system

Abstract

The system according to this embodiment aims to summarize private information without transmitting it externally and to communicate it to others. [Solution] The system according to the embodiment comprises an acquisition unit, a summarization unit, and an output unit. The acquisition unit acquires lifestyle logs from a smartphone or wearable device. The summarization unit summarizes the lifestyle logs acquired by the acquisition unit. The output unit outputs the information summarized by the summarization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that there is not enough means to summarize private information without transmitting it externally and convey it to others.

[0005] The system according to the embodiment aims to summarize private information without transmitting it externally and convey it to others.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a summarization unit, and an output unit. The acquisition unit acquires a life log from a smartphone or a wearable device. The summarization unit summarizes the life log acquired by the acquisition unit. The output unit outputs the information summarized by the summarization unit. [Effects of the Invention]

[0007] The system according to this embodiment can summarize private information and communicate it to others without transmitting it externally. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The information transmission system according to an embodiment of the present invention is a system that provides a means of communicating information to others by using a generating AI in the user's local environment to compile information without transmitting private information externally. The information transmission system acquires lifestyle logs from smartphones and wearable devices, and the generating AI summarizes the acquired lifestyle logs and creates expressions and content for output. This mechanism makes it easier to communicate events in daily life to others. For example, the information transmission system includes individuals who have difficulty communicating events in daily life to others, such as individuals with developmental disabilities, their families, and caregivers. Because these individuals have difficulty communicating events in daily life to others, they face the challenge of not realizing that daily difficulties become major problems until they arise. To solve this problem, the information transmission system makes it easier to communicate events in daily life to others by having a generating AI built in the user's local environment summarize lifestyle logs acquired from smartphones and wearable devices and create expressions and content for output. For example, if a child with a developmental disability has difficulty telling their parents about what happened at school, the generating AI can summarize the lifestyle logs acquired from the child's smartphone or wearable device and create content for the parents to communicate. This makes it easier for parents to understand events in their child's daily life and take action earlier. Furthermore, the information transmission system protects privacy by creating summarized content in the user's local environment without transmitting data stored on smartphones or other devices externally. In addition, the information transmission system can be used as an extended library based on service information such as maps and transit information, and by integrating with messaging apps, summarized content can be sent via messaging apps. Thus, the information transmission system provides a means of summarizing information using AI generated in the user's local environment and communicating it to others without transmitting private information externally, making it a useful tool for those who find it difficult to communicate everyday events to others. In short, the information transmission system allows information to be summarized and communicated to others in the user's local environment without transmitting private information externally.

[0029] The information transmission system according to this embodiment comprises an acquisition unit, a summarization unit, and an output unit. The acquisition unit acquires lifestyle logs from a smartphone or wearable device. The acquisition unit can, for example, use the smartphone's sensors to collect the user's activity history and health data. The acquisition unit can also acquire data such as heart rate and steps from a wearable device. Furthermore, the acquisition unit can acquire location information and record the user's movement history. For example, the acquisition unit can acquire the user's location information using the smartphone's GPS function. The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. For example, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI can use a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. The summarization unit can also use the generation AI to extract particularly important parts of the lifestyle logs and perform a summary. For example, the generation AI uses keyword extraction technology to pick out particularly important information from the lifestyle logs and performs a summary based on that. The output unit outputs the information summarized by the summarization unit. The output unit can, for example, display summarized information in text format. It can also output summarized information in audio format. Furthermore, it can display summarized information in graphic format. For example, the output unit can provide summarized information through a messaging application. This allows the information transmission system to gather information within the user's local environment and communicate it to others without transmitting private information externally.

[0030] The data acquisition unit acquires lifestyle logs from smartphones and wearable devices. Specifically, it can collect user activity history and health data using the smartphone's sensors. Smartphones are equipped with accelerometers, gyroscopes, GPS, heart rate sensors, etc., and by utilizing these sensors, detailed data such as the user's steps, distance traveled, heart rate, and sleep patterns can be acquired. For example, by combining the accelerometer and gyroscope, the user's walking patterns and exercise intensity can be analyzed, and daily activity levels can be accurately recorded. In addition, by using the GPS function, the user's location information can be acquired in real time, and their movement history can be recorded in detail. This makes it possible to understand what route the user took and where they stayed. Furthermore, data such as heart rate and steps can also be acquired from wearable devices. Wearable devices include wristbands and smartwatches, and since these devices are always worn on the user's body, data such as heart rate, steps, and calories burned can be continuously acquired. For example, by using a heart rate sensor, heart rate during exercise and rest can be monitored, and health status can be understood in real time. This allows the data acquisition unit to collect diverse data from smartphones and wearable devices, enabling it to record the user's lifestyle in detail.

[0031] The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. Specifically, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI can use a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. For example, the generation AI analyzes the user's activity history and health data, extracting particularly important events and outliers from daily activities. This allows the user to efficiently grasp only the important information from a vast amount of data. The generation AI utilizes natural language processing technology to extract keywords and important phrases from the lifestyle logs and performs a summary based on them. For example, it can pick out particularly noteworthy data, such as days when the user's exercise volume increased sharply or times when their heart rate was abnormally high, and summarize it in a concise text format. Furthermore, the generation AI can also consider the user's past data and trends to summarize long-term changes and trends in their health status. As a result, the summarization unit can efficiently analyze the vast amount of data obtained from the acquisition unit and provide useful information to the user. In addition, the generation AI can continuously improve the accuracy of the summaries based on user feedback. For example, when users provide ratings and comments on the summary content, the generating AI can learn from that feedback and incorporate it into future summaries. This allows the summarization unit to always provide highly accurate summaries based on the latest information, meeting the needs of users.

