System

Abstract

To provide a system according to an embodiment that predicts a future on the basis of behavior history of a user, and provides proper advice.SOLUTION: A system according to an embodiment comprises: a collection unit; a prediction unit; and an advice unit. The collection unit is configured to collect a behavior history of a user. The prediction unit is configured to predict a future of the user on the basis of the behavior history collected by the collection unit. The advice unit is configured to advise the user on the basis of a prediction result obtained by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 techniques do not adequately predict the future based on a user's behavioral history and provide appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the future based on the user's behavior history and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, and an advice unit. The collection unit collects a behavioral history of a user. The prediction unit predicts the user's future based on the behavioral history collected by the collection unit. The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can predict the future based on the user's behavior history and provide appropriate advice. [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 a 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 support system according to the embodiment of the present invention is a system that predicts the future based on the user's behavior history and provides specific advice. This support system collects the user's behavior history, such as daily life activities, work progress, and hobby activities, and AI predicts the user's future based on the collected behavior history. For example, it predicts the user's health condition if the current lifestyle habits are continued, and how the current skill set will affect the user's future career. Next, specific advice is provided to the user based on the prediction results. For example, advice is provided on how to manage health, improve lifestyle habits, and acquire new skills. This service allows the user to grasp future risks in advance and correct their behavior so as not to regret. This allows the support system to allow the user to grasp future risks in advance and correct their behavior so as not to regret.

[0029] The support system according to the embodiment includes a collection unit, a prediction unit, and an advice unit. The collection unit collects the user's behavior history. The user's behavior history includes, for example, location information, activity log, browsing history, etc., but is not limited to such examples. The collection unit collects data on the user's daily life activities, work progress, and hobby activities, for example, using a smartphone or a wearable device. For example, the collection unit collects the user's location information as GPS data and records the activity log using a sensor of the smartphone. The collection unit can also acquire the user's browsing history from an Internet browser. The prediction unit predicts the user's future based on the behavior history collected by the collection unit. The prediction is performed using, for example, a statistical model or a machine learning algorithm, but is not limited to such examples. For example, the prediction unit applies a health prediction algorithm to predict the health state if the current lifestyle continues. The prediction unit can also apply a career prediction algorithm to predict how the current skill set will affect the future career. Furthermore, the prediction unit can apply a hobby prediction algorithm to predict the impact of hobby activities on the future life. The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, but is not limited to, methods of health management, improving lifestyle habits, and acquiring new skills. For example, the advice unit recommends a balanced diet and regular exercise as methods of health management. The advice unit can also suggest methods of stress management and time management as methods of improving lifestyle habits. Furthermore, the advice unit can provide methods of programming and language learning as methods of acquiring new skills. In this way, the support system according to the embodiment allows the user to grasp future risks in advance and correct their behavior so as not to regret.

[0030] The collection unit collects the user's behavior history. The user's behavior history includes, but is not limited to, for example, location information, activity log, browsing history, etc. The collection unit collects data on the user's daily life activities, work progress, and hobby activities, for example, using a smartphone or a wearable device. Specifically, the GPS function of the smartphone is used to obtain the user's location information in real time, and the places the user visited and the route of movement are recorded. In addition, the acceleration sensor and gyro sensor mounted on the smartphone or wearable device can be used to record the user's walking and exercise patterns in detail. This makes it possible to accurately grasp the user's amount of exercise and activity level. Furthermore, the collection unit can obtain Internet browser history data and analyze what websites the user visited and what information the user is interested in. This makes it possible to grasp the user's interests and concerns and collect basic data for providing more personalized services. The collection unit centrally manages these data and stores them on a cloud server while ensuring security. The frequency and accuracy of data collection can be adjusted according to the user's settings and system requirements, allowing for flexible response to specific situations and conditions. This enables the collection unit to efficiently and effectively collect a variety of behavioral data of users, thereby improving the performance of the entire system.

[0031] The prediction unit predicts the user's future based on the behavior history collected by the collection unit. The prediction is performed using, for example, a statistical model or a machine learning algorithm, but is not limited to such examples. Specifically, the prediction unit applies a health prediction algorithm based on past health data and lifestyle data to predict the user's health condition. For example, data such as the user's amount of exercise, diet, and sleep patterns are analyzed to evaluate future health risks. In addition, the prediction unit applies a career prediction algorithm based on the user's current skill set and work history to predict the user's career. This makes it possible to suggest what skills the user should acquire in the future and what career path is optimal. Furthermore, the prediction unit applies a hobby prediction algorithm to predict the user's future quality of life and satisfaction based on the user's hobbies and interests. For example, it analyzes what hobbies the user spends time on and evaluates what impact the hobbies will have in the future. When making these predictions, the prediction unit can utilize past data and statistical information to grasp long-term trends and patterns. Furthermore, the prediction unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This enables the prediction unit to predict the user's future risks with high accuracy and provide information for taking appropriate measures.

[0032] The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, for example, a method of health management, improvement of lifestyle habits, acquisition of new skills, and the like, but is not limited to such examples. Specifically, the advice unit recommends a balanced diet and regular exercise as a method of health management based on the user's current health condition and lifestyle habits. For example, the advice unit analyzes the user's dietary content and proposes a nutritionally balanced meal plan. In addition, based on the user's amount of exercise, an appropriate exercise program is provided and the importance of regular exercise is emphasized. Furthermore, the advice unit can also propose a method of stress management and time management as an improvement of lifestyle habits. For example, the advice unit monitors the user's stress level and proposes a relaxation method and a stress relief method. In addition, the advice unit analyzes how the user uses his or her time and provides an efficient time management method. Furthermore, the advice unit can provide a method of programming or language learning based on the user's interests and career goals as an acquisition of new skills. For example, the advice unit introduces online courses and learning resources related to the field in which the user is interested, and supports the progress of learning. When providing these pieces of advice, the advice unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the advice content. For example, the advice content can be reviewed based on the results of the user's actual implementation of the advice, and more effective advice can be provided. The advice unit can also reliably transmit information using multiple communication methods. For example, important information can be delivered reliably by using not only smartphone notifications, but also voice calls, SMS, emails, etc. in combination. This enables the advice unit to provide advice to the user quickly and reliably, enabling the user to grasp future risks in advance and correct their behavior so as not to regret it.

