System

Abstract

To provide a system for providing support to enable a dual-income household to balance work and personal life.SOLUTION: A system includes an acquisition part, a proposal part, a notification part, and a menu proposal part. The acquisition part acquires a schedule of a user. The proposal part proposes housekeeping tasks on the basis of the schedule acquired by the acquisition part. The notification part notifies the user of a content proposed by the proposal part. The menu proposal part proposes a menu on the basis of commodities being sold through electric commerce.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, dual-income households had the challenge of finding it difficult to balance work, housework, and childcare within the limited time available.

[0005] The system according to the embodiment aims to provide support for dual-income households to balance their work and personal lives. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a suggestion unit, a notification unit, and a menu suggestion unit. The acquisition unit acquires a user's schedule. The suggestion unit suggests household tasks based on the schedule acquired by the acquisition unit. The notification unit notifies the user of the content suggested by the suggestion unit. The menu suggestion unit suggests a menu based on products sold in electronic commerce. Effect of the Invention

[0007] The system according to the embodiment can provide support for dual-income families to balance their work and private lives. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​assistant system according to the embodiment of the present invention is a system for supporting working families with children. In modern society, where the number of dual-income households is increasing and it is difficult to balance work, housework, and childcare, this system provides support for making effective use of limited time. Specifically, the system acquires the user's schedule, suggests housework tasks, notifies the user of the proposed content, and suggests a menu based on products sold in e-commerce. For example, the system acquires schedule information entered by the user into a smartphone or calendar app and analyzes it using AI. Next, based on the acquired schedule, the system suggests simple housework tasks that the user can do between meetings. This allows the user to do housework efficiently. Furthermore, the system notifies the user of the proposed housework tasks and menu. For example, the notification function of the smartphone is used to inform the user of the proposed content. This allows the user to do housework and shopping without forgetting. In addition, the AI ​​suggests an optimal menu based on the shopping list of the online store. This allows the user to use up ingredients efficiently. Furthermore, the system estimates the user's emotions and adjusts the timing of schedule acquisition. For example, if the user is feeling stressed, the AI ​​delays schedule acquisition to reduce the burden on the user. In this way, the present invention provides the necessary support for working families to balance work, housework, and childcare. This allows users to make effective use of their limited time and improve their quality of life. This allows the AI ​​assistant system to help working families efficiently manage their lives and balance work, housework, and childcare.

[0029] The AI ​​assistant system according to the embodiment includes an acquisition unit, a suggestion unit, a notification unit, and a menu suggestion unit. The acquisition unit acquires a user's schedule. The user's schedule includes, for example, a calendar app schedule, handwritten notes, and email schedules, but is not limited to such examples. The acquisition unit acquires, for example, schedule information input to a smartphone or a calendar app. The suggestion unit suggests a household task based on the schedule acquired by the acquisition unit. The suggestion unit suggests, for example, an easy household task that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest an optimal household task. The notification unit notifies the user of the content suggested by the suggestion unit. The notification unit notifies the user of the suggested content, for example, using a notification function of a smartphone. The notification unit uses AI to select an optimal notification method based on the user's schedule and the suggested content. The menu suggestion unit suggests a menu based on products sold in electronic commerce. The menu suggestion unit suggests an optimal menu based on, for example, a shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggest optimal menus. As a result, the AI ​​assistant system according to the embodiment can suggest household tasks and menus based on the user's schedule and efficiently support the user's life.

[0030] The acquisition unit acquires the user's schedule. The user's schedule includes, for example, a schedule in a calendar app, a handwritten note, an email schedule, and the like, but is not limited to such examples. The acquisition unit acquires, for example, schedule information input to a smartphone or a calendar app. Specifically, the acquisition unit cooperates with a smartphone's calendar app or a cloud-based schedule management service to automatically synchronize the schedule input by the user. In addition, for handwritten notes, the memo can be photographed using a smartphone camera and converted into text data using OCR (optical character recognition) technology, and the memo can be acquired as schedule information. For email schedules, the acquisition unit cooperates with a mail server to extract schedules based on specific keywords and formats. Furthermore, the acquisition unit can acquire schedule information from social media and message apps with the user's permission. In this way, the acquisition unit comprehensively acquires schedules from various information sources of the user, and realizes accurate schedule management. In addition, the acquisition unit can centrally manage the acquired schedule information and efficiently share information in cooperation with other departments. For example, the acquisition unit stores the schedule information in a cloud server, allowing the proposal unit and the notification unit to access the schedule information. This enables the acquisition unit to acquire user schedules efficiently, thereby improving the performance of the entire system.

[0031] The suggestion unit suggests household tasks based on the schedule acquired by the acquisition unit. For example, the suggestion unit suggests simple household tasks that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household tasks. Specifically, the suggestion unit analyzes the user's schedule and selects appropriate household tasks taking into consideration free time and task priority. For example, if the user has 30 minutes of free time, the suggestion unit suggests tasks such as simple cleaning and laundry that can be completed within that time. In addition, the suggestion unit can learn the user's past behavior history and preferences and make more personalized suggestions. For example, if the user has done a specific household chore at a specific time in the past, the suggestion unit learns that pattern and makes a similar suggestion. In addition, the suggestion unit can consider the priority of the household tasks and preferentially suggest tasks with high importance. For example, if an ingredient in the refrigerator is about to expire, the suggestion unit suggests a dish using that ingredient. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. This enables the suggestion unit to suggest optimal household tasks based on the user's schedule and support an efficient lifestyle.

[0032] The notification unit notifies the user of the content proposed by the suggestion unit. For example, the notification unit uses the notification function of a smartphone to inform the user of the proposed content. The notification unit uses AI to select the optimal notification method based on the user's schedule and the proposed content. Specifically, the notification unit notifies the user at the optimal timing, taking into account the user's current situation and device usage. For example, if the user is in a meeting, the notification is made immediately after the meeting ends. In addition, the notification unit customizes the content and format of the notification to provide information in a form that is easy for the user to understand. For example, the user's attention can be attracted by using not only text messages but also voice notifications and vibration notifications in combination. In addition, the notification unit can collect user feedback and continuously improve the accuracy of the notification method and timing. For example, if the user misses a notification, the notification unit analyzes the cause and adjusts the next notification method. In addition, the notification unit synchronizes notifications between multiple devices, allowing the user to receive important information regardless of which device they are using. This allows the notification unit to quickly and reliably notify the user of the proposed content and support an efficient life.

