system

Abstract

The system according to this embodiment aims to appropriately manage and suggest necessary items for going out, and to provide support that is particularly considerate of individuals with developmental disabilities. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a notification unit. The collection unit collects personal data. The analysis unit analyzes the data collected by the collection unit and generates a list of belongings. The suggestion unit suggests belongings based on the list of belongings generated by the analysis unit. The notification unit sends a reminder based on the list of belongings suggested by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to appropriately manage and propose the items necessary when going out, and there is a problem that it is difficult to manage items especially for people with developmental disabilities due to lack of attention.

[0005] The system according to the embodiment aims to appropriately manage and propose the items necessary when going out and provide support considering especially people with developmental disabilities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a notification unit. The collection unit collects personal data. The analysis unit analyzes the data collected by the collection unit and generates a list of belongings. The suggestion unit suggests belongings based on the list of belongings generated by the analysis unit. The notification unit sends a reminder based on the list of belongings suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately manage and suggest necessary items for going out, and can provide support that is particularly considerate of individuals with developmental disabilities. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The generation AI agent system according to an embodiment of the present invention is a system that supports daily outings from the perspective of "things to bring." This generation AI agent system collects personal data such as calendars, weather forecasts, and past packing data, and the AI ​​agent analyzes this data to automatically generate and suggest a list of things needed for daily outings and sends confirmation reminders. The generation AI agent system autonomously sets the priority of items and optimizes what to bring according to the destination, type of event, and environment of the day. The generation AI agent system integrates the calendars and schedules of all family members and manages and shares necessary items among family members. For families with children up to high school age, the generation AI agent system reads school packing lists from images, photos, and text data, integrates and manages them along with the necessary dates, and notifies parents of what to bring. The generation AI agent system also manages the storage location of items, making preparation and management more efficient. The generation AI agent system takes into account the attention deficit characteristics of individuals with developmental disabilities such as ADHD and ADD, and employs real-time notifications of items and a visual interface such as photos of items. The AI-generating agent system suggests purchasing items that users do not yet own or are expected to need in the future, leading to purchases on e-commerce sites. For example, the AI-generating agent system retrieves schedules from calendars, checks weather forecasts, and automatically generates a list of items needed when going out. The AI-generating agent system analyzes past item data and suggests items based on the user's preferences and behavioral patterns. The AI-generating agent system integrates the calendars of all family members and shares necessary items among family members. The AI-generating agent system reads school supply lists from images and photos, and integrates and manages them along with the necessary dates. The AI-generating agent system manages the storage location of items, streamlining preparation and management. The AI-generating agent system employs a visual interface, such as real-time notifications of items and photos of items. The AI-generating agent system suggests purchasing items that users do not yet own or are expected to need in the future, leading to purchases on e-commerce sites.This allows the AI ​​agent system to streamline users' daily preparations for going out and support them in managing their belongings.

[0029] The generation AI agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a notification unit. The collection unit collects personal data. The collection unit collects personal data such as calendars, weather forecasts, and past belongings data. The collection unit can also use AI to collect user behavior history and preference information. The analysis unit analyzes the data collected by the collection unit and generates a list of belongings. The analysis unit, for example, uses AI to analyze the collected data and generates a list of belongings needed for daily outings. The analysis unit can also use generation AI to generate a list of belongings based on the user's behavior patterns and preferences. The suggestion unit suggests belongings based on the list of belongings generated by the analysis unit. The suggestion unit, for example, uses AI to suggest belongings based on the generated list of belongings. The suggestion unit can also use generation AI to suggest belongings based on the user's behavior patterns and preferences. The notification unit sends reminders based on the list of belongings suggested by the suggestion unit. The notification unit, for example, uses AI to send reminders based on the suggested list of belongings. The notification unit can also use generative AI to send reminders based on the user's behavior patterns and preferences. This allows the generative AI agent system according to the embodiment to streamline the user's daily preparation for going out and support the management of their belongings.

[0030] The data collection unit collects personal data. Specifically, it obtains the user's calendar information to understand the details of scheduled events and meetings. It collects weather forecast data to predict weather conditions when the user goes out. It refers to past belongings data to check what items the user carried on previous outings. This data is automatically collected from the user's smartphone or computer and stored in a cloud-based database. The data collection unit can also use AI to collect user behavior history and preference information. For example, it collects information such as places the user frequently visits, modes of transportation used, and favorite restaurants to understand the user's behavior patterns. This allows the data collection unit to efficiently collect data tailored to the user's lifestyle and provide it to the analysis unit. Furthermore, the data collection unit implements advanced security measures in data collection and storage to protect user privacy. For example, it implements data encryption and access control to prevent unauthorized access by third parties. This allows the data collection unit to safely collect necessary data while gaining the user's trust.

[0031] The analysis unit analyzes the data collected by the collection unit and generates a packing list. Specifically, it uses AI to analyze the type and location of scheduled events from calendar information and lists the necessary items. For example, for a business meeting, a laptop, documents, and business cards would be required. Based on weather forecast data, it adds an umbrella and raincoat to the packing list for rainy days, and sunglasses and sunscreen for sunny days. It also refers to past packing data and considers items carried on previous outings to reflect necessary items in the list. The analysis unit can also use generation AI to generate packing lists based on the user's behavior patterns and preferences. For example, it suggests necessary items based on the user's frequently used modes of transportation and places they visit. Furthermore, it can consider the user's preference information and add items from their favorite brands and designs to the list. This allows the analysis unit to efficiently generate packing lists tailored to the user's needs and provide them to the suggestion unit.

[0032] The suggestion unit proposes items to bring based on the item list generated by the analysis unit. Specifically, it uses AI to analyze the generated item list and proposes the most suitable items for the user. For example, for a business meeting, it suggests essentials such as a laptop, documents, and business cards. Based on weather forecast data, it suggests an umbrella or raincoat in rainy weather, and sunglasses or sunscreen in sunny weather. It also refers to past item data and suggests necessary items considering items carried on previous outings. The suggestion unit can also use generation AI to suggest items based on the user's behavior patterns and preferences. For example, it suggests necessary items depending on the mode of transportation the user frequently uses and the places they visit. Furthermore, it can also consider the user's preference information and suggest items from their favorite brands and designs. This allows the suggestion unit to efficiently propose items tailored to the user's needs and provide them to the notification unit.

[0033] The notification unit sends reminders based on the packing list proposed by the suggestion unit. Specifically, it uses AI to send reminders based on the proposed packing list. For example, it can send a notification to the smartphone before leaving the house, prompting the user to check the packing list. Since the reminders are sent based on the user's behavior patterns and preferences, they help the user avoid forgetting things. The notification unit can also use generative AI to send reminders based on the user's behavior patterns and preferences. For example, it can remind users of necessary items depending on the mode of transportation they frequently use and the places they visit. Furthermore, it can consider the user's preference information and remind users of items from their favorite brands and designs. This allows the notification unit to efficiently send reminders tailored to the user's needs and support the management of their belongings. In addition, the notification unit can flexibly respond to the user's lifestyle by adjusting the timing and frequency of reminder sending. For example, it can send frequent reminders before important events and send only the minimum necessary reminders for daily outings. This allows the notification unit to reduce user stress and streamline the management of belongings.

[0034] The suggestion unit may include a priority setting unit that autonomously sets the priority of items. The suggestion unit may, for example, use AI to autonomously set the priority of items. The suggestion unit may set the priority of items based on criteria such as importance, frequency of use, and urgency. The suggestion unit may also use generative AI to set the priority of items based on the user's behavior patterns and preferences. By autonomously setting the priority of items, the user's preparation for going out can be optimized.

[0035] The suggestion unit can integrate the calendars and schedules of all family members and include a sharing unit for managing and sharing necessary items among family members. For example, the suggestion unit can use AI to integrate the calendars and schedules of all family members. The suggestion unit can also use generative AI to analyze the schedules of all family members and suggest necessary items. This streamlines the management and sharing of belongings among family members by integrating their calendars and schedules.

[0036] The proposed system could include a management unit that reads school supply lists from image, photo, and text data, integrates and manages them along with the necessary dates, and notifies parents of the required items, for families with children up to high school age. For example, the proposed unit could use AI to read school supply lists from images and photos. It could also use OCR technology to extract text data from images and photos. Furthermore, it could use generative AI to analyze school supply lists and integrate and manage them along with the necessary dates. This integrated management of school supply lists would allow parents to efficiently manage their children's belongings.

