System

Abstract

To provide a system which conducts movement prediction based on positional information of an article, and proposes an optimal movement timing.SOLUTION: A system includes a collection part, an analysis part, a prediction part, and a proposal part. The collection part collects positional information of an object from a sensor attached to an article. The analysis part analyzes movement transition of an article, based on the positional information collected by the collection part. The prediction part predicts future movement of an article, based on an analysis result obtained by the analysis part. The proposal part proposes a timing for moving an article, based on a prediction result obtained by the prediction part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately predict the movement of items or suggest optimal movement timing based on their location information, and there is room for improvement.

[0005] The system according to the embodiment aims to predict movement based on the position information of an item and propose optimal movement timing. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a suggestion unit. The collection unit collects position information of the items from a sensor attached to the items. The analysis unit analyzes the movement of the items based on the position information collected by the collection unit. The prediction unit predicts future movement of the items based on the analysis result obtained by the analysis unit. The suggestion unit suggests the timing to move the items based on the prediction result obtained by the prediction unit. Effect of the Invention

[0007] The system according to the embodiment can predict movement based on the location information of items and propose optimal movement timing. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The item movement prediction system according to the embodiment of the present invention is a system that collects location information from a sensor attached to an item, analyzes the movement of the item based on the information, and predicts future movement. This item movement prediction system collects location information from a sensor (e.g., an RFID logger) attached to the item. The location information includes the current location of the item and past movement records. Next, the collected location information is analyzed to grasp the movement of the item. For the analysis, AI or generation AI is used to analyze the movement pattern of the item in detail. Next, based on the analysis result, the future movement of the item is predicted. For the prediction, the current location and past movement records are taken into consideration, and the AI ​​or generation AI performs the movement prediction. For example, it predicts when and where the item will move. Furthermore, based on the prediction result, the optimal timing for moving the item is proposed. The proposal includes the timing of lending and borrowing the item and the timing of reloading the item. For example, the optimal timing for lending and borrowing the item and the optimal timing for reloading the item are proposed. In addition, if the item is lost, the current location and situation are predicted from past location information logs, and the location of the item is proposed. This system makes the management of item movement more efficient and reduces the risk of losing items. For example, in the logistics industry, managing the movement of items is important, and by using this system, the movement of items can be optimized and efficient logistics operations can be achieved. It can also be applied to the management of personal belongings, reducing the risk of losing items. As a result, the item movement prediction system can efficiently manage the movement of items and reduce the risk of losing items.

[0029] The item movement prediction system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects position information from a sensor attached to the item. The sensor attached to the item includes, but is not limited to, an RFID logger, a GPS device, Wi-Fi position information, a Bluetooth (registered trademark) beacon, and the like. The collection unit obtains the item's position information using, for example, an RFID logger. The RFID logger is a device that reads position information from an RFID tag attached to the item, and records the current position and past movement history of the item. The analysis unit analyzes the movement progress of the item based on the position information collected by the collection unit. For the analysis, AI and generation AI are used to analyze the movement pattern of the item in detail. For example, the analysis unit analyzes the position information of the item in chronological order to grasp the movement speed and movement direction. The analysis unit can also cluster the movement pattern of the item and group items having similar movement patterns. The prediction unit predicts the future movement of the item based on the analysis result obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or the generation AI performs the movement prediction. For example, the prediction unit predicts a future movement route and movement time based on the movement pattern of the item. The prediction unit can also predict the movement destination and movement timing of the item. The suggestion unit proposes the timing to move the item based on the prediction result obtained by the prediction unit. The proposal includes the timing of lending and borrowing the item and transshipment. For example, the suggestion unit proposes the optimal timing to lend and borrow the item and the optimal timing to transship the item. In addition, when the item is lost, the suggestion unit can predict the current location and situation from the past location information log and propose the location of the item. As a result, the item movement prediction system according to the embodiment can efficiently manage the movement of the item and reduce the risk of loss.

[0030] The collection unit collects location information from sensors attached to the items. Examples of sensors attached to the items include, but are not limited to, RFID loggers, GPS devices, Wi-Fi location information, Bluetooth beacons, and the like. Specifically, the RFID logger is a device that reads location information from an RFID tag attached to an item, and records the current location and past movement history of the item. The GPS device uses satellite signals to identify the exact location of the item and provides location information in real time. The Wi-Fi location information estimates the location of the item based on the signal strength of a Wi-Fi access point, and is particularly effective for location identification in indoor environments. The Bluetooth beacon identifies the location of the item using short-range wireless communication and provides detailed location information within a specific area. The collection unit centrally manages the location information obtained from these sensors and transmits it to a central database. The database records the location information of each item in chronological order, making it easy to refer to past movement history and current location. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, when an item enters a critical area or during a specific time period, more detailed location information can be obtained by increasing the frequency of data collection, allowing the collection unit to efficiently and effectively collect data and grasp the movement status of the item in real time.

[0031] The analysis unit analyzes the movement of the items based on the location information collected by the collection unit. AI and generation AI are used for the analysis, and the movement patterns of the items are analyzed in detail. Specifically, the location information of the items is analyzed in chronological order to grasp the movement speed and movement direction. AI uses machine learning algorithms to extract features from the movement data of the items and model the movement pattern. For example, if an item frequently moves along a specific route, the route can be identified and the movement trend can be grasped. The generation AI generates future movement patterns based on past movement data, improving the accuracy of prediction. In addition, the analysis unit can cluster the movement patterns of the items and group items with similar movement patterns. This makes it possible to grasp the movement trends of items belonging to a specific group and manage them efficiently. The analysis unit can detect unusual movement patterns or abnormal data using an anomaly detection algorithm and issue an early warning. For example, if an item moves off a normal route or moves abnormally fast, it can detect abnormal situations and respond quickly. This enables the analysis unit to quickly and accurately analyze the collected data, not only to grasp the movement status of goods in real time, but also to respond to abnormal situations, thereby improving the reliability and safety of the entire system.

[0032] The prediction unit predicts the future movement of the goods based on the analysis results obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or generation AI performs the movement prediction. Specifically, the future movement route and movement time are predicted based on the movement pattern of the goods. The AI ​​predicts future movement with high accuracy by learning past movement data and modeling the movement tendency of the goods. The generation AI simulates multiple scenarios based on past data and generates the most likely movement pattern. For example, if the goods tend to move along a specific route at a specific time, it is possible to predict future movement based on that tendency. The prediction unit can also predict the destination and movement timing of the goods. This enables efficient management of the goods and the formulation of optimal movement plans. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the movement speed or direction of the goods changes suddenly, the prediction unit immediately takes in new data and updates the prediction results. The prediction unit can also make more accurate movement predictions by taking into account the characteristics of each region and past movement history. This enables the prediction unit to always provide highly accurate movement predictions based on the latest information, thereby supporting efficient management of goods and the creation of optimal movement plans.

[0033] The suggestion unit proposes the timing of moving the item based on the prediction result obtained by the prediction unit. Specifically, this includes the timing of lending and borrowing the item and transshipping. For example, the suggestion unit proposes the optimal timing for lending and borrowing the item and the optimal timing for transshipping the item. The AI ​​analyzes the movement pattern and usage status of the item and calculates the optimal timing. For example, if the item is frequently used during a specific time period, efficient operation is possible by avoiding that time period for lending and borrowing and transshipping. In addition, if the item is lost, the suggestion unit can predict the current location and situation from past location information logs and propose the location of the item. This reduces the risk of losing the item and enables a quick response. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal. For example, it evaluates whether the movement of the item at the proposed timing was actually efficient and reflects it in the next proposal. In addition, the suggestion unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the suggestion unit to quickly and reliably provide suggestions to the user, thereby supporting efficient management and operation of items.

