system

Abstract

Provide a system. 【Solution means】 Means for collecting attribute information of information data from an information processing device connected to a network, Means for investigating usage frequency and duplication based on the collected attribute information, Means for identifying unnecessary information data based on the investigation results, Means for notifying the user of the identified unnecessary information, Means for deleting unnecessary information based on the user's selection, Means for optimizing the storage medium after deletion, Means for improving the analysis results of information using a generated AI model, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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 recent years, due to the evolution of cloud technology and the acceleration of digitalization, the digital data generated from corporate and personal terminals has been increasing rapidly. However, much of this digital data has become unnecessary data that is no longer used, so-called digital garbage, and the accumulation of this data has become a problem in terms of increased server load, increased power consumption, and ultimately environmental burden. With conventional methods, it is difficult to efficiently identify and delete such digital garbage, and storage optimization has not been fully achieved. In such a situation, there is a need for a method to effectively identify digital garbage, perform deletion, and optimize storage.

Means for Solving the Problems

[0005] This invention provides a system that identifies unnecessary digital data by collecting metadata of digital data from network-connected terminals and analyzing its usage frequency and duplication using AI. Users receive notifications of identified unnecessary data and, upon approval of deletion, the server deletes the designated digital junk. Furthermore, after deletion, the system automatically optimizes storage, performing defragmentation and storage compression to improve server efficiency. Additional features, such as automatic backup before deleting identified unnecessary digital data and recording deletion history, ensure the security and reliability of data management.

[0006] A "network" is a system in which multiple computers and devices are interconnected to communicate and share data.

[0007] A "terminal" refers to a computer or device that a user directly operates to input and output digital data.

[0008] "Digital data" is a general term for information and content stored in electronic format, and includes files, documents, photographs, videos, and more.

[0009] "Metadata" refers to supplementary information about digital data, including attributes such as file name, size, creation date, and last access date.

[0010] "AI" is an abbreviation for artificial intelligence, and refers to the technology in which computer systems imitate intelligent human behavior.

[0011] "Usage frequency" refers to the degree or number of times a particular piece of data or file is used by a user or system.

[0012] "Duplicate" refers to a state in which multiple identical or very similar pieces of data exist.

[0013] "Digital junk" refers to unnecessary digital data that has not been used for a long period of time or that exists in duplicate.

[0014] "Notification" is the act of a system informing a user of a specific message or information.

[0015] "Deletion" refers to the operation of completely removing digital data from a storage device.

[0016] "Storage" refers to a physical or virtual storage area used to hold digital data.

[0017] "Optimization" is the act of adjusting and improving resources and processes in order to improve the performance and efficiency of a system.

[0018] "Backup" refers to the process of copying and saving data to another location to protect against loss or corruption of the original data.

[0019] "History" refers to a record of past actions or changes that have occurred. [Brief explanation of the drawing]

[0020] [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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] 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).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] This invention relates to a system for improving the efficiency of digital data management, and in particular provides a specific method for identifying, deleting, and optimizing unnecessary digital data, so-called digital junk. This system consists of a server, a terminal, and a user.

[0042] The server periodically collects metadata about digital data from all terminals on the network. This metadata includes file names, sizes, creation dates, and last access dates, and plays an important role in understanding data usage.

[0043] The server uses AI to perform a deep analysis of the collected metadata. This analysis allows for the identification of infrequently used files, duplicate data, and outdated data. The server notifies the user of any identified digital junk. The notification includes the reason why the file was identified as digital junk and detailed information about the file.

[0044] Users can view notifications sent from the server and select the digital data they wish to delete. Because the deletion selection is made directly by the user, they can review and approve the data being deleted. This prevents the accidental deletion of important data.

[0045] After receiving user approval for deletion, the server deletes the specified digital junk from the device. Furthermore, it optimizes storage to make the most of the freed-up storage space resulting from the deletion. This optimization includes operations to defragment storage and increasing capacity using compression techniques.

[0046] As a concrete example, the server identifies old photo albums that are rarely accessed from the user's device and labels them as digital waste. The user confirms the notification from the server and agrees to delete the albums. Based on the user's consent, the server deletes the albums and then optimizes storage. This not only reduces the server load and saves energy, but also provides the user with a more convenient data management environment.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The server accesses each terminal on the network and collects metadata for all digital data stored in the storage. In this process, the server obtains information such as file name, size, creation date, and last access date.

[0050] Step 2:

[0051] The server passes the collected metadata to the AI ​​agent, which analyzes data usage frequency and redundancy. It then runs algorithms to identify files that have been unused for a long time, as well as files with similar or identical content.

[0052] Step 3:

[0053] Based on the analysis results, the server generates a list of files identified as digital junk. This list includes the reasons why each file was deemed unnecessary and detailed metadata for each file.

[0054] Step 4:

[0055] The server notifies the user of a list of identified digital clutter. This notification is presented in a visually easy-to-understand format and includes recommendations for deletion.

[0056] Step 5:

[0057] The user receives a notification and selects the data to delete. The selected data is added to the deletion approval list, and the following actions are taken based on this list.

[0058] Step 6:

[0059] The server deletes digital data authorized by the user from the device. This deletion is logged to ensure security.

[0060] Step 7:

[0061] After the deletion process is complete, the server optimizes the free storage. This includes performing defragmentation and applying compression techniques to expand storage capacity.

[0062] Step 8:

[0063] The server reports the optimization results to the user. This report includes information on increased free space and improvements in system performance.

[0064] (Example 1)

[0065] 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."

[0066] In modern society, the dramatic increase in digital information has strained the capacity of storage media, leading to the accumulation of vast amounts of unnecessary digital information, or so-called digital junk. This situation can result in decreased performance of storage devices and wasted energy. Furthermore, manually sorting through unnecessary information is time-consuming and laborious, and carries the risk of accidental deletion, thus highlighting the need for efficient management of digital information.

[0067] 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.

[0068] In this invention, the server includes means for collecting metadata of digital information from information processing devices connected to a communication network, means for analyzing usage frequency and duplication based on the collected metadata, and means for identifying unnecessary digital information based on the analysis results. This makes it possible for users to efficiently manage unnecessary digital information and optimize the capacity of the storage device.

[0069] A "communication network" is a network infrastructure used to exchange data between information processing devices.

[0070] An "information processing device" is an electronic device used for inputting, outputting, storing, and processing digital data.

[0071] "Digital information" refers to bit-level data processed by a computer, stored in forms such as text, images, and audio.

[0072] "Metadata" refers to attribute information associated with digital information, including file name, file size, creation date, and last access date.

[0073] "Analysis" is the process of using collected data to analyze information based on specific conditions, such as identifying unnecessary information or estimating its frequency of use.

[0074] A "storage device" is a hardware device capable of storing and reading digital information.

[0075] "Optimization" refers to the process of rearranging and compressing data to improve the efficiency of storage device usage and increase free space.

[0076] A "user" is a person who manages and manipulates digital information through an information processing device.

[0077] This invention is a system for efficiently managing digital information from network-connected information processing devices. This system primarily consists of three elements: a server, a terminal, and a user.

[0078] The server connects to each terminal via a communication network and collects metadata of the digital information contained within the terminal. SSH and HTTPS are used as secure communication methods for this process. The collected metadata is stored on the server and analyzed using AI frameworks such as TENSORFLOW® and PyTorch. The purpose of the analysis is to identify infrequently used files, duplicate data, and old data that has not been accessed for a certain period.

[0079] On the device, metadata is extracted using the OS file system API. This information is sent to the server and updated periodically. Users who receive the analysis results from the server can view a list of identified unnecessary information, for example, through a management interface on a web browser.

[0080] Users can review the digital junk they are notified of using a dashboard provided by the server and approve its deletion. This process also provides detailed information to prevent accidental deletion. Once deletion is approved, the server deletes the digital information in question and performs defragmentation to prevent storage fragmentation. It also optimizes storage space by using data compression techniques.

[0081] As a concrete example, the server identifies a folder named "family_photos_2005" from the terminal and determines it to be digital junk through metadata analysis. Once the user confirms this information on the dashboard and approves its deletion, the server deletes the folder and optimizes storage allocation. An example of a prompt for the generating AI is, "Generate code to create a program that identifies and deletes unused files."

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server connects to each terminal via a communication network and periodically collects metadata of digital information. The input data obtained from the terminals includes file names, sizes, creation dates, and last access dates. By organizing this data and storing it in a metadata database, the foundation for overall data management is established.

[0085] Step 2:

[0086] The server inputs the collected metadata into an AI model for analysis, including frequency of use, redundancy, and age. Data processing techniques such as clustering and natural language processing are employed, with file access frequency being a particularly important metric. The output generates a list of infrequently used files and duplicate data.

[0087] Step 3:

[0088] The server notifies the user of a list of unnecessary digital information derived from the analysis. The notification is sent via email or a web dashboard and includes details of the identified digital junk and the reasons why it was deemed unnecessary. This notification allows the user to explicitly confirm the data being considered for deletion.

[0089] Step 4:

[0090] Users review notifications sent from the server and filter out digital information they deem unnecessary. An intuitive interface is provided, allowing users to view the specific contents of files and decide what to delete. Based on the user's selection, a list of data to be deleted is created and returned to the server.

[0091] Step 5:

[0092] Based on user approval for deletion, the server deletes specified digital information from the terminal. This operation securely erases the target files and frees up storage space. Furthermore, after the deletion operation, defragmentation and data compression technologies are used to optimize storage and enable efficient data utilization.

[0093] (Application Example 1)

[0094] 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."

[0095] In today's information processing environment, information data is rapidly increasing, and much of it is infrequently used and duplicated. This leads to problems such as insufficient storage capacity and decreased efficiency. Furthermore, manually selecting and deleting unnecessary information is time-consuming and carries the risk of accidentally deleting important data. To solve this problem, there is a need for a method that automatically analyzes usage frequency and duplication and manages unnecessary information safely and efficiently.

[0096] 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.

[0097] In this invention, the server includes means for collecting attribute information of information data from information processing devices connected to a network, means for investigating usage frequency and duplication based on the collected attribute information, and means for improving the information analysis results using a generative AI model. This enables users to safely and efficiently confirm the necessity of information data and appropriately manage unnecessary information.

[0098] An "information processing device" is an electronic device that is connected to a network and has the function of collecting, processing, and analyzing information data.

[0099] "Attribute information" refers to information that indicates the characteristics associated with information data, and includes metadata such as file name, size, and frequency of use.

[0100] "Frequency of use" is an indicator that shows how often specific information data is used over a certain period of time.

[0101] "Duplicate" refers to a situation where information data with the same content exists in different locations.

[0102] A "generative AI model" is an artificial intelligence technology used to analyze large amounts of information data and find useful patterns and features.

[0103] "Improving analysis results" refers to the process of improving the accuracy of the analysis of attribute information collected using a generative AI model.

[0104] "Unnecessary information" refers to information data that is used infrequently or is duplicated, and therefore should be deleted or managed.

[0105] The system for implementing the present invention mainly consists of a server, an information processing terminal, and a generation AI model. The server is responsible for periodically collecting attribute information of information data from each information processing terminal connected to the network. The attribute information includes metadata such as file name, size, last used date, and usage frequency, which makes it possible to understand the usage status of digital data.

[0106] The server performs detailed analysis using a generative AI model based on the collected attribute information. This model identifies patterns and trends in the collected data and efficiently identifies infrequently used information and duplicate data. For identified unnecessary data, the server sends a notification to the user. The notification includes the reason why the data was deemed unnecessary and related details, helping the user evaluate the value of the data.

[0107] Users can select which digital data to delete after receiving a notification from the server. The user has the right to choose what to delete, which helps prevent the accidental deletion of important data. Once the server receives user approval for deletion, it will delete the specified unnecessary data from each device.

[0108] After deletion, the server optimizes the storage medium. This includes processes that maximize the use of free space by utilizing defragmentation and data compression techniques. As a result, overall system efficiency is improved and energy consumption is reduced.

[0109] For example, if a data center manages multiple duplicate backup data sets, the server will identify older backups, notify the user, delete them after obtaining approval, and optimize storage.

[0110] An example of a prompt might be: "Consider the process of detecting and optimizing old backups in the data center, and explain how to efficiently delete unnecessary backup data and reclaim storage space."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects attribute information from each information processing terminal via the network.

