system

Abstract

The system according to the embodiment aims to predict the behavioral patterns of a user and provide assistance for efficiently performing housework. [Solution] A system according to an embodiment includes a collection unit, a prediction unit, a proposal unit, an understanding unit, and an instruction unit. The collection unit collects user behavior data. The prediction unit predicts the user's behavior patterns based on the data collected by the collection unit. The proposal unit proposes a schedule based on the prediction results obtained by the prediction unit. The understanding unit understands the user's instructions. The instruction unit issues instructions to a household robot based on the instructions understood by the understanding unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately predict users' behavioral patterns and provide support for efficiently performing housework, and there is room for improvement.

[0005] The system according to the embodiment aims to predict the behavioral patterns of a user and provide assistance for efficiently performing housework. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, a proposal unit, an understanding unit, and an instruction unit. The collection unit collects user behavior data. The prediction unit predicts the user's behavior pattern based on the data collected by the collection unit. The proposal unit proposes a schedule based on the prediction result obtained by the prediction unit. The understanding unit understands the user's instructions. The instruction unit issues instructions to the household robot based on the instructions understood by the understanding unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict the behavioral patterns of the user and provide assistance for efficiently performing housework. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention improves housework efficiency by coordinating an AI-based scheduler that predicts user behavior and suggests what to do next with a multimodal generative AI and a household robot. This system collects user behavior data, analyzes the data, and predicts what to do next. Based on the prediction, the system then proposes a schedule. Furthermore, the multimodal generative AI understands the user's instructions and issues instructions to the household robot to improve housework efficiency. For example, the system collects user behavior data, such as what actions the user takes at what time of day and which tasks they prioritize. This data is input into the AI. The AI ​​then analyzes the collected data and predicts the user's behavioral patterns. For example, it predicts that the user wakes up at 7:00 a.m. every morning and eats breakfast at 8:00 a.m. Based on this prediction, it suggests what to do next. Furthermore, the multimodal generative AI understands the user's instructions. For example, if the user says, "Clean up," the generative AI understands the instruction and issues specific instructions to the household robot. The household robot then cleans according to the generative AI's instructions. This system makes users' actions more efficient and reduces the burden of housework. For example, the household robot can be set to finish cleaning before the user returns home from work. It also makes schedule management easier because the user can always know what to do next. This allows the system to predict the user's actions, suggest what to do next, and operate the household robot efficiently.

[0029] A scheduler system according to an embodiment includes a collection unit, a prediction unit, a suggestion unit, an understanding unit, and an instruction unit. The collection unit collects user behavior data. The user behavior data includes, but is not limited to, location information, activity logs, and sensor information. The collection unit collects, for example, data on what behaviors the user takes at what time of day and which tasks the user prioritizes. For example, the collection unit can acquire location information from the user's smartphone or wearable device. The collection unit can also collect an activity log that records the user's behavior. The collection unit can also record the user's behavior in detail using sensor information. The prediction unit predicts the user's behavior pattern based on the data collected by the collection unit. The prediction unit analyzes the user's behavior pattern using, for example, a machine learning algorithm. For example, the prediction unit can predict that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. The prediction unit can also analyze the user's behavior pattern by time period and predict a reaction to a specific event. The suggestion unit suggests what to do next based on the prediction results obtained by the prediction unit. The suggestion unit can, for example, suggest task priorities and timetables for the user. For example, the suggestion unit can list the tasks the user should do next and indicate their priorities. The suggestion unit can also optimize the user's schedule and suggest an efficient timetable. The understanding unit understands the user's instructions. The understanding unit can, for example, use a generation AI to analyze the user's voice instructions or text instructions. For example, if the user says, "Clean up," the understanding unit can understand the instruction and generate specific instructions. The understanding unit can also analyze the user's gesture instructions and understand their intentions. The instruction unit issues instructions to the household robot based on the instructions understood by the understanding unit. The instruction unit can, for example, issue specific cleaning instructions to the household robot. For example, the instruction unit can issue the instruction to the household robot, such as, "Clean the living room." The instruction unit can also issue instructions to the household robot to do laundry or cook.As a result, the scheduler system according to the embodiment can predict the user's behavior, suggest what to do next, and efficiently operate the household robot.

[0030] The scheduler system further includes a household unit that causes a household robot to perform specific household chores. The household unit causes the household robot to perform specific household chores. Specific household chores include, but are not limited to, cleaning, laundry, and cooking. The household unit can, for example, clean a room using a cleaning robot. For example, the household unit can instruct the cleaning robot to clean the floor of the room. The household unit can also do laundry using a laundry robot. For example, the household unit can instruct the laundry robot to wash laundry. The household unit can also cook using a cooking robot. For example, the household unit can instruct the cooking robot to cook ingredients. This allows the household robot to perform specific household chores, thereby reducing the housework burden on the user.

[0031] The scheduler system further includes a reception unit that receives user instructions. The reception unit receives the user instructions. The reception unit can receive, for example, voice instructions, text instructions, gesture instructions, and the like. For example, when a user issues a voice instruction such as "clean up," the reception unit can receive the voice instruction. Furthermore, when a user issues a text instruction such as "clean up," the reception unit can also receive the text instruction. Furthermore, when a user issues a gesture instruction to clean, the reception unit can also receive the gesture instruction. In this way, by receiving the user's instructions, the system can perform an operation according to the user's request.

[0032] The collection unit can collect data on what actions the user takes at what time of day and which tasks the user prioritizes. The collection unit can, for example, acquire location information from the user's smartphone or wearable device. For example, the collection unit can collect data on what actions the user takes at what time of day. The collection unit can also collect an activity log that records the user's actions. For example, the collection unit can collect data on which tasks the user prioritizes. Furthermore, the collection unit can record the user's actions in detail using sensor information. For example, the collection unit can collect sensor information such as the user's heart rate and number of steps. In this way, by collecting detailed user behavior data, more accurate predictions can be made.

[0033] The prediction unit can predict a user's behavioral patterns based on the collected data. The prediction unit, for example, uses a machine learning algorithm to analyze the user's behavioral patterns. For example, the prediction unit can predict that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. The prediction unit can also analyze the user's behavioral patterns by time period and predict reactions to specific events. For example, the prediction unit can predict whether the user will tend to take specific actions during specific time periods. Furthermore, the prediction unit can analyze the user's behavioral patterns over the long term and predict behavioral patterns according to seasons or events. For example, the prediction unit can predict whether the user will tend to take specific actions during summer vacation. By predicting the user's behavioral patterns, the prediction unit can suggest what the user should do next.

[0034] The suggestion unit can suggest what to do next based on the prediction results. The suggestion unit can, for example, suggest task priorities and timetables for the user. For example, the suggestion unit can list tasks that the user should do next and indicate their priorities. The suggestion unit can also optimize the user's schedule and suggest an efficient timetable. For example, the suggestion unit can suggest tasks that the user should do next for each time period. Furthermore, the suggestion unit can prioritize and suggest specific tasks based on the user's behavioral patterns. For example, the suggestion unit can suggest tasks that the user should do every morning. In this way, by suggesting what to do next based on the prediction results, the user's behavior can be made more efficient.

[0035] The understanding unit can understand user instructions and issue specific instructions to the household robot. The understanding unit, for example, uses a generation AI to analyze the user's voice instructions and text instructions. For example, if the user says, "Clean up," the understanding unit can understand the instruction and generate specific instructions. The understanding unit can also analyze the user's gesture instructions and understand their intention. For example, if the user gestures to indicate cleaning instructions, the understanding unit can analyze the gesture and generate specific instructions. Furthermore, the understanding unit can understand the user's instructions based on the context. For example, if the user says, "Clean up," the understanding unit can analyze the context and generate specific cleaning instructions. This allows the household robot to understand the user's instructions and issue specific instructions, thereby making housework more efficient.

