentially proposes highly relevant schedule information input by the user to a calendar app. The suggestion unit can also postpone less relevant plans. For example, the suggestion unit postpones less relevant schedule information received by the user via email. This allows the suggestion unit to adjust the order of proposals based on the relevance of the plans, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can collect the user's health data and combine it with the schedule information to optimize the plan. For example, the collection unit can collect heart rate and sleep data from the user's wearable device and combine it with the schedule information to propose a plan according to the user's health condition. The collection unit can also collect the user's meal record and adjust the schedule based on the timing and content of meals. Furthermore, the collection unit can collect the user's exercise data and propose a plan that takes into account the rest time after exercise. This allows the collection unit to provide a more appropriate plan based on the user's health condition.

[0050] The processing unit can analyze the user's past behavioral patterns and predict future plans. For example, the processing unit predicts and suggests future plans based on activities that the user frequently performed in the past. The processing unit can also analyze the user's past movement history and predict future movement patterns. Furthermore, the processing unit can analyze the user's past communication history and predict future communication needs. In this way, the processing unit can predict future plans based on the user's past behavioral patterns and provide a more appropriate plan.

[0051] The suggestion unit can suggest a customized plan based on the user's hobbies and interests. For example, the suggestion unit can suggest related events based on events and activities the user has participated in in the past. The suggestion unit can also suggest related books and movies based on books the user has read in the past and movies the user has watched in the past. Furthermore, the suggestion unit can suggest related travel destinations based on places the user has visited in the past. In this way, the suggestion unit can provide a customized plan based on the user's hobbies and interests.

[0052] The collection unit can synchronize data between the user's devices and collect information from the multiple devices in an integrated manner. For example, the collection unit can synchronize schedule information from the user's smartphone, tablet, and PC and manage it in a centralized manner. The collection unit can also synchronize health data from the user's smart watch and fitness tracker and collect the data in combination with the schedule information. Furthermore, the collection unit can synchronize information from the user's smart home devices and collect schedule information within the home. This allows the collection unit to collect information from the user's multiple devices in an integrated manner and provide a more accurate plan.

[0053] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit can preferentially collect schedule information related to the location. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar application. In addition, when the user is moving, the collection unit can also preferentially collect schedule information related to the destination. For example, the collection unit preferentially collects "travel" information posted by the user on SNS. Furthermore, the collection unit can filter and collect highly relevant schedule information based on the user's current location. For example, the collection unit acquires the user's current location using GPS data or Wi-Fi location information, and collects highly relevant schedule information based on the information. This allows the collection unit to collect highly relevant information based on the user's current location.

[0054] The suggestion unit can apply different proposal algorithms depending on the category of the plan when making a proposal. For example, the suggestion unit can apply a proposal algorithm that emphasizes efficiency to a business-related plan. For example, the suggestion unit makes a proposal that emphasizes efficiency based on a business-related schedule entered by the user in a calendar application. The suggestion unit can also apply a proposal algorithm that emphasizes fun to a leisure-related plan. For example, the suggestion unit makes a proposal that emphasizes fun based on a leisure-related schedule posted by the user on a SNS. Furthermore, the suggestion unit can apply a proposal algorithm that emphasizes health to a health-related plan. For example, the suggestion unit makes a proposal that emphasizes health based on a health-related schedule received by the user by email. This allows the suggestion unit to make more appropriate proposals by applying different proposal algorithms depending on the category of the plan.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection unit collects the user's schedule information. The user's schedule information includes meeting schedules, personal schedules, task deadlines, etc. The collection unit can collect schedule information from calendar apps, emails, SNS, etc. The collection unit can also use AI to collect future data where the user's intent is strong. Step 2: The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information with future data to optimize the user's immediate plan. Step 3: The suggestion unit proposes to the user the upcoming plan determined or adjusted by the processing unit. The suggestion unit can use AI to estimate the user's emotions and adjust the way the proposal is expressed based on the estimated user's emotions. For example, the suggestion unit can make a simple and highly visible proposal when the user is feeling stressed. Also, the suggestion unit can make a proposal that includes detailed information when the user is relaxed.

