system

Abstract

The system according to this embodiment aims to manage anniversaries and provide reminders and arrangements. [Solution] The system according to the embodiment comprises a management unit, a reminder unit, a suggestion unit, and an arrangement unit. The management unit manages anniversaries. The reminder unit sends reminders based on the anniversaries managed by the management unit. The suggestion unit makes suggestions based on the anniversaries reminded by the reminder unit. The arrangement unit makes arrangements based on the content suggested by the suggestion unit.

Description

Technical Field

[0004] ,

[0006] , , , , ,

[0005] , , , , ,

[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

[0007] The system according to this embodiment can manage anniversaries and make reminders and arrangements. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI ​​agent for generating anniversaries according to an embodiment of the present invention is a system that manages anniversaries for family, lovers, and friends, and provides reminders and makes arrangements. In this system, when a user registers an anniversary, the generating AI agent manages the date. For example, important dates such as birthdays and wedding anniversaries can be registered. Next, a reminder is sent at a specified time (for example, one month in advance). This allows the user to prepare in advance without forgetting the anniversary. Furthermore, the generating AI agent makes suggestions for events and gifts according to various occasions. For example, for a wedding anniversary, it may suggest a restaurant meal, a message plate, and a gift. Specific suggestions include presenting a list of restaurants and gift options. For example, depending on the user's financial situation, it may suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. The generating AI agent also makes arrangements such as restaurant reservations and gift orders. When the user confirms the suggestions and gives specific instructions, the generating AI agent makes the arrangements based on those instructions. For example, if the user reserves an Italian restaurant and instructs them to provide a dessert plate with a message and a pearl necklace at the same time, the generating AI agent will make the arrangements. In this way, the AI ​​agent that automatically generates anniversaries reduces the burden on users and makes anniversaries more special by handling everything from managing and reminding users to suggesting and arranging them. This allows the AI ​​agent to efficiently manage users' anniversaries and plan special events.

[0029] The AI ​​agent for automatically generating anniversaries according to this embodiment comprises a management unit, a reminder unit, a suggestion unit, and an arrangement unit. The management unit manages anniversaries. For example, when a user registers an anniversary, the management unit manages its date. The management unit can register important dates such as birthdays and wedding anniversaries. The reminder unit makes reminders based on the anniversaries managed by the management unit. For example, the reminder unit makes reminders at a specified time (for example, one month in advance). The reminder unit ensures that users do not forget anniversaries and can prepare in advance. The suggestion unit makes suggestions based on the anniversaries reminded by the reminder unit. For example, the suggestion unit makes suggestions for events and gifts appropriate for various occasions. For wedding anniversaries, the suggestion unit makes suggestions for restaurant meals, message plates, and gifts. As specific suggestions, the suggestion unit presents a list of restaurants and gift options. For example, depending on the financial situation, the suggestion unit can suggest pearl jewelry, which is the wife's birthstone, instead of a Sweet Ten diamond. The arrangement unit makes arrangements based on the suggestions made by the suggestion unit. The arrangement unit handles arrangements such as making restaurant reservations or ordering gifts. The arrangement unit confirms the user's suggestions and, once specific instructions are given, makes arrangements based on those instructions. For example, if the user requests an Italian restaurant reservation and instructs the system to provide a dessert plate with a message and a pearl necklace simultaneously, the arrangement unit will make those arrangements. In this way, the anniversary-generating AI agent according to this embodiment reduces the user's burden and makes the anniversary more special by handling everything from anniversary management and reminders to suggestions and arrangements in one go.

[0030] The management department manages anniversaries. For example, when a user registers an anniversary, the management department manages its dates. Important dates such as birthdays and wedding anniversaries can be registered. Specifically, when a user enters an anniversary through an application or web interface, that information is stored in the database. The management department displays this anniversary information in a calendar format so that users can see it at a glance. The management department also categorizes anniversaries according to their type, allowing for management by different categories, such as family birthdays, wedding anniversaries, and friends' birthdays. Furthermore, the management department provides a function that allows users to add specific notes and tags when registering an anniversary. This makes it easy for users to record special information and memories related to the anniversary. For example, they can leave notes such as the name of the restaurant they first visited on their wedding anniversary or details of the gift they gave. The management department securely stores this information and provides it to users as needed. In addition, the management department has a mechanism in place to prevent data loss by regularly backing up data so that users do not forget anniversaries. In this way, the management department reliably manages users' important anniversaries and makes them accessible at any time.

[0031] The reminder unit sends reminders based on anniversaries managed by the management unit. The reminder unit sends reminders at specified times (e.g., one month in advance). The reminder unit ensures users don't forget anniversaries and can prepare in advance. Specifically, the reminder unit sends notifications based on the reminder timing set by the user. Notifications are sent via multiple methods, including smartphone push notifications, email, and SMS. Users can freely choose their notification method and receive reminders in the way that suits them best. The reminder unit also provides a function to customize notification content. For example, specific messages or images can be attached to reminder messages. This allows users to immediately recall special memories or information related to the anniversary when they receive a reminder notification. Furthermore, the reminder unit has a function to track user actions after receiving a reminder notification. For example, it records whether the user viewed the reminder notification and what action was taken in response. This allows the reminder unit to understand whether users are making appropriate preparations for the anniversary and send additional reminders as needed. In this way, the reminder unit supports users in preparing in advance and ensuring they don't forget anniversaries.

[0032] The suggestion department makes suggestions based on anniversaries reminded by the reminder department. For example, the suggestion department suggests events and gifts tailored to various occasions. For wedding anniversaries, the suggestion department suggests restaurant meals, message plates, and gifts. As specific suggestions, the suggestion department presents a list of restaurants and gift options. For example, depending on the user's financial situation, the suggestion department might suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. Specifically, the suggestion department uses AI to analyze the user's past behavior history and preferences to make optimal suggestions. For example, based on data of restaurants the user has visited and gifts they have purchased in the past, it suggests restaurants and gifts that match the user's preferences. The suggestion department also considers the user's budget and current trends to make optimal suggestions. For example, if the user sets a budget, it suggests the best restaurants and gifts within that budget. Furthermore, the suggestion department provides a function to visually display the suggested content so that the user can easily review it. For example, it displays restaurant photos and menus, and gift images and detailed information so that the user can easily compare and consider options. The suggestion department also has a function that allows users to provide feedback on the suggested content. This allows the proposal department to continuously improve its proposals based on user feedback, enabling them to offer more satisfying suggestions. As a result, the proposal department can provide optimal suggestions to help users make their anniversaries even more special.