[0032] The output unit outputs the information summarized by the summarization unit. Specifically, the summarized information can be displayed in text format. For example, the summarized information can be displayed on a smartphone screen for easy user review. The output unit can also output the summarized information in audio format. For example, by using speech synthesis technology to read the summarized information aloud, users can acquire information without relying on visual input. This allows users to check information even when their hands are occupied, such as while driving or exercising. Furthermore, the output unit can display the summarized information in graphic format. For example, the summarized information can be visually displayed as graphs or charts, allowing users to grasp data trends and changes at a glance. This enables users to intuitively understand the overall picture of the data. In addition, the output unit can provide the summarized information through messaging apps. For example, the summarized information can be sent to the messaging app that the user normally uses, allowing the user to check the information anytime, anywhere. This allows the information transmission system to compile information in the user's local environment and communicate it to others without transmitting private information externally. Because the output unit can provide information in the most optimal format according to the user's needs and circumstances, users can acquire information in the most convenient way for them.

[0033] The summarization unit can summarize life logs using a generative AI. For example, the generative AI analyzes the life log, extracts important information, and creates a summary. The generative AI can use a text generation AI (e.g., LLM) to concisely summarize the life log. The summarization unit can also use the generative AI to extract particularly important parts of the life log and summarize them. For example, the generative AI can use keyword extraction technology to pick out particularly important information from the life log and create a summary based on that. This improves the accuracy of life log summarization by using the generative AI. The generative AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The generative AI can understand the content of the life log, extract important information, and create a summary. For example, the generative AI can receive a prompt such as "Please summarize the main points of this life log" and extract the main points to create a summary. This allows the summarization unit to efficiently summarize life logs using the generative AI.

[0034] The output unit can provide summarized information through a messaging app. For example, the output unit can send summarized information to a messaging app in text format. It can also send summarized information to a messaging app in audio format. Furthermore, it can send summarized information to a messaging app in graphic format. For example, by providing summarized information through a messaging app, the output unit facilitates information sharing. Messaging apps can send information in formats such as text messages, audio messages, and image messages. This makes it easier for users to share information with others by providing summarized information through a messaging app.

[0035] The summarization unit can be used as an extended library based on service information such as maps and transit information. For example, the summarization unit can use map information to summarize information related to a specific location within a life log. It can also use transit information to summarize information related to a specific movement within a life log. Furthermore, it can use weather forecast information to summarize information related to a specific weather condition within a life log. For example, the summarization unit improves the accuracy of summarization by using map information to summarize information related to a specific location within a life log. This improves the accuracy of summarization by utilizing service information such as maps and transit information. Service information includes, but is not limited to, map information, transit information, and weather forecasts. The summarization unit can improve the accuracy of summarization by using service information to summarize specific information within a life log. This improves the accuracy of summarization by utilizing service information such as maps and transit information.

[0036] The data acquisition unit can analyze the user's past lifestyle logs and select the optimal data acquisition method. For example, the data acquisition unit can set the optimal acquisition timing based on activities the user has frequently performed in the past. The data acquisition unit can also focus on acquiring data during specific time periods from the user's past lifestyle logs. Furthermore, the data acquisition unit can analyze the user's past behavioral patterns and propose efficient data acquisition methods. For example, by analyzing the user's past lifestyle logs and selecting the optimal data acquisition method, the data acquisition unit can perform efficient data collection. Thus, by analyzing past lifestyle logs, the data acquisition unit can propose efficient data acquisition methods. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past lifestyle log data into a generating AI and have the generating AI select the optimal data acquisition method.

[0037] The data acquisition unit can filter the acquired lifestyle logs based on the user's current activity status and areas of interest. For example, if the user is exercising, the data acquisition unit will acquire only data related to exercise. Furthermore, if the user is working, the data acquisition unit can prioritize acquiring work-related data. Additionally, if the user is engrossed in a hobby, the data acquisition unit can acquire data related to that hobby. For example, the data acquisition unit can detect the user's current activity status using sensors and acquire relevant data. This allows the data acquisition unit to collect highly relevant information by acquiring data based on the user's activity status and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user activity data into a generating AI and have the generating AI perform the filtering of relevant data.

[0038] The data acquisition unit can prioritize the acquisition of highly relevant logs based on the user's geographical location information when acquiring lifestyle logs. For example, if the user is in a specific location, the data acquisition unit will prioritize the acquisition of data related to that location. Furthermore, if the user is traveling, the data acquisition unit can prioritize the acquisition of data related to the travel destination. Additionally, if the user is at home, the data acquisition unit can prioritize the acquisition of data related to activities at home. For example, the data acquisition unit can acquire the user's geographical location information using GPS functionality and prioritize the acquisition of relevant data. This allows the data acquisition unit to collect highly relevant information by acquiring data based on geographical location information. Some or all of the above-described processes in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of determining the priority of relevant data.

[0039] The acquisition unit can analyze the user's social media activity and acquire relevant logs when acquiring lifestyle logs. For example, the acquisition unit can acquire relevant data based on information shared by the user on social media. The acquisition unit can also acquire relevant data based on the activity of accounts that the user follows on social media. Furthermore, the acquisition unit can acquire relevant data based on the activity of groups that the user participates in on social media. For example, the acquisition unit analyzes the user's social media activity and acquires relevant data. In this way, the acquisition unit can collect highly relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant data.

[0040] The summarization unit can adjust the level of detail in the summary based on the importance of the life log when generating the summary. For example, the summarization unit generates a detailed summary for important events. It can also generate a concise summary for everyday events. Furthermore, if the events are related to a specific theme, the summarization unit can generate a theme-focused summary. For example, the summarization unit evaluates the importance of the life log and summarizes important information in detail. This allows the summarization unit to summarize important information in detail by adjusting the level of detail based on the importance of the life log. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the summarization unit can input life log data into a generation AI and have the generation AI perform the adjustment of the level of detail in the summary based on importance.