[0057] (Example 2) The system according to the embodiment of the present invention is a system that uses a generation AI to collect a user's schedule information, and proposes and adjusts an upcoming plan. This system collects a user's schedule information, determines or adjusts an upcoming plan, and proposes it to the user. For example, if a user inputs "meeting" into a calendar app, the system can collect that information and propose appropriate break times and travel times before and after the meeting. Also, if a user writes "business trip" in an email, the system can adjust the business trip schedule based on that information and propose the optimal means of transportation and accommodation. Furthermore, if a user posts "travel" on SNS, the system can collect that information and propose a travel plan. This system allows users to reduce the cost of collecting and utilizing information and efficiently manage their schedules. Also, by collecting and using future data rather than predictions based on past data, more accurate plans can be proposed. This allows users to live happier lives. This allows the system to efficiently collect a user's schedule information and propose and adjust an upcoming plan.

[0058] The system according to the embodiment includes a collection unit, a processing unit, and a suggestion unit. The collection unit collects schedule information of a user. The schedule information of the user includes, for example, a meeting schedule, a personal schedule, a task deadline, and the like, but is not limited to such examples. The collection unit can collect schedule information from, for example, a calendar app, email, SNS, and the like. The collection unit can also use AI to collect future data in which the user's intent is strong. The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information and future data to optimize the user's immediate plan. The suggestion unit proposes the immediate plan determined or adjusted by the processing unit to the user. The suggestion unit can use AI to estimate the user's emotions and adjust the way the proposal is expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. In addition, when the user is relaxed, the suggestion unit can also make a suggestion including detailed information. This allows the system to efficiently collect the user's schedule information and propose / adjust immediate plans.

[0059] The collection unit collects the user's schedule information. The user's schedule information includes, but is not limited to, for example, meeting schedules, personal schedules, task deadlines, and the like. The collection unit can collect schedule information from, for example, a calendar app, email, SNS, and the like. Specifically, the collection unit obtains meeting and event information entered by the user from the calendar app, and analyzes received invitations and reminders from email to extract schedules. From SNS, information on events and group activities that the user plans to participate in can be collected. The collection unit can also use AI to collect future data in which the user's intent is strong. For example, natural language processing technology is used to analyze the user's intention from email and SNS messages and extract important schedules. Furthermore, the collection unit can learn the user's past behavioral patterns and history and predict future schedules. For example, the collection unit analyzes patterns of meetings and events that the user has frequently participated in in the past and predicts the next schedule. In addition, the collection unit can synchronize data between the user's devices to provide consistent schedule information. This allows the user to always check the latest schedule information even when using multiple devices. The Collection Department places importance on security and privacy, and has implemented encryption technology and access control to safely handle user data, thereby minimizing the risk of users' personal information being leaked to third parties.

[0060] The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information with future data to optimize the user's immediate plan. Specifically, the AI ​​uses a machine learning algorithm to learn the user's past behavior patterns and priorities and generate an optimal plan. For example, the AI ​​analyzes the priorities of meetings and tasks that the user considered important in the past and reflects them in the immediate plan. The processing unit can also adjust the plan taking into account the user's current situation and environment. For example, if the user is on the move, the processing unit adjusts the plan taking into account the travel time and proposes a reasonable schedule. Furthermore, the processing unit can change or adjust the plan in real time. For example, if the time of a meeting is changed or a new task is added, the processing unit immediately updates the plan and notifies the user. The processing unit can also propose an optimal solution when multiple schedules overlap or when a time-sensitive schedule occurs. For example, the processing unit reschedules less important schedules or reevaluates the priorities of tasks to create an efficient schedule. This allows the processing unit to efficiently manage the user's schedule and provide an optimal plan while reducing stress.

