system

Abstract

The system according to this embodiment aims to analyze user behavior and automatically place orders and make notes. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, an ordering unit, a memo unit, and an interlocking unit. The recording unit records the user's actions. The analysis unit analyzes the actions recorded by the recording unit. The ordering unit places orders based on the actions analyzed by the analysis unit. The memo unit makes memos based on the actions analyzed by the analysis unit. The interlocking unit coordinates the orders and memos made by the ordering unit and memo unit with each company.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's actions are not sufficiently automatically recorded and ordering or memo-making is not performed based on them, and there is room for improvement.

[0005] [[ID=3৮]] The system according to the embodiment aims to analyze the user's actions and automatically perform ordering or memo-making.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recording unit, an analysis unit, an ordering unit, a memo unit, and an interlocking unit. The recording unit records the user's actions. The analysis unit analyzes the actions recorded by the recording unit. The ordering unit places orders based on the actions analyzed by the analysis unit. The memo unit makes memos based on the actions analyzed by the analysis unit. The interlocking unit coordinates the orders and memos made by the ordering unit and memo unit with each company. [Effects of the Invention]

[0007] The system according to this embodiment can analyze user behavior and automatically place orders and make notes. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The behavior recording system according to an embodiment of the present invention is a system that automatically records a user's actions and automatically places orders and takes notes based on those actions. This behavior recording system automatically records the user's actions using voice and camera, and a generating AI analyzes the recorded actions and automatically places the necessary orders and takes notes. For example, if a user says, "Please take notes on what was said in today's meeting," the behavior recording system records the voice and the generating AI analyzes it. It is also possible to record the user's actions using a camera. For example, the camera can record the user shopping and save the content as a note. Next, the generating AI analyzes the recorded actions and automatically places the necessary orders and takes notes. For example, if a user says, "Please prepare the materials for tomorrow's meeting," the generating AI analyzes the instruction and automatically places an order for the necessary materials. Also, if a user says, "Please take notes on what was said in today's meeting," the generating AI analyzes the content and saves it as a note. This eliminates the need for the user to take notes and simplifies the process. For example, even when a user is busy, the behavior recording system automatically takes notes, saving them time and effort. Furthermore, since the procedures are automated, users can complete the necessary steps without any hassle. In addition, the activity tracking system not only records user activity but can also link with various companies to place orders and take notes. For example, if a user says, "Prepare the materials for tomorrow's meeting," the activity tracking system analyzes the instruction and automatically orders the necessary materials. Similarly, if a user says, "Take notes on today's meeting," the activity tracking system analyzes the content and saves it as a note. In this way, the activity tracking system reduces the user's effort and simplifies procedures.

[0029] The behavior recording system according to this embodiment comprises a recording unit, an analysis unit, an ordering unit, a memo unit, and an interlocking unit. The recording unit records the user's actions. The recording unit can record the user's actions using, for example, voice or camera. For example, if the user says, "Please make a note of what was said in today's meeting," the recording unit will record the voice. The recording unit can also record the user's actions using a camera. For example, it can record the user shopping with a camera and save the content as a memo. The analysis unit analyzes the actions recorded by the recording unit. The analysis unit can analyze the recorded actions using, for example, generative AI, and identify necessary orders or memos. For example, if the user says, "Please prepare the materials for tomorrow's meeting," the analysis unit will analyze the instruction and identify the necessary materials. The ordering unit places orders based on the actions analyzed by the analysis unit. The ordering unit can automatically order the materials identified by the analysis unit. For example, if the user says, "Please prepare the materials for tomorrow's meeting," the ordering unit will order the materials based on that instruction. The memo unit takes notes based on the actions analyzed by the analysis unit. The memo unit can automatically save content identified by the analysis unit as a memo. For example, if a user says, "Take notes on today's meeting," the memo unit will save that content as a memo. The linking unit coordinates orders and notes made by the ordering unit and memo unit with each company. The linking unit can coordinate materials ordered by the ordering unit with each company. For example, if a user says, "Prepare the materials for tomorrow's meeting," the linking unit will arrange for the materials based on that instruction. As a result, the action recording system according to this embodiment can automatically record, analyze, order, take notes, and link user actions, saving effort and simplifying procedures.

[0030] The recording unit records user actions. For example, it can record user actions using audio and camera. Specifically, a high-sensitivity microphone is used for audio recording to clearly capture user speech. Audio data is processed using noise reduction technology to remove unwanted background noise and clarify important statements. A high-resolution camera with a wide-angle lens is used to record user actions in detail. The camera is equipped with facial recognition and motion detection technologies to track user movements and automatically adjusts focus even when the user moves. For example, if a user says, "Please make a note of what was said in today's meeting," the recording unit will record that audio. Audio recording is done in real time, and the recorded data is immediately transmitted to a cloud server. The recording unit can also record user actions using a camera. For example, it can record a user shopping and save the footage as a memo. Camera recording records user actions chronologically, making it easy to play back later. This allows the recording unit to record user actions in detail and accurately, providing data necessary for subsequent analysis, ordering, and memo creation. Furthermore, the recording unit ensures data security by encrypting recorded data and controlling access to protect user privacy. This allows the recording unit to efficiently and securely record user behavior, improving the overall reliability of the system.

[0031] The analysis unit analyzes the actions recorded by the recording unit. For example, the analysis unit can use generative AI to analyze recorded actions and identify necessary orders or memos. Specifically, the generative AI uses natural language processing technology to convert speech data into text and analyze the content of the user's speech. For example, if a user says, "Prepare the materials for tomorrow's meeting," the generative AI analyzes the instruction and identifies the necessary materials. The generative AI uses speech recognition technology to accurately recognize the user's speech and employs contextual analysis algorithms to understand the context. This allows the generative AI to accurately grasp the user's intent and generate appropriate instructions. The analysis unit also analyzes video data recorded by a camera to identify the user's actions. For example, it can analyze video footage of a user shopping and generate a list of purchased items. Image recognition and object detection technologies are used in the video analysis to analyze the user's actions and surrounding environment in detail. This allows the analysis unit to analyze the recorded data from multiple angles and identify appropriate orders or memos based on the user's actions. Furthermore, the analysis unit can learn from past data and user behavior patterns to perform more accurate analyses. This allows the analysis unit to quickly and accurately analyze user behavior, thereby improving the overall efficiency of the system.

[0032] The ordering department places orders based on behaviors analyzed by the analytics department. For example, the ordering department can automatically order materials identified by the analytics department. Specifically, the ordering department automates the ordering process for necessary materials and products based on information provided by the analytics department. For example, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department will order the materials based on that instruction. The ordering department is linked to an online ordering system, checks the inventory status of necessary materials and products, and selects the optimal supplier. The ordering process is carried out in real time, and the user is notified of the order status. The ordering department provides a user interface to handle cases where the order details need to be confirmed or changed. This allows the user to check the order details and make corrections as needed. Furthermore, the ordering department can manage the order history and perform analysis and report generation based on past order data. This allows the ordering department to respond quickly and accurately to user needs and improve the efficiency of ordering operations.