[0114] The input is metadata of the information data transmitted from each terminal. The output is a dataset that aggregates the collected attribute information. The server uses this dataset in subsequent analysis steps.

[0115] Step 2:

[0116] The server performs analysis using a generated AI model based on the collected attribute information.

[0117] The input is a dataset of attribute information obtained in Step 1. The output is a judgment result that includes infrequently used information and duplicate data. The server uses this judgment result to identify unnecessary information.

[0118] Step 3:

[0119] The server notifies the user of any identified unnecessary information.

[0120] The input is the judgment result obtained in step 2. The output is a notification message sent to the user's device. The notification includes the reason why the data was deemed unnecessary and other detailed information, allowing the user to evaluate the data.

[0121] Step 4:

[0122] Users check notifications from the server and select the data they want to delete.

[0123] The input is a notification message sent from the server. The output is feedback that includes instructions to approve deletion or changes. The user uses their terminal to send their deletion selection to the server.

[0124] Step 5:

[0125] The server deletes the specified unnecessary information data from the terminal based on the user's instructions.

[0126] The input is a deletion instruction from the user. The output is the updated storage status. This deletion operation removes unnecessary information from the storage medium.

[0127] Step 6:

[0128] The server will perform optimization of the storage medium after deletion.

[0129] The input represents the state of the storage medium with increased free space. The output represents the state of the storage medium after optimization. The server performs operations to optimize the storage medium using defragmentation and compression techniques.

[0130] 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.

[0131] This invention is a digital data management system that takes into account the emotional state of the user, and aims to make data management more user-friendly by optimizing the identification, deletion, and storage optimization of unnecessary digital data based on the user's emotions. This system consists of a server, terminals, users, and an emotion engine.

[0132] The server collects metadata on digital data from all devices connected to the network and performs a thorough analysis of that data using AI. The server identifies digital clutter based on criteria such as frequency of use, duplication, and elapsed time. The server then generates a list of analysis results and notifies the user, including detailed information about the identified digital clutter.

[0133] This system utilizes an emotion engine to analyze the user's emotional state in real time while they are operating their device. The emotion engine collects the user's facial expressions and voice tone via sensor devices such as cameras and microphones, and identifies their emotional state while respecting their privacy.

[0134] The server uses analysis results from its emotion engine to help users decide whether to delete digital clutter, taking into account their current emotional state. For example, if a user is feeling stressed, the server may refrain from sending notifications or modify the content of notifications to allow them to make decisions in a more relaxed state.

[0135] As a concrete example, the server identifies infrequently used files detected on the device as digital clutter and recommends that the user delete them. The emotion engine assesses whether the user is focused or relaxed, and can adjust the timing and method of notifications accordingly. If the user's emotions are calm, the server prompts them to proceed with the deletion process, and the user confirms. After approval, the server deletes the specified digital clutter and optimizes storage.

[0136] In this way, the present invention can provide a more comfortable and less stressful digital environment by taking user emotions into consideration when managing data, and enables data management that more accurately reflects user intentions.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server periodically collects metadata about digital data from each terminal on the network. This allows it to obtain attribute information such as file size, last access date, and frequency of access.

[0140] Step 2:

[0141] The server passes the acquired metadata to an AI engine for analysis. This AI engine detects infrequently used files and duplicate files and classifies them as digital junk.

[0142] Step 3:

[0143] The server lists files identified as digital junk and sends this list to the emotion engine. The emotion engine analyzes human emotions to determine the appropriate timing and content for notifications.

[0144] Step 4:

[0145] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and works in conjunction with an emotion engine to evaluate the user's current emotional state.

[0146] Step 5:

[0147] The emotion engine adjusts the timing of notifications to the user based on the acquired emotional state. For example, if the user is relaxed, the server will immediately send a notification, but if they are stressed, it will postpone the notification.

[0148] Step 6:

[0149] The user reviews a list of digital waste received from the server and decides whether to delete it. Based on the user's instructions, the system selects the data to be deleted.

[0150] Step 7:

[0151] The server deletes the digital data selected by the user. If specified beforehand, a backup is performed before physically deleting the data.

[0152] Step 8:

[0153] The server optimizes the free space created after deletion. This includes processes such as storage defragmentation and data compression.

[0154] Step 9:

[0155] The server reports the optimization results and available storage status to the user, and provides appropriate feedback that takes into account the user's emotional state.

[0156] (Example 2)

[0157] 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".

[0158] In today's world, the volume of electronic data is increasing rapidly, and managing it is a significant burden for many users. Traditional data management systems focus on identifying and deleting unnecessary data, but they lack consideration for the user's emotional state during this process. This can lead to risks such as notifications being sent at inappropriate times or data manipulation that causes stress to users.

[0159] 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.

[0160] In this invention, the server includes means for collecting attribute information of electronic data from an information processing device connected to a network, means for analyzing usage counts and redundancies based on the collected attribute information, and sentiment analysis means for evaluating the user's emotional state. This enables notifications and deletion suggestions at appropriate timings and in appropriate methods according to the user's emotional state, providing a more user-friendly data management environment.

[0161] A "network" is a system that connects multiple information processing devices to each other, enabling data communication.

[0162] An "information processing device" is a device used to process and manage electronic data, and includes computers and servers.

[0163] "Electronic data" refers to information that is stored and processed in digital format, including files, documents, and images.

[0164] "Attribute information" refers to various types of information related to electronic data, including file type, size, creation date, and last access date.

[0165] "Number of uses" is an indicator that shows how often a particular electronic data item was used within a specified period.

[0166] "Duplicity" refers to the characteristic of indicating whether multiple electronic data are identical or similar.

[0167] "Analysis" is the process of analyzing data and information to identify specific trends and characteristics.

[0168] "Emotional analysis" is an analytical method that uses data such as a user's facial expressions and voice to evaluate their emotional state.

[0169] "Notification" refers to a method of providing information to a user to convey specific information or suggestions.

[0170] "Optimization" is the process of adjusting the state of a system or data to the best possible condition according to specifications and requirements.

[0171] This system consists of a server connected to a network and multiple terminals. The server collects attribute information of electronic data from the multiple terminals via the network. Agent software running on the terminals is involved in this process, sending attribute information to the server using HTTP or HTTPS protocols.

[0172] The server uses the collected attribute information to execute an AI analysis program that utilizes the Python language and data analysis libraries such as Pandas. This analysis program analyzes the usage frequency and redundancy of electronic data to identify which data is unnecessary.

[0173] The device is equipped with sensors such as a camera and microphone for emotion analysis, collecting the user's facial expressions and voice in real time. The server uses AI libraries such as TensorFlow to perform emotion analysis and identify the user's emotional state.

[0174] Based on the analysis, the server generates and sends a notification to the user suggesting the deletion of unnecessary data at the optimal timing and method according to the user's emotional state. If the user approves the notification, the server automatically deletes the unnecessary electronic data and optimizes the storage.

[0175] As a concrete example, if the server identifies a photo file that hasn't been used for a long time and confirms through sentiment analysis that the user is relaxed, the server will send a notification asking, "Is it okay to delete this photo?" If the user approves, the file will be deleted immediately.

[0176] Specific examples of prompt statements given to the generating AI model include, "Explain how the digital data management system can be optimized based on the user's emotional data." In this way, the present invention has a form that performs data management that takes user emotions into consideration.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The server collects attribute information of electronic data from terminals connected to the network. The input is metadata of the electronic data stored on the terminal, and the output is a list of attribute information aggregated on the server. Specifically, agent software on each terminal collects information such as file name, size, creation date, and last access date, and sends it to the server using HTTP / HTTPS.

[0180] Step 2:

[0181] The server performs AI analysis based on the collected attribute information. The input is a list of attribute information collected in the previous step, and the output is a list of electronic data deemed unnecessary. Using Python and the Pandas library, the system analyzes data usage counts and redundancy, classifying data that meets certain conditions as "unnecessary." Specifically, data that has not been accessed for one month is labeled as unnecessary.

[0182] Step 3:

[0183] The device acquires user emotion data using its camera and microphone. Input is user image and audio data, and output is digital information indicating emotional state. Using the data collected from the sensors, real-time emotion analysis is performed using TensorFlow. This analysis quantifies the user's stress and relaxation levels.

[0184] Step 4:

[0185] The server sends notifications to the user based on a list of unnecessary data and sentiment analysis results. The input is a list of unnecessary data and the user's emotional state, and the output is an optimized notification message. The content and timing of the notification are adjusted accordingly: immediate notifications when the user is relaxed, and more subdued messages when the user is stressed.

[0186] Step 5:

[0187] The user receives a notification and approves the deletion of unnecessary data. The input is the notification from the server and a list of suggested unnecessary data, and the output is feedback confirming the deletion approval. The user performs the approval operation via the UI, and that information is sent to the server.

[0188] Step 6:

[0189] The server deletes unnecessary data based on user approval. The input is a list of data whose deletion has been approved, and the output is the optimized storage space. Data deletion is performed immediately on the server, and the storage status is updated. As a result, unnecessary data is physically erased, and the available storage capacity increases.

[0190] (Application Example 2)

[0191] 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".

[0192] In today's information society, a large amount of digital information is stored on users' devices. This excessive information can lead to a decrease in the performance of storage devices and contribute to user stress. Therefore, there is a need to manage digital information efficiently and in a way that takes into account the user's mental state, and to optimize storage devices.

[0193] 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.

[0194] In this invention, the server includes means for collecting metadata of digital information from network-connected information processing devices, means for evaluating the frequency of use and duplication of information based on the collected metadata, and means for analyzing the user's emotional state in real time. This enables effective notification and management of digital information based on the user's emotions.

[0195] "Network-connected information processing devices" refer to electronic devices such as computers and mobile devices that can send and receive data via the internet or other communication networks.

[0196] "Metadata of digital information" refers to supplementary information that describes the attributes, structure, and other characteristics of data, and is not the data itself, but rather information about how that data is being handled.

[0197] "Evaluating usage frequency and duplication" means qualitatively and quantitatively analyzing how much digital information is being used and whether identical or similar information exists.

[0198] "Identifying unnecessary digital information" means identifying information that is inefficient to have on a storage device due to reasons such as infrequent use or duplication.

[0199] A "user" is an individual or organization that operates an information processing device and is involved in the management of its digital information.

[0200] "Optimizing notification methods and timing" means reducing user stress and improving the efficiency of information management by providing information through the most appropriate means and timing for the user.

[0201] "Optimizing memory storage" means improving the performance of information processing devices by eliminating unnecessary digital information and making efficient use of storage capacity.

[0202] "Real-time analysis of emotional state" means using sensors or other devices to instantly evaluate the user's current psychological state based on their facial expressions and voice, and then adjusting their behavior based on the evaluation results.

[0203] To implement this invention, it is first necessary to use an information processing device connected to a network. The server collects metadata of digital information from each terminal and evaluates the frequency of use and duplication of that data. The evaluated data is identified by the server as unnecessary digital information. The server then analyzes the user's emotional state in real time through sensors and optimizes the notification method and timing of unnecessary data according to that emotion.

[0204] If the emotion engine detects that the user is relaxed, the server sends a gentle notification about deleting unnecessary data. Conversely, if the system determines that the user is stressed, it refrains from sending notifications or adjusts their timing. By providing notifications at the optimal time for the user, the system reduces the user's mental burden and enables efficient data management.

[0205] The hardware used includes cameras and microphones from smartphones and smart glasses, while the software utilizes the "EmotionRecognition" library and the "DataManagement" library for emotion analysis. Furthermore, if the user approves, unnecessary digital information can be deleted and storage devices optimized, thereby improving the performance of the information processing device.

[0206] As a concrete example, while a user is relaxing in a park, their smartphone can organize floating screenshots and unread messages and send a suggestion to delete them. By prompting the user to enter a text message such as "Delete this data," the device's performance can be improved.

[0207] An example of a prompt is: "Generate a notification message suggesting data deletion when the user is in a calm state of mind."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The server collects metadata of digital information from terminals connected to the network. This collection involves sending attribute and structural information of the data held by the terminals to the server over the network. It receives metadata of all digital information on the terminals as input and stores it in the server as structured data.

[0211] Step 2:

[0212] The server evaluates the frequency of use and duplication of digital information based on the collected metadata. The server analyzes the size of the data itself, the update date and time, and the access history to determine which information qualifies as "digital junk." As a result, information that is rarely used or is duplicated is output.