[0037] The proposal unit can be equipped with a management unit that manages the location of personal belongings. The proposal unit can manage the location of personal belongings using, for example, AI. The proposal unit can manage the location of personal belongings using methods such as room-by-room arrangement and identification of storage locations. The proposal unit can also use generative AI to analyze the location of personal belongings and propose the optimal arrangement. As a result, the preparation and management of personal belongings becomes more efficient by managing the location of personal belongings.

[0038] The suggestion unit may include a purchase suggestion unit that makes purchase suggestions for items not yet owned or expected to be needed in the future. The suggestion unit can, for example, use AI to make purchase suggestions for items not yet owned or expected to be needed in the future. The suggestion unit can make purchase suggestions based on criteria such as seasonal necessities or event-specific necessities. The suggestion unit can also use generative AI to make purchase suggestions for items based on the user's behavior patterns and preferences. This streamlines the user's possession management by making purchase suggestions for items not yet owned or expected to be needed in the future.

[0039] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can use AI to analyze a user's past behavior history. The data collection unit prioritizes data collection methods that the user has frequently used in the past. The data collection unit analyzes user behavior patterns and collects data at the optimal time. The data collection unit prioritizes collecting data related to specific events from the user's past behavior history. The data collection unit can also use generative AI to analyze a user's behavior history and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavior history.

[0040] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can use AI to analyze the user's current lifestyle and areas of interest. The data collection unit prioritizes collecting data related to the user's current areas of interest. The data collection unit collects only the necessary data according to the user's lifestyle. The data collection unit filters and collects highly relevant data based on the user's areas of interest. The data collection unit can also use generative AI to analyze the user's areas of interest and lifestyle and perform filtering during data collection. This allows for the collection of only the necessary data by filtering it based on the user's lifestyle and areas of interest.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can use AI to analyze the user's geographical location information. If the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit will select the optimal data collection method based on the user's current location. The data collection unit will collect highly relevant data by referring to the user's movement history. The data collection unit can also use generative AI to analyze the user's geographical location information and prioritize the collection of highly relevant data during data collection. This allows for the priority collection of highly relevant data by considering the user's geographical location information.

[0042] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze users' social media activity. The data collection unit collects relevant data based on information shared by users on social media. The data collection unit collects highly relevant data by referencing the activities of users' social media followers and friends. The data collection unit analyzes the content of users' social media posts and collects data based on their interests. The data collection unit can also use generative AI to analyze users' social media activity and collect relevant data during data collection. This allows for the collection of relevant data by analyzing users' social media activity.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the data. The analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit adjusts the priority of the analysis according to the importance of the data. The analysis unit can also use generative AI to analyze the importance of the data and adjust the level of detail of the analysis. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can use AI to classify data categories. For weather forecast data, the analysis unit applies a meteorological analysis algorithm. For calendar data, the analysis unit applies a schedule analysis algorithm. For past personal belongings data, the analysis unit applies a pattern recognition algorithm. The analysis unit can also use generative AI to analyze data categories and apply different analysis algorithms. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category.

[0045] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can use AI to evaluate the data submission date. The analysis unit prioritizes analyzing the most recent data. The analysis unit prioritizes analyzing data with approaching submission deadlines. The analysis unit adjusts the order of analysis based on the submission date. The analysis unit can also use generative AI to analyze the data submission date and determine the analysis priority. This allows for prioritizing the analysis based on the data submission date, thereby prioritizing the analysis of data with approaching submission deadlines.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can use AI to evaluate the relevance of the data. The analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit optimizes the order of analysis according to the relevance of the data. The analysis unit can also use generative AI to analyze the relevance of the data and adjust the order of analysis. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0047] The proposal function can adjust the level of detail of a proposal based on the importance of the item. For example, the proposal function can use AI to evaluate the importance of an item. The proposal function provides detailed proposals for high-importance items and simplified proposals for low-importance items. The proposal function adjusts the priority of proposals according to the importance of the item. The proposal function can also use generative AI to analyze the importance of an item and adjust the level of detail of the proposal. This allows for detailed proposals for important items by adjusting the level of detail of the proposal based on the importance of the item.

[0048] The suggestion function can apply different suggestion algorithms depending on the item category when making suggestions. For example, the suggestion function can use AI to classify item categories. For clothing, the suggestion function applies a suggestion algorithm based on weather forecast data. For documents, the suggestion function applies a suggestion algorithm based on schedule data. For food items, the suggestion function applies a suggestion algorithm based on expiration date data. The suggestion function can also use generative AI to analyze item categories and apply different suggestion algorithms. This improves the accuracy of suggestions by applying different suggestion algorithms depending on the item category.

[0049] The proposal team can prioritize proposals based on the submission timing of each item. For example, the proposal team can use AI to evaluate the submission timing of items. The proposal team will prioritize proposing items with approaching deadlines. The proposal team will adjust the order of proposals based on submission timing. The proposal team will adjust the level of detail of proposals according to submission timing. The proposal team can also use generative AI to analyze the submission timing of items and determine the priority of proposals. This allows the team to prioritize proposals based on the submission timing of items, thereby prioritizing items with approaching deadlines.

[0050] The suggestion function can adjust the order of suggestions based on the relevance of the items during the suggestion process. For example, the suggestion function can use AI to evaluate the relevance of items. The suggestion function prioritizes suggesting highly relevant items. The suggestion function postpones suggesting less relevant items. The suggestion function optimizes the order of suggestions according to the relevance of the items. The suggestion function can also use generative AI to analyze the relevance of items and adjust the order of suggestions. This allows the system to prioritize suggesting highly relevant items by adjusting the order of suggestions based on the relevance of the items.

[0051] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can use AI to analyze the user's past notification history. The notification unit prioritizes selecting notification methods that the user has preferred to use in the past. The notification unit analyzes the user's notification history and sends notifications at the optimal time. The notification unit selects a notification method related to a specific event from the user's past notification history. The notification unit can also use generative AI to analyze the user's notification history and select the optimal notification method. This allows the system to select the optimal notification method by referring to the user's past notification history.

[0052] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, the notification unit can use AI to analyze the user's device information. If the user is using a smartphone, the notification unit will prioritize push notifications. If the user is using a tablet, the notification unit will prioritize email notifications. If the user is using a smartwatch, the notification unit will prioritize vibration notifications. The notification unit can also use generative AI to analyze the user's device information and select the optimal notification method. This allows the system to select the most suitable notification method by considering the user's device information.

[0053] The priority setting unit can adjust the priority of items based on their importance during the priority setting process. For example, the priority setting unit can use AI to evaluate the importance of items. The priority setting unit sets a higher priority for items with high importance. The priority setting unit sets a lower priority for items with low importance. The priority setting unit adjusts the order of priorities according to the importance of the items. The priority setting unit can also use generating AI to analyze the importance of items and adjust the priority accordingly. This allows for setting a higher priority for important items by adjusting the priority based on their importance.

[0054] The priority setting unit can apply different priority setting algorithms depending on the item category when setting priorities. For example, the priority setting unit uses AI to classify item categories. For clothing, the priority setting unit applies a priority setting algorithm based on weather forecast data. For documents, the priority setting unit applies a priority setting algorithm based on schedule data. For food items, the priority setting unit applies a priority setting algorithm based on expiration date data. The priority setting unit can also use generative AI to analyze item categories and apply different priority setting algorithms. This improves the accuracy of priority setting by applying different priority setting algorithms depending on the item category.

[0055] The priority setting unit can determine priority based on the submission timing of items during the priority setting process. For example, the priority setting unit uses AI to evaluate the submission timing of items. The priority setting unit sets a higher priority for items with approaching submission deadlines. The priority setting unit adjusts the priority order based on the submission timing. The priority setting unit adjusts the level of detail of priority according to the submission timing. The priority setting unit can also analyze the submission timing of items and determine priority using generating AI. This allows for setting a higher priority for items with approaching submission deadlines by determining priority based on the submission timing of items.

[0056] The priority setting unit can adjust the priority order based on the relevance of items when setting priorities. For example, the priority setting unit uses AI to evaluate the relevance of items. The priority setting unit sets a higher priority for highly relevant items. The priority setting unit sets a lower priority for less relevant items. The priority setting unit optimizes the priority order according to the relevance of items. The priority setting unit can also use generative AI to analyze the relevance of items and adjust the priority order. This allows for prioritizing highly relevant items by adjusting the priority order based on the relevance of items.