[0036] The collection unit can analyze the user's past behavioral history and select an efficient data collection method. The collection unit can, for example, select the optimal data collection method based on the user's past behavioral history. For example, the collection unit can select the optimal data collection method based on behaviors the user frequently performed in the past. The collection unit can also select the optimal data collection method for a specific time period from the user's past behavioral history. For example, the collection unit can analyze the user's past behavioral patterns and select an efficient data collection method. Furthermore, the collection unit can analyze the user's past behavioral history over the long term and select a data collection method according to the season or event. For example, the collection unit can select the optimal data collection method based on the user's tendency to perform specific behaviors during summer vacation. This enables efficient data collection by analyzing past behavioral history.

[0037] When collecting behavioral data, the collection unit can filter the behavioral data based on the user's current living situation and areas of interest. The collection unit can, for example, preferentially collect highly relevant data according to the user's current living situation. For example, the collection unit can preferentially collect highly relevant data according to the user's current living situation. The collection unit can also filter the data to be collected based on the user's areas of interest. For example, the collection unit can filter the data to be collected based on the user's areas of interest. Furthermore, the collection unit can exclude unnecessary data by taking the user's living situation and areas of interest into consideration. For example, the collection unit can exclude unnecessary data by taking the user's living situation and areas of interest into consideration. In this way, highly relevant data can be collected by filtering the data based on the user's living situation and areas of interest.

[0038] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, select an optimal data collection method based on the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to that location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, the collection unit can analyze the user's geographical location information over the long term and prioritize collecting data related to a specific location. For example, when the user frequently visits a specific location, the collection unit can prioritize collecting data related to that location. In this way, highly relevant data can be collected by taking the user's geographical location information into account.

[0039] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can select an optimal data collection method based on the user's social media activities. For example, the collection unit can select an optimal data collection method based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. For example, the collection unit can prioritize collecting data related to areas of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's social media activities over the long term and collect data based on trend information. For example, the collection unit can prioritize collecting data related to topics in which the user has shown interest on social media. This makes it possible to collect highly relevant data by analyzing the user's social media activities.

[0040] The prediction unit can improve the prediction algorithm by referring to past behavioral data when making predictions. The prediction unit can, for example, optimize the prediction algorithm based on the user's past behavioral data. For example, the prediction unit can analyze the user's past behavioral patterns and adjust the prediction algorithm. The prediction unit can also improve prediction accuracy by referring to the user's past behavioral data. For example, the prediction unit can optimize the prediction algorithm based on the user's past behavioral data. Furthermore, the prediction unit can analyze the user's past behavioral data over the long term and improve the prediction algorithm according to seasons and events. For example, the prediction unit can optimize the prediction algorithm based on the user's tendency to engage in specific behavior during summer vacation. In this way, the prediction algorithm can be optimized and prediction accuracy can be improved by referring to the past behavioral data.

[0041] The prediction unit can apply multiple prediction methods according to the user's lifestyle rhythm when making predictions. The prediction unit can, for example, apply an optimal prediction method according to the user's lifestyle rhythm. For example, the prediction unit can adjust the prediction method taking the user's lifestyle rhythm into consideration. The prediction unit can also improve prediction accuracy based on the user's lifestyle rhythm. For example, the prediction unit can apply an optimal prediction method according to the user's lifestyle rhythm. Furthermore, the prediction unit can analyze the user's lifestyle rhythm over the long term and apply a prediction method according to seasons or events. For example, the prediction unit can adjust the prediction method based on the user's tendency to engage in specific behavior during summer vacation. In this way, prediction accuracy can be improved by applying a prediction method according to the user's lifestyle rhythm.

[0042] The prediction unit can improve prediction accuracy by taking into account the user's geographical location information when making predictions. The prediction unit can improve prediction accuracy, for example, based on the user's geographical location information. For example, if the user is in a specific location, the prediction unit can make a prediction related to that location. The prediction unit can also apply an optimal prediction method based on the user's geographical location information. For example, if the user is traveling, the prediction unit can make a prediction related to the user's destination. Furthermore, the prediction unit can analyze the user's geographical location information over the long term and make a prediction related to a specific location. For example, if the user frequently visits a specific location, the prediction unit can make a prediction related to that location. In this way, by taking the user's geographical location information into account, prediction accuracy can be improved.

[0043] The prediction unit can improve the accuracy of prediction by referring to the user's related literature when making predictions. The prediction unit can improve the prediction accuracy, for example, based on the user's related literature. For example, the prediction unit can optimize the prediction algorithm based on literature referenced by the user in the past. The prediction unit can also analyze the user's related literature and apply an optimal prediction method. For example, the prediction unit can improve the prediction accuracy based on the user's related literature. Furthermore, the prediction unit can analyze the user's related literature over the long term and make predictions related to a specific topic. For example, if the user shows interest in a specific topic, the prediction unit can make predictions related to that topic. In this way, the prediction accuracy can be improved by referring to the user's related literature.

[0044] The suggestion unit can change the level of detail of the suggestion based on the importance of the task when making a suggestion. The suggestion unit can, for example, make a detailed suggestion based on the importance of the task. For example, the suggestion unit can make a detailed suggestion for an important task. The suggestion unit can also make a concise suggestion for a low-importance task. For example, the suggestion unit can adjust the level of detail of the suggestion according to the importance of the task. Furthermore, the suggestion unit can analyze the importance of tasks over the long term and make an optimal suggestion for a specific task. For example, if a user indicates high importance for a specific task, the suggestion unit can make a detailed suggestion for the task. In this way, by adjusting the level of detail of the suggestion according to the importance of the task, it is possible to make an appropriate suggestion for an important task.

[0045] When making a suggestion, the suggestion unit can apply multiple suggestion algorithms depending on the task category. The suggestion unit can, for example, apply an optimal suggestion algorithm depending on the task category. For example, the suggestion unit can apply a suggestion algorithm specialized for housework to a housework task. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for work to a work task. For example, the suggestion unit can apply an optimal suggestion algorithm depending on the task category. Furthermore, the suggestion unit can analyze task categories over the long term and make optimal suggestions for specific categories. For example, if a user shows a high interest in tasks in a specific category, the suggestion unit can make optimal suggestions for that category. In this way, more appropriate suggestions can be made by applying an optimal suggestion algorithm depending on the task category.

[0046] When making a proposal, the suggestion unit can set a priority of the proposal based on the submission time of the task. The suggestion unit can, for example, prioritize proposals based on the submission time of the task. For example, the suggestion unit can prioritize proposals for tasks with an approaching deadline. The suggestion unit can also postpone proposals for tasks with a more distant submission time. For example, the suggestion unit can determine the priority of proposals based on the submission time of the task. Furthermore, the suggestion unit can analyze the submission time of tasks over the long term and make optimal suggestions for specific tasks. For example, if a user indicates a high priority for a specific task, the suggestion unit can prioritize proposals for that task. In this way, by determining the priority of proposals based on the submission time of the task, important tasks can be preferentially proposed.

[0047] The suggestion unit can change the order of suggestions based on the relevance of the tasks when making suggestions. The suggestion unit can, for example, prioritize suggestions based on the relevance of the tasks. For example, the suggestion unit can prioritize suggestions of highly relevant tasks. The suggestion unit can also postpone suggestions of less relevant tasks. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the tasks. Furthermore, the suggestion unit can analyze the relevance of tasks over the long term and make optimal suggestions for specific tasks. For example, if a user indicates high relevance to a specific task, the suggestion unit can prioritize suggestions for that task. In this way, by adjusting the order of suggestions based on the relevance of the tasks, highly relevant tasks can be prioritized.

[0048] The understanding unit can improve the understanding algorithm by referring to past instruction data during understanding. The understanding unit can, for example, optimize the understanding algorithm based on the user's past instruction data. For example, the understanding unit can analyze the user's past instruction patterns and adjust the understanding algorithm. The understanding unit can also improve understanding accuracy by referring to the user's past instruction data. For example, the understanding unit can optimize the understanding algorithm based on the user's past instruction data. Furthermore, the understanding unit can analyze the user's past instruction data over the long term and optimally understand a specific instruction. For example, if the user demonstrates a high level of understanding of a specific instruction, the understanding unit can optimally understand that instruction. In this way, by referring to the past instruction data, the understanding algorithm can be optimized and understanding accuracy can be improved.