[0033] The memo section takes notes based on the actions analyzed by the analysis section. For example, the memo section can automatically save content identified by the analysis section as a memo. Specifically, the memo section saves the user's statements and actions in text format based on the information provided by the analysis section. For example, if a user says, "Take notes on what we discussed in today's meeting," the memo section saves that content as a memo. The memo section synchronizes the saved memos to a cloud server, allowing users to access them at any time. The memos are organized by date and content, and can be easily referenced using the search function. Furthermore, the memo section provides editing and sharing functions for memos, allowing users to share memos with other users and modify their content. This enables the memo section to efficiently record user statements and actions and quickly provide necessary information.

[0034] The Integration Unit coordinates orders and memos made by the Ordering Unit and Memo Unit with each company. For example, the Integration Unit can arrange for materials ordered by the Ordering Unit in conjunction with each company. Specifically, based on the order information provided by the Ordering Unit, the Integration Unit coordinates with each company's ordering system to arrange for necessary materials and products. For example, if a user says, "Prepare the materials for tomorrow's meeting," the Integration Unit will arrange for the materials based on that instruction. The Integration Unit communicates with each company's ordering system in real time to check the order status and delivery date. Once an order is completed, the Integration Unit notifies the user and provides order details and delivery date information. Furthermore, the Integration Unit shares memos saved by the Memo Unit with each company, quickly transmitting necessary information. For example, if a user says, "Make notes on today's meeting," the Integration Unit shares the notes with the relevant companies and transmits the meeting content. In this way, the Integration Unit can efficiently manage order and memo information in coordination with each company and respond quickly to user needs.

[0035] The recording unit can record user actions using voice and camera. For example, if a user says, "Please make a note of what was said in today's meeting," the recording unit will record that voice. For example, the recording unit can record a user shopping with a camera and save the content as a memo. For example, if a user says, "Please prepare the materials for tomorrow's meeting," the recording unit can record that instruction by voice. In this way, user actions can be accurately recorded using voice and camera. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's voice data into a generating AI and have the generating AI generate text data from the voice data.

[0036] The analysis unit can analyze recorded actions and identify necessary orders and memos. For example, the analysis unit can use a generative AI to analyze recorded actions and identify necessary orders and memos. For example, if a user says, "Prepare the materials for tomorrow's meeting," the analysis unit can analyze that instruction and identify the necessary materials. For example, if a user says, "Make notes on today's meeting," the analysis unit can analyze the content and save it as a memo. In this way, necessary orders and memos can be automatically identified by analyzing recorded actions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input recorded action data into a generative AI and have the generative AI perform the analysis of the action data.

[0037] The ordering department can automatically order necessary materials based on the analysis results. For example, the ordering department can automatically order materials identified by the analysis department. For example, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department can order the materials based on that instruction. For example, if a user says, "Order the necessary materials," the ordering department can order the materials based on that instruction. This reduces the user's workload by automatically placing orders based on the analysis results. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the analysis results into a generating AI and have the generating AI execute the ordering procedure.

[0038] The memo unit can automatically save memos based on the analysis results. For example, the memo unit can automatically save content identified by the analysis unit as a memo. For example, if a user says, "Make a memo of today's meeting," the memo unit can save that content as a memo. For example, if a user says, "Save the necessary memos," the memo unit can save that content as a memo. This reduces the user's effort by automatically saving memos based on the analysis results. Some or all of the above processing in the memo unit may be performed using AI, for example, or without AI. For example, the memo unit can input the analysis results into a generating AI and have the generating AI perform the memo saving.

[0039] The linking unit can place orders and make memos in conjunction with each company. For example, the linking unit can arrange for materials ordered by the ordering unit in conjunction with each company. For example, if a user says, "Prepare the materials for tomorrow's meeting," the linking unit can arrange for the materials based on that instruction. For example, if a user says, "Arrange the necessary materials," the linking unit can arrange for the materials based on that instruction. In this way, the ordering and memo procedures can be simplified by linking with each company. Some or all of the above processing in the linking unit may be performed using AI, for example, or not using AI. For example, the linking unit can input data from the ordering unit and the memo unit into a generating AI and have the generating AI execute the linking procedures.

[0040] The recording unit can analyze the user's past behavior history and select the optimal recording method. For example, if the user has preferred using voice recording in the past, the recording unit can prioritize voice recording. For example, if the user has frequently used camera recording in the past, the recording unit can prioritize camera recording. For example, based on the user's behavior patterns, the recording unit can suggest using both voice and camera recording. In this way, by analyzing past behavior history, the recording unit can provide the user with the most suitable recording method. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal recording method.

[0041] The recording unit can filter the recording of actions based on the user's current activity status and areas of interest. For example, if the user is in a meeting, the recording unit can record only actions related to the meeting content. For example, if the user is shopping, the recording unit can prioritize recording information about purchased items. For example, if the user is exercising, the recording unit can record data related to the exercise. This allows for prioritizing the recording of important actions by filtering based on the current activity status and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's current activity data into a generating AI and have the generating AI perform the filtering process.

[0042] The recording unit can prioritize recording highly relevant actions by considering the user's geographical location information when recording actions. For example, if the user is in a specific location, the recording unit can prioritize recording actions related to that location. For example, if the user is traveling, the recording unit can prioritize recording actions at tourist destinations. For example, if the user is at work, the recording unit can prioritize recording work-related actions. This allows for the priority recording of highly relevant actions by considering geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of actions.

[0043] The recording unit can analyze a user's social media activity and record relevant actions when recording behavior. For example, the recording unit can record behavior based on content shared by the user on social media. For example, the recording unit can record behavior based on the activity of accounts followed by the user on social media. For example, the recording unit can record behavior based on events the user participates in on social media. This allows relevant behavior to be recorded by analyzing social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media data into a generating AI and have the generating AI perform the behavior recording.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the actions during the analysis. For example, the analysis unit can analyze the contents of important meetings in detail. For example, the analysis unit can analyze everyday actions concisely. For example, the analysis unit can analyze urgent actions quickly. By adjusting the level of detail of the analysis based on the importance of the actions, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input action importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of behavior during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to business-related behaviors. For example, the analysis unit can apply a private-use analysis algorithm to private-use behaviors. For example, the analysis unit can apply a health-use analysis algorithm to health-related behaviors. By applying different analysis algorithms depending on the category of behavior, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input behavior category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the timing of the behavior recordings during the analysis. For example, the analysis unit can prioritize the analysis of recently recorded behaviors. For example, the analysis unit can prioritize the analysis of important past behaviors. For example, the analysis unit can prioritize the analysis of highly urgent behaviors. By determining the priority of analysis based on the timing of the behavior recordings, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input behavior recording timing data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the actions during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant actions. For example, the analysis unit can postpone the analysis of less relevant actions. For example, the analysis unit can group related actions together for analysis. By adjusting the order of analysis based on the relevance of the actions, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance data of the actions into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0048] The ordering department can analyze the user's past ordering history to select the optimal ordering method when placing an order. For example, the ordering department can prioritize suggesting ordering methods that the user has used in the past. For example, the ordering department can suggest the optimal ordering timing based on the user's past ordering history. For example, the ordering department can analyze the user's past ordering history and suggest the optimal ordering method. In this way, the optimal ordering method can be provided by analyzing past ordering history. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's past ordering data into a generating AI and have the generating AI select the optimal ordering method.