[0033] The collection unit can collect data on the user's daily life, work progress, and hobby. The data on daily life includes, but is not limited to, a meal record, an exercise record, and a sleep pattern. For example, the collection unit collects the user's meal record using a smartphone app. The collection unit can also collect the user's exercise record using a wearable device. The collection unit can also record the user's sleep pattern using a smart watch. The work progress data includes, but is not limited to, a task completion status, a project progress status, and the like. The collection unit collects the user's task completion status using, for example, a project management tool. The collection unit can also obtain the project progress status from a company's system. The hobby data includes, but is not limited to, a reading record, a sports activity, and music appreciation. For example, the collection unit collects the user's reading record using a reading app. The collection unit can also record the user's sports activity using a fitness app. The collection unit can also obtain the user's music appreciation history using a music streaming service. As a result, the collection unit can obtain a more accurate behavioral history by collecting data on the user's daily activities, work progress, and hobby activities. Some 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.

[0034] The advice unit can provide advice on health management, improvement of lifestyle habits, and new skills. Health management methods include, but are not limited to, for example, a balanced diet, frequency of exercise, and quality of sleep. For example, the advice unit recommends a balanced diet. The advice unit can also recommend regular exercise. Furthermore, the advice unit can also suggest a method for ensuring good quality sleep. Improvement of lifestyle habits includes, but are not limited to, for example, stress management, time management, habit formation, and the like. For example, the advice unit suggests a relaxation method for reducing stress. The advice unit can also provide an efficient time management method. Furthermore, the advice unit can also suggest a method for forming new habits. Acquisition of new skills includes, but are not limited to, for example, programming, language learning, creative skills, and the like. For example, the advice unit provides resources for learning the basics of programming. The advice unit can also suggest an effective method for language learning. Furthermore, the advice unit can also provide a training method for improving creative skills. As a result, the advice unit can provide advice on health management methods, lifestyle improvement, and acquisition of new skills, allowing the user to grasp future risks in advance and modify their behavior. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI.

[0035] The collection unit can analyze the user's past behavior history and select an efficient collection method. The collection unit customizes the collection method based on, for example, actions frequently performed by the user in the past. The collection unit can also analyze the user's past behavior patterns and select an efficient collection method. Furthermore, the collection unit can select a method for preferentially collecting important data from the user's past behavior history. In this way, by analyzing the user's past behavior history, an efficient collection method can be selected and important data can be preferentially collected. 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. For example, the collection unit can input the user's past behavior history data to the generation AI and cause the generation AI to select an efficient collection method.

[0036] The collection unit can perform filtering based on the user's current life data and interest data when collecting the behavioral history. For example, the collection unit preferentially collects behavioral history related to the field in which the user is currently interested. The collection unit can also adjust the type of data to be collected according to the user's life situation. Furthermore, the collection unit can filter unnecessary data based on the user's field of interest. As a result, by filtering data based on the user's life situation and field of interest, unnecessary data can be eliminated and important data can be preferentially collected. 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. For example, the collection unit can input the user's interest data to the generation AI and cause the generation AI to perform filtering.

[0037] When collecting the behavioral history, the collection unit can preferentially collect a history with high relevance based on the geographical location information of the user. For example, when the user is in a specific location, the collection unit preferentially collects a behavioral history related to the location. The collection unit can also collect a behavioral history with high relevance by considering the distance from the user's current location. Furthermore, when the user is moving, the collection unit can also preferentially collect a behavioral history related to the moving route. In this way, by considering the geographical location information of the user, it is possible to preferentially collect a behavioral history with high relevance. 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. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of a behavioral history with high relevance.

[0038] The collection unit can analyze the user's social media activities and collect related histories when collecting the behavioral history. The collection unit collects the related behavioral history based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect important data. Furthermore, the collection unit can also collect the related behavioral history by referring to the activities of the user's social media followers and friends. This makes it possible to efficiently collect the related behavioral history by analyzing the user's social media activities. 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. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect the related behavioral history.

[0039] The prediction unit can adjust the accuracy of prediction based on the importance of the behavioral history at the time of prediction. The prediction unit provides a detailed prediction result based on, for example, an important behavioral history. The prediction unit can also provide a simplified prediction result based on a behavioral history with a low importance. Furthermore, the prediction unit can gradually adjust the level of detail of the prediction result according to the importance of the behavioral history. As a result, by adjusting the level of detail of the prediction based on the importance of the behavioral history, it is possible to provide an important prediction result in detail. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prediction unit can input the importance data of the behavioral history to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0040] The prediction unit can apply different prediction algorithms depending on the type of behavioral history at the time of prediction. For example, the prediction unit applies a health prediction algorithm to behavioral history related to health. The prediction unit can also apply a career prediction algorithm to behavioral history related to career. Furthermore, the prediction unit can apply a hobby prediction algorithm to behavioral history related to hobby. In this way, by applying different prediction algorithms depending on the category of behavioral history, more accurate prediction results can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input category data of the behavioral history to the generation AI and cause the generation AI to apply the prediction algorithm.

