system

Abstract

The system according to this embodiment aims to quickly and accurately locate and provide users with the location of items that are easily lost. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects location information. The analysis unit analyzes the information collected by the collection unit and identifies the location of the item. The provision unit provides the location information identified by the analysis unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to quickly and accurately identify the position of an item that is easy to lose.

[0005] The system according to the embodiment aims to quickly and accurately identify the position of an item that is easy to lose and provide it to the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects position information. The analysis unit analyzes the information collected by the collection unit and identifies the position of the item. The provision unit provides the position information identified by the analysis unit to the user.

Effects of the Invention

[0007] The system according to this embodiment can quickly and accurately locate and provide to the user the location of items that are easily lost. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The item-finding assistance system according to an embodiment of the present invention is a system that uses generative AI to track items that are easily lost in daily life and helps users easily find them. The item-finding assistance system works by the user launching an application and inputting information about the lost item. For example, the user might input "I'm looking for my keys." This information is sent to the generative AI, which then proposes the best way to search based on past behavior patterns and item movement data. The generative AI analyzes real-time data from smart tags and cameras to determine the item's current location. Next, the generative AI provides the user with the identified location information. The user can then view the item's location on a map through the application. The generative AI also proposes the best way to search to the user. For example, it provides specific instructions such as "Check under the sofa in the living room." Furthermore, the generative AI learns the user's behavior patterns and provides optimal suggestions for future item searches. For example, if a user frequently loses their keys, the generative AI can identify places where keys are likely to be lost and notify the user in advance. This mechanism allows users to easily find items that are easily lost in daily life, significantly reducing the stress of searching for lost items. Furthermore, the generative AI utilizes natural language processing to facilitate smooth conversations with users and provides immediate answers to questions about lost items. For example, in response to a question like, "Where are the keys?", the generative AI will instantly reply, "They're under the sofa in the living room." This application is particularly useful for people leading busy lives or those who have difficulty managing their belongings. For instance, for dual-income couples or elderly households, where items are frequently lost in daily life, a lost-item assistance system powered by generative AI would be a great help. This allows the lost-item assistance system to efficiently find items that users tend to misplace.

[0029] The item-finding support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects location information. The collection unit can collect location information such as GPS data, Wi-Fi location information, and Bluetooth® beacons. For example, the collection unit uses GPS data to identify the user's current location. The collection unit can also use Wi-Fi location information to identify a location within a building. Furthermore, the collection unit can use Bluetooth beacons to identify a location within a specific area. The analysis unit analyzes the information collected by the collection unit to identify the location of an item. The analysis unit uses, for example, a generative AI to analyze past behavior patterns and item movement data. The generative AI can use deep learning models or natural language generation models to analyze the collected data. For example, the generative AI predicts the item's location based on past behavior patterns. The generative AI can also identify the item's current location based on item movement data. Furthermore, the generative AI can analyze the user's behavior log and suggest the optimal way to search. The provision unit provides the location information identified by the analysis unit to the user. The provisioning unit can provide location information based, for example, on the notification method and timing of provision. The provisioning unit can provide location information using, for example, the notification function of a smartphone. The provisioning unit can also provide location information using email or messaging apps. Furthermore, the provisioning unit can also provide location information using a voice assistant. As a result, the item-finding assistance system according to the embodiment can efficiently find items that users tend to lose. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using, for example, AI, or not using AI. For example, the collection unit can use AI to collect location information. The analysis unit can use generative AI to analyze the collected data. The provisioning unit can use AI to provide location information to the user.

[0030] The data collection unit collects location information. For example, it can collect location information such as GPS data, Wi-Fi location information, and Bluetooth beacons. Specifically, when using GPS data to determine the user's current location, the data collection unit receives signals from satellites and obtains the user's latitude and longitude with high accuracy. This enables location determination outdoors. On the other hand, when using Wi-Fi location information, the data collection unit measures the signal strength of surrounding Wi-Fi access points and compares it with the location information of known access points to determine the location within a building. This enables high-precision location determination even indoors where GPS signals are difficult to receive. Furthermore, when using Bluetooth beacons, the data collection unit receives signals transmitted from the beacons and determines the location within a specific area based on the signal strength and arrival time. This enables detailed location determination within a specific room or area. By combining these different technologies, the data collection unit can accurately and in real time determine the user's location. Additionally, by adjusting the frequency and accuracy of location information collection, the data collection unit can optimize battery consumption while reliably collecting necessary information. This allows the data collection unit to efficiently collect user location information and improve the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit to identify the location of items. For example, the analysis unit uses generative AI to analyze past behavioral patterns and item movement data. Generative AI can analyze collected data using deep learning models and natural language generation models. Specifically, the generative AI learns the user's past behavioral patterns and predicts where specific items are likely to be placed. For example, it learns where users frequently place their keys or wallets and uses this information to predict item locations. The generative AI can also analyze item movement data to identify the item's current location. For example, it analyzes the last place the user used an item and their movement path to determine its location. Furthermore, the generative AI can analyze user behavior logs and suggest the optimal search method. For example, it can suggest the most efficient order in which to check locations when a user is searching for a specific item. This allows the analysis unit to quickly and accurately analyze collected data and provide information to help users efficiently find lost items. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term behavioral patterns and predict trends. This allows the analysis unit to handle not only real-time location identification but also long-term behavioral analysis and prediction, improving the overall reliability and usefulness of the system.

