system

Abstract

The system according to this embodiment aims to automatically discover the user's past memories and notify them in an appropriate format. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a discovery unit, a generation unit, and a notification unit. The reception unit receives input from the user. The analysis unit analyzes past logs based on the information received by the reception unit. The discovery unit discovers memories from the past logs analyzed by the analysis unit. The generation unit compiles the memories discovered by the discovery unit into an appropriate format. The notification unit notifies the user of the format generated by the generation unit.

Description

Technical Field

[0003]

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that in a messenger service, there is a lack of a function to automatically discover past memories and notify the user.

[0005] ​​​​​​​​The system according to this embodiment comprises a reception unit, an analysis unit, a discovery unit, a generation unit, and a notification unit. The reception unit receives input from the user. The analysis unit analyzes past logs based on the information received by the reception unit. The discovery unit discovers memories from the past logs analyzed by the analysis unit. The generation unit compiles the memories discovered by the discovery unit into an appropriate format. The notification unit notifies the user of the format generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically discover a user's past memories and notify them in an appropriate format. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The messenger service according to an embodiment of the present invention is a system that uses AI to automatically search for past memories and provides an agent function to remind the user. When the user activates the memory discovery agent function, the AI ​​periodically searches past logs (group chats, individual chats, albums, etc.) and discovers past memories. The discovered memories are compiled in an appropriate format and notified to the user. For example, notifications are sent at appropriate times for each episode, such as "Three years ago today" or "X years since I first chatted with XX." This mechanism allows users to look back on forgotten past memories. First, the user activates the memory discovery agent function. The user does not need to make any specific settings; simply activating the function is sufficient. For example, simply turning on "Memory Discovery Agent" from the messenger app's settings screen automatically starts the AI's search for past logs. Next, the AI ​​periodically searches the past logs. The AI ​​analyzes data such as group chats, individual chats, and albums to discover past memories. For example, the AI ​​can identify the content of a chat from "Three years ago today" or the message from "The day I first chatted with XX." This allows the user to rediscover forgotten past events. Discovered memories are compiled into an appropriate format and notified to the user. For example, a notification summarizing the conversation from "3 years ago today" or an episode such as "X years since I first talked with XX" might be sent. In this way, users can look back on past memories. This system saves users the trouble of manually going back through past logs. Also, because the AI ​​spontaneously searches for past memories, events that the user may have forgotten are also reminded. This allows users to enjoy past memories even more. For example, if a user is notified of the conversation from "3 years ago today," they can look back on what happened that day. Also, if they are notified of an episode such as "X years since I first talked with XX," they can reaffirm their relationship with that friend. In this way, users can rediscover and enjoy various memories through past logs.Furthermore, since AI primarily uses text data when analyzing past logs, large-scale language models (LLMs) are expected to play a particularly important role. This will allow the AI ​​to perform more accurate analysis and remind users of more relevant memories. In this way, the memory retrieval agent function is expected to enhance the appeal of messenger apps by reminding users of past memories and contribute to attracting younger users. This will enable messenger services to automatically search for and remind users of their past memories.

[0029] The messenger service according to the embodiment comprises a reception unit, an analysis unit, a discovery unit, a generation unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, specific keywords, dates, and events. The reception unit accepts, for example, settings for the user to enable the "Memory Discovery Agent." The reception unit can also accept requests from the user to be reminded of specific memories. For example, a user can request to be reminded of the content of a conversation from three years ago today. The analysis unit analyzes past logs based on the information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums. The analysis unit analyzes, for example, past chat content, photos, and videos, and extracts information related to specific keywords, dates, and events. The discovery unit discovers memories from the past logs analyzed by the analysis unit. The discovery unit identifies, for example, messages from "three years ago today" or "the day I first chatted with XX." The discovery unit can discover past messages based on specific dates or events. The generation unit compiles the memories discovered by the discovery unit into an appropriate format. For example, the generation unit compiles the discovered memories into an appropriate format such as text, image, or table. The generation unit generates notifications such as a notification summarizing the content of a conversation from "3 years ago today" or a notification summarizing an episode from "X years since I first talked with XX." The notification unit notifies the user of the format generated by the generation unit. For example, the notification unit notifies the user at an appropriate time. For example, the notification unit notifies the user of a notification summarizing the content of a conversation from "3 years ago today" or an episode from "X years since I first talked with XX." As a result, the messenger service according to this embodiment analyzes past logs based on user input, discovers memories, formats them, and notifies the user, allowing the user to look back on past memories they had forgotten.

[0030] The reception desk receives input from users. User input may include, but is not limited to, specific keywords, dates, or events. The reception desk can, for example, accept settings for users to activate the "Memory Discovery Agent." It can also accept requests from users to be reminded of specific memories. For example, a user might request to be reminded of a conversation from three years ago today. The reception desk receives this input through the user interface. The user interface is designed to be intuitive and easy to use, allowing users to easily input the necessary information. For example, users can select a specific date using a calendar widget or enter keywords in the search bar. Furthermore, the reception desk also supports voice input, allowing users to make requests by voice. Using speech recognition technology, it accurately understands the user's voice instructions and processes them as appropriate requests. This makes it easy for users to request memory reminders. After receiving user input, the reception desk sends this information to the analysis department. To protect user privacy, the reception desk encrypts the entered information before transmission. This minimizes the risk of users' personal information being leaked to third parties. The reception department can efficiently process user input and quickly transmit it to the analysis department, thereby improving the overall system response time.

[0031] The analysis unit analyzes past logs based on information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums. For example, it analyzes past chat content, photos, and videos to extract information related to specific keywords, dates, and events. The analysis unit utilizes AI technology to quickly and accurately extract relevant information from vast amounts of data. For example, it uses natural language processing (NLP) technology to extract specific keywords and phrases from chat content, and image recognition technology to identify specific events and people from photos and videos. The analysis unit combines these technologies to efficiently analyze information in response to user requests. Before sending the analysis results of past logs to the discovery unit, the analysis unit verifies the integrity and consistency of the data. For example, if there are multiple chat contents or photos related to the same event, they are integrated and combined into a single episode. Furthermore, the analysis unit anonymizes the analysis results to protect user privacy. This minimizes the risk of users' personal information being leaked to third parties. When sending the analysis results of past logs to the discovery unit, the analysis unit sets data priorities. For example, if a user requests information related to a specific date or event, that information is sent to the discovery unit with the highest priority. This allows for a quick response to user requests.