[0034] The suggestion unit can suggest the timing of lending or borrowing an item, or the timing of replacing an item. The suggestion unit can suggest the optimal timing of lending or borrowing an item, for example, based on a demand forecast for the item. For example, the suggestion unit can suggest the timing of lending or borrowing an item, taking into account the frequency of use of the item and fluctuations in demand. The suggestion unit can also suggest the optimal timing of replacing an item based on the deterioration state or expiration date of the item. For example, the suggestion unit monitors the deterioration state of the item and suggests the timing of replacing an item whose expiration date is approaching. This allows the suggestion unit to suggest the optimal timing of lending or borrowing an item or reshipping. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input demand forecast data for the item to the generation AI and cause the generation AI to execute a suggestion of the optimal lending or borrowing timing.

[0035] The suggestion unit can suggest the location of the item when the item is lost. The suggestion unit suggests the location of the item based on, for example, the last recorded location information of the item. For example, the suggestion unit analyzes the last recorded location information of the item to identify a location where the item may have been lost. The suggestion unit can also suggest the location of the item by using nearby sensor information. For example, the suggestion unit analyzes location information acquired from a nearby sensor to identify the location of the item. In this way, the suggestion unit can suggest the location of the item when the item is lost. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the last recorded location information of the item to the generation AI and cause the generation AI to execute a suggestion of the location of the item.

[0036] The collection unit can acquire the location information using a radio frequency identification logger. The collection unit acquires the location information of the item using, for example, an RFID logger. The RFID logger is a device that reads location information from an RFID tag attached to an item, and records the current location and past movement history of the item. For example, the collection unit acquires the location information of the item in real time using an RFID logger. The collection unit can also periodically acquire the location information of the item using an RFID logger. For example, the collection unit acquires location information from the RFID logger at regular time intervals and records the movement progress of the item. In this way, the collection unit can accurately acquire the location information by using the RFID logger. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the location information acquired from the RFID logger to the generation AI and cause the generation AI to execute analysis of the location information.

[0037] The prediction unit can perform movement prediction based on the current position and past movement history. The prediction unit predicts a future movement pattern based on, for example, the current position and past movement history of the item. For example, the prediction unit analyzes the current position and past movement history of the item to predict a future movement route and movement time. The prediction unit can also predict the optimal movement timing based on the current position and past movement history of the item. For example, the prediction unit analyzes the movement pattern of the item and proposes the optimal movement timing. This allows the prediction unit to accurately predict future movement based on the current position and past movement performance. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the current position and past movement history of the item to the generation AI and cause the generation AI to execute a prediction of the future movement pattern.

[0038] The collection unit can acquire the usage status and status information of the item in real time and record it together with the location information. The collection unit, for example, acquires the remaining battery power of the item in real time and records it together with the location information. For example, the collection unit monitors the remaining battery power of the item and records it together with the location information. The collection unit can also acquire the operation status (on / off) of the item in real time and record it together with the location information. For example, the collection unit monitors the operation status of the item and records it together with the location information. The collection unit can also acquire the frequency of use of the item in real time and record it together with the location information. For example, the collection unit monitors the frequency of use of the item and records it together with the location information. As a result, the collection unit acquires the usage status and status information of the item in real time and records it together with the location information, thereby making the management of the item more efficient. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the remaining battery power data of the item to the generation AI and cause the generation AI to execute an analysis of the remaining battery power.

[0039] The collection unit can simultaneously collect environmental data of the item when acquiring the position information. For example, the collection unit acquires the temperature around the item in real time and records it together with the position information. For example, the collection unit monitors the temperature around the item and records it together with the position information. The collection unit can also acquire the humidity around the item in real time and record it together with the position information. For example, the collection unit monitors the humidity around the item and records it together with the position information. The collection unit can also acquire the air pressure around the item in real time and record it together with the position information. For example, the collection unit monitors the air pressure around the item and records it together with the position information. This allows the collection unit to simultaneously collect the environmental data of the item, thereby managing the item in more detail. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the temperature data around the item to the generation AI and cause the generation AI to execute the analysis of the temperature data.

[0040] When acquiring location information, the collection unit can preferentially acquire highly relevant information in consideration of the user's behavioral pattern. For example, when a user frequently uses a specific route, the collection unit preferentially acquires location information related to the route. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific route. In addition, when a user visits a specific place in a specific time period, the collection unit can also preferentially acquire location information related to the time period. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific time period. In addition, when a user participates in a specific event, the collection unit can also preferentially acquire location information related to the event. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific event. As a result, the collection unit preferentially acquires highly relevant information in consideration of the user's behavioral pattern, thereby enabling efficient information collection. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user behavior pattern data into the generation AI and have the generation AI prioritize highly relevant information.

[0041] The collection unit can simultaneously record the moving speed and direction of the item when acquiring the position information. The collection unit, for example, acquires the moving speed of the item in real time and records it together with the position information. For example, the collection unit monitors the moving speed of the item and records it together with the position information. The collection unit can also acquire the moving direction of the item in real time and record it together with the position information. For example, the collection unit monitors the moving direction of the item and records it together with the position information. The collection unit can also acquire the acceleration of the item in real time and record it together with the position information. For example, the collection unit monitors the acceleration of the item and records it together with the position information. As a result, the collection unit can grasp the moving situation of the item in detail by simultaneously recording the moving speed and direction of the item. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the moving speed data of the item to the generation AI and cause the generation AI to execute the analysis of the moving speed.

[0042] The analysis unit can take into consideration the use status and environmental data of the item when analyzing the movement pattern of the item. The analysis unit, for example, analyzes the movement pattern taking into consideration the use frequency of the item. For example, the analysis unit analyzes the movement pattern based on the use frequency data of the item. The analysis unit can also analyze the movement pattern taking into consideration the temperature around the item. For example, the analysis unit analyzes the movement pattern based on the temperature data around the item. The analysis unit can also analyze the movement pattern taking into consideration the operation state (on / off) of the item. For example, the analysis unit analyzes the movement pattern based on the operation state data of the item. As a result, the analysis unit improves the analysis accuracy of the movement pattern by taking into consideration the use status and environmental data of the item. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute the analysis of the movement pattern.

[0043] The analysis unit updates the analysis results in real time, and can always keep the movement transition of the item up to date. The analysis unit updates the analysis results in real time, for example, every time the location information of the item is updated. For example, the analysis unit monitors the location information of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the usage status of the item changes. For example, the analysis unit monitors the usage status data of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the environmental data of the item changes. For example, the analysis unit monitors the environmental data of the item in real time and updates the analysis results. As a result, the analysis unit can always grasp the latest movement transition by updating the analysis results in real time. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the analysis result.

[0044] During the analysis, the analysis unit can improve the analysis accuracy by referring to related literature and databases together with the movement history of the item. The analysis unit, for example, improves the analysis accuracy by referring to related literature based on the movement history of the item. For example, the analysis unit refers to related academic papers and industry databases based on the movement history data of the item. The analysis unit can also improve the analysis accuracy by referring to related databases based on the movement history of the item. For example, the analysis unit improves the analysis accuracy by referring to past data based on the movement history data of the item. As a result, the analysis unit improves the analysis accuracy by referring to related literature and databases. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the movement history data of the item to the generation AI and cause the generation AI to refer to related literature and databases.