[0061] The suggestion unit proposes to the user the immediate plan determined or adjusted by the processing unit. The suggestion unit can use AI to estimate the user's emotions and adjust the way in which the suggestion is expressed based on the estimated user's emotions. Specifically, the AI ​​analyzes the user's past reactions and behavioral data to estimate the current emotional state. For example, the AI ​​learns situations in which the user felt stressed or relaxed in the past and predicts the current emotional state. If the user is feeling stressed, the suggestion unit makes a simple and highly visible suggestion. For example, by emphasizing only important plans and omitting unnecessary information, the user can quickly understand. In addition, the suggestion unit can also make a suggestion including detailed information if the user is relaxed. For example, the suggestion unit provides background information of the plan and details of related tasks so that the user can grasp the overall situation. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggested content. For example, the suggestion unit records how the user responded to the suggestion and reflects it in the next suggestion. In addition, the suggestion unit can provide suggestions to the user using multiple communication means. For example, the suggestion unit conveys the suggestion to the user in the most optimal way through smartphone notifications, emails, voice assistants, etc. This enables the suggestion unit to make flexible suggestions according to the user's emotional state and situation, thereby improving the user's satisfaction.

[0062] The collection unit can collect schedule information from a calendar app, email, and SNS. The collection unit can collect meeting schedules from a calendar app, for example. For example, the collection unit automatically acquires meeting schedules entered by the user in the calendar app. The collection unit can also collect schedule information from email. For example, the collection unit analyzes meeting invitations received by the user via email and collects them as schedule information. The collection unit can also collect schedule information from SNS. For example, the collection unit collects information about "travel" posted by the user on the SNS and imports it as schedule information. This allows the collection unit to collect schedule information from a variety of information sources.

[0063] The collection unit can collect future data that has a strong user intent. For example, when a user inputs an important meeting schedule, the collection unit can collect the information. For example, when a user inputs "important meeting" in a calendar app, the collection unit collects the information preferentially. In addition, when a user makes a travel plan, the collection unit can also collect the information. For example, when a user posts "travel" on an SNS, the collection unit collects the information and imports it as schedule information. This allows the collection unit to collect future data based on the user intent. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0064] The processing unit can determine or adjust a plan based on the collected schedule information and future data. The processing unit can, for example, suggest appropriate break times and travel times before and after a meeting based on the collected meeting schedule information and future data. For example, the processing unit combines a meeting schedule entered by the user in a calendar application with future data in which the user's intent is strong to suggest appropriate break times and travel times before and after a meeting. The processing unit can also adjust a business trip schedule based on the collected business trip schedule information and future data. For example, the processing unit combines a business trip schedule received by the user by email with future data in which the user's intent is strong to suggest optimal transportation and accommodation. This allows the processing unit to optimize a plan based on the collected schedule information and future data.