[0049] The ordering department can customize the ordering process based on the user's current needs at the time of ordering. For example, if the user is in a hurry, the ordering department can provide a quick ordering method. For example, if the user needs a specific product, the ordering department can provide an ordering method specifically for that product. For example, if the user needs multiple products, the ordering department can provide a bulk ordering method. By customizing the ordering method based on current needs, appropriate ordering becomes possible. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's current needs data into a generating AI and have the generating AI perform the customization of the ordering method.

[0050] The ordering department can select the optimal ordering method when placing an order, taking into account the user's geographical location information. For example, if the user is in a specific location, the ordering department can prioritize orders related to that location. For example, if the user is traveling, the ordering department can prioritize orders at the travel destination. For example, if the user is at work, the ordering department can prioritize orders related to work. In this way, the ordering department can provide the optimal ordering method by taking geographical location information into consideration. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's geographical location data into a generating AI and have the generating AI select the optimal ordering method.

[0051] The ordering department can analyze a user's social media activity and propose ordering methods when placing an order. For example, the ordering department can propose orders based on content shared by the user on social media. For example, the ordering department can propose orders based on the activity of accounts followed by the user on social media. For example, the ordering department can propose orders based on events the user participates in on social media. In this way, by analyzing social media activity, the appropriate ordering method can be proposed. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's social media data into a generating AI and have the generating AI execute the proposal of ordering methods.

[0052] The memo section can analyze the user's past memo history and select the optimal saving method when saving a memo. For example, the memo section can prioritize suggesting memo saving methods the user has used in the past. For example, the memo section can suggest the optimal saving timing based on the user's past memo history. For example, the memo section can analyze the user's past memo history and suggest the optimal saving method. In this way, by analyzing past memo history, the optimal saving method can be provided. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's past memo data into a generating AI and have the generating AI select the optimal saving method.

[0053] The memo section can customize the memo format based on the user's current activity when saving memos. For example, if the user is in a meeting, the memo section can take detailed notes on the meeting content. For example, if the user is shopping, the memo section can take notes on information about purchased items. For example, if the user is exercising, the memo section can take notes on data related to the exercise. This allows for the saving of appropriate memos by customizing the memo format based on the user's current activity. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's current activity data into a generating AI and have the generating AI perform the customization of the memo format.

[0054] The memo section can select the optimal saving method when saving memos, taking into account the user's geographical location information. For example, if the user is in a specific location, the memo section can prioritize saving memos related to that location. For example, if the user is traveling, the memo section can prioritize saving memos from their travel destination. For example, if the user is at work, the memo section can prioritize saving work-related memos. In this way, the memo section can provide the optimal saving method by taking geographical location information into consideration. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's geographical location data into a generating AI and have the generating AI select the optimal saving method.

[0055] The memo section can analyze the user's social media activity and suggest memo methods when saving memos. For example, the memo section can suggest memos based on content the user has shared on social media. For example, the memo section can suggest memos based on the activity of accounts the user follows on social media. For example, the memo section can suggest memos based on events the user has participated in on social media. In this way, by analyzing social media activity, it is possible to suggest appropriate memo methods. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting memo methods.

[0056] The integration unit can analyze the user's past integration history and select the optimal integration method during integration. For example, the integration unit can prioritize suggesting integration methods that the user has used in the past. For example, the integration unit can suggest the optimal integration timing based on the user's past integration history. For example, the integration unit can analyze the user's past integration history and suggest the optimal integration means. In this way, by analyzing past integration history, the optimal integration method can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past integration data into a generating AI and have the generating AI select the optimal integration method.

[0057] The integration unit can customize the means of integration based on the user's current needs during integration. For example, if the user is in a hurry, the integration unit can provide a rapid integration method. For example, if the user needs a specific service, the integration unit can provide an integration method specialized for that service. For example, if the user needs multiple services, the integration unit can provide a batch integration method. This enables appropriate integration by customizing the integration method based on current needs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current needs data into a generating AI and have the generating AI perform the customization of the integration method.

[0058] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific location, the integration unit can prioritize integrations related to that location. For example, if the user is traveling, the integration unit can prioritize integrations at the travel destination. For example, if the user is at work, the integration unit can prioritize integrations related to work. In this way, the optimal integration method can be provided by considering geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal integration method.

[0059] The integration unit can analyze the user's social media activity and propose integration methods during integration. For example, the integration unit can propose integration based on content shared by the user on social media. For example, the integration unit can propose integration based on the activity of accounts followed by the user on social media. For example, the integration unit can propose integration based on events the user participates in on social media. In this way, by analyzing social media activity, an appropriate integration method can be proposed. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of integration methods.

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

[0061] The recording unit can adjust the recording method when recording user behavior, taking into account the user's health condition. For example, if the user is tired, the recording unit can reduce the recording frequency. For example, if the user is in good health, the recording unit can perform detailed recording. For example, if the user is ill, the recording unit can temporarily suspend recording. This allows for appropriate recording by adjusting the recording method according to the user's health condition. Some or all of the above processing in the recording unit can be performed, for example, by inputting health data into a generating AI and having the generating AI perform the adjustment of the recording method.

[0062] The analysis unit can adjust its analysis method when analyzing user behavior, taking into account the user's past behavior patterns. For example, the analysis unit can prioritize the analysis of actions the user has frequently performed in the past. For example, the analysis unit can analyze in detail actions that the user has considered important in the past. For example, the analysis unit can briefly analyze actions that the user has ignored in the past. By adjusting the analysis method based on the user's past behavior patterns, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit can be performed, for example, by inputting past behavior data into a generating AI and having the generating AI perform the adjustment of the analysis method.

[0063] The ordering unit can adjust the timing of orders based on the results of analyzing user behavior. For example, if the user is in a hurry, the ordering unit can place an order quickly. For example, if the user is relaxed, the ordering unit can delay the order. For example, if the user wishes to place an order during a specific time period, the ordering unit can place an order during that time period. In this way, appropriate orders can be placed by adjusting the timing of orders based on the results of user behavior analysis. Some or all of the above processing in the ordering unit can be performed, for example, by inputting behavior analysis data into a generating AI and having the generating AI perform the adjustment of the order timing.

[0064] The memo section can adjust the format of memos based on user preferences when recording user behavior. For example, if the user prefers text format, the memo section can save memos in text format. For example, if the user prefers audio format, the memo section can save memos in audio format. For example, if the user prefers image format, the memo section can save memos in image format. This allows for the saving of appropriate memos by adjusting the memo format according to user preferences. Some or all of the above processing in the memo section can be performed, for example, by inputting user preference data into a generating AI and having the generating AI perform the memo format adjustment.