[0049] The understanding unit can apply multiple understanding methods according to the user's language habits during understanding. The understanding unit can apply, for example, an optimal understanding method according to the user's language habits. For example, the understanding unit can adjust the understanding method based on the language and expression methods that the user uses daily. The understanding unit can also apply an optimal understanding method taking the user's language habits into consideration. For example, the understanding unit can improve understanding accuracy based on the user's language habits. Furthermore, the understanding unit can analyze the user's language habits over the long term and perform optimal understanding for specific language habits. For example, if the user shows a high level of understanding for a specific language habit, the understanding unit can perform optimal understanding for that language habit. In this way, understanding accuracy can be improved by applying an understanding method according to the user's language habits.

[0050] The understanding unit can improve the accuracy of understanding by taking into account the user's geographical location information during understanding. The understanding unit can improve the accuracy of understanding, for example, based on the user's geographical location information. For example, if the user is in a specific location, the understanding unit can understand instructions related to that location. The understanding unit can also apply an optimal understanding method based on the user's geographical location information. For example, if the user is traveling, the understanding unit can understand instructions related to the user's destination. Furthermore, the understanding unit can analyze the user's geographical location information over the long term and understand instructions related to a specific location. For example, if the user frequently visits a specific location, the understanding unit can understand instructions related to that location. In this way, the understanding accuracy can be improved by taking into account the user's geographical location information.

[0051] The understanding unit can improve the accuracy of understanding by referring to the user's related literature during understanding. The understanding unit can, for example, improve the understanding accuracy based on the user's related literature. For example, the understanding unit can optimize the understanding algorithm based on literature previously referenced by the user. The understanding unit can also analyze the user's related literature and apply an optimal understanding method. For example, the understanding unit can improve the understanding accuracy based on the user's related literature. Furthermore, the understanding unit can analyze the user's related literature over the long term and perform understanding related to a specific topic. For example, if the user shows interest in a specific topic, the understanding unit can perform understanding related to that topic. In this way, the understanding accuracy can be improved by referring to the user's related literature.

[0052] The instruction unit can improve the instruction algorithm by referring to past instruction data when giving an instruction. The instruction unit can, for example, optimize the instruction algorithm based on the user's past instruction data. For example, the instruction unit can analyze the user's past instruction patterns and adjust the instruction algorithm. The instruction unit can also improve instruction accuracy by referring to the user's past instruction data. For example, the instruction unit can optimize the instruction algorithm based on the user's past instruction data. Furthermore, the instruction unit can analyze the user's past instruction data over the long term and provide an optimal instruction for a specific instruction. For example, if the user demonstrates a high level of understanding of a specific instruction, the instruction unit can provide an optimal instruction for that instruction. In this way, by referring to the past instruction data, the instruction algorithm can be optimized and instruction accuracy can be improved.

[0053] The instruction unit can apply multiple instruction methods depending on the performance of the household robot when giving instructions. The instruction unit can, for example, apply an optimal instruction method depending on the performance of the household robot. For example, the instruction unit can adjust the instruction method taking into account the performance of the household robot. The instruction unit can also improve the accuracy of instructions based on the performance of the household robot. For example, the instruction unit can apply an optimal instruction method depending on the performance of the household robot. Furthermore, the instruction unit can analyze the performance of the household robot over the long term and give instructions that are optimal for a specific performance. For example, if the household robot shows high ability in a specific performance, the instruction unit can give instructions that are optimal for that performance. In this way, by applying an instruction method depending on the performance of the household robot, the accuracy of instructions can be improved.

[0054] When issuing instructions, the instruction unit can select an efficient instruction method by taking into account the geographical location information of the household robot. The instruction unit can select the optimal instruction method based on, for example, the geographical location information of the household robot. For example, if the household robot is in a specific location, the instruction unit can issue instructions related to that location. The instruction unit can also apply the optimal instruction method based on the geographical location information of the household robot. For example, if the household robot is moving, the instruction unit can issue instructions related to the destination. Furthermore, the instruction unit can analyze the geographical location information of the household robot over the long term and issue instructions related to a specific location. For example, if the household robot frequently visits a specific location, the instruction unit can issue instructions related to that location. In this way, the optimal instruction method can be selected by taking into account the geographical location information of the household robot.

[0055] The instruction unit can improve the accuracy of instructions by referring to literature related to the household robot when giving instructions. The instruction unit can improve the accuracy of instructions, for example, based on literature related to the household robot. For example, the instruction unit can optimize an instruction algorithm based on literature previously referred to by the household robot. The instruction unit can also analyze literature related to the household robot and apply an optimal instruction method. For example, the instruction unit can improve the accuracy of instructions based on literature related to the household robot. Furthermore, the instruction unit can analyze literature related to the household robot over the long term and provide instructions related to a specific topic. For example, if the household robot shows interest in a specific topic, the instruction unit can provide instructions related to that topic. In this way, the accuracy of instructions can be improved by referring to literature related to the household robot.

[0056] When performing a chore, the household chore unit can improve the execution algorithm by referring to past household chore data. The household chore unit can, for example, optimize the execution algorithm based on the user's past household chore data. For example, the household chore unit can analyze the user's past household chore patterns and adjust the execution algorithm. The household chore unit can also improve execution accuracy by referring to the user's past household chore data. For example, the household chore unit can optimize the execution algorithm based on the user's past household chore data. Furthermore, the household chore unit can analyze the user's past household chore data over the long term and optimally execute a specific household chore. For example, if the user shows a high degree of execution for a specific household chore, the household chore unit can optimally execute that household chore. In this way, by referring to the past household chore data, the execution algorithm can be optimized and execution accuracy can be improved.

[0057] When performing household chores, the household unit can select an efficient execution method by taking into account the geographical location information of the household robot. The household unit can select an optimal execution method, for example, based on the geographical location information of the household robot. For example, if the household robot is in a specific location, the household unit can perform household chores related to that location. The household unit can also apply an optimal execution method based on the geographical location information of the household robot. For example, if the household robot is moving, the household unit can perform household chores related to the destination. Furthermore, the household unit can analyze the geographical location information of the household robot over the long term and perform household chores related to a specific location. For example, if the household robot frequently visits a specific location, the household unit can perform household chores related to that location. In this way, the optimal execution method can be selected by taking into account the geographical location information of the household robot.

[0058] The reception unit can improve the reception algorithm by referring to past instruction data when receiving a command. The reception unit can, for example, optimize the reception algorithm based on the user's past instruction data. For example, the reception unit can analyze the user's past instruction patterns and adjust the reception algorithm. The reception unit can also improve reception accuracy by referring to the user's past instruction data. For example, the reception unit can optimize the reception algorithm based on the user's past instruction data. Furthermore, the reception unit can analyze the user's past instruction data over the long term and optimally receive a specific command. For example, if the user demonstrates a high level of understanding of a specific command, the reception unit can optimally receive the command. In this way, by referring to the past instruction data, the reception algorithm can be optimized and reception accuracy can be improved.

[0059] The reception unit can select an efficient reception method by taking into consideration the user's geographical location information when receiving a call. The reception unit can select the optimal reception method based on the user's geographical location information, for example. For example, if the user is in a specific location, the reception unit can receive instructions related to that location. The reception unit can also apply the optimal reception method based on the user's geographical location information. For example, if the user is traveling, the reception unit can receive instructions related to the user's destination. Furthermore, the reception unit can analyze the user's geographical location information over the long term and receive instructions related to a specific location. For example, if the user frequently visits a specific location, the reception unit can receive instructions related to that location. In this way, the optimal reception method can be selected by taking into consideration the user's geographical location information.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting user behavioral data, the collection unit can adjust the frequency of data collection taking into account the user's health condition. For example, if the user is in poor health, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is exercising, the collection unit can collect data in real time and analyze the effects of the exercise. Furthermore, if the user is sleeping, the collection unit can collect data to evaluate the quality of sleep. This makes it possible to collect appropriate data according to the user's health condition.

