system

Abstract

Provide a system. 【Solution means】 Image processing means for detecting the position of an article, Database means for storing the position information of the article, Calculation means for proposing an optimal arrangement method of the article based on the position information, Input means having a voice recognition function for inquiring about the location of an article, Monitoring means for issuing a warning in case of an abnormality, Means for predicting the behavior of a user using the information of the database means and generating a notification according to time, Means for efficiently managing the resources of public facilities and infrastructure, Means for visually displaying the position of an article in real time, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In daily life, the loss of items and the stress associated with the time spent searching for them have become problems. Especially for the elderly and disabled, it is difficult to manage items, and there is a need to improve efficiency in daily life. Also, it takes a lot of effort to check the location of items, and there is a current situation where the improvement of storage methods cannot keep up. Furthermore, sufficient security measures against the loss or inappropriate movement of valuables are required.

Means for Solving the Problems

[0005] This invention provides a system that updates location information in real time by accumulating location data of items in a cloud database, and includes an image processing device for visually managing the location of items. This system uses a computing device that can propose the optimal storage method based on location information, and can query the location of items from an input device with voice recognition capabilities. Furthermore, it includes a security device that issues warnings in the event of improper movement or loss of items, and a reminder generation device that analyzes the user's behavior patterns to predict the items needed at a specific time, thereby solving these problems.

[0006] An "image processing device" is a device that analyzes acquired video data to recognize the position of an object.

[0007] A "cloud database" is an online data storage system that stores and provides location information of items as needed, accessible via the internet.

[0008] A "calculating unit" is a computer or processor that has the function of processing data to calculate and propose the optimal storage method for items.

[0009] "Voice recognition functionality" is a technology that converts voice-input information into text data and performs information retrieval and operations based on that content.

[0010] An "input device" is a device that provides an interface for inputting data such as voice or text into a system.

[0011] A "security device" is a device that monitors for the movement or loss of inappropriate items and issues a warning when an anomaly is detected.

[0012] A "reminder generator" is a system that analyzes a user's behavior patterns and notifies them of necessary items or appointments according to the time. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered 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), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The present invention constructs a system that manages the location of items in real time, provides users with an efficient method for storing items, and warns of improper movement or loss of items. This system includes an image processing device, a cloud database, a computing device, an input device with voice recognition capabilities, a security device, and a reminder generation device.

[0035] Item location detection and management

[0036] The server analyzes the video data transmitted from the image processing device and obtains the location information of the objects. Based on this, the location data is stored in a cloud database.

[0037] Suggestions for the optimal storage method

[0038] The server analyzes location data stored in the cloud database using a computing device to calculate the optimal storage method. This suggestion is immediately notified to the user via the terminal and can be viewed on the screen.

[0039] Response to the user regarding the location of the item

[0040] When a user inquires about the location of a designated item using voice recognition, the terminal converts the voice input into text, and the server retrieves the information from its database to provide a response. The information is then displayed visually on the terminal as a map, allowing the user to easily confirm the location.

[0041] Implementation of security measures

[0042] The server monitors the location of important items through security devices. If any inappropriate movement is detected, it immediately issues a warning and notifies the user via a terminal.

[0043] Providing reminders to users

[0044] The server collects and analyzes user behavior patterns and predicts the items needed at specific times and in specific situations. Based on this, reminders are generated and notifications are sent to the user from their device.

[0045] For example, if a user asks "Where's my umbrella?", the device converts this into text, the server searches a cloud database, and replies "It's by the front door." Furthermore, on rainy mornings, the device sends a reminder asking, "Did you bring your umbrella?"

[0046] This system improves the efficiency of managing belongings in daily life and provides users with a secure and comfortable living environment.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user uses voice recognition to inquire about the location of an item, asking "Where is XX?".

[0050] Step 2:

[0051] The device converts the received audio into text using speech recognition technology.

[0052] Step 3:

[0053] The server analyzes the converted text data and searches a cloud database to detect the location of the specified item.

[0054] Step 4:

[0055] The server acquires location information and generates a visual map showing the current location of the corresponding item.

[0056] Step 5:

[0057] The device displays the generated map on the user's screen and provides answers to inquiries.

[0058] Step 6:

[0059] The server uses the stored location data of items to calculate the optimal storage method for the items using a computing device, and then retrieves the result.

[0060] Step 7:

[0061] The device notifies the user of suggestions for the optimal storage method and displays the information for the user to review.

[0062] Step 8:

[0063] The server monitors the movement or disappearance of important items through security devices and generates a warning message if an anomaly is detected.

[0064] Step 9:

[0065] The device notifies the user of any warnings that have occurred and provides information to enable the user to take prompt action.

[0066] Step 10:

[0067] The server analyzes user behavior patterns, predicts situations where a reminder is needed, and generates a reminder.

[0068] Step 11:

[0069] The device notifies the user's device of the generated reminder, providing the necessary information without delay.

[0070] (Example 1)

[0071] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0072] In modern society, the loss or improper management of belongings is a source of stress in daily life, and there is a need for efficient methods of managing and storing items. Furthermore, accurately identifying the items users need and notifying them in advance with reminders is a challenge in preventing items from being forgotten or lost.

[0073] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0074] In this invention, the server includes a visual processing means for detecting the location of an item, a data storage means for accumulating location information of the item, and a calculation means for proposing the optimal storage method for the item based on the location information. As a result, the user can grasp the precise location information of the item and receive efficient storage suggestions, improving the convenience of daily life. Furthermore, real-time updates of location information enable immediate detection of inappropriate item movements and prompt response.

[0075] "Visual processing means" refers to means for analyzing video data to extract location information of an object.

[0076] A "data storage means" is a storage device that stores location information of analyzed items and can retrieve it as needed.

[0077] A "calculation method" is a means of calculating an efficient method for storing items based on accumulated location information.

[0078] "Voice recognition functionality" is a feature that converts voice input into text data and understands user commands.

[0079] A "warning mechanism" is a means of issuing a warning and notifying the user when the improper movement of an item is detected.

[0080] "Means for generating notifications" refers to means of analyzing user behavior patterns and creating and notifying users of predicted necessary items as reminders.

[0081] "Means of visual presentation" refers to means of providing users with the location information of analyzed items visually using maps or diagrams.

[0082] "Means of immediate notification" refers to methods of communicating suggested storage methods and warnings to users in real time.

[0083] As an embodiment of this invention, a system is provided that manages the location of items and proposes efficient storage solutions. Specifically, the components work together as follows.

[0084] Item location detection and management

[0085] The terminal acquires video data of an object using image processing hardware (e.g., a camera system). The video data is analyzed in real time using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to extract the object's location information.

[0086] The server stores the location information transmitted from the visual processing means in a data storage means (e.g., cloud storage) in preparation for subsequent processing.

[0087] Suggestions for the optimal storage method

[0088] The server analyzes the stored location information using computational methods. Generative AI models (e.g., TensorFlow) are used in the calculations to propose efficient storage methods for items.

[0089] This suggestion will be notified to the user from the terminal via an immediate notification system. Based on the information presented, the user can then store items optimally.

[0090] Response to the user regarding the location of the item

[0091] When a user inquires about the location of an item through an input device with voice recognition capabilities, the terminal converts the voice into text and sends it to the server.

[0092] The server searches for the relevant location information from its data storage system and displays the location visually using a map or other means. For example, if a user asks, "Where is my umbrella?", the server will respond, "It's in the entryway."

[0093] Security features and reminder provision

[0094] The server uses warning mechanisms to alert the user if it detects the inappropriate movement of items.

[0095] Furthermore, the server uses behavioral prediction analytics to analyze user behavior based on information stored in the data storage system and generates reminders regarding necessary items. An example of a prompt message would be, "It's raining today. Did you bring an umbrella?"

[0096] The introduction of this system will allow users to improve the efficiency of managing their belongings and live their daily lives with peace of mind.

[0097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0098] Step 1:

[0099] The terminal acquires video data of an object using an image processing device. Specifically, a camera system captures an image of the area where the object is located and transmits it to a visual processing device. The input to this step is the captured still image or video data, and the output is prepared video data for image analysis.

[0100] Step 2:

[0101] The server analyzes video data acquired using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to identify the location of objects. The input is the video data obtained from step 1, and the output is the location information of the objects. Specifically, the server identifies objects within the video and extracts their locations as coordinates.

[0102] Step 3:

[0103] The server stores the location information in a data storage device. Specifically, the location information is stored in a cloud database, making it possible to process and retrieve it later. The input to this step is the location information extracted in step 2, and the output is the location information stored in the database.

[0104] Step 4:

[0105] The server performs calculations based on the stored location information. Using this location information, it proposes the optimal storage method for items using a generative AI model (e.g., TENSORFLOW®). The input for this step is location information obtained from the database, and the output is the calculated storage method proposal.

[0106] Step 5:

[0107] The device receives notifications from the server and informs the user of the optimal storage method. Specifically, it displays storage method suggestions on the smartphone or PC display. The input for this step is the storage suggestion obtained in step 4, and the output is a visual notification to the user.

[0108] Step 6:

[0109] When a user inquires about the location of an item using voice, the terminal converts the voice into text and sends it to the server. The input is the user's voice data, and the output is the query in text format. Specifically, a speech recognition engine is used to analyze the intent of the question.

[0110] Step 7:

[0111] The server searches the cloud database based on a text-based query and retrieves the location information of the corresponding item. The input for this step is the text data from step 6, and the output is the location information of the item corresponding to the query.

[0112] Step 8:

[0113] The terminal visually displays location information obtained from the server. Specifically, it marks the location of an item on a map application. The input is location information from the server, and the output is a visual display that the user can confirm.

[0114] Step 9:

[0115] The server, upon detecting the movement of inappropriate items through its warning system, issues a warning and notifies the user via a terminal. The input for this step is movement information from security sensors, and the output is an alert notification. Specifically, it monitors sensor information and detects anomalies.

[0116] Step 10:

[0117] The server analyzes user behavior, generates reminders about anticipated necessary items, and notifies the user via the terminal. The input for this step is the user's past behavior data, and the output is the generated reminder. A concrete example is sending a notification such as, "It's raining today. Did you bring an umbrella?"

[0118] (Application Example 1)

[0119] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0120] In modern smart cities, managing public facilities and infrastructure is crucial. However, the dispersion of numerous items and resources presents challenges in efficient management. Furthermore, the loss or unauthorized movement of items can impact public safety and efficient operation, necessitating solutions to prevent these issues.

[0121] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0122] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and calculation means for proposing an optimal arrangement method for the item based on the location information. This enables efficient management of resources in public facilities and infrastructure, and prevents the loss or unauthorized movement of items.

[0123] "Image processing means" refers to technology for visually detecting the position of an object and collecting that information.

[0124] A "database means" is a storage device for accumulating and efficiently managing location information of collected items.

[0125] A "calculation device" is a device that calculates and proposes the optimal arrangement method for items based on location information.

[0126] "Voice recognition functionality" is a technology that understands the user's voice commands and converts them into predetermined instructions.

[0127] "Monitoring measures" refer to functions that detect abnormal movement of goods or fraudulent activity, and issue warnings as necessary.

[0128] A "user behavior prediction tool" is a function that uses accumulated information to predict the user's behavior and necessary items, and then provides assistance.

[0129] A "real-time display means" is a technology for rapidly processing the location information of an item and presenting it visually to the user.

[0130] "Public facility management systems" are systems for efficiently supervising and managing resources and goods in various public spaces.

[0131] The system that realizes this invention is composed of a combination of multiple hardware and software components. To determine the location of an object, the system utilizes an image processing device. This device acquires and analyzes visual data from a device such as a smart camera. The server uses an image processing library such as OpenCV to detect the location of the object from the obtained video data. Furthermore, this location information is stored in a cloud database and managed by a cloud service such as AWS®.

[0132] The server has the capability to perform calculations based on collected location information and propose the optimal placement method for items. This computational means evaluates location data using analytical algorithms and generates efficient placement and storage plans. This enables efficient management of items and resources within public facilities and smart cities.

[0133] The speech recognition function uses services such as Google® Speech-to-Text to convert the user's voice commands into text. The commands entered via speech recognition are interpreted by the system, and an appropriate response is generated in real time.

[0134] As a monitoring measure, sensor technology is incorporated to detect the unauthorized movement of important items. This allows the server to immediately send a warning to the user terminal if it detects any unusual activity.

[0135] This system provides a means to predict users' behavioral patterns and generates reminders according to specific times and situations. This function helps users prevent forgetting things and allows them to live their daily lives more smoothly.

[0136] For example, if a library user asks, "Where is a specific book?", speech recognition converts the question into text, and the server quickly searches the database and displays the results. These results are presented as a map on the user's terminal, guiding them to the exact location of the item. Furthermore, during peak hours, the system predicts the need for additional equipment or staff, supporting efficient operation.

[0137] Examples of prompts to be input into the generating AI model include questions such as, "Which shelf should new books be placed on in the library?" This system is expected to significantly improve the efficiency of resource management in public facilities and smart cities.

[0138] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0139] Step 1:

[0140] The server receives visual data acquired from a smart camera as input. This data is analyzed using an image processing algorithm based on OpenCV to identify the location of objects. As output, it generates location information for the detected objects.