[0032] The service provider provides users with location information identified by the analysis unit. The service provider can provide location information based on, for example, the notification method and timing. Specifically, when providing location information using a smartphone's notification function, the service provider sends a push notification to the user's smartphone informing them of the item's location. Notifications include text messages and pinpoint displays on maps, designed to be intuitively understandable to the user. The service provider can also provide location information via email or messaging apps. For example, it can send location information to an email address or messaging app specified by the user, allowing them to check it at any time. Furthermore, the service provider can provide location information using a voice assistant. For example, if a user asks a voice assistant, "Where are my keys?", the voice assistant will guide the user to the key's location based on the location information obtained from the analysis unit. This allows the service provider to provide location information in the most optimal way, tailored to the user's situation and preferences, enabling users to efficiently find items they tend to misplace. Additionally, the service provider can collect user feedback and continuously improve the accuracy of the delivery method and timing. For example, it can analyze user behavior after receiving location information notifications to optimize the timing and content of notifications. Furthermore, the service provider can reliably transmit information using multiple communication methods. This allows the service provider to quickly and reliably provide location information to users, maximizing the effectiveness of the lost item assistance system.

[0033] The data collection unit can collect location information from smart tags and cameras. For example, the data collection unit can collect item location information using smart tags. Smart tags include, for example, RFID tags and Bluetooth tags. The data collection unit can receive signals from smart tags and determine the location of items. The data collection unit can also collect item location information using cameras. Cameras include, for example, fixed cameras and mobile cameras. The data collection unit can analyze images from cameras and determine the location of items. For example, the data collection unit can use a fixed camera to acquire images of an entire room and determine the location of items. The data collection unit can also use a mobile camera to determine the location of items within a specific area. This improves the accuracy of location information collection by using smart tags and cameras. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can use AI to receive signals from smart tags and determine the location of items. The data collection unit can use generative AI to analyze images from cameras and determine the location of items.

[0034] The analysis unit can propose the optimal search method based on past behavioral patterns and item movement data using generative AI. For example, the analysis unit can analyze past behavioral patterns using generative AI. Generative AI can analyze past behavioral patterns using deep learning models or natural language generation models. For example, generative AI can analyze user behavior logs and predict the location of items. The analysis unit can also analyze item movement data using generative AI. Generative AI can determine the current location of items based on item movement data. Furthermore, the analysis unit can also propose the optimal search method using generative AI. Generative AI can propose the optimal search method based on user behavioral patterns and item movement data. For example, generative AI can identify the location of items that users frequently lose and propose the optimal search method. This improves the item discovery rate by proposing the optimal search method based on past behavioral patterns and item movement data. Some or all of the above processing in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit can use generative AI to analyze past behavioral patterns. The analysis unit can use generating AI to analyze the movement data of items.

[0035] The service provider can suggest the optimal search method to the user. For example, the service provider can suggest the optimal search method using generative AI. Generative AI can suggest the optimal search method based on the user's behavior patterns and item movement data. For example, generative AI can identify the location of items that the user frequently loses and suggest the optimal search method. The service provider can also provide specific instructions to the user. For example, the service provider can provide specific instructions such as, "Please check under the sofa in the living room." By suggesting the optimal search method to the user, it becomes easier to find the item. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to suggest the optimal search method. The service provider can use generative AI to provide specific instructions.

[0036] The service provider can engage in dialogue with users using natural language processing and respond immediately to questions about lost items. For example, the service provider can use natural language processing to interact with users. Natural language processing is implemented using technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, if a user asks, "Where are the keys?", the service provider can use natural language processing to analyze the intent of the question and immediately respond, "They are under the sofa in the living room." The service provider can also collect detailed information about lost items through dialogue with users. For example, if a user asks, "When was the last time you saw the keys?", the service provider can use natural language processing to analyze the intent of the question and respond based on the user's activity log. In this way, by utilizing natural language processing, dialogue with users becomes smoother and questions about lost items can be responded to immediately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to engage in dialogue with users using natural language processing. The service provider can use AI to generate responses to questions about lost items immediately.

[0037] The data collection unit can analyze the user's past behavior history during data collection and select the optimal collection method. For example, the data collection unit collects the user's location history and activity logs and analyzes their past behavior history. Based on the past behavior history, the data collection unit selects the optimal collection method. For example, the data collection unit can identify places where the user has frequently lost things in the past and prioritize collecting information from those locations. The data collection unit can also analyze the user's behavior patterns and collect information from places where things are likely to be lost at specific times of day. Furthermore, the data collection unit can prioritize selecting collection methods (smart tags, cameras, etc.) that the user has used in the past. This allows the optimal collection method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to analyze the user's past behavior history. The data collection unit can use AI to select the optimal collection method.

[0038] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the collection process. For example, the data collection unit collects information on the user's daily behavior patterns and schedules to understand their current lifestyle. The data collection unit then filters the information to be collected based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit prioritizes collecting location information for important items. If the user is traveling, the data collection unit can prioritize collecting location information for items at their travel destination. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting location information for items related to that hobby. This allows for the collection of more relevant information by filtering data based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to understand the user's current lifestyle and areas of interest. The data collection unit can use AI to filter the information to be collected.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can determine the user's geographical location using GPS data or map information. Based on the user's geographical location, the data collection unit prioritizes the collection of highly relevant information. For example, if the user is at home, the data collection unit prioritizes the collection of location information for items within the home. Similarly, if the user is at the office, the data collection unit can prioritize the collection of location information for items within the office. Furthermore, if the user is out, the data collection unit can prioritize the collection of location information for items at their destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can use AI to determine the user's geographical location. The data collection unit can use AI to prioritize the collection of highly relevant information.