[0041] The prediction unit can determine the priority of the prediction based on the recording time of the behavior history at the time of prediction. The prediction unit, for example, preferentially reflects the most recent behavior history in the prediction. In addition, the prediction unit can also lower the priority of the prediction for behavior history that has been submitted earlier. Furthermore, the prediction unit can adjust the importance of the prediction result based on the submission time. As a result, by determining the priority of the prediction based on the submission time of the behavior history, the most recent behavior history can be preferentially reflected in the prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the recording time data of the behavior history to the generation AI and cause the generation AI to execute the determination of the priority of the prediction.

[0042] The prediction unit can adjust the order of prediction based on the relevance of the behavioral history at the time of prediction. For example, the prediction unit preferentially reflects highly relevant behavioral history in the prediction. In addition, the prediction unit can postpone the order of prediction for behavioral history with low relevance. Furthermore, the prediction unit can adjust the order of prediction results based on the relevance of the behavioral history. As a result, by adjusting the order of prediction based on the relevance of the behavioral history, it is possible to preferentially reflect highly relevant behavioral history in the prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input relevance data of the behavioral history to the generation AI and cause the generation AI to adjust the order of prediction.

[0043] The advice unit can adjust the accuracy of the advice based on the importance of the prediction result when giving advice. The advice unit provides detailed advice based on, for example, an important prediction result. The advice unit can also provide simplified advice based on a prediction result with a low importance. Furthermore, the advice unit can gradually adjust the level of detail of the advice according to the importance of the prediction result. As a result, important advice can be provided in detail by adjusting the level of detail of the advice based on the importance of the prediction result. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input importance data of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0044] The advice unit can apply different advice algorithms depending on the type of prediction result when giving advice. For example, the advice unit applies a health management advice algorithm to a prediction result related to health. The advice unit can also apply a career advice algorithm to a prediction result related to a career. The advice unit can also apply a hobby advice algorithm to a prediction result related to a hobby. In this way, by applying different advice algorithms depending on the category of the prediction result, more appropriate advice can be provided. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input category data of the prediction result to the generation AI and cause the generation AI to apply the advice algorithm.

[0045] The advice unit can determine the priority of advice based on the recording time of the prediction result when giving advice. The advice unit can, for example, preferentially provide advice based on the most recent prediction result. The advice unit can also lower the priority of advice for prediction results that have been submitted earlier. Furthermore, the advice unit can adjust the importance of advice based on the submission time. In this way, by determining the priority of advice based on the submission time of the prediction result, the latest prediction result can be preferentially reflected in the advice. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input recording time data of the prediction result to the generation AI and cause the generation AI to determine the priority of advice.

[0046] The advice unit can adjust the order of advice based on the relevance of the prediction result when giving advice. The advice unit, for example, preferentially provides advice based on a highly relevant prediction result. The advice unit can also postpone the order of advice for a less relevant prediction result. Furthermore, the advice unit can adjust the order of advice based on the relevance of the prediction result. As a result, by adjusting the order of advice based on the relevance of the prediction result, it is possible to preferentially reflect a highly relevant prediction result in the advice. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input relevance data of the prediction result to the generation AI and cause the generation AI to adjust the order of advice.

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

[0048] The support system can also predict the influence of a user's social network based on the user's behavior history. For example, if a user frequently interacts with certain friends or colleagues, it can predict how the behavior and opinions of those friends and colleagues will affect the user's future behavior. Furthermore, if a user joins a new social network, it can predict the impact that network will have on the user's career or hobbies. This allows the user to understand the influence of their social network in advance and take appropriate action.

[0049] The support system can also predict the user's adaptability to environmental changes based on the user's behavioral history. For example, if the user moves to a new workplace or living environment, it can predict how the change in environment will affect the user's stress level and productivity. Furthermore, if the user starts a new hobby or activity, it can predict the impact that activity will have on the user's entire life. This allows the user to understand their adaptability to environmental changes in advance and take appropriate measures.

[0050] The support system can also predict the user's consumption behavior based on the user's behavioral history. For example, if the user frequently purchases a particular product or service, it can predict how that consumption behavior will affect the user's future financial situation. Furthermore, if the user adopts a new consumption pattern, it can predict the impact that pattern will have on the user's entire life. This allows the user to understand the impact of their consumption behavior in advance and manage their finances appropriately.

[0051] The support system can also predict the user's health risks based on the user's behavioral history. For example, if the user continues to follow a certain diet or exercise habit, it can predict how that habit will affect the user's future health condition. Furthermore, if the user adopts a new health habit, it can predict the impact that habit will have on the user's overall health. This allows the user to understand health risks in advance and take appropriate health management.

[0052] The support system can also predict the user's learning effect based on the user's behavior history. For example, if the user uses a specific learning method or learning materials, it can predict how that method or learning material will affect future learning outcomes. Furthermore, if the user adopts a new learning method, it can predict the impact that method will have on the user's overall learning. This allows the user to understand the learning effect in advance and create an appropriate learning plan.

[0053] The process flow of the first embodiment will be briefly described below.

[0054] Step 1: The collection unit collects the user's behavior history. The user's behavior history includes, for example, location information, activity log, browsing history, etc. The collection unit uses a smartphone or a wearable device to collect data on the user's daily life activities, work progress, and hobby activities. For example, the collection unit collects the user's location information as GPS data and records the activity log using a smartphone sensor. The collection unit can also obtain the user's browsing history from an Internet browser. Step 2: The prediction unit predicts the user's future based on the behavioral history collected by the collection unit. The prediction is performed using statistical models and machine learning algorithms. For example, the prediction unit applies a health prediction algorithm to predict the health state if the current lifestyle continues. The prediction unit can also apply a career prediction algorithm to predict how the current skill set will affect the future career. The prediction unit can also apply a hobby prediction algorithm to predict the impact of hobby activities on future life. Step 3: The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, for example, health management methods, lifestyle improvement, and acquisition of new skills. For example, the advice unit recommends a balanced diet and regular exercise as health management methods. The advice unit can also suggest stress management and time management methods as lifestyle improvement. Furthermore, the advice unit can provide programming and language learning methods as new skill acquisition.