[0033] The arrangement department makes arrangements based on the proposals made by the proposal department. For example, the arrangement department makes arrangements such as restaurant reservations and gift orders. Once the user confirms the proposal and gives specific instructions, the arrangement department makes arrangements based on those instructions. For example, if the user requests that an Italian restaurant be reserved and that a dessert plate with a message and a pearl necklace be delivered at the same time, the arrangement department will make those arrangements. Specifically, the arrangement department makes reservations and orders for the restaurant and gifts selected by the user based on the information provided by the proposal department. The arrangement department integrates with restaurant reservation systems and online shopping sites to allow users to easily make reservations and orders. The arrangement department also provides a function that allows users to check the status of reservations and orders in real time. For example, it sends a notification when a reservation is confirmed, so that users can celebrate their special day with peace of mind. Furthermore, the arrangement department also responds to special requests from users. For example, it makes arrangements according to the user's requests, such as requests for special seats at restaurants or adding a message card to a gift. The arrangement department also provides confirmations and reminders in advance so that users can enjoy their special day smoothly. This allows the arrangement department to efficiently and reliably make arrangements to make the anniversary even more special for the user.

[0034] The asset tracking unit can understand the user's asset status. For example, the asset tracking unit can understand the user's bank account balance, real estate value, investment status, etc. By understanding the user's asset status, the asset tracking unit can tailor its recommendations to the user's financial situation. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or not using AI. For example, the asset tracking unit can input the user's bank account balance into the AI, and the AI ​​can analyze that data to understand the user's asset status.

[0035] The data management unit can manage past anniversary data. For example, the data management unit manages the type of past anniversary, the date, and related events. By managing past anniversary data, the data management unit can make users' anniversary management more efficient. Some or all of the above processing in the data management unit may be performed using AI, for example, or not using AI. For example, the data management unit can input past anniversary data into AI, and the AI ​​can analyze and manage that data.

[0036] The timing setting unit can set the timing of reminders. For example, the timing of notifications and the frequency of notifications can be set. By setting the timing of reminders, the timing setting unit allows users to prepare in advance so that they do not forget anniversaries. Some or all of the above-described processes in the timing setting unit may be performed using AI, for example, or not using AI. For example, the timing setting unit can input the reminder timing into the AI, and the AI ​​can analyze that data to set the optimal timing.

[0037] The suggestion unit can make suggestions based on at least one of the user's asset status and past anniversary data. For example, the suggestion unit can make economically feasible suggestions based on the user's asset status. The suggestion unit can also make suggestions that the user has liked in the past based on past anniversary data. By making suggestions based on the user's asset status and past anniversary data, the suggestion unit can make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's asset status and past anniversary data into AI, and the AI ​​can analyze that data and make suggestions.

[0038] The arrangement unit can make restaurant reservations and order gifts based on user instructions. For example, if a user requests a reservation at an Italian restaurant and instructs that a dessert plate with a message and a pearl necklace be delivered simultaneously, the arrangement unit will make the necessary arrangements. By making restaurant reservations and ordering gifts based on user instructions, the arrangement unit can reduce the burden on the user. Some or all of the above-described processes in the arrangement unit may be performed using AI, for example, or not. For example, the arrangement unit can input user instructions into an AI, which can then make arrangements based on those instructions.

[0039] The management department can select the optimal management method by referring to the user's past anniversary data when registering anniversaries. For example, the management department can prioritize managing anniversaries that the user frequently forgot in the past. The management department can also specially manage anniversaries that the user considered important in the past. The management department can also find specific patterns from the user's past anniversary data and propose the optimal management method. This improves the accuracy of anniversary management by selecting the optimal management method by referring to the user's past anniversary data. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's past anniversary data into AI, and the AI ​​can analyze that data to select the optimal management method.

[0040] The management unit can filter anniversaries based on the user's lifestyle and areas of interest. For example, during busy periods, the management unit will only notify users of high-priority anniversaries. The management unit can also prioritize the management of relevant anniversaries based on the user's areas of interest. The management unit can also adjust the frequency of anniversary notifications according to the user's lifestyle. This allows for more appropriate anniversary management by filtering based on the user's lifestyle and areas of interest. Some or all of the above processes in the management unit may be performed using AI, or not. For example, the management unit can input data on the user's lifestyle and areas of interest into an AI, which can then analyze and filter that data.

[0041] The reminder unit can adjust the level of detail in reminders based on the importance of the anniversary. For example, the reminder unit sends a detailed reminder message for highly important anniversaries. For less important anniversaries, it can send a concise reminder message. The reminder unit can also adjust the content of the reminder message according to the importance of the anniversary. This allows for more appropriate reminders by adjusting the level of detail based on the importance of the anniversary. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input anniversary importance data into AI, and the AI ​​can analyze that data to adjust the level of detail in the reminders.

[0042] The reminder unit can apply different reminder algorithms depending on the category of the anniversary. For example, the reminder unit can send a special reminder message on birthdays. It can also send a romantic reminder message on wedding anniversaries. It can also send an appropriate reminder message on other anniversaries. By applying different reminder algorithms depending on the category of the anniversary, more appropriate reminders can be provided. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input anniversary category data into an AI, which can then analyze the data and apply different reminder algorithms.

[0043] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the anniversary. For example, it can send a detailed suggestion message for highly important anniversaries, and a concise suggestion message for less important anniversaries. The suggestion unit can also adjust the content of the suggestion message according to the importance of the anniversary. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the anniversary. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input anniversary importance data into AI, which can then analyze the data and adjust the level of detail in the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the anniversary when making a suggestion. For example, the suggestion unit can send a special suggestion message on a birthday. It can also send a romantic suggestion message on a wedding anniversary. It can also send an appropriate suggestion message on other anniversaries. By applying different suggestion algorithms depending on the category of the anniversary, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input anniversary category data into an AI, which can then analyze the data and apply different suggestion algorithms.

[0045] The arrangement unit can select the optimal arrangement method by referring to the user's past arrangement history during the arrangement process. For example, the arrangement unit can propose the optimal arrangement method based on the user's past arrangements. The arrangement unit can also prioritize suggesting successful arrangement methods based on the user's past arrangement history. The arrangement unit can also analyze the user's past arrangement history and propose the most effective arrangement method. This improves the accuracy of the arrangement by selecting the optimal arrangement method by referring to the user's past arrangement history. Some or all of the above processes in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's past arrangement history data into AI, which can then analyze the data to select the optimal arrangement method.