[0041] The summarization unit can apply different summarization algorithms depending on the category of the lifestyle log when generating a summary. For example, if the log is health-related, the summarization unit can apply a health-specific summarization algorithm. Similarly, if the log is work-related, the summarization unit can apply a work-specific summarization algorithm. Furthermore, if the log is hobby-related, the summarization unit can apply a hobby-specific summarization algorithm. For example, the summarization unit selects an appropriate summarization algorithm based on the category of the lifestyle log and performs the summarization. This allows the summarization unit to generate a more appropriate summary by applying a summarization algorithm according to the category of the lifestyle log. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the summarization unit can input lifestyle log data into a generation AI and have the generation AI apply a category-specific summarization algorithm.

[0042] The summarization unit can determine the priority of summaries based on when the lifestyle logs were acquired during summary generation. For example, the summarization unit may prioritize summarizing recently acquired logs. It can also prioritize summarizing logs acquired during a specific period. Furthermore, it can prioritize summarizing logs from a period specified by the user. For example, the summarization unit may evaluate when the lifestyle logs were acquired and prioritize summarizing the most recent information. In this way, the summarization unit can prioritize summarizing the most recent information by determining the priority of summaries based on when the lifestyle logs were acquired. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the summarization unit can input lifestyle log data into a generation AI and have the generation AI perform the determination of summary priority based on acquisition time.

[0043] The summarization unit can adjust the order of summaries based on the relevance of the life logs during summary generation. For example, the summarization unit prioritizes summarizing logs that are highly relevant. It can also prioritize summarizing logs related to a specific theme. Furthermore, the summarization unit can adjust the order of summaries based on user interests. For example, the summarization unit evaluates the relevance of the life logs and prioritizes summarizing information that is highly relevant. In this way, the summarization unit can prioritize summarizing highly relevant information by adjusting the order of summaries based on the relevance of the life logs. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the summarization unit can input life log data into a generation AI and have the generation AI perform the adjustment of the order of summaries based on relevance.

[0044] The output unit can adjust the level of detail in the output based on the importance of the summarized information. For example, the output unit will produce a detailed output if the information is important. It can also produce a concise output if the information is everyday. Furthermore, if the information relates to a specific theme, the output unit can focus on that theme. For example, the output unit can evaluate the importance of the summarized information and output important information in detail. This allows the output unit to output important information in detail by adjusting the level of detail based on the importance of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI adjust the level of detail in the output based on importance.

[0045] The output unit can apply different output formats depending on the category of the summarized information at the time of output. For example, if the information is health-related, the output unit can apply a health-specific output format. Similarly, if the information is work-related, the output unit can apply a work-specific output format. Furthermore, if the information is hobby-related, the output unit can apply a hobby-specific output format. For example, the output unit selects an appropriate output format according to the category of the summarized information and performs the output. This allows the output unit to generate more appropriate output by applying the appropriate output format according to the category of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI apply the appropriate output format according to the category.

[0046] The output unit can determine the priority of outputs based on when the summarized information was acquired. For example, the output unit may prioritize outputting recently acquired information. The output unit can also prioritize outputting information acquired during a specific period. Furthermore, the output unit can prioritize outputting information from a period specified by the user. For example, the output unit may evaluate when the summarized information was acquired and prioritize outputting the most recent information. This allows the output unit to prioritize outputting the most recent information by determining the priority of outputs based on when the summarized information was acquired. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI perform the determination of output priority based on the acquisition time.

[0047] The output unit can adjust the order of outputs based on the relevance of the summarized information during output. For example, the output unit may prioritize outputting information that is highly relevant. It can also prioritize outputting information related to a specific theme. Furthermore, the output unit can adjust the order of outputs based on user interests. For example, the output unit may evaluate the relevance of the summarized information and prioritize outputting information that is highly relevant. This allows the output unit to prioritize outputting highly relevant information by adjusting the order of outputs based on the relevance of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI perform the adjustment of the output order based on relevance.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The data acquisition unit can analyze the user's past lifestyle logs and select the optimal data acquisition method. For example, it can set the optimal acquisition timing based on activities the user frequently performed in the past. The data acquisition unit can also focus on acquiring data during specific time periods based on the user's past lifestyle logs. Furthermore, the data acquisition unit can analyze the user's past behavioral patterns and propose efficient data acquisition methods. Thus, by analyzing past lifestyle logs, the data acquisition unit can propose efficient data acquisition methods.

[0050] The data acquisition unit can filter the acquired lifestyle logs based on the user's current activity status and areas of interest. For example, if the user is exercising, only data related to exercise will be acquired. Similarly, if the user is working, data related to work can be prioritized. Furthermore, if the user is engrossed in a hobby, data related to that hobby can be acquired. This allows the data acquisition unit to collect highly relevant information by basing data acquisition on the user's activity status and areas of interest.

[0051] The data acquisition unit can prioritize acquiring highly relevant logs based on the user's geographical location information when acquiring lifestyle logs. For example, if the user is in a specific location, it will prioritize acquiring data related to that location. Similarly, if the user is traveling, it can prioritize acquiring data related to their travel destination. Furthermore, if the user is at home, it can prioritize acquiring data related to activities at home. In this way, the data acquisition unit can collect highly relevant information by acquiring data based on geographical location information.

[0052] The data acquisition unit can analyze a user's social media activity and acquire relevant logs when acquiring lifestyle logs. For example, it can acquire relevant data based on information shared by the user on social media. The data acquisition unit can also acquire relevant data based on the activity of accounts the user follows on social media. Furthermore, it can acquire relevant data based on the activity of groups the user participates in on social media. In this way, the data acquisition unit can collect highly relevant information by analyzing social media activity.

[0053] The summarization unit can adjust the level of detail in the summary based on the importance of the life log entries during summary generation. For example, it can generate a detailed summary for important events, and a concise summary for everyday occurrences. Furthermore, for events related to a specific theme, it can generate a summary that focuses on that theme. This allows the summarization unit to summarize important information in detail by adjusting the level of detail based on the importance of the life log entries.