[0065] The integration unit can adjust the timing of integration when recording user behavior, taking the user's schedule into consideration. For example, the integration unit can delay integration if the user is busy. For example, the integration unit can perform integration during times when the user is free. For example, if the user requests integration at a specific time, the integration unit can perform integration at that time. In this way, appropriate integration can be performed by adjusting the timing of integration according to the user's schedule. Some or all of the above processing in the integration unit can be performed, for example, by inputting schedule data into a generating AI and having the generating AI perform the adjustment of the integration timing.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The recording unit records the user's actions. The recording unit can record the user's actions using, for example, audio or camera. If the user says, "Please make a note of what was said in today's meeting," the recording will record that audio. It can also record the user's actions using a camera. For example, the camera can record the user shopping and save the content as a memo. Step 2: The analysis unit analyzes the actions recorded by the recording unit. The analysis unit can, for example, use generative AI to analyze the recorded actions and identify necessary orders or notes. For example, if a user says, "Prepare the materials for tomorrow's meeting," the analysis unit will analyze that instruction and identify the necessary materials. Step 3: The ordering department places orders based on the behavior analyzed by the analysis department. The ordering department can, for example, automatically order materials identified by the analysis department. For instance, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department will order the materials based on that instruction. Step 4: The memo unit takes notes based on the actions analyzed by the analysis unit. For example, the memo unit can automatically save the content identified by the analysis unit as a memo. For instance, if a user says, "Take notes on what we discussed in today's meeting," the memo unit will save that content as a memo. Step 5: The integration unit coordinates orders and memos made by the ordering unit and memo unit with each company. For example, the integration unit can coordinate with each company to arrange for materials ordered by the ordering unit. For instance, if a user says, "Prepare the materials for tomorrow's meeting," the integration unit will arrange for the materials based on that instruction.

[0068] (Example of form 2) The behavior recording system according to an embodiment of the present invention is a system that automatically records a user's actions and automatically places orders and takes notes based on those actions. This behavior recording system automatically records the user's actions using voice and camera, and a generating AI analyzes the recorded actions and automatically places the necessary orders and takes notes. For example, if a user says, "Please take notes on what was said in today's meeting," the behavior recording system records the voice and the generating AI analyzes it. It is also possible to record the user's actions using a camera. For example, the camera can record the user shopping and save the content as a note. Next, the generating AI analyzes the recorded actions and automatically places the necessary orders and takes notes. For example, if a user says, "Please prepare the materials for tomorrow's meeting," the generating AI analyzes the instruction and automatically places an order for the necessary materials. Also, if a user says, "Please take notes on what was said in today's meeting," the generating AI analyzes the content and saves it as a note. This eliminates the need for the user to take notes and simplifies the process. For example, even when a user is busy, the behavior recording system automatically takes notes, saving them time and effort. Furthermore, since the procedures are automated, users can complete the necessary steps without any hassle. In addition, the activity tracking system not only records user activity but can also link with various companies to place orders and take notes. For example, if a user says, "Prepare the materials for tomorrow's meeting," the activity tracking system analyzes the instruction and automatically orders the necessary materials. Similarly, if a user says, "Take notes on today's meeting," the activity tracking system analyzes the content and saves it as a note. In this way, the activity tracking system reduces the user's effort and simplifies procedures.

[0069] The behavior recording system according to this embodiment comprises a recording unit, an analysis unit, an ordering unit, a memo unit, and an interlocking unit. The recording unit records the user's actions. The recording unit can record the user's actions using, for example, voice or camera. For example, if the user says, "Please make a note of what was said in today's meeting," the recording unit will record the voice. The recording unit can also record the user's actions using a camera. For example, it can record the user shopping with a camera and save the content as a memo. The analysis unit analyzes the actions recorded by the recording unit. The analysis unit can analyze the recorded actions using, for example, generative AI, and identify necessary orders or memos. For example, if the user says, "Please prepare the materials for tomorrow's meeting," the analysis unit will analyze the instruction and identify the necessary materials. The ordering unit places orders based on the actions analyzed by the analysis unit. The ordering unit can automatically order the materials identified by the analysis unit. For example, if the user says, "Please prepare the materials for tomorrow's meeting," the ordering unit will order the materials based on that instruction. The memo unit takes notes based on the actions analyzed by the analysis unit. The memo unit can automatically save content identified by the analysis unit as a memo. For example, if a user says, "Take notes on today's meeting," the memo unit will save that content as a memo. The linking unit coordinates orders and notes made by the ordering unit and memo unit with each company. The linking unit can coordinate materials ordered by the ordering unit with each company. For example, if a user says, "Prepare the materials for tomorrow's meeting," the linking unit will arrange for the materials based on that instruction. As a result, the action recording system according to this embodiment can automatically record, analyze, order, take notes, and link user actions, saving effort and simplifying procedures.

[0070] The recording unit records user actions. For example, it can record user actions using audio and camera. Specifically, a high-sensitivity microphone is used for audio recording to clearly capture user speech. Audio data is processed using noise reduction technology to remove unwanted background noise and clarify important statements. A high-resolution camera with a wide-angle lens is used to record user actions in detail. The camera is equipped with facial recognition and motion detection technologies to track user movements and automatically adjusts focus even when the user moves. For example, if a user says, "Please make a note of what was said in today's meeting," the recording unit will record that audio. Audio recording is done in real time, and the recorded data is immediately transmitted to a cloud server. The recording unit can also record user actions using a camera. For example, it can record a user shopping and save the footage as a memo. Camera recording records user actions chronologically, making it easy to play back later. This allows the recording unit to record user actions in detail and accurately, providing data necessary for subsequent analysis, ordering, and memo creation. Furthermore, the recording unit ensures data security by encrypting recorded data and controlling access to protect user privacy. This allows the recording unit to efficiently and securely record user behavior, improving the overall reliability of the system.

[0071] The analysis unit analyzes the actions recorded by the recording unit. For example, the analysis unit can use generative AI to analyze recorded actions and identify necessary orders or memos. Specifically, the generative AI uses natural language processing technology to convert speech data into text and analyze the content of the user's speech. For example, if a user says, "Prepare the materials for tomorrow's meeting," the generative AI analyzes the instruction and identifies the necessary materials. The generative AI uses speech recognition technology to accurately recognize the user's speech and employs contextual analysis algorithms to understand the context. This allows the generative AI to accurately grasp the user's intent and generate appropriate instructions. The analysis unit also analyzes video data recorded by a camera to identify the user's actions. For example, it can analyze video footage of a user shopping and generate a list of purchased items. Image recognition and object detection technologies are used in the video analysis to analyze the user's actions and surrounding environment in detail. This allows the analysis unit to analyze the recorded data from multiple angles and identify appropriate orders or memos based on the user's actions. Furthermore, the analysis unit can learn from past data and user behavior patterns to perform more accurate analyses. This allows the analysis unit to quickly and accurately analyze user behavior, thereby improving the overall efficiency of the system.