[0213] Step 3:

[0214] The device analyzes the user's emotional state in real time. This analysis includes collecting the user's facial expressions and voice tone using the device's built-in camera and microphone. Emotion analysis software processes the collected data to identify the user's emotional state. As a result, emotional state data is output.

[0215] Step 4:

[0216] The server integrates the emotion analysis results with information on duplicate and unnecessary data. Based on this integrated information, it generates a notification suggesting the deletion of unnecessary data only when the user is relaxed. Using a generation AI model, it creates prompt messages appropriate to the emotional state and sends them to the device.

[0217] Step 5:

[0218] Based on notification messages received from the device, the user approves or rejects the deletion of digital junk. If the user approves, the device sends its selection to the server and begins the deletion process. Ultimately, unnecessary digital information is removed, and storage is optimized.

[0219] 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.

[0220] Data generation model 58 is a 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention relates to a system for improving the efficiency of digital data management, and in particular provides a specific method for identifying, deleting, and optimizing unnecessary digital data, so-called digital junk. This system consists of a server, a terminal, and a user.

[0236] The server periodically collects metadata about digital data from all terminals on the network. This metadata includes file names, sizes, creation dates, and last access dates, and plays an important role in understanding data usage.

[0237] The server uses AI to perform a deep analysis of the collected metadata. This analysis allows for the identification of infrequently used files, duplicate data, and outdated data. The server notifies the user of any identified digital junk. The notification includes the reason why the file was identified as digital junk and detailed information about the file.

[0238] Users can view notifications sent from the server and select the digital data they wish to delete. Because the deletion selection is made directly by the user, they can review and approve the data being deleted. This prevents the accidental deletion of important data.

[0239] After receiving user approval for deletion, the server deletes the specified digital junk from the device. Furthermore, it optimizes storage to make the most of the freed-up storage space resulting from the deletion. This optimization includes operations to defragment storage and increasing capacity using compression techniques.

[0240] As a concrete example, the server identifies old photo albums that are rarely accessed from the user's device and labels them as digital waste. The user confirms the notification from the server and agrees to delete the albums. Based on the user's consent, the server deletes the albums and then optimizes storage. This not only reduces the server load and saves energy, but also provides the user with a more convenient data management environment.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The server accesses each terminal on the network and collects metadata for all digital data stored in the storage. In this process, the server obtains information such as file name, size, creation date, and last access date.

[0244] Step 2:

[0245] The server passes the collected metadata to the AI ​​agent, which analyzes data usage frequency and redundancy. It then runs algorithms to identify files that have been unused for a long time, as well as files with similar or identical content.

[0246] Step 3:

[0247] Based on the analysis results, the server generates a list of files identified as digital junk. This list includes the reasons why each file was deemed unnecessary and detailed metadata for each file.

[0248] Step 4:

[0249] The server notifies the user of a list of identified digital clutter. This notification is presented in a visually easy-to-understand format and includes recommendations for deletion.

[0250] Step 5:

[0251] The user receives a notification and selects the data to delete. The selected data is added to the deletion approval list, and the following actions are taken based on this list.

[0252] Step 6:

[0253] The server deletes digital data authorized by the user from the device. This deletion is logged to ensure security.

[0254] Step 7:

[0255] After the deletion process is complete, the server optimizes the free storage. This includes performing defragmentation and applying compression techniques to expand storage capacity.

[0256] Step 8:

[0257] The server reports the optimization results to the user. This report includes information on increased free space and improvements in system performance.

[0258] (Example 1)

[0259] 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 glasses 214 will be referred to as the "terminal".

[0260] In modern society, the dramatic increase in digital information has strained the capacity of storage media, leading to the accumulation of vast amounts of unnecessary digital information, or so-called digital junk. This situation can result in decreased performance of storage devices and wasted energy. Furthermore, manually sorting through unnecessary information is time-consuming and laborious, and carries the risk of accidental deletion, thus highlighting the need for efficient management of digital information.

[0261] 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.

[0262] In this invention, the server includes means for collecting metadata of digital information from information processing devices connected to a communication network, means for analyzing usage frequency and duplication based on the collected metadata, and means for identifying unnecessary digital information based on the analysis results. This makes it possible for users to efficiently manage unnecessary digital information and optimize the capacity of the storage device.

[0263] A "communication network" is a network infrastructure used to exchange data between information processing devices.

[0264] An "information processing device" is an electronic device used for inputting, outputting, storing, and processing digital data.

[0265] "Digital information" refers to bit-level data processed by a computer, stored in forms such as text, images, and audio.

[0266] "Metadata" refers to attribute information associated with digital information, including file name, file size, creation date, and last access date.

[0267] "Analysis" is the process of using collected data to analyze information based on specific conditions, such as identifying unnecessary information or estimating its frequency of use.

[0268] A "storage device" is a hardware device capable of storing and reading digital information.

[0269] "Optimization" refers to the process of rearranging and compressing data to improve the efficiency of storage device usage and increase free space.

[0270] A "user" is a person who manages and manipulates digital information through an information processing device.

[0271] This invention is a system for efficiently managing digital information from network-connected information processing devices. This system primarily consists of three elements: a server, a terminal, and a user.

[0272] The server connects to each terminal via a communication network and collects metadata of the digital information contained within the terminal. SSH and HTTPS are used as secure communication methods for this process. The collected metadata is stored on the server and analyzed using AI frameworks such as TensorFlow and PyTorch. The purpose of the analysis is to identify infrequently used files, duplicate data, and old data that has not been accessed for a certain period.

[0273] On the device, metadata is extracted using the OS file system API. This information is sent to the server and updated periodically. Users who receive the analysis results from the server can view a list of identified unnecessary information, for example, through a management interface on a web browser.

[0274] Users can review the digital junk they are notified of using a dashboard provided by the server and approve its deletion. This process also provides detailed information to prevent accidental deletion. Once deletion is approved, the server deletes the digital information in question and performs defragmentation to prevent storage fragmentation. It also optimizes storage space by using data compression techniques.

[0275] As a concrete example, the server identifies a folder named "family_photos_2005" from the terminal and determines it to be digital junk through metadata analysis. Once the user confirms this information on the dashboard and approves its deletion, the server deletes the folder and optimizes storage allocation. An example of a prompt for the generating AI is, "Generate code to create a program that identifies and deletes unused files."

[0276] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0277] Step 1:

[0278] The server connects to each terminal via a communication network and periodically collects metadata of digital information. The input data obtained from the terminals includes file names, sizes, creation dates, and last access dates. By organizing this data and storing it in a metadata database, the foundation for overall data management is established.

[0279] Step 2:

[0280] The server inputs the collected metadata into an AI model for analysis, including frequency of use, redundancy, and age. Data processing techniques such as clustering and natural language processing are employed, with file access frequency being a particularly important metric. The output generates a list of infrequently used files and duplicate data.

[0281] Step 3:

[0282] The server notifies the user of the list of unnecessary digital information derived from the analysis. The notification is sent via email or a web dashboard, including details of the identified digital waste and the reasons why it was determined to be unnecessary. This notification allows the user to explicitly confirm the data candidates for deletion.

[0283] Step 4:

[0284] The user checks the notification sent by the server and screens the digital information deemed unnecessary. An intuitive interface is provided, where the user can check the specific content of the file and then decide what to delete. Based on the user's selection, the data to be deleted is listed and sent back to the server.

[0285] Step 5:

[0286] Based on the user's deletion approval, the server deletes the specified digital information from the terminal. This operation securely erases the target file and ensures free space on the storage device. Furthermore, after the deletion operation, storage is optimized using defragmentation and data compression techniques to achieve efficient data utilization.

[0287] (Application Example 1)

[0288] Next, Application 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".

[0289] In a modern information processing environment, information data is increasing rapidly, and it may contain a lot of data with low usage frequency and duplicates. This leads to problems such as insufficient capacity of information storage devices and reduced efficiency. Also, manually screening and deleting unnecessary information is time-consuming and may lead to accidental deletion of important data. To solve this problem, there is a need for means to automatically analyze usage frequency and duplication and manage unnecessary information safely and efficiently.

[0290] 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.

[0291] In this invention, the server includes means for collecting attribute information of information data from information processing devices connected to a network, means for investigating usage frequency and duplication based on the collected attribute information, and means for improving the information analysis results using a generative AI model. This enables users to safely and efficiently confirm the necessity of information data and appropriately manage unnecessary information.

[0292] An "information processing device" is an electronic device that is connected to a network and has the function of collecting, processing, and analyzing information data.

[0293] "Attribute information" refers to information that indicates the characteristics associated with information data, and includes metadata such as file name, size, and frequency of use.

[0294] "Frequency of use" is an indicator that shows how often specific information data is used over a certain period of time.

[0295] "Duplicate" refers to a situation where information data with the same content exists in different locations.

[0296] A "generative AI model" is an artificial intelligence technology used to analyze large amounts of information data and find useful patterns and features.

[0297] "Improving analysis results" refers to the process of improving the accuracy of the analysis of attribute information collected using a generative AI model.

[0298] "Unnecessary information" refers to information data that is used infrequently or is duplicated, and therefore should be deleted or managed.

[0299] The system for implementing the present invention mainly consists of a server, an information processing terminal, and a generation AI model. The server is responsible for periodically collecting attribute information of information data from each information processing terminal connected to the network. The attribute information includes metadata such as file name, size, last used date, and usage frequency, which makes it possible to understand the usage status of digital data.

[0300] The server performs detailed analysis using a generative AI model based on the collected attribute information. This model identifies patterns and trends in the collected data and efficiently identifies infrequently used information and duplicate data. For identified unnecessary data, the server sends a notification to the user. The notification includes the reason why the data was deemed unnecessary and related details, helping the user evaluate the value of the data.

[0301] Users can select which digital data to delete after receiving a notification from the server. The user has the right to choose what to delete, which helps prevent the accidental deletion of important data. Once the server receives user approval for deletion, it will delete the specified unnecessary data from each device.

[0302] After deletion, the server optimizes the storage medium. This includes processes that maximize the use of free space by utilizing defragmentation and data compression techniques. As a result, overall system efficiency is improved and energy consumption is reduced.

[0303] For example, if a data center manages multiple duplicate backup data sets, the server will identify older backups, notify the user, delete them after obtaining approval, and optimize storage.

[0304] An example of a prompt might be: "Consider the process of detecting and optimizing old backups in the data center, and explain how to efficiently delete unnecessary backup data and reclaim storage space."

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1:

[0307] The server collects attribute information from each information processing terminal through the network.

[0308] The input is the metadata of the information data transmitted from each terminal. The output is a data set obtained by aggregating the collected attribute information. The server uses this data set in subsequent analysis steps.

[0309] Step 2:

[0310] The server performs analysis using an AI model generated based on the collected attribute information.

[0311] The input is the data set of the attribute information obtained in Step 1. The output is a determination result including information with low usage frequency and duplicate data. The server performs an operation to identify unnecessary information based on this determination result.

[0312] Step 3:

[0313] The server notifies the user of the identified unnecessary information.

[0314] The input is the determination result obtained in Step 2. The output is a notification message transmitted to the user's terminal. Since the notification includes the reason and detailed information for which the data is judged unnecessary, the user can evaluate the data.

[0315] Step 4:

[0316] The user checks the notification from the server and selects the data to be deleted.

[0317] The input is a notification message sent from the server. The output is feedback that includes instructions to approve deletion or changes. The user uses their terminal to send their deletion selection to the server.

[0318] Step 5:

[0319] The server deletes the specified unnecessary information data from the terminal based on the user's instructions.

[0320] The input is a deletion instruction from the user. The output is the updated storage status. This deletion operation removes unnecessary information from the storage medium.

[0321] Step 6:

[0322] The server will perform optimization of the storage medium after deletion.

[0323] The input represents the state of the storage medium with increased free space. The output represents the state of the storage medium after optimization. The server performs operations to optimize the storage medium using defragmentation and compression techniques.

[0324] 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.

[0325] This invention is a digital data management system that takes into account the emotional state of the user, and aims to make data management more user-friendly by optimizing the identification, deletion, and storage optimization of unnecessary digital data based on the user's emotions. This system consists of a server, terminals, users, and an emotion engine.