[0057] The sharing function can select the optimal sharing method by referring to the family's past sharing history when sharing. For example, the sharing function can use AI to analyze the family's past sharing history. The sharing function prioritizes selecting sharing methods that the family has preferred to use in the past. The sharing function analyzes the family's sharing history and shares at the optimal time. The sharing function selects sharing methods related to specific events from the family's past sharing history. The sharing function can also use generative AI to analyze the family's sharing history and select the optimal sharing method. This allows the optimal sharing method to be selected by referring to the family's past sharing history.

[0058] The sharing function can select the optimal sharing method by considering the family's device information when sharing. For example, the sharing function uses AI to analyze the family's device information. If the family is using a smartphone, the sharing function prioritizes push notifications. If the family is using a tablet, the sharing function prioritizes email notifications. If the family is using a smartwatch, the sharing function prioritizes vibration notifications. The sharing function can also use generative AI to analyze the family's device information and select the optimal sharing method. This allows the system to select the optimal sharing method by considering the family's device information.

[0059] The management department can adjust the level of detail in management based on the importance of the items. For example, the management department can use AI to evaluate the importance of items. The management department will perform detailed management for high-importance items and simplified management for low-importance items. The management department will adjust the priority of management according to the importance of the items. The management department can also use generative AI to analyze the importance of items and adjust the level of detail in management. This allows for detailed management of important items by adjusting the level of detail in management based on the importance of the items.

[0060] The management department can apply different management algorithms depending on the item category during management. For example, the management department can use AI to classify item categories. For clothing, the management department can apply a management algorithm based on weather forecast data. For documents, the management department can apply a management algorithm based on schedule data. For food products, the management department can apply a management algorithm based on expiration date data. The management department can also use generative AI to analyze item categories and apply different management algorithms. This improves the accuracy of management by applying different management algorithms depending on the item category.

[0061] The management department can determine management priorities based on the submission timing of items during management. For example, the management department can use AI to evaluate the submission timing of items. The management department will manage items with approaching deadlines with a higher priority. The management department will adjust the order of management based on the submission timing. The management department will adjust the level of detail of management according to the submission timing. The management department can also use generative AI to analyze the submission timing of items and determine management priorities. This allows for priority management of items with approaching deadlines by determining management priorities based on the submission timing.

[0062] The management department can adjust the order of management based on the relevance of items during the management process. For example, the management department can use AI to evaluate the relevance of items. The management department will manage highly relevant items with a higher priority. The management department will manage less relevant items with a lower priority. The management department will optimize the order of management according to the relevance of items. The management department can also use generative AI to analyze the relevance of items and adjust the order of management. This allows for the priority management of highly relevant items by adjusting the order of management based on the relevance of items.

[0063] The purchase proposal unit can adjust the level of detail in purchase proposals based on the importance of the items. For example, the purchase proposal unit uses AI to evaluate the importance of items. The purchase proposal unit provides detailed purchase proposals for high-importance items. The purchase proposal unit provides simplified purchase proposals for low-importance items. The purchase proposal unit adjusts the priority of purchase proposals according to the importance of the items. The purchase proposal unit can also use generative AI to analyze the importance of items and adjust the level of detail in purchase proposals. This allows for detailed purchase proposals for important items by adjusting the level of detail in purchase proposals based on the importance of the items.

[0064] The purchase suggestion unit can apply different purchase suggestion algorithms depending on the item category when making a purchase suggestion. For example, the purchase suggestion unit uses AI to classify item categories. For clothing, the purchase suggestion unit applies a purchase suggestion algorithm based on seasonal data. For books, the purchase suggestion unit applies a purchase suggestion algorithm based on the user's reading history. For food, the purchase suggestion unit applies a purchase suggestion algorithm based on expiration date data. The purchase suggestion unit can also use generative AI to analyze item categories and apply different purchase suggestion algorithms. This improves the accuracy of purchase suggestions by applying different purchase suggestion algorithms depending on the item category.

[0065] The purchase proposal department can determine the priority of purchase proposals based on the submission timing of items. For example, the purchase proposal department can use AI to evaluate the submission timing of items. The purchase proposal department will make purchase proposals with a higher priority for items whose submission deadlines are approaching. The purchase proposal department will adjust the order of purchase proposals based on the submission timing. The purchase proposal department will adjust the level of detail of purchase proposals according to the submission timing. The purchase proposal department can also use generative AI to analyze the submission timing of items and determine the priority of purchase proposals. This allows the department to prioritize purchase proposals for items whose submission deadlines are approaching by determining the priority of purchase proposals based on the submission timing of items.

[0066] The purchase suggestion unit can adjust the order of purchase suggestions based on the relevance of items when making suggestions. For example, the purchase suggestion unit uses AI to evaluate the relevance of items. The purchase suggestion unit makes purchase suggestions with a high priority for highly relevant items. The purchase suggestion unit makes purchase suggestions with a low priority for less relevant items. The purchase suggestion unit optimizes the order of purchase suggestions according to the relevance of items. The purchase suggestion unit can also use generative AI to analyze the relevance of items and adjust the order of purchase suggestions. By adjusting the order of purchase suggestions based on the relevance of items, it can prioritize purchase suggestions for highly relevant items.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The data collection unit collects the user's health data, and the analysis unit can generate a list of items tailored to the user's health condition based on the collected data. For example, the data collection unit collects the user's heart rate, blood pressure, sleep data, etc. The analysis unit analyzes this data and can add necessary medications and health management items to the list of items based on the user's health condition. The suggestion unit suggests items suitable for the user's health condition based on the generated list of items. In this way, by generating a list of items tailored to the user's health condition, it is possible to support the user's health management.

[0069] The suggestion function can propose items that users can enjoy while out and about, based on their hobbies and interests. For example, if a user enjoys reading, the suggestion function can add books to read while out and about to their packing list. If a user enjoys photography, the suggestion function can add cameras and related accessories to their packing list. If a user enjoys sports, the suggestion function can add sports equipment to their packing list. In this way, by suggesting items based on the user's hobbies and interests, it can increase the enjoyment of users while out and about.

[0070] The suggestion function can analyze a user's past outing history and suggest items needed for similar outings. For example, it can suggest items needed for a future camping trip based on a user's packing list from a past camping trip. It can also suggest items needed for a future business trip based on a user's packing list from a past business trip. Similarly, it can suggest items needed for a future trip based on a user's packing list from a past vacation. By referencing the user's past outing history, the accuracy of the packing suggestions can be improved.

[0071] The suggestion function can suggest necessary items based on the user's activities while out and about. For example, if the user plans to go for a run while out, the suggestion function can add running shoes and sportswear to the packing list. If the user plans to attend a meeting while out, the suggestion function can add a laptop and meeting materials to the packing list. If the user plans to have a meal with friends while out, the suggestion function can add a wallet and smartphone to the packing list. This allows users to carry out their activities while out by suggesting items based on their planned activities.

[0072] The suggestion function can suggest necessary items to bring based on the weather at the user's destination. For example, if the weather forecast for the destination is rain, the suggestion function can add an umbrella and raincoat to the packing list. If the weather forecast for the destination is cold, the suggestion function can add a coat and gloves to the packing list. If the weather forecast for the destination is hot, the suggestion function can add a hat and sunglasses to the packing list. In this way, by suggesting items to bring based on the weather at the destination, the system can support users in going out comfortably.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit collects personal data. The data collection unit collects personal data such as calendars, weather forecasts, and past possession data. The data collection unit can also use AI to collect user behavior history and preference information. Step 2: The analysis unit analyzes the data collected by the collection unit and generates a packing list. The analysis unit can, for example, use AI to analyze the collected data and generate a packing list necessary for daily outings. The analysis unit can also use generation AI to generate a packing list based on the user's behavior patterns and preferences. Step 3: The suggestion unit proposes items based on the item list generated by the analysis unit. The suggestion unit can, for example, use AI to propose items based on the generated item list. The suggestion unit can also use generation AI to propose items based on the user's behavior patterns and preferences. Step 4: The notification unit sends a reminder based on the packing list proposed by the suggestion unit. The notification unit can, for example, use AI to send a reminder based on the proposed packing list. The notification unit can also use generative AI to send a reminder based on the user's behavior patterns and preferences.