[0072] The ordering department places orders based on behaviors analyzed by the analytics department. For example, the ordering department can automatically order materials identified by the analytics department. Specifically, the ordering department automates the ordering process for necessary materials and products based on information provided by the analytics department. For example, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department will order the materials based on that instruction. The ordering department is linked to an online ordering system, checks the inventory status of necessary materials and products, and selects the optimal supplier. The ordering process is carried out in real time, and the user is notified of the order status. The ordering department provides a user interface to handle cases where the order details need to be confirmed or changed. This allows the user to check the order details and make corrections as needed. Furthermore, the ordering department can manage the order history and perform analysis and report generation based on past order data. This allows the ordering department to respond quickly and accurately to user needs and improve the efficiency of ordering operations.

[0073] The memo section takes notes based on the actions analyzed by the analysis section. For example, the memo section can automatically save content identified by the analysis section as a memo. Specifically, the memo section saves the user's statements and actions in text format based on the information provided by the analysis section. For example, if a user says, "Take notes on what we discussed in today's meeting," the memo section saves that content as a memo. The memo section synchronizes the saved memos to a cloud server, allowing users to access them at any time. The memos are organized by date and content, and can be easily referenced using the search function. Furthermore, the memo section provides editing and sharing functions for memos, allowing users to share memos with other users and modify their content. This enables the memo section to efficiently record user statements and actions and quickly provide necessary information.

[0074] The Integration Unit coordinates orders and memos made by the Ordering Unit and Memo Unit with each company. For example, the Integration Unit can arrange for materials ordered by the Ordering Unit in conjunction with each company. Specifically, based on the order information provided by the Ordering Unit, the Integration Unit coordinates with each company's ordering system to arrange for necessary materials and products. For example, if a user says, "Prepare the materials for tomorrow's meeting," the Integration Unit will arrange for the materials based on that instruction. The Integration Unit communicates with each company's ordering system in real time to check the order status and delivery date. Once an order is completed, the Integration Unit notifies the user and provides order details and delivery date information. Furthermore, the Integration Unit shares memos saved by the Memo Unit with each company, quickly transmitting necessary information. For example, if a user says, "Make notes on today's meeting," the Integration Unit shares the notes with the relevant companies and transmits the meeting content. In this way, the Integration Unit can efficiently manage order and memo information in coordination with each company and respond quickly to user needs.

[0075] The recording unit can record user actions using voice and camera. For example, if a user says, "Please make a note of what was said in today's meeting," the recording unit will record that voice. For example, the recording unit can record a user shopping with a camera and save the content as a memo. For example, if a user says, "Please prepare the materials for tomorrow's meeting," the recording unit can record that instruction by voice. In this way, user actions can be accurately recorded using voice and camera. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's voice data into a generating AI and have the generating AI generate text data from the voice data.

[0076] The analysis unit can analyze recorded actions and identify necessary orders and memos. For example, the analysis unit can use a generative AI to analyze recorded actions and identify necessary orders and memos. For example, if a user says, "Prepare the materials for tomorrow's meeting," the analysis unit can analyze that instruction and identify the necessary materials. For example, if a user says, "Make notes on today's meeting," the analysis unit can analyze the content and save it as a memo. In this way, necessary orders and memos can be automatically identified by analyzing recorded actions. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input recorded action data into a generative AI and have the generative AI perform the analysis of the action data.

[0077] The ordering department can automatically order necessary materials based on the analysis results. For example, the ordering department can automatically order materials identified by the analysis department. For example, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department can order the materials based on that instruction. For example, if a user says, "Order the necessary materials," the ordering department can order the materials based on that instruction. This reduces the user's workload by automatically placing orders based on the analysis results. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the analysis results into a generating AI and have the generating AI execute the ordering procedure.

[0078] The memo unit can automatically save memos based on the analysis results. For example, the memo unit can automatically save content identified by the analysis unit as a memo. For example, if a user says, "Make a memo of today's meeting," the memo unit can save that content as a memo. For example, if a user says, "Save the necessary memos," the memo unit can save that content as a memo. This reduces the user's effort by automatically saving memos based on the analysis results. Some or all of the above processing in the memo unit may be performed using AI, for example, or without AI. For example, the memo unit can input the analysis results into a generating AI and have the generating AI perform the memo saving.

[0079] The linking unit can place orders and make memos in conjunction with each company. For example, the linking unit can arrange for materials ordered by the ordering unit in conjunction with each company. For example, if a user says, "Prepare the materials for tomorrow's meeting," the linking unit can arrange for the materials based on that instruction. For example, if a user says, "Arrange the necessary materials," the linking unit can arrange for the materials based on that instruction. In this way, the ordering and memo procedures can be simplified by linking with each company. Some or all of the above processing in the linking unit may be performed using AI, for example, or not using AI. For example, the linking unit can input data from the ordering unit and the memo unit into a generating AI and have the generating AI execute the linking procedures.

[0080] The recording unit can estimate the user's emotions and adjust the timing of recording actions based on the estimated emotions. For example, if the user is stressed, the recording unit can delay recording until the user relaxes. For example, if the user is concentrating, the recording unit can immediately start recording actions. For example, if the user is tired, the recording unit can resume recording actions after a break. This allows actions to be recorded at the appropriate time by adjusting the recording timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The recording unit can analyze the user's past behavior history and select the optimal recording method. For example, if the user has preferred using voice recording in the past, the recording unit can prioritize voice recording. For example, if the user has frequently used camera recording in the past, the recording unit can prioritize camera recording. For example, based on the user's behavior patterns, the recording unit can suggest using both voice and camera recording. In this way, by analyzing past behavior history, the recording unit can provide the user with the most suitable recording method. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's past behavior data into a generating AI and have the generating AI select the optimal recording method.

[0082] The recording unit can filter the recording of actions based on the user's current activity status and areas of interest. For example, if the user is in a meeting, the recording unit can record only actions related to the meeting content. For example, if the user is shopping, the recording unit can prioritize recording information about purchased items. For example, if the user is exercising, the recording unit can record data related to the exercise. This allows for prioritizing the recording of important actions by filtering based on the current activity status and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's current activity data into a generating AI and have the generating AI perform the filtering process.

[0083] The recording unit can estimate the user's emotions and determine the priority of actions to record based on the estimated emotions. For example, if the user is stressed, the recording unit can postpone recording less important actions. For example, if the user is relaxed, the recording unit can prioritize recording detailed actions. For example, if the user is in a hurry, the recording unit can record only important actions. This allows for the priority recording of important actions by determining the priority of actions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI determine the priority of actions.