[0326] The server collects metadata on digital data from all devices connected to the network and performs a thorough analysis of that data using AI. The server identifies digital clutter based on criteria such as frequency of use, duplication, and elapsed time. The server then generates a list of analysis results and notifies the user, including detailed information about the identified digital clutter.

[0327] This system utilizes an emotion engine to analyze the user's emotional state in real time while they are operating their device. The emotion engine collects the user's facial expressions and voice tone via sensor devices such as cameras and microphones, and identifies their emotional state while respecting their privacy.

[0328] The server uses analysis results from its emotion engine to help users decide whether to delete digital clutter, taking into account their current emotional state. For example, if a user is feeling stressed, the server may refrain from sending notifications or modify the content of notifications to allow them to make decisions in a more relaxed state.

[0329] As a concrete example, the server identifies infrequently used files detected on the device as digital clutter and recommends that the user delete them. The emotion engine assesses whether the user is focused or relaxed, and can adjust the timing and method of notifications accordingly. If the user's emotions are calm, the server prompts them to proceed with the deletion process, and the user confirms. After approval, the server deletes the specified digital clutter and optimizes storage.

[0330] In this way, the present invention can provide a more comfortable and less stressful digital environment by taking user emotions into consideration when managing data, and enables data management that more accurately reflects user intentions.

[0331] The following describes the processing flow.

[0332] Step 1:

[0333] The server periodically collects metadata about digital data from each terminal on the network. This allows it to obtain attribute information such as file size, last access date, and frequency of access.

[0334] Step 2:

[0335] The server passes the acquired metadata to an AI engine for analysis. This AI engine detects infrequently used files and duplicate files and classifies them as digital junk.

[0336] Step 3:

[0337] The server lists files identified as digital junk and sends this list to the emotion engine. The emotion engine analyzes human emotions to determine the appropriate timing and content for notifications.

[0338] Step 4:

[0339] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and works in conjunction with an emotion engine to evaluate the user's current emotional state.

[0340] Step 5:

[0341] The emotion engine adjusts the timing of notifications to the user based on the acquired emotional state. For example, if the user is relaxed, the server will immediately send a notification, but if they are stressed, it will postpone the notification.

[0342] Step 6:

[0343] The user reviews a list of digital waste received from the server and decides whether to delete it. Based on the user's instructions, the system selects the data to be deleted.

[0344] Step 7:

[0345] The server deletes the digital data selected by the user. If specified beforehand, a backup is performed before physically deleting the data.

[0346] Step 8:

[0347] The server optimizes the free space created after deletion. This includes processes such as storage defragmentation and data compression.

[0348] Step 9:

[0349] The server reports the optimization results and available storage status to the user, and provides appropriate feedback that takes into account the user's emotional state.

[0350] (Example 2)

[0351] 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".

[0352] In today's world, the volume of electronic data is increasing rapidly, and managing it is a significant burden for many users. Traditional data management systems focus on identifying and deleting unnecessary data, but they lack consideration for the user's emotional state during this process. This can lead to risks such as notifications being sent at inappropriate times or data manipulation that causes stress to users.

[0353] 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.

[0354] In this invention, the server includes means for collecting attribute information of electronic data from an information processing device connected to a network, means for analyzing usage counts and redundancies based on the collected attribute information, and sentiment analysis means for evaluating the user's emotional state. This enables notifications and deletion suggestions at appropriate timings and in appropriate methods according to the user's emotional state, providing a more user-friendly data management environment.

[0355] A "network" is a system that connects multiple information processing devices to each other, enabling data communication.

[0356] An "information processing device" is a device used to process and manage electronic data, and includes computers and servers.

[0357] "Electronic data" refers to information that is stored and processed in digital format, including files, documents, and images.

[0358] "Attribute information" refers to various types of information related to electronic data, including file type, size, creation date, and last access date.

[0359] "Number of uses" is an indicator that shows how often a particular electronic data item was used within a specified period.

[0360] "Duplicity" refers to the characteristic of indicating whether multiple electronic data are identical or similar.

[0361] "Analysis" is the process of analyzing data and information to identify specific trends and characteristics.

[0362] "Emotional analysis" is an analytical method that uses data such as a user's facial expressions and voice to evaluate their emotional state.

[0363] "Notification" refers to a method of providing information to a user to convey specific information or suggestions.

[0364] "Optimization" is the process of adjusting the state of a system or data to the best possible condition according to specifications and requirements.

[0365] This system consists of a server connected to a network and multiple terminals. The server collects attribute information of electronic data from the multiple terminals via the network. Agent software running on the terminals is involved in this process, sending attribute information to the server using HTTP or HTTPS protocols.

[0366] The server uses the collected attribute information to execute an AI analysis program that utilizes the Python language and data analysis libraries such as Pandas. This analysis program analyzes the usage frequency and redundancy of electronic data to identify which data is unnecessary.

[0367] The device is equipped with sensors such as a camera and microphone for emotion analysis, collecting the user's facial expressions and voice in real time. The server uses AI libraries such as TensorFlow to perform emotion analysis and identify the user's emotional state.

[0368] Based on the analysis, the server generates and sends a notification to the user suggesting the deletion of unnecessary data at the optimal timing and method according to the user's emotional state. If the user approves the notification, the server automatically deletes the unnecessary electronic data and optimizes the storage.

[0369] As a concrete example, if the server identifies a photo file that hasn't been used for a long time and confirms through sentiment analysis that the user is relaxed, the server will send a notification asking, "Is it okay to delete this photo?" If the user approves, the file will be deleted immediately.

[0370] Specific examples of prompt statements given to the generating AI model include, "Explain how the digital data management system can be optimized based on the user's emotional data." In this way, the present invention has a form that performs data management that takes user emotions into consideration.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The server collects attribute information of electronic data from terminals connected to the network. The input is metadata of the electronic data stored on the terminal, and the output is a list of attribute information aggregated on the server. Specifically, agent software on each terminal collects information such as file name, size, creation date, and last access date, and sends it to the server using HTTP / HTTPS.

[0374] Step 2:

[0375] The server performs AI analysis based on the collected attribute information. The input is a list of attribute information collected in the previous step, and the output is a list of electronic data deemed unnecessary. Using Python and the Pandas library, the system analyzes data usage counts and redundancy, classifying data that meets certain conditions as "unnecessary." Specifically, data that has not been accessed for one month is labeled as unnecessary.

[0376] Step 3:

[0377] The device acquires user emotion data using its camera and microphone. Input is user image and audio data, and output is digital information indicating emotional state. Using the data collected from the sensors, real-time emotion analysis is performed using TensorFlow. This analysis quantifies the user's stress and relaxation levels.

[0378] Step 4:

[0379] The server sends notifications to the user based on a list of unnecessary data and sentiment analysis results. The input is a list of unnecessary data and the user's emotional state, and the output is an optimized notification message. The content and timing of the notification are adjusted accordingly: immediate notifications when the user is relaxed, and more subdued messages when the user is stressed.

[0380] Step 5:

[0381] The user receives a notification and approves the deletion of unnecessary data. The input is the notification from the server and a list of suggested unnecessary data, and the output is feedback confirming the deletion approval. The user performs the approval operation via the UI, and that information is sent to the server.

[0382] Step 6:

[0383] The server deletes unnecessary data based on user approval. The input is a list of data whose deletion has been approved, and the output is the optimized storage space. Data deletion is performed immediately on the server, and the storage status is updated. As a result, unnecessary data is physically erased, and the available storage capacity increases.

[0384] (Application Example 2)

[0385] 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."

[0386] In today's information society, a large amount of digital information is stored on users' devices. This excessive information can lead to a decrease in the performance of storage devices and contribute to user stress. Therefore, there is a need to manage digital information efficiently and in a way that takes into account the user's mental state, and to optimize storage devices.

[0387] 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.

[0388] In this invention, the server includes means for collecting metadata of digital information from network-connected information processing devices, means for evaluating the frequency of use and duplication of information based on the collected metadata, and means for analyzing the user's emotional state in real time. This enables effective notification and management of digital information based on the user's emotions.

[0389] "Network-connected information processing devices" refer to electronic devices such as computers and mobile devices that can send and receive data via the internet or other communication networks.

[0390] "Metadata of digital information" refers to supplementary information that describes the attributes, structure, and other characteristics of data, and is not the data itself, but rather information about how that data is being handled.

[0391] "Evaluating usage frequency and duplication" means qualitatively and quantitatively analyzing how much digital information is being used and whether identical or similar information exists.

[0392] "Identifying unnecessary digital information" means identifying information that is inefficient to have on a storage device due to reasons such as infrequent use or duplication.

[0393] A "user" is an individual or organization that operates an information processing device and is involved in the management of its digital information.

[0394] "Optimizing notification methods and timing" means reducing user stress and improving the efficiency of information management by providing information through the most appropriate means and timing for the user.

[0395] "Optimizing memory storage" means improving the performance of information processing devices by eliminating unnecessary digital information and making efficient use of storage capacity.

[0396] "Real-time analysis of emotional state" means using sensors or other devices to instantly evaluate the user's current psychological state based on their facial expressions and voice, and then adjusting their behavior based on the evaluation results.

[0397] To implement this invention, it is first necessary to use an information processing device connected to a network. The server collects metadata of digital information from each terminal and evaluates the frequency of use and duplication of that data. The evaluated data is identified by the server as unnecessary digital information. The server then analyzes the user's emotional state in real time through sensors and optimizes the notification method and timing of unnecessary data according to that emotion.

[0398] If the emotion engine detects that the user is relaxed, the server sends a gentle notification about deleting unnecessary data. Conversely, if the system determines that the user is stressed, it refrains from sending notifications or adjusts their timing. By providing notifications at the optimal time for the user, the system reduces the user's mental burden and enables efficient data management.

[0399] The hardware used includes cameras and microphones from smartphones and smart glasses, while the software utilizes the "EmotionRecognition" library and the "DataManagement" library for emotion analysis. Furthermore, if the user approves, unnecessary digital information can be deleted and storage devices optimized, thereby improving the performance of the information processing device.

[0400] As a concrete example, while a user is relaxing in a park, their smartphone can organize floating screenshots and unread messages and send a suggestion to delete them. By prompting the user to enter a text message such as "Delete this data," the device's performance can be improved.

[0401] An example of a prompt is: "Generate a notification message suggesting data deletion when the user is in a calm state of mind."

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The server collects metadata of digital information from terminals connected to the network. This collection involves sending attribute and structural information of the data held by the terminals to the server over the network. It receives metadata of all digital information on the terminals as input and stores it in the server as structured data.

[0405] Step 2:

[0406] The server evaluates the frequency of use and duplication of digital information based on the collected metadata. The server analyzes the size of the data itself, the update date and time, and the access history to determine which information qualifies as "digital junk." As a result, information that is rarely used or is duplicated is output.

[0407] Step 3:

[0408] The device analyzes the user's emotional state in real time. This analysis includes collecting the user's facial expressions and voice tone using the device's built-in camera and microphone. Emotion analysis software processes the collected data to identify the user's emotional state. As a result, emotional state data is output.

[0409] Step 4:

[0410] The server integrates the emotion analysis results with information on duplicate and unnecessary data. Based on this integrated information, it generates a notification suggesting the deletion of unnecessary data only when the user is relaxed. Using a generation AI model, it creates prompt messages appropriate to the emotional state and sends them to the device.

[0411] Step 5:

[0412] Based on notification messages received from the device, the user approves or rejects the deletion of digital junk. If the user approves, the device sends its selection to the server and begins the deletion process. Ultimately, unnecessary digital information is removed, and storage is optimized.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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".

[0429] This invention relates to a system for improving the efficiency of digital data management, and in particular provides a specific method for identifying, deleting, and optimizing unnecessary digital data, so-called digital junk. This system consists of a server, a terminal, and a user.

[0430] The server periodically collects metadata about digital data from all terminals on the network. This metadata includes file names, sizes, creation dates, and last access dates, and plays an important role in understanding data usage.

[0431] The server uses AI to perform a deep analysis of the collected metadata. This analysis allows for the identification of infrequently used files, duplicate data, and outdated data. The server notifies the user of any identified digital junk. The notification includes the reason why the file was identified as digital junk and detailed information about the file.

[0432] Users can view notifications sent from the server and select the digital data they wish to delete. Because the deletion selection is made directly by the user, they can review and approve the data being deleted. This prevents the accidental deletion of important data.