[0040] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit collects the user's social media posts, like history, follower information, etc., and analyzes their social media activity. Based on the user's social media activity, the data collection unit collects relevant information. For example, if a user posts on social media that they are in a specific location, the data collection unit collects location information for items in that location. Also, if a user mentions a specific item on social media, the data collection unit can collect location information for that item. Furthermore, if a friend of the user posts on social media that they are in a specific location, the data collection unit can also collect location information for items in that location. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's social media activity. The data collection unit can use AI to collect relevant information.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit evaluates the importance of items based on their frequency of use or value. The analysis unit adjusts the level of detail of the analysis based on the importance of the items. For example, for important items, the analysis unit provides detailed analysis results. For less important items, the analysis unit can provide concise analysis results. Furthermore, for items that users frequently lose, the analysis unit can also provide detailed analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the items, more detailed analysis results can be provided for more important items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the importance of items. The analysis unit can use a generative AI to adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the item category during analysis. For example, the analysis unit classifies items into categories such as electronic devices, daily necessities, and valuables. The analysis unit applies different analysis algorithms depending on the item category. For example, in the case of electronic devices, the analysis unit applies an algorithm that analyzes based on radio signals. In the case of clothing, the analysis unit can apply an algorithm that analyzes based on camera images. Furthermore, in the case of small items, the analysis unit can apply an algorithm that analyzes based on the location information of smart tags. By applying different analysis algorithms depending on the item category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to classify item categories. The analysis unit can use generative AI to apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the item's movement history during analysis. For example, the analysis unit collects GPS data and movement logs of items and analyzes their movement history. Based on the item's movement history, the analysis unit determines the priority of analysis. For example, if an item moves frequently, the analysis unit will prioritize analyzing that item. Also, if an item remains in a specific location for a long time, the analysis unit may postpone analyzing that item. Furthermore, the analysis of important items can also be prioritized based on the item's movement history. This allows for prioritizing the analysis of more important items by determining the priority of analysis based on the item's movement history. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to analyze the item's movement history. The analysis unit can use a generative AI to determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of items during analysis. The analysis unit evaluates the relevance of items based on common attributes or related events, for example. The analysis unit adjusts the order of analysis based on the relevance of items. For example, it may prioritize the analysis of items that the user frequently uses. It may also prioritize the analysis of items that the user uses during specific time periods. Furthermore, it may prioritize the analysis of items that the user uses in specific locations. By adjusting the order of analysis based on the relevance of items, more relevant items can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the relevance of items. The analysis unit can use a generative AI to adjust the order of analysis.

[0045] The service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit collects the user's location history and activity logs and analyzes their past behavior history. Based on the user's past behavior history, the service delivery unit selects the optimal delivery method. For example, it may prioritize delivery methods that the user has previously preferred (text, voice, etc.). It can also analyze the user's behavior patterns and select the optimal delivery method for a specific time period. Furthermore, it can select the optimal delivery method from the user's past behavior history. In this way, the optimal delivery method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can use AI to analyze the user's past behavior history. The service delivery unit can use AI to select the optimal delivery method.

[0046] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can collect information on the user's daily behavior patterns and schedules to understand their current living situation. The service provider can customize the means of delivery based on the user's current living situation. For example, if the user is busy, the service provider can provide concise and quick information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is traveling, the service provider can provide information about their travel destination. By customizing the means of delivery based on the user's current living situation, it becomes possible to provide more appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to understand the user's current living situation. The service provider can use AI to customize the means of delivery.

[0047] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. The service provider can determine the user's geographical location information using, for example, GPS data or map information. Based on the user's geographical location information, the service provider selects the optimal service delivery method. For example, if the user is at home, the service provider can provide information within the home. If the user is at the office, the service provider can provide information within the office. Furthermore, if the user is out, the service provider can provide information at their location. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can use AI to determine the user's geographical location information. The service provider can use AI to select the optimal service delivery method.

[0048] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, the service provider collects information such as the user's social media posts, like history, and follower information to analyze social media activity. Based on the user's social media activity, the service provider proposes a means of provision. For example, if a user posts on social media that they are in a specific location, the service provider can provide information about that location. Also, if a user mentions a specific item on social media, the service provider can provide information about that item. Furthermore, if a user's social media friend posts that they are in a specific location, the service provider can also provide information about that location. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the user's social media activity. The service provider can use AI to propose a means of provision.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, it can identify places where the user has frequently lost things in the past and prioritize collecting information from those locations. It can also analyze the user's behavioral patterns and collect information from places where things are likely to be lost at specific times of day. Furthermore, it can prioritize selecting data collection methods (such as smart tags or cameras) that the user has used in the past. This allows the optimal data collection method to be selected by analyzing the user's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's past behavioral history. The data collection unit can use AI to select the optimal data collection method.

[0051] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, it can collect information on the user's daily behavior patterns and schedules to understand their current lifestyle. The data collection unit filters the information it collects based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit prioritizes collecting location information for important items. If the user is traveling, the data collection unit can prioritize collecting location information for items at their travel destination. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting location information for items related to that hobby. This allows for the collection of more relevant information by filtering data based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to understand the user's current lifestyle and areas of interest. The data collection unit can use AI to filter the information it collects.

[0052] The analysis unit can adjust the level of detail of the analysis based on the importance of the item. For example, it can evaluate the importance of an item based on its frequency of use or its value. The analysis unit adjusts the level of detail of the analysis based on the importance of the item. For example, for important items, the analysis unit provides detailed analysis results. For less important items, the analysis unit can provide concise analysis results. Furthermore, for items that users frequently lose, the analysis unit can also provide detailed analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the item, more detailed analysis results can be provided for more important items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the importance of an item. The analysis unit can use a generative AI to adjust the level of detail of the analysis.