[0033] The menu suggestion unit proposes a menu based on products sold in electronic commerce. In the menu suggestion unit, for example, AI proposes an optimal menu based on a shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and proposes an optimal menu. Specifically, the menu suggestion unit acquires the shopping list of the online store and analyzes the types and quantities of ingredients and products purchased. Next, the AI ​​generates a balanced and nutritious menu based on this information. For example, if the ingredients purchased are mainly vegetables, healthy dishes with vegetables as the main ingredient are proposed. In addition, the menu suggestion unit can learn the user's past purchase history and preferences and propose a more personalized menu. For example, if the user liked a particular dish in the past, the dish is proposed again. In addition, the menu suggestion unit can propose an appropriate menu taking into account the user's allergy information and dietary restrictions. For example, if the user requires a gluten-free diet, dishes using ingredients that do not contain gluten are proposed. Furthermore, the menu suggestion unit provides detailed information on the necessary ingredients and cooking procedures based on the proposed menu. This allows the user to easily execute the suggested menu. This allows the menu suggestion unit to suggest an optimal menu based on the user's shopping list and support efficient meal planning.

[0034] The suggestion unit can suggest household chores that can be done in free time based on the user's schedule. For example, the suggestion unit suggests household chores that can be done in free time of one hour or more based on the user's schedule. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household chores. For example, the suggestion unit suggests simple household chores that the user can do between meetings. The suggestion unit can also suggest household chores that can be done during specific time periods. This allows the user to effectively use their free time and do housework efficiently.

[0035] The menu suggestion unit can suggest a menu based on a shopping list of an online store. The menu suggestion unit suggests a menu in a format such as text, image, or product ID based on the shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggest an optimal menu. For example, the menu suggestion unit suggests a menu that efficiently uses up ingredients based on ingredients purchased by the user. The menu suggestion unit can also suggest a menu that takes into consideration the user's preferences and nutritional balance. This makes it possible to efficiently suggest menus by utilizing the shopping list of an online store.

[0036] The notification unit can notify the user of the housework task or menu proposed by the suggestion unit. The notification unit notifies the user of the housework task or menu proposed by the suggestion unit, for example, by push notification, email, SMS, or other methods. The notification unit uses AI to select the optimal notification method based on the user's schedule and the contents of the proposal. For example, the notification unit sends a push notification if the user is using a smartphone. Also, if the user prefers email, the notification unit can notify the user by email. This allows the user to perform the suggested housework task or menu without forgetting.

[0037] The suggestion unit can suggest household chores based on the child's schedule. For example, the suggestion unit suggests household chores based on the child's schedule, taking into consideration the child's school timetable, extracurricular activities, playtime, and the like. The suggestion unit uses AI to analyze the child's schedule and suggest optimal household chores. For example, the suggestion unit suggests household chores that can be done while the child is at school. It can also suggest household chores to coincide with the child's extracurricular activities. This improves the efficiency of the entire household by suggesting household chores to coincide with the child's schedule.

[0038] The suggestion unit can make suggestions for health management for the user. For example, the suggestion unit manages meals, suggests exercise, manages sleep, and the like for the user's health management. The suggestion unit uses AI to analyze the user's health condition and makes suggestions for optimal health management. For example, the suggestion unit can analyze the user's diet and suggest nutritionally balanced meals. It can also analyze the user's exercise habits and suggest an appropriate exercise plan. Furthermore, it can analyze the user's sleep patterns and provide advice for getting good quality sleep. This can support the user's health management and improve the quality of life.

[0039] The acquisition unit can analyze the user's past schedule history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past schedule history and selects the optimal acquisition method. The acquisition unit uses AI to analyze the user's past schedule history. For example, the acquisition unit prioritizes schedule acquisition methods that the user has frequently used in the past. In addition, the acquisition unit can also suggest the optimal acquisition timing from the user's past schedule history. Furthermore, the user's past schedule history is analyzed and the most efficient acquisition method is selected. In this way, the optimal schedule acquisition method can be selected by analyzing the past schedule history.

[0040] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring a schedule. For example, the acquisition unit performs filtering based on the user's current living situation and areas of interest when acquiring a schedule. The acquisition unit uses AI to analyze the user's current living situation and areas of interest. For example, the acquisition unit preferentially acquires schedules related to a project the user is currently working on. In addition, the acquisition unit can filter related schedules based on the user's areas of interest. Furthermore, the acquisition unit acquires a schedule based on the user's living situation (for example, a child's school events, etc.). This makes it possible to filter schedules based on the user's living situation and areas of interest and acquire highly relevant schedules.

[0041] The acquisition unit can preferentially acquire a highly relevant schedule in consideration of the user's geographical location information when acquiring a schedule. For example, the acquisition unit preferentially acquires a highly relevant schedule in consideration of the user's geographical location information when acquiring a schedule. The acquisition unit uses AI to analyze the user's geographical location information. For example, the acquisition unit preferentially acquires schedules of events or meetings close to the user's current location. In addition, the acquisition unit can filter related schedules based on the user's geographical location information. Furthermore, when the user is moving, the acquisition unit preferentially acquires schedules related to the destination. This allows for the acquisition of highly relevant schedules based on the user's geographical location information, and efficient schedule management.

[0042] The acquisition unit can analyze the user's social media activity when acquiring the schedule and acquire the related schedule. For example, the acquisition unit analyzes the user's social media activity when acquiring the schedule and acquires the related schedule. The acquisition unit uses AI to analyze the user's social media activity. For example, the acquisition unit acquires schedules of events and meetings mentioned by the user on social media. In addition, the acquisition unit can filter related schedules based on the user's social media activity. Furthermore, the event schedules of accounts that the user follows on social media are preferentially acquired. This allows the acquisition of related schedules based on the user's social media activity and efficient schedule management.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the household task at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the household task at the time of suggestion. The suggestion unit uses AI to evaluate the importance of the household task. For example, the suggestion unit suggests detailed steps for household tasks with high importance. Also, the suggestion unit can suggest simple steps for household tasks with low importance. Furthermore, the suggestion unit adjusts the level of detail of the proposal according to the importance of the household task. This allows the level of detail of the proposal to be adjusted according to the importance of the household task, allowing housework to be performed efficiently.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the household task at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the household task at the time of suggestion. The suggestion unit classifies the category of the household task using AI. For example, the suggestion unit applies an algorithm that suggests an efficient cleaning method to a cleaning task. Also, a recipe suggestion algorithm can be applied to a cooking task. Furthermore, an algorithm that suggests an optimal laundry method is applied to a laundry task. In this way, the optimal suggestion algorithm can be applied depending on the category of the household task, and housework can be performed efficiently.