[0075] (Example of form 2) The generation AI agent system according to an embodiment of the present invention is a system that supports daily outings from the perspective of "things to bring." This generation AI agent system collects personal data such as calendars, weather forecasts, and past packing data, and the AI ​​agent analyzes this data to automatically generate and suggest a list of things needed for daily outings and sends confirmation reminders. The generation AI agent system autonomously sets the priority of items and optimizes what to bring according to the destination, type of event, and environment of the day. The generation AI agent system integrates the calendars and schedules of all family members and manages and shares necessary items among family members. For families with children up to high school age, the generation AI agent system reads school packing lists from images, photos, and text data, integrates and manages them along with the necessary dates, and notifies parents of what to bring. The generation AI agent system also manages the storage location of items, making preparation and management more efficient. The generation AI agent system takes into account the attention deficit characteristics of individuals with developmental disabilities such as ADHD and ADD, and employs real-time notifications of items and a visual interface such as photos of items. The AI-generating agent system suggests purchasing items that users do not yet own or are expected to need in the future, leading to purchases on e-commerce sites. For example, the AI-generating agent system retrieves schedules from calendars, checks weather forecasts, and automatically generates a list of items needed when going out. The AI-generating agent system analyzes past item data and suggests items based on the user's preferences and behavioral patterns. The AI-generating agent system integrates the calendars of all family members and shares necessary items among family members. The AI-generating agent system reads school supply lists from images and photos, and integrates and manages them along with the necessary dates. The AI-generating agent system manages the storage location of items, streamlining preparation and management. The AI-generating agent system employs a visual interface, such as real-time notifications of items and photos of items. The AI-generating agent system suggests purchasing items that users do not yet own or are expected to need in the future, leading to purchases on e-commerce sites.This allows the AI ​​agent system to streamline users' daily preparations for going out and support them in managing their belongings.

[0076] The generation AI agent system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, and a notification unit. The collection unit collects personal data. The collection unit collects personal data such as calendars, weather forecasts, and past belongings data. The collection unit can also use AI to collect user behavior history and preference information. The analysis unit analyzes the data collected by the collection unit and generates a list of belongings. The analysis unit, for example, uses AI to analyze the collected data and generates a list of belongings needed for daily outings. The analysis unit can also use generation AI to generate a list of belongings based on the user's behavior patterns and preferences. The suggestion unit suggests belongings based on the list of belongings generated by the analysis unit. The suggestion unit, for example, uses AI to suggest belongings based on the generated list of belongings. The suggestion unit can also use generation AI to suggest belongings based on the user's behavior patterns and preferences. The notification unit sends reminders based on the list of belongings suggested by the suggestion unit. The notification unit, for example, uses AI to send reminders based on the suggested list of belongings. The notification unit can also use generative AI to send reminders based on the user's behavior patterns and preferences. This allows the generative AI agent system according to the embodiment to streamline the user's daily preparation for going out and support the management of their belongings.

[0077] The data collection unit collects personal data. Specifically, it obtains the user's calendar information to understand the details of scheduled events and meetings. It collects weather forecast data to predict weather conditions when the user goes out. It refers to past belongings data to check what items the user carried on previous outings. This data is automatically collected from the user's smartphone or computer and stored in a cloud-based database. The data collection unit can also use AI to collect user behavior history and preference information. For example, it collects information such as places the user frequently visits, modes of transportation used, and favorite restaurants to understand the user's behavior patterns. This allows the data collection unit to efficiently collect data tailored to the user's lifestyle and provide it to the analysis unit. Furthermore, the data collection unit implements advanced security measures in data collection and storage to protect user privacy. For example, it implements data encryption and access control to prevent unauthorized access by third parties. This allows the data collection unit to safely collect necessary data while gaining the user's trust.

[0078] The analysis unit analyzes the data collected by the collection unit and generates a packing list. Specifically, it uses AI to analyze the type and location of scheduled events from calendar information and lists the necessary items. For example, for a business meeting, a laptop, documents, and business cards would be required. Based on weather forecast data, it adds an umbrella and raincoat to the packing list for rainy days, and sunglasses and sunscreen for sunny days. It also refers to past packing data and considers items carried on previous outings to reflect necessary items in the list. The analysis unit can also use generation AI to generate packing lists based on the user's behavior patterns and preferences. For example, it suggests necessary items based on the user's frequently used modes of transportation and places they visit. Furthermore, it can consider the user's preference information and add items from their favorite brands and designs to the list. This allows the analysis unit to efficiently generate packing lists tailored to the user's needs and provide them to the suggestion unit.

[0079] The suggestion unit proposes items to bring based on the item list generated by the analysis unit. Specifically, it uses AI to analyze the generated item list and proposes the most suitable items for the user. For example, for a business meeting, it suggests essentials such as a laptop, documents, and business cards. Based on weather forecast data, it suggests an umbrella or raincoat in rainy weather, and sunglasses or sunscreen in sunny weather. It also refers to past item data and suggests necessary items considering items carried on previous outings. The suggestion unit can also use generation AI to suggest items based on the user's behavior patterns and preferences. For example, it suggests necessary items depending on the mode of transportation the user frequently uses and the places they visit. Furthermore, it can also consider the user's preference information and suggest items from their favorite brands and designs. This allows the suggestion unit to efficiently propose items tailored to the user's needs and provide them to the notification unit.

[0080] The notification unit sends reminders based on the packing list proposed by the suggestion unit. Specifically, it uses AI to send reminders based on the proposed packing list. For example, it can send a notification to the smartphone before leaving the house, prompting the user to check the packing list. Since the reminders are sent based on the user's behavior patterns and preferences, they help the user avoid forgetting things. The notification unit can also use generative AI to send reminders based on the user's behavior patterns and preferences. For example, it can remind users of necessary items depending on the mode of transportation they frequently use and the places they visit. Furthermore, it can consider the user's preference information and remind users of items from their favorite brands and designs. This allows the notification unit to efficiently send reminders tailored to the user's needs and support the management of their belongings. In addition, the notification unit can flexibly respond to the user's lifestyle by adjusting the timing and frequency of reminder sending. For example, it can send frequent reminders before important events and send only the minimum necessary reminders for daily outings. This allows the notification unit to reduce user stress and streamline the management of belongings.

[0081] The suggestion unit may include a priority setting unit that autonomously sets the priority of items. The suggestion unit may, for example, use AI to autonomously set the priority of items. The suggestion unit may set the priority of items based on criteria such as importance, frequency of use, and urgency. The suggestion unit may also use generative AI to set the priority of items based on the user's behavior patterns and preferences. By autonomously setting the priority of items, the user's preparation for going out can be optimized.

[0082] The suggestion unit can integrate the calendars and schedules of all family members and include a sharing unit for managing and sharing necessary items among family members. For example, the suggestion unit can use AI to integrate the calendars and schedules of all family members. The suggestion unit can also use generative AI to analyze the schedules of all family members and suggest necessary items. This streamlines the management and sharing of belongings among family members by integrating their calendars and schedules.

[0083] The proposed system could include a management unit that reads school supply lists from image, photo, and text data, integrates and manages them along with the necessary dates, and notifies parents of the required items, for families with children up to high school age. For example, the proposed unit could use AI to read school supply lists from images and photos. It could also use OCR technology to extract text data from images and photos. Furthermore, it could use generative AI to analyze school supply lists and integrate and manage them along with the necessary dates. This integrated management of school supply lists would allow parents to efficiently manage their children's belongings.

[0084] The proposal unit can be equipped with a management unit that manages the location of personal belongings. The proposal unit can manage the location of personal belongings using, for example, AI. The proposal unit can manage the location of personal belongings using methods such as room-by-room arrangement and identification of storage locations. The proposal unit can also use generative AI to analyze the location of personal belongings and propose the optimal arrangement. As a result, the preparation and management of personal belongings becomes more efficient by managing the location of personal belongings.

[0085] The suggestion unit may include a purchase suggestion unit that makes purchase suggestions for items not yet owned or expected to be needed in the future. The suggestion unit can, for example, use AI to make purchase suggestions for items not yet owned or expected to be needed in the future. The suggestion unit can make purchase suggestions based on criteria such as seasonal necessities or event-specific necessities. The suggestion unit can also use generative AI to make purchase suggestions for items based on the user's behavior patterns and preferences. This streamlines the user's possession management by making purchase suggestions for items not yet owned or expected to be needed in the future.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. The data collection unit estimates the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. The data collection unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the data collection unit reduces the frequency of data collection to lessen the user's burden. If the user is relaxed, the data collection unit collects detailed data to generate a more accurate packing list. If the user is in a hurry, the data collection unit quickly collects only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection based on their emotions.