[0065] The collection unit can estimate the user's emotion and adjust the collection timing of the schedule information based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can delay the collection timing and collect the schedule information when the user is relaxed. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the collection unit can instantly collect the schedule information and quickly start processing. For example, the collection unit records the user's voice and estimates the emotion using a voice analysis technique. Furthermore, when the user is busy, the collection unit can adjust the collection timing to reduce the burden on the user. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the collection unit can reduce the burden on the user by adjusting the collection timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0066] The collection unit can analyze the user's past schedule history and select the optimal collection method. The collection unit can, for example, preferentially use a collection method (calendar, email, etc.) that the user has frequently used in the past. For example, if the user has frequently used a calendar app in the past, the collection unit preferentially uses that method. The collection unit can also collect from the user's past schedule history in a specific time period. For example, if the user has input a schedule in a specific time period in the past, the collection unit collects in that time period. Furthermore, the collection unit can analyze the user's past schedule history and select the most efficient collection method. For example, the collection unit selects the optimal collection method based on the user's past schedule history. As a result, the collection unit can efficiently collect information by selecting the optimal collection method based on the past schedule history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0067] The collection unit can perform filtering based on the user's current activity status and field of interest when collecting schedule information. For example, when the user is currently in a meeting, the collection unit can collect only schedule information related to the meeting. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar application. In addition, when the user is traveling, the collection unit can also collect only schedule information related to the travel. For example, the collection unit preferentially collects information on "travel" posted by the user on SNS. Furthermore, the collection unit can also preferentially collect related schedule information based on the user's field of interest. For example, the collection unit collects related schedule information based on keywords searched by the user in the past and social media activities. This allows the collection unit to preferentially collect related information based on the user's current situation and interests. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0068] The collection unit can estimate the user's emotion and determine the priority of the scheduled information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can postpone the scheduled information of low importance. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the collection unit can also preferentially collect the scheduled information of high importance. For example, the collection unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the collection unit can preferentially collect the scheduled information of high urgency. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the collection unit can preferentially collect important information by determining the priority of the information to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0069] When collecting schedule information, the collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit can preferentially collect schedule information related to the location. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar app. In addition, when the user is moving, the collection unit can also preferentially collect schedule information related to the destination. For example, the collection unit preferentially collects information on "travel" posted by the user on SNS. Furthermore, the collection unit can filter and collect highly relevant schedule information based on the user's current location. For example, the collection unit acquires the user's current location using GPS data or Wi-Fi location information, and collects highly relevant schedule information based on the information. This allows the collection unit to collect highly relevant information based on the user's current location. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0070] The collection unit can analyze the user's social media activity and collect related information when collecting the schedule information. For example, when a user posts "meeting" on an SNS, the collection unit can collect the information and preferentially collect schedule information related to the meeting. For example, when a user posts "travel" on an SNS, the collection unit collects the information and preferentially collects schedule information related to the travel. The collection unit can also analyze the user's social media activity and collect related schedule information by filtering. For example, when a user posts "business trip" on an SNS, the collection unit collects the information and preferentially collects schedule information related to the business trip. This allows the collection unit to collect related information based on the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The processing unit can estimate the user's emotion and change the method of adjusting the plan based on the estimated emotion of the user. For example, when the user is feeling stressed, the processing unit can minimize the adjustment of the plan. For example, the processing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, the processing unit can adjust the detailed plan when the user is relaxed. For example, the processing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, the processing unit can quickly adjust the plan when the user is busy. For example, the processing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the processing unit can provide a more appropriate plan by changing the method of adjusting the plan according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0072] The processing unit can combine the collected schedule information and future data to optimize the user's immediate plan. The processing unit can, for example, propose an optimal plan based on the collected schedule information and future data. For example, the processing unit combines a meeting schedule entered by the user in a calendar application with future data in which the user's intent is strong to propose an optimal plan. The processing unit can also adjust the collected schedule information in consideration of future data. For example, the processing unit combines a business trip schedule received by the user by email with future data to propose optimal transportation means and accommodations. As a result, the processing unit can combine the collected schedule information and future data to optimize the user's immediate plan. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The processing unit can adjust the level of detail of the plan based on the importance of the schedule information. The processing unit can create a detailed plan based on schedule information with a high level of importance, for example. For example, the processing unit creates a detailed plan based on a schedule of an important meeting input by the user to a calendar app. The processing unit can also create a simplified plan based on schedule information with a low level of importance. For example, the processing unit creates a simplified plan based on a schedule of a general task received by the user by email. This allows the processing unit to adjust the level of detail of the plan according to the importance of the schedule information, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0074] The processing unit can estimate the user's emotion and determine the priority of the plan based on the estimated emotion of the user. For example, when the user is feeling stressed, the processing unit can postpone plans with low importance. For example, the processing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the processing unit can also prioritize processing of plans with high importance. For example, the processing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the processing unit can also prioritize processing of plans with high urgency. For example, the processing unit collects the user's biodata (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the processing unit can prioritize processing of important plans by determining the priority of plans according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI.