[0045] The suggestion unit can determine the priority of the proposals based on the submission time of the household chore tasks at the time of proposal. For example, the suggestion unit determines the priority of the proposals based on the submission time of the household chore tasks at the time of proposal. The suggestion unit uses AI to evaluate the submission time of the household chore tasks. For example, the suggestion unit preferentially suggests household chore tasks with high urgency. Also, the suggestion unit can preferentially suggest household chore tasks with a close submission deadline. Furthermore, the suggestion unit determines the priority of the household chore tasks based on the submission time. This allows the priority of the proposals to be determined based on the submission time of the household chore tasks, allowing housework to be performed efficiently.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the household chore tasks when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the household chore tasks when making suggestions. The suggestion unit uses AI to evaluate the relevance of the household chore tasks. For example, the suggestion unit preferentially suggests highly relevant household chore tasks. Also, the suggestion unit can postpone less relevant household chore tasks. Furthermore, the suggestion unit adjusts the order of suggestions based on the relevance of the household chore tasks. This allows the order of suggestions to be adjusted based on the relevance of the household chore tasks, enabling efficient housework.

[0047] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. For example, the notification unit selects the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit uses AI to analyze the user's past notification history. For example, the notification unit prioritizes the notification method that the user has preferred in the past. In addition, the notification unit can also suggest the optimal notification timing from the user's past notification history. Furthermore, the user's past notification history is analyzed to select the most effective notification method. In this way, the optimal notification method can be selected by referring to the past notification history.

[0048] The notification unit can customize the content of the notification based on the user's current activity status at the time of notification. For example, the notification unit customizes the content of the notification based on the user's current activity status at the time of notification. The notification unit uses AI to analyze the user's current activity status. For example, the notification unit provides a brief notification when the user is at work. Also, the notification unit can provide a detailed notification when the user is on a break. Furthermore, the notification content is customized based on the user's current activity status. This allows the notification content to be customized based on the user's current activity status, making it possible to provide more effective notifications.

[0049] The notification unit can select the optimal notification method in consideration of the user's geographical location information at the time of notification. For example, the notification unit selects the optimal notification method in consideration of the user's geographical location information at the time of notification. The notification unit uses AI to analyze the user's geographical location information. For example, the notification unit prioritizes notifications related to the user's current location. In addition, the notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, if the user is moving, the notification is sent at the optimal timing. This allows the optimal notification method to be selected based on the user's geographical location information, making it possible to send more effective notifications.

[0050] The notification unit can adjust the content of the notification by analyzing the user's social media activity at the time of notification. For example, the notification unit adjusts the content of the notification by analyzing the user's social media activity at the time of notification. The notification unit uses AI to analyze the user's social media activity. For example, the notification unit notifies the user of events or meetings mentioned by the user on social media. The notification content can also be customized based on the user's social media activity. Furthermore, notifications of accounts that the user follows on social media are given priority. This allows the content of the notification to be adjusted based on the user's social media activity, making it possible to provide more effective notifications.

[0051] The menu suggestion unit can suggest an optimal menu by referring to the user's past meal history when proposing a menu. For example, the menu suggestion unit suggests an optimal menu by referring to the user's past meal history when proposing a menu. The menu suggestion unit uses AI to analyze the user's past meal history. For example, the menu suggestion unit suggests a menu based on dishes that the user liked to eat in the past. In addition, it can also suggest a balanced menu from the user's past meal history. Furthermore, the user's past meal history is analyzed to suggest a menu that uses up ingredients most efficiently. In this way, the optimal menu can be suggested by referring to the user's past meal history.

[0052] The menu suggestion unit can customize the menu based on the user's current health condition when proposing a menu. For example, the menu suggestion unit customizes the menu based on the user's current health condition when proposing a menu. The menu suggestion unit uses AI to analyze the user's health condition. For example, if the user is on a diet, the menu suggestion unit can suggest a low-calorie menu. Also, if the user is looking for a healthy diet, the menu suggestion unit can also suggest a nutritionally balanced menu. Furthermore, if the user has a specific health condition (e.g., high blood pressure), a menu suitable for that condition is suggested. This allows the menu to be customized according to the user's health condition, and healthier meals to be suggested.

[0053] The menu suggestion unit can propose an optimal menu taking into consideration the geographical location information of the user when proposing a menu. For example, the menu suggestion unit proposes an optimal menu taking into consideration the geographical location information of the user when proposing a menu. The menu suggestion unit uses AI to analyze the geographical location information of the user. For example, the menu suggestion unit proposes a menu using ingredients related to the user's current location. In addition, the menu suggestion unit can also propose a menu using local specialties based on the geographical location information of the user. Furthermore, if the user is traveling, a menu using ingredients from that region is proposed. This allows the optimal menu to be proposed based on the geographical location information of the user, making it possible to make more effective menu suggestions.

[0054] The menu suggestion unit can adjust the contents of the menu by analyzing the user's social media activity when proposing a menu. For example, the menu suggestion unit adjusts the contents of the menu by analyzing the user's social media activity when proposing a menu. The menu suggestion unit uses AI to analyze the user's social media activity. For example, the menu suggestion unit suggests a menu based on dishes mentioned by the user on social media. It can also suggest related menus based on the user's social media activity. Furthermore, it suggests a menu based on recipes of cooking accounts that the user follows on social media. This allows the contents of the menu to be adjusted based on the user's social media activity, making it possible to make more effective menu suggestions.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The suggestion unit can also suggest household chores based on the user's health condition. For example, if the user is tired, light household chores can be suggested. Also, if the user is in good health, time-consuming household chores can be suggested. Furthermore, if the user has a particular health condition (e.g., lower back pain), household chores suitable for that condition can be suggested. This allows household chores to be suggested according to the user's health condition, allowing the user to do housework without straining themselves.

[0057] The suggestion unit can also suggest household chores based on the user's hobbies and interests. For example, if the user is interested in gardening, the suggestion unit can suggest garden maintenance. If the user likes cooking, the suggestion unit can suggest trying new recipes. If the user is interested in DIY, the suggestion unit can suggest simple repairs or creating handmade items. This allows the user to enjoy doing housework by suggesting household chores based on their hobbies and interests.

[0058] The suggestion unit can also suggest household chores based on the user's family structure. For example, in a household with children, the suggestion unit can suggest household chores that can be done together with the children. Also, in a household with elderly people, the suggestion unit can suggest household chores that are less burdensome. Furthermore, in a household with pets, the suggestion unit can suggest household chores related to pet care. In this way, household chores are suggested according to the user's family structure, and household chores can be performed efficiently by the entire household.

[0059] The suggestion unit can also analyze the user's past housework history and suggest optimal housework tasks. For example, the suggestion unit can preferentially suggest housework tasks that the user has frequently performed in the past. The suggestion unit can also suggest particularly effective housework tasks among the housework tasks that the user has performed in the past. Furthermore, the suggestion unit can analyze the user's past housework history and suggest the most efficient housework tasks. In this way, the optimal housework tasks can be suggested by analyzing the past housework history.

[0060] The process flow of the first embodiment will be briefly described below.