[0032] The discovery unit discovers memories from past logs analyzed by the analysis unit. For example, the discovery unit identifies messages such as "3 years ago today" or "the day I first talked to XX." The discovery unit can also discover past messages based on specific dates or events. The discovery unit utilizes AI technology to efficiently search data sent from the analysis unit and identify memories that meet user requests. For example, it uses machine learning algorithms to extract specific patterns and features from past conversations, photos, and videos, and discovers related memories based on them. The discovery unit can also discover multiple memories simultaneously in response to user requests. For example, if a user requests both "conversation content from 3 years ago today" and "the day I first talked to XX," the discovery unit will simultaneously discover memories corresponding to each request and send them to the generation unit. The discovery unit evaluates the importance and relevance of the discovered memories and sets priorities. For example, it prioritizes discovering memories related to events or people that the user considers particularly important and sends them to the generation unit. This allows for the rapid delivery of memories that meet user expectations. When sending discovered memories to the generation unit, the discovery unit verifies the integrity and consistency of the data. For example, if there are multiple messages or photos related to the same event, they can be integrated and combined into a single episode. This ensures that the memories presented to the user are consistent, providing a more emotionally impactful experience.

[0033] The generation unit compiles the memories discovered by the discovery unit into an appropriate format. For example, the generation unit compiles the discovered memories into an appropriate format such as text, image, or table. For example, the generation unit generates a notification summarizing the content of a conversation "3 years ago today" or a notification summarizing an episode "X years since I first talked with XX." The generation unit provides memories in the most suitable format according to the user's request. For example, if the user requests text format, the generation unit compiles the conversation content in text format; if the user requests image format, the generation unit compiles it in image format including relevant photos and videos. The generation unit applies an appropriate design and layout depending on the content and format of the memories. For example, special designs and layouts are applied to memories related to specific events or people to provide a more emotionally impactful experience. Before sending the generated memories to the notification unit, the generation unit verifies the integrity and consistency of the data. For example, if there are multiple messages or photos related to the same event, they are integrated and compiled into a single episode. This ensures that the memories provided to the user are consistent and can provide a more emotionally impactful experience. The generation unit sets data priorities when sending the generated memories to the notification unit. For example, it prioritizes sending memories related to events or people that the user considers particularly important to the notification unit. This allows for the rapid delivery of memories that meet the user's expectations.

[0034] The notification unit notifies the user of the format generated by the generation unit. The notification unit notifies the user at an appropriate time, for example. For example, the notification unit may notify the user of a summary of the conversation from "3 years ago today" or an episode such as "X years since I first talked with XX." The notification unit adjusts the timing and method of notifications according to the user's settings. For example, if the user wishes to receive notifications during a specific time period, the notification will be sent during that time period. In addition, multiple methods such as push notifications, email, and SMS can be used as notification methods. The notification unit notifies the user in the most optimal way according to the user's preferences. The notification unit sets notification priorities according to the importance and urgency of the notification content. For example, notifications related to particularly important memories or events will be delivered to the user with priority. This allows the user to look back on memories at the appropriate time without missing important information. The notification unit collects user feedback and uses it to improve the content and method of notifications. For example, users can comment on and rate the content of notifications, and the notification unit improves the content and method of notifications based on that feedback. This improves user satisfaction and allows for the provision of a better service. The notification system encrypts and transmits notification content to protect user privacy. This minimizes the risk of users' personal information being leaked to third parties. The notification system provides users with timely and reliable notifications, enabling them to reminisce about past memories they may have forgotten.

[0035] The analysis unit can analyze data such as group chats, individual chats, and albums. For example, the analysis unit can analyze the message content of group chats and extract information related to specific keywords, dates, and events. It can also analyze the message content of individual chats and extract information related to specific keywords, dates, and events. Furthermore, the analysis unit can analyze photos and videos in albums and extract information related to specific keywords, dates, and events. In this way, the analysis unit makes it easier to discover past memories by analyzing data such as group chats, individual chats, and albums. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as group chats, individual chats, and albums into a generating AI and have the generating AI perform the analysis.

[0036] The discovery unit can identify messages such as "3 years ago today" or "the day I first talked to XX." For example, the discovery unit can identify the content of a conversation from "3 years ago today" and remind the user. It can also identify the message from "the day I first talked to XX" and remind the user. Furthermore, the discovery unit can discover past messages based on specific dates or events. In this way, the discovery unit reminds the user of important memories by discovering past messages based on specific dates or events. Some or all of the above processing in the discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input past messages based on specific dates or events into a generation AI and have the generation AI perform the discovery.

[0037] The generation unit can organize the discovered memories into an appropriate format. For example, the generation unit can organize the discovered memories into an appropriate format such as text, image, or table. For example, the generation unit can generate a notification summarizing the conversation content from "3 years ago today" or a notification summarizing episodes from "X years since I first talked with XX." In this way, the generation unit can organize the discovered memories into an appropriate format, making notifications easy for the user to view and understand. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the discovered memories into a generation AI, which can then organize them into an appropriate format.

[0038] The notification unit can notify the user at the appropriate time. For example, the notification unit may send notifications summarizing the content of conversations from "3 years ago today" or episodes such as "X years since I first talked with XX." The notification unit sends notifications at the appropriate time based on the user's activity time and notification priority. This allows the notification unit to effectively reflect on past memories by sending notifications at the right time. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input a schedule for sending notifications at the appropriate time into the generative AI, and the generative AI can determine the optimal timing.

[0039] The notification unit can remind users of events they have forgotten. For example, the notification unit can remind users of past events or important messages. For example, the notification unit can remind users of a conversation from "three years ago today" or a message from "the day I first talked to XX." In this way, the notification unit allows users to rediscover and enjoy past memories by reminding them of events they have forgotten. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input an event the user has forgotten into a generative AI, and have the generative AI perform the reminder.

[0040] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit may prioritize suggesting reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest the optimal reception timing. In this way, the reception unit can select the optimal reception method and perform efficient reception by analyzing the user's past input history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's past input history into a generative AI, and the generative AI can select the optimal reception method.