[0087] The data collection unit can analyze a user's past behavior history and select the optimal data collection method. For example, the data collection unit can use AI to analyze a user's past behavior history. The data collection unit prioritizes data collection methods that the user has frequently used in the past. The data collection unit analyzes user behavior patterns and collects data at the optimal time. The data collection unit prioritizes collecting data related to specific events from the user's past behavior history. The data collection unit can also use generative AI to analyze a user's behavior history and select the optimal data collection method. This allows the optimal data collection method to be selected by analyzing the user's past behavior history.

[0088] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can use AI to analyze the user's current lifestyle and areas of interest. The data collection unit prioritizes collecting data related to the user's current areas of interest. The data collection unit collects only the necessary data according to the user's lifestyle. The data collection unit filters and collects highly relevant data based on the user's areas of interest. The data collection unit can also use generative AI to analyze the user's areas of interest and lifestyle and perform filtering during data collection. This allows for the collection of only the necessary data by filtering it based on the user's lifestyle and areas of interest.

[0089] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those estimated emotions. For example, the data collection unit uses emotion estimation functions, such as an emotion engine or generative AI, to estimate the user's emotions. The data collection unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the data collection unit prioritizes collecting high-priority data. If the user is relaxed, the data collection unit prioritizes collecting detailed data. If the user is in a hurry, the data collection unit prioritizes collecting only the essential data. The data collection unit can also use generative AI to analyze the user's emotions and prioritize the data to collect. This allows for the priority collection of important data based on the user's emotions.

[0090] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can use AI to analyze the user's geographical location information. If the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit will select the optimal data collection method based on the user's current location. The data collection unit will collect highly relevant data by referring to the user's movement history. The data collection unit can also use generative AI to analyze the user's geographical location information and prioritize the collection of highly relevant data during data collection. This allows for the priority collection of highly relevant data by considering the user's geographical location information.

[0091] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can use AI to analyze users' social media activity. The data collection unit collects relevant data based on information shared by users on social media. The data collection unit collects highly relevant data by referencing the activities of users' social media followers and friends. The data collection unit analyzes the content of users' social media posts and collects data based on their interests. The data collection unit can also use generative AI to analyze users' social media activity and collect relevant data during data collection. This allows for the collection of relevant data by analyzing users' social media activity.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, the analysis unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The analysis unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the analysis unit applies a simplified analysis method. If the user is relaxed, the analysis unit applies a detailed analysis method. If the user is in a hurry, the analysis unit applies a method for rapid analysis. The analysis unit can also use generative AI to analyze the user's emotions and adjust the analysis method accordingly. This allows for a reduction in the user's burden by adjusting the analysis method based on their emotions.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit uses AI to evaluate the importance of the data. The analysis unit performs a detailed analysis on data with high importance. The analysis unit performs a simplified analysis on data with low importance. The analysis unit adjusts the priority of the analysis according to the importance of the data. The analysis unit can also use generative AI to analyze the importance of the data and adjust the level of detail of the analysis. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data.

[0094] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can use AI to classify data categories. For weather forecast data, the analysis unit applies a meteorological analysis algorithm. For calendar data, the analysis unit applies a schedule analysis algorithm. For past personal belongings data, the analysis unit applies a pattern recognition algorithm. The analysis unit can also use generative AI to analyze data categories and apply different analysis algorithms. This improves the accuracy of the analysis by applying different analysis algorithms depending on the data category.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, the analysis unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The analysis unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the analysis unit prioritizes analyzing high-priority data. If the user is relaxed, the analysis unit prioritizes analyzing detailed data. If the user is in a hurry, the analysis unit prioritizes analyzing only the essential data. The analysis unit can also use generative AI to analyze the user's emotions and determine the priority of analysis. This allows for prioritizing the analysis of important data based on the user's emotions.

[0096] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit can use AI to evaluate the data submission date. The analysis unit prioritizes analyzing the most recent data. The analysis unit prioritizes analyzing data with approaching submission deadlines. The analysis unit adjusts the order of analysis based on the submission date. The analysis unit can also use generative AI to analyze the data submission date and determine the analysis priority. This allows for prioritizing the analysis based on the data submission date, thereby prioritizing the analysis of data with approaching submission deadlines.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can use AI to evaluate the relevance of the data. The analysis unit prioritizes the analysis of highly relevant data. The analysis unit postpones the analysis of less relevant data. The analysis unit optimizes the order of analysis according to the relevance of the data. The analysis unit can also use generative AI to analyze the relevance of the data and adjust the order of analysis. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data.

[0098] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, the suggestion function uses an emotion estimation feature, such as an emotion engine or generative AI, to estimate the user's emotions. The suggestion function can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the suggestion function provides simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function provides suggestions that include detailed information. If the user is in a hurry, the suggestion function provides suggestions that can be quickly understood. The suggestion function can also use generative AI to analyze the user's emotions and adjust the way suggestions are presented. This allows for suggestions that are easier for the user to understand by adjusting the presentation based on the user's emotions.

[0099] The proposal function can adjust the level of detail of a proposal based on the importance of the item. For example, the proposal function can use AI to evaluate the importance of an item. The proposal function provides detailed proposals for high-importance items and simplified proposals for low-importance items. The proposal function adjusts the priority of proposals according to the importance of the item. The proposal function can also use generative AI to analyze the importance of an item and adjust the level of detail of the proposal. This allows for detailed proposals for important items by adjusting the level of detail of the proposal based on the importance of the item.

[0100] The suggestion function can apply different suggestion algorithms depending on the item category when making suggestions. For example, the suggestion function can use AI to classify item categories. For clothing, the suggestion function applies a suggestion algorithm based on weather forecast data. For documents, the suggestion function applies a suggestion algorithm based on schedule data. For food items, the suggestion function applies a suggestion algorithm based on expiration date data. The suggestion function can also use generative AI to analyze item categories and apply different suggestion algorithms. This improves the accuracy of suggestions by applying different suggestion algorithms depending on the item category.

[0101] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, the suggestion function uses emotion estimation capabilities, such as an emotion engine or generative AI, to estimate the user's emotions. The suggestion function can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function will provide longer suggestions with more detailed explanations. If the user is in a hurry, the suggestion function will provide short, easily understandable suggestions. The suggestion function can also use generative AI to analyze the user's emotions and adjust the length of the suggestions accordingly. This allows for suggestions of an appropriate length for the user by adjusting the length based on their emotions.

[0102] The proposal team can prioritize proposals based on the submission timing of each item. For example, the proposal team can use AI to evaluate the submission timing of items. The proposal team will prioritize proposing items with approaching deadlines. The proposal team will adjust the order of proposals based on submission timing. The proposal team will adjust the level of detail of proposals according to submission timing. The proposal team can also use generative AI to analyze the submission timing of items and determine the priority of proposals. This allows the team to prioritize proposals based on the submission timing of items, thereby prioritizing items with approaching deadlines.

[0103] The suggestion function can adjust the order of suggestions based on the relevance of the items during the suggestion process. For example, the suggestion function can use AI to evaluate the relevance of items. The suggestion function prioritizes suggesting highly relevant items. The suggestion function postpones suggesting less relevant items. The suggestion function optimizes the order of suggestions according to the relevance of the items. The suggestion function can also use generative AI to analyze the relevance of items and adjust the order of suggestions. This allows the system to prioritize suggesting highly relevant items by adjusting the order of suggestions based on the relevance of the items.

[0104] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, the notification unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The notification unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the notification unit reduces the frequency of notifications to lessen the user's burden. If the user is relaxed, the notification unit provides detailed notifications. If the user is in a hurry, the notification unit provides only the essential notifications quickly. The notification unit can also use generative AI to analyze the user's emotions and adjust the timing of notifications. This allows for reduced user burden by adjusting notification timing based on the user's emotions.