[0061] Step 1: The acquisition unit acquires a user's schedule. The user's schedule includes, but is not limited to, events in a calendar app, handwritten notes, and emails. The acquisition unit acquires schedule information inputted into, for example, a smartphone or a calendar app. Step 2: The suggestion unit suggests household chores based on the schedule acquired by the acquisition unit. For example, the suggestion unit suggests easy household chores that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household chores. Step 3: The notification unit notifies the user of the content proposed by the suggestion unit. The notification unit notifies the user of the proposed content, for example, by using the notification function of a smartphone. The notification unit uses AI to select the optimal notification method based on the user's schedule and the proposed content. Step 4: The menu suggestion unit suggests a menu based on products sold in e-commerce. For example, the menu suggestion unit uses AI to suggest an optimal menu based on a shopping list from an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggests an optimal menu.

[0062] (Example 2) The AI ​​assistant system according to the embodiment of the present invention is a system for supporting working families with children. In modern society, where the number of dual-income households is increasing and it is difficult to balance work, housework, and childcare, this system provides support for making effective use of limited time. Specifically, the system acquires the user's schedule, suggests housework tasks, notifies the user of the proposed content, and suggests a menu based on products sold in e-commerce. For example, the system acquires schedule information entered by the user into a smartphone or calendar app and analyzes it using AI. Next, based on the acquired schedule, the system suggests simple housework tasks that the user can do between meetings. This allows the user to do housework efficiently. Furthermore, the system notifies the user of the proposed housework tasks and menu. For example, the notification function of the smartphone is used to inform the user of the proposed content. This allows the user to do housework and shopping without forgetting. In addition, the AI ​​suggests an optimal menu based on the shopping list of the online store. This allows the user to use up ingredients efficiently. Furthermore, the system estimates the user's emotions and adjusts the timing of schedule acquisition. For example, if the user is feeling stressed, the AI ​​delays schedule acquisition to reduce the burden on the user. In this way, the present invention provides the necessary support for working families to balance work, housework, and childcare. This allows users to make effective use of their limited time and improve their quality of life. This allows the AI ​​assistant system to help working families efficiently manage their lives and balance work, housework, and childcare.

[0063] The AI ​​assistant system according to the embodiment includes an acquisition unit, a suggestion unit, a notification unit, and a menu suggestion unit. The acquisition unit acquires a user's schedule. The user's schedule includes, for example, a calendar app schedule, handwritten notes, and email schedules, but is not limited to such examples. The acquisition unit acquires, for example, schedule information input to a smartphone or a calendar app. The suggestion unit suggests a household task based on the schedule acquired by the acquisition unit. The suggestion unit suggests, for example, an easy household task that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest an optimal household task. The notification unit notifies the user of the content suggested by the suggestion unit. The notification unit notifies the user of the suggested content, for example, using a notification function of a smartphone. The notification unit uses AI to select an optimal notification method based on the user's schedule and the suggested content. The menu suggestion unit suggests a menu based on products sold in electronic commerce. The menu suggestion unit suggests an optimal menu based on, for example, a shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggest optimal menus. As a result, the AI ​​assistant system according to the embodiment can suggest household tasks and menus based on the user's schedule and efficiently support the user's life.

[0064] The acquisition unit acquires the user's schedule. The user's schedule includes, for example, a schedule in a calendar app, a handwritten note, an email schedule, and the like, but is not limited to such examples. The acquisition unit acquires, for example, schedule information input to a smartphone or a calendar app. Specifically, the acquisition unit cooperates with a smartphone's calendar app or a cloud-based schedule management service to automatically synchronize the schedule input by the user. In addition, for handwritten notes, the memo can be photographed using a smartphone camera and converted into text data using OCR (optical character recognition) technology, and the memo can be acquired as schedule information. For email schedules, the acquisition unit cooperates with a mail server to extract schedules based on specific keywords and formats. Furthermore, the acquisition unit can acquire schedule information from social media and message apps with the user's permission. In this way, the acquisition unit comprehensively acquires schedules from various information sources of the user, and realizes accurate schedule management. In addition, the acquisition unit can centrally manage the acquired schedule information and efficiently share information in cooperation with other departments. For example, the acquisition unit stores the schedule information in a cloud server, allowing the proposal unit and the notification unit to access the schedule information. This enables the acquisition unit to acquire user schedules efficiently, thereby improving the performance of the entire system.

[0065] The suggestion unit suggests household tasks based on the schedule acquired by the acquisition unit. For example, the suggestion unit suggests simple household tasks that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household tasks. Specifically, the suggestion unit analyzes the user's schedule and selects appropriate household tasks taking into consideration free time and task priority. For example, if the user has 30 minutes of free time, the suggestion unit suggests tasks such as simple cleaning and laundry that can be completed within that time. In addition, the suggestion unit can learn the user's past behavior history and preferences and make more personalized suggestions. For example, if the user has done a specific household chore at a specific time in the past, the suggestion unit learns that pattern and makes a similar suggestion. In addition, the suggestion unit can consider the priority of the household tasks and preferentially suggest tasks with high importance. For example, if an ingredient in the refrigerator is about to expire, the suggestion unit suggests a dish using that ingredient. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the suggestions. This enables the suggestion unit to suggest optimal household tasks based on the user's schedule and support an efficient lifestyle.

[0066] The notification unit notifies the user of the content proposed by the suggestion unit. For example, the notification unit uses the notification function of a smartphone to inform the user of the proposed content. The notification unit uses AI to select the optimal notification method based on the user's schedule and the proposed content. Specifically, the notification unit notifies the user at the optimal timing, taking into account the user's current situation and device usage. For example, if the user is in a meeting, the notification is made immediately after the meeting ends. In addition, the notification unit customizes the content and format of the notification to provide information in a form that is easy for the user to understand. For example, the user's attention can be attracted by using not only text messages but also voice notifications and vibration notifications in combination. In addition, the notification unit can collect user feedback and continuously improve the accuracy of the notification method and timing. For example, if the user misses a notification, the notification unit analyzes the cause and adjusts the next notification method. In addition, the notification unit synchronizes notifications between multiple devices, allowing the user to receive important information regardless of which device they are using. This allows the notification unit to quickly and reliably notify the user of the proposed content and support an efficient life.