[0041] The reception unit can filter information based on the user's current areas of interest upon receipt. For example, the reception unit prioritizes receiving information related to topics the user is currently interested in. The reception unit can also filter relevant past logs based on the user's current areas of interest. Furthermore, the reception unit can suggest the optimal reception method based on the user's current areas of interest. This allows the reception unit to receive more relevant information by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's current areas of interest into a generative AI and have the generative AI perform the filtering.

[0042] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location information during the reception process. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also suggest the optimal reception method based on the user's geographical location information. Furthermore, the reception unit can filter relevant past logs based on the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, and the generative AI can select highly relevant information.

[0043] The reception unit can analyze the user's social media activity and receive relevant information upon receiving a request. For example, the reception unit can prioritize receiving relevant information based on the user's social media activity. The reception unit can also analyze the user's social media activity and suggest the optimal reception method. Furthermore, the reception unit can filter relevant past logs based on the user's social media activity. This allows the reception unit to prioritize receiving relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, which can then select relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image analysis algorithm to image data. Furthermore, it can apply a speech recognition algorithm to audio data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, which can then apply the most suitable analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis based on the submission date. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI, and the generative AI can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0048] The discovery unit can improve the accuracy of its discovery by considering the interrelationships between data during the discovery process. For example, the discovery unit analyzes the interrelationships between data and discovers related data. The discovery unit can also improve the accuracy of its discovery based on the interrelationships between data. Furthermore, the discovery unit can apply the optimal discovery method by considering the interrelationships between data. In this way, the discovery unit can improve the accuracy of its discovery by considering the interrelationships between data. Some or all of the above-described processes in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the interrelationships between data into a generative AI, and the generative AI can improve the accuracy of its discovery.

[0049] The discovery unit can perform discoveries while considering the attribute information of the data submitter. For example, the discovery unit discovers relevant data based on the attribute information of the data submitter. The discovery unit can also improve the accuracy of discoveries by considering the attribute information of the data submitter. Furthermore, the discovery unit can apply the optimal discovery method based on the attribute information of the data submitter. In this way, the discovery unit can improve the accuracy of discoveries by considering the attribute information of the data submitter. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the attribute information of the data submitter into a generative AI and have the generative AI perform the discovery.

[0050] The discovery unit can perform discoveries while considering the geographical distribution of the data. For example, the discovery unit discovers relevant data based on the geographical distribution of the data. The discovery unit can also improve the accuracy of discoveries by considering the geographical distribution of the data. Furthermore, the discovery unit can apply the optimal discovery method based on the geographical distribution of the data. In this way, the discovery unit can improve the accuracy of discoveries by considering the geographical distribution of the data. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the discovery.

[0051] The discovery unit can improve the accuracy of its discovery by referring to relevant literature for the data during the discovery process. For example, the discovery unit can refer to relevant literature for the data and discover relevant data. The discovery unit can also improve the accuracy of its discovery based on the relevant literature for the data. Furthermore, the discovery unit can apply the optimal discovery method by considering the relevant literature for the data. In this way, the discovery unit can improve the accuracy of its discovery by referring to relevant literature for the data. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input relevant literature for the data into a generative AI and have the generative AI perform the discovery.

[0052] The generation unit can adjust the level of detail in the format based on the importance of the data during generation. For example, the generation unit can provide a detailed format for important data, and a concise format for less important data. Furthermore, the generation unit can determine the priority of the format based on the importance of the data. This allows the generation unit to provide an efficient format by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail in the format.

[0053] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply a natural language generation algorithm to text data. It can also apply an image generation algorithm to image data. Furthermore, it can apply a speech generation algorithm to speech data. This allows the generation unit to provide a more accurate format by applying different generation algorithms depending on the data category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the data category into a generation AI, which can then apply the most suitable generation algorithm.

[0054] The generation unit can determine the format priority based on the data submission date during generation. For example, the generation unit prioritizes formatting the most recent data. It can also postpone processing older data. Furthermore, the generation unit can adjust the format priority based on the submission date. This allows the generation unit to provide an efficient format by determining the format priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the data submission date into the generation AI, which can then determine the format priority.

[0055] The generation unit can adjust the formatting order based on the relevance of the data during generation. For example, the generation unit prioritizes formatting highly relevant data. It can also postpone the processing of less relevant data. Furthermore, the generation unit can adjust the formatting order based on the relevance of the data. This allows the generation unit to provide an efficient format by adjusting the formatting order based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI, and the generation AI can adjust the formatting order.

[0056] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can also select the most efficient notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification timing. In this way, the notification unit can select the optimal notification method and send notifications efficiently by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's past notification history into a generative AI, and the generative AI can select the optimal notification method.

[0057] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, if the user is at work, the notification unit may suggest a quiet notification method. It may also suggest a relaxing notification method if the user is on vacation. Furthermore, the notification unit can customize the optimal notification method based on the user's living situation. This allows the notification unit to provide more appropriate notifications by customizing the notification method based on the user's current living situation. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's current living situation into a generative AI, which can then customize the optimal notification method.

[0058] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit may prioritize suggesting notification methods relevant to the user's current location. The notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, the notification unit can filter relevant notification content based on the user's geographical location information. This allows the notification unit to select the optimal notification method and provide efficient notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI, which can then select the optimal notification method.

[0059] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. For example, the notification unit can suggest the optimal notification method based on the user's social media activity. The notification unit can also analyze the user's social media activity and filter relevant notification content. Furthermore, the notification unit can suggest the optimal notification timing based on the user's social media activity. In this way, the notification unit can suggest the optimal notification method and provide efficient notifications by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's social media activity into a generative AI, which can then suggest the optimal notification method.

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

[0061] The reception system can learn the user's past behavior patterns and suggest the optimal reception method. For example, if a user has frequently sent messages during a specific time period in the past, the system can adjust the reception process to coincide with that time. Furthermore, if a user is using a specific device, the system can suggest the most suitable reception method for that device. It can also suggest the optimal reception timing based on the user's past behavior patterns. This allows the reception system to perform more efficient reception by learning the user's past behavior patterns.

[0062] The discovery unit can improve the accuracy of its discoveries based on the user's current areas of interest. For example, it can prioritize discovering data related to topics the user is currently interested in. It can also filter relevant past logs based on the user's current areas of interest. Furthermore, it can suggest the optimal discovery method based on the user's current areas of interest. In this way, the discovery unit can improve the accuracy of its discoveries based on the user's current areas of interest.