[0046] The arrangement unit can customize the arrangement methods based on the user's current lifestyle. For example, if the user is busy, the arrangement unit can suggest an easy-to-implement arrangement method. If the user is relaxed, the arrangement unit can also suggest a more detailed arrangement method. The arrangement unit can also suggest the most suitable arrangement method depending on the user's lifestyle. This allows for more appropriate arrangements by customizing the arrangement methods based on the user's current lifestyle. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input user lifestyle data into AI, which can then analyze the data to customize the arrangement methods.

[0047] The asset tracking unit can select the optimal method of tracking an asset status by referring to the user's past asset data. For example, the asset tracking unit can propose the optimal method based on the methods the user has used to track their asset status in the past. The asset tracking unit can also prioritize and propose successful methods based on the user's past asset data. The asset tracking unit can also analyze the user's past asset data and propose the most effective method. This improves the accuracy of asset status tracking by selecting the optimal method by referring to the user's past asset data. Some or all of the above processes in the asset tracking unit may be performed using AI, for example, or without AI. For example, the asset tracking unit can input the user's past asset data into AI, which can then analyze the data to select the optimal method of tracking.

[0048] The asset tracking unit can weight asset data based on the user's lifestyle when assessing their asset status. For example, if the user is busy, the asset tracking unit will prioritize identifying important asset data. If the user is relaxed, the asset tracking unit can also identify all asset data. The asset tracking unit can also adjust the weighting of asset data according to the user's lifestyle. This allows for a more accurate assessment of the user's asset status by weighting asset data based on the user's lifestyle. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or without AI. For example, the asset tracking unit can input user lifestyle data into AI, which can then analyze that data and weight the asset data.

[0049] The data management department can select the optimal management method by referring to the user's past data history during data management. For example, the data management department can propose the optimal method based on the data management methods the user has used in the past. The data management department can also prioritize and propose successful management methods based on the user's past data history. The data management department can also analyze the user's past data history and propose the most effective management method. This improves the accuracy of data management by selecting the optimal management method by referring to the user's past data history. Some or all of the above processes in the data management department may be performed using AI, for example, or not using AI. For example, the data management department can input the user's past data history into AI, and the AI ​​can analyze that data to select the optimal management method.

[0050] The data management unit can weight data based on the user's lifestyle when managing data. For example, if the user is busy, the data management unit will prioritize managing important data. If the user is relaxed, the data management unit can manage all data. The data management unit can also adjust the weighting of data according to the user's lifestyle. This allows for more appropriate data management by weighting data based on the user's lifestyle. Some or all of the above processes in the data management unit may be performed using AI, for example, or not using AI. For example, the data management unit can input user lifestyle data into AI, which can then analyze the data and weight it.

[0051] The timing setting unit can select the optimal timing when setting reminder timing by referring to the user's past reminder history. For example, the timing setting unit can suggest the optimal timing based on the user's past reminder timings. The timing setting unit can also prioritize suggesting successful timings from the user's past reminder history. The timing setting unit can also analyze the user's past reminder history and suggest the most effective timing. This improves the accuracy of reminders by selecting the optimal timing by referring to the user's past reminder history. Some or all of the above processing in the timing setting unit may be performed using AI, for example, or without AI. For example, the timing setting unit can input the user's past reminder history data into AI, which can then analyze the data to select the optimal timing.

[0052] The timing setting unit can customize the timing of reminders based on the user's lifestyle when setting the reminder timing. For example, if the user is busy, the timing setting unit will send a reminder at the optimal time. If the user is relaxed, the timing setting unit can also send a reminder earlier. The timing setting unit can also adjust the timing of reminders according to the user's lifestyle. This allows for more appropriate reminders by customizing the timing of reminders based on the user's lifestyle. Some or all of the above processing in the timing setting unit may be performed using AI, for example, or without using AI. For example, the timing setting unit can input user lifestyle data into AI, and the AI ​​can analyze that data to customize the timing of reminders.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The suggestion department can customize suggestions based on the user's hobbies and preferences. For example, if the user is a music lover, it can suggest concert tickets or music-related gifts. If the user enjoys the outdoors, it can suggest camping equipment or outdoor activities. Furthermore, if the user enjoys cooking, it can suggest cooking class reservations or high-quality ingredients. This allows for a more personalized service by providing suggestions based on the user's hobbies and preferences.

[0055] The reminder function can analyze a user's past reminder responses and select the most suitable reminder method. For example, if a user has responded well to email reminders in the past, email reminders will be prioritized. Similarly, if a user has responded well to push notifications in the past, push notifications can be prioritized. Furthermore, if a user has responded well to reminders in their calendar app in the past, reminders in the calendar app can be prioritized. This allows the system to maximize the effectiveness of reminders by selecting the most suitable method based on the user's past reminder responses.

[0056] The arrangement function can adjust the arrangement based on the user's current health condition. For example, if the user is feeling unwell, it will suggest arrangements that are within a reasonable range. If the user is in good health, it can also suggest active events or activities. Furthermore, if the user has specific health constraints, the arrangement can be made with those constraints in mind. In this way, by making arrangements based on the user's health condition, a more appropriate service can be provided.

[0057] The suggestion department can analyze users' social media activity and customize suggestions. For example, it can make suggestions based on topics users have recently talked about on social media or events they are interested in. It can also suggest relevant products and services based on brands and influencers users follow on social media. Furthermore, it can make similar suggestions based on past anniversaries and events users have shared on social media. This allows for a more personalized service by providing suggestions based on users' social media activity.

[0058] The reminder function can adjust reminder content based on the user's geographical location. For example, if the user is traveling, it can send reminders for their travel destination. It can also send reminders for events and activities related to a specific location if the user is there. Furthermore, if the user is at home, it can suggest activities they can do at home. This allows for more relevant reminders by providing them based on the user's geographical location.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The administration department manages anniversaries. When a user registers an anniversary, the administration department manages the dates, allowing users to register important dates such as birthdays and wedding anniversaries. Step 2: The reminder unit will send reminders based on anniversaries managed by the management unit. Reminders will be sent at a specified time (for example, one month in advance) so that users do not forget the anniversary and can prepare in advance. Step 3: The proposal team makes suggestions based on the anniversaries reminded by the reminder team. They propose events and gifts appropriate for various occasions, and provide specific suggestions such as restaurant lists and gift options. For example, depending on the financial situation, they might suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. Step 4: The arrangement department makes arrangements based on the proposals made by the proposal department. They make arrangements such as restaurant reservations and gift orders, and when the user confirms the proposals and gives specific instructions, they make the arrangements based on those instructions. For example, if the user makes a reservation at an Italian restaurant and instructs that a dessert plate with a message and a pearl necklace be provided at the same time, the arrangement department will make those arrangements.