[0054] The summarization unit can apply different summarization algorithms depending on the category of the lifestyle log during summary generation. For example, for health-related logs, a health-specific summarization algorithm is applied. Similarly, for work-related logs, a work-specific summarization algorithm can be applied. Furthermore, for hobby-related logs, a hobby-specific summarization algorithm can be applied. This allows the summarization unit to generate more appropriate summaries by applying the appropriate summarization algorithm according to the category of the lifestyle log.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition unit acquires lifestyle logs from smartphones and wearable devices. For example, it collects the user's activity history and health data using the smartphone's sensors, and acquires data such as heart rate and steps from wearable devices. It can also acquire location information and record the user's movement history. For example, it acquires location information using the smartphone's GPS function. Step 2: The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. For example, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI uses a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. Alternatively, the generation AI can be used to extract particularly important parts of the lifestyle logs and perform a summary. For example, keyword extraction technology can be used to pick out important information, and a summary can be performed based on that. Step 3: The output unit outputs the information summarized by the summarization unit. For example, the summarized information can be displayed in text format. It can also be output in audio format, or even in graphic format. For example, the summarized information can be provided through a messaging app.

[0057] (Example of form 2) The information transmission system according to an embodiment of the present invention is a system that provides a means of communicating information to others by using a generating AI in the user's local environment to compile information without transmitting private information externally. The information transmission system acquires lifestyle logs from smartphones and wearable devices, and the generating AI summarizes the acquired lifestyle logs and creates expressions and content for output. This mechanism makes it easier to communicate events in daily life to others. For example, the information transmission system includes individuals who have difficulty communicating events in daily life to others, such as individuals with developmental disabilities, their families, and caregivers. Because these individuals have difficulty communicating events in daily life to others, they face the challenge of not realizing that daily difficulties become major problems until they arise. To solve this problem, the information transmission system makes it easier to communicate events in daily life to others by having a generating AI built in the user's local environment summarize lifestyle logs acquired from smartphones and wearable devices and create expressions and content for output. For example, if a child with a developmental disability has difficulty telling their parents about what happened at school, the generating AI can summarize the lifestyle logs acquired from the child's smartphone or wearable device and create content for the parents to communicate. This makes it easier for parents to understand events in their child's daily life and take action earlier. Furthermore, the information transmission system protects privacy by creating summarized content in the user's local environment without transmitting data stored on smartphones or other devices externally. In addition, the information transmission system can be used as an extended library based on service information such as maps and transit information, and by integrating with messaging apps, summarized content can be sent via messaging apps. Thus, the information transmission system provides a means of summarizing information using AI generated in the user's local environment and communicating it to others without transmitting private information externally, making it a useful tool for those who find it difficult to communicate everyday events to others. In short, the information transmission system allows information to be summarized and communicated to others in the user's local environment without transmitting private information externally.

[0058] The information transmission system according to this embodiment comprises an acquisition unit, a summarization unit, and an output unit. The acquisition unit acquires lifestyle logs from a smartphone or wearable device. The acquisition unit can, for example, use the smartphone's sensors to collect the user's activity history and health data. The acquisition unit can also acquire data such as heart rate and steps from a wearable device. Furthermore, the acquisition unit can acquire location information and record the user's movement history. For example, the acquisition unit can acquire the user's location information using the smartphone's GPS function. The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. For example, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI can use a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. The summarization unit can also use the generation AI to extract particularly important parts of the lifestyle logs and perform a summary. For example, the generation AI uses keyword extraction technology to pick out particularly important information from the lifestyle logs and performs a summary based on that. The output unit outputs the information summarized by the summarization unit. The output unit can, for example, display summarized information in text format. It can also output summarized information in audio format. Furthermore, it can display summarized information in graphic format. For example, the output unit can provide summarized information through a messaging application. This allows the information transmission system to gather information within the user's local environment and communicate it to others without transmitting private information externally.

[0059] The data acquisition unit acquires lifestyle logs from smartphones and wearable devices. Specifically, it can collect user activity history and health data using the smartphone's sensors. Smartphones are equipped with accelerometers, gyroscopes, GPS, heart rate sensors, etc., and by utilizing these sensors, detailed data such as the user's steps, distance traveled, heart rate, and sleep patterns can be acquired. For example, by combining the accelerometer and gyroscope, the user's walking patterns and exercise intensity can be analyzed, and daily activity levels can be accurately recorded. In addition, by using the GPS function, the user's location information can be acquired in real time, and their movement history can be recorded in detail. This makes it possible to understand what route the user took and where they stayed. Furthermore, data such as heart rate and steps can also be acquired from wearable devices. Wearable devices include wristbands and smartwatches, and since these devices are always worn on the user's body, data such as heart rate, steps, and calories burned can be continuously acquired. For example, by using a heart rate sensor, heart rate during exercise and rest can be monitored, and health status can be understood in real time. This allows the data acquisition unit to collect diverse data from smartphones and wearable devices, enabling it to record the user's lifestyle in detail.

[0060] The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. Specifically, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI can use a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. For example, the generation AI analyzes the user's activity history and health data, extracting particularly important events and outliers from daily activities. This allows the user to efficiently grasp only the important information from a vast amount of data. The generation AI utilizes natural language processing technology to extract keywords and important phrases from the lifestyle logs and performs a summary based on them. For example, it can pick out particularly noteworthy data, such as days when the user's exercise volume increased sharply or times when their heart rate was abnormally high, and summarize it in a concise text format. Furthermore, the generation AI can also consider the user's past data and trends to summarize long-term changes and trends in their health status. As a result, the summarization unit can efficiently analyze the vast amount of data obtained from the acquisition unit and provide useful information to the user. In addition, the generation AI can continuously improve the accuracy of the summaries based on user feedback. For example, when users provide ratings and comments on the summary content, the generating AI can learn from that feedback and incorporate it into future summaries. This allows the summarization unit to always provide highly accurate summaries based on the latest information, meeting the needs of users.