[0045] The analysis unit can visualize the movement pattern of the item during the analysis and present it to the user in an easy-to-understand manner. For example, the analysis unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the analysis unit displays the movement pattern of the item in a time series graph or a heat map. The analysis unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the analysis unit plots the movement route of the item on a map and visually displays it. The analysis unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the analysis unit reproduces the movement of the item in animation and presents it to the user. Thereby, the analysis unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0046] The prediction unit can predict the future movement pattern in detail based on the current position and past movement records of the item. The prediction unit predicts the future movement pattern based on, for example, the current position and past movement records of the item. For example, the prediction unit analyzes the current position and past movement records of the item to predict the future movement route and movement time. The prediction unit can also predict the optimal movement route based on the current position and past movement records of the item. For example, the prediction unit analyzes the movement pattern of the item and proposes the optimal movement route. The prediction unit can also predict the movement time based on the current position and past movement records of the item. For example, the prediction unit analyzes the movement pattern of the item and predicts the movement time. As a result, the prediction unit can accurately predict the future movement pattern based on the current position and past movement records of the item. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the current position and past movement records of the item to the generation AI and cause the generation AI to execute the prediction of the future movement pattern.

[0047] The prediction unit updates the prediction result in real time, and can always keep the movement prediction of the item up to date. The prediction unit updates the prediction result in real time, for example, every time the location information of the item is updated. For example, the prediction unit monitors the location information of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the usage status of the item changes. For example, the prediction unit monitors the usage status data of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the environmental data of the item changes. For example, the prediction unit monitors the environmental data of the item in real time and updates the prediction result. As a result, the prediction unit can always grasp the latest movement prediction by updating the prediction result in real time. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the prediction result.

[0048] The prediction unit can improve the prediction accuracy by referring to related market data and trends together with the movement history of the goods at the time of prediction. The prediction unit improves the prediction accuracy by referring to related market data, for example, based on the movement history of the goods. For example, the prediction unit refers to related market reports and trend analysis data based on the movement history data of the goods. The prediction unit can also improve the prediction accuracy by referring to related trends based on the movement history of the goods. For example, the prediction unit improves the prediction accuracy by referring to past data based on the movement history data of the goods. As a result, the prediction unit improves the prediction accuracy by referring to related market data and trends. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the movement history data of the goods to the generation AI and cause the generation AI to refer to the related market data and trends.

[0049] The prediction unit can visualize the movement pattern of the item at the time of prediction and present it to the user in an easy-to-understand manner. For example, the prediction unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the prediction unit displays the movement pattern of the item in a time series graph or a heat map. The prediction unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the prediction unit plots the movement route of the item on a map and visually displays it. The prediction unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the prediction unit reproduces the movement of the item in animation and presents it to the user. Thereby, the prediction unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0050] The suggestion unit can take into consideration the usage status and environmental data of the item when proposing the timing of lending / borrowing or reshipping the item. The suggestion unit, for example, takes into consideration the frequency of use of the item to propose the optimal timing of lending / borrowing. For example, the suggestion unit proposes the optimal timing of lending / borrowing based on the use frequency data of the item. The suggestion unit can also propose the optimal timing of reshipment by considering the temperature around the item. For example, the suggestion unit proposes the optimal timing of reshipment based on the temperature data around the item. The suggestion unit can also propose the optimal timing of lending / borrowing by considering the operating state (on / off) of the item. For example, the suggestion unit proposes the optimal timing of lending / borrowing based on the operating state data of the item. As a result, the suggestion unit can propose the optimal timing of lending / borrowing or reshipment by taking into consideration the usage status and environmental data of the item. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute a proposal for the optimal timing of lending / borrowing.

[0051] The suggestion unit updates the suggestion result in real time, and can always keep the item movement suggestion up to date. The suggestion unit updates the suggestion result in real time, for example, every time the location information of the item is updated. For example, the suggestion unit monitors the location information of the item in real time, and updates the suggestion result. The suggestion unit can also update the suggestion result in real time every time the usage status of the item changes. For example, the suggestion unit monitors the usage status data of the item in real time, and updates the suggestion result. The suggestion unit can also update the suggestion result in real time every time the environmental data of the item changes. For example, the suggestion unit monitors the environmental data of the item in real time, and updates the suggestion result. As a result, the suggestion unit can always grasp the latest movement suggestion by updating the suggestion result in real time. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the location information data of the item to the generation AI, and cause the generation AI to execute the update of the suggestion result.

[0052] The suggestion unit can improve the suggestion accuracy by referring to related literature and databases together with the movement history of the item at the time of suggestion. The suggestion unit improves the suggestion accuracy by referring to related literature, for example, based on the movement history of the item. For example, the suggestion unit refers to related academic papers and industry databases based on the movement history data of the item. The suggestion unit can also improve the suggestion accuracy by referring to related databases based on the movement history of the item. For example, the suggestion unit improves the suggestion accuracy by referring to past data based on the movement history data of the item. As a result, the suggestion unit improves the suggestion accuracy by referring to related literature and databases. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the movement history data of the item to the generation AI and cause the generation AI to refer to related literature and databases.

[0053] The suggestion unit can visualize the movement pattern of the item at the time of suggestion and present it to the user in an easy-to-understand manner. For example, the suggestion unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the suggestion unit displays the movement pattern of the item in a time series graph or a heat map. The suggestion unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the suggestion unit plots the movement route of the item on a map and visually displays it. The suggestion unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the suggestion unit reproduces the movement of the item in animation and presents it to the user. Thereby, the suggestion unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can take into consideration the use status and environmental data of the item when analyzing the movement pattern of the item. For example, the analysis unit analyzes the movement pattern taking into consideration the use frequency of the item. For example, the analysis unit analyzes the movement pattern based on the use frequency data of the item. The analysis unit can also analyze the movement pattern taking into consideration the temperature around the item. For example, the analysis unit analyzes the movement pattern based on the temperature data around the item. The analysis unit can also analyze the movement pattern taking into consideration the operation state (on / off) of the item. For example, the analysis unit analyzes the movement pattern based on the operation state data of the item. As a result, the analysis unit improves the analysis accuracy of the movement pattern by taking into consideration the use status and environmental data of the item. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute the analysis of the movement pattern.

[0056] The collection unit can acquire the usage status and status information of the item in real time and record it together with the location information. For example, the collection unit acquires the remaining battery power of the item in real time and records it together with the location information. For example, the collection unit monitors the remaining battery power of the item and records it together with the location information. The collection unit can also acquire the operation status (on / off) of the item in real time and record it together with the location information. For example, the collection unit monitors the operation status of the item and records it together with the location information. The collection unit can also acquire the frequency of use of the item in real time and record it together with the location information. For example, the collection unit monitors the frequency of use of the item and records it together with the location information. As a result, the collection unit acquires the usage status and status information of the item in real time and records it together with the location information, thereby making the management of the item more efficient. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the remaining battery power data of the item to the generation AI and cause the generation AI to execute an analysis of the remaining battery power.

[0057] The collection unit can simultaneously collect environmental data of the item when acquiring the position information. For example, the collection unit acquires the temperature around the item in real time and records it together with the position information. For example, the collection unit monitors the temperature around the item and records it together with the position information. The collection unit can also acquire the humidity around the item in real time and record it together with the position information. For example, the collection unit monitors the humidity around the item and records it together with the position information. The collection unit can also acquire the air pressure around the item in real time and record it together with the position information. For example, the collection unit monitors the air pressure around the item and records it together with the position information. This allows the collection unit to simultaneously collect the environmental data of the item, thereby managing the item in more detail. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the temperature data around the item to the generation AI and cause the generation AI to execute the analysis of the temperature data.