[0141] Step 2:

[0142] The server stores the location information of the items generated in Step 1 in a cloud database. This database is managed using a cloud platform such as AWS, enabling real-time data updates. The database output provides the latest item location information.

[0143] Step 3:

[0144] The device receives voice commands from the user as input using Google Speech-to-Text speech recognition technology. The voice data is converted into text format and sent to the server. As output, the content of the user's commands is generated in text format.

[0145] Step 4:

[0146] The server receives the text data obtained in step 3 as input and searches the cloud database. It then retrieves the location information of the corresponding item and outputs it to the terminal. The results are presented along with a visual map.

[0147] Step 5:

[0148] Based on the location information accumulated in step 2, the server uses a calculation algorithm to determine the optimal placement method for the items. As an output of this calculation, it generates a proposed placement plan and notifies the administrator.

[0149] Step 6:

[0150] The terminal receives the deployment plan sent from the server and notifies the user. Furthermore, it generates and outputs necessary reminders based on the user's behavior patterns.

[0151] Step 7:

[0152] The server receives data from monitoring devices as input and detects unauthorized movement of important items. If an anomaly is detected, it generates a warning and outputs it to the terminal. This output allows the user to receive information about the unauthorized movement immediately.

[0153] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0154] This invention provides more personalized interactions by combining a system for managing the location information of items with an emotion engine that recognizes the user's emotions. This system includes an image processing device, a cloud database, a computing device, an input device with speech recognition capabilities, a security device, a reminder generation device, and an emotion engine.

[0155] Item location detection and management

[0156] The server analyzes video data transmitted from the image processing device to detect the location of objects. The obtained location information is stored in a cloud database and can be updated in real time.

[0157] Suggestions for the optimal storage method

[0158] The server uses computing power to analyze location information from the cloud database and suggest the optimal storage method for the user. Location information is updated regularly, and suggestions are made based on this updated information.

[0159] Response to the user regarding the location of the item

[0160] When a user inquires about the location of an item using voice recognition, the server searches the database and notifies the user of the location of the corresponding item via the device.

[0161] Security features

[0162] The server monitors the location of critical items and issues an alert if an anomaly occurs. This alert is then sent to the user via the terminal, enabling a quick response.

[0163] Emotion recognition and response customization

[0164] The emotion engine analyzes the user's emotions from their voice and facial expressions and adjusts the system's response accordingly. For example, if the system determines that the user is stressed, it will provide more concise guidance.

[0165] Personalize reminders

[0166] The server considers the user's emotional state and dynamically changes the content and timing of reminders. Based on the analysis results from the emotion engine, information is delivered in a more effective manner.

[0167] For example, if a user asks "Where are the keys?" in an anxious voice, the emotion engine will sense the anxiety, and the server will provide a quick and clear answer. Furthermore, encouraging messages will be added as needed to enhance the user's sense of security.

[0168] This system not only streamlines the management of items in daily life, but also allows users to enjoy a more comfortable user experience through personalized interactions that take emotions into consideration.

[0169] The following describes the processing flow.

[0170] Step 1:

[0171] A user uses voice recognition to inquire about the location of an item by asking, "Where is [item name]?"

[0172] Step 2:

[0173] The device inputs the user's voice and converts it into text data using speech recognition technology. This text data is immediately sent to the server.

[0174] Step 3:

[0175] The emotion engine analyzes the user's voice to determine, for example, whether anxiety is present.

[0176] Step 4:

[0177] The server searches the cloud database to retrieve the latest location information for the specified item.

[0178] Step 5:

[0179] The server adjusts the content and tone of its response based on the results of sentiment analysis. For example, if the user is feeling anxious, it will add reassuring language.

[0180] Step 6:

[0181] The server sends the generated response to the terminal along with visual map information.

[0182] Step 7:

[0183] The device displays a visual map to the user and communicates the results via voice or text. For example, it might respond with, "The keys are on the table by the front door. Everything is fine."

[0184] Step 8:

[0185] The server continuously learns user behavior patterns and feeds them back into the cloud database. This improves the accuracy of responses to future inquiries.

[0186] Step 9:

[0187] The server monitors the user's emotional state and adjusts reminders as needed. The user is then notified of these adjustments via their device.

[0188] (Example 2)

[0189] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0190] In recent years, as the demand for efficient management of goods and services tailored to individual needs increases, the means to track the location of items, suggest appropriate storage methods, and provide emotionally sensitive responses remain limited. Furthermore, no system exists that can dynamically adjust how information is conveyed based on the user's emotional state. The challenges arising from this are to provide a user experience optimized for each individual while simultaneously improving efficiency in item management and security.

[0191] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0192] In this invention, the server includes visual information processing means for detecting the position of an object, information storage means for storing the position information of the object, and emotion analysis means, which adjusts the response according to the user's emotional state. This allows the user to easily grasp the position of an item and receive personalized service tailored to their emotions.

[0193] "Visual information processing means" refers to technologies and devices for analyzing visual data such as images and videos to determine the location of an object.

[0194] "Information storage means" refers to means of storing and managing the location and related information of objects, such as using databases or cloud storage.

[0195] "Computational means" refers to a function that uses a computer or its software to analyze data and perform processing to propose the optimal storage method for objects.

[0196] "Input means" refers to interfaces and devices that allow users to input information and instructions into a system through voice or text.

[0197] "Protective measures" refer to safety devices and systems that detect abnormalities in the position of an object and issue a warning to the user.

[0198] "Means for generating alert information" refers to methods for predicting user behavior and generating appropriate reminders and notifications at the necessary time.

[0199] "Emotional analysis tools" refer to technologies and devices that analyze data such as the tone of a user's voice and facial expressions to determine their emotional state.

[0200] This invention is a system that precisely manages the position of objects and provides efficient and personalized services to users. The main components involved are a server, a terminal, and the user.

[0201] The server detects the position of objects using visual information processing means. These means include hardware such as cameras and image sensors, and software equipped with image recognition algorithms. The server analyzes the acquired visual data to determine the object's location. This location information is stored in cloud storage, which is an information storage means. This ensures that the object's location information is updated in real time, facilitating later reference.

[0202] The user can inquire about the location of an object using an input device. This device utilizes speech recognition technology, allowing the user to ask questions in natural speech through the terminal. The terminal converts the speech into digital data and sends it to the server. Based on the received data, the server searches for the corresponding location information in its information storage device and returns the result to the terminal.

[0203] Furthermore, the server is equipped with emotion analysis capabilities that analyze the user's emotions from their voice and facial expressions. Based on this data, it customizes responses and provides support tailored to the user's psychological state.

[0204] For example, if a user anxiously asks, "Where's my wallet?", the server quickly searches its information storage devices and immediately provides the location. In some cases, it may also automatically generate an encouraging message such as, "I hope you find it," to reassure the user.

[0205] The system also includes a mechanism for generating alert information. The server analyzes user behavior patterns and has the function to set reminders at appropriate times. For example, it can support users' schedules by sending reminders such as, "Don't forget to bring the necessary documents to tomorrow's meeting."

[0206] An example of a prompt message is, "If the user's voice sounds anxious, indicate how the system should respond." In this way, the invention can continue to provide users with practical and emotionally sensitive interactions.

[0207] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0208] Step 1:

[0209] The server acquires visual data of objects from image sensors. This data is input as video of the environment in which the objects are placed. Based on this, the server applies an image recognition algorithm and outputs location information that identifies the position of each object. Specifically, the image analysis process involves object shape recognition and feature detection.

[0210] Step 2:

[0211] The server saves the identified location information to cloud storage. The input for this process is the location information of the object extracted in step 1. The server converts this into a database format and outputs it in a state where it can be updated in real time. Specifically, the location information is written and the database is synchronized.

[0212] Step 3:

[0213] The user asks questions about the location of objects using voice commands through the device. The device converts this voice input into text data and sends it to the server. Speech recognition technology is used to process natural language and analyze the content of the questions.

[0214] Step 4:

[0215] The server searches cloud storage based on the received text data and retrieves the location information of the corresponding object. The input to this process is the text data of the question generated in step 3, and the output is the location information of the object. The server uses a search algorithm to quickly extract the necessary information from the database.

[0216] Step 5:

[0217] The server transmits the location information of the acquired object to the terminal. This allows the user to receive the location information through the terminal. The output is the specific location of the object displayed to the user. The terminal then presents the information via voice or text.

[0218] Step 6:

[0219] The server analyzes the user's voice using emotion analysis tools to determine their psychological state. The user's voice data is used as input, and the output is an evaluation of their emotions. The analysis involves analyzing features such as voice tone and speed to quantify the user's emotions.

[0220] Step 7:

[0221] The server adjusts its response based on the results of sentiment analysis. The server uses the data obtained from the analysis as input to dynamically change and output the response to the user. Specific actions include processes such as changing the content of the response, including adding friendly language or encouraging messages.

[0222] Step 8:

[0223] The server generates and sends reminders based on the user's behavioral history to the device. The input is the user's past behavioral patterns and emotional state, and the output is individually tailored reminder information. The server then uses a behavioral prediction algorithm to send notifications at the appropriate time.

[0224] (Application Example 2)

[0225] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0226] In the daily lives of the elderly and those requiring care, there are problems such as the inability to manage the location of belongings and the lack of appropriate information tailored to their emotions. Furthermore, conventional technologies struggle to provide personalized responses through user emotion recognition, highlighting the need for improved user experience.

[0227] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0228] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and emotion analysis means for analyzing the user's emotions and adjusting the response. This enables more efficient management of items in elderly care and the provision of appropriate information according to the user's emotional state.

[0229] "Image processing means" refers to a device that uses cameras and sensors to acquire information for identifying the location of an object, and then processes and analyzes this information to generate location information.

[0230] A "database system" is a system that stores location information of items and user behavior data, and uses this information to search for and utilize it as needed.

[0231] A "computational means" refers to a processor or device that analyzes accumulated data and generates optimal storage methods for items and suggestions for users.

[0232] An "input means" is an interface device that allows a user to inquire about the location of an item by voice or other means.

[0233] A "security measure" is a device that constantly monitors the location information of an item and issues a warning if an anomaly is detected.

[0234] A "reminder generation device" is a device that generates notifications and reminders at appropriate times based on the user's actions and emotions.

[0235] An "emotion analysis tool" is a system that analyzes a user's voice and facial expressions to determine their emotional state.

[0236] An "information presentation device" is an interface device that provides information to the user based on the analysis results.

[0237] The system for realizing this invention is composed of a user, a terminal, and a server. The server acquires the location information of an item using image processing means and stores the generated location information in a database means. This enables real-time location tracking. Furthermore, using the information stored in the database, a calculation means proposes the optimal storage method for the item.

[0238] The terminal receives voice input from the user using its voice recognition function and can query the location information of items through the input device. The server analyzes this voice data, retrieves the necessary information from the database, and presents it to the user.

[0239] The server includes emotion analysis tools to analyze the user's voice and facial expressions. These tools utilize machine learning frameworks such as TensorFlow and Keras. Based on the user's emotional state, appropriate responses are generated and delivered to the user through information presentation tools.

[0240] Furthermore, the security system alerts the user if an anomaly is detected, prompting a quick response. The entire system is implemented as a server-side application using Flask, and speech recognition is performed using the Google Cloud Speech-to-Text API. Firebase is used for real-time data management.

[0241] For example, if a user asks the device, "Where are my medicines?", the server performs sentiment analysis and, if the user is feeling confused or anxious, responds with encouraging words such as, "They're on the shelf above the kitchen counter."

[0242] An example of a prompt for a generative AI model might be: "If a user is anxiously searching for a specific item, suggest a way to generate a reassuring response based on location information and emotional data."

[0243] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0244] Step 1:

[0245] The user inputs voice data inquiring about the location of an item through the device's voice input function. This data is converted into text data on the device using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. At this stage, the voice data is internally sent to the API, and the returned text is retrieved.

[0246] Step 2:

[0247] Text data is sent to a server, which analyzes the sentiment of the text data using sentiment analysis tools. Here, TensorFlow or Keras is used to label the sentiment of the input text, and the sentiment label is obtained as output. Specifically, the model estimates the sentiment (e.g., anxious, confused, neutral) based on the text data and outputs that sentiment.

[0248] Step 3:

[0249] The server retrieves the location information of the corresponding item from the database based on the emotion label and query content. In this step, the database is queried to retrieve location data associated with the item. The input is the query text, and the output is the location information of the item. Specifically, the process involves executing a query on the database based on the text content.

[0250] Step 4:

[0251] The server generates an appropriate response through an information presentation system based on the sentiment analysis results and the location information of the items. Using a generation AI model, it selects the most appropriate expression for the user and creates a response sentence to present to the user as output. Specifically, in addition to an explanation of the location information, it also generates encouraging or cautionary messages corresponding to the sentiment label.

[0252] Step 5:

[0253] The final response is sent to the terminal and communicated to the user using the terminal's voice output function. The input is the generated response text, and the output is the voice message provided to the user. Specifically, the process involves converting the text response into speech via a speech synthesis API and outputting it through the speaker.