[0062] The suggestion unit can personalize the content of suggestions based on the user's past behavioral data. For example, based on an activity that the user liked in the past, the suggestion unit can suggest that activity again. Also, based on an activity that the user avoided in the past, the suggestion unit can avoid suggesting that activity. Furthermore, the suggestion unit can analyze the user's past behavioral data and suggest new activities. This enables personalized suggestions based on the user's preferences and behavioral patterns.

[0063] The suggestion unit can dynamically change the priority of suggestions based on the user's behavioral data. For example, if the user is in a hurry, the suggestion unit can prioritize suggesting important tasks. On the other hand, if the user has time to spare, the suggestion unit can suggest tasks for relaxation. Furthermore, the suggestion unit can analyze the user's behavioral data in real time and change the priority of suggestions as appropriate. This makes it possible to make appropriate suggestions according to the user's situation.

[0064] The collection unit may have a filtering function for protecting the user's privacy when collecting user behavioral data. For example, the collection unit may automatically filter data including the user's personal information so as not to collect it. The collection unit may also limit the scope of data collection based on privacy settings specified by the user. Furthermore, the collection unit may notify the user when collecting data and obtain permission for collection. This allows data collection while protecting the user's privacy.

[0065] The collection unit may include a correction function for improving the accuracy of the data when collecting user behavior data. For example, the collection unit may correct the data using an algorithm for correcting sensor errors. The collection unit may also integrate data from multiple data sources to generate highly accurate data. Furthermore, the collection unit may detect and automatically correct outliers in the data. This may improve the accuracy of the collected data.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects user behavior data. The user behavior data includes location information, activity logs, sensor information, etc. For example, the collection unit obtains location information from the user's smartphone or wearable device, and collects activity logs and sensor information that record the user's behavior. Step 2: The prediction unit predicts the user's behavioral patterns based on the data collected by the collection unit. The prediction unit analyzes the user's behavioral patterns using a machine learning algorithm and predicts, for example, that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. Step 3: The suggestion unit suggests what to do next based on the prediction results obtained by the prediction unit. The suggestion unit suggests the user's task priorities and timetables, for example, by listing the tasks the user should do next and indicating their priorities. Step 4: The understanding unit understands the user's instructions. The understanding unit uses the generation AI to analyze the user's voice and text instructions. For example, if the user says "Clean up," the understanding unit understands the instruction and generates specific instructions. Step 5: The instruction unit issues instructions to the household robot based on the instructions understood by the understanding unit. The instruction unit issues specific cleaning instructions to the household robot, for example, "Clean the living room."

[0068] (Example 2) A system according to an embodiment of the present invention improves housework efficiency by coordinating an AI-based scheduler that predicts user behavior and suggests what to do next with a multimodal generative AI and a household robot. This system collects user behavior data, analyzes the data, and predicts what to do next. Based on the prediction, the system then proposes a schedule. Furthermore, the multimodal generative AI understands the user's instructions and issues instructions to the household robot to improve housework efficiency. For example, the system collects user behavior data, such as what actions the user takes at what time of day and which tasks they prioritize. This data is input into the AI. The AI ​​then analyzes the collected data and predicts the user's behavioral patterns. For example, it predicts that the user wakes up at 7:00 a.m. every morning and eats breakfast at 8:00 a.m. Based on this prediction, it suggests what to do next. Furthermore, the multimodal generative AI understands the user's instructions. For example, if the user says, "Clean up," the generative AI understands the instruction and issues specific instructions to the household robot. The household robot then cleans according to the generative AI's instructions. This system makes users' actions more efficient and reduces the burden of housework. For example, the household robot can be set to finish cleaning before the user returns home from work. It also makes schedule management easier because the user can always know what to do next. This allows the system to predict the user's actions, suggest what to do next, and operate the household robot efficiently.

[0069] A scheduler system according to an embodiment includes a collection unit, a prediction unit, a suggestion unit, an understanding unit, and an instruction unit. The collection unit collects user behavior data. The user behavior data includes, but is not limited to, location information, activity logs, and sensor information. The collection unit collects, for example, data on what behaviors the user takes at what time of day and which tasks the user prioritizes. For example, the collection unit can acquire location information from the user's smartphone or wearable device. The collection unit can also collect an activity log that records the user's behavior. The collection unit can also record the user's behavior in detail using sensor information. The prediction unit predicts the user's behavior pattern based on the data collected by the collection unit. The prediction unit analyzes the user's behavior pattern using, for example, a machine learning algorithm. For example, the prediction unit can predict that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. The prediction unit can also analyze the user's behavior pattern by time period and predict a reaction to a specific event. The suggestion unit suggests what to do next based on the prediction results obtained by the prediction unit. The suggestion unit can, for example, suggest task priorities and timetables for the user. For example, the suggestion unit can list the tasks the user should do next and indicate their priorities. The suggestion unit can also optimize the user's schedule and suggest an efficient timetable. The understanding unit understands the user's instructions. The understanding unit can, for example, use a generation AI to analyze the user's voice instructions or text instructions. For example, if the user says, "Clean up," the understanding unit can understand the instruction and generate specific instructions. The understanding unit can also analyze the user's gesture instructions and understand their intentions. The instruction unit issues instructions to the household robot based on the instructions understood by the understanding unit. The instruction unit can, for example, issue specific cleaning instructions to the household robot. For example, the instruction unit can issue the instruction to the household robot, such as, "Clean the living room." The instruction unit can also issue instructions to the household robot to do laundry or cook.As a result, the scheduler system according to the embodiment can predict the user's behavior, suggest what to do next, and efficiently operate the household robot.

[0070] The scheduler system further includes a household unit that causes a household robot to perform specific household chores. The household unit causes the household robot to perform specific household chores. Specific household chores include, but are not limited to, cleaning, laundry, and cooking. The household unit can, for example, clean a room using a cleaning robot. For example, the household unit can instruct the cleaning robot to clean the floor of the room. The household unit can also do laundry using a laundry robot. For example, the household unit can instruct the laundry robot to wash laundry. The household unit can also cook using a cooking robot. For example, the household unit can instruct the cooking robot to cook ingredients. This allows the household robot to perform specific household chores, thereby reducing the housework burden on the user.

[0071] The scheduler system further includes a reception unit that receives user instructions. The reception unit receives the user instructions. The reception unit can receive, for example, voice instructions, text instructions, gesture instructions, and the like. For example, when a user issues a voice instruction such as "clean up," the reception unit can receive the voice instruction. Furthermore, when a user issues a text instruction such as "clean up," the reception unit can also receive the text instruction. Furthermore, when a user issues a gesture instruction to clean, the reception unit can also receive the gesture instruction. In this way, by receiving the user's instructions, the system can perform an operation according to the user's request.

[0072] The collection unit can collect data on what actions the user takes at what time of day and which tasks the user prioritizes. The collection unit can, for example, acquire location information from the user's smartphone or wearable device. For example, the collection unit can collect data on what actions the user takes at what time of day. The collection unit can also collect an activity log that records the user's actions. For example, the collection unit can collect data on which tasks the user prioritizes. Furthermore, the collection unit can record the user's actions in detail using sensor information. For example, the collection unit can collect sensor information such as the user's heart rate and number of steps. In this way, by collecting detailed user behavior data, more accurate predictions can be made.

[0073] The prediction unit can predict a user's behavioral patterns based on the collected data. The prediction unit, for example, uses a machine learning algorithm to analyze the user's behavioral patterns. For example, the prediction unit can predict that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. The prediction unit can also analyze the user's behavioral patterns by time period and predict reactions to specific events. For example, the prediction unit can predict whether the user will tend to take specific actions during specific time periods. Furthermore, the prediction unit can analyze the user's behavioral patterns over the long term and predict behavioral patterns according to seasons or events. For example, the prediction unit can predict whether the user will tend to take specific actions during summer vacation. By predicting the user's behavioral patterns, the prediction unit can suggest what the user should do next.