[0075] The processing unit can determine the priority of the plan based on the submission time of the collected schedule information. The processing unit can, for example, preferentially process schedule information with a close submission deadline. For example, the processing unit preferentially processes a schedule of a task with a close submission deadline input by the user to a calendar application. The processing unit can also postpone schedule information with a distant submission deadline. For example, the processing unit postpones a schedule of a task with a distant submission deadline received by the user by email. This allows the processing unit to determine the priority of the plan based on the submission time, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed using, for example, AI, or may be performed without using AI.

[0076] The processing unit can adjust the order of the plans based on the relevance of the collected schedule information. The processing unit can, for example, preferentially process schedule information with high relevance. For example, the processing unit preferentially processes schedule information with high relevance input by the user to a calendar application. The processing unit can also postpone schedule information with low relevance. For example, the processing unit postpones schedule information with low relevance received by the user by email. This allows the processing unit to adjust the order of the plans based on the relevance of the schedule information, thereby enabling efficient plan creation. A part or all of the above-mentioned processing in the processing unit may be performed, for example, using AI, or may be performed without using AI.

[0077] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit can make a simple and highly visible proposal. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the suggestion unit can make a proposal including detailed information. For example, the suggestion unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the suggestion unit can make a proposal that focuses on the main points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the suggestion unit can make a more appropriate proposal by adjusting the expression method of the proposal according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The suggestion unit can adjust the level of detail of the proposal based on the importance of the plan when making a proposal. The suggestion unit can make a detailed proposal based on a plan with high importance, for example. For example, the suggestion unit makes a detailed proposal based on a schedule of an important meeting input by the user to a calendar app. The suggestion unit can also make a simplified proposal based on a plan with low importance. For example, the suggestion unit makes a simplified proposal based on a schedule of a general task received by the user by email. This allows the suggestion unit to adjust the level of detail of the proposal according to the importance of the plan, thereby enabling efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The suggestion unit can apply different proposal algorithms depending on the category of the plan when making a proposal. For example, the suggestion unit can apply a proposal algorithm that emphasizes efficiency to a business-related plan. For example, the suggestion unit makes a proposal that emphasizes efficiency based on a business-related schedule input by the user to a calendar app. The suggestion unit can also apply a proposal algorithm that emphasizes fun to a leisure-related plan. For example, the suggestion unit makes a proposal that emphasizes fun based on a leisure-related schedule posted by the user on a SNS. Furthermore, the suggestion unit can apply a proposal algorithm that emphasizes health to a health-related plan. For example, the suggestion unit makes a proposal that emphasizes health based on a health-related schedule received by the user by email. This allows the suggestion unit to make a more appropriate proposal by applying different proposal algorithms depending on the category of the plan. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. For example, when the user is stressed, the suggestion unit can make a short and to the point suggestion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the suggestion unit can make a longer suggestion including a detailed explanation. For example, the suggestion unit records the user's voice and estimates the emotion using a voice analysis technique. In addition, when the user is busy, the suggestion unit can make a quick and concise suggestion. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. Thereby, the suggestion unit can make a more appropriate suggestion by adjusting the length of the suggestion according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The suggestion unit can determine the priority of the proposal based on the submission time of the plan at the time of proposal. The suggestion unit can, for example, preferentially propose a plan with a near submission deadline. For example, the suggestion unit preferentially proposes a schedule of a task with a near submission deadline input by the user to a calendar app. The suggestion unit can also postpone a plan with a distant submission deadline. For example, the suggestion unit postpones a schedule of a task with a far submission deadline received by the user by email. This allows the suggestion unit to determine the priority of the proposal based on the submission time, thereby enabling efficient proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The suggestion unit can adjust the order of proposals based on the relevance of the plans when making a proposal. The suggestion unit can, for example, preferentially propose highly relevant plans. For example, the suggestion unit preferentially proposes highly relevant schedule information input by the user to a calendar app. The suggestion unit can also postpone less relevant plans. For example, the suggestion unit postpones less relevant schedule information received by the user via email. This allows the suggestion unit to adjust the order of proposals based on the relevance of the plans, thereby enabling efficient proposals. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can collect the user's health data and combine it with the schedule information to optimize the plan. For example, the collection unit can collect heart rate and sleep data from the user's wearable device and combine it with the schedule information to propose a plan according to the user's health condition. The collection unit can also collect the user's meal record and adjust the schedule based on the timing and content of meals. Furthermore, the collection unit can collect the user's exercise data and propose a plan that takes into account the rest time after exercise. This allows the collection unit to provide a more appropriate plan based on the user's health condition.