[0061] The output unit outputs the information summarized by the summarization unit. Specifically, the summarized information can be displayed in text format. For example, the summarized information can be displayed on a smartphone screen for easy user review. The output unit can also output the summarized information in audio format. For example, by using speech synthesis technology to read the summarized information aloud, users can acquire information without relying on visual input. This allows users to check information even when their hands are occupied, such as while driving or exercising. Furthermore, the output unit can display the summarized information in graphic format. For example, the summarized information can be visually displayed as graphs or charts, allowing users to grasp data trends and changes at a glance. This enables users to intuitively understand the overall picture of the data. In addition, the output unit can provide the summarized information through messaging apps. For example, the summarized information can be sent to the messaging app that the user normally uses, allowing the user to check the information anytime, anywhere. This allows the information transmission system to compile information in the user's local environment and communicate it to others without transmitting private information externally. Because the output unit can provide information in the most optimal format according to the user's needs and circumstances, users can acquire information in the most convenient way for them.

[0062] The summarization unit can summarize life logs using a generative AI. For example, the generative AI analyzes the life log, extracts important information, and creates a summary. The generative AI can use a text generation AI (e.g., LLM) to concisely summarize the life log. The summarization unit can also use the generative AI to extract particularly important parts of the life log and summarize them. For example, the generative AI can use keyword extraction technology to pick out particularly important information from the life log and create a summary based on that. This improves the accuracy of life log summarization by using the generative AI. The generative AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The generative AI can understand the content of the life log, extract important information, and create a summary. For example, the generative AI can receive a prompt such as "Please summarize the main points of this life log" and extract the main points to create a summary. This allows the summarization unit to efficiently summarize life logs using the generative AI.

[0063] The output unit can provide summarized information through a messaging app. For example, the output unit can send summarized information to a messaging app in text format. It can also send summarized information to a messaging app in audio format. Furthermore, it can send summarized information to a messaging app in graphic format. For example, by providing summarized information through a messaging app, the output unit facilitates information sharing. Messaging apps can send information in formats such as text messages, audio messages, and image messages. This makes it easier for users to share information with others by providing summarized information through a messaging app.

[0064] The summarization unit can be used as an extended library based on service information such as maps and transit information. For example, the summarization unit can use map information to summarize information related to a specific location within a life log. It can also use transit information to summarize information related to a specific movement within a life log. Furthermore, it can use weather forecast information to summarize information related to a specific weather condition within a life log. For example, the summarization unit improves the accuracy of summarization by using map information to summarize information related to a specific location within a life log. This improves the accuracy of summarization by utilizing service information such as maps and transit information. Service information includes, but is not limited to, map information, transit information, and weather forecasts. The summarization unit can improve the accuracy of summarization by using service information to summarize specific information within a life log. This improves the accuracy of summarization by utilizing service information such as maps and transit information.

[0065] The data acquisition unit can estimate the user's emotions and adjust the timing of life log acquisition based on the estimated emotions. For example, if the user is stressed, the data acquisition unit can reduce the frequency of life log acquisition to alleviate the user's burden. Furthermore, if the user is relaxed, the data acquisition unit can acquire more detailed life logs and gather more information. Additionally, if the user is in a hurry, the data acquisition unit can prioritize acquiring only important events. For example, the data acquisition unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the data acquisition unit to reduce the user's burden by adjusting the timing of life log acquisition according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. The data acquisition unit can reduce the user's burden by estimating the user's emotions and adjusting the timing of life log acquisition based on the estimated emotions.

[0066] The data acquisition unit can analyze the user's past lifestyle logs and select the optimal data acquisition method. For example, the data acquisition unit can set the optimal acquisition timing based on activities the user has frequently performed in the past. The data acquisition unit can also focus on acquiring data during specific time periods from the user's past lifestyle logs. Furthermore, the data acquisition unit can analyze the user's past behavioral patterns and propose efficient data acquisition methods. For example, by analyzing the user's past lifestyle logs and selecting the optimal data acquisition method, the data acquisition unit can perform efficient data collection. Thus, by analyzing past lifestyle logs, the data acquisition unit can propose efficient data acquisition methods. The optimal data acquisition method includes, but is not limited to, data type, acquisition frequency, and acquisition method. Some or all of the above-described processes in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past lifestyle log data into a generating AI and have the generating AI select the optimal data acquisition method.

[0067] The data acquisition unit can filter the acquired lifestyle logs based on the user's current activity status and areas of interest. For example, if the user is exercising, the data acquisition unit will acquire only data related to exercise. Furthermore, if the user is working, the data acquisition unit can prioritize acquiring work-related data. Additionally, if the user is engrossed in a hobby, the data acquisition unit can acquire data related to that hobby. For example, the data acquisition unit can detect the user's current activity status using sensors and acquire relevant data. This allows the data acquisition unit to collect highly relevant information by acquiring data based on the user's activity status and areas of interest. Some or all of the above processing in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input user activity data into a generating AI and have the generating AI perform the filtering of relevant data.

[0068] The data acquisition unit can estimate the user's emotions and determine the priority of lifestyle logs to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring data related to stress reduction. It can also prioritize acquiring data related to relaxation if the user is relaxed. Furthermore, if the user is excited, it can prioritize acquiring data related to excitement. For example, the data acquisition unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the data acquisition unit to prioritize important information by determining the priority of lifestyle logs according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. The data acquisition unit can prioritize acquiring important information by estimating the user's emotions and determining the priority of lifestyle logs based on the estimated emotions.