[0254] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0255] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0256] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0257] [Second Embodiment]

[0258] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0259] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0260] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0261] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0262] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0263] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0264] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0265] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0266] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0267] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0268] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0269] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0270] The present invention constructs a system that manages the location of items in real time, provides users with an efficient method for storing items, and warns of improper movement or loss of items. This system includes an image processing device, a cloud database, a computing device, an input device with voice recognition capabilities, a security device, and a reminder generation device.

[0271] Item location detection and management

[0272] The server analyzes the video data transmitted from the image processing device and obtains the location information of the objects. Based on this, the location data is stored in a cloud database.

[0273] Suggestions for the optimal storage method

[0274] The server analyzes location data stored in the cloud database using a computing device to calculate the optimal storage method. This suggestion is immediately notified to the user via the terminal and can be viewed on the screen.

[0275] Response to the user regarding the location of the item

[0276] When a user inquires about the location of a designated item using voice recognition, the terminal converts the voice input into text, and the server retrieves the information from its database to provide a response. The information is then displayed visually on the terminal as a map, allowing the user to easily confirm the location.

[0277] Implementation of security measures

[0278] The server monitors the location of important items through security devices. If any inappropriate movement is detected, it immediately issues a warning and notifies the user via a terminal.

[0279] Providing reminders to users

[0280] The server collects and analyzes user behavior patterns and predicts the items needed at specific times and in specific situations. Based on this, reminders are generated and notifications are sent to the user from their device.

[0281] For example, if a user asks "Where's my umbrella?", the device converts this into text, the server searches a cloud database, and replies "It's by the front door." Furthermore, on rainy mornings, the device sends a reminder asking, "Did you bring your umbrella?"

[0282] This system improves the efficiency of managing belongings in daily life and provides users with a secure and comfortable living environment.

[0283] The following describes the processing flow.

[0284] Step 1:

[0285] The user inquires about the location of an item, such as "Where is ○○?", through the voice recognition function.

[0286] Step 2:

[0287] The terminal converts the received voice into text using voice recognition technology.

[0288] Step 3:

[0289] The server analyzes the converted text data and searches the cloud database to detect the location of the specified item.

[0290] Step 4:

[0291] The server obtains the location information and generates a visual map to show the current location of the corresponding item.

[0292] Step 5:

[0293] The terminal displays the generated map on the user's screen and provides an answer to the inquiry.

[0294] Step 6:

[0295] The server calculates, using an arithmetic unit, a proposal for the optimal storage method of the item based on the accumulated location data of the item and obtains the result. <0934> Step 7:

[0297] The terminal notifies the user of the proposal for the optimal storage method and displays information so that the user can confirm it.

[0298] Step 8: [[ID=X]]

[0299] The server monitors the movement and disappearance of important items through a security device and generates a warning message when an abnormality is detected.

[0300] Step 9:

[0301] The terminal notifies the user of the generated warning and provides information so that the user can respond promptly.

[0302] Step 10:

[0303] The server analyzes the user's behavior pattern, predicts situations where a reminder is needed, and generates a reminder.

[0304] Step 11:

[0305] The terminal notifies the generated reminder to the user's device and provides the necessary information without missing the opportunity.

[0306] (Example 1)

[0307] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0308] In modern society, the loss and improper management of items have become stress factors in daily life, and there is a demand for providing efficient methods for item management and storage. Also, it is an issue to accurately grasp the items required by the user and prevent forgetting or losing items by notifying them in advance as a reminder.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0310] In this invention, the server includes a visual processing means for detecting the location of an item, a data storage means for accumulating location information of the item, and a calculation means for proposing the optimal storage method for the item based on the location information. As a result, the user can grasp the precise location information of the item and receive efficient storage suggestions, improving the convenience of daily life. Furthermore, real-time updates of location information enable immediate detection of inappropriate item movements and prompt response.

[0311] "Visual processing means" refers to means for analyzing video data to extract location information of an object.

[0312] A "data storage means" is a storage device that stores location information of analyzed items and can retrieve it as needed.

[0313] A "calculation method" is a means of calculating an efficient method for storing items based on accumulated location information.

[0314] "Voice recognition functionality" is a feature that converts voice input into text data and understands user commands.

[0315] A "warning mechanism" is a means of issuing a warning and notifying the user when the improper movement of an item is detected.

[0316] "Means for generating notifications" refers to means of analyzing user behavior patterns and creating and notifying users of predicted necessary items as reminders.

[0317] "Means of visual presentation" refers to means of providing users with the location information of analyzed items visually using maps or diagrams.

[0318] "Means of immediate notification" refers to methods of communicating suggested storage methods and warnings to users in real time.

[0319] As an embodiment of this invention, a system is provided that manages the location of items and proposes efficient storage solutions. Specifically, the components work together as follows.

[0320] Item location detection and management

[0321] The terminal acquires video data of an object using image processing hardware (e.g., a camera system). The video data is analyzed in real time using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to extract the object's location information.

[0322] The server stores the location information transmitted from the visual processing means in a data storage means (e.g., cloud storage) in preparation for subsequent processing.

[0323] Suggestions for the optimal storage method

[0324] The server analyzes the stored location information using computational methods. Generative AI models (e.g., TensorFlow) are used in the calculations to propose efficient storage methods for items.

[0325] This suggestion will be notified to the user from the terminal via an immediate notification system. Based on the information presented, the user can then store items optimally.

[0326] Response to the user regarding the location of the item

[0327] When a user inquires about the location of an item through an input device with voice recognition capabilities, the terminal converts the voice into text and sends it to the server.

[0328] The server searches for the relevant location information from its data storage system and displays the location visually using a map or other means. For example, if a user asks, "Where is my umbrella?", the server will respond, "It's in the entryway."

[0329] Security features and reminder provision

[0330] The server uses warning mechanisms to alert the user if it detects the inappropriate movement of items.

[0331] Furthermore, the server uses behavioral prediction analytics to analyze user behavior based on information stored in the data storage system and generates reminders regarding necessary items. An example of a prompt message would be, "It's raining today. Did you bring an umbrella?"

[0332] The introduction of this system will allow users to improve the efficiency of managing their belongings and live their daily lives with peace of mind.

[0333] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0334] Step 1:

[0335] The terminal acquires video data of an object using an image processing device. Specifically, a camera system captures an image of the area where the object is located and transmits it to a visual processing device. The input to this step is the captured still image or video data, and the output is prepared video data for image analysis.

[0336] Step 2:

[0337] The server analyzes video data acquired using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to identify the location of objects. The input is the video data obtained from step 1, and the output is the location information of the objects. Specifically, the server identifies objects within the video and extracts their locations as coordinates.

[0338] Step 3:

[0339] The server stores the location information in a data storage device. Specifically, the location information is stored in a cloud database, making it possible to process and retrieve it later. The input to this step is the location information extracted in step 2, and the output is the location information stored in the database.

[0340] Step 4:

[0341] The server performs calculations based on the stored location information. Using this location information, it proposes the optimal storage method for items using a generative AI model (e.g., TensorFlow). The input for this step is location information obtained from the database, and the output is the calculated storage method proposal.

[0342] Step 5:

[0343] The device receives notifications from the server and informs the user of the optimal storage method. Specifically, it displays storage method suggestions on the smartphone or PC display. The input for this step is the storage suggestion obtained in step 4, and the output is a visual notification to the user.

[0344] Step 6:

[0345] When a user inquires about the location of an item using voice, the terminal converts the voice into text and sends it to the server. The input is the user's voice data, and the output is the query in text format. Specifically, a speech recognition engine is used to analyze the intent of the question.

[0346] Step 7:

[0347] The server searches the cloud database based on a text-based query and retrieves the location information of the corresponding item. The input for this step is the text data from step 6, and the output is the location information of the item corresponding to the query.

[0348] Step 8:

[0349] The terminal visually displays location information obtained from the server. Specifically, it marks the location of an item on a map application. The input is location information from the server, and the output is a visual display that the user can confirm.

[0350] Step 9:

[0351] The server, upon detecting the movement of inappropriate items through its warning system, issues a warning and notifies the user via a terminal. The input for this step is movement information from security sensors, and the output is an alert notification. Specifically, it monitors sensor information and detects anomalies.

[0352] Step 10:

[0353] The server analyzes user behavior, generates reminders about anticipated necessary items, and notifies the user via the terminal. The input for this step is the user's past behavior data, and the output is the generated reminder. A concrete example is sending a notification such as, "It's raining today. Did you bring an umbrella?"

[0354] (Application Example 1)

[0355] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0356] In modern smart cities, managing public facilities and infrastructure is crucial. However, the dispersion of numerous items and resources presents challenges in efficient management. Furthermore, the loss or unauthorized movement of items can impact public safety and efficient operation, necessitating solutions to prevent these issues.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0358] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and calculation means for proposing an optimal arrangement method for the item based on the location information. This enables efficient management of resources in public facilities and infrastructure, and prevents the loss or unauthorized movement of items.

[0359] "Image processing means" refers to technology for visually detecting the position of an object and collecting that information.

[0360] A "database means" is a storage device for accumulating and efficiently managing location information of collected items.

[0361] A "calculation device" is a device that calculates and proposes the optimal arrangement method for items based on location information.

[0362] "Voice recognition functionality" is a technology that understands the user's voice commands and converts them into predetermined instructions.

[0363] "Monitoring measures" refer to functions that detect abnormal movement of goods or fraudulent activity, and issue warnings as necessary.

[0364] A "user behavior prediction tool" is a function that uses accumulated information to predict the user's behavior and necessary items, and then provides assistance.

[0365] A "real-time display means" is a technology for rapidly processing the location information of an item and presenting it visually to the user.

[0366] "Public facility management systems" are systems for efficiently supervising and managing resources and goods in various public spaces.

[0367] The system that realizes this invention is composed of a combination of multiple hardware and software components. To determine the location of an object, the system utilizes an image processing device. This device acquires and analyzes visual data from a device such as a smart camera. The server uses an image processing library such as OpenCV to detect the location of the object from the obtained video data. Furthermore, this location information is stored in a cloud database and managed by a cloud service such as AWS.

[0368] The server has the capability to perform calculations based on collected location information and propose the optimal placement method for items. This computational means evaluates location data using analytical algorithms and generates efficient placement and storage plans. This enables efficient management of items and resources within public facilities and smart cities.

[0369] The speech recognition function uses services like Google Speech-to-Text to convert the user's voice commands into text. The commands entered via speech recognition are interpreted by the system, and an appropriate response is generated in real time.

[0370] As a monitoring measure, sensor technology is incorporated to detect the unauthorized movement of important items. This allows the server to immediately send a warning to the user terminal if it detects any unusual activity.

[0371] This system provides a means to predict users' behavioral patterns and generates reminders according to specific times and situations. This function helps users prevent forgetting things and allows them to live their daily lives more smoothly.

[0372] For example, if a library user asks, "Where is a specific book?", speech recognition converts the question into text, and the server quickly searches the database and displays the results. These results are presented as a map on the user's terminal, guiding them to the exact location of the item. Furthermore, during peak hours, the system predicts the need for additional equipment or staff, supporting efficient operation.

[0373] Examples of prompts to be input into the generating AI model include questions such as, "Which shelf should new books be placed on in the library?" This system is expected to significantly improve the efficiency of resource management in public facilities and smart cities.

[0374] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0375] Step 1:

[0376] The server receives visual data acquired from a smart camera as input. This data is analyzed using an image processing algorithm based on OpenCV to identify the location of objects. As output, it generates location information for the detected objects.

[0377] Step 2:

[0378] The server stores the location information of the items generated in Step 1 in a cloud database. This database is managed using a cloud platform such as AWS, enabling real-time data updates. The database output provides the latest item location information.

[0379] Step 3:

[0380] The device receives voice commands from the user as input using Google Speech-to-Text speech recognition technology. The voice data is converted into text format and sent to the server. As output, the content of the user's commands is generated in text format.

[0381] Step 4:

[0382] The server receives the text data obtained in step 3 as input and searches the cloud database. It then retrieves the location information of the corresponding item and outputs it to the terminal. The results are presented along with a visual map.

[0383] Step 5:

[0384] Based on the location information accumulated in step 2, the server uses a calculation algorithm to determine the optimal placement method for the items. As an output of this calculation, it generates a proposed placement plan and notifies the administrator.

[0385] Step 6:

[0386] The terminal receives the deployment plan sent from the server and notifies the user. Furthermore, it generates and outputs necessary reminders based on the user's behavior patterns.

[0387] Step 7:

[0388] The server receives data from monitoring devices as input and detects unauthorized movement of important items. If an anomaly is detected, it generates a warning and outputs it to the terminal. This output allows the user to receive information about the unauthorized movement immediately.

[0389] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0390] This invention provides more personalized interactions by combining a system for managing the location information of items with an emotion engine that recognizes the user's emotions. This system includes an image processing device, a cloud database, a computing device, an input device with speech recognition capabilities, a security device, a reminder generation device, and an emotion engine.

[0391] Item location detection and management

[0392] The server analyzes video data transmitted from the image processing device to detect the location of objects. The obtained location information is stored in a cloud database and can be updated in real time.