[0061] (Example of form 2) The AI ​​agent for generating anniversaries according to an embodiment of the present invention is a system that manages anniversaries for family, lovers, and friends, and provides reminders and makes arrangements. In this system, when a user registers an anniversary, the generating AI agent manages the date. For example, important dates such as birthdays and wedding anniversaries can be registered. Next, a reminder is sent at a specified time (for example, one month in advance). This allows the user to prepare in advance without forgetting the anniversary. Furthermore, the generating AI agent makes suggestions for events and gifts according to various occasions. For example, for a wedding anniversary, it may suggest a restaurant meal, a message plate, and a gift. Specific suggestions include presenting a list of restaurants and gift options. For example, depending on the user's financial situation, it may suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. The generating AI agent also makes arrangements such as restaurant reservations and gift orders. When the user confirms the suggestions and gives specific instructions, the generating AI agent makes the arrangements based on those instructions. For example, if the user reserves an Italian restaurant and instructs them to provide a dessert plate with a message and a pearl necklace at the same time, the generating AI agent will make the arrangements. In this way, the AI ​​agent that automatically generates anniversaries reduces the burden on users and makes anniversaries more special by handling everything from managing and reminding users to suggesting and arranging them. This allows the AI ​​agent to efficiently manage users' anniversaries and plan special events.

[0062] The AI ​​agent for automatically generating anniversaries according to this embodiment comprises a management unit, a reminder unit, a suggestion unit, and an arrangement unit. The management unit manages anniversaries. For example, when a user registers an anniversary, the management unit manages its date. The management unit can register important dates such as birthdays and wedding anniversaries. The reminder unit makes reminders based on the anniversaries managed by the management unit. For example, the reminder unit makes reminders at a specified time (for example, one month in advance). The reminder unit ensures that users do not forget anniversaries and can prepare in advance. The suggestion unit makes suggestions based on the anniversaries reminded by the reminder unit. For example, the suggestion unit makes suggestions for events and gifts appropriate for various occasions. For wedding anniversaries, the suggestion unit makes suggestions for restaurant meals, message plates, and gifts. As specific suggestions, the suggestion unit presents a list of restaurants and gift options. For example, depending on the financial situation, the suggestion unit can suggest pearl jewelry, which is the wife's birthstone, instead of a Sweet Ten diamond. The arrangement unit makes arrangements based on the suggestions made by the suggestion unit. The arrangement unit handles arrangements such as making restaurant reservations or ordering gifts. The arrangement unit confirms the user's suggestions and, once specific instructions are given, makes arrangements based on those instructions. For example, if the user requests an Italian restaurant reservation and instructs the system to provide a dessert plate with a message and a pearl necklace simultaneously, the arrangement unit will make those arrangements. In this way, the anniversary-generating AI agent according to this embodiment reduces the user's burden and makes the anniversary more special by handling everything from anniversary management and reminders to suggestions and arrangements in one go.

[0063] The management department manages anniversaries. For example, when a user registers an anniversary, the management department manages its dates. Important dates such as birthdays and wedding anniversaries can be registered. Specifically, when a user enters an anniversary through an application or web interface, that information is stored in the database. The management department displays this anniversary information in a calendar format so that users can see it at a glance. The management department also categorizes anniversaries according to their type, allowing for management by different categories, such as family birthdays, wedding anniversaries, and friends' birthdays. Furthermore, the management department provides a function that allows users to add specific notes and tags when registering an anniversary. This makes it easy for users to record special information and memories related to the anniversary. For example, they can leave notes such as the name of the restaurant they first visited on their wedding anniversary or details of the gift they gave. The management department securely stores this information and provides it to users as needed. In addition, the management department has a mechanism in place to prevent data loss by regularly backing up data so that users do not forget anniversaries. In this way, the management department reliably manages users' important anniversaries and makes them accessible at any time.

[0064] The reminder unit sends reminders based on anniversaries managed by the management unit. The reminder unit sends reminders at specified times (e.g., one month in advance). The reminder unit ensures users don't forget anniversaries and can prepare in advance. Specifically, the reminder unit sends notifications based on the reminder timing set by the user. Notifications are sent via multiple methods, including smartphone push notifications, email, and SMS. Users can freely choose their notification method and receive reminders in the way that suits them best. The reminder unit also provides a function to customize notification content. For example, specific messages or images can be attached to reminder messages. This allows users to immediately recall special memories or information related to the anniversary when they receive a reminder notification. Furthermore, the reminder unit has a function to track user actions after receiving a reminder notification. For example, it records whether the user viewed the reminder notification and what action was taken in response. This allows the reminder unit to understand whether users are making appropriate preparations for the anniversary and send additional reminders as needed. In this way, the reminder unit supports users in preparing in advance and ensuring they don't forget anniversaries.

[0065] The suggestion department makes suggestions based on anniversaries reminded by the reminder department. For example, the suggestion department suggests events and gifts tailored to various occasions. For wedding anniversaries, the suggestion department suggests restaurant meals, message plates, and gifts. As specific suggestions, the suggestion department presents a list of restaurants and gift options. For example, depending on the user's financial situation, the suggestion department might suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. Specifically, the suggestion department uses AI to analyze the user's past behavior history and preferences to make optimal suggestions. For example, based on data of restaurants the user has visited and gifts they have purchased in the past, it suggests restaurants and gifts that match the user's preferences. The suggestion department also considers the user's budget and current trends to make optimal suggestions. For example, if the user sets a budget, it suggests the best restaurants and gifts within that budget. Furthermore, the suggestion department provides a function to visually display the suggested content so that the user can easily review it. For example, it displays restaurant photos and menus, and gift images and detailed information so that the user can easily compare and consider options. The suggestion department also has a function that allows users to provide feedback on the suggested content. This allows the proposal department to continuously improve its proposals based on user feedback, enabling them to offer more satisfying suggestions. As a result, the proposal department can provide optimal suggestions to help users make their anniversaries even more special.