[0074] The suggestion unit can suggest what to do next based on the prediction results. The suggestion unit can, for example, suggest task priorities and timetables for the user. For example, the suggestion unit can list tasks that the user should do next and indicate their priorities. The suggestion unit can also optimize the user's schedule and suggest an efficient timetable. For example, the suggestion unit can suggest tasks that the user should do next for each time period. Furthermore, the suggestion unit can prioritize and suggest specific tasks based on the user's behavioral patterns. For example, the suggestion unit can suggest tasks that the user should do every morning. In this way, by suggesting what to do next based on the prediction results, the user's behavior can be made more efficient.

[0075] The understanding unit can understand user instructions and issue specific instructions to the household robot. The understanding unit, for example, uses a generation AI to analyze the user's voice instructions and text instructions. For example, if the user says, "Clean up," the understanding unit can understand the instruction and generate specific instructions. The understanding unit can also analyze the user's gesture instructions and understand their intention. For example, if the user gestures to indicate cleaning instructions, the understanding unit can analyze the gesture and generate specific instructions. Furthermore, the understanding unit can understand the user's instructions based on the context. For example, if the user says, "Clean up," the understanding unit can analyze the context and generate specific cleaning instructions. This allows the household robot to understand the user's instructions and issue specific instructions, thereby making housework more efficient.

[0076] Furthermore, the collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The collection unit can estimate emotions using, for example, facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The collection unit can also estimate emotions using a user's voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate emotions. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. For example, the collection unit can estimate emotions based on fluctuations in the user's heart rate. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The collection unit can analyze the user's past behavioral history and select an efficient data collection method. The collection unit can, for example, select the optimal data collection method based on the user's past behavioral history. For example, the collection unit can select the optimal data collection method based on behaviors the user frequently performed in the past. The collection unit can also select the optimal data collection method for a specific time period from the user's past behavioral history. For example, the collection unit can analyze the user's past behavioral patterns and select an efficient data collection method. Furthermore, the collection unit can analyze the user's past behavioral history over the long term and select a data collection method according to the season or event. For example, the collection unit can select the optimal data collection method based on the user's tendency to perform specific behaviors during summer vacation. This enables efficient data collection by analyzing past behavioral history.

[0078] When collecting behavioral data, the collection unit can filter the behavioral data based on the user's current living situation and areas of interest. The collection unit can, for example, preferentially collect highly relevant data according to the user's current living situation. For example, the collection unit can preferentially collect highly relevant data according to the user's current living situation. The collection unit can also filter the data to be collected based on the user's areas of interest. For example, the collection unit can filter the data to be collected based on the user's areas of interest. Furthermore, the collection unit can exclude unnecessary data by taking the user's living situation and areas of interest into consideration. For example, the collection unit can exclude unnecessary data by taking the user's living situation and areas of interest into consideration. In this way, highly relevant data can be collected by filtering the data based on the user's living situation and areas of interest.

[0079] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. The collection unit can estimate emotions using, for example, facial expression recognition technology. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also estimate emotions using a user's voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can estimate the emotions based on fluctuations in the user's heart rate. This allows important data to be collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, select an optimal data collection method based on the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to that location. The collection unit can also select an optimal data collection method based on the user's geographical location information. For example, when the user is traveling, the collection unit can prioritize collecting data related to the user's destination. Furthermore, the collection unit can analyze the user's geographical location information over the long term and prioritize collecting data related to a specific location. For example, when the user frequently visits a specific location, the collection unit can prioritize collecting data related to that location. In this way, highly relevant data can be collected by taking the user's geographical location information into account.

[0081] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can select an optimal data collection method based on the user's social media activities. For example, the collection unit can select an optimal data collection method based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect related data. For example, the collection unit can prioritize collecting data related to areas of interest from the user's social media activities. Furthermore, the collection unit can analyze the user's social media activities over the long term and collect data based on trend information. For example, the collection unit can prioritize collecting data related to topics in which the user has shown interest on social media. This makes it possible to collect highly relevant data by analyzing the user's social media activities.

[0082] The prediction unit can estimate the user's emotion and adjust the behavioral pattern prediction method based on the estimated user emotion. The prediction unit can estimate the emotion using, for example, facial expression recognition technology. For example, the prediction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The prediction unit can also estimate the emotion using a user's voice analysis technology. For example, the prediction unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the prediction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit can estimate the emotion based on fluctuations in the user's heart rate. This allows for more appropriate behavioral pattern prediction by adjusting the prediction method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The prediction unit can improve the prediction algorithm by referring to past behavioral data when making predictions. The prediction unit can, for example, optimize the prediction algorithm based on the user's past behavioral data. For example, the prediction unit can analyze the user's past behavioral patterns and adjust the prediction algorithm. The prediction unit can also improve prediction accuracy by referring to the user's past behavioral data. For example, the prediction unit can optimize the prediction algorithm based on the user's past behavioral data. Furthermore, the prediction unit can analyze the user's past behavioral data over the long term and improve the prediction algorithm according to seasons and events. For example, the prediction unit can optimize the prediction algorithm based on the user's tendency to engage in specific behavior during summer vacation. In this way, the prediction algorithm can be optimized and prediction accuracy can be improved by referring to the past behavioral data.

[0084] The prediction unit can apply multiple prediction methods according to the user's lifestyle rhythm when making predictions. The prediction unit can, for example, apply an optimal prediction method according to the user's lifestyle rhythm. For example, the prediction unit can adjust the prediction method taking the user's lifestyle rhythm into consideration. The prediction unit can also improve prediction accuracy based on the user's lifestyle rhythm. For example, the prediction unit can apply an optimal prediction method according to the user's lifestyle rhythm. Furthermore, the prediction unit can analyze the user's lifestyle rhythm over the long term and apply a prediction method according to seasons or events. For example, the prediction unit can adjust the prediction method based on the user's tendency to engage in specific behavior during summer vacation. In this way, prediction accuracy can be improved by applying a prediction method according to the user's lifestyle rhythm.

[0085] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user emotion. The prediction unit can estimate the emotion using, for example, facial expression recognition technology. For example, the prediction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The prediction unit can also estimate the emotion using user voice analysis technology. For example, the prediction unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the prediction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit can estimate the emotion based on fluctuations in the user's heart rate. This allows for highly visible prediction results to be provided by adjusting the display method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The prediction unit can improve prediction accuracy by taking into account the user's geographical location information when making predictions. The prediction unit can improve prediction accuracy, for example, based on the user's geographical location information. For example, if the user is in a specific location, the prediction unit can make a prediction related to that location. The prediction unit can also apply an optimal prediction method based on the user's geographical location information. For example, if the user is traveling, the prediction unit can make a prediction related to the user's destination. Furthermore, the prediction unit can analyze the user's geographical location information over the long term and make a prediction related to a specific location. For example, if the user frequently visits a specific location, the prediction unit can make a prediction related to that location. In this way, by taking the user's geographical location information into account, prediction accuracy can be improved.

[0087] The prediction unit can improve the accuracy of prediction by referring to the user's related literature when making predictions. The prediction unit can improve the prediction accuracy, for example, based on the user's related literature. For example, the prediction unit can optimize the prediction algorithm based on literature referenced by the user in the past. The prediction unit can also analyze the user's related literature and apply an optimal prediction method. For example, the prediction unit can improve the prediction accuracy based on the user's related literature. Furthermore, the prediction unit can analyze the user's related literature over the long term and make predictions related to a specific topic. For example, if the user shows interest in a specific topic, the prediction unit can make predictions related to that topic. In this way, the prediction accuracy can be improved by referring to the user's related literature.