[0433] After receiving user approval for deletion, the server deletes the specified digital junk from the device. Furthermore, it optimizes storage to make the most of the freed-up storage space resulting from the deletion. This optimization includes operations to defragment storage and increasing capacity using compression techniques.

[0434] As a concrete example, the server identifies old photo albums that are rarely accessed from the user's device and labels them as digital waste. The user confirms the notification from the server and agrees to delete the albums. Based on the user's consent, the server deletes the albums and then optimizes storage. This not only reduces the server load and saves energy, but also provides the user with a more convenient data management environment.

[0435] The following describes the processing flow.

[0436] Step 1:

[0437] The server accesses each terminal on the network and collects metadata for all digital data stored in the storage. In this process, the server obtains information such as file name, size, creation date, and last access date.

[0438] Step 2:

[0439] The server passes the collected metadata to the AI ​​agent, which analyzes data usage frequency and redundancy. It then runs algorithms to identify files that have been unused for a long time, as well as files with similar or identical content.

[0440] Step 3:

[0441] Based on the analysis results, the server generates a list of files identified as digital junk. This list includes the reasons why each file was deemed unnecessary and detailed metadata for each file.

[0442] Step 4:

[0443] The server notifies the user of a list of identified digital clutter. This notification is presented in a visually easy-to-understand format and includes recommendations for deletion.

[0444] Step 5:

[0445] The user receives a notification and selects the data to delete. The selected data is added to the deletion approval list, and the following actions are taken based on this list.

[0446] Step 6:

[0447] The server deletes digital data authorized by the user from the device. This deletion is logged to ensure security.

[0448] Step 7:

[0449] After the deletion process is complete, the server optimizes the free storage. This includes performing defragmentation and applying compression techniques to expand storage capacity.

[0450] Step 8:

[0451] The server reports the optimization results to the user. This report includes information on increased free space and improvements in system performance.

[0452] (Example 1)

[0453] 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."

[0454] In modern society, the dramatic increase in digital information has strained the capacity of storage media, leading to the accumulation of vast amounts of unnecessary digital information, or so-called digital junk. This situation can result in decreased performance of storage devices and wasted energy. Furthermore, manually sorting through unnecessary information is time-consuming and laborious, and carries the risk of accidental deletion, thus highlighting the need for efficient management of digital information.

[0455] 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.

[0456] In this invention, the server includes means for collecting metadata of digital information from information processing devices connected to a communication network, means for analyzing usage frequency and duplication based on the collected metadata, and means for identifying unnecessary digital information based on the analysis results. This makes it possible for users to efficiently manage unnecessary digital information and optimize the capacity of the storage device.

[0457] A "communication network" is a network infrastructure used to exchange data between information processing devices.

[0458] An "information processing device" is an electronic device used for inputting, outputting, storing, and processing digital data.

[0459] "Digital information" refers to bit-level data processed by a computer, stored in forms such as text, images, and audio.

[0460] "Metadata" refers to attribute information associated with digital information, including file name, file size, creation date, and last access date.

[0461] "Analysis" is the process of using collected data to analyze information based on specific conditions, such as identifying unnecessary information or estimating its frequency of use.

[0462] A "storage device" is a hardware device capable of storing and reading digital information.

[0463] "Optimization" refers to the process of rearranging and compressing data to improve the efficiency of storage device usage and increase free space.

[0464] A "user" is a person who manages and manipulates digital information through an information processing device.

[0465] This invention is a system for efficiently managing digital information from network-connected information processing devices. This system primarily consists of three elements: a server, a terminal, and a user.

[0466] The server connects to each terminal via a communication network and collects metadata of the digital information contained within the terminal. SSH and HTTPS are used as secure communication methods for this process. The collected metadata is stored on the server and analyzed using AI frameworks such as TensorFlow and PyTorch. The purpose of the analysis is to identify infrequently used files, duplicate data, and old data that has not been accessed for a certain period.

[0467] On the device, metadata is extracted using the OS file system API. This information is sent to the server and updated periodically. Users who receive the analysis results from the server can view a list of identified unnecessary information, for example, through a management interface on a web browser.

[0468] Users can review the digital junk they are notified of using a dashboard provided by the server and approve its deletion. This process also provides detailed information to prevent accidental deletion. Once deletion is approved, the server deletes the digital information in question and performs defragmentation to prevent storage fragmentation. It also optimizes storage space by using data compression techniques.

[0469] As a concrete example, the server identifies a folder named "family_photos_2005" from the terminal and determines it to be digital junk through metadata analysis. Once the user confirms this information on the dashboard and approves its deletion, the server deletes the folder and optimizes storage allocation. An example of a prompt for the generating AI is, "Generate code to create a program that identifies and deletes unused files."

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] The server connects to each terminal via a communication network and periodically collects metadata of digital information. The input data obtained from the terminals includes file names, sizes, creation dates, and last access dates. By organizing this data and storing it in a metadata database, the foundation for overall data management is established.

[0473] Step 2:

[0474] The server inputs the collected metadata into an AI model for analysis, including frequency of use, redundancy, and age. Data processing techniques such as clustering and natural language processing are employed, with file access frequency being a particularly important metric. The output generates a list of infrequently used files and duplicate data.

[0475] Step 3:

[0476] The server notifies the user of a list of unnecessary digital information derived from the analysis. The notification is sent via email or a web dashboard and includes details of the identified digital junk and the reasons why it was deemed unnecessary. This notification allows the user to explicitly confirm the data being considered for deletion.

[0477] Step 4:

[0478] Users review notifications sent from the server and filter out digital information they deem unnecessary. An intuitive interface is provided, allowing users to view the specific contents of files and decide what to delete. Based on the user's selection, a list of data to be deleted is created and returned to the server.

[0479] Step 5:

[0480] Based on user approval for deletion, the server deletes specified digital information from the terminal. This operation securely erases the target files and frees up storage space. Furthermore, after the deletion operation, defragmentation and data compression technologies are used to optimize storage and enable efficient data utilization.

[0481] (Application Example 1)

[0482] 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."

[0483] In today's information processing environment, information data is rapidly increasing, and much of it is infrequently used and duplicated. This leads to problems such as insufficient storage capacity and decreased efficiency. Furthermore, manually selecting and deleting unnecessary information is time-consuming and carries the risk of accidentally deleting important data. To solve this problem, there is a need for a method that automatically analyzes usage frequency and duplication and manages unnecessary information safely and efficiently.

[0484] 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.

[0485] In this invention, the server includes means for collecting attribute information of information data from information processing devices connected to a network, means for investigating usage frequency and duplication based on the collected attribute information, and means for improving the information analysis results using a generative AI model. This enables users to safely and efficiently confirm the necessity of information data and appropriately manage unnecessary information.

[0486] An "information processing device" is an electronic device that is connected to a network and has the function of collecting, processing, and analyzing information data.

[0487] "Attribute information" refers to information that indicates the characteristics associated with information data, and includes metadata such as file name, size, and frequency of use.

[0488] "Frequency of use" is an indicator that shows how often specific information data is used over a certain period of time.

[0489] "Duplicate" refers to a situation where information data with the same content exists in different locations.

[0490] A "generative AI model" is an artificial intelligence technology used to analyze large amounts of information data and find useful patterns and features.

[0491] "Improving analysis results" refers to the process of improving the accuracy of the analysis of attribute information collected using a generative AI model.

[0492] "Unnecessary information" refers to information data that is used infrequently or is duplicated, and therefore should be deleted or managed.

[0493] The system for implementing the present invention mainly consists of a server, an information processing terminal, and a generation AI model. The server is responsible for periodically collecting attribute information of information data from each information processing terminal connected to the network. The attribute information includes metadata such as file name, size, last used date, and usage frequency, which makes it possible to understand the usage status of digital data.

[0494] The server performs detailed analysis using a generative AI model based on the collected attribute information. This model identifies patterns and trends in the collected data and efficiently identifies infrequently used information and duplicate data. For identified unnecessary data, the server sends a notification to the user. The notification includes the reason why the data was deemed unnecessary and related details, helping the user evaluate the value of the data.

[0495] Users can select which digital data to delete after receiving a notification from the server. The user has the right to choose what to delete, which helps prevent the accidental deletion of important data. Once the server receives user approval for deletion, it will delete the specified unnecessary data from each device.

[0496] After deletion, the server optimizes the storage medium. This includes processes that maximize the use of free space by utilizing defragmentation and data compression techniques. As a result, overall system efficiency is improved and energy consumption is reduced.

[0497] For example, if a data center manages multiple duplicate backup data sets, the server will identify older backups, notify the user, delete them after obtaining approval, and optimize storage.

[0498] An example of a prompt might be: "Consider the process of detecting and optimizing old backups in the data center, and explain how to efficiently delete unnecessary backup data and reclaim storage space."

[0499] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0500] Step 1:

[0501] The server collects attribute information from each information processing terminal via the network.

[0502] The input is metadata of the information data transmitted from each terminal. The output is a dataset that aggregates the collected attribute information. The server uses this dataset in subsequent analysis steps.

[0503] Step 2:

[0504] The server performs analysis using a generated AI model based on the collected attribute information.

[0505] The input is a dataset of attribute information obtained in Step 1. The output is a judgment result that includes infrequently used information and duplicate data. The server uses this judgment result to identify unnecessary information.

[0506] Step 3:

[0507] The server notifies the user of any identified unnecessary information.

[0508] The input is the judgment result obtained in step 2. The output is a notification message sent to the user's device. The notification includes the reason why the data was deemed unnecessary and other detailed information, allowing the user to evaluate the data.

[0509] Step 4:

[0510] Users check notifications from the server and select the data they want to delete.

[0511] The input is a notification message sent from the server. The output is feedback that includes instructions to approve deletion or changes. The user uses their terminal to send their deletion selection to the server.

[0512] Step 5:

[0513] The server deletes the specified unnecessary information data from the terminal based on the user's instructions.

[0514] The input is a deletion instruction from the user. The output is the updated storage status. This deletion operation removes unnecessary information from the storage medium.

[0515] Step 6:

[0516] The server will perform optimization of the storage medium after deletion.

[0517] The input represents the state of the storage medium with increased free space. The output represents the state of the storage medium after optimization. The server performs operations to optimize the storage medium using defragmentation and compression techniques.

[0518] 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.

[0519] This invention is a digital data management system that takes into account the emotional state of the user, and aims to make data management more user-friendly by optimizing the identification, deletion, and storage optimization of unnecessary digital data based on the user's emotions. This system consists of a server, terminals, users, and an emotion engine.

[0520] The server collects metadata on digital data from all devices connected to the network and performs a thorough analysis of that data using AI. The server identifies digital clutter based on criteria such as frequency of use, duplication, and elapsed time. The server then generates a list of analysis results and notifies the user, including detailed information about the identified digital clutter.

[0521] This system utilizes an emotion engine to analyze the user's emotional state in real time while they are operating their device. The emotion engine collects the user's facial expressions and voice tone via sensor devices such as cameras and microphones, and identifies their emotional state while respecting their privacy.

[0522] The server uses analysis results from its emotion engine to help users decide whether to delete digital clutter, taking into account their current emotional state. For example, if a user is feeling stressed, the server may refrain from sending notifications or modify the content of notifications to allow them to make decisions in a more relaxed state.

[0523] As a concrete example, the server identifies infrequently used files detected on the device as digital clutter and recommends that the user delete them. The emotion engine assesses whether the user is focused or relaxed, and can adjust the timing and method of notifications accordingly. If the user's emotions are calm, the server prompts them to proceed with the deletion process, and the user confirms. After approval, the server deletes the specified digital clutter and optimizes storage.

[0524] In this way, the present invention can provide a more comfortable and less stressful digital environment by taking user emotions into consideration when managing data, and enables data management that more accurately reflects user intentions.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The server periodically collects metadata about digital data from each terminal on the network. This allows it to obtain attribute information such as file size, last access date, and frequency of access.

[0528] Step 2:

[0529] The server passes the acquired metadata to an AI engine for analysis. This AI engine detects infrequently used files and duplicate files and classifies them as digital junk.

[0530] Step 3:

[0531] The server lists files identified as digital junk and sends this list to the emotion engine. The emotion engine analyzes human emotions to determine the appropriate timing and content for notifications.

[0532] Step 4:

[0533] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and works in conjunction with an emotion engine to evaluate the user's current emotional state.