[0069] The data acquisition unit can prioritize the acquisition of highly relevant logs based on the user's geographical location information when acquiring lifestyle logs. For example, if the user is in a specific location, the data acquisition unit will prioritize the acquisition of data related to that location. Furthermore, if the user is traveling, the data acquisition unit can prioritize the acquisition of data related to the travel destination. Additionally, if the user is at home, the data acquisition unit can prioritize the acquisition of data related to activities at home. For example, the data acquisition unit can acquire the user's geographical location information using GPS functionality and prioritize the acquisition of relevant data. This allows the data acquisition unit to collect highly relevant information by acquiring data based on geographical location information. Some or all of the above-described processes in the data acquisition unit may be performed using AI, or not. For example, the data acquisition unit can input the user's geographical location information data into a generating AI and have the generating AI perform the process of determining the priority of relevant data.

[0070] The acquisition unit can analyze the user's social media activity and acquire relevant logs when acquiring lifestyle logs. For example, the acquisition unit can acquire relevant data based on information shared by the user on social media. The acquisition unit can also acquire relevant data based on the activity of accounts that the user follows on social media. Furthermore, the acquisition unit can acquire relevant data based on the activity of groups that the user participates in on social media. For example, the acquisition unit analyzes the user's social media activity and acquires relevant data. In this way, the acquisition unit can collect highly relevant information by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant data.

[0071] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is relaxed, the summarization unit will use soft language. If the user is in a hurry, the summarization unit can use concise and to-the-point language. Furthermore, if the user is excited, the summarization unit can use visually stimulating language. For example, the summarization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the summarization unit to produce a more appropriate summary by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. The summarization unit can produce a more appropriate summary by estimating the user's emotions and adjusting the way the summary is presented based on those emotions.

[0072] The summarization unit can adjust the level of detail in the summary based on the importance of the life log when generating the summary. For example, the summarization unit generates a detailed summary for important events. It can also generate a concise summary for everyday events. Furthermore, if the events are related to a specific theme, the summarization unit can generate a theme-focused summary. For example, the summarization unit evaluates the importance of the life log and summarizes important information in detail. This allows the summarization unit to summarize important information in detail by adjusting the level of detail based on the importance of the life log. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the summarization unit can input life log data into a generation AI and have the generation AI perform the adjustment of the level of detail in the summary based on importance.

[0073] The summarization unit can apply different summarization algorithms depending on the category of the lifestyle log when generating a summary. For example, if the log is health-related, the summarization unit can apply a health-specific summarization algorithm. Similarly, if the log is work-related, the summarization unit can apply a work-specific summarization algorithm. Furthermore, if the log is hobby-related, the summarization unit can apply a hobby-specific summarization algorithm. For example, the summarization unit selects an appropriate summarization algorithm based on the category of the lifestyle log and performs the summarization. This allows the summarization unit to generate a more appropriate summary by applying a summarization algorithm according to the category of the lifestyle log. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the summarization unit can input lifestyle log data into a generation AI and have the generation AI apply a category-specific summarization algorithm.

[0074] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit will generate a short, concise summary. If the user is relaxed, the summarization unit can also generate a longer summary with more detailed explanations. Furthermore, if the user is excited, the summarization unit can generate a visually stimulating summary. For example, the summarization unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the summarization unit to generate a more appropriate summary by adjusting the length of the summary according to the user's emotions. Emotion estimation is performed using techniques such as facial recognition, speech analysis, and text analysis. The summarization unit can generate a more appropriate summary by estimating the user's emotions and adjusting the length of the summary based on the estimated emotions.

[0075] The summarization unit can determine the priority of summaries based on when the lifestyle logs were acquired during summary generation. For example, the summarization unit may prioritize summarizing recently acquired logs. It can also prioritize summarizing logs acquired during a specific period. Furthermore, it can prioritize summarizing logs from a period specified by the user. For example, the summarization unit may evaluate when the lifestyle logs were acquired and prioritize summarizing the most recent information. In this way, the summarization unit can prioritize summarizing the most recent information by determining the priority of summaries based on when the lifestyle logs were acquired. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or without a generation AI. For example, the summarization unit can input lifestyle log data into a generation AI and have the generation AI perform the determination of summary priority based on acquisition time.

[0076] The summarization unit can adjust the order of summaries based on the relevance of the life logs during summary generation. For example, the summarization unit prioritizes summarizing logs that are highly relevant. It can also prioritize summarizing logs related to a specific theme. Furthermore, the summarization unit can adjust the order of summaries based on user interests. For example, the summarization unit evaluates the relevance of the life logs and prioritizes summarizing information that is highly relevant. In this way, the summarization unit can prioritize summarizing highly relevant information by adjusting the order of summaries based on the relevance of the life logs. Some or all of the above processing in the summarization unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the summarization unit can input life log data into a generation AI and have the generation AI perform the adjustment of the order of summaries based on relevance.

[0077] The output unit can estimate the user's emotions and adjust the output's presentation based on those emotions. For example, if the user is relaxed, the output unit will use softer language. If the user is in a hurry, the output unit can use concise and to-the-point language. Furthermore, if the user is excited, the output unit can use visually stimulating language. For example, the output unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the output unit to produce more appropriate output by adjusting its presentation based on the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. The output unit can produce more appropriate output by estimating the user's emotions and adjusting the output's presentation based on those emotions.

[0078] The output unit can adjust the level of detail in the output based on the importance of the summarized information. For example, the output unit will produce a detailed output if the information is important. It can also produce a concise output if the information is everyday. Furthermore, if the information relates to a specific theme, the output unit can focus on that theme. For example, the output unit can evaluate the importance of the summarized information and output important information in detail. This allows the output unit to output important information in detail by adjusting the level of detail based on the importance of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI adjust the level of detail in the output based on importance.