[0055] (Example 2) The support system according to the embodiment of the present invention is a system that predicts the future based on the user's behavior history and provides specific advice. This support system collects the user's behavior history, such as daily life activities, work progress, and hobby activities, and AI predicts the user's future based on the collected behavior history. For example, it predicts the user's health condition if the current lifestyle habits are continued, and how the current skill set will affect the user's future career. Next, specific advice is provided to the user based on the prediction results. For example, advice is provided on how to manage health, improve lifestyle habits, and acquire new skills. This service allows the user to grasp future risks in advance and correct their behavior so as not to regret. This allows the support system to allow the user to grasp future risks in advance and correct their behavior so as not to regret.

[0056] The support system according to the embodiment includes a collection unit, a prediction unit, and an advice unit. The collection unit collects the user's behavior history. The user's behavior history includes, for example, location information, activity log, browsing history, etc., but is not limited to such examples. The collection unit collects data on the user's daily life activities, work progress, and hobby activities, for example, using a smartphone or a wearable device. For example, the collection unit collects the user's location information as GPS data and records the activity log using a sensor of the smartphone. The collection unit can also acquire the user's browsing history from an Internet browser. The prediction unit predicts the user's future based on the behavior history collected by the collection unit. The prediction is performed using, for example, a statistical model or a machine learning algorithm, but is not limited to such examples. For example, the prediction unit applies a health prediction algorithm to predict the health state if the current lifestyle continues. The prediction unit can also apply a career prediction algorithm to predict how the current skill set will affect the future career. Furthermore, the prediction unit can apply a hobby prediction algorithm to predict the impact of hobby activities on the future life. The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, but is not limited to, methods of health management, improving lifestyle habits, and acquiring new skills. For example, the advice unit recommends a balanced diet and regular exercise as methods of health management. The advice unit can also suggest methods of stress management and time management as methods of improving lifestyle habits. Furthermore, the advice unit can provide methods of programming and language learning as methods of acquiring new skills. In this way, the support system according to the embodiment allows the user to grasp future risks in advance and correct their behavior so as not to regret.

[0057] The collection unit collects the user's behavior history. The user's behavior history includes, but is not limited to, for example, location information, activity log, browsing history, etc. The collection unit collects data on the user's daily life activities, work progress, and hobby activities, for example, using a smartphone or a wearable device. Specifically, the GPS function of the smartphone is used to obtain the user's location information in real time, and the places the user visited and the route of movement are recorded. In addition, the acceleration sensor and gyro sensor mounted on the smartphone or wearable device can be used to record the user's walking and exercise patterns in detail. This makes it possible to accurately grasp the user's amount of exercise and activity level. Furthermore, the collection unit can obtain Internet browser history data and analyze what websites the user visited and what information the user is interested in. This makes it possible to grasp the user's interests and concerns and collect basic data for providing more personalized services. The collection unit centrally manages these data and stores them on a cloud server while ensuring security. The frequency and accuracy of data collection can be adjusted according to the user's settings and system requirements, allowing for flexible response to specific situations and conditions. This enables the collection unit to efficiently and effectively collect a variety of behavioral data of users, thereby improving the performance of the entire system.

[0058] The prediction unit predicts the user's future based on the behavior history collected by the collection unit. The prediction is performed using, for example, a statistical model or a machine learning algorithm, but is not limited to such examples. Specifically, the prediction unit applies a health prediction algorithm based on past health data and lifestyle data to predict the user's health condition. For example, data such as the user's amount of exercise, diet, and sleep patterns are analyzed to evaluate future health risks. In addition, the prediction unit applies a career prediction algorithm based on the user's current skill set and work history to predict the user's career. This makes it possible to suggest what skills the user should acquire in the future and what career path is optimal. Furthermore, the prediction unit applies a hobby prediction algorithm to predict the user's future quality of life and satisfaction based on the user's hobbies and interests. For example, it analyzes what hobbies the user spends time on and evaluates what impact the hobbies will have in the future. When making these predictions, the prediction unit can utilize past data and statistical information to grasp long-term trends and patterns. Furthermore, the prediction unit can use an anomaly detection algorithm to detect unusual patterns and abnormal data and issue an early warning. This enables the prediction unit to predict the user's future risks with high accuracy and provide information for taking appropriate measures.

[0059] The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, for example, a method of health management, improvement of lifestyle habits, acquisition of new skills, and the like, but is not limited to such examples. Specifically, the advice unit recommends a balanced diet and regular exercise as a method of health management based on the user's current health condition and lifestyle habits. For example, the advice unit analyzes the user's dietary content and proposes a nutritionally balanced meal plan. In addition, based on the user's amount of exercise, an appropriate exercise program is provided and the importance of regular exercise is emphasized. Furthermore, the advice unit can also propose a method of stress management and time management as an improvement of lifestyle habits. For example, the advice unit monitors the user's stress level and proposes a relaxation method and a stress relief method. In addition, the advice unit analyzes how the user uses his or her time and provides an efficient time management method. Furthermore, the advice unit can provide a method of programming or language learning based on the user's interests and career goals as an acquisition of new skills. For example, the advice unit introduces online courses and learning resources related to the field in which the user is interested, and supports the progress of learning. When providing these pieces of advice, the advice unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the advice content. For example, the advice content can be reviewed based on the results of the user's actual implementation of the advice, and more effective advice can be provided. The advice unit can also reliably transmit information using multiple communication methods. For example, important information can be delivered reliably by using not only smartphone notifications, but also voice calls, SMS, emails, etc. in combination. This enables the advice unit to provide advice to the user quickly and reliably, enabling the user to grasp future risks in advance and correct their behavior so as not to regret it.