[0058] When acquiring location information, the collection unit can preferentially acquire highly relevant information in consideration of the user's behavioral pattern. For example, when the user frequently uses a specific route, the collection unit preferentially acquires location information related to the route. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific route. In addition, when the user visits a specific place in a specific time period, the collection unit can also preferentially acquire location information related to the time period. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific time period. In addition, when the user participates in a specific event, the collection unit can also preferentially acquire location information related to the event. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific event. As a result, the collection unit preferentially acquires highly relevant information in consideration of the user's behavioral pattern, thereby enabling efficient information collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user behavior pattern data into the generation AI and have the generation AI prioritize highly relevant information.

[0059] The analysis unit updates the analysis results in real time, and can always keep the movement transition of the item up to date. For example, the analysis unit updates the analysis results in real time every time the location information of the item is updated. For example, the analysis unit monitors the location information of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the usage status of the item changes. For example, the analysis unit monitors the usage status data of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the environmental data of the item changes. For example, the analysis unit monitors the environmental data of the item in real time and updates the analysis results. As a result, the analysis unit can always grasp the latest movement transition by updating the analysis results in real time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the analysis result.

[0060] The prediction unit updates the prediction result in real time, and can always keep the movement prediction of the item up to date. For example, the prediction unit updates the prediction result in real time every time the location information of the item is updated. For example, the prediction unit monitors the location information of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the usage status of the item changes. For example, the prediction unit monitors the usage status data of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the environmental data of the item changes. For example, the prediction unit monitors the environmental data of the item in real time and updates the prediction result. As a result, the prediction unit can always grasp the latest movement prediction by updating the prediction result in real time. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the prediction result.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects location information from a sensor attached to the item. The sensors attached to the item include an RFID logger, a GPS device, Wi-Fi location information, a Bluetooth beacon, etc. For example, an RFID logger is used to obtain the location information of the item, and the current location and past movement history of the item are recorded. Step 2: The analysis unit analyzes the movement of the items based on the location information collected by the collection unit. AI and generative AI are used for the analysis, and the movement patterns of the items are analyzed in detail. For example, the location information of the items is analyzed over time to determine the movement speed and direction. In addition, the movement patterns of the items can be clustered, and items with similar movement patterns can be grouped together. Step 3: The prediction unit predicts the future movement of the item based on the analysis results obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or generation AI performs the movement prediction. For example, based on the movement pattern of the item, the future movement route, movement time, movement destination, and movement timing are predicted. Step 4: The suggestion unit suggests the timing to move the item based on the prediction results obtained by the prediction unit. The suggestions include the timing to lend or borrow an item or to reship an item. For example, it suggests the optimal timing to lend or borrow an item or to reship an item. In addition, if an item is lost, it can predict the current location and situation from past location information logs and suggest the location of the item.

[0063] (Example 2) The item movement prediction system according to the embodiment of the present invention is a system that collects location information from a sensor attached to an item, analyzes the movement of the item based on the information, and predicts future movement. This item movement prediction system collects location information from a sensor (e.g., an RFID logger) attached to the item. The location information includes the current location of the item and past movement records. Next, the collected location information is analyzed to grasp the movement of the item. For the analysis, AI or generation AI is used to analyze the movement pattern of the item in detail. Next, based on the analysis result, the future movement of the item is predicted. For the prediction, the current location and past movement records are taken into consideration, and the AI ​​or generation AI performs the movement prediction. For example, it predicts when and where the item will move. Furthermore, based on the prediction result, the optimal timing for moving the item is proposed. The proposal includes the timing of lending and borrowing the item and the timing of reloading the item. For example, the optimal timing for lending and borrowing the item and the optimal timing for reloading the item are proposed. In addition, if the item is lost, the current location and situation are predicted from past location information logs, and the location of the item is proposed. This system makes the management of item movement more efficient and reduces the risk of losing items. For example, in the logistics industry, managing the movement of items is important, and by using this system, the movement of items can be optimized and efficient logistics operations can be achieved. It can also be applied to the management of personal belongings, reducing the risk of losing items. As a result, the item movement prediction system can efficiently manage the movement of items and reduce the risk of losing items.

[0064] The item movement prediction system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, and a proposal unit. The collection unit collects position information from a sensor attached to the item. The sensor attached to the item includes, but is not limited to, an RFID logger, a GPS device, Wi-Fi position information, a Bluetooth beacon, and the like. The collection unit obtains the item's position information using, for example, an RFID logger. The RFID logger is a device that reads position information from an RFID tag attached to the item, and records the current position and past movement history of the item. The analysis unit analyzes the movement progress of the item based on the position information collected by the collection unit. For the analysis, AI and generation AI are used to analyze the movement pattern of the item in detail. For example, the analysis unit analyzes the position information of the item in chronological order to grasp the movement speed and movement direction. The analysis unit can also cluster the movement pattern of the item and group items having similar movement patterns. The prediction unit predicts the future movement of the item based on the analysis result obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or the generation AI performs the movement prediction. For example, the prediction unit predicts a future movement route and movement time based on the movement pattern of the item. The prediction unit can also predict the movement destination and movement timing of the item. The suggestion unit proposes the timing to move the item based on the prediction result obtained by the prediction unit. The proposal includes the timing of lending and borrowing the item and transshipment. For example, the suggestion unit proposes the optimal timing to lend and borrow the item and the optimal timing to transship the item. In addition, when the item is lost, the suggestion unit can predict the current location and situation from the past location information log and propose the location of the item. As a result, the item movement prediction system according to the embodiment can efficiently manage the movement of the item and reduce the risk of loss.

[0065] The collection unit collects location information from sensors attached to the items. Examples of sensors attached to the items include, but are not limited to, RFID loggers, GPS devices, Wi-Fi location information, Bluetooth beacons, and the like. Specifically, the RFID logger is a device that reads location information from an RFID tag attached to an item, and records the current location and past movement history of the item. The GPS device uses satellite signals to identify the exact location of the item and provides location information in real time. The Wi-Fi location information estimates the location of the item based on the signal strength of a Wi-Fi access point, and is particularly effective for location identification in indoor environments. The Bluetooth beacon identifies the location of the item using short-range wireless communication and provides detailed location information within a specific area. The collection unit centrally manages the location information obtained from these sensors and transmits it to a central database. The database records the location information of each item in chronological order, making it easy to refer to past movement history and current location. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, when an item enters a critical area or during a specific time period, more detailed location information can be obtained by increasing the frequency of data collection, allowing the collection unit to efficiently and effectively collect data and grasp the movement status of the item in real time.

[0066] The analysis unit analyzes the movement of the items based on the location information collected by the collection unit. AI and generation AI are used for the analysis, and the movement patterns of the items are analyzed in detail. Specifically, the location information of the items is analyzed in chronological order to grasp the movement speed and movement direction. AI uses machine learning algorithms to extract features from the movement data of the items and model the movement pattern. For example, if an item frequently moves along a specific route, the route can be identified and the movement trend can be grasped. The generation AI generates future movement patterns based on past movement data, improving the accuracy of prediction. In addition, the analysis unit can cluster the movement patterns of the items and group items with similar movement patterns. This makes it possible to grasp the movement trends of items belonging to a specific group and manage them efficiently. The analysis unit can detect unusual movement patterns or abnormal data using an anomaly detection algorithm and issue an early warning. For example, if an item moves off a normal route or moves abnormally fast, it can detect abnormal situations and respond quickly. This enables the analysis unit to quickly and accurately analyze the collected data, not only to grasp the movement status of goods in real time, but also to respond to abnormal situations, thereby improving the reliability and safety of the entire system.