[0067] The menu suggestion unit proposes a menu based on products sold in electronic commerce. In the menu suggestion unit, for example, AI proposes an optimal menu based on a shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and proposes an optimal menu. Specifically, the menu suggestion unit acquires the shopping list of the online store and analyzes the types and quantities of ingredients and products purchased. Next, the AI ​​generates a balanced and nutritious menu based on this information. For example, if the ingredients purchased are mainly vegetables, healthy dishes with vegetables as the main ingredient are proposed. In addition, the menu suggestion unit can learn the user's past purchase history and preferences and propose a more personalized menu. For example, if the user liked a particular dish in the past, the dish is proposed again. In addition, the menu suggestion unit can propose an appropriate menu taking into account the user's allergy information and dietary restrictions. For example, if the user requires a gluten-free diet, dishes using ingredients that do not contain gluten are proposed. Furthermore, the menu suggestion unit provides detailed information on the necessary ingredients and cooking procedures based on the proposed menu. This allows the user to easily execute the suggested menu. This allows the menu suggestion unit to suggest an optimal menu based on the user's shopping list and support efficient meal planning.

[0068] The suggestion unit can suggest household chores that can be done in free time based on the user's schedule. For example, the suggestion unit suggests household chores that can be done in free time of one hour or more based on the user's schedule. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household chores. For example, the suggestion unit suggests simple household chores that the user can do between meetings. The suggestion unit can also suggest household chores that can be done during specific time periods. This allows the user to effectively use their free time and do housework efficiently.

[0069] The menu suggestion unit can suggest a menu based on a shopping list of an online store. The menu suggestion unit suggests a menu in a format such as text, image, or product ID based on the shopping list of an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggest an optimal menu. For example, the menu suggestion unit suggests a menu that efficiently uses up ingredients based on ingredients purchased by the user. The menu suggestion unit can also suggest a menu that takes into consideration the user's preferences and nutritional balance. This makes it possible to efficiently suggest menus by utilizing the shopping list of an online store.

[0070] The notification unit can notify the user of the housework task or menu proposed by the suggestion unit. The notification unit notifies the user of the housework task or menu proposed by the suggestion unit, for example, by push notification, email, SMS, or other methods. The notification unit uses AI to select the optimal notification method based on the user's schedule and the contents of the proposal. For example, the notification unit sends a push notification if the user is using a smartphone. Also, if the user prefers email, the notification unit can notify the user by email. This allows the user to perform the suggested housework task or menu without forgetting.

[0071] The suggestion unit can suggest household chores based on the child's schedule. For example, the suggestion unit suggests household chores based on the child's schedule, taking into consideration the child's school timetable, extracurricular activities, playtime, and the like. The suggestion unit uses AI to analyze the child's schedule and suggest optimal household chores. For example, the suggestion unit suggests household chores that can be done while the child is at school. It can also suggest household chores to coincide with the child's extracurricular activities. This improves the efficiency of the entire household by suggesting household chores to coincide with the child's schedule.

[0072] The suggestion unit can make suggestions for health management for the user. For example, the suggestion unit manages meals, suggests exercise, manages sleep, and the like for the user's health management. The suggestion unit uses AI to analyze the user's health condition and makes suggestions for optimal health management. For example, the suggestion unit can analyze the user's diet and suggest nutritionally balanced meals. It can also analyze the user's exercise habits and suggest an appropriate exercise plan. Furthermore, it can analyze the user's sleep patterns and provide advice for getting good quality sleep. This can support the user's health management and improve the quality of life.

[0073] The acquisition unit can estimate the user's emotion and adjust the schedule acquisition timing based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and adjusts the schedule acquisition timing based on the estimated user's emotion. The acquisition unit estimates the user's emotion using AI. For example, the acquisition unit delays schedule acquisition when the user is stressed. Also, when the user is relaxed, the acquisition unit can speed up schedule acquisition. Furthermore, when the user is busy, the schedule is acquired at an optimal timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to adjust the schedule acquisition timing according to the user's emotion and reduce the burden.

[0074] The acquisition unit can analyze the user's past schedule history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past schedule history and selects the optimal acquisition method. The acquisition unit uses AI to analyze the user's past schedule history. For example, the acquisition unit prioritizes schedule acquisition methods that the user has frequently used in the past. In addition, the acquisition unit can also suggest the optimal acquisition timing from the user's past schedule history. Furthermore, the user's past schedule history is analyzed and the most efficient acquisition method is selected. In this way, the optimal schedule acquisition method can be selected by analyzing the past schedule history.

[0075] The acquisition unit can perform filtering based on the user's current living situation and areas of interest when acquiring a schedule. For example, the acquisition unit performs filtering based on the user's current living situation and areas of interest when acquiring a schedule. The acquisition unit uses AI to analyze the user's current living situation and areas of interest. For example, the acquisition unit preferentially acquires schedules related to a project the user is currently working on. In addition, the acquisition unit can filter related schedules based on the user's areas of interest. Furthermore, the acquisition unit acquires a schedule based on the user's living situation (for example, a child's school events, etc.). This makes it possible to filter schedules based on the user's living situation and areas of interest and acquire highly relevant schedules.

[0076] The acquisition unit can estimate the user's emotion and determine the priority of the schedule to be acquired based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and determines the priority of the schedule to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion using AI. For example, when the user is feeling stressed, the acquisition unit postpones schedules with low importance. In addition, when the user is relaxed, the acquisition unit can prioritize schedules with high importance. Furthermore, when the user is busy, the most important schedule is acquired preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the priority of the schedule can be determined according to the user's emotion, and the schedule can be efficiently managed.

[0077] The acquisition unit can preferentially acquire a highly relevant schedule in consideration of the user's geographical location information when acquiring a schedule. For example, the acquisition unit preferentially acquires a highly relevant schedule in consideration of the user's geographical location information when acquiring a schedule. The acquisition unit uses AI to analyze the user's geographical location information. For example, the acquisition unit preferentially acquires schedules of events or meetings close to the user's current location. In addition, the acquisition unit can filter related schedules based on the user's geographical location information. Furthermore, when the user is moving, the acquisition unit preferentially acquires schedules related to the destination. This allows for the acquisition of highly relevant schedules based on the user's geographical location information, and efficient schedule management.

[0078] The acquisition unit can analyze the user's social media activity when acquiring the schedule and acquire the related schedule. For example, the acquisition unit analyzes the user's social media activity when acquiring the schedule and acquires the related schedule. The acquisition unit uses AI to analyze the user's social media activity. For example, the acquisition unit acquires schedules of events and meetings mentioned by the user on social media. In addition, the acquisition unit can filter related schedules based on the user's social media activity. Furthermore, the event schedules of accounts that the user follows on social media are preferentially acquired. This allows the acquisition of related schedules based on the user's social media activity and efficient schedule management.

[0079] The suggestion unit can estimate the user's emotion and adjust the way of expressing the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way of expressing the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using AI. For example, the suggestion unit makes a simple and easy-to-understand suggestion when the user is stressed. Also, when the user is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit makes a quick and concise suggestion. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the way of expressing the suggestion can be adjusted according to the user's emotion, and more effective suggestions can be made.