[0060] The collection unit can collect data on the user's daily life, work progress, and hobby. The data on daily life includes, but is not limited to, a meal record, an exercise record, and a sleep pattern. For example, the collection unit collects the user's meal record using a smartphone app. The collection unit can also collect the user's exercise record using a wearable device. The collection unit can also record the user's sleep pattern using a smart watch. The work progress data includes, but is not limited to, a task completion status, a project progress status, and the like. The collection unit collects the user's task completion status using, for example, a project management tool. The collection unit can also obtain the project progress status from a company's system. The hobby data includes, but is not limited to, a reading record, a sports activity, and music appreciation. For example, the collection unit collects the user's reading record using a reading app. The collection unit can also record the user's sports activity using a fitness app. The collection unit can also obtain the user's music appreciation history using a music streaming service. As a result, the collection unit can obtain a more accurate behavioral history by collecting data on the user's daily activities, work progress, and hobby activities. Some 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.

[0061] The advice unit can provide advice on health management, improvement of lifestyle habits, and new skills. Health management methods include, but are not limited to, for example, a balanced diet, frequency of exercise, and quality of sleep. For example, the advice unit recommends a balanced diet. The advice unit can also recommend regular exercise. Furthermore, the advice unit can also suggest a method for ensuring good quality sleep. Improvement of lifestyle habits includes, but are not limited to, for example, stress management, time management, habit formation, and the like. For example, the advice unit suggests a relaxation method for reducing stress. The advice unit can also provide an efficient time management method. Furthermore, the advice unit can also suggest a method for forming new habits. Acquisition of new skills includes, but are not limited to, for example, programming, language learning, creative skills, and the like. For example, the advice unit provides resources for learning the basics of programming. The advice unit can also suggest an effective method for language learning. Furthermore, the advice unit can also provide a training method for improving creative skills. As a result, the advice unit can provide advice on health management methods, lifestyle improvement, and acquisition of new skills, allowing the user to grasp future risks in advance and modify their behavior. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI.

[0062] The collection unit can estimate the user's emotion and adjust the collection timing of the behavior history based on the estimated user's emotion. For example, when the user is stressed, the collection unit reduces the collection timing to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can also increase the collection timing to collect detailed behavior history. Furthermore, when the user is busy, the collection unit can adjust the collection timing to collect only important data. In this way, by adjusting the collection timing according to the user's emotion, it is possible to reduce the burden on the user and collect detailed behavior history. The estimation of 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0063] The collection unit can analyze the user's past behavior history and select an efficient collection method. The collection unit customizes the collection method based on, for example, actions frequently performed by the user in the past. The collection unit can also analyze the user's past behavior patterns and select an efficient collection method. Furthermore, the collection unit can select a method for preferentially collecting important data from the user's past behavior history. In this way, by analyzing the user's past behavior history, an efficient collection method can be selected and important data can be preferentially collected. 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. For example, the collection unit can input the user's past behavior history data to the generation AI and cause the generation AI to select an efficient collection method.

[0064] The collection unit can perform filtering based on the user's current life data and interest data when collecting the behavioral history. For example, the collection unit preferentially collects behavioral history related to the field in which the user is currently interested. The collection unit can also adjust the type of data to be collected according to the user's life situation. Furthermore, the collection unit can filter unnecessary data based on the user's field of interest. As a result, by filtering data based on the user's life situation and field of interest, unnecessary data can be eliminated and important data can be preferentially collected. 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. For example, the collection unit can input the user's interest data to the generation AI and cause the generation AI to perform filtering.

[0065] The collection unit can estimate the user's emotion and determine the priority of the behavior history to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects behavior history related to stress reduction. In addition, when the user is relaxed, the collection unit can also preferentially collect behavior history related to relaxation. Furthermore, when the user is excited, the collection unit can also preferentially collect behavior history related to excitement. In this way, important data can be preferentially collected by determining the priority of the behavior history 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. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the behavior history.

[0066] When collecting the behavioral history, the collection unit can preferentially collect a history with high relevance based on the geographical location information of the user. For example, when the user is in a specific location, the collection unit preferentially collects a behavioral history related to the location. The collection unit can also collect a behavioral history with high relevance by considering the distance from the user's current location. Furthermore, when the user is moving, the collection unit can also preferentially collect a behavioral history related to the moving route. In this way, by considering the geographical location information of the user, it is possible to preferentially collect a behavioral history with high relevance. 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. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute collection of a behavioral history with high relevance.

[0067] The collection unit can analyze the user's social media activities and collect related histories when collecting the behavioral history. The collection unit collects the related behavioral history based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect important data. Furthermore, the collection unit can also collect the related behavioral history by referring to the activities of the user's social media followers and friends. This makes it possible to efficiently collect the related behavioral history by analyzing the user's social media activities. 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. For example, the collection unit can input the user's social media data to the generation AI and cause the generation AI to collect the related behavioral history.

[0068] The prediction unit can estimate the user's emotion and adjust the expression method of the prediction based on the estimated user's emotion. For example, when the user is nervous, the prediction unit provides a simple and highly visible prediction result. In addition, the prediction unit can also provide a detailed prediction result when the user is relaxed. Furthermore, when the user is excited, the prediction unit can also provide a visually stimulating prediction result. In this way, by adjusting the expression method of the prediction according to the user's emotion, it is possible to provide a prediction result that is easy for the user to understand. 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. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the prediction.