[0053] The analysis unit can apply different analysis algorithms depending on the item category. For example, item categories can be classified into electronic devices, daily necessities, valuables, etc. The analysis unit applies different analysis algorithms depending on the item category. For example, in the case of electronic devices, the analysis unit applies an algorithm that analyzes based on radio signals. In the case of clothing, the analysis unit can apply an algorithm that analyzes based on camera images. Furthermore, in the case of small items, the analysis unit can apply an algorithm that analyzes based on the location information of smart tags. By applying different analysis algorithms depending on the item category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to classify item categories. The analysis unit can use generative AI to apply different analysis algorithms.

[0054] The service provider can select the optimal delivery method by considering the user's geographical location. For example, it can determine the user's geographical location using GPS data or map information. Based on the user's geographical location, the service provider selects the optimal delivery method. For example, if the user is at home, the service provider can provide information within the home. If the user is at the office, the service provider can provide information within the office. Furthermore, if the user is out, the service provider can provide information at their location. In this way, the service provider can select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to determine the user's geographical location. The service provider can use AI to select the optimal delivery method.

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

[0056] Step 1: The collection unit collects location information. The collection unit can collect location information such as GPS data, Wi-Fi location information, and Bluetooth beacons. For example, the collection unit can use GPS data to determine the user's current location. The collection unit can also use Wi-Fi location information to determine the location within a building. Furthermore, the collection unit can use Bluetooth beacons to determine the location within a specific area. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the location of the item. The analysis unit analyzes past behavior patterns and item movement data, for example, using generative AI. The generative AI can analyze the collected data using deep learning models or natural language generation models. For example, the generative AI can predict the location of an item based on past behavior patterns. The generative AI can also determine the current location of an item based on item movement data. Furthermore, the generative AI can analyze the user's behavior log and suggest the optimal way to search for the item. Step 3: The service provider provides the user with the location information identified by the analysis unit. The service provider can provide location information based on, for example, the notification method and timing. For example, the service provider can provide location information using the notification function of a smartphone. The service provider can also provide location information using email or messaging apps. Furthermore, the service provider can also provide location information using a voice assistant.

[0057] (Example of form 2) The item-finding assistance system according to an embodiment of the present invention is a system that uses generative AI to track items that are easily lost in daily life and helps users easily find them. The item-finding assistance system works by the user launching an application and inputting information about the lost item. For example, the user might input "I'm looking for my keys." This information is sent to the generative AI, which then proposes the best way to search based on past behavior patterns and item movement data. The generative AI analyzes real-time data from smart tags and cameras to determine the item's current location. Next, the generative AI provides the user with the identified location information. The user can then view the item's location on a map through the application. The generative AI also proposes the best way to search to the user. For example, it provides specific instructions such as "Check under the sofa in the living room." Furthermore, the generative AI learns the user's behavior patterns and provides optimal suggestions for future item searches. For example, if a user frequently loses their keys, the generative AI can identify places where keys are likely to be lost and notify the user in advance. This mechanism allows users to easily find items that are easily lost in daily life, significantly reducing the stress of searching for lost items. Furthermore, the generative AI utilizes natural language processing to facilitate smooth conversations with users and provides immediate answers to questions about lost items. For example, in response to a question like, "Where are the keys?", the generative AI will instantly reply, "They're under the sofa in the living room." This application is particularly useful for people leading busy lives or those who have difficulty managing their belongings. For instance, for dual-income couples or elderly households, where items are frequently lost in daily life, a lost-item assistance system powered by generative AI would be a great help. This allows the lost-item assistance system to efficiently find items that users tend to misplace.

[0058] The item-finding support system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects location information. The collection unit can collect location information such as GPS data, Wi-Fi location information, and Bluetooth beacons. For example, the collection unit uses GPS data to identify the user's current location. The collection unit can also use Wi-Fi location information to identify a location within a building. Furthermore, the collection unit can use Bluetooth beacons to identify a location within a specific area. The analysis unit analyzes the information collected by the collection unit to identify the location of an item. The analysis unit uses, for example, a generative AI to analyze past behavior patterns and item movement data. The generative AI can use deep learning models or natural language generation models to analyze the collected data. For example, the generative AI predicts the item's location based on past behavior patterns. The generative AI can also identify the item's current location based on item movement data. Furthermore, the generative AI can analyze the user's behavior log and suggest the optimal way to search. The provision unit provides the location information identified by the analysis unit to the user. The provisioning unit can provide location information based, for example, on the notification method and timing of provision. The provisioning unit can provide location information using, for example, the notification function of a smartphone. The provisioning unit can also provide location information using email or messaging apps. Furthermore, the provisioning unit can also provide location information using a voice assistant. As a result, the item-finding assistance system according to the embodiment can efficiently find items that users tend to lose. Some or all of the above-described processes in the collection unit, analysis unit, and provisioning unit may be performed using, for example, AI, or not using AI. For example, the collection unit can use AI to collect location information. The analysis unit can use generative AI to analyze the collected data. The provisioning unit can use AI to provide location information to the user.

[0059] The data collection unit collects location information. For example, it can collect location information such as GPS data, Wi-Fi location information, and Bluetooth beacons. Specifically, when using GPS data to determine the user's current location, the data collection unit receives signals from satellites and obtains the user's latitude and longitude with high accuracy. This enables location determination outdoors. On the other hand, when using Wi-Fi location information, the data collection unit measures the signal strength of surrounding Wi-Fi access points and compares it with the location information of known access points to determine the location within a building. This enables high-precision location determination even indoors where GPS signals are difficult to receive. Furthermore, when using Bluetooth beacons, the data collection unit receives signals transmitted from the beacons and determines the location within a specific area based on the signal strength and arrival time. This enables detailed location determination within a specific room or area. By combining these different technologies, the data collection unit can accurately and in real time determine the user's location. Additionally, by adjusting the frequency and accuracy of location information collection, the data collection unit can optimize battery consumption while reliably collecting necessary information. This allows the data collection unit to efficiently collect user location information and improve the overall system performance.