[0393] Suggestions for the optimal storage method

[0394] The server uses computing power to analyze location information from the cloud database and suggest the optimal storage method for the user. Location information is updated regularly, and suggestions are made based on this updated information.

[0395] Response to the user regarding the location of the item

[0396] When a user inquires about the location of an item using voice recognition, the server searches the database and notifies the user of the location of the corresponding item via the device.

[0397] Security features

[0398] The server monitors the location of critical items and issues an alert if an anomaly occurs. This alert is then sent to the user via the terminal, enabling a quick response.

[0399] Emotion recognition and response customization

[0400] The emotion engine analyzes the user's emotions from their voice and facial expressions and adjusts the system's response accordingly. For example, if the system determines that the user is stressed, it will provide more concise guidance.

[0401] Personalize reminders

[0402] The server considers the user's emotional state and dynamically changes the content and timing of reminders. Based on the analysis results from the emotion engine, information is delivered in a more effective manner.

[0403] For example, if a user asks "Where are the keys?" in an anxious voice, the emotion engine will sense the anxiety, and the server will provide a quick and clear answer. Furthermore, encouraging messages will be added as needed to enhance the user's sense of security.

[0404] This system not only streamlines the management of items in daily life, but also allows users to enjoy a more comfortable user experience through personalized interactions that take emotions into consideration.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] A user uses voice recognition to inquire about the location of an item by asking, "Where is [item name]?"

[0408] Step 2:

[0409] The device inputs the user's voice and converts it into text data using speech recognition technology. This text data is immediately sent to the server.

[0410] Step 3:

[0411] The emotion engine analyzes the user's voice to determine, for example, whether anxiety is present.

[0412] Step 4:

[0413] The server searches the cloud database to retrieve the latest location information for the specified item.

[0414] Step 5:

[0415] The server adjusts the content and tone of its response based on the results of sentiment analysis. For example, if the user is feeling anxious, it will add reassuring language.

[0416] Step 6:

[0417] The server sends the generated response to the terminal along with visual map information.

[0418] Step 7:

[0419] The device displays a visual map to the user and communicates the results via voice or text. For example, it might respond with, "The keys are on the table by the front door. Everything is fine."

[0420] Step 8:

[0421] The server continuously learns user behavior patterns and feeds them back into the cloud database. This improves the accuracy of responses to future inquiries.

[0422] Step 9:

[0423] The server monitors the user's emotional state and adjusts reminders as needed. The user is then notified of these adjustments via their device.

[0424] (Example 2)

[0425] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0426] In recent years, as the demand for efficient management of goods and services tailored to individual needs increases, the means to track the location of items, suggest appropriate storage methods, and provide emotionally sensitive responses remain limited. Furthermore, no system exists that can dynamically adjust how information is conveyed based on the user's emotional state. The challenges arising from this are to provide a user experience optimized for each individual while simultaneously improving efficiency in item management and security.

[0427] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0428] In this invention, the server includes visual information processing means for detecting the position of an object, information storage means for storing the position information of the object, and emotion analysis means, which adjusts the response according to the user's emotional state. This allows the user to easily grasp the position of an item and receive personalized service tailored to their emotions.

[0429] "Visual information processing means" refers to technologies and devices for analyzing visual data such as images and videos to determine the location of an object.

[0430] "Information storage means" refers to means of storing and managing the location and related information of objects, such as using databases or cloud storage.

[0431] "Computational means" refers to a function that uses a computer or its software to analyze data and perform processing to propose the optimal storage method for objects.

[0432] "Input means" refers to interfaces and devices that allow users to input information and instructions into a system through voice or text.

[0433] "Protective measures" refer to safety devices and systems that detect abnormalities in the position of an object and issue a warning to the user.

[0434] "Means for generating alert information" refers to methods for predicting user behavior and generating appropriate reminders and notifications at the necessary time.

[0435] "Emotional analysis tools" refer to technologies and devices that analyze data such as the tone of a user's voice and facial expressions to determine their emotional state.

[0436] This invention is a system that precisely manages the position of objects and provides efficient and personalized services to users. The main components involved are a server, a terminal, and the user.

[0437] The server detects the position of objects using visual information processing means. These means include hardware such as cameras and image sensors, and software equipped with image recognition algorithms. The server analyzes the acquired visual data to determine the object's location. This location information is stored in cloud storage, which is an information storage means. This ensures that the object's location information is updated in real time, facilitating later reference.

[0438] The user can inquire about the location of an object using an input device. This device utilizes speech recognition technology, allowing the user to ask questions in natural speech through the terminal. The terminal converts the speech into digital data and sends it to the server. Based on the received data, the server searches for the corresponding location information in its information storage device and returns the result to the terminal.

[0439] Furthermore, the server is equipped with emotion analysis capabilities that analyze the user's emotions from their voice and facial expressions. Based on this data, it customizes responses and provides support tailored to the user's psychological state.

[0440] For example, if a user anxiously asks, "Where's my wallet?", the server quickly searches its information storage devices and immediately provides the location. In some cases, it may also automatically generate an encouraging message such as, "I hope you find it," to reassure the user.

[0441] The system also includes a mechanism for generating alert information. The server analyzes user behavior patterns and has the function to set reminders at appropriate times. For example, it can support users' schedules by sending reminders such as, "Don't forget to bring the necessary documents to tomorrow's meeting."

[0442] An example of a prompt message is, "If the user's voice sounds anxious, indicate how the system should respond." In this way, the invention can continue to provide users with practical and emotionally sensitive interactions.

[0443] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0444] Step 1:

[0445] The server acquires visual data of objects from image sensors. This data is input as video of the environment in which the objects are placed. Based on this, the server applies an image recognition algorithm and outputs location information that identifies the position of each object. Specifically, the image analysis process involves object shape recognition and feature detection.

[0446] Step 2:

[0447] The server saves the identified location information to cloud storage. The input for this process is the location information of the object extracted in step 1. The server converts this into a database format and outputs it in a state where it can be updated in real time. Specifically, the location information is written and the database is synchronized.

[0448] Step 3:

[0449] The user asks questions about the location of objects using voice commands through the device. The device converts this voice input into text data and sends it to the server. Speech recognition technology is used to process natural language and analyze the content of the questions.

[0450] Step 4:

[0451] The server searches cloud storage based on the received text data and retrieves the location information of the corresponding object. The input to this process is the text data of the question generated in step 3, and the output is the location information of the object. The server uses a search algorithm to quickly extract the necessary information from the database.

[0452] Step 5:

[0453] The server transmits the location information of the acquired object to the terminal. This allows the user to receive the location information through the terminal. The output is the specific location of the object displayed to the user. The terminal then presents the information via voice or text.

[0454] Step 6:

[0455] The server analyzes the user's voice using emotion analysis tools to determine their psychological state. The user's voice data is used as input, and the output is an evaluation of their emotions. The analysis involves analyzing features such as voice tone and speed to quantify the user's emotions.

[0456] Step 7:

[0457] The server adjusts its response based on the results of sentiment analysis. The server uses the data obtained from the analysis as input to dynamically change and output the response to the user. Specific actions include processes such as changing the content of the response, including adding friendly language or encouraging messages.

[0458] Step 8:

[0459] The server generates and sends reminders based on the user's behavioral history to the device. The input is the user's past behavioral patterns and emotional state, and the output is individually tailored reminder information. The server then uses a behavioral prediction algorithm to send notifications at the appropriate time.

[0460] (Application Example 2)

[0461] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0462] In the daily lives of the elderly and those requiring care, there are problems such as the inability to manage the location of belongings and the lack of appropriate information tailored to their emotions. Furthermore, conventional technologies struggle to provide personalized responses through user emotion recognition, highlighting the need for improved user experience.

[0463] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0464] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and emotion analysis means for analyzing the user's emotions and adjusting the response. This enables more efficient management of items in elderly care and the provision of appropriate information according to the user's emotional state.

[0465] "Image processing means" refers to a device that uses cameras and sensors to acquire information for identifying the location of an object, and then processes and analyzes this information to generate location information.

[0466] A "database system" is a system that stores location information of items and user behavior data, and uses this information to search for and utilize it as needed.

[0467] A "computational means" refers to a processor or device that analyzes accumulated data and generates optimal storage methods for items and suggestions for users.

[0468] An "input means" is an interface device that allows a user to inquire about the location of an item by voice or other means.

[0469] A "security measure" is a device that constantly monitors the location information of an item and issues a warning if an anomaly is detected.

[0470] A "reminder generation device" is a device that generates notifications and reminders at appropriate times based on the user's actions and emotions.

[0471] An "emotion analysis tool" is a system that analyzes a user's voice and facial expressions to determine their emotional state.

[0472] An "information presentation device" is an interface device that provides information to the user based on the analysis results.

[0473] The system for realizing this invention is composed of a user, a terminal, and a server. The server acquires the location information of an item using image processing means and stores the generated location information in a database means. This enables real-time location tracking. Furthermore, using the information stored in the database, a calculation means proposes the optimal storage method for the item.

[0474] The terminal receives voice input from the user using its voice recognition function and can query the location information of items through the input device. The server analyzes this voice data, retrieves the necessary information from the database, and presents it to the user.

[0475] The server includes emotion analysis tools to analyze the user's voice and facial expressions. These tools utilize machine learning frameworks such as TensorFlow and Keras. Based on the user's emotional state, appropriate responses are generated and delivered to the user through information presentation tools.

[0476] Furthermore, the security system alerts the user if an anomaly is detected, prompting a quick response. The entire system is implemented as a server-side application using Flask, and speech recognition is performed using the Google Cloud Speech-to-Text API. Firebase is used for real-time data management.

[0477] For example, if a user asks the device, "Where are my medicines?", the server performs sentiment analysis and, if the user is feeling confused or anxious, responds with encouraging words such as, "They're on the shelf above the kitchen counter."

[0478] An example of a prompt for a generative AI model might be: "If a user is anxiously searching for a specific item, suggest a way to generate a reassuring response based on location information and emotional data."

[0479] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0480] Step 1:

[0481] The user inputs voice data inquiring about the location of an item through the device's voice input function. This data is converted into text data on the device using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. At this stage, the voice data is internally sent to the API, and the returned text is retrieved.

[0482] Step 2:

[0483] Text data is sent to a server, which analyzes the sentiment of the text data using sentiment analysis tools. Here, TensorFlow or Keras is used to label the sentiment of the input text, and the sentiment label is obtained as output. Specifically, the model estimates the sentiment (e.g., anxious, confused, neutral) based on the text data and outputs that sentiment.

[0484] Step 3:

[0485] The server retrieves the location information of the corresponding item from the database based on the emotion label and query content. In this step, the database is queried to retrieve location data associated with the item. The input is the query text, and the output is the location information of the item. Specifically, the process involves executing a query on the database based on the text content.

[0486] Step 4:

[0487] The server generates an appropriate response through an information presentation system based on the sentiment analysis results and the location information of the items. Using a generation AI model, it selects the most appropriate expression for the user and creates a response sentence to present to the user as output. Specifically, in addition to an explanation of the location information, it also generates encouraging or cautionary messages corresponding to the sentiment label.

[0488] Step 5:

[0489] The final response is sent to the terminal and communicated to the user using the terminal's voice output function. The input is the generated response text, and the output is the voice message provided to the user. Specifically, the process involves converting the text response into speech via a speech synthesis API and outputting it through the speaker.

[0490] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0491] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0492] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0493] [Third Embodiment]

[0494] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0495] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0496] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0497] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0498] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0499] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0500] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0501] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0502] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0503] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0504] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0505] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0506] The present invention constructs a system that manages the location of items in real time, provides users with an efficient method for storing items, and warns of improper movement or loss of items. This system includes an image processing device, a cloud database, a computing device, an input device with voice recognition capabilities, a security device, and a reminder generation device.

[0507] Item location detection and management

[0508] The server analyzes the video data transmitted from the image processing device and obtains the location information of the objects. Based on this, the location data is stored in a cloud database.

[0509] Suggestions for the optimal storage method

[0510] The server analyzes location data stored in the cloud database using a computing device to calculate the optimal storage method. This suggestion is immediately notified to the user via the terminal and can be viewed on the screen.

[0511] Response to the user regarding the location of the item

[0512] When a user inquires about the location of a designated item using voice recognition, the terminal converts the voice input into text, and the server retrieves the information from its database to provide a response. The information is then displayed visually on the terminal as a map, allowing the user to easily confirm the location.

[0513] Implementation of security measures

[0514] The server monitors the location of important items through security devices. If any inappropriate movement is detected, it immediately issues a warning and notifies the user via a terminal.

[0515] Providing reminders to users

[0516] The server collects and analyzes user behavior patterns and predicts the items needed at specific times and in specific situations. Based on this, reminders are generated and notifications are sent to the user from their device.

[0517] For example, if a user asks "Where's my umbrella?", the device converts this into text, the server searches a cloud database, and replies "It's by the front door." Furthermore, on rainy mornings, the device sends a reminder asking, "Did you bring your umbrella?"

[0518] This system improves the efficiency of managing belongings in daily life and provides users with a secure and comfortable living environment.

[0519] The following describes the processing flow.