[0069] The prediction unit can adjust the accuracy of prediction based on the importance of the behavioral history at the time of prediction. The prediction unit provides a detailed prediction result based on, for example, an important behavioral history. The prediction unit can also provide a simplified prediction result based on a behavioral history with a low importance. Furthermore, the prediction unit can gradually adjust the level of detail of the prediction result according to the importance of the behavioral history. As a result, by adjusting the level of detail of the prediction based on the importance of the behavioral history, it is possible to provide an important prediction result in detail. A part or all of the above-mentioned processing in the prediction unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the prediction unit can input the importance data of the behavioral history to the generation AI and cause the generation AI to adjust the level of detail of the prediction.

[0070] The prediction unit can apply different prediction algorithms depending on the type of behavioral history at the time of prediction. For example, the prediction unit applies a health prediction algorithm to behavioral history related to health. The prediction unit can also apply a career prediction algorithm to behavioral history related to career. Furthermore, the prediction unit can apply a hobby prediction algorithm to behavioral history related to hobby. In this way, by applying different prediction algorithms depending on the category of behavioral history, more accurate prediction results can be provided. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input category data of the behavioral history to the generation AI and cause the generation AI to apply the prediction algorithm.

[0071] The prediction unit can estimate the user's emotion and adjust the length of the prediction based on the estimated user's emotion. For example, when the user is in a hurry, the prediction unit provides a short and to the point prediction result. In addition, when the user is relaxed, the prediction unit can also provide a longer prediction result including detailed explanations. In addition, when the user is excited, the prediction unit can also provide a visually stimulating prediction result. In this way, by adjusting the length of the prediction according to the user's emotion, a prediction result of an appropriate length for the user can be provided. 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. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the prediction.

[0072] The prediction unit can determine the priority of the prediction based on the recording time of the behavior history at the time of prediction. The prediction unit, for example, preferentially reflects the most recent behavior history in the prediction. In addition, the prediction unit can also lower the priority of the prediction for behavior history that has been submitted earlier. Furthermore, the prediction unit can adjust the importance of the prediction result based on the submission time. As a result, by determining the priority of the prediction based on the submission time of the behavior history, the most recent behavior history can be preferentially reflected in the prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the recording time data of the behavior history to the generation AI and cause the generation AI to execute the determination of the priority of the prediction.

[0073] The prediction unit can adjust the order of prediction based on the relevance of the behavioral history at the time of prediction. For example, the prediction unit preferentially reflects highly relevant behavioral history in the prediction. In addition, the prediction unit can postpone the order of prediction for behavioral history with low relevance. Furthermore, the prediction unit can adjust the order of prediction results based on the relevance of the behavioral history. As a result, by adjusting the order of prediction based on the relevance of the behavioral history, it is possible to preferentially reflect highly relevant behavioral history in the prediction. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input relevance data of the behavioral history to the generation AI and cause the generation AI to adjust the order of prediction.

[0074] The advice unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. For example, when the user is nervous, the advice unit provides simple and highly visible advice. In addition, the advice unit can also provide detailed advice when the user is relaxed. Furthermore, when the user is excited, the advice unit can also provide visually stimulating advice. In this way, by adjusting the way of expressing advice according to the user's emotion, it is possible to provide advice that is easy for the user to understand. The estimation of 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. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the way of expressing advice.

[0075] The advice unit can adjust the accuracy of the advice based on the importance of the prediction result when giving advice. The advice unit provides detailed advice based on, for example, an important prediction result. The advice unit can also provide simplified advice based on a prediction result with a low importance. Furthermore, the advice unit can gradually adjust the level of detail of the advice according to the importance of the prediction result. As a result, important advice can be provided in detail by adjusting the level of detail of the advice based on the importance of the prediction result. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input importance data of the prediction result to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0076] The advice unit can apply different advice algorithms depending on the type of prediction result when giving advice. For example, the advice unit applies a health management advice algorithm to a prediction result related to health. The advice unit can also apply a career advice algorithm to a prediction result related to a career. The advice unit can also apply a hobby advice algorithm to a prediction result related to a hobby. In this way, by applying different advice algorithms depending on the category of the prediction result, more appropriate advice can be provided. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input category data of the prediction result to the generation AI and cause the generation AI to apply the advice algorithm.

[0077] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. For example, when the user is in a hurry, the advice unit provides short advice that is to the point. Also, when the user is relaxed, the advice unit can provide longer advice that includes detailed explanations. Furthermore, when the user is excited, the advice unit can provide visually stimulating advice. In this way, by adjusting the length of the advice according to the user's emotion, advice of an appropriate length for the user can be provided. 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. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the advice.

[0078] The advice unit can determine the priority of advice based on the recording time of the prediction result when giving advice. The advice unit can, for example, preferentially provide advice based on the most recent prediction result. The advice unit can also lower the priority of advice for prediction results that have been submitted earlier. Furthermore, the advice unit can adjust the importance of advice based on the submission time. In this way, by determining the priority of advice based on the submission time of the prediction result, the latest prediction result can be preferentially reflected in the advice. A part or all of the above-mentioned processing in the advice unit can be performed, for example, using AI or can be performed without using AI. For example, the advice unit can input recording time data of the prediction result to the generation AI and cause the generation AI to determine the priority of advice.