[0060] The analysis unit analyzes the information collected by the collection unit to identify the location of items. For example, the analysis unit uses generative AI to analyze past behavioral patterns and item movement data. Generative AI can analyze collected data using deep learning models and natural language generation models. Specifically, the generative AI learns the user's past behavioral patterns and predicts where specific items are likely to be placed. For example, it learns where users frequently place their keys or wallets and uses this information to predict item locations. The generative AI can also analyze item movement data to identify the item's current location. For example, it analyzes the last place the user used an item and their movement path to determine its location. Furthermore, the generative AI can analyze user behavior logs and suggest the optimal search method. For example, it can suggest the most efficient order in which to check locations when a user is searching for a specific item. This allows the analysis unit to quickly and accurately analyze collected data and provide information to help users efficiently find lost items. Additionally, the analysis unit can utilize historical data and statistical information to analyze long-term behavioral patterns and predict trends. This allows the analysis unit to handle not only real-time location identification but also long-term behavioral analysis and prediction, improving the overall reliability and usefulness of the system.

[0061] The service provider provides users with location information identified by the analysis unit. The service provider can provide location information based on, for example, the notification method and timing. Specifically, when providing location information using a smartphone's notification function, the service provider sends a push notification to the user's smartphone informing them of the item's location. Notifications include text messages and pinpoint displays on maps, designed to be intuitively understandable to the user. The service provider can also provide location information via email or messaging apps. For example, it can send location information to an email address or messaging app specified by the user, allowing them to check it at any time. Furthermore, the service provider can provide location information using a voice assistant. For example, if a user asks a voice assistant, "Where are my keys?", the voice assistant will guide the user to the key's location based on the location information obtained from the analysis unit. This allows the service provider to provide location information in the most optimal way, tailored to the user's situation and preferences, enabling users to efficiently find items they tend to misplace. Additionally, the service provider can collect user feedback and continuously improve the accuracy of the delivery method and timing. For example, it can analyze user behavior after receiving location information notifications to optimize the timing and content of notifications. Furthermore, the service provider can reliably transmit information using multiple communication methods. This allows the service provider to quickly and reliably provide location information to users, maximizing the effectiveness of the lost item assistance system.

[0062] The data collection unit can collect location information from smart tags and cameras. For example, the data collection unit can collect item location information using smart tags. Smart tags include, for example, RFID tags and Bluetooth tags. The data collection unit can receive signals from smart tags and determine the location of items. The data collection unit can also collect item location information using cameras. Cameras include, for example, fixed cameras and mobile cameras. The data collection unit can analyze images from cameras and determine the location of items. For example, the data collection unit can use a fixed camera to acquire images of an entire room and determine the location of items. The data collection unit can also use a mobile camera to determine the location of items within a specific area. This improves the accuracy of location information collection by using smart tags and cameras. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can use AI to receive signals from smart tags and determine the location of items. The data collection unit can use generative AI to analyze images from cameras and determine the location of items.

[0063] The analysis unit can propose the optimal search method based on past behavioral patterns and item movement data using generative AI. For example, the analysis unit can analyze past behavioral patterns using generative AI. Generative AI can analyze past behavioral patterns using deep learning models or natural language generation models. For example, generative AI can analyze user behavior logs and predict the location of items. The analysis unit can also analyze item movement data using generative AI. Generative AI can determine the current location of items based on item movement data. Furthermore, the analysis unit can also propose the optimal search method using generative AI. Generative AI can propose the optimal search method based on user behavioral patterns and item movement data. For example, generative AI can identify the location of items that users frequently lose and propose the optimal search method. This improves the item discovery rate by proposing the optimal search method based on past behavioral patterns and item movement data. Some or all of the above processing in the analysis unit may be performed using generative AI, for example, or without using generative AI. For example, the analysis unit can use generative AI to analyze past behavioral patterns. The analysis unit can use generating AI to analyze the movement data of items.

[0064] The service provider can suggest the optimal search method to the user. For example, the service provider can suggest the optimal search method using generative AI. Generative AI can suggest the optimal search method based on the user's behavior patterns and item movement data. For example, generative AI can identify the location of items that the user frequently loses and suggest the optimal search method. The service provider can also provide specific instructions to the user. For example, the service provider can provide specific instructions such as, "Please check under the sofa in the living room." By suggesting the optimal search method to the user, it becomes easier to find the item. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to suggest the optimal search method. The service provider can use generative AI to provide specific instructions.

[0065] The service provider can engage in dialogue with users using natural language processing and respond immediately to questions about lost items. For example, the service provider can use natural language processing to interact with users. Natural language processing is implemented using technologies such as morphological analysis, grammatical analysis, and semantic analysis. For example, if a user asks, "Where are the keys?", the service provider can use natural language processing to analyze the intent of the question and immediately respond, "They are under the sofa in the living room." The service provider can also collect detailed information about lost items through dialogue with users. For example, if a user asks, "When was the last time you saw the keys?", the service provider can use natural language processing to analyze the intent of the question and respond based on the user's activity log. In this way, by utilizing natural language processing, dialogue with users becomes smoother and questions about lost items can be responded to immediately. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use generative AI to engage in dialogue with users using natural language processing. The service provider can use AI to generate responses to questions about lost items immediately.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of location data collection based on the estimated emotions. The data collection unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. For example, the data collection unit can analyze the user's facial expressions using a camera and estimate their emotions. It can also analyze the user's voice using a microphone and estimate their emotions. Furthermore, it can analyze the user's text input and estimate their emotions. The data collection unit adjusts the timing of location data collection based on the estimated emotions. For example, if the user is anxious, the data collection unit updates location data frequently in real time to provide information quickly. If the user is relaxed, the data collection unit collects location data at regular intervals to conserve battery power. Furthermore, if the user is feeling uneasy, the data collection unit collects detailed location data frequently to provide reassurance. This allows for more appropriate timing of information collection by adjusting the timing of location data collection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data acquired by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0067] The data collection unit can analyze the user's past behavior history during data collection and select the optimal collection method. For example, the data collection unit collects the user's location history and activity logs and analyzes their past behavior history. Based on the past behavior history, the data collection unit selects the optimal collection method. For example, the data collection unit can identify places where the user has frequently lost things in the past and prioritize collecting information from those locations. The data collection unit can also analyze the user's behavior patterns and collect information from places where things are likely to be lost at specific times of day. Furthermore, the data collection unit can prioritize selecting collection methods (smart tags, cameras, etc.) that the user has used in the past. This allows the optimal collection method to be selected by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to analyze the user's past behavior history. The data collection unit can use AI to select the optimal collection method.