[0534] Step 5:

[0535] The emotion engine adjusts the timing of notifications to the user based on the acquired emotional state. For example, if the user is relaxed, the server will immediately send a notification, but if they are stressed, it will postpone the notification.

[0536] Step 6:

[0537] The user reviews a list of digital waste received from the server and decides whether to delete it. Based on the user's instructions, the system selects the data to be deleted.

[0538] Step 7:

[0539] The server deletes the digital data selected by the user. If specified beforehand, a backup is performed before physically deleting the data.

[0540] Step 8:

[0541] The server optimizes the free space created after deletion. This includes processes such as storage defragmentation and data compression.

[0542] Step 9:

[0543] The server reports the optimization results and available storage status to the user, and provides appropriate feedback that takes into account the user's emotional state.

[0544] (Example 2)

[0545] 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."

[0546] In today's world, the volume of electronic data is increasing rapidly, and managing it is a significant burden for many users. Traditional data management systems focus on identifying and deleting unnecessary data, but they lack consideration for the user's emotional state during this process. This can lead to risks such as notifications being sent at inappropriate times or data manipulation that causes stress to users.

[0547] 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.

[0548] In this invention, the server includes means for collecting attribute information of electronic data from an information processing device connected to a network, means for analyzing usage counts and redundancies based on the collected attribute information, and sentiment analysis means for evaluating the user's emotional state. This enables notifications and deletion suggestions at appropriate timings and in appropriate methods according to the user's emotional state, providing a more user-friendly data management environment.

[0549] A "network" is a system that connects multiple information processing devices to each other, enabling data communication.

[0550] An "information processing device" is a device used to process and manage electronic data, and includes computers and servers.

[0551] "Electronic data" refers to information that is stored and processed in digital format, including files, documents, and images.

[0552] "Attribute information" refers to various types of information related to electronic data, including file type, size, creation date, and last access date.

[0553] "Number of uses" is an indicator that shows how often a particular electronic data item was used within a specified period.

[0554] "Duplicity" refers to the characteristic of indicating whether multiple electronic data are identical or similar.

[0555] "Analysis" is the process of analyzing data and information to identify specific trends and characteristics.

[0556] "Emotional analysis" is an analytical method that uses data such as a user's facial expressions and voice to evaluate their emotional state.

[0557] "Notification" refers to a method of providing information to a user to convey specific information or suggestions.

[0558] "Optimization" is the process of adjusting the state of a system or data to the best possible condition according to specifications and requirements.

[0559] This system consists of a server connected to a network and multiple terminals. The server collects attribute information of electronic data from the multiple terminals via the network. Agent software running on the terminals is involved in this process, sending attribute information to the server using HTTP or HTTPS protocols.

[0560] The server uses the collected attribute information to execute an AI analysis program that utilizes the Python language and data analysis libraries such as Pandas. This analysis program analyzes the usage frequency and redundancy of electronic data to identify which data is unnecessary.

[0561] The device is equipped with sensors such as a camera and microphone for emotion analysis, collecting the user's facial expressions and voice in real time. The server uses AI libraries such as TensorFlow to perform emotion analysis and identify the user's emotional state.

[0562] Based on the analysis, the server generates and sends a notification to the user suggesting the deletion of unnecessary data at the optimal timing and method according to the user's emotional state. If the user approves the notification, the server automatically deletes the unnecessary electronic data and optimizes the storage.

[0563] As a concrete example, if the server identifies a photo file that hasn't been used for a long time and confirms through sentiment analysis that the user is relaxed, the server will send a notification asking, "Is it okay to delete this photo?" If the user approves, the file will be deleted immediately.

[0564] Specific examples of prompt statements given to the generating AI model include, "Explain how the digital data management system can be optimized based on the user's emotional data." In this way, the present invention has a form that performs data management that takes user emotions into consideration.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The server collects attribute information of electronic data from terminals connected to the network. The input is metadata of the electronic data stored on the terminal, and the output is a list of attribute information aggregated on the server. Specifically, agent software on each terminal collects information such as file name, size, creation date, and last access date, and sends it to the server using HTTP / HTTPS.

[0568] Step 2:

[0569] The server performs AI analysis based on the collected attribute information. The input is a list of attribute information collected in the previous step, and the output is a list of electronic data deemed unnecessary. Using Python and the Pandas library, the system analyzes data usage counts and redundancy, classifying data that meets certain conditions as "unnecessary." Specifically, data that has not been accessed for one month is labeled as unnecessary.

[0570] Step 3:

[0571] The device acquires user emotion data using its camera and microphone. Input is user image and audio data, and output is digital information indicating emotional state. Using the data collected from the sensors, real-time emotion analysis is performed using TensorFlow. This analysis quantifies the user's stress and relaxation levels.

[0572] Step 4:

[0573] The server sends notifications to the user based on a list of unnecessary data and sentiment analysis results. The input is a list of unnecessary data and the user's emotional state, and the output is an optimized notification message. The content and timing of the notification are adjusted accordingly: immediate notifications when the user is relaxed, and more subdued messages when the user is stressed.

[0574] Step 5:

[0575] The user receives a notification and approves the deletion of unnecessary data. The input is the notification from the server and a list of suggested unnecessary data, and the output is feedback confirming the deletion approval. The user performs the approval operation via the UI, and that information is sent to the server.

[0576] Step 6:

[0577] The server deletes unnecessary data based on user approval. The input is a list of data whose deletion has been approved, and the output is the optimized storage space. Data deletion is performed immediately on the server, and the storage status is updated. As a result, unnecessary data is physically erased, and the available storage capacity increases.

[0578] (Application Example 2)

[0579] 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."

[0580] In today's information society, a large amount of digital information is stored on users' devices. This excessive information can lead to a decrease in the performance of storage devices and contribute to user stress. Therefore, there is a need to manage digital information efficiently and in a way that takes into account the user's mental state, and to optimize storage devices.

[0581] 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.

[0582] In this invention, the server includes means for collecting metadata of digital information from network-connected information processing devices, means for evaluating the frequency of use and duplication of information based on the collected metadata, and means for analyzing the user's emotional state in real time. This enables effective notification and management of digital information based on the user's emotions.

[0583] "Network-connected information processing devices" refer to electronic devices such as computers and mobile devices that can send and receive data via the internet or other communication networks.

[0584] "Metadata of digital information" refers to supplementary information that describes the attributes, structure, and other characteristics of data, and is not the data itself, but rather information about how that data is being handled.

[0585] "Evaluating usage frequency and duplication" means qualitatively and quantitatively analyzing how much digital information is being used and whether identical or similar information exists.

[0586] "Identifying unnecessary digital information" means identifying information that is inefficient to have on a storage device due to reasons such as infrequent use or duplication.

[0587] A "user" is an individual or organization that operates an information processing device and is involved in the management of its digital information.

[0588] "Optimizing notification methods and timing" means reducing user stress and improving the efficiency of information management by providing information through the most appropriate means and timing for the user.

[0589] "Optimizing memory storage" means improving the performance of information processing devices by eliminating unnecessary digital information and making efficient use of storage capacity.

[0590] "Real-time analysis of emotional state" means using sensors or other devices to instantly evaluate the user's current psychological state based on their facial expressions and voice, and then adjusting their behavior based on the evaluation results.

[0591] To implement this invention, it is first necessary to use an information processing device connected to a network. The server collects metadata of digital information from each terminal and evaluates the frequency of use and duplication of that data. The evaluated data is identified by the server as unnecessary digital information. The server then analyzes the user's emotional state in real time through sensors and optimizes the notification method and timing of unnecessary data according to that emotion.

[0592] If the emotion engine detects that the user is relaxed, the server sends a gentle notification about deleting unnecessary data. Conversely, if the system determines that the user is stressed, it refrains from sending notifications or adjusts their timing. By providing notifications at the optimal time for the user, the system reduces the user's mental burden and enables efficient data management.

[0593] The hardware used includes cameras and microphones from smartphones and smart glasses, while the software utilizes the "EmotionRecognition" library and the "DataManagement" library for emotion analysis. Furthermore, if the user approves, unnecessary digital information can be deleted and storage devices optimized, thereby improving the performance of the information processing device.

[0594] As a concrete example, while a user is relaxing in a park, their smartphone can organize floating screenshots and unread messages and send a suggestion to delete them. By prompting the user to enter a text message such as "Delete this data," the device's performance can be improved.

[0595] An example of a prompt is: "Generate a notification message suggesting data deletion when the user is in a calm state of mind."

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The server collects metadata of digital information from terminals connected to the network. This collection involves sending attribute and structural information of the data held by the terminals to the server over the network. It receives metadata of all digital information on the terminals as input and stores it in the server as structured data.

[0599] Step 2:

[0600] The server evaluates the frequency of use and duplication of digital information based on the collected metadata. The server analyzes the size of the data itself, the update date and time, and the access history to determine which information qualifies as "digital junk." As a result, information that is rarely used or is duplicated is output.

[0601] Step 3:

[0602] The device analyzes the user's emotional state in real time. This analysis includes collecting the user's facial expressions and voice tone using the device's built-in camera and microphone. Emotion analysis software processes the collected data to identify the user's emotional state. As a result, emotional state data is output.

[0603] Step 4:

[0604] The server integrates the emotion analysis results with information on duplicate and unnecessary data. Based on this integrated information, it generates a notification suggesting the deletion of unnecessary data only when the user is relaxed. Using a generation AI model, it creates prompt messages appropriate to the emotional state and sends them to the device.

[0605] Step 5:

[0606] Based on notification messages received from the device, the user approves or rejects the deletion of digital junk. If the user approves, the device sends its selection to the server and begins the deletion process. Ultimately, unnecessary digital information is removed, and storage is optimized.

[0607] 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.

[0608] 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.

[0609] 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.

[0610] [Fourth Embodiment]

[0611] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0612] 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.

[0613] 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).

[0614] 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.

[0615] 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.

[0616] 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).

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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".

[0624] This invention relates to a system for improving the efficiency of digital data management, and in particular provides a specific method for identifying, deleting, and optimizing unnecessary digital data, so-called digital junk. This system consists of a server, a terminal, and a user.

[0625] The server periodically collects metadata about digital data from all terminals on the network. This metadata includes file names, sizes, creation dates, and last access dates, and plays an important role in understanding data usage.

[0626] The server uses AI to perform a deep analysis of the collected metadata. This analysis allows for the identification of infrequently used files, duplicate data, and outdated data. The server notifies the user of any identified digital junk. The notification includes the reason why the file was identified as digital junk and detailed information about the file.

[0627] Users can view notifications sent from the server and select the digital data they wish to delete. Because the deletion selection is made directly by the user, they can review and approve the data being deleted. This prevents the accidental deletion of important data.

[0628] After receiving user approval for deletion, the server deletes the specified digital junk from the device. Furthermore, it optimizes storage to make the most of the freed-up storage space resulting from the deletion. This optimization includes operations to defragment storage and increasing capacity using compression techniques.

[0629] As a concrete example, the server identifies old photo albums that are rarely accessed from the user's device and labels them as digital waste. The user confirms the notification from the server and agrees to delete the albums. Based on the user's consent, the server deletes the albums and then optimizes storage. This not only reduces the server load and saves energy, but also provides the user with a more convenient data management environment.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The server accesses each terminal on the network and collects metadata for all digital data stored in the storage. In this process, the server obtains information such as file name, size, creation date, and last access date.

[0633] Step 2:

[0634] The server passes the collected metadata to the AI ​​agent, which analyzes data usage frequency and redundancy. It then runs algorithms to identify files that have been unused for a long time, as well as files with similar or identical content.

[0635] Step 3:

[0636] Based on the analysis results, the server generates a list of files identified as digital junk. This list includes the reasons why each file was deemed unnecessary and detailed metadata for each file.

[0637] Step 4:

[0638] The server notifies the user of a list of identified digital clutter. This notification is presented in a visually easy-to-understand format and includes recommendations for deletion.

[0639] Step 5:

[0640] The user receives a notification and selects the data to delete. The selected data is added to the deletion approval list, and the following actions are taken based on this list.

[0641] Step 6:

[0642] The server deletes digital data authorized by the user from the device. This deletion is logged to ensure security.

[0643] Step 7:

[0644] After the deletion process is complete, the server optimizes the free storage. This includes performing defragmentation and applying compression techniques to expand storage capacity.