[0079] The advice unit can adjust the order of advice based on the relevance of the prediction result when giving advice. The advice unit, for example, preferentially provides advice based on a highly relevant prediction result. The advice unit can also postpone the order of advice for a less relevant prediction result. Furthermore, the advice unit can adjust the order of advice based on the relevance of the prediction result. As a result, by adjusting the order of advice based on the relevance of the prediction result, it is possible to preferentially reflect a highly relevant prediction result in the advice. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input relevance data of the prediction result to the generation AI and cause the generation AI to adjust the order of advice.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The support system can also predict the influence of a user's social network based on the user's behavior history. For example, if a user frequently interacts with certain friends or colleagues, it can predict how the behavior and opinions of those friends and colleagues will affect the user's future behavior. Furthermore, if a user joins a new social network, it can predict the impact that network will have on the user's career or hobbies. This allows the user to understand the influence of their social network in advance and take appropriate action.

[0082] The support system can also predict the user's adaptability to environmental changes based on the user's behavioral history. For example, if the user moves to a new workplace or living environment, it can predict how the change in environment will affect the user's stress level and productivity. Furthermore, if the user starts a new hobby or activity, it can predict the impact that activity will have on the user's entire life. This allows the user to understand their adaptability to environmental changes in advance and take appropriate measures.

[0083] The support system can also predict the user's consumption behavior based on the user's behavioral history. For example, if the user frequently purchases a particular product or service, it can predict how that consumption behavior will affect the user's future financial situation. Furthermore, if the user adopts a new consumption pattern, it can predict the impact that pattern will have on the user's entire life. This allows the user to understand the impact of their consumption behavior in advance and manage their finances appropriately.

[0084] The support system can also predict the user's health risks based on the user's behavioral history. For example, if the user continues to follow a certain diet or exercise habit, it can predict how that habit will affect the user's future health condition. Furthermore, if the user adopts a new health habit, it can predict the impact that habit will have on the user's overall health. This allows the user to understand health risks in advance and take appropriate health management.

[0085] The support system can also predict the user's learning effect based on the user's behavior history. For example, if the user uses a specific learning method or learning materials, it can predict how that method or learning material will affect future learning outcomes. Furthermore, if the user adopts a new learning method, it can predict the impact that method will have on the user's overall learning. This allows the user to understand the learning effect in advance and create an appropriate learning plan.

[0086] The support system can estimate the user's emotions and provide advice to improve the user's motivation based on the estimated emotions. For example, if the user is feeling down, the system can improve the user's motivation by sending encouraging messages or sharing success stories. If the user is motivated, the system can provide advice to encourage the user to take on further challenges. Furthermore, if the user is tired, the system can provide advice to encourage the user to take a rest. In this way, the system can effectively support the user's actions by providing advice to improve the user's motivation according to the user's emotions.

[0087] The support system can estimate the user's emotions and provide advice to support the user's stress management based on the estimated emotions. For example, if the user feels stressed, the support system can suggest relaxation methods and activities for relieving stress. If the user is relaxed, the support system can provide advice for maintaining that state. Furthermore, if the user is excited, the support system can suggest methods for calming down. In this way, the support system can maintain the user's mental health by providing advice to support stress management according to the user's emotions.

[0088] The support system can estimate the user's emotions and provide advice to improve the user's communication skills based on the estimated emotions. For example, if the user is nervous, the support system can suggest breathing techniques and speaking tips to relax the user. If the user is confident, the support system can also provide a communication strategy to utilize that confidence. Furthermore, if the user is angry, the support system can suggest ways to stay calm. In this way, the support system can improve the user's interpersonal relationships by providing advice to improve communication skills according to the user's emotions.

[0089] The support system can estimate the user's emotions and provide advice to bring out the user's creativity based on the estimated emotions. For example, if the user is relaxed, the support system can suggest a brainstorming method to bring out creative ideas. If the user is excited, the support system can also suggest a way to utilize that energy in a creative project. Furthermore, if the user is depressed, the support system can suggest a creative activity to lift the user's mood. In this way, the support system can support the user's creative activities by providing advice to bring out creativity according to the user's emotions.

[0090] The support system can estimate the user's emotions and provide advice to promote the user's self-growth based on the estimated emotions. For example, if the user has self-doubt, a method to increase self-esteem can be suggested. Also, if the user has confidence, a challenge to further strengthen the user's confidence can be suggested. Furthermore, if the user feels anxious, a relaxation method to reduce anxiety can be provided. In this way, the system can support the user's personal growth by providing advice to promote self-growth according to the user's emotions.

[0091] The process flow of the second embodiment will be briefly described below.

[0092] Step 1: The collection unit collects the user's behavior history. The user's behavior history includes, for example, location information, activity log, browsing history, etc. The collection unit uses a smartphone or a wearable device to collect data on the user's daily life activities, work progress, and hobby activities. For example, the collection unit collects the user's location information as GPS data and records the activity log using a smartphone sensor. The collection unit can also obtain the user's browsing history from an Internet browser. Step 2: The prediction unit predicts the user's future based on the behavioral history collected by the collection unit. The prediction is performed using statistical models and machine learning algorithms. For example, the prediction unit applies a health prediction algorithm to predict the health state if the current lifestyle continues. The prediction unit can also apply a career prediction algorithm to predict how the current skill set will affect the future career. The prediction unit can also apply a hobby prediction algorithm to predict the impact of hobby activities on future life. Step 3: The advice unit provides advice to the user based on the prediction result obtained by the prediction unit. The advice includes, for example, health management methods, lifestyle improvement, and acquisition of new skills. For example, the advice unit recommends a balanced diet and regular exercise as health management methods. The advice unit can also suggest stress management and time management methods as lifestyle improvement. Furthermore, the advice unit can provide programming and language learning methods as new skill acquisition.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] For example, the collection unit collects the user's behavior history using the camera 42 and microphone 38B of the smart device 14. For example, the collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects the user's location information and activity log. The prediction unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and predicts the user's future using a statistical model or a machine learning algorithm based on the collected behavior history. The advice unit is, for example, realized by the control unit 46A of the smart device 14, and provides specific advice to the user based on the prediction result. The advice unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and provides advice such as health management, improvement of lifestyle habits, and acquisition of new skills. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the collection unit collects the user's behavior history using the camera 42 and microphone 238 of the smart glasses 214. For example, the collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects the user's location information and activity log. The prediction unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and predicts the user's future using a statistical model or a machine learning algorithm based on the collected behavior history. The advice unit is, for example, realized by the control unit 46A of the smart glasses 214, and provides specific advice to the user based on the prediction result. The advice unit is, for example, also realized by the specific processing unit 290 of the data processing device 12, and provides advice such as health management, improvement of lifestyle habits, and acquisition of new skills. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0116] 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0118] 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).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0120] 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0123] 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0127] 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.