[0067] The prediction unit predicts the future movement of the goods based on the analysis results obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or generation AI performs the movement prediction. Specifically, the future movement route and movement time are predicted based on the movement pattern of the goods. The AI ​​predicts future movement with high accuracy by learning past movement data and modeling the movement tendency of the goods. The generation AI simulates multiple scenarios based on past data and generates the most likely movement pattern. For example, if the goods tend to move along a specific route at a specific time, it is possible to predict future movement based on that tendency. The prediction unit can also predict the destination and movement timing of the goods. This enables efficient management of the goods and the formulation of optimal movement plans. Furthermore, the prediction unit can continuously revise the prediction results based on data updated in real time to respond to the latest situation. For example, if the movement speed or direction of the goods changes suddenly, the prediction unit immediately takes in new data and updates the prediction results. The prediction unit can also make more accurate movement predictions by taking into account the characteristics of each region and past movement history. This enables the prediction unit to always provide highly accurate movement predictions based on the latest information, thereby supporting efficient management of goods and the creation of optimal movement plans.

[0068] The suggestion unit proposes the timing of moving the item based on the prediction result obtained by the prediction unit. Specifically, this includes the timing of lending and borrowing the item and transshipping. For example, the suggestion unit proposes the optimal timing for lending and borrowing the item and the optimal timing for transshipping the item. The AI ​​analyzes the movement pattern and usage status of the item and calculates the optimal timing. For example, if the item is frequently used during a specific time period, efficient operation is possible by avoiding that time period for lending and borrowing and transshipping. In addition, if the item is lost, the suggestion unit can predict the current location and situation from past location information logs and propose the location of the item. This reduces the risk of losing the item and enables a quick response. Furthermore, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of the proposal. For example, it evaluates whether the movement of the item at the proposed timing was actually efficient and reflects it in the next proposal. In addition, the suggestion unit can reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the suggestion unit to quickly and reliably provide suggestions to the user, thereby supporting efficient management and operation of items.

[0069] The suggestion unit can suggest the timing of lending or borrowing an item, or the timing of replacing an item. The suggestion unit can suggest the optimal timing of lending or borrowing an item, for example, based on a demand forecast for the item. For example, the suggestion unit can suggest the timing of lending or borrowing an item, taking into account the frequency of use of the item and fluctuations in demand. The suggestion unit can also suggest the optimal timing of replacing an item based on the deterioration state or expiration date of the item. For example, the suggestion unit monitors the deterioration state of the item and suggests the timing of replacing an item whose expiration date is approaching. This allows the suggestion unit to suggest the optimal timing of lending or borrowing an item or reshipping. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input demand forecast data for the item to the generation AI and cause the generation AI to execute a suggestion of the optimal lending or borrowing timing.

[0070] The suggestion unit can suggest the location of the item when the item is lost. The suggestion unit suggests the location of the item based on, for example, the last recorded location information of the item. For example, the suggestion unit analyzes the last recorded location information of the item to identify a location where the item may have been lost. The suggestion unit can also suggest the location of the item by using nearby sensor information. For example, the suggestion unit analyzes location information acquired from a nearby sensor to identify the location of the item. In this way, the suggestion unit can suggest the location of the item when the item is lost. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the last recorded location information of the item to the generation AI and cause the generation AI to execute a suggestion of the location of the item.

[0071] The collection unit can acquire the location information using a radio frequency identification logger. The collection unit acquires the location information of the item using, for example, an RFID logger. The RFID logger is a device that reads location information from an RFID tag attached to an item, and records the current location and past movement history of the item. For example, the collection unit acquires the location information of the item in real time using an RFID logger. The collection unit can also periodically acquire the location information of the item using an RFID logger. For example, the collection unit acquires location information from the RFID logger at regular time intervals and records the movement progress of the item. In this way, the collection unit can accurately acquire the location information by using the RFID logger. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the location information acquired from the RFID logger to the generation AI and cause the generation AI to execute analysis of the location information.

[0072] The prediction unit can perform movement prediction based on the current position and past movement history. The prediction unit predicts a future movement pattern based on, for example, the current position and past movement history of the item. For example, the prediction unit analyzes the current position and past movement history of the item to predict a future movement route and movement time. The prediction unit can also predict the optimal movement timing based on the current position and past movement history of the item. For example, the prediction unit analyzes the movement pattern of the item and proposes the optimal movement timing. This allows the prediction unit to accurately predict future movement based on the current position and past movement performance. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the current position and past movement history of the item to the generation AI and cause the generation AI to execute a prediction of the future movement pattern.

[0073] The collection unit can estimate the user's emotion and adjust the timing of acquiring the location information based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on a change in facial expression and adjusts the timing of acquiring the location information. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of acquiring the location information. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of acquiring the location information. As a result, the collection unit can reduce the load on the system and collect detailed data by adjusting the timing of acquiring the location information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0074] The collection unit can acquire the usage status and status information of the item in real time and record it together with the location information. The collection unit, for example, acquires the remaining battery power of the item in real time and records it together with the location information. For example, the collection unit monitors the remaining battery power of the item and records it together with the location information. The collection unit can also acquire the operation status (on / off) of the item in real time and record it together with the location information. For example, the collection unit monitors the operation status of the item and records it together with the location information. The collection unit can also acquire the frequency of use of the item in real time and record it together with the location information. For example, the collection unit monitors the frequency of use of the item and records it together with the location information. As a result, the collection unit acquires the usage status and status information of the item in real time and records it together with the location information, thereby making the management of the item more efficient. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the remaining battery power data of the item to the generation AI and cause the generation AI to execute an analysis of the remaining battery power.

[0075] The collection unit can simultaneously collect environmental data of the item when acquiring the position information. For example, the collection unit acquires the temperature around the item in real time and records it together with the position information. For example, the collection unit monitors the temperature around the item and records it together with the position information. The collection unit can also acquire the humidity around the item in real time and record it together with the position information. For example, the collection unit monitors the humidity around the item and records it together with the position information. The collection unit can also acquire the air pressure around the item in real time and record it together with the position information. For example, the collection unit monitors the air pressure around the item and records it together with the position information. This allows the collection unit to simultaneously collect the environmental data of the item, thereby managing the item in more detail. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the temperature data around the item to the generation AI and cause the generation AI to execute the analysis of the temperature data.

[0076] The collection unit can estimate the user's emotion and determine the priority of the location information to be acquired based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in facial expression and determines the priority of the location information to be acquired. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the location information to be acquired. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate and determines the priority of the location information to be acquired. As a result, the collection unit can prioritize important information by determining the priority of the location information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0077] When acquiring location information, the collection unit can preferentially acquire highly relevant information in consideration of the user's behavioral pattern. For example, when a user frequently uses a specific route, the collection unit preferentially acquires location information related to the route. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific route. In addition, when a user visits a specific place in a specific time period, the collection unit can also preferentially acquire location information related to the time period. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific time period. In addition, when a user participates in a specific event, the collection unit can also preferentially acquire location information related to the event. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific event. As a result, the collection unit preferentially acquires highly relevant information in consideration of the user's behavioral pattern, thereby enabling efficient information collection. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user behavior pattern data into the generation AI and have the generation AI prioritize highly relevant information.