[0645] Step 8:

[0646] The server reports the optimization results to the user. This report includes information on increased free space and improvements in system performance.

[0647] (Example 1)

[0648] 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".

[0649] In modern society, the dramatic increase in digital information has strained the capacity of storage media, leading to the accumulation of vast amounts of unnecessary digital information, or so-called digital junk. This situation can result in decreased performance of storage devices and wasted energy. Furthermore, manually sorting through unnecessary information is time-consuming and laborious, and carries the risk of accidental deletion, thus highlighting the need for efficient management of digital information.

[0650] 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.

[0651] In this invention, the server includes means for collecting metadata of digital information from information processing devices connected to a communication network, means for analyzing usage frequency and duplication based on the collected metadata, and means for identifying unnecessary digital information based on the analysis results. This makes it possible for users to efficiently manage unnecessary digital information and optimize the capacity of the storage device.

[0652] A "communication network" is a network infrastructure used to exchange data between information processing devices.

[0653] An "information processing device" is an electronic device used for inputting, outputting, storing, and processing digital data.

[0654] "Digital information" refers to bit-level data processed by a computer, stored in forms such as text, images, and audio.

[0655] "Metadata" refers to attribute information associated with digital information, including file name, file size, creation date, and last access date.

[0656] "Analysis" is the process of using collected data to analyze information based on specific conditions, such as identifying unnecessary information or estimating its frequency of use.

[0657] A "storage device" is a hardware device capable of storing and reading digital information.

[0658] "Optimization" refers to the process of rearranging and compressing data to improve the efficiency of storage device usage and increase free space.

[0659] A "user" is a person who manages and manipulates digital information through an information processing device.

[0660] This invention is a system for efficiently managing digital information from network-connected information processing devices. This system primarily consists of three elements: a server, a terminal, and a user.

[0661] The server connects to each terminal via a communication network and collects metadata of the digital information contained within the terminal. SSH and HTTPS are used as secure communication methods for this process. The collected metadata is stored on the server and analyzed using AI frameworks such as TensorFlow and PyTorch. The purpose of the analysis is to identify infrequently used files, duplicate data, and old data that has not been accessed for a certain period.

[0662] On the device, metadata is extracted using the OS file system API. This information is sent to the server and updated periodically. Users who receive the analysis results from the server can view a list of identified unnecessary information, for example, through a management interface on a web browser.

[0663] Users can review the digital junk they are notified of using a dashboard provided by the server and approve its deletion. This process also provides detailed information to prevent accidental deletion. Once deletion is approved, the server deletes the digital information in question and performs defragmentation to prevent storage fragmentation. It also optimizes storage space by using data compression techniques.

[0664] As a concrete example, the server identifies a folder named "family_photos_2005" from the terminal and determines it to be digital junk through metadata analysis. Once the user confirms this information on the dashboard and approves its deletion, the server deletes the folder and optimizes storage allocation. An example of a prompt for the generating AI is, "Generate code to create a program that identifies and deletes unused files."

[0665] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0666] Step 1:

[0667] The server connects to each terminal via a communication network and periodically collects metadata of digital information. The input data obtained from the terminals includes file names, sizes, creation dates, and last access dates. By organizing this data and storing it in a metadata database, the foundation for overall data management is established.

[0668] Step 2:

[0669] The server inputs the collected metadata into an AI model for analysis, including frequency of use, redundancy, and age. Data processing techniques such as clustering and natural language processing are employed, with file access frequency being a particularly important metric. The output generates a list of infrequently used files and duplicate data.

[0670] Step 3:

[0671] The server notifies the user of a list of unnecessary digital information derived from the analysis. The notification is sent via email or a web dashboard and includes details of the identified digital junk and the reasons why it was deemed unnecessary. This notification allows the user to explicitly confirm the data being considered for deletion.

[0672] Step 4:

[0673] Users review notifications sent from the server and filter out digital information they deem unnecessary. An intuitive interface is provided, allowing users to view the specific contents of files and decide what to delete. Based on the user's selection, a list of data to be deleted is created and returned to the server.

[0674] Step 5:

[0675] Based on user approval for deletion, the server deletes specified digital information from the terminal. This operation securely erases the target files and frees up storage space. Furthermore, after the deletion operation, defragmentation and data compression technologies are used to optimize storage and enable efficient data utilization.

[0676] (Application Example 1)

[0677] 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".

[0678] In today's information processing environment, information data is rapidly increasing, and much of it is infrequently used and duplicated. This leads to problems such as insufficient storage capacity and decreased efficiency. Furthermore, manually selecting and deleting unnecessary information is time-consuming and carries the risk of accidentally deleting important data. To solve this problem, there is a need for a method that automatically analyzes usage frequency and duplication and manages unnecessary information safely and efficiently.

[0679] 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.

[0680] In this invention, the server includes means for collecting attribute information of information data from information processing devices connected to a network, means for investigating usage frequency and duplication based on the collected attribute information, and means for improving the information analysis results using a generative AI model. This enables users to safely and efficiently confirm the necessity of information data and appropriately manage unnecessary information.

[0681] An "information processing device" is an electronic device that is connected to a network and has the function of collecting, processing, and analyzing information data.

[0682] "Attribute information" refers to information that indicates the characteristics associated with information data, and includes metadata such as file name, size, and frequency of use.

[0683] "Frequency of use" is an indicator that shows how often specific information data is used over a certain period of time.

[0684] "Duplicate" refers to a situation where information data with the same content exists in different locations.

[0685] A "generative AI model" is an artificial intelligence technology used to analyze large amounts of information data and find useful patterns and features.

[0686] "Improving analysis results" refers to the process of improving the accuracy of the analysis of attribute information collected using a generative AI model.

[0687] "Unnecessary information" refers to information data that is used infrequently or is duplicated, and therefore should be deleted or managed.

[0688] The system for implementing the present invention mainly consists of a server, an information processing terminal, and a generation AI model. The server is responsible for periodically collecting attribute information of information data from each information processing terminal connected to the network. The attribute information includes metadata such as file name, size, last used date, and usage frequency, which makes it possible to understand the usage status of digital data.

[0689] The server performs detailed analysis using a generative AI model based on the collected attribute information. This model identifies patterns and trends in the collected data and efficiently identifies infrequently used information and duplicate data. For identified unnecessary data, the server sends a notification to the user. The notification includes the reason why the data was deemed unnecessary and related details, helping the user evaluate the value of the data.

[0690] Users can select which digital data to delete after receiving a notification from the server. The user has the right to choose what to delete, which helps prevent the accidental deletion of important data. Once the server receives user approval for deletion, it will delete the specified unnecessary data from each device.

[0691] After deletion, the server optimizes the storage medium. This includes processes that maximize the use of free space by utilizing defragmentation and data compression techniques. As a result, overall system efficiency is improved and energy consumption is reduced.

[0692] For example, if a data center manages multiple duplicate backup data sets, the server will identify older backups, notify the user, delete them after obtaining approval, and optimize storage.

[0693] An example of a prompt might be: "Consider the process of detecting and optimizing old backups in the data center, and explain how to efficiently delete unnecessary backup data and reclaim storage space."

[0694] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0695] Step 1:

[0696] The server collects attribute information from each information processing terminal via the network.

[0697] The input is metadata of the information data transmitted from each terminal. The output is a dataset that aggregates the collected attribute information. The server uses this dataset in subsequent analysis steps.

[0698] Step 2:

[0699] The server performs analysis using a generated AI model based on the collected attribute information.

[0700] The input is a dataset of attribute information obtained in Step 1. The output is a judgment result that includes infrequently used information and duplicate data. The server uses this judgment result to identify unnecessary information.

[0701] Step 3:

[0702] The server notifies the user of any identified unnecessary information.

[0703] The input is the judgment result obtained in step 2. The output is a notification message sent to the user's device. The notification includes the reason why the data was deemed unnecessary and other detailed information, allowing the user to evaluate the data.

[0704] Step 4:

[0705] Users check notifications from the server and select the data they want to delete.

[0706] The input is a notification message sent from the server. The output is feedback that includes instructions to approve deletion or changes. The user uses their terminal to send their deletion selection to the server.

[0707] Step 5:

[0708] The server deletes the specified unnecessary information data from the terminal based on the user's instructions.

[0709] The input is a deletion instruction from the user. The output is the updated storage status. This deletion operation removes unnecessary information from the storage medium.

[0710] Step 6:

[0711] The server will perform optimization of the storage medium after deletion.

[0712] The input represents the state of the storage medium with increased free space. The output represents the state of the storage medium after optimization. The server performs operations to optimize the storage medium using defragmentation and compression techniques.

[0713] 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.

[0714] This invention is a digital data management system that takes into account the emotional state of the user, and aims to make data management more user-friendly by optimizing the identification, deletion, and storage optimization of unnecessary digital data based on the user's emotions. This system consists of a server, terminals, users, and an emotion engine.

[0715] The server collects metadata on digital data from all devices connected to the network and performs a thorough analysis of that data using AI. The server identifies digital clutter based on criteria such as frequency of use, duplication, and elapsed time. The server then generates a list of analysis results and notifies the user, including detailed information about the identified digital clutter.

[0716] This system utilizes an emotion engine to analyze the user's emotional state in real time while they are operating their device. The emotion engine collects the user's facial expressions and voice tone via sensor devices such as cameras and microphones, and identifies their emotional state while respecting their privacy.

[0717] The server uses analysis results from its emotion engine to help users decide whether to delete digital clutter, taking into account their current emotional state. For example, if a user is feeling stressed, the server may refrain from sending notifications or modify the content of notifications to allow them to make decisions in a more relaxed state.

[0718] As a concrete example, the server identifies infrequently used files detected on the device as digital clutter and recommends that the user delete them. The emotion engine assesses whether the user is focused or relaxed, and can adjust the timing and method of notifications accordingly. If the user's emotions are calm, the server prompts them to proceed with the deletion process, and the user confirms. After approval, the server deletes the specified digital clutter and optimizes storage.

[0719] In this way, the present invention can provide a more comfortable and less stressful digital environment by taking user emotions into consideration when managing data, and enables data management that more accurately reflects user intentions.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] The server periodically collects metadata about digital data from each terminal on the network. This allows it to obtain attribute information such as file size, last access date, and frequency of access.

[0723] Step 2:

[0724] The server passes the acquired metadata to an AI engine for analysis. This AI engine detects infrequently used files and duplicate files and classifies them as digital junk.

[0725] Step 3:

[0726] The server lists files identified as digital junk and sends this list to the emotion engine. The emotion engine analyzes human emotions to determine the appropriate timing and content for notifications.

[0727] Step 4:

[0728] The device uses its built-in camera and microphone to analyze the user's facial expressions and voice tone, and works in conjunction with an emotion engine to evaluate the user's current emotional state.

[0729] Step 5:

[0730] The emotion engine adjusts the timing of notifications to the user based on the acquired emotional state. For example, if the user is relaxed, the server will immediately send a notification, but if they are stressed, it will postpone the notification.

[0731] Step 6:

[0732] The user reviews a list of digital waste received from the server and decides whether to delete it. Based on the user's instructions, the system selects the data to be deleted.

[0733] Step 7:

[0734] The server deletes the digital data selected by the user. If specified beforehand, a backup is performed before physically deleting the data.

[0735] Step 8:

[0736] The server optimizes the free space created after deletion. This includes processes such as storage defragmentation and data compression.

[0737] Step 9:

[0738] The server reports the optimization results and available storage status to the user, and provides appropriate feedback that takes into account the user's emotional state.

[0739] (Example 2)

[0740] 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".

[0741] In today's world, the volume of electronic data is increasing rapidly, and managing it is a significant burden for many users. Traditional data management systems focus on identifying and deleting unnecessary data, but they lack consideration for the user's emotional state during this process. This can lead to risks such as notifications being sent at inappropriate times or data manipulation that causes stress to users.

[0742] 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.

[0743] In this invention, the server includes means for collecting attribute information of electronic data from an information processing device connected to a network, means for analyzing usage counts and redundancies based on the collected attribute information, and sentiment analysis means for evaluating the user's emotional state. This enables notifications and deletion suggestions at appropriate timings and in appropriate methods according to the user's emotional state, providing a more user-friendly data management environment.

[0744] A "network" is a system that connects multiple information processing devices to each other, enabling data communication.

[0745] An "information processing device" is a device used to process and manage electronic data, and includes computers and servers.