[0085] The processing unit can analyze the user's past behavioral patterns and predict future plans. For example, the processing unit predicts and suggests future plans based on activities that the user frequently performed in the past. The processing unit can also analyze the user's past movement history and predict future movement patterns. Furthermore, the processing unit can analyze the user's past communication history and predict future communication needs. In this way, the processing unit can predict future plans based on the user's past behavioral patterns and provide a more appropriate plan.

[0086] The suggestion unit can suggest a customized plan based on the user's hobbies and interests. For example, the suggestion unit can suggest related events based on events and activities the user has participated in in the past. The suggestion unit can also suggest related books and movies based on books the user has read in the past and movies the user has watched in the past. Furthermore, the suggestion unit can suggest related travel destinations based on places the user has visited in the past. In this way, the suggestion unit can provide a customized plan based on the user's hobbies and interests.

[0087] The collection unit can synchronize data between the user's devices and collect information from the multiple devices in an integrated manner. For example, the collection unit can synchronize schedule information from the user's smartphone, tablet, and PC and manage it in a centralized manner. The collection unit can also synchronize health data from the user's smart watch and fitness tracker and collect the data in combination with the schedule information. Furthermore, the collection unit can synchronize information from the user's smart home devices and collect schedule information within the home. This allows the collection unit to collect information from the user's multiple devices in an integrated manner and provide a more accurate plan.

[0088] The processing unit can estimate the user's emotion and determine the priority of the plans based on the estimated emotion of the user. For example, when the user is stressed, the processing unit can postpone plans with low importance. For example, the processing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the processing unit can also preferentially process plans with high importance. For example, the processing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the processing unit can also preferentially process plans with high urgency. For example, the processing unit collects the user's biodata (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. As a result, the processing unit can preferentially process important plans by determining the priority of plans according to the user's emotion.

[0089] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated emotion of the user. For example, when the user is feeling stressed, the suggestion unit can make a simple and highly visible suggestion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the suggestion unit can make a suggestion including detailed information. For example, the suggestion unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the suggestion unit can make a suggestion that focuses on the main points. For example, the suggestion unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. As a result, the suggestion unit can make a more appropriate suggestion by adjusting the way of expressing the suggestion according to the user's emotion.

[0090] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit can preferentially collect schedule information related to the location. For example, the collection unit preferentially collects meeting schedules entered by the user in a calendar application. In addition, when the user is moving, the collection unit can also preferentially collect schedule information related to the destination. For example, the collection unit preferentially collects "travel" information posted by the user on SNS. Furthermore, the collection unit can filter and collect highly relevant schedule information based on the user's current location. For example, the collection unit acquires the user's current location using GPS data or Wi-Fi location information, and collects highly relevant schedule information based on the information. This allows the collection unit to collect highly relevant information based on the user's current location.

[0091] The processing unit can estimate the user's emotion and change the method of adjusting the plan based on the estimated emotion of the user. For example, the processing unit can minimize the adjustment of the plan when the user is stressed. For example, the processing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The processing unit can also adjust the detailed plan when the user is relaxed. For example, the processing unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, the processing unit can quickly adjust the plan when the user is busy. For example, the processing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. As a result, the processing unit can provide a more appropriate plan by changing the method of adjusting the plan according to the user's emotion.