[0520] Step 1:

[0521] The user uses voice recognition to inquire about the location of an item, asking "Where is XX?".

[0522] Step 2:

[0523] The device converts the received audio into text using speech recognition technology.

[0524] Step 3:

[0525] The server analyzes the converted text data and searches a cloud database to detect the location of the specified item.

[0526] Step 4:

[0527] The server acquires location information and generates a visual map showing the current location of the corresponding item.

[0528] Step 5:

[0529] The device displays the generated map on the user's screen and provides answers to inquiries.

[0530] Step 6:

[0531] The server uses the stored location data of items to calculate the optimal storage method for the items using a computing device, and then retrieves the result.

[0532] Step 7:

[0533] The device notifies the user of suggestions for the optimal storage method and displays the information for the user to review.

[0534] Step 8:

[0535] The server monitors the movement or disappearance of important items through security devices and generates a warning message if an anomaly is detected.

[0536] Step 9:

[0537] The device notifies the user of any warnings that have occurred and provides information to enable the user to take prompt action.

[0538] Step 10:

[0539] The server analyzes user behavior patterns, predicts situations where a reminder is needed, and generates a reminder.

[0540] Step 11:

[0541] The device notifies the user's device of the generated reminder, providing the necessary information without delay.

[0542] (Example 1)

[0543] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In modern society, the loss or improper management of belongings is a source of stress in daily life, and there is a need for efficient methods of managing and storing items. Furthermore, accurately identifying the items users need and notifying them in advance with reminders is a challenge in preventing items from being forgotten or lost.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0546] In this invention, the server includes a visual processing means for detecting the location of an item, a data storage means for accumulating location information of the item, and a calculation means for proposing the optimal storage method for the item based on the location information. As a result, the user can grasp the precise location information of the item and receive efficient storage suggestions, improving the convenience of daily life. Furthermore, real-time updates of location information enable immediate detection of inappropriate item movements and prompt response.

[0547] "Visual processing means" refers to means for analyzing video data to extract location information of an object.

[0548] A "data storage means" is a storage device that stores location information of analyzed items and can retrieve it as needed.

[0549] A "calculation method" is a means of calculating an efficient method for storing items based on accumulated location information.

[0550] "Voice recognition functionality" is a feature that converts voice input into text data and understands user commands.

[0551] A "warning mechanism" is a means of issuing a warning and notifying the user when the improper movement of an item is detected.

[0552] "Means for generating notifications" refers to means of analyzing user behavior patterns and creating and notifying users of predicted necessary items as reminders.

[0553] "Means of visual presentation" refers to means of providing users with the location information of analyzed items visually using maps or diagrams.

[0554] "Means of immediate notification" refers to methods of communicating suggested storage methods and warnings to users in real time.

[0555] As an embodiment of this invention, a system is provided that manages the location of items and proposes efficient storage solutions. Specifically, the components work together as follows.

[0556] Item location detection and management

[0557] The terminal acquires video data of an object using image processing hardware (e.g., a camera system). The video data is analyzed in real time using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to extract the object's location information.

[0558] The server stores the location information transmitted from the visual processing means in a data storage means (e.g., cloud storage) in preparation for subsequent processing.

[0559] Suggestions for the optimal storage method

[0560] The server analyzes the stored location information using computational methods. Generative AI models (e.g., TensorFlow) are used in the calculations to propose efficient storage methods for items.

[0561] This suggestion will be notified to the user from the terminal via an immediate notification system. Based on the information presented, the user can then store items optimally.

[0562] Response to the user regarding the location of the item

[0563] When a user inquires about the location of an item through an input device with voice recognition capabilities, the terminal converts the voice into text and sends it to the server.

[0564] The server searches for the relevant location information from its data storage system and displays the location visually using a map or other means. For example, if a user asks, "Where is my umbrella?", the server will respond, "It's in the entryway."

[0565] Security features and reminder provision

[0566] The server uses warning mechanisms to alert the user if it detects the inappropriate movement of items.

[0567] Furthermore, the server uses behavioral prediction analytics to analyze user behavior based on information stored in the data storage system and generates reminders regarding necessary items. An example of a prompt message would be, "It's raining today. Did you bring an umbrella?"

[0568] The introduction of this system will allow users to improve the efficiency of managing their belongings and live their daily lives with peace of mind.

[0569] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0570] Step 1:

[0571] The terminal acquires video data of an object using an image processing device. Specifically, a camera system captures an image of the area where the object is located and transmits it to a visual processing device. The input to this step is the captured still image or video data, and the output is prepared video data for image analysis.

[0572] Step 2:

[0573] The server analyzes video data acquired using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to identify the location of objects. The input is the video data obtained from step 1, and the output is the location information of the objects. Specifically, the server identifies objects within the video and extracts their locations as coordinates.

[0574] Step 3:

[0575] The server stores the location information in a data storage device. Specifically, the location information is stored in a cloud database, making it possible to process and retrieve it later. The input to this step is the location information extracted in step 2, and the output is the location information stored in the database.

[0576] Step 4:

[0577] The server performs calculations based on the stored location information. Using this location information, it proposes the optimal storage method for items using a generative AI model (e.g., TensorFlow). The input for this step is location information obtained from the database, and the output is the calculated storage method proposal.

[0578] Step 5:

[0579] The device receives notifications from the server and informs the user of the optimal storage method. Specifically, it displays storage method suggestions on the smartphone or PC display. The input for this step is the storage suggestion obtained in step 4, and the output is a visual notification to the user.

[0580] Step 6:

[0581] When a user inquires about the location of an item using voice, the terminal converts the voice into text and sends it to the server. The input is the user's voice data, and the output is the query in text format. Specifically, a speech recognition engine is used to analyze the intent of the question.

[0582] Step 7:

[0583] The server searches the cloud database based on a text-based query and retrieves the location information of the corresponding item. The input for this step is the text data from step 6, and the output is the location information of the item corresponding to the query.

[0584] Step 8:

[0585] The terminal visually displays location information obtained from the server. Specifically, it marks the location of an item on a map application. The input is location information from the server, and the output is a visual display that the user can confirm.

[0586] Step 9:

[0587] The server, upon detecting the movement of inappropriate items through its warning system, issues a warning and notifies the user via a terminal. The input for this step is movement information from security sensors, and the output is an alert notification. Specifically, it monitors sensor information and detects anomalies.

[0588] Step 10:

[0589] The server analyzes user behavior, generates reminders about anticipated necessary items, and notifies the user via the terminal. The input for this step is the user's past behavior data, and the output is the generated reminder. A concrete example is sending a notification such as, "It's raining today. Did you bring an umbrella?"

[0590] (Application Example 1)

[0591] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0592] In modern smart cities, managing public facilities and infrastructure is crucial. However, the dispersion of numerous items and resources presents challenges in efficient management. Furthermore, the loss or unauthorized movement of items can impact public safety and efficient operation, necessitating solutions to prevent these issues.

[0593] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0594] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and calculation means for proposing an optimal arrangement method for the item based on the location information. This enables efficient management of resources in public facilities and infrastructure, and prevents the loss or unauthorized movement of items.

[0595] "Image processing means" refers to technology for visually detecting the position of an object and collecting that information.

[0596] A "database means" is a storage device for accumulating and efficiently managing location information of collected items.

[0597] A "calculation device" is a device that calculates and proposes the optimal arrangement method for items based on location information.

[0598] "Voice recognition functionality" is a technology that understands the user's voice commands and converts them into predetermined instructions.

[0599] "Monitoring measures" refer to functions that detect abnormal movement of goods or fraudulent activity, and issue warnings as necessary.

[0600] A "user behavior prediction tool" is a function that uses accumulated information to predict the user's behavior and necessary items, and then provides assistance.

[0601] A "real-time display means" is a technology for rapidly processing the location information of an item and presenting it visually to the user.

[0602] "Public facility management systems" are systems for efficiently supervising and managing resources and goods in various public spaces.

[0603] The system that realizes this invention is composed of a combination of multiple hardware and software components. To determine the location of an object, the system utilizes an image processing device. This device acquires and analyzes visual data from a device such as a smart camera. The server uses an image processing library such as OpenCV to detect the location of the object from the obtained video data. Furthermore, this location information is stored in a cloud database and managed by a cloud service such as AWS.

[0604] The server has the capability to perform calculations based on collected location information and propose the optimal placement method for items. This computational means evaluates location data using analytical algorithms and generates efficient placement and storage plans. This enables efficient management of items and resources within public facilities and smart cities.

[0605] The speech recognition function uses services like Google Speech-to-Text to convert the user's voice commands into text. The commands entered via speech recognition are interpreted by the system, and an appropriate response is generated in real time.

[0606] As a monitoring measure, sensor technology is incorporated to detect the unauthorized movement of important items. This allows the server to immediately send a warning to the user terminal if it detects any unusual activity.

[0607] This system provides a means to predict users' behavioral patterns and generates reminders according to specific times and situations. This function helps users prevent forgetting things and allows them to live their daily lives more smoothly.

[0608] For example, if a library user asks, "Where is a specific book?", speech recognition converts the question into text, and the server quickly searches the database and displays the results. These results are presented as a map on the user's terminal, guiding them to the exact location of the item. Furthermore, during peak hours, the system predicts the need for additional equipment or staff, supporting efficient operation.

[0609] Examples of prompts to be input into the generating AI model include questions such as, "Which shelf should new books be placed on in the library?" This system is expected to significantly improve the efficiency of resource management in public facilities and smart cities.

[0610] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0611] Step 1:

[0612] The server receives visual data acquired from a smart camera as input. This data is analyzed using an image processing algorithm based on OpenCV to identify the location of objects. As output, it generates location information for the detected objects.

[0613] Step 2:

[0614] The server stores the location information of the items generated in Step 1 in a cloud database. This database is managed using a cloud platform such as AWS, enabling real-time data updates. The database output provides the latest item location information.

[0615] Step 3:

[0616] The device receives voice commands from the user as input using Google Speech-to-Text speech recognition technology. The voice data is converted into text format and sent to the server. As output, the content of the user's commands is generated in text format.

[0617] Step 4:

[0618] The server receives the text data obtained in step 3 as input and searches the cloud database. It then retrieves the location information of the corresponding item and outputs it to the terminal. The results are presented along with a visual map.

[0619] Step 5:

[0620] Based on the location information accumulated in step 2, the server uses a calculation algorithm to determine the optimal placement method for the items. As an output of this calculation, it generates a proposed placement plan and notifies the administrator.

[0621] Step 6:

[0622] The terminal receives the deployment plan sent from the server and notifies the user. Furthermore, it generates and outputs necessary reminders based on the user's behavior patterns.

[0623] Step 7:

[0624] The server receives data from monitoring devices as input and detects unauthorized movement of important items. If an anomaly is detected, it generates a warning and outputs it to the terminal. This output allows the user to receive information about the unauthorized movement immediately.

[0625] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0626] This invention provides more personalized interactions by combining a system for managing the location information of items with an emotion engine that recognizes the user's emotions. This system includes an image processing device, a cloud database, a computing device, an input device with speech recognition capabilities, a security device, a reminder generation device, and an emotion engine.

[0627] Item location detection and management

[0628] The server analyzes video data transmitted from the image processing device to detect the location of objects. The obtained location information is stored in a cloud database and can be updated in real time.

[0629] Suggestions for the optimal storage method

[0630] The server uses computing power to analyze location information from the cloud database and suggest the optimal storage method for the user. Location information is updated regularly, and suggestions are made based on this updated information.

[0631] Response to the user regarding the location of the item

[0632] When a user inquires about the location of an item using voice recognition, the server searches the database and notifies the user of the location of the corresponding item via the device.

[0633] Security features

[0634] The server monitors the location of critical items and issues an alert if an anomaly occurs. This alert is then sent to the user via the terminal, enabling a quick response.

[0635] Emotion recognition and response customization

[0636] The emotion engine analyzes the user's emotions from their voice and facial expressions and adjusts the system's response accordingly. For example, if the system determines that the user is stressed, it will provide more concise guidance.

[0637] Personalize reminders

[0638] The server considers the user's emotional state and dynamically changes the content and timing of reminders. Based on the analysis results from the emotion engine, information is delivered in a more effective manner.

[0639] For example, if a user asks "Where are the keys?" in an anxious voice, the emotion engine will sense the anxiety, and the server will provide a quick and clear answer. Furthermore, encouraging messages will be added as needed to enhance the user's sense of security.

[0640] This system not only streamlines the management of items in daily life, but also allows users to enjoy a more comfortable user experience through personalized interactions that take emotions into consideration.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] A user uses voice recognition to inquire about the location of an item by asking, "Where is [item name]?"

[0644] Step 2:

[0645] The device inputs the user's voice and converts it into text data using speech recognition technology. This text data is immediately sent to the server.

[0646] Step 3:

[0647] The emotion engine analyzes the user's voice to determine, for example, whether anxiety is present.

[0648] Step 4:

[0649] The server searches the cloud database to retrieve the latest location information for the specified item.

[0650] Step 5:

[0651] The server adjusts the content and tone of its response based on the results of sentiment analysis. For example, if the user is feeling anxious, it will add reassuring language.