[0078] The collection unit can simultaneously record the moving speed and direction of the item when acquiring the position information. The collection unit, for example, acquires the moving speed of the item in real time and records it together with the position information. For example, the collection unit monitors the moving speed of the item and records it together with the position information. The collection unit can also acquire the moving direction of the item in real time and record it together with the position information. For example, the collection unit monitors the moving direction of the item and records it together with the position information. The collection unit can also acquire the acceleration of the item in real time and record it together with the position information. For example, the collection unit monitors the acceleration of the item and records it together with the position information. As a result, the collection unit can grasp the moving situation of the item in detail by simultaneously recording the moving speed and direction of the item. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the moving speed data of the item to the generation AI and cause the generation AI to execute the analysis of the moving speed.

[0079] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the analysis algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the analysis algorithm. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the analysis algorithm. As a result, the analysis unit adjusts the analysis algorithm according to the user's emotion, thereby enabling rapid and highly accurate analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0080] The analysis unit can take into consideration the use status and environmental data of the item when analyzing the movement pattern of the item. The analysis unit, for example, analyzes the movement pattern taking into consideration the use frequency of the item. For example, the analysis unit analyzes the movement pattern based on the use frequency data of the item. The analysis unit can also analyze the movement pattern taking into consideration the temperature around the item. For example, the analysis unit analyzes the movement pattern based on the temperature data around the item. The analysis unit can also analyze the movement pattern taking into consideration the operation state (on / off) of the item. For example, the analysis unit analyzes the movement pattern based on the operation state data of the item. As a result, the analysis unit improves the analysis accuracy of the movement pattern by taking into consideration the use status and environmental data of the item. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute the analysis of the movement pattern.

[0081] The analysis unit updates the analysis results in real time, and can always keep the movement transition of the item up to date. The analysis unit updates the analysis results in real time, for example, every time the location information of the item is updated. For example, the analysis unit monitors the location information of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the usage status of the item changes. For example, the analysis unit monitors the usage status data of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the environmental data of the item changes. For example, the analysis unit monitors the environmental data of the item in real time and updates the analysis results. As a result, the analysis unit can always grasp the latest movement transition by updating the analysis results in real time. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the analysis result.

[0082] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the display method of the analysis result. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the analysis result. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the display method of the analysis result. As a result, the analysis unit adjusts the display method of the analysis result according to the user's emotion, thereby enabling a highly visible display. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate emotions.

[0083] During the analysis, the analysis unit can improve the analysis accuracy by referring to related literature and databases together with the movement history of the item. The analysis unit, for example, improves the analysis accuracy by referring to related literature based on the movement history of the item. For example, the analysis unit refers to related academic papers and industry databases based on the movement history data of the item. The analysis unit can also improve the analysis accuracy by referring to related databases based on the movement history of the item. For example, the analysis unit improves the analysis accuracy by referring to past data based on the movement history data of the item. As a result, the analysis unit improves the analysis accuracy by referring to related literature and databases. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the movement history data of the item to the generation AI and cause the generation AI to refer to related literature and databases.

[0084] The analysis unit can visualize the movement pattern of the item during the analysis and present it to the user in an easy-to-understand manner. For example, the analysis unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the analysis unit displays the movement pattern of the item in a time series graph or a heat map. The analysis unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the analysis unit plots the movement route of the item on a map and visually displays it. The analysis unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the analysis unit reproduces the movement of the item in animation and presents it to the user. Thereby, the analysis unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0085] The prediction unit can estimate the user's emotion and adjust the movement prediction algorithm based on the estimated user's emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the change in facial expression and adjusts the movement prediction algorithm. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the movement prediction algorithm. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the movement prediction algorithm. As a result, the prediction unit adjusts the movement prediction algorithm according to the user's emotion, thereby enabling rapid and highly accurate prediction. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate emotions.

[0086] The prediction unit can predict the future movement pattern in detail based on the current position and past movement records of the item. The prediction unit predicts the future movement pattern based on, for example, the current position and past movement records of the item. For example, the prediction unit analyzes the current position and past movement records of the item to predict the future movement route and movement time. The prediction unit can also predict the optimal movement route based on the current position and past movement records of the item. For example, the prediction unit analyzes the movement pattern of the item and proposes the optimal movement route. The prediction unit can also predict the movement time based on the current position and past movement records of the item. For example, the prediction unit analyzes the movement pattern of the item and predicts the movement time. As a result, the prediction unit can accurately predict the future movement pattern based on the current position and past movement records of the item. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the current position and past movement records of the item to the generation AI and cause the generation AI to execute the prediction of the future movement pattern.

[0087] The prediction unit updates the prediction result in real time, and can always keep the movement prediction of the item up to date. The prediction unit updates the prediction result in real time, for example, every time the location information of the item is updated. For example, the prediction unit monitors the location information of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the usage status of the item changes. For example, the prediction unit monitors the usage status data of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the environmental data of the item changes. For example, the prediction unit monitors the environmental data of the item in real time and updates the prediction result. As a result, the prediction unit can always grasp the latest movement prediction by updating the prediction result in real time. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the prediction result.

[0088] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the change in facial expression and adjusts the display method of the prediction result. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the prediction result. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the display method of the prediction result. As a result, the prediction unit adjusts the display method of the prediction result according to the user's emotion, thereby enabling a highly visible display. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate emotions.

[0089] The prediction unit can improve the prediction accuracy by referring to related market data and trends together with the movement history of the goods at the time of prediction. The prediction unit improves the prediction accuracy by referring to related market data, for example, based on the movement history of the goods. For example, the prediction unit refers to related market reports and trend analysis data based on the movement history data of the goods. The prediction unit can also improve the prediction accuracy by referring to related trends based on the movement history of the goods. For example, the prediction unit improves the prediction accuracy by referring to past data based on the movement history data of the goods. As a result, the prediction unit improves the prediction accuracy by referring to related market data and trends. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the movement history data of the goods to the generation AI and cause the generation AI to refer to the related market data and trends.

[0090] The prediction unit can visualize the movement pattern of the item at the time of prediction and present it to the user in an easy-to-understand manner. For example, the prediction unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the prediction unit displays the movement pattern of the item in a time series graph or a heat map. The prediction unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the prediction unit plots the movement route of the item on a map and visually displays it. The prediction unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the prediction unit reproduces the movement of the item in animation and presents it to the user. Thereby, the prediction unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. Part or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0091] The suggestion unit can estimate the user's emotion and adjust the proposed expression method based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in facial expression and adjusts the proposed expression method. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the proposed expression method. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the proposed expression method. As a result, the suggestion unit can adjust the proposed expression method according to the user's emotion, thereby making it possible to make a more appropriate suggestion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0092] The suggestion unit can take into consideration the usage status and environmental data of the item when proposing the timing of lending / borrowing or reshipping the item. The suggestion unit, for example, takes into consideration the frequency of use of the item to propose the optimal timing of lending / borrowing. For example, the suggestion unit proposes the optimal timing of lending / borrowing based on the use frequency data of the item. The suggestion unit can also propose the optimal timing of reshipment by considering the temperature around the item. For example, the suggestion unit proposes the optimal timing of reshipment based on the temperature data around the item. The suggestion unit can also propose the optimal timing of lending / borrowing by considering the operating state (on / off) of the item. For example, the suggestion unit proposes the optimal timing of lending / borrowing based on the operating state data of the item. As a result, the suggestion unit can propose the optimal timing of lending / borrowing or reshipment by taking into consideration the usage status and environmental data of the item. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute a proposal for the optimal timing of lending / borrowing.