[0092] The suggestion unit can apply different proposal algorithms depending on the category of the plan when making a proposal. For example, the suggestion unit can apply a proposal algorithm that emphasizes efficiency to a business-related plan. For example, the suggestion unit makes a proposal that emphasizes efficiency based on a business-related schedule entered by the user in a calendar application. The suggestion unit can also apply a proposal algorithm that emphasizes fun to a leisure-related plan. For example, the suggestion unit makes a proposal that emphasizes fun based on a leisure-related schedule posted by the user on a SNS. Furthermore, the suggestion unit can apply a proposal algorithm that emphasizes health to a health-related plan. For example, the suggestion unit makes a proposal that emphasizes health based on a health-related schedule received by the user by email. This allows the suggestion unit to make more appropriate proposals by applying different proposal algorithms depending on the category of the plan.

[0093] The collection unit can estimate the user's emotion and determine the priority of the scheduled information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can postpone the schedule information of low importance. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. In addition, when the user is relaxed, the collection unit can also preferentially collect the schedule information of high importance. For example, the collection unit records the user's voice and estimates the emotion using a voice analysis technology. Furthermore, when the user is busy, the collection unit can also preferentially collect the schedule information of high urgency. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. In this way, the collection unit can preferentially collect important information by determining the priority of the information to be collected according to the user's emotion.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects the user's schedule information. The user's schedule information includes meeting schedules, personal schedules, task deadlines, etc. The collection unit can collect schedule information from calendar apps, emails, SNS, etc. The collection unit can also use AI to collect future data where the user's intent is strong. Step 2: The processing unit determines or adjusts the user's immediate plan based on the schedule information collected by the collection unit. The processing unit can use AI to combine the collected schedule information with future data to optimize the user's immediate plan. Step 3: The suggestion unit proposes to the user the upcoming plan determined or adjusted by the processing unit. The suggestion unit can use AI to estimate the user's emotions and adjust the way the proposal is expressed based on the estimated user's emotions. For example, the suggestion unit can make a simple and highly visible proposal when the user is feeling stressed. Also, the suggestion unit can make a proposal that includes detailed information when the user is relaxed.

[0096] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0097] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0098] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0099] For example, the collection unit can collect the user's schedule information using the camera 42 or microphone 38B of the smart device 14. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects schedule information from a calendar app, email, SNS, etc. The processing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and determines or adjusts the user's upcoming plan based on the collected schedule information. The suggestion unit is realized by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12, and proposes the determined or adjusted upcoming plan to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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, the RAM 30, and the 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.

[0103] 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.

[0104] 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 the voice according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0106] 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 performed in a secure state.

[0107] Fig. 4 shows an example of 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0113] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0114] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] For example, the collection unit can collect the user's schedule information using the camera 42 or microphone 238 of the smart glasses 214. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects schedule information from a calendar app, email, SNS, etc. The processing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and determines or adjusts the user's upcoming plan based on the collected schedule information. The suggestion unit is realized by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and suggests the determined or adjusted upcoming plan to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] 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, the RAM 30, and the 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.

[0119] 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.

[0120] 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] 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 performed in a secure state.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0127] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 voice indicating a user input for 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.

[0129] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] For example, the collection unit can collect the user's schedule information using the camera 42 or microphone 238 of the headset type terminal 314. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects schedule information from a calendar app, email, SNS, etc. The processing unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and determines or adjusts the user's upcoming plan based on the collected schedule information. The suggestion unit is realized by the display 343 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and proposes the determined or adjusted upcoming plan to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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, the RAM 30, and the 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0138] 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 performed in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0148] For example, the collection unit can collect the user's schedule information using the camera 42 or microphone 238 of the robot 414. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects schedule information from a calendar app, email, SNS, etc. The processing unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and determines or adjusts the user's upcoming plan based on the collected schedule information. The suggestion unit is realized by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12, and suggests the determined or adjusted upcoming plan to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0150] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0152] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0154] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0156] 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.