[0084] The recording unit can prioritize recording highly relevant actions by considering the user's geographical location information when recording actions. For example, if the user is in a specific location, the recording unit can prioritize recording actions related to that location. For example, if the user is traveling, the recording unit can prioritize recording actions at tourist destinations. For example, if the user is at work, the recording unit can prioritize recording work-related actions. This allows for the priority recording of highly relevant actions by considering geographical location information. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of actions.

[0085] The recording unit can analyze a user's social media activity and record relevant actions when recording behavior. For example, the recording unit can record behavior based on content shared by the user on social media. For example, the recording unit can record behavior based on the activity of accounts followed by the user on social media. For example, the recording unit can record behavior based on events the user participates in on social media. This allows relevant behavior to be recorded by analyzing social media activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's social media data into a generating AI and have the generating AI perform the behavior recording.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, appropriate analysis results can be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the actions during the analysis. For example, the analysis unit can analyze the contents of important meetings in detail. For example, the analysis unit can analyze everyday actions concisely. For example, the analysis unit can analyze urgent actions quickly. By adjusting the level of detail of the analysis based on the importance of the actions, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input action importance data into a generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the category of behavior during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to business-related behaviors. For example, the analysis unit can apply a private-use analysis algorithm to private-use behaviors. For example, the analysis unit can apply a health-use analysis algorithm to health-related behaviors. By applying different analysis algorithms depending on the category of behavior, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input behavior category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the timing of the behavior recordings during the analysis. For example, the analysis unit can prioritize the analysis of recently recorded behaviors. For example, the analysis unit can prioritize the analysis of important past behaviors. For example, the analysis unit can prioritize the analysis of highly urgent behaviors. By determining the priority of analysis based on the timing of the behavior recordings, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input behavior recording timing data into a generative AI and have the generative AI perform the determination of the analysis priority.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the actions during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant actions. For example, the analysis unit can postpone the analysis of less relevant actions. For example, the analysis unit can group related actions together for analysis. By adjusting the order of analysis based on the relevance of the actions, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance data of the actions into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0092] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is relaxed, the ordering unit can provide detailed ordering instructions. For example, if the user is in a hurry, the ordering unit can provide concise ordering instructions. For example, if the user is excited, the ordering unit can provide visually stimulating ordering instructions. This allows for the provision of appropriate ordering instructions by adjusting the ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering unit may be performed using AI or not. For example, the ordering unit can input user emotion data into a generative AI and have the generative AI adjust the ordering method.

[0093] The ordering department can analyze the user's past ordering history to select the optimal ordering method when placing an order. For example, the ordering department can prioritize suggesting ordering methods that the user has used in the past. For example, the ordering department can suggest the optimal ordering timing based on the user's past ordering history. For example, the ordering department can analyze the user's past ordering history and suggest the optimal ordering method. In this way, the optimal ordering method can be provided by analyzing past ordering history. Some or all of the above processes in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's past ordering data into a generating AI and have the generating AI select the optimal ordering method.

[0094] The ordering department can customize the ordering process based on the user's current needs at the time of ordering. For example, if the user is in a hurry, the ordering department can provide a quick ordering method. For example, if the user needs a specific product, the ordering department can provide an ordering method specifically for that product. For example, if the user needs multiple products, the ordering department can provide a bulk ordering method. By customizing the ordering method based on current needs, appropriate ordering becomes possible. Some or all of the above processes in the ordering department may be performed using AI, for example, or not using AI. For example, the ordering department can input the user's current needs data into a generating AI and have the generating AI perform the customization of the ordering method.

[0095] The ordering department can estimate the user's emotions and determine the priority of orders based on the estimated emotions. For example, if the user is stressed, the ordering department can postpone less important orders. For example, if the user is relaxed, the ordering department can prioritize detailed ordering procedures. For example, if the user is in a hurry, the ordering department can prioritize important orders. This allows for prioritizing important orders by determining the priority of orders according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering department may be performed using AI or not. For example, the ordering department can input user emotion data into a generative AI and have the generative AI determine the priority of orders.

[0096] The ordering department can select the optimal ordering method when placing an order, taking into account the user's geographical location information. For example, if the user is in a specific location, the ordering department can prioritize orders related to that location. For example, if the user is traveling, the ordering department can prioritize orders at the travel destination. For example, if the user is at work, the ordering department can prioritize orders related to work. In this way, the ordering department can provide the optimal ordering method by taking geographical location information into consideration. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's geographical location data into a generating AI and have the generating AI select the optimal ordering method.

[0097] The ordering department can analyze a user's social media activity and propose ordering methods when placing an order. For example, the ordering department can propose orders based on content shared by the user on social media. For example, the ordering department can propose orders based on the activity of accounts followed by the user on social media. For example, the ordering department can propose orders based on events the user participates in on social media. In this way, by analyzing social media activity, the appropriate ordering method can be proposed. Some or all of the above processing in the ordering department may be performed using AI, for example, or without AI. For example, the ordering department can input the user's social media data into a generating AI and have the generating AI execute the proposal of ordering methods.

[0098] The memo section can estimate the user's emotions and adjust how notes are saved based on the estimated emotions. For example, if the user is relaxed, the memo section can save detailed notes. If the user is in a hurry, the memo section can save concise notes. If the user is excited, the memo section can save visually stimulating notes. This allows for the saving of appropriate notes by adjusting how notes are saved according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the memo section may be performed using AI or not. For example, the memo section can input user emotion data into a generative AI and have the generative AI adjust how notes are saved.

[0099] The memo section can analyze the user's past memo history and select the optimal saving method when saving a memo. For example, the memo section can prioritize suggesting memo saving methods the user has used in the past. For example, the memo section can suggest the optimal saving timing based on the user's past memo history. For example, the memo section can analyze the user's past memo history and suggest the optimal saving method. In this way, by analyzing past memo history, the optimal saving method can be provided. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's past memo data into a generating AI and have the generating AI select the optimal saving method.

[0100] The memo section can customize the memo format based on the user's current activity when saving memos. For example, if the user is in a meeting, the memo section can take detailed notes on the meeting content. For example, if the user is shopping, the memo section can take notes on information about purchased items. For example, if the user is exercising, the memo section can take notes on data related to the exercise. This allows for the saving of appropriate memos by customizing the memo format based on the user's current activity. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's current activity data into a generating AI and have the generating AI perform the customization of the memo format.

[0101] The note-taking section can estimate the user's emotions and prioritize notes based on those emotions. For example, if the user is stressed, the note-taking section can postpone less important notes. For example, if the user is relaxed, the note-taking section can prioritize detailed notes. For example, if the user is in a hurry, the note-taking section can prioritize only important notes. This allows important notes to be prioritized by determining the priority of notes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the note-taking section may be performed using AI or not. For example, the note-taking section can input user emotion data into a generative AI and have the generative AI determine the priority of notes.

[0102] The memo section can select the optimal saving method when saving memos, taking into account the user's geographical location information. For example, if the user is in a specific location, the memo section can prioritize saving memos related to that location. For example, if the user is traveling, the memo section can prioritize saving memos from their travel destination. For example, if the user is at work, the memo section can prioritize saving work-related memos. In this way, the memo section can provide the optimal saving method by taking geographical location information into consideration. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's geographical location data into a generating AI and have the generating AI select the optimal saving method.