[0068] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the collection process. For example, the data collection unit collects information on the user's daily behavior patterns and schedules to understand their current lifestyle. The data collection unit then filters the information to be collected based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit prioritizes collecting location information for important items. If the user is traveling, the data collection unit can prioritize collecting location information for items at their travel destination. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting location information for items related to that hobby. This allows for the collection of more relevant information by filtering data based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to understand the user's current lifestyle and areas of interest. The data collection unit can use AI to filter the information to be collected.

[0069] The data collection unit can estimate the user's emotions and determine the priority of location information to collect based on the estimated emotions. The data collection unit estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Based on the estimated emotions, the data collection unit determines the priority of location information to collect. For example, if the user is anxious, the data collection unit will prioritize collecting location information for important items. If the user is relaxed, the data collection unit can collect location information for all items equally. Furthermore, if the user is feeling uneasy, the data collection unit can prioritize collecting location information for items that are frequently lost. In this way, by determining the priority of location information to collect according to the user's emotions, more important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user image data acquired by a camera into a generating AI, which can then perform the estimation of the user's emotions.

[0070] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during the collection process. For example, the data collection unit can determine the user's geographical location using GPS data or map information. Based on the user's geographical location, the data collection unit prioritizes the collection of highly relevant information. For example, if the user is at home, the data collection unit prioritizes the collection of location information for items within the home. Similarly, if the user is at the office, the data collection unit can prioritize the collection of location information for items within the office. Furthermore, if the user is out, the data collection unit can prioritize the collection of location information for items at their destination. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above-described processes in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can use AI to determine the user's geographical location. The data collection unit can use AI to prioritize the collection of highly relevant information.

[0071] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit collects the user's social media posts, like history, follower information, etc., and analyzes their social media activity. Based on the user's social media activity, the data collection unit collects relevant information. For example, if a user posts on social media that they are in a specific location, the data collection unit collects location information for items in that location. Also, if a user mentions a specific item on social media, the data collection unit can collect location information for that item. Furthermore, if a friend of the user posts on social media that they are in a specific location, the data collection unit can also collect location information for items in that location. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's social media activity. The data collection unit can use AI to collect relevant information.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The analysis unit adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is anxious, the analysis unit can provide a concise and quick analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling uneasy, the analysis unit can provide a reassuring analysis result. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions. The analysis unit can use generative AI to adjust the way the analysis is represented.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the items during the analysis. For example, the analysis unit evaluates the importance of items based on their frequency of use or value. The analysis unit adjusts the level of detail of the analysis based on the importance of the items. For example, for important items, the analysis unit provides detailed analysis results. For less important items, the analysis unit can provide concise analysis results. Furthermore, for items that users frequently lose, the analysis unit can also provide detailed analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the items, more detailed analysis results can be provided for more important items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the importance of items. The analysis unit can use a generative AI to adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the item category during analysis. For example, the analysis unit classifies items into categories such as electronic devices, daily necessities, and valuables. The analysis unit applies different analysis algorithms depending on the item category. For example, in the case of electronic devices, the analysis unit applies an algorithm that analyzes based on radio signals. In the case of clothing, the analysis unit can apply an algorithm that analyzes based on camera images. Furthermore, in the case of small items, the analysis unit can apply an algorithm that analyzes based on the location information of smart tags. By applying different analysis algorithms depending on the item category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to classify item categories. The analysis unit can use generative AI to apply different analysis algorithms.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit estimates the user's emotions using technologies such as facial recognition, speech analysis, and text analysis. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is feeling anxious, the analysis unit can provide a reassuring analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. 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-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions. The analysis unit can use generative AI to adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on the item's movement history during analysis. For example, the analysis unit collects GPS data and movement logs of items and analyzes their movement history. Based on the item's movement history, the analysis unit determines the priority of analysis. For example, if an item moves frequently, the analysis unit will prioritize analyzing that item. Also, if an item remains in a specific location for a long time, the analysis unit may postpone analyzing that item. Furthermore, the analysis of important items can also be prioritized based on the item's movement history. This allows for prioritizing the analysis of more important items by determining the priority of analysis based on the item's movement history. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to analyze the item's movement history. The analysis unit can use a generative AI to determine the priority of analysis.

[0077] The analysis unit can adjust the order of analysis based on the relevance of items during analysis. The analysis unit evaluates the relevance of items based on common attributes or related events, for example. The analysis unit adjusts the order of analysis based on the relevance of items. For example, it may prioritize the analysis of items that the user frequently uses. It may also prioritize the analysis of items that the user uses during specific time periods. Furthermore, it may prioritize the analysis of items that the user uses in specific locations. By adjusting the order of analysis based on the relevance of items, more relevant items can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the relevance of items. The analysis unit can use a generative AI to adjust the order of analysis.