[0105] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit can use AI to analyze the user's past notification history. The notification unit prioritizes selecting notification methods that the user has preferred to use in the past. The notification unit analyzes the user's notification history and sends notifications at the optimal time. The notification unit selects a notification method related to a specific event from the user's past notification history. The notification unit can also use generative AI to analyze the user's notification history and select the optimal notification method. This allows the system to select the optimal notification method by referring to the user's past notification history.

[0106] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, the notification unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The notification unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the notification unit prioritizes high-priority notifications. If the user is relaxed, the notification unit prioritizes detailed notifications. If the user is in a hurry, the notification unit prioritizes essential notifications. The notification unit can also use generative AI to analyze the user's emotions and determine notification priorities. This allows for prioritizing important notifications based on the user's emotions.

[0107] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, the notification unit can use AI to analyze the user's device information. If the user is using a smartphone, the notification unit will prioritize push notifications. If the user is using a tablet, the notification unit will prioritize email notifications. If the user is using a smartwatch, the notification unit will prioritize vibration notifications. The notification unit can also use generative AI to analyze the user's device information and select the optimal notification method. This allows the system to select the most suitable notification method by considering the user's device information.

[0108] The priority setting unit can estimate the user's emotions and adjust the priority of items based on those emotions. For example, the priority setting unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The priority setting unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the priority setting unit prioritizes high-importance items. If the user is relaxed, the priority setting unit prioritizes detailed items. If the user is in a hurry, the priority setting unit prioritizes essential items. The priority setting unit can also use generative AI to analyze the user's emotions and adjust item priorities accordingly. This allows for prioritizing important items by adjusting item priorities based on the user's emotions.

[0109] The priority setting unit can adjust the priority of items based on their importance during the priority setting process. For example, the priority setting unit can use AI to evaluate the importance of items. The priority setting unit sets a higher priority for items with high importance. The priority setting unit sets a lower priority for items with low importance. The priority setting unit adjusts the order of priorities according to the importance of the items. The priority setting unit can also use generating AI to analyze the importance of items and adjust the priority accordingly. This allows for setting a higher priority for important items by adjusting the priority based on their importance.

[0110] The priority setting unit can apply different priority setting algorithms depending on the item category when setting priorities. For example, the priority setting unit uses AI to classify item categories. For clothing, the priority setting unit applies a priority setting algorithm based on weather forecast data. For documents, the priority setting unit applies a priority setting algorithm based on schedule data. For food items, the priority setting unit applies a priority setting algorithm based on expiration date data. The priority setting unit can also use generative AI to analyze item categories and apply different priority setting algorithms. This improves the accuracy of priority setting by applying different priority setting algorithms depending on the item category.

[0111] The priority setting unit can estimate the user's emotions and adjust the display method of priorities based on the estimated user emotions. For example, the priority setting unit estimates the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. The priority setting unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. When the user is stressed, the priority setting unit provides a simple and easy-to-understand display method. When the user is relaxed, the priority setting unit provides a display method that includes detailed information. When the user is in a hurry, the priority setting unit provides a display method that can be quickly understood. The priority setting unit can also analyze the user's emotions using generative AI and adjust the display method of priorities accordingly. This allows for a user-friendly display by adjusting the priority display method based on the user's emotions.

[0112] The priority setting unit can determine priority based on the submission timing of items during the priority setting process. For example, the priority setting unit uses AI to evaluate the submission timing of items. The priority setting unit sets a higher priority for items with approaching submission deadlines. The priority setting unit adjusts the priority order based on the submission timing. The priority setting unit adjusts the level of detail of priority according to the submission timing. The priority setting unit can also analyze the submission timing of items and determine priority using generating AI. This allows for setting a higher priority for items with approaching submission deadlines by determining priority based on the submission timing of items.

[0113] The priority setting unit can adjust the priority order based on the relevance of items when setting priorities. For example, the priority setting unit uses AI to evaluate the relevance of items. The priority setting unit sets a higher priority for highly relevant items. The priority setting unit sets a lower priority for less relevant items. The priority setting unit optimizes the priority order according to the relevance of items. The priority setting unit can also use generative AI to analyze the relevance of items and adjust the priority order. This allows for prioritizing highly relevant items by adjusting the priority order based on the relevance of items.

[0114] The sharing function can estimate the user's emotions and adjust the sharing method based on those emotions. For example, the sharing function uses an emotion estimation feature, such as an emotion engine or generative AI, to estimate the user's emotions. The sharing function can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the sharing function provides a simple and easy-to-understand sharing method. If the user is relaxed, the sharing function provides a sharing method that includes detailed information. If the user is in a hurry, the sharing function provides a sharing method that can be quickly understood. The sharing function can also use generative AI to analyze the user's emotions and adjust the sharing method accordingly. This allows for sharing that is easier for the user to understand by adjusting the sharing method based on the user's emotions.

[0115] The sharing function can select the optimal sharing method by referring to the family's past sharing history when sharing. For example, the sharing function can use AI to analyze the family's past sharing history. The sharing function prioritizes selecting sharing methods that the family has preferred to use in the past. The sharing function analyzes the family's sharing history and shares at the optimal time. The sharing function selects sharing methods related to specific events from the family's past sharing history. The sharing function can also use generative AI to analyze the family's sharing history and select the optimal sharing method. This allows the optimal sharing method to be selected by referring to the family's past sharing history.

[0116] The sharing function can estimate the user's emotions and determine the priority of sharing based on those emotions. For example, the sharing function uses an emotion estimation feature, such as an emotion engine or generative AI, to estimate the user's emotions. The sharing function can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the sharing function prioritizes high-priority sharing. If the user is relaxed, the sharing function prioritizes detailed sharing. If the user is in a hurry, the sharing function prioritizes sharing only the essentials. The sharing function can also use generative AI to analyze the user's emotions and determine the priority of sharing. This allows for prioritizing important sharing based on the user's emotions.

[0117] The sharing function can select the optimal sharing method by considering the family's device information when sharing. For example, the sharing function uses AI to analyze the family's device information. If the family is using a smartphone, the sharing function prioritizes push notifications. If the family is using a tablet, the sharing function prioritizes email notifications. If the family is using a smartwatch, the sharing function prioritizes vibration notifications. The sharing function can also use generative AI to analyze the family's device information and select the optimal sharing method. This allows the system to select the optimal sharing method by considering the family's device information.

[0118] The management department can estimate the user's emotions and adjust management methods based on those estimated emotions. For example, the management department uses emotion estimation functions, such as an emotion engine or generative AI, to estimate the user's emotions. The management department can also estimate user emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the management department provides a simple and easy-to-understand management method. If the user is relaxed, the management department provides a management method that includes detailed information. If the user is in a hurry, the management department provides a management method that can be quickly understood. The management department can also use generative AI to analyze the user's emotions and adjust management methods accordingly. This allows for management that is easy for the user to understand by adjusting management methods based on the user's emotions.

[0119] The management department can adjust the level of detail in management based on the importance of the items. For example, the management department can use AI to evaluate the importance of items. The management department will perform detailed management for high-importance items and simplified management for low-importance items. The management department will adjust the priority of management according to the importance of the items. The management department can also use generative AI to analyze the importance of items and adjust the level of detail in management. This allows for detailed management of important items by adjusting the level of detail in management based on the importance of the items.

[0120] The management department can apply different management algorithms depending on the item category during management. For example, the management department can use AI to classify item categories. For clothing, the management department can apply a management algorithm based on weather forecast data. For documents, the management department can apply a management algorithm based on schedule data. For food products, the management department can apply a management algorithm based on expiration date data. The management department can also use generative AI to analyze item categories and apply different management algorithms. This improves the accuracy of management by applying different management algorithms depending on the item category.

[0121] The management department can estimate the user's emotions and determine management priorities based on those estimated emotions. For example, the management department can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. The management department can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the management department will prioritize high-priority management tasks. If the user is relaxed, the management department will prioritize detailed management tasks. If the user is in a hurry, the management department will prioritize minimal management tasks. The management department can also use generative AI to analyze the user's emotions and determine management priorities. This allows for prioritizing important management tasks based on the user's emotions.

[0122] The management department can determine management priorities based on the submission timing of items during management. For example, the management department can use AI to evaluate the submission timing of items. The management department will manage items with approaching deadlines with a higher priority. The management department will adjust the order of management based on the submission timing. The management department will adjust the level of detail of management according to the submission timing. The management department can also use generative AI to analyze the submission timing of items and determine management priorities. This allows for priority management of items with approaching deadlines by determining management priorities based on the submission timing.