[0103] The memo section can analyze the user's social media activity and suggest memo methods when saving memos. For example, the memo section can suggest memos based on content the user has shared on social media. For example, the memo section can suggest memos based on the activity of accounts the user follows on social media. For example, the memo section can suggest memos based on events the user has participated in on social media. In this way, by analyzing social media activity, it is possible to suggest appropriate memo methods. Some or all of the above processing in the memo section may be performed using AI, for example, or without AI. For example, the memo section can input the user's social media data into a generating AI and have the generating AI perform the task of suggesting memo methods.

[0104] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated user emotions. For example, if the user is relaxed, the interaction unit can provide detailed interaction procedures. For example, if the user is in a hurry, the interaction unit can provide concise interaction procedures. For example, if the user is excited, the interaction unit can provide visually stimulating interaction procedures. In this way, by adjusting the interaction method according to the user's emotions, an appropriate interaction procedure can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI adjust the interaction method.

[0105] The integration unit can analyze the user's past integration history and select the optimal integration method during integration. For example, the integration unit can prioritize suggesting integration methods that the user has used in the past. For example, the integration unit can suggest the optimal integration timing based on the user's past integration history. For example, the integration unit can analyze the user's past integration history and suggest the optimal integration means. In this way, by analyzing past integration history, the optimal integration method can be provided. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's past integration data into a generating AI and have the generating AI select the optimal integration method.

[0106] The integration unit can customize the means of integration based on the user's current needs during integration. For example, if the user is in a hurry, the integration unit can provide a rapid integration method. For example, if the user needs a specific service, the integration unit can provide an integration method specialized for that service. For example, if the user needs multiple services, the integration unit can provide a batch integration method. This enables appropriate integration by customizing the integration method based on current needs. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's current needs data into a generating AI and have the generating AI perform the customization of the integration method.

[0107] The interaction unit can estimate the user's emotions and determine the priority of interactions based on the estimated user emotions. For example, if the user is stressed, the interaction unit can postpone less important interactions. For example, if the user is relaxed, the interaction unit can prioritize detailed interaction steps. For example, if the user is in a hurry, the interaction unit can prioritize important interactions. In this way, important interactions can be prioritized by determining the priority of interactions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or not using AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI perform the determination of interaction priorities.

[0108] The integration unit can select the optimal integration method by considering the user's geographical location information during integration. For example, if the user is in a specific location, the integration unit can prioritize integrations related to that location. For example, if the user is traveling, the integration unit can prioritize integrations at the travel destination. For example, if the user is at work, the integration unit can prioritize integrations related to work. In this way, the optimal integration method can be provided by considering geographical location information. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal integration method.

[0109] The integration unit can analyze the user's social media activity and propose integration methods during integration. For example, the integration unit can propose integration based on content shared by the user on social media. For example, the integration unit can propose integration based on the activity of accounts followed by the user on social media. For example, the integration unit can propose integration based on events the user participates in on social media. In this way, by analyzing social media activity, an appropriate integration method can be proposed. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of integration methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The recording unit can adjust the recording method when recording user behavior, taking into account the user's health condition. For example, if the user is tired, the recording unit can reduce the recording frequency. For example, if the user is in good health, the recording unit can perform detailed recording. For example, if the user is ill, the recording unit can temporarily suspend recording. This allows for appropriate recording by adjusting the recording method according to the user's health condition. Some or all of the above processing in the recording unit can be performed, for example, by inputting health data into a generating AI and having the generating AI perform the adjustment of the recording method.

[0112] The analysis unit can adjust its analysis method when analyzing user behavior, taking into account the user's past behavior patterns. For example, the analysis unit can prioritize the analysis of actions the user has frequently performed in the past. For example, the analysis unit can analyze in detail actions that the user has considered important in the past. For example, the analysis unit can briefly analyze actions that the user has ignored in the past. By adjusting the analysis method based on the user's past behavior patterns, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit can be performed, for example, by inputting past behavior data into a generating AI and having the generating AI perform the adjustment of the analysis method.

[0113] The ordering unit can adjust the timing of orders based on the results of analyzing user behavior. For example, if the user is in a hurry, the ordering unit can place an order quickly. For example, if the user is relaxed, the ordering unit can delay the order. For example, if the user wishes to place an order during a specific time period, the ordering unit can place an order during that time period. In this way, appropriate orders can be placed by adjusting the timing of orders based on the results of user behavior analysis. Some or all of the above processing in the ordering unit can be performed, for example, by inputting behavior analysis data into a generating AI and having the generating AI perform the adjustment of the order timing.

[0114] The memo section can adjust the format of memos based on user preferences when recording user behavior. For example, if the user prefers text format, the memo section can save memos in text format. For example, if the user prefers audio format, the memo section can save memos in audio format. For example, if the user prefers image format, the memo section can save memos in image format. This allows for the saving of appropriate memos by adjusting the memo format according to user preferences. Some or all of the above processing in the memo section can be performed, for example, by inputting user preference data into a generating AI and having the generating AI perform the memo format adjustment.

[0115] The integration unit can adjust the timing of integration when recording user behavior, taking the user's schedule into consideration. For example, the integration unit can delay integration if the user is busy. For example, the integration unit can perform integration during times when the user is free. For example, if the user requests integration at a specific time, the integration unit can perform integration at that time. In this way, appropriate integration can be performed by adjusting the timing of integration according to the user's schedule. Some or all of the above processing in the integration unit can be performed, for example, by inputting schedule data into a generating AI and having the generating AI perform the adjustment of the integration timing.

[0116] The recording unit can estimate the user's emotions and determine the priority of actions to record based on the estimated emotions. For example, if the user is stressed, the recording unit can postpone recording less important actions. For example, if the user is relaxed, the recording unit can prioritize recording detailed actions. For example, if the user is in a hurry, the recording unit can record only important actions. This allows for the priority recording of important actions by determining the priority of actions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not using AI. For example, the recording unit can input user emotion data into a generative AI and have the generative AI determine the priority of actions.

[0117] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can provide visually stimulating analysis results. In this way, appropriate analysis results can be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0118] The ordering unit can estimate the user's emotions and adjust the ordering method based on the estimated emotions. For example, if the user is relaxed, the ordering unit can provide detailed ordering instructions. For example, if the user is in a hurry, the ordering unit can provide concise ordering instructions. For example, if the user is excited, the ordering unit can provide visually stimulating ordering instructions. This allows for the provision of appropriate ordering instructions by adjusting the ordering method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the ordering unit may be performed using AI or not. For example, the ordering unit can input user emotion data into a generative AI and have the generative AI adjust the ordering method.