[0157] 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 Universal Serial Bus (USB) 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.

[0158] In addition, 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 upon request from the data processing device 12.

[0159] 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.

[0160] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0161] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0162] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0163] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0165] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0167] (Appendix 1) A collection unit that collects schedule information of a user; A processing unit that determines or adjusts an immediate plan for the user based on the schedule information collected by the collection unit; A proposal unit that proposes the immediate plan determined or adjusted by the processing unit to the user. A system characterized by: (Appendix 2) The collecting unit includes: Collect the above schedule information from calendar apps, emails, and social media 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect future data when the user's intent is strong 2. The system of claim 1, (Appendix 4) The processing unit includes: determining or adjusting the user's immediate plan based on the schedule information collected by the collection unit and the future data; 2. The system of claim 1, (Appendix 5) The collecting unit includes: To estimate a user's emotion and adjust a timing of collection of schedule information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the user's past schedule history and select the appropriate collection method 2. The system of claim 1, (Appendix 7) The collecting unit includes: Filter event information as it is collected based on the user's current activities and interests 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and determine a priority of information to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting schedule information, the system takes into account the user's geographic location information and prioritizes collection of information that is highly relevant. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting scheduled information, analyze users' social media activities and collect related information 2. The system of claim 1, (Appendix 11) The processing unit includes: Infer user emotions and change how the plan is adjusted based on the inferred user emotions 2. The system of claim 1, (Appendix 12) The processing unit includes: Combine collected schedule information with future data to optimize users' immediate plans 2. The system of claim 1, (Appendix 13) The processing unit includes: Adjust the level of detail in your plan based on the importance of the event information 2. The system of claim 1, (Appendix 14) The processing unit includes: Infer user emotions and prioritize plans based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The processing unit includes: Prioritize plans based on time of submission of collected schedule information 2. The system of claim 1, (Appendix 16) The processing unit includes: Adjust the order of plans based on the relevance of the collected schedule information 2. The system of claim 1, (Appendix 17) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The suggestion unit, When making a proposal, adjust the level of detail in the proposal based on the importance of the plan 2. The system of claim 1, (Appendix 19) The suggestion unit, When making suggestions, apply different suggestion algorithms depending on the plan category 2. The system of claim 1, (Appendix 20) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 21) The suggestion unit, At the time of proposal, prioritize proposals based on when the plans were submitted 2. The system of claim 1, (Appendix 22) The suggestion unit, When making suggestions, adjust the order of suggestions based on plan relevance 2. The system of claim 1, [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects schedule information of a user; A processing unit that determines or adjusts an immediate plan for the user based on the schedule information collected by the collection unit; A proposal unit that proposes the immediate plan determined or adjusted by the processing unit to the user. A system characterized by:

2. The collecting unit includes: Collect the above schedule information from calendar apps, emails, and social media 2. The system of claim 1 .

3. The collecting unit includes: Collect future data when the user's intent is strong 2. The system of claim 1 .

4. The processing unit includes: determining or adjusting the user's immediate plan based on the schedule information collected by the collection unit and the future data; 4. The system of claim 3.

5. The collecting unit includes: A feeling of the user is estimated, and a timing of collection of schedule information is adjusted based on the estimated feeling of the user.

2. The system of claim 1 .

6. The collecting unit includes: Analyze the user's past schedule history and select an appropriate collection method.

2. The system of claim 1 .

7. The collecting unit includes: When collecting the schedule information, filtering is performed based on the user's current activities and interests.

2. The system of claim 1 .

8. The collecting unit includes: Estimating the user's emotion and determining a priority order of information to be collected based on the estimated user's emotion.

2. The system of claim 1 .

9. The collecting unit includes: When collecting the schedule information, highly relevant information is preferentially collected in consideration of the geographical location information of the user.

2. The system of claim 1 .

10. The collecting unit includes: When collecting the schedule information, the user's social media activity is analyzed to collect related information.

2. The system of claim 1 .

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