[0063] The notification unit can customize notification content by considering the user's geographical location. For example, it can prioritize notifications related to the user's current location. It can also suggest the optimal notification method based on the user's geographical location. Furthermore, it can filter relevant past logs based on the user's geographical location. As a result, the notification unit can provide more relevant notifications by considering the user's geographical location.

[0064] The reception department can analyze a user's social media activity and suggest the optimal reception method. For example, if a user frequently uses a particular social media platform, it can suggest the most suitable reception method for that platform. It can also filter relevant past logs based on the user's social media activity. Furthermore, it can suggest the optimal reception timing based on the user's social media activity. In this way, the reception department can perform reception more efficiently by analyzing the user's social media activity.

[0065] The discovery unit can improve the accuracy of its discoveries by considering the attribute information of the data submitters. For example, it can prioritize the discovery of relevant data based on the attribute information of the data submitters. It can also determine the priority of discoveries by considering the attribute information of the data submitters. Furthermore, it can suggest the optimal discovery method based on the attribute information of the data submitters. In this way, the discovery unit can improve the accuracy of its discoveries by considering the attribute information of the data submitters.

[0066] The notification unit can analyze the user's past notification history and suggest the most suitable notification method. For example, if a user has previously preferred a particular notification method, it will prioritize suggesting that method. It can also suggest the optimal notification timing based on the user's past notification history. Furthermore, it can analyze the user's past notification history and suggest the most suitable notification content. As a result, the notification unit can provide more efficient notifications by analyzing the user's past notification history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk receives input from users. User input may include specific keywords, dates, events, etc. For example, it can receive settings for users to activate the "Memory Discovery Agent" or requests to be reminded of specific memories. Step 2: The analysis unit analyzes past logs based on the information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums, and extracts information related to specific keywords, dates, and events by analyzing past chat content, photos, videos, etc. Step 3: The discovery unit discovers memories from past logs analyzed by the analysis unit. The discovery unit can discover past messages based on specific dates or events. For example, it can identify messages from "three years ago today" or "the day I first talked to XX." Step 4: The generation unit compiles the memories discovered by the discovery unit into an appropriate format. The generation unit compiles the discovered memories into an appropriate format such as text, image, or table. For example, it generates a notification summarizing the conversation content from "3 years ago today" or a notification summarizing episodes from "X years since I first talked with XX." Step 5: The notification unit notifies the user of the format generated by the generation unit. The notification unit sends notifications to the user at the appropriate time. For example, a notification summarizing the conversation content from "3 years ago today" or an episode such as "It's been X years since I first talked with XX" might be sent.

[0069] (Example of form 2) The messenger service according to an embodiment of the present invention is a system that uses AI to automatically search for past memories and provides an agent function to remind the user. When the user activates the memory discovery agent function, the AI ​​periodically searches past logs (group chats, individual chats, albums, etc.) and discovers past memories. The discovered memories are compiled in an appropriate format and notified to the user. For example, notifications are sent at appropriate times for each episode, such as "Three years ago today" or "X years since I first chatted with XX." This mechanism allows users to look back on forgotten past memories. First, the user activates the memory discovery agent function. The user does not need to make any specific settings; simply activating the function is sufficient. For example, simply turning on "Memory Discovery Agent" from the messenger app's settings screen automatically starts the AI's search for past logs. Next, the AI ​​periodically searches the past logs. The AI ​​analyzes data such as group chats, individual chats, and albums to discover past memories. For example, the AI ​​can identify the content of a chat from "Three years ago today" or the message from "The day I first chatted with XX." This allows the user to rediscover forgotten past events. Discovered memories are compiled into an appropriate format and notified to the user. For example, a notification summarizing the conversation from "3 years ago today" or an episode such as "X years since I first talked with XX" might be sent. In this way, users can look back on past memories. This system saves users the trouble of manually going back through past logs. Also, because the AI ​​spontaneously searches for past memories, events that the user may have forgotten are also reminded. This allows users to enjoy past memories even more. For example, if a user is notified of the conversation from "3 years ago today," they can look back on what happened that day. Also, if they are notified of an episode such as "X years since I first talked with XX," they can reaffirm their relationship with that friend. In this way, users can rediscover and enjoy various memories through past logs.Furthermore, since AI primarily uses text data when analyzing past logs, large-scale language models (LLMs) are expected to play a particularly important role. This will allow the AI ​​to perform more accurate analysis and remind users of more relevant memories. In this way, the memory retrieval agent function is expected to enhance the appeal of messenger apps by reminding users of past memories and contribute to attracting younger users. This will enable messenger services to automatically search for and remind users of their past memories.

[0070] The messenger service according to the embodiment comprises a reception unit, an analysis unit, a discovery unit, a generation unit, and a notification unit. The reception unit receives input from the user. User input includes, but is not limited to, specific keywords, dates, and events. The reception unit accepts, for example, settings for the user to enable the "Memory Discovery Agent." The reception unit can also accept requests from the user to be reminded of specific memories. For example, a user can request to be reminded of the content of a conversation from three years ago today. The analysis unit analyzes past logs based on the information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums. The analysis unit analyzes, for example, past chat content, photos, and videos, and extracts information related to specific keywords, dates, and events. The discovery unit discovers memories from the past logs analyzed by the analysis unit. The discovery unit identifies, for example, messages from "three years ago today" or "the day I first chatted with XX." The discovery unit can discover past messages based on specific dates or events. The generation unit compiles the memories discovered by the discovery unit into an appropriate format. For example, the generation unit compiles the discovered memories into an appropriate format such as text, image, or table. The generation unit generates notifications such as a notification summarizing the content of a conversation from "3 years ago today" or a notification summarizing an episode from "X years since I first talked with XX." The notification unit notifies the user of the format generated by the generation unit. For example, the notification unit notifies the user at an appropriate time. For example, the notification unit notifies the user of a notification summarizing the content of a conversation from "3 years ago today" or an episode from "X years since I first talked with XX." As a result, the messenger service according to this embodiment analyzes past logs based on user input, discovers memories, formats them, and notifies the user, allowing the user to look back on past memories they had forgotten.