[0088] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit can estimate the emotion using, for example, facial expression recognition technology. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the emotion using a user's voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can estimate the emotion based on fluctuations in the user's heart rate. This allows for more appropriate suggestions to be made by adjusting the way the suggestion is presented based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The suggestion unit can change the level of detail of the suggestion based on the importance of the task when making a suggestion. The suggestion unit can, for example, make a detailed suggestion based on the importance of the task. For example, the suggestion unit can make a detailed suggestion for an important task. The suggestion unit can also make a concise suggestion for a low-importance task. For example, the suggestion unit can adjust the level of detail of the suggestion according to the importance of the task. Furthermore, the suggestion unit can analyze the importance of tasks over the long term and make an optimal suggestion for a specific task. For example, if a user indicates high importance for a specific task, the suggestion unit can make a detailed suggestion for the task. In this way, by adjusting the level of detail of the suggestion according to the importance of the task, it is possible to make an appropriate suggestion for an important task.

[0090] When making a suggestion, the suggestion unit can apply multiple suggestion algorithms depending on the task category. The suggestion unit can, for example, apply an optimal suggestion algorithm depending on the task category. For example, the suggestion unit can apply a suggestion algorithm specialized for housework to a housework task. Furthermore, the suggestion unit can apply a suggestion algorithm specialized for work to a work task. For example, the suggestion unit can apply an optimal suggestion algorithm depending on the task category. Furthermore, the suggestion unit can analyze task categories over the long term and make optimal suggestions for specific categories. For example, if a user shows a high interest in tasks in a specific category, the suggestion unit can make optimal suggestions for that category. In this way, more appropriate suggestions can be made by applying an optimal suggestion algorithm depending on the task category.

[0091] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the emotion using, for example, facial expression recognition technology. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The suggestion unit can also estimate the emotion using a user's voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the suggestion unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can estimate the emotion based on fluctuations in the user's heart rate. This allows for more appropriate suggestions to be made by adjusting the length of the suggestion based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] When making a proposal, the suggestion unit can set a priority of the proposal based on the submission time of the task. The suggestion unit can, for example, prioritize proposals based on the submission time of the task. For example, the suggestion unit can prioritize proposals for tasks with an approaching deadline. The suggestion unit can also postpone proposals for tasks with a more distant submission time. For example, the suggestion unit can determine the priority of proposals based on the submission time of the task. Furthermore, the suggestion unit can analyze the submission time of tasks over the long term and make optimal suggestions for specific tasks. For example, if a user indicates a high priority for a specific task, the suggestion unit can prioritize proposals for that task. In this way, by determining the priority of proposals based on the submission time of the task, important tasks can be preferentially proposed.

[0093] The suggestion unit can change the order of suggestions based on the relevance of the tasks when making suggestions. The suggestion unit can, for example, prioritize suggestions based on the relevance of the tasks. For example, the suggestion unit can prioritize suggestions of highly relevant tasks. The suggestion unit can also postpone suggestions of less relevant tasks. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the tasks. Furthermore, the suggestion unit can analyze the relevance of tasks over the long term and make optimal suggestions for specific tasks. For example, if a user indicates high relevance to a specific task, the suggestion unit can prioritize suggestions for that task. In this way, by adjusting the order of suggestions based on the relevance of the tasks, highly relevant tasks can be prioritized.

[0094] The understanding unit can estimate the user's emotions and adjust the method of understanding instructions based on the estimated user emotions. The understanding unit can estimate emotions using, for example, facial expression recognition technology. For example, the understanding unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The understanding unit can also estimate emotions using a user's voice analysis technology. For example, the understanding unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the understanding unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the understanding unit can estimate the user's emotions based on fluctuations in the user's heart rate. This allows the user to understand instructions more appropriately by adjusting the method of understanding instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0095] The understanding unit can improve the understanding algorithm by referring to past instruction data during understanding. The understanding unit can, for example, optimize the understanding algorithm based on the user's past instruction data. For example, the understanding unit can analyze the user's past instruction patterns and adjust the understanding algorithm. The understanding unit can also improve understanding accuracy by referring to the user's past instruction data. For example, the understanding unit can optimize the understanding algorithm based on the user's past instruction data. Furthermore, the understanding unit can analyze the user's past instruction data over the long term and optimally understand a specific instruction. For example, if the user demonstrates a high level of understanding of a specific instruction, the understanding unit can optimally understand that instruction. In this way, by referring to the past instruction data, the understanding algorithm can be optimized and understanding accuracy can be improved.

[0096] The understanding unit can apply multiple understanding methods according to the user's language habits during understanding. The understanding unit can apply, for example, an optimal understanding method according to the user's language habits. For example, the understanding unit can adjust the understanding method based on the language and expression methods that the user uses daily. The understanding unit can also apply an optimal understanding method taking the user's language habits into consideration. For example, the understanding unit can improve understanding accuracy based on the user's language habits. Furthermore, the understanding unit can analyze the user's language habits over the long term and perform optimal understanding for specific language habits. For example, if the user shows a high level of understanding for a specific language habit, the understanding unit can perform optimal understanding for that language habit. In this way, understanding accuracy can be improved by applying an understanding method according to the user's language habits.

[0097] The understanding unit can estimate the user's emotion and adjust the display method of the instructions based on the estimated user emotion. The understanding unit can estimate the emotion using, for example, facial expression recognition technology. For example, the understanding unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The understanding unit can also estimate the emotion using a user's voice analysis technology. For example, the understanding unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the understanding unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the understanding unit can estimate the emotion based on fluctuations in the user's heart rate. This allows for highly visible instructions to be provided by adjusting the display method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the understanding unit may be performed using, for example, AI, or may be performed without using AI. For example, the understanding unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0098] The understanding unit can improve the accuracy of understanding by taking into account the user's geographical location information during understanding. The understanding unit can improve the accuracy of understanding, for example, based on the user's geographical location information. For example, if the user is in a specific location, the understanding unit can understand instructions related to that location. The understanding unit can also apply an optimal understanding method based on the user's geographical location information. For example, if the user is traveling, the understanding unit can understand instructions related to the user's destination. Furthermore, the understanding unit can analyze the user's geographical location information over the long term and understand instructions related to a specific location. For example, if the user frequently visits a specific location, the understanding unit can understand instructions related to that location. In this way, the understanding accuracy can be improved by taking into account the user's geographical location information.

[0099] The understanding unit can improve the accuracy of understanding by referring to the user's related literature during understanding. The understanding unit can, for example, improve the understanding accuracy based on the user's related literature. For example, the understanding unit can optimize the understanding algorithm based on literature previously referenced by the user. The understanding unit can also analyze the user's related literature and apply an optimal understanding method. For example, the understanding unit can improve the understanding accuracy based on the user's related literature. Furthermore, the understanding unit can analyze the user's related literature over the long term and perform understanding related to a specific topic. For example, if the user shows interest in a specific topic, the understanding unit can perform understanding related to that topic. In this way, the understanding accuracy can be improved by referring to the user's related literature.

[0100] The instruction unit can estimate the user's emotion and adjust the instruction method for the household robot based on the estimated user's emotion. The instruction unit can estimate the emotion using, for example, facial expression recognition technology. For example, the instruction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The instruction unit can also estimate the emotion using a user's voice analysis technology. For example, the instruction unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the instruction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the instruction unit can estimate the emotion based on fluctuations in the user's heart rate. This allows the instruction method to be adjusted according to the user's emotion, thereby more appropriately instructing the household robot. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0101] The instruction unit can improve the instruction algorithm by referring to past instruction data when giving an instruction. The instruction unit can, for example, optimize the instruction algorithm based on the user's past instruction data. For example, the instruction unit can analyze the user's past instruction patterns and adjust the instruction algorithm. The instruction unit can also improve instruction accuracy by referring to the user's past instruction data. For example, the instruction unit can optimize the instruction algorithm based on the user's past instruction data. Furthermore, the instruction unit can analyze the user's past instruction data over the long term and provide an optimal instruction for a specific instruction. For example, if the user demonstrates a high level of understanding of a specific instruction, the instruction unit can provide an optimal instruction for that instruction. In this way, by referring to the past instruction data, the instruction algorithm can be optimized and instruction accuracy can be improved.