[0652] Step 6:

[0653] The server sends the generated response to the terminal along with visual map information.

[0654] Step 7:

[0655] The device displays a visual map to the user and communicates the results via voice or text. For example, it might respond with, "The keys are on the table by the front door. Everything is fine."

[0656] Step 8:

[0657] The server continuously learns user behavior patterns and feeds them back into the cloud database. This improves the accuracy of responses to future inquiries.

[0658] Step 9:

[0659] The server monitors the user's emotional state and adjusts reminders as needed. The user is then notified of these adjustments via their device.

[0660] (Example 2)

[0661] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0662] In recent years, as the demand for efficient management of goods and services tailored to individual needs increases, the means to track the location of items, suggest appropriate storage methods, and provide emotionally sensitive responses remain limited. Furthermore, no system exists that can dynamically adjust how information is conveyed based on the user's emotional state. The challenges arising from this are to provide a user experience optimized for each individual while simultaneously improving efficiency in item management and security.

[0663] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0664] In this invention, the server includes visual information processing means for detecting the position of an object, information storage means for storing the position information of the object, and emotion analysis means, which adjusts the response according to the user's emotional state. This allows the user to easily grasp the position of an item and receive personalized service tailored to their emotions.

[0665] "Visual information processing means" refers to technologies and devices for analyzing visual data such as images and videos to determine the location of an object.

[0666] "Information storage means" refers to means of storing and managing the location and related information of objects, such as using databases or cloud storage.

[0667] "Computational means" refers to a function that uses a computer or its software to analyze data and perform processing to propose the optimal storage method for objects.

[0668] "Input means" refers to interfaces and devices that allow users to input information and instructions into a system through voice or text.

[0669] "Protective measures" refer to safety devices and systems that detect abnormalities in the position of an object and issue a warning to the user.

[0670] "Means for generating alert information" refers to methods for predicting user behavior and generating appropriate reminders and notifications at the necessary time.

[0671] "Emotional analysis tools" refer to technologies and devices that analyze data such as the tone of a user's voice and facial expressions to determine their emotional state.

[0672] This invention is a system that precisely manages the position of objects and provides efficient and personalized services to users. The main components involved are a server, a terminal, and the user.

[0673] The server detects the position of objects using visual information processing means. These means include hardware such as cameras and image sensors, and software equipped with image recognition algorithms. The server analyzes the acquired visual data to determine the object's location. This location information is stored in cloud storage, which is an information storage means. This ensures that the object's location information is updated in real time, facilitating later reference.

[0674] The user can inquire about the location of an object using an input device. This device utilizes speech recognition technology, allowing the user to ask questions in natural speech through the terminal. The terminal converts the speech into digital data and sends it to the server. Based on the received data, the server searches for the corresponding location information in its information storage device and returns the result to the terminal.

[0675] Furthermore, the server is equipped with emotion analysis capabilities that analyze the user's emotions from their voice and facial expressions. Based on this data, it customizes responses and provides support tailored to the user's psychological state.

[0676] For example, if a user anxiously asks, "Where's my wallet?", the server quickly searches its information storage devices and immediately provides the location. In some cases, it may also automatically generate an encouraging message such as, "I hope you find it," to reassure the user.

[0677] The system also includes a mechanism for generating alert information. The server analyzes user behavior patterns and has the function to set reminders at appropriate times. For example, it can support users' schedules by sending reminders such as, "Don't forget to bring the necessary documents to tomorrow's meeting."

[0678] An example of a prompt message is, "If the user's voice sounds anxious, indicate how the system should respond." In this way, the invention can continue to provide users with practical and emotionally sensitive interactions.

[0679] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0680] Step 1:

[0681] The server acquires visual data of objects from image sensors. This data is input as video of the environment in which the objects are placed. Based on this, the server applies an image recognition algorithm and outputs location information that identifies the position of each object. Specifically, the image analysis process involves object shape recognition and feature detection.

[0682] Step 2:

[0683] The server saves the identified location information to cloud storage. The input for this process is the location information of the object extracted in step 1. The server converts this into a database format and outputs it in a state where it can be updated in real time. Specifically, the location information is written and the database is synchronized.

[0684] Step 3:

[0685] The user asks questions about the location of objects using voice commands through the device. The device converts this voice input into text data and sends it to the server. Speech recognition technology is used to process natural language and analyze the content of the questions.

[0686] Step 4:

[0687] The server searches cloud storage based on the received text data and retrieves the location information of the corresponding object. The input to this process is the text data of the question generated in step 3, and the output is the location information of the object. The server uses a search algorithm to quickly extract the necessary information from the database.

[0688] Step 5:

[0689] The server transmits the location information of the acquired object to the terminal. This allows the user to receive the location information through the terminal. The output is the specific location of the object displayed to the user. The terminal then presents the information via voice or text.

[0690] Step 6:

[0691] The server analyzes the user's voice using emotion analysis tools to determine their psychological state. The user's voice data is used as input, and the output is an evaluation of their emotions. The analysis involves analyzing features such as voice tone and speed to quantify the user's emotions.

[0692] Step 7:

[0693] The server adjusts its response based on the results of sentiment analysis. The server uses the data obtained from the analysis as input to dynamically change and output the response to the user. Specific actions include processes such as changing the content of the response, including adding friendly language or encouraging messages.

[0694] Step 8:

[0695] The server generates and sends reminders based on the user's behavioral history to the device. The input is the user's past behavioral patterns and emotional state, and the output is individually tailored reminder information. The server then uses a behavioral prediction algorithm to send notifications at the appropriate time.

[0696] (Application Example 2)

[0697] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0698] In the daily lives of the elderly and those requiring care, there are problems such as the inability to manage the location of belongings and the lack of appropriate information tailored to their emotions. Furthermore, conventional technologies struggle to provide personalized responses through user emotion recognition, highlighting the need for improved user experience.

[0699] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0700] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and emotion analysis means for analyzing the user's emotions and adjusting the response. This enables more efficient management of items in elderly care and the provision of appropriate information according to the user's emotional state.

[0701] "Image processing means" refers to a device that uses cameras and sensors to acquire information for identifying the location of an object, and then processes and analyzes this information to generate location information.

[0702] A "database system" is a system that stores location information of items and user behavior data, and uses this information to search for and utilize it as needed.

[0703] A "computational means" refers to a processor or device that analyzes accumulated data and generates optimal storage methods for items and suggestions for users.

[0704] An "input means" is an interface device that allows a user to inquire about the location of an item by voice or other means.

[0705] A "security measure" is a device that constantly monitors the location information of an item and issues a warning if an anomaly is detected.

[0706] A "reminder generation device" is a device that generates notifications and reminders at appropriate times based on the user's actions and emotions.

[0707] An "emotion analysis tool" is a system that analyzes a user's voice and facial expressions to determine their emotional state.

[0708] An "information presentation device" is an interface device that provides information to the user based on the analysis results.

[0709] The system for realizing this invention is composed of a user, a terminal, and a server. The server acquires the location information of an item using image processing means and stores the generated location information in a database means. This enables real-time location tracking. Furthermore, using the information stored in the database, a calculation means proposes the optimal storage method for the item.

[0710] The terminal receives voice input from the user using its voice recognition function and can query the location information of items through the input device. The server analyzes this voice data, retrieves the necessary information from the database, and presents it to the user.

[0711] The server includes emotion analysis tools to analyze the user's voice and facial expressions. These tools utilize machine learning frameworks such as TensorFlow and Keras. Based on the user's emotional state, appropriate responses are generated and delivered to the user through information presentation tools.

[0712] Furthermore, the security system alerts the user if an anomaly is detected, prompting a quick response. The entire system is implemented as a server-side application using Flask, and speech recognition is performed using the Google Cloud Speech-to-Text API. Firebase is used for real-time data management.

[0713] For example, if a user asks the device, "Where are my medicines?", the server performs sentiment analysis and, if the user is feeling confused or anxious, responds with encouraging words such as, "They're on the shelf above the kitchen counter."

[0714] An example of a prompt for a generative AI model might be: "If a user is anxiously searching for a specific item, suggest a way to generate a reassuring response based on location information and emotional data."

[0715] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0716] Step 1:

[0717] The user inputs voice data inquiring about the location of an item through the device's voice input function. This data is converted into text data on the device using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. At this stage, the voice data is internally sent to the API, and the returned text is retrieved.

[0718] Step 2:

[0719] Text data is sent to a server, which analyzes the sentiment of the text data using sentiment analysis tools. Here, TensorFlow or Keras is used to label the sentiment of the input text, and the sentiment label is obtained as output. Specifically, the model estimates the sentiment (e.g., anxious, confused, neutral) based on the text data and outputs that sentiment.

[0720] Step 3:

[0721] The server retrieves the location information of the corresponding item from the database based on the emotion label and query content. In this step, the database is queried to retrieve location data associated with the item. The input is the query text, and the output is the location information of the item. Specifically, the process involves executing a query on the database based on the text content.

[0722] Step 4:

[0723] The server generates an appropriate response through an information presentation system based on the sentiment analysis results and the location information of the items. Using a generation AI model, it selects the most appropriate expression for the user and creates a response sentence to present to the user as output. Specifically, in addition to an explanation of the location information, it also generates encouraging or cautionary messages corresponding to the sentiment label.

[0724] Step 5:

[0725] The final response is sent to the terminal and communicated to the user using the terminal's voice output function. The input is the generated response text, and the output is the voice message provided to the user. Specifically, the process involves converting the text response into speech via a speech synthesis API and outputting it through the speaker.

[0726] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0727] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0728] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0729] [Fourth Embodiment]

[0730] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0731] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0732] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0733] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0734] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0735] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0736] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0737] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0738] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0739] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0740] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0741] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0742] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0743] The present invention constructs a system that manages the location of items in real time, provides users with an efficient method for storing items, and warns of improper movement or loss of items. This system includes an image processing device, a cloud database, a computing device, an input device with voice recognition capabilities, a security device, and a reminder generation device.

[0744] Item location detection and management

[0745] The server analyzes the video data transmitted from the image processing device and obtains the location information of the objects. Based on this, the location data is stored in a cloud database.

[0746] Suggestions for the optimal storage method

[0747] The server analyzes location data stored in the cloud database using a computing device to calculate the optimal storage method. This suggestion is immediately notified to the user via the terminal and can be viewed on the screen.

[0748] Response to the user regarding the location of the item

[0749] When a user inquires about the location of a designated item using voice recognition, the terminal converts the voice input into text, and the server retrieves the information from its database to provide a response. The information is then displayed visually on the terminal as a map, allowing the user to easily confirm the location.

[0750] Implementation of security measures

[0751] The server monitors the location of important items through security devices. If any inappropriate movement is detected, it immediately issues a warning and notifies the user via a terminal.

[0752] Providing reminders to users

[0753] The server collects and analyzes user behavior patterns and predicts the items needed at specific times and in specific situations. Based on this, reminders are generated and notifications are sent to the user from their device.

[0754] For example, if a user asks "Where's my umbrella?", the device converts this into text, the server searches a cloud database, and replies "It's by the front door." Furthermore, on rainy mornings, the device sends a reminder asking, "Did you bring your umbrella?"

[0755] This system improves the efficiency of managing belongings in daily life and provides users with a secure and comfortable living environment.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] The user uses voice recognition to inquire about the location of an item, asking "Where is XX?".

[0759] Step 2:

[0760] The device converts the received audio into text using speech recognition technology.

[0761] Step 3:

[0762] The server analyzes the converted text data and searches a cloud database to detect the location of the specified item.

[0763] Step 4:

[0764] The server acquires location information and generates a visual map showing the current location of the corresponding item.

[0765] Step 5:

[0766] The device displays the generated map on the user's screen and provides answers to inquiries.

[0767] Step 6:

[0768] The server uses the stored location data of items to calculate the optimal storage method for the items using a computing device, and then retrieves the result.

[0769] Step 7:

[0770] The device notifies the user of suggestions for the optimal storage method and displays the information for the user to review.

[0771] Step 8:

[0772] The server monitors the movement or disappearance of important items through security devices and generates a warning message if an anomaly is detected.

[0773] Step 9:

[0774] The device notifies the user of any warnings that have occurred and provides information to enable the user to take prompt action.

[0775] Step 10:

[0776] The server analyzes user behavior patterns, predicts situations where a reminder is needed, and generates a reminder.

[0777] Step 11:

[0778] The device notifies the user's device of the generated reminder, providing the necessary information without delay.

[0779] (Example 1)

[0780] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0781] In modern society, the loss or improper management of belongings is a source of stress in daily life, and there is a need for efficient methods of managing and storing items. Furthermore, accurately identifying the items users need and notifying them in advance with reminders is a challenge in preventing items from being forgotten or lost.

[0782] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0783] In this invention, the server includes a visual processing means for detecting the location of an item, a data storage means for accumulating location information of the item, and a calculation means for proposing the optimal storage method for the item based on the location information. As a result, the user can grasp the precise location information of the item and receive efficient storage suggestions, improving the convenience of daily life. Furthermore, real-time updates of location information enable immediate detection of inappropriate item movements and prompt response.