[0080] The suggestion unit can adjust the level of detail of the proposal based on the importance of the household task at the time of suggestion. For example, the suggestion unit adjusts the level of detail of the proposal based on the importance of the household task at the time of suggestion. The suggestion unit uses AI to evaluate the importance of the household task. For example, the suggestion unit suggests detailed steps for household tasks with high importance. Also, the suggestion unit can suggest simple steps for household tasks with low importance. Furthermore, the suggestion unit adjusts the level of detail of the proposal according to the importance of the household task. This allows the level of detail of the proposal to be adjusted according to the importance of the household task, allowing housework to be performed efficiently.

[0081] The suggestion unit can apply different suggestion algorithms depending on the category of the household task at the time of suggestion. For example, the suggestion unit applies different suggestion algorithms depending on the category of the household task at the time of suggestion. The suggestion unit classifies the category of the household task using AI. For example, the suggestion unit applies an algorithm that suggests an efficient cleaning method to a cleaning task. Also, a recipe suggestion algorithm can be applied to a cooking task. Furthermore, an algorithm that suggests an optimal laundry method is applied to a laundry task. In this way, the optimal suggestion algorithm can be applied depending on the category of the household task, and housework can be performed efficiently.

[0082] 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, for example, estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. The suggestion unit estimates the user's emotion using AI. For example, the suggestion unit makes a short and to the point suggestion when the user is stressed. Also, the suggestion unit can make a detailed suggestion when the user is relaxed. Furthermore, when the user is in a hurry, a quick and concise suggestion is made. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the length of the suggestion to be adjusted according to the user's emotion, and more effective suggestions to be made.

[0083] The suggestion unit can determine the priority of the proposals based on the submission time of the household chore tasks at the time of proposal. For example, the suggestion unit determines the priority of the proposals based on the submission time of the household chore tasks at the time of proposal. The suggestion unit uses AI to evaluate the submission time of the household chore tasks. For example, the suggestion unit preferentially suggests household chore tasks with high urgency. Also, the suggestion unit can preferentially suggest household chore tasks with a close submission deadline. Furthermore, the suggestion unit determines the priority of the household chore tasks based on the submission time. This allows the priority of the proposals to be determined based on the submission time of the household chore tasks, allowing housework to be performed efficiently.

[0084] The suggestion unit can adjust the order of suggestions based on the relevance of the household chore tasks when making suggestions. For example, the suggestion unit adjusts the order of suggestions based on the relevance of the household chore tasks when making suggestions. The suggestion unit uses AI to evaluate the relevance of the household chore tasks. For example, the suggestion unit preferentially suggests highly relevant household chore tasks. Also, the suggestion unit can postpone less relevant household chore tasks. Furthermore, the suggestion unit adjusts the order of suggestions based on the relevance of the household chore tasks. This allows the order of suggestions to be adjusted based on the relevance of the household chore tasks, enabling efficient housework.

[0085] The notification unit can estimate the user's emotion and adjust the timing of the notification based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and adjusts the timing of the notification based on the estimated user's emotion. The notification unit estimates the user's emotion using AI. For example, the notification unit delays the timing of the notification when the user is stressed. Also, when the user is relaxed, the notification can be advanced. Furthermore, when the user is busy, the notification is performed at an optimal timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Thereby, the timing of the notification can be adjusted according to the user's emotion, and more effective notification can be performed.

[0086] The notification unit can select the optimal notification method by referring to the user's past notification history at the time of notification. For example, the notification unit selects the optimal notification method by referring to the user's past notification history at the time of notification. The notification unit uses AI to analyze the user's past notification history. For example, the notification unit prioritizes the notification method that the user has preferred in the past. In addition, the notification unit can also suggest the optimal notification timing from the user's past notification history. Furthermore, the user's past notification history is analyzed to select the most effective notification method. In this way, the optimal notification method can be selected by referring to the past notification history.

[0087] The notification unit can customize the content of the notification based on the user's current activity status at the time of notification. For example, the notification unit customizes the content of the notification based on the user's current activity status at the time of notification. The notification unit uses AI to analyze the user's current activity status. For example, the notification unit provides a brief notification when the user is at work. Also, the notification unit can provide a detailed notification when the user is on a break. Furthermore, the notification content is customized based on the user's current activity status. This allows the notification content to be customized based on the user's current activity status, making it possible to provide more effective notifications.

[0088] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit, for example, estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion using AI. For example, when the user is stressed, the notification unit postpones notifications with low importance. Also, when the user is relaxed, the notification unit can prioritize notifications with high importance. Furthermore, when the user is busy, the most important notification is prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the priority of notifications to be determined according to the user's emotion, and more effective notifications to be performed.

[0089] The notification unit can select the optimal notification method in consideration of the user's geographical location information at the time of notification. For example, the notification unit selects the optimal notification method in consideration of the user's geographical location information at the time of notification. The notification unit uses AI to analyze the user's geographical location information. For example, the notification unit prioritizes notifications related to the user's current location. In addition, the notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, if the user is moving, the notification is sent at the optimal timing. This allows the optimal notification method to be selected based on the user's geographical location information, making it possible to send more effective notifications.

[0090] The notification unit can adjust the content of the notification by analyzing the user's social media activity at the time of notification. For example, the notification unit adjusts the content of the notification by analyzing the user's social media activity at the time of notification. The notification unit uses AI to analyze the user's social media activity. For example, the notification unit notifies the user of events or meetings mentioned by the user on social media. The notification content can also be customized based on the user's social media activity. Furthermore, notifications of accounts that the user follows on social media are given priority. This allows the content of the notification to be adjusted based on the user's social media activity, making it possible to provide more effective notifications.

[0091] The menu suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. The menu suggestion unit, for example, estimates the user's emotions and adjusts the menu suggestion method based on the estimated user's emotions. The menu suggestion unit estimates the user's emotions using AI. For example, the menu suggestion unit can suggest a simple and easy menu when the user is stressed. Also, when the user is relaxed, it can suggest a menu including detailed recipes. Furthermore, when the user is in a hurry, it suggests a menu that can be cooked quickly. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows the menu suggestion method to be adjusted according to the user's emotions, and more effective menu suggestions can be made.

[0092] The menu suggestion unit can suggest an optimal menu by referring to the user's past meal history when proposing a menu. For example, the menu suggestion unit suggests an optimal menu by referring to the user's past meal history when proposing a menu. The menu suggestion unit uses AI to analyze the user's past meal history. For example, the menu suggestion unit suggests a menu based on dishes that the user liked to eat in the past. In addition, it can also suggest a balanced menu from the user's past meal history. Furthermore, the user's past meal history is analyzed to suggest a menu that uses up ingredients most efficiently. In this way, the optimal menu can be suggested by referring to the user's past meal history.