[0066] The arrangement department makes arrangements based on the proposals made by the proposal department. For example, the arrangement department makes arrangements such as restaurant reservations and gift orders. Once the user confirms the proposal and gives specific instructions, the arrangement department makes arrangements based on those instructions. For example, if the user requests that an Italian restaurant be reserved and that a dessert plate with a message and a pearl necklace be delivered at the same time, the arrangement department will make those arrangements. Specifically, the arrangement department makes reservations and orders for the restaurant and gifts selected by the user based on the information provided by the proposal department. The arrangement department integrates with restaurant reservation systems and online shopping sites to allow users to easily make reservations and orders. The arrangement department also provides a function that allows users to check the status of reservations and orders in real time. For example, it sends a notification when a reservation is confirmed, so that users can celebrate their special day with peace of mind. Furthermore, the arrangement department also responds to special requests from users. For example, it makes arrangements according to the user's requests, such as requests for special seats at restaurants or adding a message card to a gift. The arrangement department also provides confirmations and reminders in advance so that users can enjoy their special day smoothly. This allows the arrangement department to efficiently and reliably make arrangements to make the anniversary even more special for the user.

[0067] The asset tracking unit can understand the user's asset status. For example, the asset tracking unit can understand the user's bank account balance, real estate value, investment status, etc. By understanding the user's asset status, the asset tracking unit can tailor its recommendations to the user's financial situation. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or not using AI. For example, the asset tracking unit can input the user's bank account balance into the AI, and the AI ​​can analyze that data to understand the user's asset status.

[0068] The data management unit can manage past anniversary data. For example, the data management unit manages the type of past anniversary, the date, and related events. By managing past anniversary data, the data management unit can make users' anniversary management more efficient. Some or all of the above processing in the data management unit may be performed using AI, for example, or not using AI. For example, the data management unit can input past anniversary data into AI, and the AI ​​can analyze and manage that data.

[0069] The timing setting unit can set the timing of reminders. For example, the timing of notifications and the frequency of notifications can be set. By setting the timing of reminders, the timing setting unit allows users to prepare in advance so that they do not forget anniversaries. Some or all of the above-described processes in the timing setting unit may be performed using AI, for example, or not using AI. For example, the timing setting unit can input the reminder timing into the AI, and the AI ​​can analyze that data to set the optimal timing.

[0070] The suggestion unit can make suggestions based on at least one of the user's asset status and past anniversary data. For example, the suggestion unit can make economically feasible suggestions based on the user's asset status. The suggestion unit can also make suggestions that the user has liked in the past based on past anniversary data. By making suggestions based on the user's asset status and past anniversary data, the suggestion unit can make more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's asset status and past anniversary data into AI, and the AI ​​can analyze that data and make suggestions.

[0071] The arrangement unit can make restaurant reservations and order gifts based on user instructions. For example, if a user requests a reservation at an Italian restaurant and instructs that a dessert plate with a message and a pearl necklace be delivered simultaneously, the arrangement unit will make the necessary arrangements. By making restaurant reservations and ordering gifts based on user instructions, the arrangement unit can reduce the burden on the user. Some or all of the above-described processes in the arrangement unit may be performed using AI, for example, or not. For example, the arrangement unit can input user instructions into an AI, which can then make arrangements based on those instructions.

[0072] The management unit can estimate the user's emotions and adjust the importance of anniversaries based on the estimated emotions. For example, if the user is stressed, the management unit may refrain from notifying users of less important anniversaries. If the user is relaxed, the management unit may notify users of all anniversaries. If the user is busy, the management unit may notify users of only important anniversaries. This allows for more appropriate notifications by adjusting the importance of anniversaries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the importance of anniversaries.

[0073] The management department can select the optimal management method by referring to the user's past anniversary data when registering anniversaries. For example, the management department can prioritize managing anniversaries that the user frequently forgot in the past. The management department can also specially manage anniversaries that the user considered important in the past. The management department can also find specific patterns from the user's past anniversary data and propose the optimal management method. This improves the accuracy of anniversary management by selecting the optimal management method by referring to the user's past anniversary data. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's past anniversary data into AI, and the AI ​​can analyze that data to select the optimal management method.

[0074] The management unit can filter anniversaries based on the user's lifestyle and areas of interest. For example, during busy periods, the management unit will only notify users of high-priority anniversaries. The management unit can also prioritize the management of relevant anniversaries based on the user's areas of interest. The management unit can also adjust the frequency of anniversary notifications according to the user's lifestyle. This allows for more appropriate anniversary management by filtering based on the user's lifestyle and areas of interest. Some or all of the above processes in the management unit may be performed using AI, or not. For example, the management unit can input data on the user's lifestyle and areas of interest into an AI, which can then analyze and filter that data.

[0075] The reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on the estimated emotions. For example, if the user is stressed, the reminder unit can send a simple reminder message. If the user is relaxed, the reminder unit can also send a detailed reminder message. If the user is busy, the reminder unit can also send a concise reminder message. This allows for more appropriate reminders by adjusting the way the reminder is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into the generative AI, which can analyze the data to estimate emotions and adjust the way the reminder is expressed.

[0076] The reminder unit can adjust the level of detail in reminders based on the importance of the anniversary. For example, the reminder unit sends a detailed reminder message for highly important anniversaries. For less important anniversaries, it can send a concise reminder message. The reminder unit can also adjust the content of the reminder message according to the importance of the anniversary. This allows for more appropriate reminders by adjusting the level of detail based on the importance of the anniversary. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input anniversary importance data into AI, and the AI ​​can analyze that data to adjust the level of detail in the reminders.

[0077] The reminder unit can apply different reminder algorithms depending on the category of the anniversary. For example, the reminder unit can send a special reminder message on birthdays. It can also send a romantic reminder message on wedding anniversaries. It can also send an appropriate reminder message on other anniversaries. By applying different reminder algorithms depending on the category of the anniversary, more appropriate reminders can be provided. Some or all of the above processing in the reminder unit may be performed using AI, for example, or without AI. For example, the reminder unit can input anniversary category data into an AI, which can then analyze the data and apply different reminder algorithms.

[0078] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can send a simple suggestion message. If the user is relaxed, the suggestion unit can also send a detailed suggestion message. If the user is busy, the suggestion unit can send a concise suggestion message. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the way suggestions are presented.

[0079] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the anniversary. For example, it can send a detailed suggestion message for highly important anniversaries, and a concise suggestion message for less important anniversaries. The suggestion unit can also adjust the content of the suggestion message according to the importance of the anniversary. This allows for more appropriate suggestions by adjusting the level of detail based on the importance of the anniversary. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input anniversary importance data into AI, which can then analyze the data and adjust the level of detail in the suggestions.