[0123] The management department can adjust the order of management based on the relevance of items during the management process. For example, the management department can use AI to evaluate the relevance of items. The management department will manage highly relevant items with a higher priority. The management department will manage less relevant items with a lower priority. The management department will optimize the order of management according to the relevance of items. The management department can also use generative AI to analyze the relevance of items and adjust the order of management. This allows for the priority management of highly relevant items by adjusting the order of management based on the relevance of items.

[0124] The purchase suggestion unit can estimate the user's emotions and adjust its purchase suggestion methods based on those emotions. For example, the purchase suggestion unit uses emotion estimation functions, such as an emotion engine or generative AI, to estimate the user's emotions. It can also estimate user emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the purchase suggestion unit provides simple and easy-to-understand purchase suggestions. If the user is relaxed, it provides purchase suggestions with detailed information. If the user is in a hurry, it provides purchase suggestions that can be quickly understood. The purchase suggestion unit can also use generative AI to analyze the user's emotions and adjust its purchase suggestion methods. This allows for purchase suggestions that are easier for the user to understand by adjusting the method based on the user's emotions.

[0125] The purchase proposal unit can adjust the level of detail in purchase proposals based on the importance of the items. For example, the purchase proposal unit uses AI to evaluate the importance of items. The purchase proposal unit provides detailed purchase proposals for high-importance items. The purchase proposal unit provides simplified purchase proposals for low-importance items. The purchase proposal unit adjusts the priority of purchase proposals according to the importance of the items. The purchase proposal unit can also use generative AI to analyze the importance of items and adjust the level of detail in purchase proposals. This allows for detailed purchase proposals for important items by adjusting the level of detail in purchase proposals based on the importance of the items.

[0126] The purchase suggestion unit can apply different purchase suggestion algorithms depending on the item category when making a purchase suggestion. For example, the purchase suggestion unit uses AI to classify item categories. For clothing, the purchase suggestion unit applies a purchase suggestion algorithm based on seasonal data. For books, the purchase suggestion unit applies a purchase suggestion algorithm based on the user's reading history. For food, the purchase suggestion unit applies a purchase suggestion algorithm based on expiration date data. The purchase suggestion unit can also use generative AI to analyze item categories and apply different purchase suggestion algorithms. This improves the accuracy of purchase suggestions by applying different purchase suggestion algorithms depending on the item category.

[0127] The purchase suggestion unit can estimate the user's emotions and prioritize purchase suggestions based on those emotions. For example, the purchase suggestion unit uses an emotion estimation function, such as an emotion engine or generative AI, to estimate the user's emotions. The purchase suggestion unit can also estimate the user's emotions using methods such as facial recognition, voice analysis, and survey results. If the user is stressed, the purchase suggestion unit prioritizes high-priority purchase suggestions. If the user is relaxed, the purchase suggestion unit prioritizes detailed purchase suggestions. If the user is in a hurry, the purchase suggestion unit prioritizes only the essential purchase suggestions. The purchase suggestion unit can also use generative AI to analyze the user's emotions and determine the priority of purchase suggestions. This allows for prioritizing important purchase suggestions based on the user's emotions.

[0128] The purchase proposal department can determine the priority of purchase proposals based on the submission timing of items. For example, the purchase proposal department can use AI to evaluate the submission timing of items. The purchase proposal department will make purchase proposals with a higher priority for items whose submission deadlines are approaching. The purchase proposal department will adjust the order of purchase proposals based on the submission timing. The purchase proposal department will adjust the level of detail of purchase proposals according to the submission timing. The purchase proposal department can also use generative AI to analyze the submission timing of items and determine the priority of purchase proposals. This allows the department to prioritize purchase proposals for items whose submission deadlines are approaching by determining the priority of purchase proposals based on the submission timing of items.

[0129] The purchase suggestion unit can adjust the order of purchase suggestions based on the relevance of items when making suggestions. For example, the purchase suggestion unit uses AI to evaluate the relevance of items. The purchase suggestion unit makes purchase suggestions with a high priority for highly relevant items. The purchase suggestion unit makes purchase suggestions with a low priority for less relevant items. The purchase suggestion unit optimizes the order of purchase suggestions according to the relevance of items. The purchase suggestion unit can also use generative AI to analyze the relevance of items and adjust the order of purchase suggestions. By adjusting the order of purchase suggestions based on the relevance of items, it can prioritize purchase suggestions for highly relevant items.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The data collection unit collects the user's health data, and the analysis unit can generate a list of items tailored to the user's health condition based on the collected data. For example, the data collection unit collects the user's heart rate, blood pressure, sleep data, etc. The analysis unit analyzes this data and can add necessary medications and health management items to the list of items based on the user's health condition. The suggestion unit suggests items suitable for the user's health condition based on the generated list of items. In this way, by generating a list of items tailored to the user's health condition, it is possible to support the user's health management.

[0132] The suggestion function can propose items that users can enjoy while out and about, based on their hobbies and interests. For example, if a user enjoys reading, the suggestion function can add books to read while out and about to their packing list. If a user enjoys photography, the suggestion function can add cameras and related accessories to their packing list. If a user enjoys sports, the suggestion function can add sports equipment to their packing list. In this way, by suggesting items based on the user's hobbies and interests, it can increase the enjoyment of users while out and about.

[0133] The suggestion function can analyze a user's past outing history and suggest items needed for similar outings. For example, it can suggest items needed for a future camping trip based on a user's packing list from a past camping trip. It can also suggest items needed for a future business trip based on a user's packing list from a past business trip. Similarly, it can suggest items needed for a future trip based on a user's packing list from a past vacation. By referencing the user's past outing history, the accuracy of the packing suggestions can be improved.

[0134] The suggestion function can suggest necessary items based on the user's activities while out and about. For example, if the user plans to go for a run while out, the suggestion function can add running shoes and sportswear to the packing list. If the user plans to attend a meeting while out, the suggestion function can add a laptop and meeting materials to the packing list. If the user plans to have a meal with friends while out, the suggestion function can add a wallet and smartphone to the packing list. This allows users to carry out their activities while out by suggesting items based on their planned activities.

[0135] The suggestion function can suggest necessary items to bring based on the weather at the user's destination. For example, if the weather forecast for the destination is rain, the suggestion function can add an umbrella and raincoat to the packing list. If the weather forecast for the destination is cold, the suggestion function can add a coat and gloves to the packing list. If the weather forecast for the destination is hot, the suggestion function can add a hat and sunglasses to the packing list. In this way, by suggesting items to bring based on the weather at the destination, the system can support users in going out comfortably.

[0136] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. If the user is relaxed, the unit can collect more detailed data to generate a more accurate packing list. If the user is in a hurry, the unit can quickly collect only the minimum necessary data. This reduces the user's burden by adjusting the timing of data collection based on their emotions.

[0137] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is stressed, the analysis unit can apply a simplified analysis method. If the user is relaxed, the analysis unit can apply a detailed analysis method. If the user is in a hurry, the analysis unit can apply a method that performs a rapid analysis. By adjusting the analysis method based on the user's emotions, the burden on the user can be reduced.

[0138] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function can offer simple and easy-to-understand suggestions. If the user is relaxed, the suggestion function can offer suggestions that include detailed information. If the user is in a hurry, the suggestion function can offer suggestions that can be quickly understood. By adjusting the way suggestions are presented based on the user's emotions, it becomes possible to create suggestions that are easy for the user to understand.

[0139] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications to lessen the user's burden. If the user is relaxed, the notification unit can provide detailed notifications. If the user is in a hurry, the notification unit can quickly provide only the essential notifications. In this way, by adjusting the timing of notifications based on the user's emotions, the user's burden can be reduced.