[0079] The output unit can apply different output formats depending on the category of the summarized information at the time of output. For example, if the information is health-related, the output unit can apply a health-specific output format. Similarly, if the information is work-related, the output unit can apply a work-specific output format. Furthermore, if the information is hobby-related, the output unit can apply a hobby-specific output format. For example, the output unit selects an appropriate output format according to the category of the summarized information and performs the output. This allows the output unit to generate more appropriate output by applying the appropriate output format according to the category of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI apply the appropriate output format according to the category.

[0080] The output unit can estimate the user's emotions and adjust the length of the output based on the estimated emotions. For example, if the user is in a hurry, the output unit will produce a short, concise output. If the user is relaxed, the output unit can produce a longer output that includes detailed explanations. Furthermore, if the user is excited, the output unit can produce a visually stimulating output. For example, the output unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the output unit to produce more appropriate output by adjusting the length of the output according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. The output unit can produce more appropriate output by estimating the user's emotions and adjusting the length of the output based on the estimated emotions.

[0081] The output unit can determine the priority of outputs based on when the summarized information was acquired. For example, the output unit may prioritize outputting recently acquired information. The output unit can also prioritize outputting information acquired during a specific period. Furthermore, the output unit can prioritize outputting information from a period specified by the user. For example, the output unit may evaluate when the summarized information was acquired and prioritize outputting the most recent information. This allows the output unit to prioritize outputting the most recent information by determining the priority of outputs based on when the summarized information was acquired. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI perform the determination of output priority based on the acquisition time.

[0082] The output unit can adjust the order of outputs based on the relevance of the summarized information during output. For example, the output unit may prioritize outputting information that is highly relevant. It can also prioritize outputting information related to a specific theme. Furthermore, the output unit can adjust the order of outputs based on user interests. For example, the output unit may evaluate the relevance of the summarized information and prioritize outputting information that is highly relevant. This allows the output unit to prioritize outputting highly relevant information by adjusting the order of outputs based on the relevance of the summarized information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input summarized information data into a generating AI and have the generating AI perform the adjustment of the output order based on relevance.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The data acquisition unit can estimate the user's emotions and adjust how it acquires lifestyle logs based on those emotions. For example, if the user is stressed, the unit reduces the frequency of lifestyle log acquisition to lessen the user's burden. Conversely, if the user is relaxed, the unit can acquire more detailed lifestyle logs to gather more information. Furthermore, if the user is in a hurry, the unit can prioritize acquiring only important events. In this way, the data acquisition unit can reduce the user's burden and efficiently collect necessary information by adjusting how it acquires lifestyle logs according to the user's emotions.

[0085] The summarization function can estimate the user's emotions and adjust the way the summary is presented based on those emotions. For example, if the user is relaxed, the summarization function will use gentle language. If the user is in a hurry, the summarization function can use concise and to-the-point language. Furthermore, if the user is excited, the summarization function can use visually stimulating language. In this way, the summarization function can generate more appropriate summaries by adjusting the way the summary is presented according to the user's emotions.

[0086] The output unit can estimate the user's emotions and adjust the output's presentation based on those emotions. For example, if the user is relaxed, the output unit will use softer language. If the user is in a hurry, the output unit can use concise and to-the-point language. Furthermore, if the user is excited, the output unit can use visually stimulating language. In this way, the output unit can generate more appropriate output by adjusting its presentation according to the user's emotions.

[0087] The data acquisition unit can analyze the user's past lifestyle logs and select the optimal data acquisition method. For example, it can set the optimal acquisition timing based on activities the user frequently performed in the past. The data acquisition unit can also focus on acquiring data during specific time periods based on the user's past lifestyle logs. Furthermore, the data acquisition unit can analyze the user's past behavioral patterns and propose efficient data acquisition methods. Thus, by analyzing past lifestyle logs, the data acquisition unit can propose efficient data acquisition methods.

[0088] The data acquisition unit can filter the acquired lifestyle logs based on the user's current activity status and areas of interest. For example, if the user is exercising, only data related to exercise will be acquired. Similarly, if the user is working, data related to work can be prioritized. Furthermore, if the user is engrossed in a hobby, data related to that hobby can be acquired. This allows the data acquisition unit to collect highly relevant information by basing data acquisition on the user's activity status and areas of interest.

[0089] The data acquisition unit can estimate the user's emotions and determine the priority of lifestyle logs to acquire based on those emotions. For example, if the user is stressed, it can prioritize acquiring data related to stress reduction. Similarly, if the user is relaxed, it can prioritize acquiring data related to relaxation. Furthermore, if the user is excited, it can prioritize acquiring data related to excitement. In this way, the data acquisition unit can prioritize acquiring important information by determining the priority of lifestyle logs according to the user's emotions.

[0090] The data acquisition unit can prioritize acquiring highly relevant logs based on the user's geographical location information when acquiring lifestyle logs. For example, if the user is in a specific location, it will prioritize acquiring data related to that location. Similarly, if the user is traveling, it can prioritize acquiring data related to their travel destination. Furthermore, if the user is at home, it can prioritize acquiring data related to activities at home. In this way, the data acquisition unit can collect highly relevant information by acquiring data based on geographical location information.

[0091] The data acquisition unit can analyze a user's social media activity and acquire relevant logs when acquiring lifestyle logs. For example, it can acquire relevant data based on information shared by the user on social media. The data acquisition unit can also acquire relevant data based on the activity of accounts the user follows on social media. Furthermore, it can acquire relevant data based on the activity of groups the user participates in on social media. In this way, the data acquisition unit can collect highly relevant information by analyzing social media activity.

[0092] The summarization unit can adjust the level of detail in the summary based on the importance of the life log entries during summary generation. For example, it can generate a detailed summary for important events, and a concise summary for everyday occurrences. Furthermore, for events related to a specific theme, it can generate a summary that focuses on that theme. This allows the summarization unit to summarize important information in detail by adjusting the level of detail based on the importance of the life log entries.