[0080] The suggestion unit can apply different suggestion algorithms depending on the category of the anniversary when making a suggestion. For example, the suggestion unit can send a special suggestion message on a birthday. It can also send a romantic suggestion message on a wedding anniversary. It can also send an appropriate suggestion message on other anniversaries. By applying different suggestion algorithms depending on the category of the anniversary, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input anniversary category data into an AI, which can then analyze the data and apply different suggestion algorithms.

[0081] The arrangement unit can estimate the user's emotions and adjust the arrangement method based on the estimated emotions. For example, if the user is stressed, the arrangement unit can suggest a simple arrangement method. If the user is relaxed, the arrangement unit can also suggest a detailed arrangement method. If the user is busy, the arrangement unit can suggest a concise arrangement method. By adjusting the arrangement method based on the user's emotions, a more appropriate arrangement becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the arrangement unit may be performed using AI, or not using AI. For example, the arrangement unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the arrangement method.

[0082] The arrangement unit can select the optimal arrangement method by referring to the user's past arrangement history during the arrangement process. For example, the arrangement unit can propose the optimal arrangement method based on the user's past arrangements. The arrangement unit can also prioritize suggesting successful arrangement methods based on the user's past arrangement history. The arrangement unit can also analyze the user's past arrangement history and propose the most effective arrangement method. This improves the accuracy of the arrangement by selecting the optimal arrangement method by referring to the user's past arrangement history. Some or all of the above processes in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input the user's past arrangement history data into AI, which can then analyze the data to select the optimal arrangement method.

[0083] The arrangement unit can customize the arrangement methods based on the user's current lifestyle. For example, if the user is busy, the arrangement unit can suggest an easy-to-implement arrangement method. If the user is relaxed, the arrangement unit can also suggest a more detailed arrangement method. The arrangement unit can also suggest the most suitable arrangement method depending on the user's lifestyle. This allows for more appropriate arrangements by customizing the arrangement methods based on the user's current lifestyle. Some or all of the above processing in the arrangement unit may be performed using AI, for example, or without AI. For example, the arrangement unit can input user lifestyle data into AI, which can then analyze the data to customize the arrangement methods.

[0084] The asset tracking unit can estimate the user's emotions and adjust the method of understanding the asset situation based on the estimated emotions. For example, if the user is stressed, the asset tracking unit can suggest a concise method of understanding the asset situation. If the user is relaxed, the asset tracking unit can also suggest a detailed method of understanding the asset situation. If the user is busy, the asset tracking unit can also suggest a concise method of understanding the asset situation. By adjusting the method of understanding the asset situation based on the user's emotions, a more appropriate understanding of the asset situation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or without AI. For example, the asset tracking unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the method of understanding the asset situation.

[0085] The asset tracking unit can select the optimal method of tracking an asset status by referring to the user's past asset data. For example, the asset tracking unit can propose the optimal method based on the methods the user has used to track their asset status in the past. The asset tracking unit can also prioritize and propose successful methods based on the user's past asset data. The asset tracking unit can also analyze the user's past asset data and propose the most effective method. This improves the accuracy of asset status tracking by selecting the optimal method by referring to the user's past asset data. Some or all of the above processes in the asset tracking unit may be performed using AI, for example, or without AI. For example, the asset tracking unit can input the user's past asset data into AI, which can then analyze the data to select the optimal method of tracking.

[0086] The asset tracking unit can estimate the user's emotions and adjust the frequency of asset status monitoring based on the estimated emotions. For example, if the user is stressed, the asset tracking unit can reduce the frequency of asset status monitoring. If the user is relaxed, the asset tracking unit can increase the frequency of asset status monitoring. If the user is busy, the asset tracking unit can optimize the frequency of asset status monitoring. This allows for more appropriate asset status monitoring by adjusting the frequency of asset status monitoring based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the asset tracking unit may be performed using AI or not using AI. For example, the asset tracking unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the frequency of asset status monitoring.

[0087] The asset tracking unit can weight asset data based on the user's lifestyle when assessing their asset status. For example, if the user is busy, the asset tracking unit will prioritize identifying important asset data. If the user is relaxed, the asset tracking unit can also identify all asset data. The asset tracking unit can also adjust the weighting of asset data according to the user's lifestyle. This allows for a more accurate assessment of the user's asset status by weighting asset data based on the user's lifestyle. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or without AI. For example, the asset tracking unit can input user lifestyle data into AI, which can then analyze that data and weight the asset data.

[0088] The data management unit can estimate the user's emotions and adjust the data management method based on the estimated emotions. For example, if the user is stressed, the data management unit can suggest a concise data management method. If the user is relaxed, the data management unit can also suggest a detailed data management method. If the user is busy, the data management unit can also suggest a concise data management method. This allows for more appropriate data management by adjusting the data management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data management unit may be performed using AI, or not using AI. For example, the data management unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the data management method.

[0089] The data management department can select the optimal management method by referring to the user's past data history during data management. For example, the data management department can propose the optimal method based on the data management methods the user has used in the past. The data management department can also prioritize and propose successful management methods based on the user's past data history. The data management department can also analyze the user's past data history and propose the most effective management method. This improves the accuracy of data management by selecting the optimal management method by referring to the user's past data history. Some or all of the above processes in the data management department may be performed using AI, for example, or not using AI. For example, the data management department can input the user's past data history into AI, and the AI ​​can analyze that data to select the optimal management method.

[0090] The data management unit can estimate the user's emotions and determine data management priorities based on those estimated emotions. For example, if the user is stressed, the data management unit may refrain from managing low-priority data. If the user is relaxed, the data management unit may manage all data. If the user is busy, the data management unit may manage only high-priority data. This allows for more appropriate data management by determining data management priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data management unit may be performed using AI or not. For example, the data management unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and determine data management priorities.

[0091] The data management unit can weight data based on the user's lifestyle when managing data. For example, if the user is busy, the data management unit will prioritize managing important data. If the user is relaxed, the data management unit can manage all data. The data management unit can also adjust the weighting of data according to the user's lifestyle. This allows for more appropriate data management by weighting data based on the user's lifestyle. Some or all of the above processes in the data management unit may be performed using AI, for example, or not using AI. For example, the data management unit can input user lifestyle data into AI, which can then analyze the data and weight it.

[0092] The timing setting unit can estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. For example, if the user is stressed, the timing setting unit can delay the reminder timing. If the user is relaxed, the timing setting unit can also advance the reminder timing. If the user is busy, the timing setting unit can also optimize the reminder timing. This allows for more appropriate reminders by adjusting the timing of reminders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the timing setting unit may be performed using AI, or not using AI. For example, the timing setting unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the timing of reminders.