[0093] The suggestion unit updates the suggestion result in real time, and can always keep the item movement suggestion up to date. The suggestion unit updates the suggestion result in real time, for example, every time the location information of the item is updated. For example, the suggestion unit monitors the location information of the item in real time, and updates the suggestion result. The suggestion unit can also update the suggestion result in real time every time the usage status of the item changes. For example, the suggestion unit monitors the usage status data of the item in real time, and updates the suggestion result. The suggestion unit can also update the suggestion result in real time every time the environmental data of the item changes. For example, the suggestion unit monitors the environmental data of the item in real time, and updates the suggestion result. As a result, the suggestion unit can always grasp the latest movement suggestion by updating the suggestion result in real time. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the location information data of the item to the generation AI, and cause the generation AI to execute the update of the suggestion result.

[0094] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on changes in facial expression and determines the priority of suggestions. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of suggestions. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on heart rate fluctuations and determines the priority of suggestions. As a result, the suggestion unit can prioritize important suggestions by determining the priority of suggestions according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate emotions.

[0095] The suggestion unit can improve the suggestion accuracy by referring to related literature and databases together with the movement history of the item at the time of suggestion. The suggestion unit improves the suggestion accuracy by referring to related literature, for example, based on the movement history of the item. For example, the suggestion unit refers to related academic papers and industry databases based on the movement history data of the item. The suggestion unit can also improve the suggestion accuracy by referring to related databases based on the movement history of the item. For example, the suggestion unit improves the suggestion accuracy by referring to past data based on the movement history data of the item. As a result, the suggestion unit improves the suggestion accuracy by referring to related literature and databases. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the movement history data of the item to the generation AI and cause the generation AI to refer to related literature and databases.

[0096] The suggestion unit can visualize the movement pattern of the item at the time of suggestion and present it to the user in an easy-to-understand manner. For example, the suggestion unit visualizes the movement pattern of the item in a graph and presents it to the user. For example, the suggestion unit displays the movement pattern of the item in a time series graph or a heat map. The suggestion unit can also visualize the movement pattern of the item on a map and present it to the user. For example, the suggestion unit plots the movement route of the item on a map and visually displays it. The suggestion unit can also visualize the movement pattern of the item in animation and present it to the user. For example, the suggestion unit reproduces the movement of the item in animation and presents it to the user. Thereby, the suggestion unit can present the movement pattern of the item to the user in an easy-to-understand manner by visualizing it. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the movement pattern data of the item to the generation AI and cause the generation AI to execute the visualization processing.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The analysis unit can take into consideration the use status and environmental data of the item when analyzing the movement pattern of the item. For example, the analysis unit analyzes the movement pattern taking into consideration the use frequency of the item. For example, the analysis unit analyzes the movement pattern based on the use frequency data of the item. The analysis unit can also analyze the movement pattern taking into consideration the temperature around the item. For example, the analysis unit analyzes the movement pattern based on the temperature data around the item. The analysis unit can also analyze the movement pattern taking into consideration the operation state (on / off) of the item. For example, the analysis unit analyzes the movement pattern based on the operation state data of the item. As a result, the analysis unit improves the analysis accuracy of the movement pattern by taking into consideration the use status and environmental data of the item. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the use frequency data of the item to the generation AI and cause the generation AI to execute the analysis of the movement pattern.

[0099] The collection unit can acquire the usage status and status information of the item in real time and record it together with the location information. For example, the collection unit acquires the remaining battery power of the item in real time and records it together with the location information. For example, the collection unit monitors the remaining battery power of the item and records it together with the location information. The collection unit can also acquire the operation status (on / off) of the item in real time and record it together with the location information. For example, the collection unit monitors the operation status of the item and records it together with the location information. The collection unit can also acquire the frequency of use of the item in real time and record it together with the location information. For example, the collection unit monitors the frequency of use of the item and records it together with the location information. As a result, the collection unit acquires the usage status and status information of the item in real time and records it together with the location information, thereby making the management of the item more efficient. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the remaining battery power data of the item to the generation AI and cause the generation AI to execute an analysis of the remaining battery power.

[0100] The collection unit can simultaneously collect environmental data of the item when acquiring the position information. For example, the collection unit acquires the temperature around the item in real time and records it together with the position information. For example, the collection unit monitors the temperature around the item and records it together with the position information. The collection unit can also acquire the humidity around the item in real time and record it together with the position information. For example, the collection unit monitors the humidity around the item and records it together with the position information. The collection unit can also acquire the air pressure around the item in real time and record it together with the position information. For example, the collection unit monitors the air pressure around the item and records it together with the position information. This allows the collection unit to simultaneously collect the environmental data of the item, thereby managing the item in more detail. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the temperature data around the item to the generation AI and cause the generation AI to execute the analysis of the temperature data.

[0101] The collection unit can estimate the user's emotion and adjust the timing of acquiring the location information based on the estimated user's emotion. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the change in the facial expression and adjusts the timing of acquiring the location information. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of acquiring the location information. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the timing of acquiring the location information. As a result, the collection unit can reduce the load on the system and collect detailed data by adjusting the timing of acquiring the location information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0102] When acquiring location information, the collection unit can preferentially acquire highly relevant information in consideration of the user's behavioral pattern. For example, when the user frequently uses a specific route, the collection unit preferentially acquires location information related to the route. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific route. In addition, when the user visits a specific place in a specific time period, the collection unit can also preferentially acquire location information related to the time period. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific time period. In addition, when the user participates in a specific event, the collection unit can also preferentially acquire location information related to the event. For example, the collection unit analyzes the user's behavioral pattern and preferentially acquires location information related to the specific event. As a result, the collection unit preferentially acquires highly relevant information in consideration of the user's behavioral pattern, thereby enabling efficient information collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input user behavior pattern data into the generation AI and have the generation AI prioritize highly relevant information.

[0103] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the change in facial expression and adjusts the analysis algorithm. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technique. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the analysis algorithm. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the analysis algorithm. As a result, the analysis unit adjusts the analysis algorithm according to the user's emotion, thereby enabling rapid and highly accurate analysis. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate emotions.

[0104] The analysis unit updates the analysis results in real time, and can always keep the movement transition of the item up to date. For example, the analysis unit updates the analysis results in real time every time the location information of the item is updated. For example, the analysis unit monitors the location information of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the usage status of the item changes. For example, the analysis unit monitors the usage status data of the item in real time and updates the analysis results. The analysis unit can also update the analysis results in real time every time the environmental data of the item changes. For example, the analysis unit monitors the environmental data of the item in real time and updates the analysis results. As a result, the analysis unit can always grasp the latest movement transition by updating the analysis results in real time. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the analysis result.

[0105] The prediction unit can estimate the user's emotion and adjust the movement prediction algorithm based on the estimated user's emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the change in facial expression and adjusts the movement prediction algorithm. The prediction unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the movement prediction algorithm. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the movement prediction algorithm. As a result, the prediction unit adjusts the movement prediction algorithm according to the user's emotion, thereby enabling rapid and highly accurate prediction. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit may input image data of a user captured by a camera to the generation AI and cause the generation AI to estimate emotions.

[0106] The prediction unit updates the prediction result in real time, and can always keep the movement prediction of the item up to date. For example, the prediction unit updates the prediction result in real time every time the location information of the item is updated. For example, the prediction unit monitors the location information of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the usage status of the item changes. For example, the prediction unit monitors the usage status data of the item in real time and updates the prediction result. The prediction unit can also update the prediction result in real time every time the environmental data of the item changes. For example, the prediction unit monitors the environmental data of the item in real time and updates the prediction result. As a result, the prediction unit can always grasp the latest movement prediction by updating the prediction result in real time. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the location information data of the item to the generation AI and cause the generation AI to execute the update of the prediction result.