[0102] The instruction unit can apply multiple instruction methods depending on the performance of the household robot when giving instructions. The instruction unit can, for example, apply an optimal instruction method depending on the performance of the household robot. For example, the instruction unit can adjust the instruction method taking into account the performance of the household robot. The instruction unit can also improve the accuracy of instructions based on the performance of the household robot. For example, the instruction unit can apply an optimal instruction method depending on the performance of the household robot. Furthermore, the instruction unit can analyze the performance of the household robot over the long term and give instructions that are optimal for a specific performance. For example, if the household robot shows high ability in a specific performance, the instruction unit can give instructions that are optimal for that performance. In this way, by applying an instruction method depending on the performance of the household robot, the accuracy of instructions can be improved.

[0103] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated user emotions. The instruction unit can estimate the emotions using, for example, facial expression recognition technology. For example, the instruction unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The instruction unit can also estimate the emotions using a user's voice analysis technology. For example, the instruction unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the instruction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the instruction unit can estimate the emotions based on fluctuations in the user's heart rate. This allows important instructions to be given priority by determining the priority of instructions according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0104] When issuing instructions, the instruction unit can select an efficient instruction method by taking into account the geographical location information of the household robot. The instruction unit can select the optimal instruction method based on, for example, the geographical location information of the household robot. For example, if the household robot is in a specific location, the instruction unit can issue instructions related to that location. The instruction unit can also apply the optimal instruction method based on the geographical location information of the household robot. For example, if the household robot is moving, the instruction unit can issue instructions related to the destination. Furthermore, the instruction unit can analyze the geographical location information of the household robot over the long term and issue instructions related to a specific location. For example, if the household robot frequently visits a specific location, the instruction unit can issue instructions related to that location. In this way, the optimal instruction method can be selected by taking into account the geographical location information of the household robot.

[0105] The instruction unit can improve the accuracy of instructions by referring to literature related to the household robot when giving instructions. The instruction unit can improve the accuracy of instructions, for example, based on literature related to the household robot. For example, the instruction unit can optimize an instruction algorithm based on literature previously referred to by the household robot. The instruction unit can also analyze literature related to the household robot and apply an optimal instruction method. For example, the instruction unit can improve the accuracy of instructions based on literature related to the household robot. Furthermore, the instruction unit can analyze literature related to the household robot over the long term and provide instructions related to a specific topic. For example, if the household robot shows interest in a specific topic, the instruction unit can provide instructions related to that topic. In this way, the accuracy of instructions can be improved by referring to literature related to the household robot.

[0106] The housekeeping unit can estimate the user's emotions and adjust the way the housekeeping task is performed based on the estimated user's emotions. The housekeeping unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the housekeeping unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The housekeeping unit can also estimate the user's emotions using a voice analysis technology. For example, the housekeeping unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the housekeeping unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the housekeeping unit can estimate the user's emotions based on fluctuations in the user's heart rate. This allows the housekeeping task to be performed more appropriately by adjusting the way the housekeeping task is performed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the housekeeping unit may be performed using AI, or may be performed without using AI. For example, the housekeeping unit may input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0107] When performing a chore, the household chore unit can improve the execution algorithm by referring to past household chore data. The household chore unit can, for example, optimize the execution algorithm based on the user's past household chore data. For example, the household chore unit can analyze the user's past household chore patterns and adjust the execution algorithm. The household chore unit can also improve execution accuracy by referring to the user's past household chore data. For example, the household chore unit can optimize the execution algorithm based on the user's past household chore data. Furthermore, the household chore unit can analyze the user's past household chore data over the long term and optimally execute a specific household chore. For example, if the user shows a high degree of execution for a specific household chore, the household chore unit can optimally execute that household chore. In this way, by referring to the past household chore data, the execution algorithm can be optimized and execution accuracy can be improved.

[0108] The housekeeping unit can estimate the user's emotions and prioritize housekeeping tasks based on the estimated user emotions. The housekeeping unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the housekeeping unit can capture the user's facial expressions with a camera and estimate the user's emotions using an emotion estimation algorithm. The housekeeping unit can also estimate the user's emotions using a voice analysis technology. For example, the housekeeping unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the housekeeping unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the housekeeping unit can estimate the user's emotions based on fluctuations in the user's heart rate. This allows the user to prioritize important housekeeping tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the housekeeping unit may be performed using AI, or may be performed without using AI. For example, the housekeeping unit may input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0109] When performing household chores, the household unit can select an efficient execution method by taking into account the geographical location information of the household robot. The household unit can select an optimal execution method, for example, based on the geographical location information of the household robot. For example, if the household robot is in a specific location, the household unit can perform household chores related to that location. The household unit can also apply an optimal execution method based on the geographical location information of the household robot. For example, if the household robot is moving, the household unit can perform household chores related to the destination. Furthermore, the household unit can analyze the geographical location information of the household robot over the long term and perform household chores related to a specific location. For example, if the household robot frequently visits a specific location, the household unit can perform household chores related to that location. In this way, the optimal execution method can be selected by taking into account the geographical location information of the household robot.

[0110] The reception unit can estimate the user's emotion and adjust the instruction reception method based on the estimated user emotion. The reception unit can estimate the emotion using, for example, facial expression recognition technology. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also estimate the emotion using a user's voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit can estimate the user's emotion based on fluctuations in the user's heart rate. This allows the reception of more appropriate instructions by adjusting the reception method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0111] The reception unit can improve the reception algorithm by referring to past instruction data when receiving a command. The reception unit can, for example, optimize the reception algorithm based on the user's past instruction data. For example, the reception unit can analyze the user's past instruction patterns and adjust the reception algorithm. The reception unit can also improve reception accuracy by referring to the user's past instruction data. For example, the reception unit can optimize the reception algorithm based on the user's past instruction data. Furthermore, the reception unit can analyze the user's past instruction data over the long term and optimally receive a specific command. For example, if the user demonstrates a high level of understanding of a specific command, the reception unit can optimally receive the command. In this way, by referring to the past instruction data, the reception algorithm can be optimized and reception accuracy can be improved.

[0112] The reception unit can estimate the user's emotions and determine the priority of reception based on the estimated user emotions. The reception unit can estimate the emotions using, for example, facial expression recognition technology. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also estimate the emotions using a user's voice analysis technology. For example, the reception unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit can estimate the emotions based on fluctuations in the user's heart rate. This allows important instructions to be received preferentially by determining the priority of reception based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0113] The reception unit can select an efficient reception method by taking into consideration the user's geographical location information when receiving a call. The reception unit can select the optimal reception method based on the user's geographical location information, for example. For example, if the user is in a specific location, the reception unit can receive instructions related to that location. The reception unit can also apply the optimal reception method based on the user's geographical location information. For example, if the user is traveling, the reception unit can receive instructions related to the user's destination. Furthermore, the reception unit can analyze the user's geographical location information over the long term and receive instructions related to a specific location. For example, if the user frequently visits a specific location, the reception unit can receive instructions related to that location. In this way, the optimal reception method can be selected by taking into consideration the user's geographical location information. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, prediction unit, suggestion unit, understanding unit, instruction unit, and housework unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and sensors of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's behavior pattern based on the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests what to do next based on the prediction result. The understanding unit is realized by the control unit 46A of the smart device 14 and analyzes the user's voice instructions and text instructions. The instruction unit is realized by the control unit 46A of the smart device 14 and issues specific instructions to the housework robot. The housework unit is realized by the control unit 46A of the smart device 14 and causes the housework robot to perform specific housework. The reception unit is realized by the control unit 46A of the smart device 14 and receives user instructions. The collection unit estimates the user's emotions using the specific processing unit 290 of the data processing device 12, and adjusts the collection timing. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, prediction unit, suggestion unit, understanding unit, instruction unit, and household chore unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and sensors of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's behavior pattern based on the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests what to do next based on the prediction result. The understanding unit is realized by the control unit 46A of the smart glasses 214 and analyzes the user's voice instructions and text instructions. The instruction unit is realized by the control unit 46A of the smart glasses 214 and issues specific instructions to the household robot. The household chore unit is realized by the control unit 46A of the smart glasses 214 and causes the household chore robot to perform specific household chores. The reception unit is realized by the control unit 46A of the smart glasses 214, and receives instructions from the user. The collection unit estimates the user's emotions using the specific processing unit 290 of the data processing device 12, and adjusts the collection timing. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, prediction unit, suggestion unit, understanding unit, instruction unit, and household chore unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and sensors of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's behavior pattern based on the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests what to do next based on the prediction result. The understanding unit is realized by the control unit 46A of the headset-type terminal 314 and analyzes the user's voice instructions and text instructions. The instruction unit is realized by the control unit 46A of the headset-type terminal 314 and issues specific instructions to the household robot. The household chore unit is realized by the control unit 46A of the headset-type terminal 314 and causes the household robot to perform specific household chores. The reception unit is realized by the control unit 46A of the headset type terminal 314, and receives instructions from the user. The collection unit estimates the user's emotions using the specific processing unit 290 of the data processing device 12, and adjusts the collection timing. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, prediction unit, suggestion unit, understanding unit, instruction unit, and housework unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and sensors of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the user's behavior pattern based on the collected data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests what to do next based on the prediction result. The understanding unit is realized by the control unit 46A of the robot 414 and analyzes the user's voice instructions and text instructions. The instruction unit is realized by the control unit 46A of the robot 414 and issues specific instructions to the housework robot. The housework unit is realized by the control unit 46A of the robot 414 and causes the housework robot to perform specific housework. The reception unit is realized by the control unit 46A of the robot 414 and receives user instructions. The collection unit estimates the user's emotions using the specific processing unit 290 of the data processing device 12, and adjusts the collection timing.