[0093] The summarization unit can apply different summarization algorithms depending on the category of the lifestyle log during summary generation. For example, for health-related logs, a health-specific summarization algorithm is applied. Similarly, for work-related logs, a work-specific summarization algorithm can be applied. Furthermore, for hobby-related logs, a hobby-specific summarization algorithm can be applied. This allows the summarization unit to generate more appropriate summaries by applying the appropriate summarization algorithm according to the category of the lifestyle log.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The acquisition unit acquires lifestyle logs from smartphones and wearable devices. For example, it collects the user's activity history and health data using the smartphone's sensors, and acquires data such as heart rate and steps from wearable devices. It can also acquire location information and record the user's movement history. For example, it acquires location information using the smartphone's GPS function. Step 2: The summarization unit uses a generation AI to summarize the lifestyle logs acquired by the acquisition unit. For example, the generation AI analyzes the lifestyle logs, extracts important information, and performs a summary. The generation AI uses a text generation AI (e.g., LLM) to concisely summarize the lifestyle logs. Alternatively, the generation AI can be used to extract particularly important parts of the lifestyle logs and perform a summary. For example, keyword extraction technology can be used to pick out important information, and a summary can be performed based on that. Step 3: The output unit outputs the information summarized by the summarization unit. For example, the summarized information can be displayed in text format. It can also be output in audio format, or even in graphic format. For example, the summarized information can be provided through a messaging app.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the acquisition unit, summarization unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can collect the user's activity history and health data using the sensors of the smart device 14. The acquisition unit can also acquire location information using the GPS function of the smart device 14 and record the user's movement history. The summarization unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired lifestyle log using a generation AI, extracts important information, and performs summarization. The output unit is implemented in the control unit 46A of the smart device 14, which can display the summarized information in text or audio format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the acquisition unit, summarization unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can collect the user's activity history and health data using the sensors of the smart glasses 214. The acquisition unit can also acquire location information using the GPS function of the smart glasses 214 and record the user's movement history. The summarization unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired lifestyle log using a generation AI, extracts important information, and performs summarization. The output unit is implemented in the control unit 46A of the smart glasses 214, which can display the summarized information in text or audio format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the acquisition unit, summarization unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can collect the user's activity history and health data using the sensors of the headset terminal 314. The acquisition unit can also acquire location information using the GPS function of the headset terminal 314 and record the user's movement history. The summarization unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired lifestyle log using a generation AI, extracts important information, and performs summarization. The output unit is implemented in the control unit 46A of the headset terminal 314, which can display the summarized information in text or audio format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the acquisition unit, summarization unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can collect the user's activity history and health data using the sensors of the robot 414. The acquisition unit can also acquire location information using the GPS function of the robot 414 and record the user's movement history. The summarization unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired lifestyle log using a generation AI, extracts important information, and performs summarization. The output unit is implemented in the control unit 46A of the robot 414, which can display the summarized information in text or audio format. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) The acquisition unit obtains lifestyle logs from smartphones and wearable devices, A summarization unit that summarizes the lifestyle log acquired by the acquisition unit, The system comprises an output unit that outputs the information summarized by the summarization unit. A system characterized by the following features. (Note 2) The summary section above is, Summarizing lifestyle logs using generation AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The output unit is, Summarized information is provided through messaging apps. The system described in Appendix 1, characterized by the features described herein. (Note 4) The summary section above is, It will be used as an extension library based on service information such as maps and transit information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring lifestyle logs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, Analyze the user's past lifestyle logs and select the optimal method for acquiring them. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring lifestyle logs, filtering is performed based on the user's current activity status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, It estimates the user's emotions and determines the priority of lifestyle logs to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring lifestyle logs, the system prioritizes acquiring logs that are highly relevant based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring lifestyle logs, the system analyzes the user's social media activity and retrieves relevant logs. The system described in Appendix 1, characterized by the features described herein. (Note 11) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, When generating a summary, adjust the level of detail in the summary based on the importance of the lifestyle log. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the lifestyle log. The system described in Appendix 1, characterized by the features described herein. (Note 14) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The summary section above is, When generating summaries, the priority of summaries is determined based on when the lifestyle logs were acquired. The system described in Appendix 1, characterized by the features described herein. (Note 16) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the lifestyle logs. The system described in Appendix 1, characterized by the features described herein. (Note 17) The output unit is, It estimates the user's emotions and adjusts the way the output is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The output unit is, When outputting, adjust the level of detail in the output based on the importance of the summarized information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, When outputting, different output formats are applied depending on the category of the summarized information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, It estimates the user's emotions and adjusts the length of the output based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, When outputting, prioritize the output based on when the summarized information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, When outputting, adjust the order of the output based on the relevance of the summarized information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit obtains lifestyle logs from smartphones and wearable devices, A summarization unit that summarizes the lifestyle log acquired by the acquisition unit, The system comprises an output unit that outputs the information summarized by the summarization unit. A system characterized by the following features.

2. The summary section above is, Summarizing lifestyle logs using generation AI. The system according to feature 1.

3. The output unit is, Summarized information is provided through messaging apps. The system according to feature 1.

4. The summary section above is, It will be used as an extension library based on service information such as maps and transit information. The system according to feature 1.

5. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring lifestyle logs based on those estimated emotions. The system according to feature 1.

6. The acquisition unit is, Analyze the user's past lifestyle logs and select the optimal method for acquiring them. The system according to feature 1.

7. The acquisition unit is, When acquiring lifestyle logs, filtering is performed based on the user's current activity status and areas of interest. The system according to feature 1.

8. The acquisition unit is, It estimates the user's emotions and determines the priority of lifestyle logs to collect based on the estimated user emotions. The system according to feature 1.

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