[0078] The service provider can estimate the user's emotions and adjust its delivery method based on the estimated emotions. The service provider estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Based on the estimated emotions, the service provider adjusts its delivery method. For example, if the user is anxious, the service provider provides concise and rapid information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is feeling uneasy, the service provider can provide reassuring information. By adjusting the delivery method according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can use generative AI to estimate the user's emotions. The service provider can use generative AI to adjust its delivery method.

[0079] The service delivery unit can analyze the user's past behavior history to select the optimal delivery method at the time of delivery. For example, the service delivery unit collects the user's location history and activity logs and analyzes their past behavior history. Based on the user's past behavior history, the service delivery unit selects the optimal delivery method. For example, it may prioritize delivery methods that the user has previously preferred (text, voice, etc.). It can also analyze the user's behavior patterns and select the optimal delivery method for a specific time period. Furthermore, it can select the optimal delivery method from the user's past behavior history. In this way, the optimal delivery method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can use AI to analyze the user's past behavior history. The service delivery unit can use AI to select the optimal delivery method.

[0080] The service provider can customize the means of delivery based on the user's current living situation at the time of delivery. For example, the service provider can collect information on the user's daily behavior patterns and schedules to understand their current living situation. The service provider can customize the means of delivery based on the user's current living situation. For example, if the user is busy, the service provider can provide concise and quick information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is traveling, the service provider can provide information about their travel destination. By customizing the means of delivery based on the user's current living situation, it becomes possible to provide more appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to understand the user's current living situation. The service provider can use AI to customize the means of delivery.

[0081] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. The service provider estimates the user's emotions using technologies such as facial recognition, voice analysis, and text analysis. Based on the estimated user emotions, the service provider determines the priority of its offerings. For example, if the user is anxious, the service provider prioritizes providing important information. If the user is relaxed, the service provider can provide all information equally. Furthermore, if the user is feeling uneasy, the service provider can prioritize providing information that provides reassurance. This allows for the prioritization of more important information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 service provider may be performed using AI, or not. For example, the service provider can use generative AI to estimate the user's emotions. The service provider can use generative AI to determine the priority of its offerings.

[0082] The service provider can select the optimal service delivery method by considering the user's geographical location information at the time of delivery. The service provider can determine the user's geographical location information using, for example, GPS data or map information. Based on the user's geographical location information, the service provider selects the optimal service delivery method. For example, if the user is at home, the service provider can provide information within the home. If the user is at the office, the service provider can provide information within the office. Furthermore, if the user is out, the service provider can provide information at their location. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can use AI to determine the user's geographical location information. The service provider can use AI to select the optimal service delivery method.

[0083] The service provider can analyze the user's social media activity and propose a means of provision at the time of provision. For example, the service provider collects information such as the user's social media posts, like history, and follower information to analyze social media activity. Based on the user's social media activity, the service provider proposes a means of provision. For example, if a user posts on social media that they are in a specific location, the service provider can provide information about that location. Also, if a user mentions a specific item on social media, the service provider can provide information about that item. Furthermore, if a user's social media friend posts that they are in a specific location, the service provider can also provide information about that location. This makes it possible to provide more relevant information by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to analyze the user's social media activity. The service provider can use AI to propose a means of provision.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is anxious, the analysis unit will prioritize the analysis of important items. If the user is relaxed, the analysis unit can analyze all items equally. Furthermore, if the user is feeling uneasy, the analysis unit can prioritize the analysis of items that provide a sense of security. In this way, by determining the priority of analysis according to the user's emotions, more important information can be analyzed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions. The analysis unit can use generative AI to determine the priority of analysis.

[0086] The information delivery unit can estimate the user's emotions and adjust the timing of delivery based on the estimated emotions. For example, if the user is anxious, the information delivery unit can provide information quickly. If the user is relaxed, the information delivery unit can provide information at regular intervals. Furthermore, if the user is feeling uneasy, the information delivery unit can provide information at a time that provides reassurance. In this way, by adjusting the timing of delivery according to the user's emotions, information can be delivered at a more appropriate time. 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 information delivery unit may be performed using, for example, generative AI, or without generative AI. For example, the information delivery unit can use generative AI to estimate the user's emotions. The information delivery unit can use generative AI to adjust the timing of delivery.

[0087] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is anxious, the data collection unit will prioritize collecting data on important items. If the user is relaxed, the data collection unit can collect data on all items equally. Furthermore, if the user is feeling uneasy, the data collection unit can prioritize collecting data that provides a sense of security. In this way, by adjusting the types of data collected according to the user's emotions, more important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can use generative AI to estimate the user's emotions. The data collection unit can use generative AI to adjust the types of data collected.

[0088] The service provider can estimate the user's emotions and adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is anxious, the service provider can provide concise and to-the-point information. If the user is relaxed, the service provider can provide detailed information. Furthermore, if the user is feeling uneasy, the service provider can provide detailed information that provides a sense of security. In this way, by adjusting the level of detail of the information provided according to the user's emotions, more appropriate information can be provided. 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 service provider may be performed using, for example, generative AI, or without generative AI. For example, the service provider can use generative AI to estimate the user's emotions. The service provider can use generative AI to adjust the level of detail of the information provided.

[0089] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is anxious, the analysis unit can adopt a quick and concise analysis method. If the user is relaxed, the analysis unit can adopt a detailed and time-consuming analysis method. Furthermore, if the user is feeling uneasy, the analysis unit can adopt an analysis method that provides reassurance. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. 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-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to estimate the user's emotions. The analysis unit can use generative AI to adjust the analysis method.