[0093] The menu suggestion unit can customize the menu based on the user's current health condition when proposing a menu. For example, the menu suggestion unit customizes the menu based on the user's current health condition when proposing a menu. The menu suggestion unit uses AI to analyze the user's health condition. For example, if the user is on a diet, the menu suggestion unit can suggest a low-calorie menu. Also, if the user is looking for a healthy diet, the menu suggestion unit can also suggest a nutritionally balanced menu. Furthermore, if the user has a specific health condition (e.g., high blood pressure), a menu suitable for that condition is suggested. This allows the menu to be customized according to the user's health condition, and healthier meals to be suggested.

[0094] The menu suggestion unit can estimate the user's emotions and determine the priority order of menus based on the estimated user's emotions. The menu suggestion unit, for example, estimates the user's emotions and determines the priority order of menus based on the estimated user's emotions. The menu suggestion unit estimates the user's emotions using AI. For example, when the user is stressed, the menu suggestion unit preferentially suggests a simple and easy menu. In addition, when the user is relaxed, the menu suggestion unit can preferentially suggest a menu including detailed recipes. Furthermore, when the user is in a hurry, the menu suggestion unit preferentially suggests a menu that can be cooked quickly. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. As a result, the priority order of menus can be determined according to the user's emotions, and more effective menu suggestions can be made.

[0095] The menu suggestion unit can propose an optimal menu taking into consideration the geographical location information of the user when proposing a menu. For example, the menu suggestion unit proposes an optimal menu taking into consideration the geographical location information of the user when proposing a menu. The menu suggestion unit uses AI to analyze the geographical location information of the user. For example, the menu suggestion unit proposes a menu using ingredients related to the user's current location. In addition, the menu suggestion unit can also propose a menu using local specialties based on the geographical location information of the user. Furthermore, if the user is traveling, a menu using ingredients from that region is proposed. This allows the optimal menu to be proposed based on the geographical location information of the user, making it possible to make more effective menu suggestions.

[0096] The menu suggestion unit can adjust the contents of the menu by analyzing the user's social media activity when proposing a menu. For example, the menu suggestion unit adjusts the contents of the menu by analyzing the user's social media activity when proposing a menu. The menu suggestion unit uses AI to analyze the user's social media activity. For example, the menu suggestion unit suggests a menu based on dishes mentioned by the user on social media. It can also suggest related menus based on the user's social media activity. Furthermore, it suggests a menu based on recipes of cooking accounts that the user follows on social media. This allows the contents of the menu to be adjusted based on the user's social media activity, making it possible to make more effective menu suggestions.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The suggestion unit can also suggest household chores based on the user's health condition. For example, if the user is tired, light household chores can be suggested. Also, if the user is in good health, time-consuming household chores can be suggested. Furthermore, if the user has a particular health condition (e.g., lower back pain), household chores suitable for that condition can be suggested. This allows household chores to be suggested according to the user's health condition, allowing the user to do housework without straining themselves.

[0099] The suggestion unit can also suggest household chores based on the user's hobbies and interests. For example, if the user is interested in gardening, the suggestion unit can suggest garden maintenance. If the user likes cooking, the suggestion unit can suggest trying new recipes. If the user is interested in DIY, the suggestion unit can suggest simple repairs or creating handmade items. This allows the user to enjoy doing housework by suggesting household chores based on their hobbies and interests.

[0100] The suggestion unit can also suggest household chores based on the user's family structure. For example, in a household with children, the suggestion unit can suggest household chores that can be done together with the children. Also, in a household with elderly people, the suggestion unit can suggest household chores that are less burdensome. Furthermore, in a household with pets, the suggestion unit can suggest household chores related to pet care. In this way, household chores are suggested according to the user's family structure, and household chores can be performed efficiently by the entire household.

[0101] The suggestion unit can also analyze the user's past housework history and suggest optimal housework tasks. For example, the suggestion unit can preferentially suggest housework tasks that the user has frequently performed in the past. The suggestion unit can also suggest particularly effective housework tasks among the housework tasks that the user has performed in the past. Furthermore, the suggestion unit can analyze the user's past housework history and suggest the most efficient housework tasks. In this way, the optimal housework tasks can be suggested by analyzing the past housework history.

[0102] The suggestion unit can estimate the user's emotions and adjust the method of suggesting household chore tasks based on the estimated user's emotions. For example, if the user is stressed, easy and relaxing household chore tasks can be suggested. Also, if the user is relaxed, time-consuming household chore tasks can be suggested. Furthermore, if the user is in a hurry, household chore tasks that can be completed quickly can be suggested. In this way, the method of suggesting household chore tasks can be adjusted according to the user's emotions, and more effective suggestions can be made.

[0103] The suggestion unit can estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions. For example, if the user is stressed, a simple and easy menu can be suggested. If the user is relaxed, a menu including detailed recipes can be suggested. Furthermore, if the user is in a hurry, a menu that can be quickly prepared can be suggested. In this way, the menu suggestion method can be adjusted according to the user's emotions, and more effective menu suggestions can be made.

[0104] The suggestion unit can estimate the user's emotions and determine the priority order of housework tasks based on the estimated user's emotions. For example, if the user is stressed, housework tasks with low importance can be postponed. Also, if the user is relaxed, housework tasks with high importance can be prioritized. Furthermore, if the user is busy, the most important housework tasks are suggested with priority. In this way, the priority order of housework tasks can be determined according to the user's emotions, and housework can be performed efficiently.

[0105] The suggestion unit can estimate the user's emotions and adjust the timing of suggesting housework tasks based on the estimated user's emotions. For example, if the user is stressed, the suggestion of housework tasks can be delayed. Also, if the user is relaxed, the suggestion of housework tasks can be accelerated. Furthermore, if the user is busy, housework tasks can be suggested at optimal timing. In this way, the timing of suggesting housework tasks can be adjusted according to the user's emotions, reducing the burden on the user.

[0106] The suggestion unit can estimate the user's emotions and customize the suggested content of the housework tasks based on the estimated user's emotions. For example, if the user is stressed, the suggestion unit can suggest relaxing housework tasks. If the user is relaxed, the suggestion unit can suggest challenging housework tasks. Furthermore, if the user is in a hurry, the suggestion unit can suggest housework tasks that can be completed quickly. In this way, the suggested content of the housework tasks can be customized according to the user's emotions, and more effective suggestions can be made.