[0746] "Electronic data" refers to information that is stored and processed in digital format, including files, documents, and images.

[0747] "Attribute information" refers to various types of information related to electronic data, including file type, size, creation date, and last access date.

[0748] "Number of uses" is an indicator that shows how often a particular electronic data item was used within a specified period.

[0749] "Duplicity" refers to the characteristic of indicating whether multiple electronic data are identical or similar.

[0750] "Analysis" is the process of analyzing data and information to identify specific trends and characteristics.

[0751] "Emotional analysis" is an analytical method that uses data such as a user's facial expressions and voice to evaluate their emotional state.

[0752] "Notification" refers to a method of providing information to a user to convey specific information or suggestions.

[0753] "Optimization" is the process of adjusting the state of a system or data to the best possible condition according to specifications and requirements.

[0754] This system consists of a server connected to a network and multiple terminals. The server collects attribute information of electronic data from the multiple terminals via the network. Agent software running on the terminals is involved in this process, sending attribute information to the server using HTTP or HTTPS protocols.

[0755] The server uses the collected attribute information to execute an AI analysis program that utilizes the Python language and data analysis libraries such as Pandas. This analysis program analyzes the usage frequency and redundancy of electronic data to identify which data is unnecessary.

[0756] The device is equipped with sensors such as a camera and microphone for emotion analysis, collecting the user's facial expressions and voice in real time. The server uses AI libraries such as TensorFlow to perform emotion analysis and identify the user's emotional state.

[0757] Based on the analysis, the server generates and sends a notification to the user suggesting the deletion of unnecessary data at the optimal timing and method according to the user's emotional state. If the user approves the notification, the server automatically deletes the unnecessary electronic data and optimizes the storage.

[0758] As a concrete example, if the server identifies a photo file that hasn't been used for a long time and confirms through sentiment analysis that the user is relaxed, the server will send a notification asking, "Is it okay to delete this photo?" If the user approves, the file will be deleted immediately.

[0759] Specific examples of prompt statements given to the generating AI model include, "Explain how the digital data management system can be optimized based on the user's emotional data." In this way, the present invention has a form that performs data management that takes user emotions into consideration.

[0760] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0761] Step 1:

[0762] The server collects attribute information of electronic data from terminals connected to the network. The input is metadata of the electronic data stored on the terminal, and the output is a list of attribute information aggregated on the server. Specifically, agent software on each terminal collects information such as file name, size, creation date, and last access date, and sends it to the server using HTTP / HTTPS.

[0763] Step 2:

[0764] The server performs AI analysis based on the collected attribute information. The input is a list of attribute information collected in the previous step, and the output is a list of electronic data deemed unnecessary. Using Python and the Pandas library, the system analyzes data usage counts and redundancy, classifying data that meets certain conditions as "unnecessary." Specifically, data that has not been accessed for one month is labeled as unnecessary.

[0765] Step 3:

[0766] The device acquires user emotion data using its camera and microphone. Input is user image and audio data, and output is digital information indicating emotional state. Using the data collected from the sensors, real-time emotion analysis is performed using TensorFlow. This analysis quantifies the user's stress and relaxation levels.

[0767] Step 4:

[0768] The server sends notifications to the user based on a list of unnecessary data and sentiment analysis results. The input is a list of unnecessary data and the user's emotional state, and the output is an optimized notification message. The content and timing of the notification are adjusted accordingly: immediate notifications when the user is relaxed, and more subdued messages when the user is stressed.

[0769] Step 5:

[0770] The user receives a notification and approves the deletion of unnecessary data. The input is the notification from the server and a list of suggested unnecessary data, and the output is feedback confirming the deletion approval. The user performs the approval operation via the UI, and that information is sent to the server.

[0771] Step 6:

[0772] The server deletes unnecessary data based on user approval. The input is a list of data whose deletion has been approved, and the output is the optimized storage space. Data deletion is performed immediately on the server, and the storage status is updated. As a result, unnecessary data is physically erased, and the available storage capacity increases.

[0773] (Application Example 2)

[0774] 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".

[0775] In today's information society, a large amount of digital information is stored on users' devices. This excessive information can lead to a decrease in the performance of storage devices and contribute to user stress. Therefore, there is a need to manage digital information efficiently and in a way that takes into account the user's mental state, and to optimize storage devices.

[0776] 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.

[0777] In this invention, the server includes means for collecting metadata of digital information from network-connected information processing devices, means for evaluating the frequency of use and duplication of information based on the collected metadata, and means for analyzing the user's emotional state in real time. This enables effective notification and management of digital information based on the user's emotions.

[0778] "Network-connected information processing devices" refer to electronic devices such as computers and mobile devices that can send and receive data via the internet or other communication networks.

[0779] "Metadata of digital information" refers to supplementary information that describes the attributes, structure, and other characteristics of data, and is not the data itself, but rather information about how that data is being handled.

[0780] "Evaluating usage frequency and duplication" means qualitatively and quantitatively analyzing how much digital information is being used and whether identical or similar information exists.

[0781] "Identifying unnecessary digital information" means identifying information that is inefficient to have on a storage device due to reasons such as infrequent use or duplication.

[0782] A "user" is an individual or organization that operates an information processing device and is involved in the management of its digital information.

[0783] "Optimizing notification methods and timing" means reducing user stress and improving the efficiency of information management by providing information through the most appropriate means and timing for the user.

[0784] "Optimizing memory storage" means improving the performance of information processing devices by eliminating unnecessary digital information and making efficient use of storage capacity.

[0785] "Real-time analysis of emotional state" means using sensors or other devices to instantly evaluate the user's current psychological state based on their facial expressions and voice, and then adjusting their behavior based on the evaluation results.

[0786] To implement this invention, it is first necessary to use an information processing device connected to a network. The server collects metadata of digital information from each terminal and evaluates the frequency of use and duplication of that data. The evaluated data is identified by the server as unnecessary digital information. The server then analyzes the user's emotional state in real time through sensors and optimizes the notification method and timing of unnecessary data according to that emotion.

[0787] If the emotion engine detects that the user is relaxed, the server sends a gentle notification about deleting unnecessary data. Conversely, if the system determines that the user is stressed, it refrains from sending notifications or adjusts their timing. By providing notifications at the optimal time for the user, the system reduces the user's mental burden and enables efficient data management.

[0788] The hardware used includes cameras and microphones from smartphones and smart glasses, while the software utilizes the "EmotionRecognition" library and the "DataManagement" library for emotion analysis. Furthermore, if the user approves, unnecessary digital information can be deleted and storage devices optimized, thereby improving the performance of the information processing device.

[0789] As a concrete example, while a user is relaxing in a park, their smartphone can organize floating screenshots and unread messages and send a suggestion to delete them. By prompting the user to enter a text message such as "Delete this data," the device's performance can be improved.

[0790] An example of a prompt is: "Generate a notification message suggesting data deletion when the user is in a calm state of mind."

[0791] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0792] Step 1:

[0793] The server collects metadata of digital information from terminals connected to the network. This collection involves sending attribute and structural information of the data held by the terminals to the server over the network. It receives metadata of all digital information on the terminals as input and stores it in the server as structured data.

[0794] Step 2:

[0795] The server evaluates the frequency of use and duplication of digital information based on the collected metadata. The server analyzes the size of the data itself, the update date and time, and the access history to determine which information qualifies as "digital junk." As a result, information that is rarely used or is duplicated is output.

[0796] Step 3:

[0797] The device analyzes the user's emotional state in real time. This analysis includes collecting the user's facial expressions and voice tone using the device's built-in camera and microphone. Emotion analysis software processes the collected data to identify the user's emotional state. As a result, emotional state data is output.

[0798] Step 4:

[0799] The server integrates the emotion analysis results with information on duplicate and unnecessary data. Based on this integrated information, it generates a notification suggesting the deletion of unnecessary data only when the user is relaxed. Using a generation AI model, it creates prompt messages appropriate to the emotional state and sends them to the device.

[0800] Step 5:

[0801] Based on notification messages received from the device, the user approves or rejects the deletion of digital junk. If the user approves, the device sends its selection to the server and begins the deletion process. Ultimately, unnecessary digital information is removed, and storage is optimized.

[0802] 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.

[0803] 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.

[0804] In the above embodiment, an example was given in which the 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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."

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] The following is further disclosed regarding the embodiments described above.

[0824] (Claim 1)

[0825] A means for collecting metadata of digital data from terminals connected to a network,

[0826] A means of analyzing usage frequency and duplication based on collected metadata,

[0827] A means of identifying unnecessary digital data based on the analysis results,

[0828] A means of notifying users of unwanted data that has been identified,

[0829] A means of deleting unnecessary data based on user selection,

[0830] A means to optimize storage after deletion,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for automatically backing up data before deleting identified unwanted digital data.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for recording the deletion history of digital data whose deletion has been approved based on the user's intent.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A means for collecting metadata of digital information from an information processing device connected to a communication network,

[0839] A means of analyzing usage frequency and duplication based on collected metadata,

[0840] A means of identifying unnecessary digital information based on the analysis results,

[0841] A means of notifying users of unnecessary information that they have identified,

[0842] A means of deleting unnecessary information based on the user's choice,

[0843] A means for optimizing the storage device after deletion,

[0844] A system that includes this.

[0845] (Claim 2)

[0846] The system according to claim 1, further comprising means for automatically saving information before deleting identified unwanted digital information.

[0847] (Claim 3)

[0848] The system according to claim 1, further comprising means for storing a record of the deletion of digital information whose deletion has been approved based on the user's consent.

[0849] "Application Example 1"

[0850] (Claim 1)

[0851] A means for collecting attribute information of information data from an information processing device connected to a network,

[0852] A means of investigating usage frequency and overlap based on collected attribute information,

[0853] A means of identifying unnecessary information data based on the survey results,

[0854] A means of notifying users of unnecessary information that they have identified,

[0855] A means of deleting unnecessary information based on the user's choice,

[0856] A means for optimizing the storage medium after deletion,

[0857] A means of improving the results of information analysis using a generative AI model,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, further comprising means for automatically pre-storing information before deleting identified unnecessary information data.

[0861] (Claim 3)

[0862] The system according to claim 1, further comprising means for recording the deletion history of information data whose deletion has been approved based on the user's consent.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] A means for collecting attribute information of electronic data from an information processing device connected to a network,

[0866] A means of analyzing usage count and overlap based on collected attribute information,

[0867] A means for identifying unnecessary electronic data based on the analysis results,

[0868] A means of notifying users of unwanted data that has been identified,

[0869] A means of sentiment analysis for evaluating the user's emotional state,

[0870] A means of adjusting the timing and method of notifications based on the sentiment analysis results,

[0871] A means of deleting unnecessary data based on user selection,

[0872] A means for optimizing the storage device after deletion,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, further comprising means for automatically saving data before deleting identified unwanted electronic data.

[0876] (Claim 3)

[0877] The system according to claim 1, further comprising means for storing a deletion history of electronic data whose deletion has been approved based on the user's intent.

[0878] "Application example 2 when combining with an emotional engine"

[0879] (Claim 1)

[0880] A means for collecting metadata of digital information from an information processing device connected to a network,

[0881] A means of evaluating the frequency of use and duplication of information based on collected metadata,

[0882] A means of identifying unnecessary digital information based on evaluation results,

[0883] A means of notifying users of unnecessary information they have identified,

[0884] A means of removing unnecessary information based on the user's choice,

[0885] A means for optimizing the storage device after removal,

[0886] A means of analyzing the emotional state of users in real time,

[0887] A means to optimize the method and timing of notifications based on the user's emotions,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for automatically saving information before removing identified unwanted digital information.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for recording the removal history of digital information whose removal has been approved based on the user's intent. [Explanation of symbols]

[0893] 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. A means for collecting attribute information of information data from an information processing device connected to a network, A means of investigating usage frequency and overlap based on collected attribute information, A means of identifying unnecessary information data based on the survey results, A means of notifying users of unnecessary information that they have identified, A means of deleting unnecessary information based on the user's choice, A means for optimizing the storage medium after deletion, A means of improving the results of information analysis using a generative AI model, A system that includes this.

2. The system according to claim 1, further comprising means for automatically pre-storing information before deleting identified unnecessary information data.

3. The system according to claim 1, further comprising means for recording the deletion history of information data whose deletion has been approved based on the user's consent.

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