[0140] The priority setting unit can estimate the user's emotions and adjust the priority of items based on those emotions. For example, if the user is stressed, the priority setting unit can prioritize high-importance items. If the user is relaxed, the priority setting unit can prioritize detailed items. If the user is in a hurry, the priority setting unit can prioritize essential items. In this way, by adjusting the priority of items based on the user's emotions, important items can be prioritized.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The data collection unit collects personal data. The data collection unit collects personal data such as calendars, weather forecasts, and past possession data. The data collection unit can also use AI to collect user behavior history and preference information. Step 2: The analysis unit analyzes the data collected by the collection unit and generates a packing list. The analysis unit can, for example, use AI to analyze the collected data and generate a packing list necessary for daily outings. The analysis unit can also use generation AI to generate a packing list based on the user's behavior patterns and preferences. Step 3: The suggestion unit proposes items based on the item list generated by the analysis unit. The suggestion unit can, for example, use AI to propose items based on the generated item list. The suggestion unit can also use generation AI to propose items based on the user's behavior patterns and preferences. Step 4: The notification unit sends a reminder based on the packing list proposed by the suggestion unit. The notification unit can, for example, use AI to send a reminder based on the proposed packing list. The notification unit can also use generative AI to send a reminder based on the user's behavior patterns and preferences.

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0144] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0145] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects personal data through the calendar and weather forecast applications of the smart device 14 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates a packing list based on the collected data. The suggestion unit is implemented in the control unit 46A of the smart device 14 and suggests items to bring based on the generated packing list. The notification unit is implemented in the control unit 46A of the smart device 14 and sends a reminder based on the suggested packing list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0150] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

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

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects personal data through the calendar and weather forecast applications of the smart glasses 214 and analyzes it by the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates a list of items to bring based on the collected data. The suggestion unit is implemented in the control unit 46A of the smart glasses 214 and suggests items to bring based on the generated list of items. The notification unit is implemented in the control unit 46A of the smart glasses 214 and sends a reminder based on the suggested list of items to bring. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0166] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects personal data through the calendar and weather forecast applications of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and generates a packing list based on the collected data. The suggestion unit is implemented, for example, by the control unit 46A of the headset terminal 314 and suggests items to bring based on the generated packing list. The notification unit is implemented, for example, by the control unit 46A of the headset terminal 314 and sends a reminder based on the suggested packing list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] As shown in Figure 7, the 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.

[0181] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0182] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0183] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0185] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0186] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0187] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0188] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0189] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0190] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0191] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0192] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0193] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0194] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0195] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects personal data through the robot 414's calendar and weather forecast applications and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, using the identification processing unit 290 of the data processing unit 12 and generates a packing list based on the collected data. The suggestion unit is implemented, for example, using the control unit 46A of the robot 414 and suggests items to bring based on the generated packing list. The notification unit is implemented, for example, using the control unit 46A of the robot 414 and sends a reminder based on the suggested packing list. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0196] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0197] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0198] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0199] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0200] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0201] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0202] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0203] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0204] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0205] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0206] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0207] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0208] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0209] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0210] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0211] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0212] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0213] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0214] (Note 1) A system characterized by comprising: a collection unit for collecting personal data; an analysis unit for analyzing the data collected by the collection unit and generating a list of belongings; a suggestion unit for proposing belongings based on the list of belongings generated by the analysis unit; and a notification unit for sending reminders based on the list of belongings proposed by the suggestion unit. (Note 2) The proposed unit is characterized by comprising a priority setting unit that autonomously sets the priority of items, as described in Appendix 1. (Note 3) The proposed unit is the system described in Appendix 1, characterized in that it includes a shared unit that integrates the calendars and schedules of all family members and manages and shares necessary belongings among family members. (Note 4) The proposed unit is a system as described in Appendix 1, characterized in that it includes a management unit that reads school supply lists from image, photograph, and text data, integrates and manages them along with the necessary dates, and notifies parents of the required items, for use by families with children up to high school age. (Note 5) The aforementioned proposal section is, It has a management department that manages where belongings are stored. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system according to Appendix 1, characterized in that the proposal unit includes a purchase proposal unit that makes purchase proposals for items not currently owned or expected to be needed in the future. (Note 7) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user's emotions. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated user's emotions. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The system according to Appendix 1, characterized in that the analysis unit estimates the user's emotions and determines the priority of analysis based on the estimated user's emotions. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The system according to Appendix 1, characterized in that the proposal unit estimates the user's emotions and adjusts the method of expressing the proposal based on the estimated user's emotions. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The system according to Appendix 1, characterized in that the suggestion unit estimates the user's emotions and adjusts the length of the suggestion based on the estimated user's emotions. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the items were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 25) The notification unit is characterized by estimating the user's emotions and adjusting the timing of notifications based on the estimated user emotions, as described in Appendix 1. (Note 26) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The notification unit is characterized by estimating the user's emotions and determining the priority of notifications based on the estimated user emotions, as described in Appendix 1. (Note 28) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The system according to Appendix 2, characterized in that the priority setting unit estimates the user's emotions and adjusts the priority of items based on the estimated user's emotions. (Note 30) The priority setting unit is, When setting priorities, adjust the priority based on the importance of the items. The system described in Appendix 2, characterized by the features described herein. (Note 31) The priority setting unit is, When setting priorities, different priority algorithms are applied depending on the item category. The system described in Appendix 2, characterized by the features described herein. (Note 32) The system according to Appendix 2, characterized in that the priority setting unit estimates the user's emotions and adjusts the method of displaying the priorities based on the estimated user emotions. (Note 33) The priority setting unit is, When setting priorities, priority is determined based on when the items were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 34) The priority setting unit is, When setting priorities, the order of priorities is adjusted based on the relevance of the items. The system described in Appendix 2, characterized by the features described herein. (Note 35) The system according to Appendix 3, characterized in that the sharing unit estimates the user's emotions and adjusts the sharing method based on the estimated user's emotions. (Note 36) The aforementioned shared portion is, When sharing, refer to the family's past sharing history to select the most suitable sharing method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The system according to Appendix 3, characterized in that the sharing unit estimates the user's emotions and determines the priority of sharing based on the estimated user's emotions. (Note 38) The aforementioned shared portion is, When sharing, select the optimal sharing method considering the family's device information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The system according to Appendix 4, characterized in that the management unit estimates the user's emotions and adjusts the management method based on the estimated user's emotions. (Note 40) The aforementioned management department, During management, adjust the level of detail based on the importance of the item. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned management department, During management, different management algorithms are applied depending on the item category. The system described in Appendix 4, characterized by the features described herein. (Note 42) The system according to Appendix 4, characterized in that the management unit estimates the user's emotions and determines management priorities based on the estimated user's emotions. (Note 43) The aforementioned management department, During management, prioritize management based on when the items were submitted. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned management department, During management, adjust the order of management based on the relationships between items. The system described in Appendix 4, characterized by the features described herein. (Note 45) The system according to Appendix 6, characterized in that the purchase proposal unit estimates the user's emotions and adjusts the purchase proposal method based on the estimated user's emotions. (Note 46) The aforementioned purchase proposal department, When making a purchase suggestion, adjust the level of detail in the suggestion based on the importance of the item. The system described in Appendix 6, characterized by the features described herein. (Note 47) The aforementioned purchase proposal department, When making purchase suggestions, different purchase suggestion algorithms are applied depending on the item category. The system described in Appendix 6, characterized by the features described herein. (Note 48) The system according to Appendix 6, characterized in that the purchase proposal unit estimates the user's emotions and determines the priority of purchase proposals based on the estimated user's emotions. (Note 49) The aforementioned purchase proposal department, When submitting a purchase proposal, we prioritize the proposals based on when the items were submitted. The system described in Appendix 6, characterized by the features described herein. (Note 50) The aforementioned purchase proposal department, When making purchase suggestions, adjust the order of the suggestions based on the relevance of the items. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: a collection unit for collecting personal data; an analysis unit for analyzing the data collected by the collection unit and generating a list of belongings; a suggestion unit for proposing belongings based on the list of belongings generated by the analysis unit; and a notification unit for sending reminders based on the list of belongings proposed by the suggestion unit.

2. The system according to claim 1, characterized in that the proposed unit includes a priority setting unit that autonomously sets the priority of items.

3. The proposed unit is characterized by having a shared unit that integrates the calendars and schedules of all family members and manages and shares necessary belongings among family members.

4. The proposed unit is a system according to claim 1, characterized in that it includes a management unit that reads a list of school supplies from image, photograph, and text data, integrates and manages it along with the necessary dates, and notifies parents of the items to be brought, for families with children up to high school age.

5. The aforementioned proposal section is, It has a management department that manages where belongings are stored. The system according to feature 1.

6. The system according to claim 1, characterized in that the proposal unit includes a purchase proposal unit that makes purchase proposals for items not currently owned or expected to be needed in the future.

7. The system according to claim 1, characterized in that the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user's emotions.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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