[0090] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. For example, it can identify places where the user has frequently lost things in the past and prioritize collecting information from those locations. It can also analyze the user's behavioral patterns and collect information from places where things are likely to be lost at specific times of day. Furthermore, it can prioritize selecting data collection methods (such as smart tags or cameras) that the user has used in the past. This allows the optimal data collection method to be selected by analyzing the user's past behavioral history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's past behavioral history. The data collection unit can use AI to select the optimal data collection method.

[0091] The data collection unit can filter data based on the user's current lifestyle and areas of interest. For example, it can collect information on the user's daily behavior patterns and schedules to understand their current lifestyle. The data collection unit filters the information it collects based on the user's current lifestyle and areas of interest. For example, if the user is busy, the data collection unit prioritizes collecting location information for important items. If the user is traveling, the data collection unit can prioritize collecting location information for items at their travel destination. Furthermore, if the user has a specific hobby, the data collection unit can prioritize collecting location information for items related to that hobby. This allows for the collection of more relevant information by filtering data based on the user's current lifestyle and areas of interest. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can use AI to understand the user's current lifestyle and areas of interest. The data collection unit can use AI to filter the information it collects.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the item. For example, it can evaluate the importance of an item based on its frequency of use or its value. The analysis unit adjusts the level of detail of the analysis based on the importance of the item. For example, for important items, the analysis unit provides detailed analysis results. For less important items, the analysis unit can provide concise analysis results. Furthermore, for items that users frequently lose, the analysis unit can also provide detailed analysis results. In this way, by adjusting the level of detail of the analysis based on the importance of the item, more detailed analysis results can be provided for more important items. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can use a generative AI to evaluate the importance of an item. The analysis unit can use a generative AI to adjust the level of detail of the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the item category. For example, item categories can be classified into electronic devices, daily necessities, valuables, etc. The analysis unit applies different analysis algorithms depending on the item category. For example, in the case of electronic devices, the analysis unit applies an algorithm that analyzes based on radio signals. In the case of clothing, the analysis unit can apply an algorithm that analyzes based on camera images. Furthermore, in the case of small items, the analysis unit can apply an algorithm that analyzes based on the location information of smart tags. By applying different analysis algorithms depending on the item category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can use generative AI to classify item categories. The analysis unit can use generative AI to apply different analysis algorithms.

[0094] The service provider can select the optimal delivery method by considering the user's geographical location. For example, it can determine the user's geographical location using GPS data or map information. Based on the user's geographical location, the service provider selects the optimal delivery method. For example, if the user is at home, the service provider can provide information within the home. If the user is at the office, the service provider can provide information within the office. Furthermore, if the user is out, the service provider can provide information at their location. In this way, the service provider can select the optimal delivery method by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use AI to determine the user's geographical location. The service provider can use AI to select the optimal delivery method.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection unit collects location information. The collection unit can collect location information such as GPS data, Wi-Fi location information, and Bluetooth beacons. For example, the collection unit can use GPS data to determine the user's current location. The collection unit can also use Wi-Fi location information to determine the location within a building. Furthermore, the collection unit can use Bluetooth beacons to determine the location within a specific area. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the location of the item. The analysis unit analyzes past behavior patterns and item movement data, for example, using generative AI. The generative AI can analyze the collected data using deep learning models or natural language generation models. For example, the generative AI can predict the location of an item based on past behavior patterns. The generative AI can also determine the current location of an item based on item movement data. Furthermore, the generative AI can analyze the user's behavior log and suggest the optimal way to search for the item. Step 3: The service provider provides the user with the location information identified by the analysis unit. The service provider can provide location information based on, for example, the notification method and timing. For example, the service provider can provide location information using the notification function of a smartphone. The service provider can also provide location information using email or messaging apps. Furthermore, the service provider can also provide location information using a voice assistant.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect location information using the camera 42 or Bluetooth beacon of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to identify the location of an item. The provision unit is implemented in the control unit 46A of the smart device 14, which provides location information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect location information using the camera 42 or Bluetooth beacon of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to identify the location of an item. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, which provides location information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect location information using the camera 42 or Bluetooth beacon of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to identify the location of the item. The provision unit is implemented in the control unit 46A of the headset terminal 314, which provides location information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect location information using the camera 42 or Bluetooth beacon of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data using a generation AI to identify the location of the item. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides location information to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A collection unit that collects location information, An analysis unit analyzes the information collected by the collection unit and identifies the location of the item, The system includes a provisioning unit that provides location information identified by the analysis unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect location information from smart tags and cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The generated AI suggests the optimal search method based on past behavioral patterns and item movement data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We propose the best search method for the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Utilizing natural language processing, it engages in conversations with users and provides immediate responses to questions about finding lost items. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of location data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the system analyzes the user's past behavior history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of location data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the item category. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the item's movement history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the items. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, At the time of delivery, the system analyzes the user's past behavior history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 collection unit that collects location information, An analysis unit analyzes the information collected by the collection unit and identifies the location of the item, The system includes a provisioning unit that provides location information identified by the analysis unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is Collect location information from smart tags and cameras. The system according to feature 1.

3. The aforementioned analysis unit, The AI ​​generates data that suggests the optimal search method based on past behavioral patterns and item movement data. The system according to feature 1.

4. The aforementioned supply unit is, We propose the best search method for the user. The system according to feature 1.

5. The aforementioned supply unit is, Utilizing natural language processing, it engages in conversations with users and provides immediate responses to questions about finding lost items. The system according to feature 1.

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

7. The aforementioned collection unit is During data collection, the system analyzes the user's past behavior history to select the most suitable collection method. The system according to feature 1.

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

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of location data to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system according to feature 1.

Images