[0093] The timing setting unit can select the optimal timing when setting reminder timing by referring to the user's past reminder history. For example, the timing setting unit can suggest the optimal timing based on the user's past reminder timings. The timing setting unit can also prioritize suggesting successful timings from the user's past reminder history. The timing setting unit can also analyze the user's past reminder history and suggest the most effective timing. This improves the accuracy of reminders by selecting the optimal timing by referring to the user's past reminder history. Some or all of the above processing in the timing setting unit may be performed using AI, for example, or without AI. For example, the timing setting unit can input the user's past reminder history data into AI, which can then analyze the data to select the optimal timing.

[0094] The timing setting unit can estimate the user's emotions and adjust the reminder frequency based on the estimated emotions. For example, if the user is stressed, the timing setting unit can reduce the reminder frequency. If the user is relaxed, the timing setting unit can also increase the reminder frequency. If the user is busy, the timing setting unit can also optimize the reminder frequency. This allows for more appropriate reminders by adjusting the reminder frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the timing setting unit may be performed using AI or not using AI. For example, the timing setting unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the reminder frequency.

[0095] The timing setting unit can customize the timing of reminders based on the user's lifestyle when setting the reminder timing. For example, if the user is busy, the timing setting unit will send a reminder at the optimal time. If the user is relaxed, the timing setting unit can also send a reminder earlier. The timing setting unit can also adjust the timing of reminders according to the user's lifestyle. This allows for more appropriate reminders by customizing the timing of reminders based on the user's lifestyle. Some or all of the above processing in the timing setting unit may be performed using AI, for example, or without using AI. For example, the timing setting unit can input user lifestyle data into AI, and the AI ​​can analyze that data to customize the timing of reminders.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The suggestion department can customize suggestions based on the user's hobbies and preferences. For example, if the user is a music lover, it can suggest concert tickets or music-related gifts. If the user enjoys the outdoors, it can suggest camping equipment or outdoor activities. Furthermore, if the user enjoys cooking, it can suggest cooking class reservations or high-quality ingredients. This allows for a more personalized service by providing suggestions based on the user's hobbies and preferences.

[0098] The reminder function can analyze a user's past reminder responses and select the most suitable reminder method. For example, if a user has responded well to email reminders in the past, email reminders will be prioritized. Similarly, if a user has responded well to push notifications in the past, push notifications can be prioritized. Furthermore, if a user has responded well to reminders in their calendar app in the past, reminders in the calendar app can be prioritized. This allows the system to maximize the effectiveness of reminders by selecting the most suitable method based on the user's past reminder responses.

[0099] The arrangement function can adjust the arrangement based on the user's current health condition. For example, if the user is feeling unwell, it will suggest arrangements that are within a reasonable range. If the user is in good health, it can also suggest active events or activities. Furthermore, if the user has specific health constraints, the arrangement can be made with those constraints in mind. In this way, by making arrangements based on the user's health condition, a more appropriate service can be provided.

[0100] The suggestion department can analyze users' social media activity and customize suggestions. For example, it can make suggestions based on topics users have recently talked about on social media or events they are interested in. It can also suggest relevant products and services based on brands and influencers users follow on social media. Furthermore, it can make similar suggestions based on past anniversaries and events users have shared on social media. This allows for a more personalized service by providing suggestions based on users' social media activity.

[0101] The reminder function can adjust reminder content based on the user's geographical location. For example, if the user is traveling, it can send reminders for their travel destination. It can also send reminders for events and activities related to a specific location if the user is there. Furthermore, if the user is at home, it can suggest activities they can do at home. This allows for more relevant reminders by providing them based on the user's geographical location.

[0102] The management unit can estimate the user's emotions and adjust the importance of anniversaries based on those emotions. For example, if the user is stressed, notifications for less important anniversaries may be withheld. If the user is relaxed, notifications for all anniversaries may be sent. If the user is busy, notifications for only important anniversaries may be sent. This allows for more appropriate notifications by adjusting the importance of anniversaries based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the importance of anniversaries.

[0103] The reminder unit can estimate the user's emotions and adjust the way the reminder is expressed based on those emotions. For example, if the user is stressed, a simple reminder message can be sent. If the user is relaxed, a more detailed reminder message can be sent. If the user is busy, a concise reminder message can be sent. By adjusting the way the reminder is expressed based on the user's emotions, more appropriate reminders can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the way the reminder is expressed.

[0104] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, a simple suggestion message can be sent. If the user is relaxed, a detailed suggestion message can be sent. If the user is busy, a concise suggestion message can be sent. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the way suggestions are presented.

[0105] The arrangement unit can estimate the user's emotions and adjust the arrangement method based on the estimated emotions. For example, if the user is stressed, it can suggest a simple arrangement method. If the user is relaxed, it can suggest a detailed arrangement method. If the user is busy, it can suggest a concise arrangement method. By adjusting the arrangement method based on the user's emotions, a more appropriate arrangement becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the arrangement unit may be performed using AI, or not using AI. For example, the arrangement unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the arrangement method.