[0071] The reception desk receives input from users. User input may include, but is not limited to, specific keywords, dates, or events. The reception desk can, for example, accept settings for users to activate the "Memory Discovery Agent." It can also accept requests from users to be reminded of specific memories. For example, a user might request to be reminded of a conversation from three years ago today. The reception desk receives this input through the user interface. The user interface is designed to be intuitive and easy to use, allowing users to easily input the necessary information. For example, users can select a specific date using a calendar widget or enter keywords in the search bar. Furthermore, the reception desk also supports voice input, allowing users to make requests by voice. Using speech recognition technology, it accurately understands the user's voice instructions and processes them as appropriate requests. This makes it easy for users to request memory reminders. After receiving user input, the reception desk sends this information to the analysis department. To protect user privacy, the reception desk encrypts the entered information before transmission. This minimizes the risk of users' personal information being leaked to third parties. The reception department can efficiently process user input and quickly transmit it to the analysis department, thereby improving the overall system response time.

[0072] The analysis unit analyzes past logs based on information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums. For example, it analyzes past chat content, photos, and videos to extract information related to specific keywords, dates, and events. The analysis unit utilizes AI technology to quickly and accurately extract relevant information from vast amounts of data. For example, it uses natural language processing (NLP) technology to extract specific keywords and phrases from chat content, and image recognition technology to identify specific events and people from photos and videos. The analysis unit combines these technologies to efficiently analyze information in response to user requests. Before sending the analysis results of past logs to the discovery unit, the analysis unit verifies the integrity and consistency of the data. For example, if there are multiple chat contents or photos related to the same event, they are integrated and combined into a single episode. Furthermore, the analysis unit anonymizes the analysis results to protect user privacy. This minimizes the risk of users' personal information being leaked to third parties. When sending the analysis results of past logs to the discovery unit, the analysis unit sets data priorities. For example, if a user requests information related to a specific date or event, that information is sent to the discovery unit with the highest priority. This allows for a quick response to user requests.

[0073] The discovery unit discovers memories from past logs analyzed by the analysis unit. For example, the discovery unit identifies messages such as "3 years ago today" or "the day I first talked to XX." The discovery unit can also discover past messages based on specific dates or events. The discovery unit utilizes AI technology to efficiently search data sent from the analysis unit and identify memories that meet user requests. For example, it uses machine learning algorithms to extract specific patterns and features from past conversations, photos, and videos, and discovers related memories based on them. The discovery unit can also discover multiple memories simultaneously in response to user requests. For example, if a user requests both "conversation content from 3 years ago today" and "the day I first talked to XX," the discovery unit will simultaneously discover memories corresponding to each request and send them to the generation unit. The discovery unit evaluates the importance and relevance of the discovered memories and sets priorities. For example, it prioritizes discovering memories related to events or people that the user considers particularly important and sends them to the generation unit. This allows for the rapid delivery of memories that meet user expectations. When sending discovered memories to the generation unit, the discovery unit verifies the integrity and consistency of the data. For example, if there are multiple messages or photos related to the same event, they can be integrated and combined into a single episode. This ensures that the memories presented to the user are consistent, providing a more emotionally impactful experience.

[0074] The generation unit compiles the memories discovered by the discovery unit into an appropriate format. For example, the generation unit compiles the discovered memories into an appropriate format such as text, image, or table. For example, the generation unit generates a notification summarizing the content of a conversation "3 years ago today" or a notification summarizing an episode "X years since I first talked with XX." The generation unit provides memories in the most suitable format according to the user's request. For example, if the user requests text format, the generation unit compiles the conversation content in text format; if the user requests image format, the generation unit compiles it in image format including relevant photos and videos. The generation unit applies an appropriate design and layout depending on the content and format of the memories. For example, special designs and layouts are applied to memories related to specific events or people to provide a more emotionally impactful experience. Before sending the generated memories to the notification unit, the generation unit verifies the integrity and consistency of the data. For example, if there are multiple messages or photos related to the same event, they are integrated and compiled into a single episode. This ensures that the memories provided to the user are consistent and can provide a more emotionally impactful experience. The generation unit sets data priorities when sending the generated memories to the notification unit. For example, it prioritizes sending memories related to events or people that the user considers particularly important to the notification unit. This allows for the rapid delivery of memories that meet the user's expectations.

[0075] The notification unit notifies the user of the format generated by the generation unit. The notification unit notifies the user at an appropriate time, for example. For example, the notification unit may notify the user of a summary of the conversation from "3 years ago today" or an episode such as "X years since I first talked with XX." The notification unit adjusts the timing and method of notifications according to the user's settings. For example, if the user wishes to receive notifications during a specific time period, the notification will be sent during that time period. In addition, multiple methods such as push notifications, email, and SMS can be used as notification methods. The notification unit notifies the user in the most optimal way according to the user's preferences. The notification unit sets notification priorities according to the importance and urgency of the notification content. For example, notifications related to particularly important memories or events will be delivered to the user with priority. This allows the user to look back on memories at the appropriate time without missing important information. The notification unit collects user feedback and uses it to improve the content and method of notifications. For example, users can comment on and rate the content of notifications, and the notification unit improves the content and method of notifications based on that feedback. This improves user satisfaction and allows for the provision of a better service. The notification system encrypts and transmits notification content to protect user privacy. This minimizes the risk of users' personal information being leaked to third parties. The notification system provides users with timely and reliable notifications, enabling them to reminisce about past memories they may have forgotten.

[0076] The analysis unit can analyze data such as group chats, individual chats, and albums. For example, the analysis unit can analyze the message content of group chats and extract information related to specific keywords, dates, and events. It can also analyze the message content of individual chats and extract information related to specific keywords, dates, and events. Furthermore, the analysis unit can analyze photos and videos in albums and extract information related to specific keywords, dates, and events. In this way, the analysis unit makes it easier to discover past memories by analyzing data such as group chats, individual chats, and albums. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as group chats, individual chats, and albums into a generating AI and have the generating AI perform the analysis.

[0077] The discovery unit can identify messages such as "3 years ago today" or "the day I first talked to XX." For example, the discovery unit can identify the content of a conversation from "3 years ago today" and remind the user. It can also identify the message from "the day I first talked to XX" and remind the user. Furthermore, the discovery unit can discover past messages based on specific dates or events. In this way, the discovery unit reminds the user of important memories by discovering past messages based on specific dates or events. Some or all of the above processing in the discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input past messages based on specific dates or events into a generation AI and have the generation AI perform the discovery.