[0784] "Visual processing means" refers to means for analyzing video data to extract location information of an object.

[0785] A "data storage means" is a storage device that stores location information of analyzed items and can retrieve it as needed.

[0786] A "calculation method" is a means of calculating an efficient method for storing items based on accumulated location information.

[0787] "Voice recognition functionality" is a feature that converts voice input into text data and understands user commands.

[0788] A "warning mechanism" is a means of issuing a warning and notifying the user when the improper movement of an item is detected.

[0789] "Means for generating notifications" refers to means of analyzing user behavior patterns and creating and notifying users of predicted necessary items as reminders.

[0790] "Means of visual presentation" refers to means of providing users with the location information of analyzed items visually using maps or diagrams.

[0791] "Means of immediate notification" refers to methods of communicating suggested storage methods and warnings to users in real time.

[0792] As an embodiment of this invention, a system is provided that manages the location of items and proposes efficient storage solutions. Specifically, the components work together as follows.

[0793] Item location detection and management

[0794] The terminal acquires video data of an object using image processing hardware (e.g., a camera system). The video data is analyzed in real time using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to extract the object's location information.

[0795] The server stores the location information transmitted from the visual processing means in a data storage means (e.g., cloud storage) in preparation for subsequent processing.

[0796] Suggestions for the optimal storage method

[0797] The server analyzes the stored location information using computational methods. Generative AI models (e.g., TensorFlow) are used in the calculations to propose efficient storage methods for items.

[0798] This suggestion will be notified to the user from the terminal via an immediate notification system. Based on the information presented, the user can then store items optimally.

[0799] Response to the user regarding the location of the item

[0800] When a user inquires about the location of an item through an input device with voice recognition capabilities, the terminal converts the voice into text and sends it to the server.

[0801] The server searches for the relevant location information from its data storage system and displays the location visually using a map or other means. For example, if a user asks, "Where is my umbrella?", the server will respond, "It's in the entryway."

[0802] Security features and reminder provision

[0803] The server uses warning mechanisms to alert the user if it detects the inappropriate movement of items.

[0804] Furthermore, the server uses behavioral prediction analytics to analyze user behavior based on information stored in the data storage system and generates reminders regarding necessary items. An example of a prompt message would be, "It's raining today. Did you bring an umbrella?"

[0805] The introduction of this system will allow users to improve the efficiency of managing their belongings and live their daily lives with peace of mind.

[0806] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0807] Step 1:

[0808] The terminal acquires video data of an object using an image processing device. Specifically, a camera system captures an image of the area where the object is located and transmits it to a visual processing device. The input to this step is the captured still image or video data, and the output is prepared video data for image analysis.

[0809] Step 2:

[0810] The server analyzes video data acquired using visual processing tools. Image processing software (e.g., OpenCV) is used for the analysis to identify the location of objects. The input is the video data obtained from step 1, and the output is the location information of the objects. Specifically, the server identifies objects within the video and extracts their locations as coordinates.

[0811] Step 3:

[0812] The server stores the location information in a data storage device. Specifically, the location information is stored in a cloud database, making it possible to process and retrieve it later. The input to this step is the location information extracted in step 2, and the output is the location information stored in the database.

[0813] Step 4:

[0814] The server performs calculations based on the stored location information. Using this location information, it proposes the optimal storage method for items using a generative AI model (e.g., TensorFlow). The input for this step is location information obtained from the database, and the output is the calculated storage method proposal.

[0815] Step 5:

[0816] The device receives notifications from the server and informs the user of the optimal storage method. Specifically, it displays storage method suggestions on the smartphone or PC display. The input for this step is the storage suggestion obtained in step 4, and the output is a visual notification to the user.

[0817] Step 6:

[0818] When a user inquires about the location of an item using voice, the terminal converts the voice into text and sends it to the server. The input is the user's voice data, and the output is the query in text format. Specifically, a speech recognition engine is used to analyze the intent of the question.

[0819] Step 7:

[0820] The server searches the cloud database based on a text-based query and retrieves the location information of the corresponding item. The input for this step is the text data from step 6, and the output is the location information of the item corresponding to the query.

[0821] Step 8:

[0822] The terminal visually displays location information obtained from the server. Specifically, it marks the location of an item on a map application. The input is location information from the server, and the output is a visual display that the user can confirm.

[0823] Step 9:

[0824] The server, upon detecting the movement of inappropriate items through its warning system, issues a warning and notifies the user via a terminal. The input for this step is movement information from security sensors, and the output is an alert notification. Specifically, it monitors sensor information and detects anomalies.

[0825] Step 10:

[0826] The server analyzes user behavior, generates reminders about anticipated necessary items, and notifies the user via the terminal. The input for this step is the user's past behavior data, and the output is the generated reminder. A concrete example is sending a notification such as, "It's raining today. Did you bring an umbrella?"

[0827] (Application Example 1)

[0828] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0829] In modern smart cities, managing public facilities and infrastructure is crucial. However, the dispersion of numerous items and resources presents challenges in efficient management. Furthermore, the loss or unauthorized movement of items can impact public safety and efficient operation, necessitating solutions to prevent these issues.

[0830] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0831] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and calculation means for proposing an optimal arrangement method for the item based on the location information. This enables efficient management of resources in public facilities and infrastructure, and prevents the loss or unauthorized movement of items.

[0832] "Image processing means" refers to technology for visually detecting the position of an object and collecting that information.

[0833] A "database means" is a storage device for accumulating and efficiently managing location information of collected items.

[0834] A "calculation device" is a device that calculates and proposes the optimal arrangement method for items based on location information.

[0835] "Voice recognition functionality" is a technology that understands the user's voice commands and converts them into predetermined instructions.

[0836] "Monitoring measures" refer to functions that detect abnormal movement of goods or fraudulent activity, and issue warnings as necessary.

[0837] A "user behavior prediction tool" is a function that uses accumulated information to predict the user's behavior and necessary items, and then provides assistance.

[0838] A "real-time display means" is a technology for rapidly processing the location information of an item and presenting it visually to the user.

[0839] "Public facility management systems" are systems for efficiently supervising and managing resources and goods in various public spaces.

[0840] The system that realizes this invention is composed of a combination of multiple hardware and software components. To determine the location of an object, the system utilizes an image processing device. This device acquires and analyzes visual data from a device such as a smart camera. The server uses an image processing library such as OpenCV to detect the location of the object from the obtained video data. Furthermore, this location information is stored in a cloud database and managed by a cloud service such as AWS.

[0841] The server has the capability to perform calculations based on collected location information and propose the optimal placement method for items. This computational means evaluates location data using analytical algorithms and generates efficient placement and storage plans. This enables efficient management of items and resources within public facilities and smart cities.

[0842] The speech recognition function uses services like Google Speech-to-Text to convert the user's voice commands into text. The commands entered via speech recognition are interpreted by the system, and an appropriate response is generated in real time.

[0843] As a monitoring measure, sensor technology is incorporated to detect the unauthorized movement of important items. This allows the server to immediately send a warning to the user terminal if it detects any unusual activity.

[0844] This system provides a means to predict users' behavioral patterns and generates reminders according to specific times and situations. This function helps users prevent forgetting things and allows them to live their daily lives more smoothly.

[0845] For example, if a library user asks, "Where is a specific book?", speech recognition converts the question into text, and the server quickly searches the database and displays the results. These results are presented as a map on the user's terminal, guiding them to the exact location of the item. Furthermore, during peak hours, the system predicts the need for additional equipment or staff, supporting efficient operation.

[0846] Examples of prompts to be input into the generating AI model include questions such as, "Which shelf should new books be placed on in the library?" This system is expected to significantly improve the efficiency of resource management in public facilities and smart cities.

[0847] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0848] Step 1:

[0849] The server receives visual data acquired from a smart camera as input. This data is analyzed using an image processing algorithm based on OpenCV to identify the location of objects. As output, it generates location information for the detected objects.

[0850] Step 2:

[0851] The server stores the location information of the items generated in Step 1 in a cloud database. This database is managed using a cloud platform such as AWS, enabling real-time data updates. The database output provides the latest item location information.

[0852] Step 3:

[0853] The device receives voice commands from the user as input using Google Speech-to-Text speech recognition technology. The voice data is converted into text format and sent to the server. As output, the content of the user's commands is generated in text format.

[0854] Step 4:

[0855] The server receives the text data obtained in step 3 as input and searches the cloud database. It then retrieves the location information of the corresponding item and outputs it to the terminal. The results are presented along with a visual map.

[0856] Step 5:

[0857] Based on the location information accumulated in step 2, the server uses a calculation algorithm to determine the optimal placement method for the items. As an output of this calculation, it generates a proposed placement plan and notifies the administrator.

[0858] Step 6:

[0859] The terminal receives the deployment plan sent from the server and notifies the user. Furthermore, it generates and outputs necessary reminders based on the user's behavior patterns.

[0860] Step 7:

[0861] The server receives data from monitoring devices as input and detects unauthorized movement of important items. If an anomaly is detected, it generates a warning and outputs it to the terminal. This output allows the user to receive information about the unauthorized movement immediately.

[0862] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0863] This invention provides more personalized interactions by combining a system for managing the location information of items with an emotion engine that recognizes the user's emotions. This system includes an image processing device, a cloud database, a computing device, an input device with speech recognition capabilities, a security device, a reminder generation device, and an emotion engine.

[0864] Item location detection and management

[0865] The server analyzes video data transmitted from the image processing device to detect the location of objects. The obtained location information is stored in a cloud database and can be updated in real time.

[0866] Suggestions for the optimal storage method

[0867] The server uses computing power to analyze location information from the cloud database and suggest the optimal storage method for the user. Location information is updated regularly, and suggestions are made based on this updated information.

[0868] Response to the user regarding the location of the item

[0869] When a user inquires about the location of an item using voice recognition, the server searches the database and notifies the user of the location of the corresponding item via the device.

[0870] Security features

[0871] The server monitors the location of critical items and issues an alert if an anomaly occurs. This alert is then sent to the user via the terminal, enabling a quick response.

[0872] Emotion recognition and response customization

[0873] The emotion engine analyzes the user's emotions from their voice and facial expressions and adjusts the system's response accordingly. For example, if the system determines that the user is stressed, it will provide more concise guidance.

[0874] Personalize reminders

[0875] The server considers the user's emotional state and dynamically changes the content and timing of reminders. Based on the analysis results from the emotion engine, information is delivered in a more effective manner.

[0876] For example, if a user asks "Where are the keys?" in an anxious voice, the emotion engine will sense the anxiety, and the server will provide a quick and clear answer. Furthermore, encouraging messages will be added as needed to enhance the user's sense of security.

[0877] This system not only streamlines the management of items in daily life, but also allows users to enjoy a more comfortable user experience through personalized interactions that take emotions into consideration.

[0878] The following describes the processing flow.

[0879] Step 1:

[0880] A user uses voice recognition to inquire about the location of an item by asking, "Where is [item name]?"

[0881] Step 2:

[0882] The device inputs the user's voice and converts it into text data using speech recognition technology. This text data is immediately sent to the server.

[0883] Step 3:

[0884] The emotion engine analyzes the user's voice to determine, for example, whether anxiety is present.

[0885] Step 4:

[0886] The server searches the cloud database to retrieve the latest location information for the specified item.

[0887] Step 5:

[0888] The server adjusts the content and tone of its response based on the results of sentiment analysis. For example, if the user is feeling anxious, it will add reassuring language.

[0889] Step 6:

[0890] The server sends the generated response to the terminal along with visual map information.

[0891] Step 7:

[0892] The device displays a visual map to the user and communicates the results via voice or text. For example, it might respond with, "The keys are on the table by the front door. Everything is fine."

[0893] Step 8:

[0894] The server continuously learns user behavior patterns and feeds them back into the cloud database. This improves the accuracy of responses to future inquiries.

[0895] Step 9:

[0896] The server monitors the user's emotional state and adjusts reminders as needed. The user is then notified of these adjustments via their device.

[0897] (Example 2)

[0898] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0899] In recent years, as the demand for efficient management of goods and services tailored to individual needs increases, the means to track the location of items, suggest appropriate storage methods, and provide emotionally sensitive responses remain limited. Furthermore, no system exists that can dynamically adjust how information is conveyed based on the user's emotional state. The challenges arising from this are to provide a user experience optimized for each individual while simultaneously improving efficiency in item management and security.

[0900] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0901] In this invention, the server includes visual information processing means for detecting the position of an object, information storage means for storing the position information of the object, and emotion analysis means, which adjusts the response according to the user's emotional state. This allows the user to easily grasp the position of an item and receive personalized service tailored to their emotions.

[0902] "Visual information processing means" refers to technologies and devices for analyzing visual data such as images and videos to determine the location of an object.

[0903] "Information storage means" refers to means of storing and managing the location and related information of objects, such as using databases or cloud storage.

[0904] "Computational means" refers to a function that uses a computer or its software to analyze data and perform processing to propose the optimal storage method for objects.

[0905] "Input means" refers to interfaces and devices that allow users to input information and instructions into a system through voice or text.

[0906] "Protective measures" refer to safety devices and systems that detect abnormalities in the position of an object and issue a warning to the user.