[0106] The asset tracking unit can estimate the user's emotions and adjust the method of understanding the user's asset status based on the estimated emotions. For example, if the user is stressed, it can suggest a concise method of understanding the user's asset status. If the user is relaxed, it can suggest a detailed method of understanding the user's asset status. If the user is busy, it can suggest a concise method of understanding the user's asset status. By adjusting the method of understanding the user's asset status based on their emotions, a more appropriate understanding of the user's asset status becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the asset tracking unit may be performed using AI, for example, or not using AI. For example, the asset tracking unit can input user emotion data into a generative AI, which can analyze the data to estimate emotions and adjust the method of understanding the user's asset status.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The administration department manages anniversaries. When a user registers an anniversary, the administration department manages the dates, allowing users to register important dates such as birthdays and wedding anniversaries. Step 2: The reminder unit will send reminders based on anniversaries managed by the management unit. Reminders will be sent at a specified time (for example, one month in advance) so that users do not forget the anniversary and can prepare in advance. Step 3: The proposal team makes suggestions based on the anniversaries reminded by the reminder team. They propose events and gifts appropriate for various occasions, and provide specific suggestions such as restaurant lists and gift options. For example, depending on the financial situation, they might suggest pearl jewelry, the wife's birthstone, instead of a Sweet Ten diamond. Step 4: The arrangement department makes arrangements based on the proposals made by the proposal department. They make arrangements such as restaurant reservations and gift orders, and when the user confirms the proposals and gives specific instructions, they make the arrangements based on those instructions. For example, if the user makes a reservation at an Italian restaurant and instructs that a dessert plate with a message and a pearl necklace be provided at the same time, the arrangement department will make those arrangements.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the management unit, reminder unit, proposal unit, arrangement unit, asset tracking unit, data management unit, and timing setting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14 and manages the date of an anniversary when the user registers it. The reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a reminder at a specified time. The proposal unit is implemented by, for example, the control unit 46A of the smart device 14 and makes suggestions for events and gifts according to various occasions. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes arrangements such as making restaurant reservations and ordering gifts. The asset tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the user's asset status. The data management unit is implemented by, for example, the control unit 46A of the smart device 14 and manages past anniversary data. The timing setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which sets the reminder timing. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements mentioned above, including the management unit, reminder unit, proposal unit, arrangement unit, asset tracking unit, data management unit, and timing setting unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214 and manages the dates of anniversaries when the user registers them. The reminder unit is implemented by the specific processing unit 290 of the data processing unit 12 and sends reminders at specified times. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and makes suggestions for events and gifts according to various occasions. The arrangement unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes arrangements such as making restaurant reservations and ordering gifts. The asset tracking unit is implemented by the specific processing unit 290 of the data processing unit 12 and grasps the user's asset status. The data management unit is implemented by the control unit 46A of the smart glasses 214 and manages past anniversary data. The timing setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which sets the reminder timing. 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.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the management unit, reminder unit, proposal unit, arrangement unit, asset tracking unit, data management unit, and timing setting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314 and manages the date of an anniversary when the user registers it. The reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a reminder at a specified time. The proposal unit is implemented by, for example, the control unit 46A of the headset terminal 314 and makes suggestions for events and gifts according to various occasions. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes arrangements such as restaurant reservations and gift orders. The asset tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the user's asset status. The data management unit is implemented by, for example, the control unit 46A of the headset terminal 314 and manages past anniversary data. The timing setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which sets the reminder timing. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the management unit, reminder unit, proposal unit, arrangement unit, asset tracking unit, data management unit, and timing setting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414 and manages the date of an anniversary when the user registers it. The reminder unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sends a reminder at a specified time. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and makes suggestions for events and gifts according to various occasions. The arrangement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes arrangements such as making restaurant reservations and ordering gifts. The asset tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and grasps the user's asset status. The data management unit is implemented by, for example, the control unit 46A of the robot 414 and manages past anniversary data. The timing setting unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which sets the reminder timing. 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The management department that manages anniversaries, A reminder unit that sends reminders based on anniversaries managed by the aforementioned management unit, A proposal unit makes a proposal based on the anniversary reminded by the aforementioned reminder unit, The system includes an arrangement unit that performs arrangements based on the content proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) It includes an asset tracking unit to understand the user's asset status. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a data management department that manages past anniversary data. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a timing setting unit for setting the reminder timing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system according to Appendix 1, characterized in that the proposal unit makes a proposal based on at least one of the user's asset status and past anniversary data. (Note 6) The aforementioned arrangement section is, Based on user instructions, the system makes restaurant reservations and orders gifts. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, It estimates the user's emotions and adjusts the importance of anniversaries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The system described in Appendix 1, characterized in that the management unit selects an appropriate management method by referring to the user's past anniversary data when registering an anniversary. (Note 9) The aforementioned management department, When managing anniversaries, filtering is performed based on the user's lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reminder unit, It estimates the user's emotions and adjusts the way reminders are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reminder unit, When sending a reminder, adjust the level of detail based on the importance of the anniversary. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reminder unit, When sending reminders, different reminder algorithms are applied depending on the category of the anniversary. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the anniversary. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, a different proposal algorithm is applied depending on the category of the anniversary. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned arrangement section is, It estimates the user's emotions and adjusts the arrangement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The system described in Appendix 1 is characterized in that the arrangement unit selects an appropriate arrangement method by referring to the user's past arrangement history when arranging. (Note 18) The aforementioned arrangement section is, During the arrangement process, the arrangement method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned asset grasping unit is We estimate the user's emotions and adjust how we understand their asset status based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 20) The system described in Appendix 2 is characterized in that the asset tracking unit selects an appropriate tracking method by referring to the user's past asset data when tracking the asset status. (Note 21) The aforementioned asset grasping unit is The system estimates the user's emotions and adjusts the frequency of monitoring their asset status based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned asset grasping unit is When assessing asset status, asset data is weighted based on the user's lifestyle. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned data management unit, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 24) The system described in Appendix 3 is characterized in that the data management unit selects an appropriate management method by referring to the user's past data history when managing data. (Note 25) The aforementioned data management unit, It estimates user sentiment and prioritizes data management based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned data management unit, When managing data, weight the data based on the user's lifestyle. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned timing setting unit, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 28) The system as described in Appendix 4, characterized in that the timing setting unit selects an appropriate timing by referring to the user's past reminder history when setting the timing of a reminder. (Note 29) The aforementioned timing setting unit, It estimates the user's emotions and adjusts the reminder frequency based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 30) The aforementioned timing setting unit, When setting reminder timing, the reminder timing can be customized based on the user's lifestyle. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0181] 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. The management department that manages anniversaries, A reminder unit that sends reminders based on anniversaries managed by the aforementioned management unit, A proposal unit makes a proposal based on the anniversary reminded by the aforementioned reminder unit, The system includes an arrangement unit that performs arrangements based on the content proposed by the aforementioned proposal unit. A system characterized by the following features.

2. It includes an asset tracking unit to understand the user's asset status. The system according to feature 1.

3. It has a data management department that manages past anniversary data. The system according to feature 1.

4. It includes a timing setting unit for setting the reminder timing. The system according to feature 1.

5. The system according to claim 1, characterized in that the proposal unit makes a proposal based on at least one of the user's asset status and past anniversary data.

6. The aforementioned arrangement section is, Based on user instructions, the system makes restaurant reservations and orders gifts. The system according to feature 1.

7. The aforementioned management department, It estimates the user's emotions and adjusts the importance of anniversaries based on those estimated emotions. The system according to feature 1.

8. The system according to claim 1, characterized in that the management unit selects an appropriate management method by referring to the user's past anniversary data when an anniversary is registered.

9. The aforementioned management department, When managing anniversaries, filtering is performed based on the user's lifestyle and areas of interest. The system according to feature 1.

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