[0078] The generation unit can organize the discovered memories into an appropriate format. For example, the generation unit can organize the discovered memories into an appropriate format such as text, image, or table. For example, the generation unit can generate a notification summarizing the conversation content from "3 years ago today" or a notification summarizing episodes from "X years since I first talked with XX." In this way, the generation unit can organize the discovered memories into an appropriate format, making notifications easy for the user to view and understand. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the discovered memories into a generation AI, which can then organize them into an appropriate format.

[0079] The notification unit can notify the user at the appropriate time. For example, the notification unit may send notifications summarizing the content of conversations from "3 years ago today" or episodes such as "X years since I first talked with XX." The notification unit sends notifications at the appropriate time based on the user's activity time and notification priority. This allows the notification unit to effectively reflect on past memories by sending notifications at the right time. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input a schedule for sending notifications at the appropriate time into the generative AI, and the generative AI can determine the optimal timing.

[0080] The notification unit can remind users of events they have forgotten. For example, the notification unit can remind users of past events or important messages. For example, the notification unit can remind users of a conversation from "three years ago today" or a message from "the day I first talked to XX." In this way, the notification unit allows users to rediscover and enjoy past memories by reminding them of events they have forgotten. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input an event the user has forgotten into a generative AI, and have the generative AI perform the reminder.

[0081] The reception desk can estimate the user's emotions and adjust the timing of the reception based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will perform the reception during a time when the user is relaxed. Similarly, if the user is busy, the reception desk can perform the reception during a less busy time. Furthermore, if the user is emotionally calm, the reception desk can perform the reception immediately. This allows the reception desk to adjust the timing of the reception according to the user's emotions, enabling them to perform the reception at a more appropriate time. 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 reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit may prioritize suggesting reception methods that the user has frequently used in the past. The reception unit can also select the most efficient reception method from the user's past input history. Furthermore, the reception unit can analyze the user's past input history and suggest the optimal reception timing. In this way, the reception unit can select the optimal reception method and perform efficient reception by analyzing the user's past input history. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's past input history into a generative AI, and the generative AI can select the optimal reception method.

[0083] The reception unit can filter information based on the user's current areas of interest upon receipt. For example, the reception unit prioritizes receiving information related to topics the user is currently interested in. The reception unit can also filter relevant past logs based on the user's current areas of interest. Furthermore, the reception unit can suggest the optimal reception method based on the user's current areas of interest. This allows the reception unit to receive more relevant information by filtering based on the user's current areas of interest. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's current areas of interest into a generative AI and have the generative AI perform the filtering.

[0084] The reception unit can estimate the user's emotions and determine the priority of information to receive based on the estimated emotions. For example, if the user is emotionally calm, the reception unit will prioritize receiving important information. If the user is stressed, the reception unit can also prioritize receiving information that helps them relax. Furthermore, if the user is busy, the reception unit can prioritize receiving information that can be processed efficiently. In this way, the reception unit can prioritize receiving more appropriate information by determining the priority of information according to 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 reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location information during the reception process. For example, the reception unit can prioritize receiving information related to the user's current location. The reception unit can also suggest the optimal reception method based on the user's geographical location information. Furthermore, the reception unit can filter relevant past logs based on the user's geographical location information. In this way, the reception unit can prioritize receiving highly relevant information by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's geographical location information into a generative AI, and the generative AI can select highly relevant information.

[0086] The reception unit can analyze the user's social media activity and receive relevant information upon receiving a request. For example, the reception unit can prioritize receiving relevant information based on the user's social media activity. The reception unit can also analyze the user's social media activity and suggest the optimal reception method. Furthermore, the reception unit can filter relevant past logs based on the user's social media activity. This allows the reception unit to prioritize receiving relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit can input the user's social media activity into a generative AI, which can then select relevant information.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is emotionally calm, the analysis unit can provide detailed analysis results. If the user is stressed, the analysis unit can also provide concise analysis results. Furthermore, if the user is busy, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a simplified analysis on less important data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. It can also apply an image analysis algorithm to image data. Furthermore, it can apply a speech recognition algorithm to audio data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, which can then apply the most suitable analysis algorithm.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is emotionally calm, the analysis unit can perform a detailed analysis. If the user is stressed, the analysis unit can perform a concise analysis. Furthermore, if the user is busy, the analysis unit can perform a summary analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis based on the submission date. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI, and the generative AI can determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0093] The discovery unit can estimate the user's emotions and adjust the discovery criteria based on the estimated emotions. For example, if the user is emotionally calm, the discovery unit can apply detailed discovery criteria. If the user is stressed, the discovery unit can also apply concise discovery criteria. Furthermore, if the user is busy, the discovery unit can apply concise discovery criteria. In this way, the discovery unit can provide more appropriate discovery results by adjusting the discovery criteria according to 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 discovery unit may be performed using AI, for example, or not using AI. For example, the discovery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The discovery unit can improve the accuracy of its discovery by considering the interrelationships between data during the discovery process. For example, the discovery unit analyzes the interrelationships between data and discovers related data. The discovery unit can also improve the accuracy of its discovery based on the interrelationships between data. Furthermore, the discovery unit can apply the optimal discovery method by considering the interrelationships between data. In this way, the discovery unit can improve the accuracy of its discovery by considering the interrelationships between data. Some or all of the above-described processes in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the interrelationships between data into a generative AI, and the generative AI can improve the accuracy of its discovery.

[0095] The discovery unit can perform discoveries while considering the attribute information of the data submitter. For example, the discovery unit discovers relevant data based on the attribute information of the data submitter. The discovery unit can also improve the accuracy of discoveries by considering the attribute information of the data submitter. Furthermore, the discovery unit can apply the optimal discovery method based on the attribute information of the data submitter. In this way, the discovery unit can improve the accuracy of discoveries by considering the attribute information of the data submitter. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the attribute information of the data submitter into a generative AI and have the generative AI perform the discovery.

[0096] The discovery unit can estimate the user's emotions and adjust the order in which the discovery results are displayed based on the estimated emotions. For example, if the user is emotionally calm, the discovery unit may prioritize displaying detailed discovery results. If the user is stressed, the discovery unit may prioritize displaying concise discovery results. Furthermore, if the user is busy, the discovery unit may prioritize displaying concise discovery results. In this way, the discovery unit can provide more appropriate discovery results by adjusting the order in which the discovery results are displayed according to 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 discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The discovery unit can perform discoveries while considering the geographical distribution of the data. For example, the discovery unit discovers relevant data based on the geographical distribution of the data. The discovery unit can also improve the accuracy of discoveries by considering the geographical distribution of the data. Furthermore, the discovery unit can apply the optimal discovery method based on the geographical distribution of the data. In this way, the discovery unit can improve the accuracy of discoveries by considering the geographical distribution of the data. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the discovery.