[0107] The suggestion unit can estimate the user's emotion and adjust the expression method of the proposal based on the estimated user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the change in facial expression and adjusts the expression method of the proposal. The suggestion unit can also record the user's voice and estimate the emotion using a voice analysis technology. For example, the suggestion unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the expression method of the proposal. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit calculates an emotion score based on the fluctuation of the heart rate and adjusts the expression method of the proposal. As a result, the suggestion unit can adjust the expression method of the proposal according to the user's emotion, thereby making it possible to make a more appropriate proposal. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit may input image data of a user captured by a camera to the generation AI, and cause the generation AI to estimate emotions.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects location information from a sensor attached to the item. The sensors attached to the item include an RFID logger, a GPS device, Wi-Fi location information, a Bluetooth beacon, etc. For example, an RFID logger is used to obtain the location information of the item, and the current location and past movement history of the item are recorded. Step 2: The analysis unit analyzes the movement of the items based on the location information collected by the collection unit. AI and generative AI are used for the analysis, and the movement patterns of the items are analyzed in detail. For example, the location information of the items is analyzed over time to determine the movement speed and direction. In addition, the movement patterns of the items can be clustered, and items with similar movement patterns can be grouped together. Step 3: The prediction unit predicts the future movement of the item based on the analysis results obtained by the analysis unit. The prediction takes into account the current location and past movement records, and the AI ​​or generation AI performs the movement prediction. For example, based on the movement pattern of the item, the future movement route, movement time, movement destination, and movement timing are predicted. Step 4: The suggestion unit suggests the timing to move the item based on the prediction results obtained by the prediction unit. The suggestions include the timing to lend or borrow an item or to reship an item. For example, it suggests the optimal timing to lend or borrow an item or to reship an item. In addition, if an item is lost, it can predict the current location and situation from past location information logs and suggest the location of the item.

[0110] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] For example, the collection unit can collect the position information of the item using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected position information. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the future movement of the item based on the analysis result. The suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and proposes the timing of the movement of the item. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0121] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0126] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0128] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0129] For example, the collection unit can collect the location information of the item using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected location information. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the future movement of the item based on the analysis result. The suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and suggests the timing of the movement of the item. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0132] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0140] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] For example, the collection unit can collect the position information of the item using the camera 42 or microphone 238 of the headset terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected position information. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts future movement of the item based on the analysis result. The suggestion unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and proposes the timing of movement of the item. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a 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.

[0148] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0158] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0159] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0161] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] For example, the collection unit can collect the position information of the item using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected position information. The prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts the future movement of the item based on the analysis result. The suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and proposes the timing of the movement of the item. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0163] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0164] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0168] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0171] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0176] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection unit that collects position information of the item from a sensor attached to the item; an analysis unit that analyzes the movement of the item based on the position information collected by the collection unit; a prediction unit that predicts future movement of an article based on the analysis result obtained by the analysis unit; and a unit for proposing a timing for moving an item based on the prediction result obtained by the prediction unit. A system characterized by: (Appendix 2) The suggestion unit, Suggest when to borrow or lend items or replace items 2. The system of claim 1, (Appendix 3) The suggestion unit, If an item is lost, suggest where it is located 2. The system of claim 1, (Appendix 4) The collecting unit includes: Obtaining location information using radio frequency identification loggers 2. The system of claim 1, (Appendix 5) The prediction unit is Predict movement based on current location and past movement history 2. The system of claim 1, (Appendix 6) The collecting unit includes: A method for estimating a user's emotions and adjusting the timing of acquiring location information based on the estimated user's emotions 2. The system of claim 1, (Appendix 7) The collecting unit includes: Obtain information on the usage and status of items in real time and record it along with location information 2. The system of claim 1, (Appendix 8) The collecting unit includes: When acquiring location information, environmental data of the item is collected at the same time. 2. The system of claim 1, (Appendix 9) The collecting unit includes: A user's emotion is estimated, and a priority order of location information to be acquired is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 10) The collecting unit includes: When acquiring location information, the system prioritizes acquiring information that is highly relevant based on the user's behavioral patterns. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When acquiring location information, the speed and direction of movement of the item are simultaneously recorded. 2. The system of claim 1, (Appendix 12) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 13) The analysis unit is Considering item usage and environmental data when analyzing item movement patterns 2. The system of claim 1, (Appendix 14) The analysis unit is Analysis results are updated in real time to keep the movement of goods up to date. 2. The system of claim 1, (Appendix 15) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, the accuracy of the analysis will be improved by referring to related literature and databases along with the movement history of the items. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the movement patterns of items are visualized and presented to the user in an easy-to-understand manner. 2. The system of claim 1, (Appendix 18) The prediction unit is Inferring user emotions and adjusting a movement prediction algorithm based on the estimated user emotions 2. The system of claim 1, (Appendix 19) The prediction unit is Predict future movement patterns in detail based on the current location of goods and their past movement records 2. The system of claim 1, (Appendix 20) The prediction unit is Update prediction results in real time to keep goods movement forecasts up to date 2. The system of claim 1, (Appendix 21) The prediction unit is Infer user emotions and adjust how prediction results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 22) The prediction unit is When making forecasts, reference relevant market data and trends along with historical goods movements to improve forecast accuracy 2. The system of claim 1, (Appendix 23) The prediction unit is When making predictions, the movement patterns of goods are visualized and presented to users in an easy-to-understand manner. 2. The system of claim 1, (Appendix 24) The suggestion unit, Infer user emotions and adjust the presentation of suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 25) The suggestion unit, Considering item usage and environmental data when proposing the timing of item lending, borrowing, or transshipment 2. The system of claim 1, (Appendix 26) The suggestion unit, Update proposal results in real time to keep your item movement proposals up to date 2. The system of claim 1, (Appendix 27) The suggestion unit, Estimate user sentiment and prioritize suggestions based on the estimated user sentiment 2. The system of claim 1, (Appendix 28) The suggestion unit, When making a proposal, the accuracy of the proposal is improved by referring to related literature and databases along with the item movement history. 2. The system of claim 1, (Appendix 29) The suggestion unit, When making a proposal, the movement patterns of items are visualized and presented to the user in an easy-to-understand manner. 2. The system of claim 1, [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects position information of the item from a sensor attached to the item; an analysis unit that analyzes a movement transition of the item based on the position information collected by the collection unit; a prediction unit that predicts a future movement of the article based on the analysis result obtained by the analysis unit; a suggestion unit that suggests a timing for moving the item based on the prediction result obtained by the prediction unit. A system characterized by:

2. The suggestion unit, Suggest the timing of lending or borrowing the item or replacing the item 2. The system of claim 1 .

3. The suggestion unit, If the item is lost, suggest the location of the item 2. The system of claim 1 .

4. The collecting unit includes: Obtaining location information using radio frequency identification loggers 2. The system of claim 1 .

5. The prediction unit is Predict movement based on current location and past movement history 2. The system of claim 1 .

6. The collecting unit includes: A method for estimating a user's emotions and adjusting the timing of acquiring location information based on the estimated user's emotions 2. The system of claim 1 .

7. The collecting unit includes: The usage status and status information of the item are acquired in real time and recorded together with the location information.

2. The system of claim 1 .

8. The collecting unit includes: When acquiring location information, environmental data of the item is collected at the same time.

2. The system of claim 1 .

9. The collecting unit includes: A user's emotion is estimated, and a priority order of location information to be acquired is determined based on the estimated user's emotion.

2. The system of claim 1 .

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