[0907] "Means for generating alert information" refers to methods for predicting user behavior and generating appropriate reminders and notifications at the necessary time.

[0908] "Emotional analysis tools" refer to technologies and devices that analyze data such as the tone of a user's voice and facial expressions to determine their emotional state.

[0909] This invention is a system that precisely manages the position of objects and provides efficient and personalized services to users. The main components involved are a server, a terminal, and the user.

[0910] The server detects the position of objects using visual information processing means. These means include hardware such as cameras and image sensors, and software equipped with image recognition algorithms. The server analyzes the acquired visual data to determine the object's location. This location information is stored in cloud storage, which is an information storage means. This ensures that the object's location information is updated in real time, facilitating later reference.

[0911] The user can inquire about the location of an object using an input device. This device utilizes speech recognition technology, allowing the user to ask questions in natural speech through the terminal. The terminal converts the speech into digital data and sends it to the server. Based on the received data, the server searches for the corresponding location information in its information storage device and returns the result to the terminal.

[0912] Furthermore, the server is equipped with emotion analysis capabilities that analyze the user's emotions from their voice and facial expressions. Based on this data, it customizes responses and provides support tailored to the user's psychological state.

[0913] For example, if a user anxiously asks, "Where's my wallet?", the server quickly searches its information storage devices and immediately provides the location. In some cases, it may also automatically generate an encouraging message such as, "I hope you find it," to reassure the user.

[0914] The system also includes a mechanism for generating alert information. The server analyzes user behavior patterns and has the function to set reminders at appropriate times. For example, it can support users' schedules by sending reminders such as, "Don't forget to bring the necessary documents to tomorrow's meeting."

[0915] An example of a prompt message is, "If the user's voice sounds anxious, indicate how the system should respond." In this way, the invention can continue to provide users with practical and emotionally sensitive interactions.

[0916] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0917] Step 1:

[0918] The server acquires visual data of objects from image sensors. This data is input as video of the environment in which the objects are placed. Based on this, the server applies an image recognition algorithm and outputs location information that identifies the position of each object. Specifically, the image analysis process involves object shape recognition and feature detection.

[0919] Step 2:

[0920] The server saves the identified location information to cloud storage. The input for this process is the location information of the object extracted in step 1. The server converts this into a database format and outputs it in a state where it can be updated in real time. Specifically, the location information is written and the database is synchronized.

[0921] Step 3:

[0922] The user asks questions about the location of objects using voice commands through the device. The device converts this voice input into text data and sends it to the server. Speech recognition technology is used to process natural language and analyze the content of the questions.

[0923] Step 4:

[0924] The server searches cloud storage based on the received text data and retrieves the location information of the corresponding object. The input to this process is the text data of the question generated in step 3, and the output is the location information of the object. The server uses a search algorithm to quickly extract the necessary information from the database.

[0925] Step 5:

[0926] The server transmits the location information of the acquired object to the terminal. This allows the user to receive the location information through the terminal. The output is the specific location of the object displayed to the user. The terminal then presents the information via voice or text.

[0927] Step 6:

[0928] The server analyzes the user's voice using emotion analysis tools to determine their psychological state. The user's voice data is used as input, and the output is an evaluation of their emotions. The analysis involves analyzing features such as voice tone and speed to quantify the user's emotions.

[0929] Step 7:

[0930] The server adjusts its response based on the results of sentiment analysis. The server uses the data obtained from the analysis as input to dynamically change and output the response to the user. Specific actions include processes such as changing the content of the response, including adding friendly language or encouraging messages.

[0931] Step 8:

[0932] The server generates and sends reminders based on the user's behavioral history to the device. The input is the user's past behavioral patterns and emotional state, and the output is individually tailored reminder information. The server then uses a behavioral prediction algorithm to send notifications at the appropriate time.

[0933] (Application Example 2)

[0934] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0935] In the daily lives of the elderly and those requiring care, there are problems such as the inability to manage the location of belongings and the lack of appropriate information tailored to their emotions. Furthermore, conventional technologies struggle to provide personalized responses through user emotion recognition, highlighting the need for improved user experience.

[0936] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0937] In this invention, the server includes image processing means for detecting the location of an item, database means for storing location information of the item, and emotion analysis means for analyzing the user's emotions and adjusting the response. This enables more efficient management of items in elderly care and the provision of appropriate information according to the user's emotional state.

[0938] "Image processing means" refers to a device that uses cameras and sensors to acquire information for identifying the location of an object, and then processes and analyzes this information to generate location information.

[0939] A "database system" is a system that stores location information of items and user behavior data, and uses this information to search for and utilize it as needed.

[0940] A "computational means" refers to a processor or device that analyzes accumulated data and generates optimal storage methods for items and suggestions for users.

[0941] An "input means" is an interface device that allows a user to inquire about the location of an item by voice or other means.

[0942] A "security measure" is a device that constantly monitors the location information of an item and issues a warning if an anomaly is detected.

[0943] A "reminder generation device" is a device that generates notifications and reminders at appropriate times based on the user's actions and emotions.

[0944] An "emotion analysis tool" is a system that analyzes a user's voice and facial expressions to determine their emotional state.

[0945] An "information presentation device" is an interface device that provides information to the user based on the analysis results.

[0946] The system for realizing this invention is composed of a user, a terminal, and a server. The server acquires the location information of an item using image processing means and stores the generated location information in a database means. This enables real-time location tracking. Furthermore, using the information stored in the database, a calculation means proposes the optimal storage method for the item.

[0947] The terminal receives voice input from the user using its voice recognition function and can query the location information of items through the input device. The server analyzes this voice data, retrieves the necessary information from the database, and presents it to the user.

[0948] The server includes emotion analysis tools to analyze the user's voice and facial expressions. These tools utilize machine learning frameworks such as TensorFlow and Keras. Based on the user's emotional state, appropriate responses are generated and delivered to the user through information presentation tools.

[0949] Furthermore, the security system alerts the user if an anomaly is detected, prompting a quick response. The entire system is implemented as a server-side application using Flask, and speech recognition is performed using the Google Cloud Speech-to-Text API. Firebase is used for real-time data management.

[0950] For example, if a user asks the device, "Where are my medicines?", the server performs sentiment analysis and, if the user is feeling confused or anxious, responds with encouraging words such as, "They're on the shelf above the kitchen counter."

[0951] An example of a prompt for a generative AI model might be: "If a user is anxiously searching for a specific item, suggest a way to generate a reassuring response based on location information and emotional data."

[0952] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0953] Step 1:

[0954] The user inputs voice data inquiring about the location of an item through the device's voice input function. This data is converted into text data on the device using the Google Cloud Speech-to-Text API. The input is voice data, and the output is text data. At this stage, the voice data is internally sent to the API, and the returned text is retrieved.

[0955] Step 2:

[0956] Text data is sent to a server, which analyzes the sentiment of the text data using sentiment analysis tools. Here, TensorFlow or Keras is used to label the sentiment of the input text, and the sentiment label is obtained as output. Specifically, the model estimates the sentiment (e.g., anxious, confused, neutral) based on the text data and outputs that sentiment.

[0957] Step 3:

[0958] The server retrieves the location information of the corresponding item from the database based on the emotion label and query content. In this step, the database is queried to retrieve location data associated with the item. The input is the query text, and the output is the location information of the item. Specifically, the process involves executing a query on the database based on the text content.

[0959] Step 4:

[0960] The server generates an appropriate response through an information presentation system based on the sentiment analysis results and the location information of the items. Using a generation AI model, it selects the most appropriate expression for the user and creates a response sentence to present to the user as output. Specifically, in addition to an explanation of the location information, it also generates encouraging or cautionary messages corresponding to the sentiment label.

[0961] Step 5:

[0962] The final response is sent to the terminal and communicated to the user using the terminal's voice output function. The input is the generated response text, and the output is the voice message provided to the user. Specifically, the process involves converting the text response into speech via a speech synthesis API and outputting it through the speaker.

[0963] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0964] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0965] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0966] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0967] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0968] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0969] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0970] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0971] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0972] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0973] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0974] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0975] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0976] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0977] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0978] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0979] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0980] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0981] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0982] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0983] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0984] The following is further disclosed regarding the embodiments described above.

[0985] (Claim 1)

[0986] An image processing device for detecting the position of an object,

[0987] A cloud database that stores location information of items,

[0988] A computing device that proposes the optimal storage method for items based on the aforementioned location information,

[0989] An input device having voice recognition capabilities for inquiring about the location of an item,

[0990] A security device that issues a warning in case of an abnormality,

[0991] A device that predicts user behavior and generates reminders using information from the aforementioned cloud database,

[0992] A system that includes this.

[0993] (Claim 2)

[0994] The system according to claim 1, having a data feedback function for updating the location information of an item in real time.

[0995] (Claim 3)

[0996] The system according to claim 1, comprising a function to analyze user behavior patterns and predict the items needed at a specific time.

[0997] "Example 1"

[0998] (Claim 1)

[0999] A visual processing means for detecting the position of an object,

[1000] A data storage means for accumulating location information of items,

[1001] A calculation means that proposes the optimal storage method for the items based on the aforementioned location information,

[1002] It has a voice recognition function and an input means for inquiring about the location of an item,

[1003] A warning system that issues a warning in case of an abnormality,

[1004] A means for predicting user behavior and generating notifications using the information from the aforementioned data storage means,

[1005] A means of visually presenting the location information of the analyzed items,

[1006] A means of immediately notifying the user of the suggested storage method,

[1007] A system that includes this.

[1008] (Claim 2)

[1009] The system according to claim 1, comprising data update means for updating the location information of an item in real time.

[1010] (Claim 3)

[1011] The system according to claim 1, comprising a function to analyze user behavior patterns and predict the items needed at a specific time.

[1012] "Application Example 1"

[1013] (Claim 1)

[1014] Image processing means for detecting the position of an object,

[1015] A database means for storing location information of items,

[1016] A calculation means that proposes the optimal arrangement method for the items based on the aforementioned location information,

[1017] It has a voice recognition function and an input means for inquiring about the location of an item,

[1018] A monitoring system that issues warnings in the event of an anomaly,

[1019] A means for predicting user behavior using information from the aforementioned database and generating time-appropriate notifications,

[1020] Means for efficiently managing resources in public facilities and infrastructure,

[1021] A means of visually displaying the location of an item in real time,

[1022] A system that includes this.

[1023] (Claim 2)

[1024] The system according to claim 1, comprising a function to update the location information of an item in real time and to monitor the unauthorized movement of an important item.

[1025] (Claim 3)

[1026] The system according to claim 1, which has the effect of analyzing user behavior patterns and proposing the optimal placement of items within a public facility.

[1027] "Example 2 of combining an emotion engine"

[1028] (Claim 1)

[1029] A visual information processing means for detecting the position of an object,

[1030] Information storage means for storing positional information of objects,

[1031] A calculation means that proposes the optimal method for storing an object based on the aforementioned positional information,

[1032] An input means for recognizing audio data and querying the position of an object,

[1033] A protective measure that issues a warning in case of an abnormality,

[1034] A means for predicting user behavior and generating warning information using data from the aforementioned information storage means,

[1035] A means of analyzing emotions and adjusting responses according to the user's emotional state,

[1036] A system that includes this.

[1037] (Claim 2)

[1038] The system according to claim 1, further comprising data feedback means for instantly updating the position information of an object.

[1039] (Claim 3)

[1040] The system according to claim 1, comprising means for analyzing the behavioral characteristics of users and predicting the objects needed at a specific time.

[1041] "Application example 2 when combining with an emotional engine"

[1042] (Claim 1)

[1043] Image processing means for detecting the position of an object,

[1044] A database means for storing location information of items,

[1045] A calculation means that proposes the optimal storage method for the items based on the aforementioned location information,

[1046] It has a voice recognition function and an input means for inquiring about the location of an item,

[1047] Security measures that issue warnings in case of abnormalities,

[1048] A means for predicting user behavior and generating reminders using information from the aforementioned database means,

[1049] A means of analyzing user emotions and adjusting responses,

[1050] A means of presenting information to the user in real time,

[1051] A system that includes this.

[1052] (Claim 2)

[1053] The system according to claim 1, having a data feedback function for updating the location information of an item and the emotional state of a user in real time.

[1054] (Claim 3)

[1055] The system according to claim 1, comprising a function to analyze the user's behavioral patterns and emotional state and predict the items needed and appropriate responses at a specific time. [Explanation of Symbols]

[1056] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image processing means for detecting the position of an object, A database means for storing location information of items, A calculation means that proposes the optimal arrangement method for the items based on the aforementioned location information, It has a voice recognition function and an input means for inquiring about the location of an item, A monitoring system that issues warnings in the event of an anomaly, A means for predicting user behavior using information from the aforementioned database and generating time-appropriate notifications, Means for efficiently managing resources in public facilities and infrastructure, A means of visually displaying the location of an item in real time, A system that includes this.

2. The system according to claim 1, comprising a function to update the location information of an item in real time and to monitor the unauthorized movement of an important item.

3. The system according to claim 1, which has the effect of analyzing user behavior patterns and proposing the optimal placement of items within a public facility.

Images