[0107] The suggestion unit can estimate the user's emotions and adjust the method of suggesting housework tasks based on the estimated user's emotions. For example, when the user is stressed, a simple and easy-to-understand suggestion can be made. When the user is relaxed, a detailed suggestion can be made. Furthermore, when the user is in a hurry, a quick and concise suggestion can be made. In this way, the method of suggesting housework tasks can be adjusted according to the user's emotions to make more effective suggestions.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The acquisition unit acquires a user's schedule. The user's schedule includes, but is not limited to, events in a calendar app, handwritten notes, and emails. The acquisition unit acquires schedule information inputted into, for example, a smartphone or a calendar app. Step 2: The suggestion unit suggests household chores based on the schedule acquired by the acquisition unit. For example, the suggestion unit suggests easy household chores that the user can do between meetings. The suggestion unit uses AI to analyze the user's schedule and suggest optimal household chores. Step 3: The notification unit notifies the user of the content proposed by the suggestion unit. The notification unit notifies the user of the proposed content, for example, by using the notification function of a smartphone. The notification unit uses AI to select the optimal notification method based on the user's schedule and the proposed content. Step 4: The menu suggestion unit suggests a menu based on products sold in e-commerce. For example, the menu suggestion unit uses AI to suggest an optimal menu based on a shopping list from an online store. The menu suggestion unit uses AI to analyze the user's shopping list and suggests an optimal menu.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned acquisition unit, suggestion unit, notification unit, and menu suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart device 14, and acquires schedule information from a calendar app or handwritten notes of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal housework tasks based on the acquired schedule. The notification unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user of the suggested content using the notification function of the smartphone. The menu suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on a shopping list of an online store. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

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

[0117] 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.

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

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

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

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements including the above-mentioned acquisition unit, suggestion unit, notification unit, and menu suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214, and acquires schedule information from a calendar app or handwritten notes of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal housework tasks based on the acquired schedule. The notification unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user of the suggested content using the notification function of the smart glasses 214. The menu suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on a shopping list of an online store. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] 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.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] 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.

[0138] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned acquisition unit, suggestion unit, notification unit, and menu suggestion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314, and acquires schedule information from a calendar application or handwritten notes of the headset type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests optimal housework tasks based on the acquired schedule. The notification unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user of the suggested content using the notification function of the headset type terminal 314. The menu suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests an optimal menu based on a shopping list of an online store. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] 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.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] 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.

[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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] Each of the multiple elements including the above-mentioned acquisition unit, suggestion unit, notification unit, and menu suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414, and acquires schedule information from a calendar application or handwritten notes of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes optimal housework tasks based on the acquired schedule. The notification unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user of the proposed content using the notification function of the robot 414. The menu suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal menu based on a shopping list of an online store. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) An acquisition unit that acquires a user's schedule; a suggestion unit that suggests housework tasks based on the schedule acquired by the acquisition unit; a notification unit that notifies a user of the content proposed by the suggestion unit; The system includes a menu suggestion unit that suggests a menu based on products sold in electronic commerce. (Appendix 2) The suggestion unit, Suggesting household chores based on the user's schedule 2. The system of claim 1, (Appendix 3) The menu suggestion unit includes: Suggest meals based on shopping lists from online stores 2. The system of claim 1, (Appendix 4) The notification unit is A system that notifies a user of household chores or menus suggested by a suggestion unit. (Appendix 5) The suggestion unit, Suggest chores based on your child's schedule 2. The system of claim 1, (Appendix 6) The suggestion unit, Providing health management suggestions to users 2. The system of claim 1, (Appendix 7) The acquisition unit is To estimate a user's emotions and adjust the timing of schedule acquisition based on the estimated user's emotions. 2. The system of claim 1, (Appendix 8) The acquisition unit is Analyze the user's past schedule history and select the acquisition method 2. The system of claim 1, (Appendix 9) The acquisition unit is Filter schedules based on the user's current life situation and interests 2. The system of claim 1, (Appendix 10) The acquisition unit is A user's emotion is estimated, and a priority of a schedule to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The acquisition unit is When retrieving schedules, the system takes into account the user's geographic location information to prioritize retrieving schedules that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The acquisition unit is When retrieving a schedule, analyze the user's social media activity and retrieve related schedules. 2. The system of claim 1, (Appendix 13) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The suggestion unit, When making suggestions, adjust the detail of the suggestions based on the importance of the household task 2. The system of claim 1, (Appendix 15) The suggestion unit, When making suggestions, different suggestion algorithms are applied depending on the category of household tasks. 2. The system of claim 1, (Appendix 16) The suggestion unit, Infer user sentiment and adjust the length of suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 17) The suggestion unit, During suggestions, prioritize suggestions based on when chore tasks were submitted 2. The system of claim 1, (Appendix 18) The suggestion unit, During suggestions, adjust the order of suggestions based on the relevance of household tasks 2. The system of claim 1, (Appendix 19) The notification unit is Infer user emotions and adjust notification timing based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The notification unit is When notifying users, the system selects the optimal notification method by referring to the user's past notification history. 2. The system of claim 1, (Appendix 21) The notification unit is Customize the content of notifications based on the user's current activity at the time of notification 2. The system of claim 1, (Appendix 22) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 2. The system of claim 1, (Appendix 23) The notification unit is When notifying users, the optimal notification method is selected based on the user's geographic location. 2. The system of claim 1, (Appendix 24) The notification unit is When notifying users, analyze their social media activity to tailor the content of the notification 2. The system of claim 1, (Appendix 25) The menu suggestion unit includes: Estimate the user's emotions and adjust the menu suggestion method based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The menu suggestion unit includes: When suggesting a menu, the system refers to the user's past meal history to suggest the optimal menu. 2. The system of claim 1, (Appendix 27) The menu suggestion unit includes: Customize meal suggestions based on the user's current health status 2. The system of claim 1, (Appendix 28) The menu suggestion unit includes: Estimate the user's emotions and prioritize menu items based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The menu suggestion unit includes: When suggesting menus, the system considers the user's geographic location to suggest the most suitable menu. 2. The system of claim 1, (Appendix 30) The menu suggestion unit includes: When suggesting meals, the menu content is adjusted based on the user's social media activity. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An acquisition unit that acquires a user's schedule; a suggestion unit that suggests housework tasks based on the schedule acquired by the acquisition unit; a notification unit that notifies a user of the content proposed by the suggestion unit; The system includes a menu suggestion unit that suggests a menu based on products sold in electronic commerce.

2. The suggestion unit, Suggesting household chores based on the user's schedule 2. The system of claim 1 .

3. The menu suggestion unit includes: Suggest meals based on shopping lists from online stores 2. The system of claim 1 .

4. The suggestion unit, Suggest chores based on your child's schedule 2. The system of claim 1 .

5. The suggestion unit, Providing health management suggestions to users 2. The system of claim 1 .

6. The acquisition unit is To estimate a user's emotions and adjust the timing of schedule acquisition based on the estimated user's emotions.

2. The system of claim 1 .

7. The acquisition unit is Analyze the user's past schedule history and select the acquisition method 2. The system of claim 1 .

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