[0119] The memo section can estimate the user's emotions and adjust how notes are saved based on the estimated emotions. For example, if the user is relaxed, the memo section can save detailed notes. If the user is in a hurry, the memo section can save concise notes. If the user is excited, the memo section can save visually stimulating notes. This allows for the saving of appropriate notes by adjusting how notes are saved according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the memo section may be performed using AI or not. For example, the memo section can input user emotion data into a generative AI and have the generative AI adjust how notes are saved.

[0120] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated user emotions. For example, if the user is relaxed, the interaction unit can provide detailed interaction procedures. For example, if the user is in a hurry, the interaction unit can provide concise interaction procedures. For example, if the user is excited, the interaction unit can provide visually stimulating interaction procedures. In this way, by adjusting the interaction method according to the user's emotions, an appropriate interaction procedure can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI adjust the interaction method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The recording unit records the user's actions. The recording unit can record the user's actions using, for example, audio or camera. If the user says, "Please make a note of what was said in today's meeting," the recording will record that audio. It can also record the user's actions using a camera. For example, the camera can record the user shopping and save the content as a memo. Step 2: The analysis unit analyzes the actions recorded by the recording unit. The analysis unit can, for example, use generative AI to analyze the recorded actions and identify necessary orders or notes. For example, if a user says, "Prepare the materials for tomorrow's meeting," the analysis unit will analyze that instruction and identify the necessary materials. Step 3: The ordering department places orders based on the behavior analyzed by the analysis department. The ordering department can, for example, automatically order materials identified by the analysis department. For instance, if a user says, "Prepare the materials for tomorrow's meeting," the ordering department will order the materials based on that instruction. Step 4: The memo unit takes notes based on the actions analyzed by the analysis unit. For example, the memo unit can automatically save the content identified by the analysis unit as a memo. For instance, if a user says, "Take notes on what we discussed in today's meeting," the memo unit will save that content as a memo. Step 5: The integration unit coordinates orders and memos made by the ordering unit and memo unit with each company. For example, the integration unit can coordinate with each company to arrange for materials ordered by the ordering unit. For instance, if a user says, "Prepare the materials for tomorrow's meeting," the integration unit will arrange for the materials based on that instruction.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the recording unit, analysis unit, ordering unit, memo unit, and linking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the user's actions using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A manages the recorded data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the recorded action data. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and orders the necessary materials based on the analysis results. The memo unit is implemented in the specific processing unit 46A of the smart device 14 and saves memos based on the analysis results. The linking unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs ordering and memo procedures in conjunction with each company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the recording unit, analysis unit, ordering unit, memo unit, and linking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the user's actions using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A manages the recorded data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the recorded action data. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and orders the necessary materials based on the analysis results. The memo unit is implemented in the specific processing unit 46A of the smart glasses 214 and saves memos based on the analysis results. The linking unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs ordering and memo procedures in conjunction with each company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the recording unit, analysis unit, ordering unit, memo unit, and linking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the user's actions using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A manages the recorded data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the recorded action data. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and orders the necessary materials based on the analysis results. The memo unit is implemented in the specific processing unit 46A of the headset terminal 314 and saves memos based on the analysis results. The linking unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs ordering and memo procedures in conjunction with each company. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the recording unit, analysis unit, ordering unit, memo unit, and linking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the recording unit records the user's actions using the camera 42 and microphone 238 of the robot 414, and the control unit 46A manages the recorded data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the recorded action data. The ordering unit is implemented in the specific processing unit 290 of the data processing unit 12 and orders the necessary materials based on the analysis results. The memo unit is implemented in the control unit 46A of the robot 414 and saves memos based on the analysis results. The linking unit is implemented in the specific processing unit 290 of the data processing unit 12 and coordinates ordering and memo procedures with each company. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A recording unit that records user behavior, An analysis unit that analyzes the actions recorded by the recording unit, An ordering unit that places orders based on the actions analyzed by the aforementioned analysis unit, A memo unit that takes notes based on the actions analyzed by the analysis unit, The system includes an interlocking unit that coordinates orders and memos made by the ordering unit and the memo unit with each company. A system characterized by the following features. (Note 2) The recording unit is, Record user behavior using audio and cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze recorded actions to identify necessary orders and notes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The ordering department said, The system automatically orders the necessary documents based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned memo section is, Automatically save notes based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned interlocking part is, Coordinate with each company to place orders and make memos. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recording unit is, It estimates the user's emotions and adjusts the timing of recording actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recording unit is, Analyze the user's past behavior history and select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recording unit is, When recording activity, 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 10) The recording unit is, It estimates the user's emotions and determines the priority of actions to record based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recording unit is, When recording user activity, the system prioritizes recording highly relevant activities by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recording unit is, When recording behavior, the system analyzes the user's social media activity and records relevant behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavior. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the behavior. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavior was recorded. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behaviors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The ordering department said, It estimates the user's emotions and adjusts the ordering method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The ordering department said, When placing an order, the system analyzes the user's past order history to select the optimal ordering method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The ordering department said, When placing an order, customize the ordering method based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 22) The ordering department said, It estimates user sentiment and determines order priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The ordering department said, When placing an order, the optimal ordering method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The ordering department said, When placing an order, we analyze the user's social media activity and suggest ordering methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned memo section is, It estimates the user's emotions and adjusts how notes are saved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned memo section is, When saving a memo, the system analyzes the user's past memo history to select the optimal saving method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned memo section is, When saving a memo, customize the memo format based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned memo section is, It estimates the user's emotions and prioritizes notes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned memo section is, When saving a memo, the system selects the optimal saving method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned memo section is, When saving a memo, the system analyzes the user's social media activity and suggests ways to take the memo. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned interlocking part is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned interlocking part is, During integration, the system analyzes the user's past integration history to select the optimal integration method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned interlocking part is, During integration, the integration method is customized based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned interlocking part is, It estimates the user's emotions and determines the priority of interactions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned interlocking part is, During integration, the system selects the optimal integration method by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned interlocking part is, During integration, the system analyzes the user's social media activity and proposes integration methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recording unit that records user behavior, An analysis unit that analyzes the actions recorded by the recording unit, An ordering unit that places orders based on the actions analyzed by the aforementioned analysis unit, A memo unit that takes notes based on the actions analyzed by the analysis unit, The system includes an interlocking unit that coordinates orders and memos made by the ordering unit and the memo unit with each company. A system characterized by the following features.

2. The recording unit is, Record user behavior using audio and cameras. The system according to feature 1.

3. The aforementioned analysis unit, Analyze recorded actions to identify necessary orders and notes. The system according to feature 1.

4. The ordering department said, The system automatically orders the necessary documents based on the analysis results. The system according to feature 1.

5. The aforementioned memo section is, Automatically save notes based on analysis results. The system according to feature 1.

6. The aforementioned interlocking part is, Coordinate with each company to place orders and make memos. The system according to feature 1.

7. The recording unit is, It estimates the user's emotions and adjusts the timing of recording actions based on the estimated user emotions. The system according to feature 1.

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

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