[0114] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0115] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can refrain from making suggestions or make suggestions to help the user relax. Furthermore, if the user is concentrating, the suggestion unit can adjust the timing of suggestions so as not to disturb the user's concentration. Furthermore, if the user is happy, the suggestion unit can make positive suggestions to maintain the user's emotions. This allows suggestions to be made at appropriate times according to the user's emotions.

[0116] When collecting user behavioral data, the collection unit can adjust the frequency of data collection taking into account the user's health condition. For example, if the user is in poor health, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is exercising, the collection unit can collect data in real time and analyze the effects of the exercise. Furthermore, if the user is sleeping, the collection unit can collect data to evaluate the quality of sleep. This makes it possible to collect appropriate data according to the user's health condition.

[0117] The understanding unit can estimate the user's emotions and adjust the interpretation method of instructions based on the estimated user's emotions. For example, if the user is angry, the understanding unit can carefully interpret instructions taking into account the user's emotions. Also, if the user is sad, the understanding unit can preferentially interpret instructions to soothe the user's emotions. Furthermore, if the user is excited, the understanding unit can interpret instructions to calm the user's emotions. This makes it possible to interpret instructions appropriately according to the user's emotions.

[0118] The suggestion unit can personalize the content of suggestions based on the user's past behavioral data. For example, based on an activity that the user liked in the past, the suggestion unit can suggest that activity again. Also, based on an activity that the user avoided in the past, the suggestion unit can avoid suggesting that activity. Furthermore, the suggestion unit can analyze the user's past behavioral data and suggest new activities. This enables personalized suggestions based on the user's preferences and behavioral patterns.

[0119] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, if the user is relaxed, the collection unit can collect detailed data. If the user is nervous, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is excited, the collection unit can record the user's emotions and analyze them later. This makes it possible to collect appropriate data according to the user's emotions.

[0120] The suggestion unit can dynamically change the priority of suggestions based on the user's behavioral data. For example, if the user is in a hurry, the suggestion unit can prioritize suggesting important tasks. On the other hand, if the user has time to spare, the suggestion unit can suggest tasks for relaxation. Furthermore, the suggestion unit can analyze the user's behavioral data in real time and change the priority of suggestions as appropriate. This makes it possible to make appropriate suggestions according to the user's situation.

[0121] The understanding unit can estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. For example, if the user is feeling stressed, the understanding unit can give priority to interpreting instructions to relieve the user's emotions. Also, if the user is relaxed, the understanding unit can give priority to interpreting instructions to maintain the user's emotions. Furthermore, if the user is concentrating, the understanding unit can adjust the priority of instructions so as not to disturb the user's concentration. This makes it possible to give appropriate priority to instructions according to the user's emotions.

[0122] The collection unit may have a filtering function for protecting the user's privacy when collecting user behavioral data. For example, the collection unit may automatically filter data including the user's personal information so as not to collect it. The collection unit may also limit the scope of data collection based on privacy settings specified by the user. Furthermore, the collection unit may notify the user when collecting data and obtain permission for collection. This allows data collection while protecting the user's privacy.

[0123] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated user's emotions. For example, if the user is tired, the suggestion unit can suggest rest. If the user is energetic, the suggestion unit can suggest active activities. Furthermore, if the user is stressed, the suggestion unit can suggest activities for relaxation. This makes it possible to make appropriate suggestions according to the user's emotions.

[0124] The collection unit may include a correction function for improving the accuracy of the data when collecting user behavior data. For example, the collection unit may correct the data using an algorithm for correcting sensor errors. The collection unit may also integrate data from multiple data sources to generate highly accurate data. Furthermore, the collection unit may detect and automatically correct outliers in the data. This may improve the accuracy of the collected data.

[0125] The processing flow of the second embodiment will be briefly explained below.

[0126] Step 1: The collection unit collects user behavior data. The user behavior data includes location information, activity logs, sensor information, etc. For example, the collection unit obtains location information from the user's smartphone or wearable device, and collects activity logs and sensor information that record the user's behavior. Step 2: The prediction unit predicts the user's behavioral patterns based on the data collected by the collection unit. The prediction unit analyzes the user's behavioral patterns using a machine learning algorithm and predicts, for example, that the user will wake up at 7:00 a.m. every morning and have breakfast at 8:00 a.m. Step 3: The suggestion unit suggests what to do next based on the prediction results obtained by the prediction unit. The suggestion unit suggests the user's task priorities and timetables, for example, by listing the tasks the user should do next and indicating their priorities. Step 4: The understanding unit understands the user's instructions. The understanding unit uses the generation AI to analyze the user's voice and text instructions. For example, if the user says "clean up," the understanding unit understands the instruction and generates specific instructions. Step 5: The instruction unit issues instructions to the household robot based on the instructions understood by the understanding unit. The instruction unit issues specific cleaning instructions to the household robot, for example, "Clean the living room."

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0129] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] 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.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0145] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] 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.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0154] 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.

[0155] 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.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0157] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] 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.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0170] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0171] 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.

[0172] 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.

[0173] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0174] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0175] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0177] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0178] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0179] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0180] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0182] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0183] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0184] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0187] 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.

[0188] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0189] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0190] 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.

[0191] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0192] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0193] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0195] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0196] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0197] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0198] [Explanation of symbols]

[0199] 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 data; a prediction unit that predicts a behavior pattern of a user based on the data collected by the collection unit; a proposal unit that proposes a schedule based on the prediction result obtained by the prediction unit; an understanding unit that understands a user's instruction; an instruction unit that issues an instruction to the household robot based on the instruction understood by the understanding unit; Equipped with A system characterized by:

2. The household robot will have a housework section that performs specific household chores.

2. The system of claim 1.

3. A reception unit is provided to receive instructions from the user.

2. The system of claim 1.

4. The collecting unit Collect data on what users do at what times and which tasks they prioritize 2. The system of claim 1.

5. The prediction unit Predicting user behavior patterns based on collected data 2. The system of claim 1.

6. The proposal unit Recommend what to do next based on the prediction results 2. The system of claim 1.

7. The understanding unit Understand user instructions and give specific instructions to the household robot 2. The system of claim 1.

8. The collecting unit The system estimates user emotions and adjusts the timing of behavioral data collection based on the estimated user emotions.

2. The system of claim 1.

9. The collecting unit Analyze users' past behavioral history and select an efficient data collection method 2. The system of claim 1.

Images