[0128] For example, the collection unit collects the user's behavior history using the camera 42 and microphone 238 of the headset type terminal 314. For example, the collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects the user's location information and activity log. The prediction unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and predicts the user's future using a statistical model or a machine learning algorithm based on the collected behavior history. The advice unit is, for example, realized by the control unit 46A of the headset type terminal 314, and provides specific advice to the user based on the prediction result. The advice unit is, for example, also realized by the specific processing unit 290 of the data processing device 12, and provides advice such as health management, improvement of lifestyle habits, and acquisition of new skills. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, 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.

[0132] 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.

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

[0134] 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).

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

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the collection unit collects the user's behavior history using the camera 42 and microphone 238 of the robot 414. For example, the collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects the user's location information and activity log. The prediction unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and predicts the user's future using a statistical model or a machine learning algorithm based on the collected behavior history. The advice unit is, for example, realized by the control unit 46A of the robot 414, and provides specific advice to the user based on the prediction result. The advice unit is, for example, realized by the specific processing unit 290 of the data processing device 12, and provides advice such as health management, improvement of lifestyle habits, and acquisition of new skills. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Appendix 1) A collection unit that collects user behavior history; A prediction unit that predicts the user's future based on the behavioral history collected by the collection unit; an advice unit that provides advice to the user based on the prediction result obtained by the prediction unit; A system characterized by: (Appendix 2) The collecting unit includes: Collect data on the user's daily life, work progress, and hobbies 2. The system of claim 1, (Appendix 3) The advice unit, Providing advice on managing your health, improving your lifestyle and learning new skills 2. The system of claim 1, (Appendix 4) The collecting unit includes: A method for predicting a user's emotions and adjusting the timing of collecting behavioral history based on the user's emotions 2. The system of claim 1, (Appendix 5) The collecting unit includes: Analyze users' past behavioral history and select the most efficient collection method 2. The system of claim 1, (Appendix 6) The collecting unit includes: When collecting behavioral history, filtering is performed based on the user's current lifestyle and interest data. 2. The system of claim 1, (Appendix 7) The collecting unit includes: The user's emotions are estimated, and the priority of the behavioral history to be collected is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting behavioral history, prioritize collection of history that is more relevant based on the user's geographic location information 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting behavioral history, analyze the user's social media activity and collect related history. 2. The system of claim 1, (Appendix 10) The prediction unit is Infer user sentiment and adjust prediction presentation based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The prediction unit is When making predictions, adjust the accuracy of the prediction based on the importance of behavioral history. 2. The system of claim 1, (Appendix 12) The prediction unit is When making predictions, different prediction algorithms are applied depending on the type of behavioral history. 2. The system of claim 1, (Appendix 13) The prediction unit is Infer user sentiment and adjust the length of the prediction based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The prediction unit is When making predictions, prioritize predictions based on when the behavioral history was recorded. 2. The system of claim 1, (Appendix 15) The prediction unit is When making predictions, adjust the order of predictions based on the relevance of behavioral history 2. The system of claim 1, (Appendix 16) The advice unit, Infer the user's emotions and adjust the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 17) The advice unit, When giving advice, adjust the accuracy of the advice based on the importance of the prediction results 2. The system of claim 1, (Appendix 18) The advice unit, When advising, apply different advice algorithms depending on the type of prediction result. 2. The system of claim 1, (Appendix 19) The advice unit, Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The advice unit, When giving advice, the priority of advice is determined based on when the prediction results were recorded. 2. The system of claim 1, (Appendix 21) The advice unit, When advising, adjust the order of advice based on the relevance of the prediction results 2. The system of claim 1, [Explanation of symbols]

[0165] 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 user behavior history; A prediction unit that predicts the user's future based on the behavioral history collected by the collection unit; an advice unit that provides advice to the user based on the prediction result obtained by the prediction unit; A system characterized by:

2. The collecting unit includes: Collecting data on the user's daily life, work progress, and hobbies 2. The system of claim 1 .

3. The advice unit, Providing advice on managing your health, improving your lifestyle and learning new skills 2. The system of claim 1 .

4. The collecting unit includes: The user's feelings are estimated, and a timing for collecting a behavior history is adjusted based on the estimated user's feelings.

2. The system of claim 1 .

5. The collecting unit includes: Analyze the user's past behavior history and select an efficient collection method.

2. The system of claim 1 .

6. The collecting unit includes: When collecting behavioral history, filtering is performed based on the user's current lifestyle and interest data.

2. The system of claim 1 .

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

2. The system of claim 1 .

8. The collecting unit includes: When collecting behavioral histories, a history highly related to the user is preferentially collected based on the geographical location information of the user.

2. The system of claim 1 .

9. The collecting unit includes: When collecting the behavioral history, the user's social media activities are analyzed and related history is collected.

2. The system of claim 1 .

10. The prediction unit is Inferring a sentiment of the user and adjusting a manner of expressing the prediction based on the estimated sentiment of the user.

2. The system of claim 1 .

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