[0098] The discovery unit can improve the accuracy of its discovery by referring to relevant literature for the data during the discovery process. For example, the discovery unit can refer to relevant literature for the data and discover relevant data. The discovery unit can also improve the accuracy of its discovery based on the relevant literature for the data. Furthermore, the discovery unit can apply the optimal discovery method by considering the relevant literature for the data. In this way, the discovery unit can improve the accuracy of its discovery by referring to relevant literature for the data. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can input relevant literature for the data into a generative AI and have the generative AI perform the discovery.

[0099] The generation unit can estimate the user's emotions and adjust the formatting based on the estimated emotions. For example, if the user is emotionally calm, the generation unit can provide a detailed format. If the user is stressed, the generation unit can provide a concise format. Furthermore, if the user is busy, the generation unit can provide a to-the-point format. In this way, the generation unit can provide a more appropriate format by adjusting the formatting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit can adjust the level of detail in the format based on the importance of the data during generation. For example, the generation unit can provide a detailed format for important data, and a concise format for less important data. Furthermore, the generation unit can determine the priority of the format based on the importance of the data. This allows the generation unit to provide an efficient format by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can adjust the level of detail in the format.

[0101] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can apply a natural language generation algorithm to text data. It can also apply an image generation algorithm to image data. Furthermore, it can apply a speech generation algorithm to speech data. This allows the generation unit to provide a more accurate format by applying different generation algorithms depending on the data category. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the data category into a generation AI, which can then apply the most suitable generation algorithm.

[0102] The generation unit can estimate the user's emotions and adjust the length of the generated format based on the estimated emotions. For example, if the user is emotionally calm, the generation unit can provide a detailed format. If the user is stressed, the generation unit can also provide a concise format. Furthermore, if the user is busy, the generation unit can provide a to-the-point format. In this way, the generation unit can provide a more appropriate format by adjusting the length of the format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit can determine the format priority based on the data submission date during generation. For example, the generation unit prioritizes formatting the most recent data. It can also postpone processing older data. Furthermore, the generation unit can adjust the format priority based on the submission date. This allows the generation unit to provide an efficient format by determining the format priority based on the data submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the data submission date into the generation AI, which can then determine the format priority.

[0104] The generation unit can adjust the formatting order based on the relevance of the data during generation. For example, the generation unit prioritizes formatting highly relevant data. It can also postpone the processing of less relevant data. Furthermore, the generation unit can adjust the formatting order based on the relevance of the data. This allows the generation unit to provide an efficient format by adjusting the formatting order based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the data into a generation AI, and the generation AI can adjust the formatting order.

[0105] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is emotionally calm, the notification unit will send a notification immediately. If the user is stressed, the notification unit can send a notification during a time when the user is relaxed. Furthermore, if the user is busy, the notification unit can send a notification during a time when the user is free. In this way, the notification unit can send notifications at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 notification unit may be performed using AI, or not using AI. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has preferred to use in the past. The notification unit can also select the most efficient notification method from the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history and suggest the optimal notification timing. In this way, the notification unit can select the optimal notification method and send notifications efficiently by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's past notification history into a generative AI, and the generative AI can select the optimal notification method.

[0107] The notification unit can customize the notification method based on the user's current living situation when a notification is sent. For example, if the user is at work, the notification unit may suggest a quiet notification method. It may also suggest a relaxing notification method if the user is on vacation. Furthermore, the notification unit can customize the optimal notification method based on the user's living situation. This allows the notification unit to provide more appropriate notifications by customizing the notification method based on the user's current living situation. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's current living situation into a generative AI, which can then customize the optimal notification method.

[0108] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is emotionally calm, the notification unit will prioritize important notifications. It can also prioritize relaxing notifications if the user is stressed. Furthermore, if the user is busy, it can prioritize notifications that can be processed efficiently. This allows the notification unit to provide more appropriate notifications by prioritizing them according to 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 notification unit may be performed using AI, or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The notification unit can select the optimal notification method when sending a notification, taking into account the user's geographical location information. For example, the notification unit may prioritize suggesting notification methods relevant to the user's current location. The notification unit can also select the optimal notification method based on the user's geographical location information. Furthermore, the notification unit can filter relevant notification content based on the user's geographical location information. This allows the notification unit to select the optimal notification method and provide efficient notifications by considering the user's geographical location information. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's geographical location information into a generative AI, which can then select the optimal notification method.

[0110] The notification unit can analyze the user's social media activity and suggest a notification method when sending a notification. For example, the notification unit can suggest the optimal notification method based on the user's social media activity. The notification unit can also analyze the user's social media activity and filter relevant notification content. Furthermore, the notification unit can suggest the optimal notification timing based on the user's social media activity. In this way, the notification unit can suggest the optimal notification method and provide efficient notifications by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or without a generative AI. For example, the notification unit can input the user's social media activity into a generative AI, which can then suggest the optimal notification method.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception system can learn the user's past behavior patterns and suggest the optimal reception method. For example, if a user has frequently sent messages during a specific time period in the past, the system can adjust the reception process to coincide with that time. Furthermore, if a user is using a specific device, the system can suggest the most suitable reception method for that device. It can also suggest the optimal reception timing based on the user's past behavior patterns. This allows the reception system to perform more efficient reception by learning the user's past behavior patterns.

[0113] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is emotionally calm, it can prioritize analyzing important data. If the user is stressed, it can prioritize analyzing data that promotes relaxation. Furthermore, if the user is busy, it can prioritize analyzing data that can be processed efficiently. In this way, the analysis unit can provide more appropriate analysis results by determining the priority of analysis according to the user's emotions.

[0114] The discovery unit can improve the accuracy of its discoveries based on the user's current areas of interest. For example, it can prioritize discovering data related to topics the user is currently interested in. It can also filter relevant past logs based on the user's current areas of interest. Furthermore, it can suggest the optimal discovery method based on the user's current areas of interest. In this way, the discovery unit can improve the accuracy of its discoveries based on the user's current areas of interest.

[0115] The generation unit can estimate the user's emotions and adjust the content of the generated format based on those emotions. For example, if the user is emotionally calm, it can provide detailed content. If the user is stressed, it can provide concise content. Furthermore, if the user is busy, it can provide content that gets straight to the point. In this way, the generation unit can provide a more appropriate format by adjusting the content according to the user's emotions.

[0116] The notification unit can customize notification content by considering the user's geographical location. For example, it can prioritize notifications related to the user's current location. It can also suggest the optimal notification method based on the user's geographical location. Furthermore, it can filter relevant past logs based on the user's geographical location. As a result, the notification unit can provide more relevant notifications by considering the user's geographical location.

[0117] The reception department can analyze a user's social media activity and suggest the optimal reception method. For example, if a user frequently uses a particular social media platform, it can suggest the most suitable reception method for that platform. It can also filter relevant past logs based on the user's social media activity. Furthermore, it can suggest the optimal reception timing based on the user's social media activity. In this way, the reception department can perform reception more efficiently by analyzing the user's social media activity.

[0118] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is emotionally calm, it can perform a detailed analysis. If the user is stressed, it can perform a concise analysis. Furthermore, if the user is busy, it can perform a summary analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the analysis method according to the user's emotions.

[0119] The discovery unit can improve the accuracy of its discoveries by considering the attribute information of the data submitters. For example, it can prioritize the discovery of relevant data based on the attribute information of the data submitters. It can also determine the priority of discoveries by considering the attribute information of the data submitters. Furthermore, it can suggest the optimal discovery method based on the attribute information of the data submitters. In this way, the discovery unit can improve the accuracy of its discoveries by considering the attribute information of the data submitters.

[0120] The generation unit can estimate the user's emotions and adjust the design of the generated format based on those emotions. For example, if the user is emotionally calm, it can provide a detailed design. If the user is stressed, it can provide a simple design. Furthermore, if the user is busy, it can provide a concise design. In this way, the generation unit can provide a more appropriate format by adjusting the format design according to the user's emotions.

[0121] The notification unit can analyze the user's past notification history and suggest the most suitable notification method. For example, if a user has previously preferred a particular notification method, it will prioritize suggesting that method. It can also suggest the optimal notification timing based on the user's past notification history. Furthermore, it can analyze the user's past notification history and suggest the most suitable notification content. As a result, the notification unit can provide more efficient notifications by analyzing the user's past notification history.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives input from users. User input may include specific keywords, dates, events, etc. For example, it can receive settings for users to activate the "Memory Discovery Agent" or requests to be reminded of specific memories. Step 2: The analysis unit analyzes past logs based on the information received by the reception unit. The analysis unit analyzes data such as group chats, individual chats, and albums, and extracts information related to specific keywords, dates, and events by analyzing past chat content, photos, videos, etc. Step 3: The discovery unit discovers memories from past logs analyzed by the analysis unit. The discovery unit can discover past messages based on specific dates or events. For example, it can identify messages from "three years ago today" or "the day I first talked to XX." Step 4: The generation unit compiles the memories discovered by the discovery unit into an appropriate format. The generation unit compiles the discovered memories into an appropriate format such as text, image, or table. For example, it generates a notification summarizing the conversation content from "3 years ago today" or a notification summarizing episodes from "X years since I first talked with XX." Step 5: The notification unit notifies the user of the format generated by the generation unit. The notification unit sends notifications to the user at the appropriate time. For example, a notification summarizing the conversation content from "3 years ago today" or an episode such as "It's been X years since I first talked with XX" might be sent.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, discovery unit, generation unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past logs. The discovery unit is implemented by the specific processing unit 290 of the data processing unit 12 and discovers memories from the analyzed past logs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and compiles the discovered memories into an appropriate format. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the user of the generated format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, discovery unit, generation unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes past logs. The discovery unit is implemented by the identification processing unit 290 of the data processing unit 12 and discovers memories from the analyzed past logs. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and compiles the discovered memories into an appropriate format. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user of the generated format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, discovery unit, generation unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past logs. The discovery unit is implemented by the specific processing unit 290 of the data processing unit 12 and discovers memories from the analyzed past logs. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and compiles the discovered memories into an appropriate format. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user of the generated format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the reception unit, analysis unit, discovery unit, generation unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes past logs. The discovery unit is implemented by the identification processing unit 290 of the data processing unit 12 and discovers memories from the analyzed past logs. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and compiles the discovered memories into an appropriate format. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the user of the generated format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A reception area that receives input from users, An analysis unit analyzes past logs based on the information received by the reception unit, A discovery unit that discovers memories from past logs analyzed by the aforementioned analysis unit, A generation unit that compiles the memories discovered by the discovery unit into an appropriate format, The system includes a notification unit that notifies the user of the format generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze data from group chats, individual chats, albums, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned detection unit is Identify messages such as "3 years ago today" or "The day I first talked to XX." The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Organize the discovered memories into an appropriate format. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify the user at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, Remind users of events they may have forgotten. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of the reception based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the information to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During the registration process, the system prioritizes processing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During registration, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned detection unit is We estimate the user's emotions and adjust the discovery criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned detection unit is When discoveries are made, the interrelationships between data are taken into consideration to improve the accuracy of the discoveries. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned detection unit is When discovering data, the data submitter's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned detection unit is It estimates the user's sentiment and adjusts the order in which the discovery results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned detection unit is When making a discovery, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned detection unit is When discoveries are made, the accuracy of the discoveries is improved by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the way the generated format is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the level of detail in the format is adjusted based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and adjusts the length of the generated format based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the format priority is determined based on the data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, the order of the format is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, customize the notification method based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, we analyze the user's social media activity and suggest notification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives input from users, An analysis unit analyzes past logs based on the information received by the reception unit, A discovery unit that discovers memories from past logs analyzed by the aforementioned analysis unit, A generation unit that compiles the memories discovered by the discovery unit into an appropriate format, The system includes a notification unit that notifies the user of the format generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze data from group chats, individual chats, albums, etc. The system according to feature 1.

3. The generating unit is Organize the discovered memories into an appropriate format. The system according to feature 1.

4. The aforementioned notification unit, Notify the user at the appropriate time. The system according to feature 1.

5. The aforementioned notification unit, Remind users of events they may have forgotten. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of the reception based on those emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past input history to select the optimal reception method. The system according to feature 1.

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